UV-VISIBLE Spectrophotometry of Waters and Soils 0323909945

Table of contents :
UV-Visible Spectrophotometry of Waters and Soils
00a.pdf
Contents
01.pdf
1 What do we need for water and soil quality monitoring?
1.1 Introduction
1.2 Water quality and human perception; the importance of color
1.3 Parameters for water quality characterization
1.4 About water quality monitoring
1.4.1 Objectives and goal of water quality monitoring
1.4.2 Selection of methods
1.4.3 Implementation of water quality monitoring
1.4.4 Main challenges
1.4.5 Water quality monitoring and water safety plans
1.5 Soil quality monitoring
1.6 Trends in water quality monitoring
1.7 Conclusion
References
02.pdf
2 The basis for good spectrophotometric UV–visible measurements
2.1 Introduction
2.2 Interaction of light with matter
2.2.1 The electromagnetic spectrum
2.2.2 The origin of spectra, absorption of radiation by atoms, ions, and molecules
2.2.2.1 Fundamental processes
2.2.2.2 Optical processes in spectrophotometry
2.2.2.3 Chromophores
2.2.3 Quantitative laws of the attenuation of light
2.2.4 Presentation of spectral data
2.2.5 Nomenclature
2.3 Factors affecting the quality of spectral data
2.3.1 Good spectroscopic practice
2.3.2 Instrumental performance criteria
2.3.3 Use of certified reference materials
2.3.4 Procedures and best practices for assuring spectrophotometer performance
2.3.4.1 Wavelength accuracy and reproducibility
2.3.4.2 Absorbance accuracy and reproducibility
2.3.4.3 Stray light
2.3.4.4 Resolution
2.3.4.5 Optimal spectrophotometric range
2.4 Sample presentation
2.4.1 Cuvettes
2.4.2 Cleaning procedures
2.5 Factors influencing spectral characteristics
2.5.1 Sample handling and storage
2.5.2 Turbidity
2.5.3 Solvent quality and polarity
2.5.4 pH
2.5.5 Ionic strength
2.5.6 Temperature
2.5.7 Data treatment
2.5.7.1 Averaging and smoothing
2.5.7.2 Derivatives
2.5.7.3 Spectral correction
2.6 Data integrity and security
References
Further reading
03.pdf
3 From spectra to qualitative and quantitative results
3.1 Introduction
3.2 Basic handling of UV spectra
3.2.1 One spectrum transformation
3.2.1.1 Colored scale
3.2.1.2 Derivative spectra
3.2.1.3 Shape factor
3.2.1.4 Smoothing–denoising
3.2.2 Two-spectra comparison
3.2.2.1 Differential spectrum
3.2.2.2 Direct comparison of two spectra
3.2.2.3 Normalization
3.2.3 Evolution study from a spectra set
3.2.3.1 Isosbestic points
3.2.3.2 Hidden isosbestic points
3.2.3.3 Application: variability estimation
3.3 Concentration calculation
3.3.1 Ideal case: pure solution with no interference
3.3.1.1 Simple absorptiometry for one analyte
3.3.1.2 Two analytes
3.3.1.2.1 Two wavelengths method
3.3.1.2.2 N wavelengths method
3.3.1.3 Multicomponent method by mutlilinear regression
3.3.1.3.1 Single or averaged standards
3.3.1.3.2 Several standards of the components and their mixtures
3.3.1.3.3 Generalized (multiple) standard addition method
3.3.2 Real samples: compensation of interferences
3.3.2.1 Two wavelengths approach
3.3.2.2 Spectra slopes
3.3.2.3 Derivative methods
3.3.2.4 Polynomial compensation of interferences
3.3.2.5 Chemometric analysis: principal component analysis, principal component regression, and partial least squares
3.3.2.6 UV semideterministic method (UVSD)
3.3.3 Real samples: pretreatment steps for improving UV response
3.4 Examples of application
Acknowledgments
References
04.pdf
4 Organic constituents
4.1 Introduction
4.2 Colored organic compounds
4.2.1 Dyes
4.2.1.1 Azoic dyes
4.2.1.2 Anthraquinonic dyes
4.2.1.3 Other dyes
4.2.2 Colored reagents
4.2.2.1 pH indicators
4.2.2.2 Redox indicator
4.2.2.3 Complexometry indicators
4.3 UV-absorbing organic compounds
4.3.1 Aldehydes and ketones
4.3.1.1 Aldehydes
4.3.1.2 Ketones
4.3.2 Amines
4.3.2.1 Aniline
4.3.2.2 Chloroanilines
4.3.2.3 Toluidine and anisidine
4.3.2.4 Other aromatic amines
4.3.2.5 Applications
4.3.3 Benzene and related compounds
4.3.3.1 BTEX
4.3.3.2 Chlorobenzene
4.3.4 Pesticides
4.3.4.1 Herbicides
4.3.4.2 Insecticides
4.3.5 Pharmaceuticals
4.3.6 Phenols
4.3.6.1 Alkylphenols
4.3.6.2 Chlorophenols
4.3.6.3 Nitrophenols
4.3.6.4 Polyphenols
4.3.6.5 Phenol index
4.3.7 Phthalates
4.3.8 Polycyclic aromatic hydrocarbons
4.3.8.1 Solvent effect
4.3.8.2 Influence of the number of aromatic rings
4.3.8.3 Isomeric polycyclic aromatic hydrocarbon UV spectra
4.3.8.4 Introduction of a five-membered cycle in the polycyclic aromatic hydrocarbon structure
4.3.8.5 Polycyclic aromatic hydrocarbon (index)
4.3.9 Sulfur organic compounds
4.3.10 Surfactants
4.4 Solid-phase extraction and UV–visible spectrophotometry
4.5 Nonabsorbing organic compounds
4.5.1 Carbonyl compounds: use of absorbing derivatives
4.5.2 Aliphatic amines and amino acids: photooxidation
4.5.3 Carbohydrates: photodegradation
Acknowledgments
References
05.pdf
5 Aggregate organic constituents
5.1 Introduction
5.2 Dissolved organic matter
5.3 Specific UV absorbance as a proxy for dissolved organic matter characterization and formation potential
5.4 Assistance of reference methods
5.4.1 Explanation of total organic carbon and dissolved organic carbon
5.4.2 Biological oxygen demand measurement
5.4.3 Final determination of chemical oxygen demand
5.5 Biodegradable dissolved organic carbon
5.6 Water quality indices and UV spectrophotometry
5.7 UV estimation of total organic carbon, dissolved organic carbon, chemical oxygen demand, and BOD5
5.7.1 UV spectra exploitation from a limited number of wavelengths
5.7.2 UV spectra exploitation from a multiwavelengths approach
5.7.3 Validation
5.8 UV recovery of organic pollution parameters
References
06.pdf
6 Mineral constituents
6.1 Introduction
6.2 Inorganic nonmetallic constituents
6.2.1 N compounds
6.2.1.1 General procedure
6.2.1.2 Nitrate measurement
6.2.1.3 Nitrite measurement
6.2.1.4 Total Kjeldahl nitrogen measurement
6.2.1.5 Ammonium measurement
6.2.2 P compounds
6.2.2.1 General procedure
6.2.2.2 Orthophosphates
6.2.2.3 Total phosphorus
6.2.3 S compounds
6.2.4 Cl compounds
6.2.4.1 Chloride
6.2.4.2 Hypochlorite
6.2.4.3 Organochlorine compounds
6.3 Metallic constituents
6.3.1 Chromium (direct measurement)
6.3.1.1 Hexavalent chromium
6.3.1.2 Trivalent chromium
6.3.2 Metallic constituents determination by complexometry
References
07.pdf
7 Physical and aggregation properties
7.1 Introduction
7.2 Color
7.2.1 Determination of color
7.2.2 Relationship between color and visible absorbance
7.3 Physical diffuse absorption
7.3.1 Some elements on the diffusion of light by particles
7.3.2 Methods for the study of heterogeneous fractions
7.3.3 UV–visible responses of mineral suspensions
7.3.4 UV responses of nanoparticles
7.3.5 Interactions of dissolved organic matter with natural and engineered colloids
7.3.6 UV responses of microorganisms
7.3.7 UV responses of wastewater
7.4 Total suspended solid estimation
7.4.1 Turbidimetry
7.4.2 UV estimation of total suspended solids
References
08.pdf
8 Natural water
8.1 Introduction
8.2 Significance of UV spectra of natural water
8.3 Quality of natural water
8.3.1 Water quality variation along a river
8.3.2 Rain influence on river water quality
8.3.3 Small tributaries quality
8.3.4 Wetland water quality
8.3.5 Lakes water quality
8.3.6 Groundwater quality
8.4 Point source and accidental discharge
8.4.1 Discharge in river
8.4.2 Discharge in sea
8.4.3 Accidental discharge
8.5 Different freshwaters but some common fate
8.5.1 About hidden isosbestic point
8.5.2 Relation between parameters (DOC/NO3)
8.6 Second derivative of UV spectra, a key parameter
Acknowledgments
References
09.pdf
9 Remote sensing and high-frequency monitoring
9.1 Introduction
9.2 Satellites applications
9.3 Other airborne applications
9.3.1 Aircrafts
9.3.2 Drones
9.4 Surface applications
9.4.1 Boats and buoys
9.4.2 High-frequency grab sampling
9.4.3 Handheld devices
9.4.4 On-site systems
9.5 Underwater applications
9.6 Wireless sensor networks
9.7 Remote sensing techniques appraisal
References
10.pdf
10 Drinking water quality assessment and management
10.1 Introduction
10.2 From source to tap water
10.2.1 Source water monitoring
10.2.2 Coagulation optimization and performance assessment
10.3 Estimation of concentrations of disinfection by-products
10.4 Early warning systems
10.5 Bottled drinking waters
10.5.1 Spring water
10.5.2 Mineral water
10.5.3 Other bottled waters
References
11.pdf
11 Urban wastewater
11.1 Introduction
11.2 Sewers
11.2.1 Fresh domestic effluent
11.2.2 Variation of quality according to time
11.2.3 Evolution along the sewer
11.2.4 Effect of rain
11.2.5 Nondomestic load in a urban wastewater network
11.2.6 Synthesis and other applications
11.3 Treatment processes
11.3.1 Primary settling assistance
11.3.2 Physicochemical treatment assistance
11.3.2.1 Jar test
11.3.2.2 Problem of sample aging
11.3.3 Biological processes
11.3.4 Complementary technique: membrane filtration and activated carbon
11.4 Applications
11.4.1 Fixed biomass treatment plant
11.4.2 Extensive process
11.4.3 Ozone treatment for treated effluent
11.5 Classification of wastewater
11.5.1 Typology of urban wastewater from UV spectra shape
11.5.2 Automatic classification of water and wastewater
References
12.pdf
12 Industrial wastewater
12.1 Introduction
12.2 Wastewater characteristics
12.2.1 Generalities
12.2.2 Influence of industry nature
12.2.3 Variability of industrial wastewater quality
12.2.4 Quantitative estimation
12.3 Treatment processes
12.3.1 Physicochemical processes
12.3.2 Biological processes
12.3.3 Hyphenated processes
12.4 Waste management
12.4.1 Sampling assistance
12.4.2 Treatability tests assistance
12.4.3 Spills detection
12.4.4 Shock-loading management
12.4.5 External waste management
12.5 Environmental impact
12.5.1 Discharge
12.5.2 Groundwater survey
References
13.pdf
13 Polluted soils, composts, and leachates
13.1 Introduction
13.2 Polluted soils
13.2.1 Characterization of polluted soils
13.2.1.1 Polycyclic aromatic hydrocarbons
13.2.1.2 Petroleum hydrocarbons
13.2.2 Treatment of polluted soils
13.2.2.1 Polycyclic aromatic hydrocarbons
13.2.2.2 Petroleum hydrocarbons
13.3 Composts
13.3.1 Characterization of solid wastes
13.3.2 Composting of solid wastes
13.4 Landfill leachates
13.4.1 Characterization of leachate
13.4.1.1 Direct examination of UV spectra
13.4.1.2 pH effect
13.4.2 Leachate treatment
13.4.3 Coagulation–flocculation with FeCl3
13.4.3.1 Photooxidation
References
14.pdf
14 Agricultural and natural soils, wetlands, and sediments
14.1 Introduction
14.2 Agricultural soils
14.3 Natural soils
14.4 Wetlands
14.5 Sediments
Ackowledgments
References
15.pdf
15 UV spectra library
15.1 Introduction
15.2 Spectra acquisition
15.3 Spectra of compounds
15.3.1 Acids and salts
15.3.1.1 Acetic acid
15.3.1.2 Butyric acid
15.3.1.3 Ethylenediaminetetraacetic acid
15.3.1.4 Formic acid
15.3.1.5 Oxalic acid
15.3.1.6 Propionic acid
15.3.1.7 Sodium salicylate
15.3.1.8 Potassium sodium tartrate
15.3.2 Aldehydes and ketones
15.3.2.1 Acetaldehyde
15.3.2.2 Acetone
15.3.2.3 Benzaldehyde
15.3.2.4 2-Butanone
15.3.2.5 Butyraldehyde
15.3.2.6 Diisobutylketone
15.3.2.7 Formaldehyde
15.3.2.8 Isobutyl methyl ketone
15.3.3 Amines and related compounds
15.3.3.1 Aniline
15.3.3.2 p-Anisidine
15.3.3.3 2-Chloroaniline
15.3.3.4 4-Chloroaniline
15.3.3.5 2-Chloro-4-methylaniline
15.3.3.6 3,4-Dichloroaniline
15.3.3.7 Diethylamine
15.3.3.8 Diethanolamine
15.3.3.9 Glutamic acid
15.3.3.10 Glycine
15.3.3.11 4-Nitroaniline
15.3.3.12 m-Toluidine
15.3.3.13 p-Toluidine
15.3.3.14 Tyrosine
15.3.3.15 4,4′-Diaminodiphenylmethane
15.3.4 Benzene and related compounds
15.3.4.1 Benzene
15.3.4.2 Chlorobenzene
15.3.4.3 Ethylbenzene
15.3.4.4 Toluene
15.3.4.5 m-Xylene
15.3.4.6 o-Xylene
15.3.4.7 p-Xylene
15.3.5 Pesticides
15.3.5.1 2-4-Dichlorophenoxy acetic acid (2-4-D)
15.3.5.2 Alachlor
15.3.5.3 Atrazine
15.3.5.4 Carbaryl
15.3.5.5 Chlorpyrifos
15.3.5.6 Chlortoluron
15.3.5.7 Diazinon
15.3.5.8 Dichlorprop
15.3.5.9 Dimethoate
15.3.5.10 Dinoterb
15.3.5.11 Diquat
15.3.5.12 Diuron
15.3.5.13 Hexazinone
15.3.5.14 Isoproturon
15.3.5.15 Linuron
15.3.5.16 Malathion
15.3.5.17 Metazachlor
15.3.5.18 Metolachlor
15.3.5.19 Paraquat
15.3.5.20 Parathion
15.3.5.21 Simazine
15.3.5.22 Terbuthylazine
15.3.5.23 Terbutryn
15.3.6 Pharmaceuticals
15.3.6.1 1,7 Ethinylestradiol
15.3.6.2 Acetaminohen
15.3.6.3 Atenolol
15.3.6.4 Caffeine
15.3.6.5 Carbamazepine
15.3.6.6 Ciprofloxacine
15.3.6.7 Clofibric acid
15.3.6.8 Diatrozoate
15.3.6.9 Diclofenac
15.3.6.10 Erythromycine
15.3.6.11 Ibuprofen
15.3.6.12 Methylparaben
15.3.6.13 Sulfamethoxazole
15.3.6.14 Trimethoprim
15.3.6.15 Warfarin
15.3.7 Phenol and related compounds
15.3.7.1 Phenol
15.3.7.2 4-Chloro-3-methylphenol
15.3.7.3 2-Chlorophenol
15.3.7.4 3-Chlorophenol
15.3.7.5 4-Chlorophenol
15.3.7.6 m-Cresol
15.3.7.7 o-Cresol
15.3.7.8 p-Cresol
15.3.7.9 4,5-Dichlorocatechol
15.3.7.10 2,3-Dichlorophenol
15.3.7.11 2,4-Dichlorophenol
15.3.7.12 2,5-Dimethylphenol
15.3.7.13 4,6-Dinitro-2-methylphenol
15.3.7.14 2-Nitrophenol
15.3.7.15 3-Nitrophenol
15.3.7.16 4-Nitrophenol
15.3.7.17 Pentachlorophenol
15.3.7.18 Pyrocatechol
15.3.7.19 2-Tert-butyl-4-methylphenol
15.3.7.20 2,4,6-Trichlorophenol
15.3.7.21 2,4,6-Trimethylphenol
15.3.7.22 Bisphenol A
15.3.8 Phthalates
15.3.8.1 Butyl benzyl phthalate
15.3.8.2 Di-butyl phthalate
15.3.8.3 Di-ethyl phthalate
15.3.8.4 Potassium hydrogen phthalate
15.3.8.5 Di(2-ethylhexyl) phthalate
15.3.9 Polycyclic aromatic hydrocarbons
15.3.9.1 Acenaphthene
15.3.9.2 Acenaphthylene
15.3.9.3 Anthracene
15.3.9.4 Benzo(a)anthracene
15.3.9.5 Benzo(a)pyrene
15.3.9.6 Benzo(b)fluoranthene
15.3.9.7 Benzo(g,h,i)perylene
15.3.9.8 Benzo(k)fluoranthene
15.3.9.9 Chrysene
15.3.9.10 Dibenz(a,h)anthracene
15.3.9.11 Fluoranthene
15.3.9.12 Fluorene
15.3.9.13 Indeno(1,2,3-cd)pyrene
15.3.9.14 Naphthalene
15.3.9.15 Phenanthrene
15.3.9.16 Pyrene
15.3.10 Surfactants
15.3.10.1 Alkyl diphenyloxide disulfonate, disodium salt
15.3.10.2 Dodecyl benzene sulfonate
15.3.10.3 Nonyl phenol ethoxylate
15.3.10.4 Octyl phenol ethoxylate
15.3.10.5 Sodium-N-methyl-N-oleoyl-taurate
15.3.11 Solvents
15.3.11.1 Acetone
15.3.11.2 Acetonitrile
15.3.11.3 Ethanol
15.3.11.4 Hexane
15.3.12 Inorganic compounds
15.3.12.1 Ammonium chloride
15.3.12.2 Hydrogen peroxide
15.3.12.3 Iodine
15.3.12.4 Potassium cyanide
15.3.12.5 Potassium dichromate
15.3.12.6 Potassium iodate
15.3.12.7 Potassium iodide
15.3.12.8 Potassium metaperiodate
15.3.12.9 Potassium permanganate
15.3.12.10 Sodium chlorate
15.3.12.11 Sodium chromate
15.3.12.12 Sodium cyanide
15.3.12.13 Sodium hypochlorite
15.3.12.14 Sodium nitrate (low concentration)
15.3.12.15 Sodium nitrate (high concentration)
15.3.12.16 Sodium nitrite (low concentration)
15.3.12.17 Sodium nitrite (high concentration)
15.3.12.18 Sodium tetraborate decahydrate
Acknowledgments
References
16.pdf
Index

Citation preview

UV-VISIBLE SPECTROPHOTOMETRY OF WATERS AND SOILS THIRD EDITION

UV-VISIBLE SPECTROPHOTOMETRY OF WATERS AND SOILS THIRD EDITION Edited by

OLIVIER THOMAS School of Public Health, Ecole des Hautes Etudes en Sante´ Publique (EHESP), Rennes, France

CHRISTOPHER BURGESS Burgess Analytical Consultancy Limited, Barnard Castle, United Kingdom

Contents

List of contributors Preface

ix xi

1. What do we need for water and soil quality monitoring?

1

Olivier Thomas and Christopher Burgess 1.1 Introduction 1.2 Water quality and human perception; the importance of color 1.3 Parameters for water quality characterization 1.4 About water quality monitoring 1.5 Soil quality monitoring 1.6 Trends in water quality monitoring 1.7 Conclusion References

2 3 5 8 15 16 19 21

2. The basis for good spectrophotometric UV visible measurements

25

Christopher Burgess 2.1 Introduction 2.2 Interaction of light with matter 2.3 Factors affecting the quality of spectral data 2.4 Sample presentation 2.5 Factors influencing spectral characteristics 2.6 Data integrity and security References Further reading

26 26 36 49 52 56 57 58

3. From spectra to qualitative and quantitative results

59

Olivier Thomas and Jean Causse 3.1 Introduction 3.2 Basic handling of UV spectra 3.3 Concentration calculation 3.4 Examples of application Acknowledgments References

59 61 74 88 90 90

v

vi

Contents

4. Organic constituents

95

Olivier Thomas and Marine Brogat 4.1 Introduction 4.2 Colored organic compounds 4.3 UV-absorbing organic compounds 4.4 Solid-phase extraction and UV visible spectrophotometry 4.5 Nonabsorbing organic compounds Acknowledgments References

5. Aggregate organic constituents

96 96 112 151 154 158 158

161

Olivier Thomas, Jean Causse and Marie-Florence Thomas 5.1 Introduction 5.2 Dissolved organic matter 5.3 Specific UV absorbance as a proxy for dissolved organic matter characterization and formation potential 5.4 Assistance of reference methods 5.5 Biodegradable dissolved organic carbon 5.6 Water quality indices and UV spectrophotometry 5.7 UV estimation of total organic carbon, dissolved organic carbon, chemical oxygen demand, and BOD5 5.8 UV recovery of organic pollution parameters References

6. Mineral constituents

162 166 167 169 175 176 178 186 188

193

Olivier Thomas and Benoit Roig 6.1 Introduction 6.2 Inorganic nonmetallic constituents 6.3 Metallic constituents References

7. Physical and aggregation properties

193 195 217 226

233

Marie-Florence Thomas, Christopher Burgess and Olivier Thomas 7.1 Introduction 7.2 Color 7.3 Physical diffuse absorption 7.4 Total suspended solid estimation References

8. Natural water

234 235 238 250 255

259

Olivier Thomas, Jean Causse and Marie-Florence Thomas 8.1 Introduction

260

Contents

8.2 Significance of UV spectra of natural water 8.3 Quality of natural water 8.4 Point source and accidental discharge 8.5 Different freshwaters but some common fate 8.6 Second derivative of UV spectra, a key parameter Acknowledgments References

9. Remote sensing and high-frequency monitoring

vii 262 266 285 291 293 294 294

297

Olivier Thomas and Jean Causse 9.1 Introduction 9.2 Satellites applications 9.3 Other airborne applications 9.4 Surface applications 9.5 Underwater applications 9.6 Wireless sensor networks 9.7 Remote sensing techniques appraisal References

10. Drinking water quality assessment and management

298 299 302 304 311 312 313 315

321

Nicolas Beauchamp, Ianis Delpla, Caetano Dorea, Christian Bouchard, Marie-Florence Thomas, Olivier Thomas and Manuel Rodriguez 10.1 Introduction 10.2 From source to tap water 10.3 Estimation of concentrations of disinfection by-products 10.4 Early warning systems 10.5 Bottled drinking waters References

11. Urban wastewater

322 322 328 333 337 340

347

Olivier Thomas and Marie-Florence Thomas 11.1 Introduction 11.2 Sewers 11.3 Treatment processes 11.4 Applications 11.5 Classification of wastewater References

12. Industrial wastewater

348 348 360 372 378 382

385

Olivier Thomas and Marie-Florence Thomas 12.1 Introduction 12.2 Wastewater characteristics 12.3 Treatment processes

386 386 395

viii

Contents

12.4 Waste management 12.5 Environmental impact References

13. Polluted soils, composts, and leachates

400 412 414

417

Olivier Thomas and Guillaume Junqua 13.1 Introduction 13.2 Polluted soils 13.3 Composts 13.4 Landfill leachates References

14. Agricultural and natural soils, wetlands, and sediments

417 418 426 430 437

439

Olivier Thomas and Marie-Florence Thomas 14.1 Introduction 14.2 Agricultural soils 14.3 Natural soils 14.4 Wetlands 14.5 Sediments Acknowledgments References

15. UV spectra library

439 440 444 445 448 451 451

455

Olivier Thomas and Marine Brogat 15.1 Introduction 15.2 Spectra acquisition 15.3 Spectra of compounds Acknowledgments References

Index

455 456 459 605 605

607

C H A P T E R

1 What do we need for water and soil quality monitoring? Olivier Thomas1 and Christopher Burgess2 1

EHESP School of Public Health, Rennes, France, 2Burgess Analytical Consultancy Ltd, Barnard Castle, United Kingdom

O U T L I N E 1.1 Introduction

2

1.2 Water quality and human perception; the importance of color

3

1.3 Parameters for water quality characterization

5

1.4 About water quality monitoring 1.4.1 Objectives and goal of water quality monitoring 1.4.2 Selection of methods 1.4.3 Implementation of water quality monitoring 1.4.4 Main challenges 1.4.5 Water quality monitoring and water safety plans

8 9 11 12 13 14

1.5 Soil quality monitoring

15

1.6 Trends in water quality monitoring

16

1.7 Conclusion

19

References

21

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00012-5

1

© 2022 Elsevier B.V. All rights reserved.

2

1. What do we need for water and soil quality monitoring?

1.1 Introduction The knowledge of water and soil quality is of great importance regarding their respective usages. Since the 19th century, the notion of water quality (WQ) was principally associated to sanitary issues for drinking water in order to prevent health risks for the population and was extended to wastewater in the early 1900s, when water pollution of rivers began to be observed in downstream cities. Concerning soils, their quality was assessed more recently principally for agricultural needs. At the end of the 20th century, polluted soils were considered taking into account the increasing number of polluted soils and sites. The association of water and soil quality is obvious if we consider the number and nature of works published in this frame, between 2010 and 2020, for example. From the Science-direct database, almost 80% of the 100,000 works related to soil quality monitoring are linked to water (associated keywords: rain, runoff, sewage sludge, or storm). In a recent paper on research priorities in water and health [1], WQ was selected as one of the main priorities identified by an international experts group. In this introductory chapter, WQ is defined in terms of parameter values, spatiotemporal variability, and human perception. After a brief presentation of the methods and techniques used for WQ measurement, the implementation of quality monitoring programs will be discussed with a focus on both standards and complementary procedures. Finally, the requirements for soil quality monitoring will be briefly introduced. Before defining the notion of WQ, a quick survey of works published on the topic for this last decade (Science-direct database, keyword: “water quality monitoring”) shows that the half of works are related to ground and wastewater (Fig. 1.1). Of the remainder, one-third deal principally with surface waters (river, lake, and sea) and the rest (12%) is related to drinking water. It is useful to consider how human observers perceive the (good or bad) quality of water and soils. There are important differences regarding the observation of the quality of water and soil. Water is a common good from which all people benefit [2]. The degradation of its quality may have huge sanitary impacts (waterborne outbreaks). In order to keep a good quality of water, the community is largely involved from water authorities to numerous volunteers’ networks. On the contrary, the perception of soil quality is often linked to agricultural practices handled by one farmer.

UV-Visible Spectrophotometry of Waters and Soils

1.2 Water quality and human perception; the importance of color

3

FIGURE 1.1 Distribution of publications on “water quality monitoring.” Credit: From Science-direct 2010 2020.

1.2 Water quality and human perception; the importance of color Before considering WQ monitoring (WQM), it is interesting to examine how people perceive WQ through the visual aspect of water. The visual perception is, by definition, linked to the visible region of light, between violet (around 400 nm) and red (700 nm). Thus color and clarity transparency are the usual criteria for human perception of WQ [3]. A recent study involving 167 participants reported on the perception of 26 photographs taken under water in a recreational river (with flow sufficient for canoe practice), a few days after rainfall [4]. At the same time, WQ parameters (transparency, turbidity, total suspended solids, and particulate phosphorus) were measured from grab samples. Evaluation of the results showed that the common human perception was in accordance with the measured parameters that influence visible characteristics of water. Another older experiment was carried out for the rapid quality assessment of several samples of wastewater by seven laboratory technicians and field scientists as observers [5]. They were asked to estimate of the amount of total suspended solids, chemical oxygen demand (COD), and biological or biochemical oxygen demand (BOD) visually and the results show that field scientists gave twice as many good

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1. What do we need for water and soil quality monitoring?

answers (with an error of 10% max) than the laboratory technicians. Even if the number of observers was small, this experiment shows that experimented people seem to be better observers. Nowadays, people can be more easily involved through social networks and smartphone applications such as Hydrocolor or Eyeonwater (EoW). This application allows anybody to take a picture of the surface of a water body and upload it on their website “eyeonwater.org.” The color is codified by virtue of a visual comparison with the Forel-Ule (FU) hue scale when the picture is uploaded by the observer (see an example in Fig. 1.2). A recent work comparing the two applications [6] concluded that there is a degree of confidence for the FU scale within the EoW App, which is appropriate and provides a fairly accurate estimation of WQ. However, the EoW App with the use of FU scale should not be considered as a surrogate for other WQ variables (parameters).

FIGURE 1.2 Example of Eyeonwater (personal) observation with the Forel-Ule scale. Credit: Screenshot from eyeonwater.org/observations.

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1.3 Parameters for water quality characterization

5

Nevertheless, a more recent study [7] showed that a relationship between the Forel-Ule Index (FUI) and the trophic state index (TSI), proposed by Carlson [8] in 1977, could be established based on in situ and remote (MODIS sensor) measurements. The TSI was calculated from the parameters of lake water (chlorophyll-A and N and P concentrations) analyzed for the characterization of their trophic status. Thus the FU scale, which should simply be an indication of the visual appearance of the water surface [5], seems to give more useful information than expected. This shows that the optical properties of water analyzed through UV visible spectrophotometry are of great importance for the assessment of WQ. The use of remote sensing for WQM will be discussed in more detail in Chapter 9.

1.3 Parameters for water quality characterization WQ is defined as the suitability of water to sustain various uses or processes [9]. In practice, a range of parameters, the values of which can limit water use, define WQ and give a quality status of a water body (e.g., bad to good). The parameters refer to physical, chemical, or biological characteristics of water (Fig. 1.2). WQ standards and guidelines as well as standardized methods are available for their measurement. Besides standard methods of analysis, [10] complementary and alternative ones are available for some parameters in order to simplify the operation in the design of monitoring programs. These types of method are not only designed for the analysis of water but also for sampling. Complementary methods can be used for the identification of problem areas, the establishment of monitoring stations (localization and sampling frequency), as well as the measurement of specific parameters (pollutants). Validated alternative methods are often applicable for the easier and faster monitoring of a pollutant instead of using an often timeconsuming standard method such as the Alternate Test Procedure defined in the US Clean Water Act [11]. UV spectrophotometry may be used nondestructively as an alternative method for nitrate analysis. The use of passive samplers is also considered as complementary methods for improving the sampling step for the long-term assessment of WQ. In Fig. 1.3, chemical parameters are principally divided into two groups: minerals and organics that being linked to a potential pollution of the water body.

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1. What do we need for water and soil quality monitoring?

FIGURE 1.3 Main groups of parameters for a water body quality assessment. Credit: O. Thomas.

Several proxies such as TOC (total organic carbon), COD, and BOD are used for the characterization of organic pollution of water. These parameters cover the main part of organic pollution (expressed in mg L21), but organic micropollutants and other organic substances (from algae), at trace level, can also be monitored concerning their potential toxicity. Physicochemical parameters are mainly analyzed after field sampling and transportation at laboratory. Except for biological and radiochemical parameters (Table 1.1), the procedures used are based on one of the main family of analytical techniques: • the development of a specific colorimetric reaction and optical comparison with a known color scale; • electrochemical measurement of a given electric signal (amperometry, voltammetry, and potentiometry); • spectrometric, with the use of a spectrophotometer and spectral evaluation; • chromatographic separation of dissolved substances according to their affinity with a column substrate, and a specific detection after separation (spectrophotometry, mass spectroscopy, atomic and fluorescence spectrometries, etc.). Other recent methods are also available such as biochemical reactions (biosensors) for trace elements, organics, toxins, and biological agents.

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1.3 Parameters for water quality characterization

TABLE 1.1

Parameters of water quality. Analytical method

Value Parameter

Unit

Turbidity

NTU

Colorimetic

Electro.

C

X 21

X

mg L

Conductivity

µS cm21

X

Redox

Diss. oxygen

X u Ph

X 21

X

mg L

Alkalinity

Sulfate Nitrogen Potassium Sodium

21

X

mg L

21

mg L

21

mg L

X

X

X X

mg L

21

µg L21

X X 21

MgO2 L

21

mgO2 L

21

mgC L 21

Chlorophyll-A µg L Algae toxins

X

21

Diss. metals

TOC

X

mg L

µg L

BOD

X

X 21

Trace elements

COD

X

X

Hardness Chloride

Other

X 

Suspended solids

pH

Bio det

X

Color Temperature

Optical Chromato.

21

µg L

21

X

X X X X X

X

X

X

Organics

µg L

Radioactive subst.

Bq, Sv

Bacteria

n/100 mL

X

Viruses

n/100 mL

X

Algae

n/100 mL

X

X X

BOD, biological or biochemical oxygen demand; COD, chemical oxygen demand; TOC, total organic carbon; NTU, Nephelometric Turbidity Unity.

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1. What do we need for water and soil quality monitoring?

Their implementation is often difficult due to interferences linked to the high selectivity of biorecognition elements. However, a recent review shows that the application of chemometric tools can provide significant improvements for the performance of biosensors [12]. Even if quantitative analysis is required at laboratory, most of the time for the best assessment of WQ, handheld or portable devices are often used when sampling for the direct measurement of some parameters (pH, dissolved oxygen, conductivity, and temperature). Semiquantitative (test kits) or qualitative (presence/absence) detection can also be used for field monitoring by volunteers, for example. Moreover, the direct exploitation of a raw analytical signal (e.g., UV absorption spectra) can be the principle of a nonparametric approach [13]. Some parameters in Table 1.1, presented according to the analytical methods used, are considered having an effect on optical WQ. These are suspended solids, dissolved organic matter (dissolved organic carbon (DOM) or proxies like TOC), algae, and chlorophyll-A. Depending on the values of these parameters, the turbidity, transparency, and color of water body may be affected. The particulate fractions with colloids (size from 1 nm to 1 µm) and suspended solids are responsible for light scattering. The chromophoric dissolved organic carbon (CDOM) is composed of humic-like substances giving the yellow-brown color of some natural waters. Some other chemical substances may also affect locally the color of natural waters such as iron hydroxides in groundwater resurgence. This observation can be made in small catchments in forest environments, for example. Besides CDOM and colored water, another parameter simple to measure is the electrical conductivity related to the quantity of electrically charged substances in water, as for example, the mineral ions. Expressed as the inverse of a resistance, a high conductivity is associated to highly mineralized water.

1.4 About water quality monitoring In past decades, monitoring programs were often designed with a focus on short-term human activities and immediate environmental concerns. However, a long-term view of the ecosystem is necessary to consider the interaction of both human and nonhuman components of the ecosystem [14]. The starting point of WQM is water sampling. However, because sampling is a time-consuming step and only provides data for a local station at a precise time, remote sensing has been widely used when satellites became available at the end of the last century. Historically, measurement of the WQ parameters was carried out from grab samples directly taken from water bodies (river, lake, ponds, etc.) or in

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1.4 About water quality monitoring

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wastewater (treatment plant, discharge) and drinking water (for treatment). Grab samples are then transported for analysis in laboratory, after the measurement of some field parameters (temperature, conductivity, and dissolved oxygen), often carried out with multiparameter probes. In some instances (e.g., for wastewater treatment efficiency monitoring), an autosampling can be implemented. In this case, a grab sample is automatically pumped every hour during 24 hours, for example, or after a define volume in order to get a composite integrated sample. Hourly fractions collected can be also be analyzed for daily variation assessment of wastewater quality, if needed. In some cases, online monitors can be used to gain a better knowledge of the WQ variability, for example, the survey of sewage overflows or wastewater discharge. Meyer et al. [15] recently described a field mobile laboratory for the real-time measurement of some basic parameters (temperature, dissolved Oxygen, pH, conductivity, turbidity, nitrate, ammonium, total P, total reactive P, and TOC). The sampling frequency was 10 minutes and all sensors were chosen from robust commercial solutions. A flowmeter and an autosampler were integrated for load calculations and further laboratory analysis if necessary. If this system provides data from a single monitoring station of a catchment, the high costs of material, consumption, and maintenance limit its use to very selected investigations. Considering that WQ sampling methods may bias evaluations of watershed management practices [16], paced-flow composite samples may provide more accurate data for the estimation of TSS (total suspended solids) loads. In recent years, the design of WQM programs (WQMP) has moved from field measurement and sampling to solutions more adapted to the need of global monitoring with remote sensing or observations from satellites complemented by local measurements with on-site data acquisition (Fig. 1.4). With remote sensing development, complementary approaches are proposed with wireless, in situ, or software sensors. The growth of the number of works on these complementary techniques published for the last 40 years shows an exponential increase, particularly for remote sensing and software sensors (Fig. 1.5). With respect to the total number of works dealing with WQM, the proportion of complementary techniques varies from 22% in the 1980s to 33% for the last decade.

1.4.1 Objectives and goal of water quality monitoring The main goal of WQM is to have adequate knowledge about WQ of all water bodies and drinking waters in order to ensure a safe use of water. The main objectives are to implement networks for the

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1. What do we need for water and soil quality monitoring?

FIGURE 1.4 Water monitoring solutions. Credit: O. Thomas.

FIGURE 1.5 Evolution of scientific works on complementary methods for WQM. WQM, water quality monitoring. Credit: From Google Scholar database.

measurement of WQ parameters in order to meet the aims of regulation compliance, status definition, quality degradation detection, monitoring remediation actions. In practice, the main goal for European water bodies is to characterize their ecological status (good to bad), with respect to the annexes of the European Water Framework Directive (WFD), for chemical and biological data [17]. Another framework, from the Canadian Council of Minister of Environment Water Quality Index (CCME-WQI), proposes a WQ rating slightly different from the one of the WFD (excellent to poor) [18].

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1.4 About water quality monitoring

For drinking water or wastewater treatment, WQM is implemented for process control as well as for raw and treated water characterization. In addition, some scientific studies are based on WQ data acquisition in order to explain the occurrence and fate of some specific dissolved substances such as pharmaceuticals, for example [19,20]. In this case, climatic and other hydrological data are also required.

1.4.2 Selection of methods Given the large number of measurement methods for WQM, the quality attribute required will be dependent on the main goals of monitoring. A nonexhaustive list of characteristics is displayed in Table 1.2. For the compliance survey, the choice of a standardized or at least validated procedure is mandatory. Thus the results obtained cannot be disputed. The validation of the method is not always necessary for other usages. For volunteer’s networks, the methods chosen should be rapid, simple, robust, and low cost, as for example field-test kits or multiparameter probes. If the chosen methodology is expensive or complex (e.g., in situ monitoring station), fragile (laboratory instruments deployed on field) or encounter frequent problems or malfunctions, the method should always be performed by experienced and trained users who can assure adequate data integrity. From the point of view of people involved in WQM, in the field, the activities should be controlled by a set of standard operating procedures covering both sampling and on-site measurements. Volunteers and field staff (professionals, technicians) from water authorities or laboratories are looking for practical and robust devices. Researchers and scientists can handle more sophisticated systems for specific needs. The methodology used by a technician can be different if they are attached to a public organization or a private company, depending on the financial TABLE 1.2

Characteristics of water quality methods for different monitoring usages. Compliance survey

Validated

üü

Sensitivity

ü

Pollution diagnosis

Scientific knowledge

Volunteers action

Process control

ü

ü ü

ü

Rapidity

ü

ü

Simplicity

ü

ü

Robustness

ü

ü

ü

ü

ü

Low cost Availability

ü

ü

ü

ü

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ü

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1. What do we need for water and soil quality monitoring?

efforts deployed for WQM. Other stakeholders like administrative people involved in WQM are far from field but often require rapid quality data especially in the case of waterborne outbreak.

1.4.3 Implementation of water quality monitoring In order to illustrate how to implement WQMP, let us consider three important programs: 1. The National Water Quality Monitoring Council (NWQMC), created in 1997 in the United States [21], is chaired by the EPA and USGS. It is built around four types of monitoring designs, defined to cover the main objectives (knowledge of quality, changes and trends, impaired waters, restoration help, and reporting): a. The targeted monitoring focuses on area(s) of interest. b. The fixed-site monitoring gives long-term data and quality trends at a localized site. c. The statistical surveys are used at a large scale. d. Remote sensing is planned as a low-cost complement of on-theground monitoring. One specificity of the NWQMC is that volunteers’ groups are very active for the implementation of the national monitoring program. 2. The WFD adopted by the European Commission in 2000 is based on three monitoring programs [17]: a. Surveillance Monitoring b. Operational Monitoring c. Investigative Monitoring. The goal of WFD was to achieve a good ecological WQ status defined by the Annex V of the Directive. The Surveillance Monitoring aims at allowing the assessment of longterm changes in natural conditions, the efficient and effective design of future monitoring programs, the validation of the impact assessment procedure detailed in Annex II of the Directive, and the assessment of long-term changes resulting from human activity. The Operational Monitoring aims at establishing the status of water bodies identified at being at risk of failing to meet their environmental objectives and assess any changes in the status of such bodies resulting from the programs of measures. The Investigative Monitoring is required where the reason for any exceedance of environmental objectives are unknown. The Surveillance Monitoring indicates that the objectives set under Article 4 of the WFD, for a body of water, are not likely to be achieved and Operational Monitoring has not already been established to determine the causes of a water body or water bodies failing to achieve the environmental objectives or to ascertain the

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1.4 About water quality monitoring

13

magnitude and impacts of accidental pollution, and for the establishment of a program of measures for the achievement of the environmental objectives and specific measures necessary to remedy the effects of accidental pollution. 3. More recently in 2017, the United Nations proposed the Integrated Water Monitoring Initiative for SDG 6 (Sustainable Development Goal), seeking to support countries in monitoring water within the framework of the 2030 Agenda for Sustainable Development and in compiling country data to report on global progress toward SDG 6. Through the accepted UN SDG indicator 6.3.2, committed parties have agreed to an assessment of their freshwaters using the “proportion of bodies of water with good ambient water quality” [22]. To measure this, a set of parameters and specific parameters are used as representative of the state of a water body, which can be assessed relatively easily using established methods. The different groups used are oxygen and related parameters (dissolved oxygen, BOD, COD); salinity (electrical conductivity, salinity, total dissolved solids); nitrogen (total oxidized nitrogen, nitrite, nitrate, ammonia, organic nitrogen, and total nitrogen); phosphorus (orthophosphate, organic phosphorus, and total phosphorous); and acidification (pH). Recently, EPA cosponsored a competition for low-cost nutrient sensor development and use. The winner device was a wet system (with colorimetric detection) integrated in an autonomous buoy and able to measure on-site nitrate, nitrite, and orthophosphate concentrations in water every 40 minutes [23]. Besides the abovementioned WQM frameworks, some scientific approaches were proposed for improving the implementation of monitoring programs: In their literature review on WQM, Behmel et al. [24] concluded that it is impossible to propose a one-size-fits-all approach, considering the regulation context and the local characteristics of watersheds. One solution could be implemented through an intelligent decision support system in order to guide the watershed manager within his site-specific context. This approach was successfully tested and reported in a recent work [25].

1.4.4 Main challenges The first challenge of WQM is to face the spatiotemporal variability issue of WQ. Several recent studies have shown the importance of the quality variability of water at different scales. For agricultural tail waters [26] as in urban context [27], the temporal variability is mainly linked to the flow of discharges, runoff, or river and thus to climate events. At a watershed scale, a seasonal effect can be seen [28] with agricultural and industrial practices and the temperature impact, on biomass production and substrate dissolution. The conclusions of these studies are that a

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WQMP based on regularly measurements (e.g., monthly) is not adequate for a relevant assessment of WQ, taking into account the variation of hydrology in the context of climate change. Thus the monitoring program should be adapted to these variations, from a shorter frequency (online measurement) to a higher spatial resolution. Chapter 9 will discuss this question in more detail and recommend solutions for relevant spatiotemporal WQM. Besides the spatiotemporal variability issue, the WQM efficiency is linked to the heterogeneity and lack of data. In certain parts of the world, WQM can be difficult to implement, rare, or nonexistent for economic reasons or because of the inefficiency of monitoring programs. In this context, volunteer’s networks are very useful [29]. A recent study based on the review of 56 volunteer’s experiences [30] shows that, in addition to remote sensing and modeling, citizen science has been actively promoted to address the lack of WQ data in general and related to SDG indicator 6.3.2 more particularly. The results show that citizen science has a strong potential to address this lack of WQ data. The factors of success for an efficient participation of volunteers are the quality of observers (experience, skills, and knowledge of environmental issues); the implication of institutions (political decision and funding); and the interactions between citizens and institutions (understanding and trust). Besides the two abovementioned technical challenges, a more scientific one remains, namely, the knowledge of occurrence and fate of DOM. The dissolved part of organic matter is more studied than the particulate organic matter (POM) but cannot be considered as a monitoring parameter as its quantification is not directly possible. However, the simultaneous study of particulate and DOM should be considered as a research priority [31]. Organic matter in water comes principally from the vegetal cover and from soils (natural part or natural organic matter (NOM)), but also from wastewater discharges (even treated), agricultural intensive practices, and urban runoffs. This anthropogenic part of organic matter is mainly responsible for the pollution of water bodies. Recently, land use and rainfall have also been responsible for optical properties variation of DOM [32]. Finally, monitoring DOM in wastewater and drinking water treatments is becoming a huge challenge [33]. The factors of success for the efficiency of volunteer’s actions cited previously are the same for the success of WQMP with professionals and technicians from water authorities. Moreover, a participative management with stakeholders in authorities, boards, and policies implementation is required.

1.4.5 Water quality monitoring and water safety plans To provide a complement to established Water Monitoring Programs for the characterization of surface water status, the approach of water

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1.5 Soil quality monitoring

15

safety plans (WSPs) was proposed in 2005 by WHO [34] for the secure supply of drinking water, based on the identification of sanitary risks for a potable distribution network, from catchment to consumer. A WSP comprises system assessment and design, operational monitoring, and management plans [35]. In this framework, control measures are implemented all along the distribution system from catchment to point of use. In general, control measures are actions designed to mitigate the risk of increased level of hazards (for catchment, water treatment, and distribution system). At the same time, operational monitoring checks the action’s efficiency regarding pathogenic and chemical hazards. Several parameters already presented in Table 1.1 are chosen for the operational monitoring, but specific ones are added for treatment efficiency control (microbiological parameters such as total coliforms, Clostridium, and residual reagents for coagulation and disinfection byproducts, etc.). As for WQMP describe earlier, the monitoring plan designed for WSO includes the following information: - parameters - sampling location - frequency - procedures and other requirements. Its implementation should include improved ways of assessing WQ using real-time parameters and online sensors for operational control (e.g., turbidity, conductivity), in addition to lengthier laboratory analyses carried out on grab samples. Because measurements of microbiological parameters require a long time of incubation (24 48 hours), alternative procedures are more and more chosen for reducing time. In a recent work, Gunnarsdottir et al. reported rapid efficient detection techniques [36]. For example, the AQV online monitoring technique has the potential to provide early warning (1 2 hours) of elevated levels of fecal contamination by measuring total enzymatic activity, total coliforms, or Escherichia coli. Finally, the sanitary inspection through WSPs and the WQ analysis through WQMP are the distinct and complementary tools, both serving important purposes in the ongoing process of ensuring water safety [37].

1.5 Soil quality monitoring Contrary to WQM, the survey of soil composition was not driven by health risks management but by agricultural efficiency (suitability for crop growth mainly) in the 1960s, and the notion of ecosystem services nowadays. However, under the growing urbanization and

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1. What do we need for water and soil quality monitoring?

industrialization, the monitoring of polluted sites began to be implemented in the past decades. As for water, sampling and analysis or visual examination for soil quality characterization is widely practiced [38]. In their bibliographical review, Bu¨nemann et al. reported that different soil quality indicators (parameters) were considered for soil quality assessment. The main indicators cited in at least 10% of the 62 studies examined were: 4 biological parameters (earthworms, microbial biomass, soil respiration, and N mineralization); 13 chemical parameters (pH, labile C and N, micronutrients, macronutrients, total N, available K and P, total organic matter/carbon, etc.); and 10 physical indicators (infiltration, aggregation, porosity, soil depth, texture, water storage, etc.). In practice, total organic matter/carbon and pH are the most frequently used parameters ( . 50% of studies), with available phosphorus and water storage. Thus for agricultural requirements, a first group of physicochemical parameters (water content, soil organic carbon, minerals, and nutriments) is often monitored. For sanitary reasons, other chemicals are more and more surveyed as for example pesticide residues or heavy metals. For polluted soils by industrial activities, specific organics are particularly monitored such as PAH (polycyclic-aromatic hydrocarbons).

1.6 Trends in water quality monitoring Considering that the monitoring of WQ needs to collect large quantities of data to check the quality status for natural water bodies (rivers, lakes, and wetlands) or for the compliance of drinking or wastewater, monitoring networks based on sampling and laboratory analysis are often not sufficient. It is the reason why complementary approaches have been developed over the past 20 years. For a better knowledge of long-term quality evolution, the use of passive samplers are sometimes proposed for the monitoring of trace organics (pesticides and pharmaceuticals) [39] as well as bioindicators’ exploitation (mussels, bryophytes) for emerging contaminants [40]. On the other hand, the improvement of analytical methods has led to the design of new sensors (wireless, in situ, software). The issue of spatiotemporal variability is challenging with remote sensing with satellites observations or with local high-frequency monitoring systems. Finally, the development of online nanobiosensors is promising for pollution detection [38]. The evolution of complementary techniques already displayed in Fig. 1.5 shows that the number of works on passive sampling and wireless sensors is limited compared with other complementary methods. The works on remote sensing decrease relatively with time which is the

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1.6 Trends in water quality monitoring

17

opposite of passive sampling ones. For the last decade, the works related to these complementary methods correspond to 30% of papers published on WQM, showing the research effort in this field. This picture shows that integrated systems for long-time monitoring (passive samplers) and global sensing for large-scale observation (remote measurement from satellites) are more and more needed as complementary tools for WQM. Finally, a strong trend in WQM lies outside the technical improvements of monitoring tools with the development of citizen science initiatives in which complement that of institutions or scientific monitoring networks. Monitoring campaigns by volunteers are regularly implemented mainly in English-speaking countries (United States, Canada, New Zealand, Australia, and England). In the United States, for example, the NWQMC (National Water Quality Monitoring Council) holds its biennial conference since 1998 [21]. This important event is regularly organized for sharing the latest monitoring experiences and also providing networking opportunities between a wide range of participants: decision-makers, scientists, academics, practitioners, industry, nonprofit/nongovernmental groups, and citizen scientists (volunteers). In the past 20 years, the monitoring tools used by volunteers have gained in relevance, from the on-site measurements with multiparameter portable probes (pH, temperature, electrical conductivity, and dissolved oxygen) to field-test kits for nitrogen species (nitrate, nitrite, ammonia) and phosphorus (orthophosphate). When possible, grab samples are taken for complementary laboratory analysis (BOD, COD, TOC, pesticides, etc.). Recently, field systems for the on-site simultaneous measurement of N and P by wet chemistry [17] or for nitrate and TOC assessment by variable optical pathlength and second derivative UV spectrophotometry [41] were proposed. Even if these systems designed for high-frequency monitoring are promising [42], they must however be commercialized for their widespread use within the framework of WQ networks by institutions, scientists, or volunteers. Another point is the possibility of using smartphone applications. The simplest application presented earlier (EoW) is linked to a database, grouping pictures taken from observers with FUI given by the observers when uploading a picture of water body, for its color characterization. Another one, The Nutrient App, facilitates the reading of field-test kits, strips for nutrients (nitrate and orthophosphate, or PO4) [43]. The approach is based on the comparison between the color developed on a test strip (nitrate) or in a vial (PO4) and reference colors displayed on a reference hue card. The accuracy of the results of these semiquantitative methods depends on the quality of the image and light conditions. Table 1.3 displays some examples of other smartphone-based applications for WQ. It is not surprising that several integrate an optical final

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1. What do we need for water and soil quality monitoring?

TABLE 1.3 Examples of smartphone applications for water quality measurement. Name

Parameter(s)

Principle

Observation

References

Smartfluo

Chlorophyll-A

Fluorescence

Source adapter for smartphone

[44]

µMPD

pH, water hardness, phenols (catechol)

µColorimetry, Detection RGB

Lab-on-a chip

[45]

Cu sensor

Pb21, COD

Electrochemistry

Cu disposable electrode

[46]

SPCE

NO2

Screen printed carbon electrode

Disposable electrode

[47]

OEW

Algae

Optoelectrowetting

Fluorescence adapter

[48]

Phenol index

Phenol

µColorimetry

Microextraction device

[49]

COD, chemical oxygen demand.

FIGURE 1.6 Evolution of works on “smartphone-based colorimetric system in water” for the last decade. Credit: From Science-direct database.

detection thanks to the camera of the smartphone. A short bibliographic research on Science-direct database shows that the works on “smartphone-based colorimetric system in water” are strongly increasing for the last decade (Fig. 1.6). Besides the diversification of analytical tools for WQM (new sensors, smartphone applications) and the efficiency of works of volunteer’s networks, the improvement of knowledge of organic matter and the relation between water and soils must be considered as one research priority.

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1.7 Conclusion

19

The environment, the season, and the precipitations affect the composition of natural water. In the case of heavy rainfalls, particulate matter, colloids and associated DOM, and other substances can be transferred from soil to water with runoffs and floods. Moreover, more close links exist permanently between groundwater and soil composition in saturated zone. Among the main common parameters considered for water and soil monitoring, organic matter more often expressed as DOM (carbon, DOC) is the most important one because of the following: • It gives with nutrients, the “power of fertilization” of agricultural soils; its loss by runoff leads to a decrease of soil fertility, and the agricultural practices try to prevent this phenomenon. • It is responsible for color of receiving water bodies and the associated diminution of transparency, often linked to the presence of colloids and particles. • It is often associated with the nutrients load (nitrogen and phosphorus) under their mineral or organic forms. When the concentration of DOC is high in waters, the phenomenon of eutrophication may lead to an increase of biomass (vegetal and animal) and correlatively to the decrease of dissolved oxygen; In the same time, the appearance of harmful algae may occur. Some forms of DOM (humic-like substances and degradation by-products) are considered as hardly degradable or refractory in water treatment processes. The characterization of DOM in water and soils will be discussed in Chapters 5 and 14.

1.7 Conclusion Fluorimetry and spectrophotometry are the most frequent optical analysis tools for WQM. Both techniques represent between 80% and 90% of analytical procedures for WQM. The number of works on UV spectrophotometry and water published over the last 10 years is 297,418 against 31,575 for soils. Regarding fluorescence applications and water, the respective numbers are 324,200 and 41,393. For UV spectrophotometry, the number of works published has regularly increased since 2000 and a high increase for online spectrophotometers has been observed from the last decade (Fig. 1.7). Optical methods like UV visible spectrophotometry have numerous advantages with respect to other analytical techniques based on electrochemistry or chromatography. In a similar manner to the vision of a human observer, it gives a full picture of a scene (the water sample observed) with several useful pieces of

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1. What do we need for water and soil quality monitoring?

FIGURE 1.7 Evolution of works annually published for UV visible spectrophotometry and water and online spectrophotometer and water. Credit: From Google Scholar.

information acquired in a same time. The evaluation of a UV visible absorption spectrum quickly shows not only the presence of suspended solids, colloids, nitrate, organic matter, and chlorophyll-A for natural waters but also aromatic compounds from surfactants or hydrocarbons or other specific pollutants in polluted waters. This will be discussed in detail in Chapters 3 to 7 for components and 8 to 14 for water and soil types. But for human vision, the picture is rarely totally sharp and complete and other analytical methods more efficient for some components have to be used. UV visible spectrophotometric techniques are one of the main technical solutions for WQM. These techniques are either built in a dedicated device (e.g., field portable spectrophotometer or fluorimeter, or remote sensor) or included as the detector in more complex systems (microfluidic lab-on-a chip or biosensors) (Fig. 1.8). This is the reason why the use of UV visible spectrophotometry for water and soil quality monitoring is always increasing. This observation is in a straight line with the first human visual perception described two centuries ago. For example, Henry already reported a link between water aspect, such as the lack of transparency (muddy waters) and possibly of the related odor, and its lack of healthiness in 1825 [49]. This book is organized in four main sections. After the presentation of the theoretical basis and good spectroscopic practice operations necessary to measure reliable UV visible spectra, the different types of physicochemical parameters (organics, aggregate organic, mineral, and physical) are considered in the second part. The third section is dedicated to practical applications and important examples relating to water and soil analysis (natural water, seawater, drinking water, wastewater,

UV-Visible Spectrophotometry of Waters and Soils

References

21

FIGURE 1.8 Optical detection in water quality monitoring. Credit: O. Thomas.

and soils). The final section provides a spectral library containing the UV spectra of almost 150 substances (ions and molecules).

References [1] K. Setty, J.F. Loret, S. Courtois, C.C. Hammer, P. Hartemann, M. Lafforgue, et al., Faster and safer: Research priorities in water and health, International Journal of Hygiene and Environmental Health 222 (2019) 593 606. Available from: https://doi. org/10.1016/j.ijheh.2019.03.003. [2] D. Morrison, The common good, The Cambridge Companion to Aristotle’s Politics (2011) 176 198. Available from: https://doi.org/10.1017/CCO9780511791581.008. [3] A.O. West, J.M. Nolan, J.T. Scott, Optical water quality and human perceptions: a synthesis, Wiley Interdisciplinary Reviews: Water 3 (2016) 167 180. Available from: https://doi.org/10.1002/wat2.1127. [4] A.O. West, J.M. Nolan, J.T. Scott, Optical water quality and human perceptions of rivers: an ethnohydrology study, Ecosystem Health and Sustainability 2 (2016). Available from: https://doi.org/10.1002/ehs2.1230. [5] O. Thomas, Metrologie des eaux residuaires, Lavoisier Tech. et Doc., Paris, Liege (1995) 33 34. [6] T.J. Malthus, R. Ohmsen, H.J. van der Woerd, An evaluation of citizen science smartphone apps for Inland water quality assessment, Remote Sensing 12 (2020). Available from: https://doi.org/10.3390/rs12101578. [7] S. Wang, J. Li, B. Zhang, E. Spyrakos, A.N. Tyler, Q. Shen, et al., Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index, Remote Sensing of Environment 217 (2018) 444 460. Available from: https://doi.org/10.1016/j. rse.2018.08.026.

UV-Visible Spectrophotometry of Waters and Soils

22

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[8] R.E. Carlson, A trophic state index for lakes, Limnology and Oceanography 22 (1977) 361 369. [9] J. Bartram, R. Ballance, Water quality monitoring - a practical guide to the design and implementation of freshwater quality studies and monitoring programmes, 1996, pp. 1 348. [10] E.W. Rice, R.B. Baird, A.D. Eaton, Standard Methods for the Examination of Water and Wastewater, 23rd Edition, APHA, AWWA, WEF, 2017. [11] EPA, Clean Water Act Alternate Test Procedure. https://www.ecfr.gov/cgi-bin/text-idx? SID 5 d8e203746d0d1b1297968109116b6dea&mc 5 true&node 5 pt40.25.136&rgn 5 div5#se40.25.136_14, 2020. [12] E. Martynko, D. Kirsanov, Application of chemometrics in biosensing: a brief review, Biosensors 10 (2020). Available from: https://doi.org/10.3390/bios10080100. [13] O. Thomas, E. Baure`s, M. Pouet, UV Spectrophotometry as a Non-parametric Measurement of Water and Wastewater Quality Variability, Water Quality Reserch Journal 40 (2005) 51 58. [14] D.D. MacDonald, M.J.R. Clark, P.H. Whitfield, M.P. Wong, Designing monitoring programs for water quality based on experience in Canada I. Theory and framework, TrAC - Trends in Analytical Chemistry 28 (2009) 204 213. Available from: https:// doi.org/10.1016/j.trac.2008.10.016. [15] A.M. Meyer, C. Klein, E. Fu¨nfrocken, R. Kautenburger, H.P. Beck, Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers, Science of the Total Environment 651 (2019) 2323 2333. Available from: https://doi. org/10.1016/j.scitotenv.2018.10.069. [16] J. Thompson, C.E. Pelc, T.E. Jordan, Water quality sampling methods may bias evaluations of watershed management practices, Science of The Total Environment (2020) 142739. Available from: https://doi.org/10.1016/j.scitotenv.2020.142739. [17] European Commission, Common Implementation Strategy for the WFD, Guidance Document No. 19, Guidance on Surface Water Chemical Monitoring, 2009. [18] G.D. Gikas, G.K. Sylaios, V.A. Tsihrintzis, I.K. Konstantinou, T. Albanis, I. Boskidis, Comparative evaluation of river chemical status based on WFD methodology and CCME water quality index, Science of the Total Environment 745 (2020) 140849. Available from: https://doi.org/10.1016/j.scitotenv.2020.140849. [19] S. Mompelat, B. Le Bot, O. Thomas, Occurrence and fate of pharmaceutical products and by-products, from resource to drinking water, Environment International 35 (2009) 803 814. [20] L. Charuaud, E. Jarde, A. Jaffrezic, M.F. Thomas, B. Le Bot, Veterinary pharmaceutical residues from natural water to tap water: sales, occurrence and fate, Journal of Hazardous Materials 361 (2019) 169 186. Available from: https://doi.org/10.1016/j. jhazmat.2018.08.075. [21] National Water Quality Monitoring Council, NWQMC. https://acwi.gov/monitoring/, 2020. [22] J. Ladel, M. Mehta, G. Gulemvuga, L. Namayanga, Water policy on SDG6.5 implementation: progress in integrated & transboundary water resources management implementation, World Water Policy 6 (2020) 115 133. Available from: https://doi. org/10.1002/wwp2.12025. [23] H.D.A. Lindquist, T. Faber, B. Patel, L. Boczek, J. Hoelle, et al, Experience using the winning sensor from the nutrient sensor challenge (using the WIZ for surface water), Low cost nutrient sensor challenge, 2019. [24] S. Behmel, M. Damour, R. Ludwig, M.J. Rodriguez, Water quality monitoring strategies—A review and future perspectives, Science of the Total Environment 571 (2016) 1312 1329. Available from: https://doi.org/10.1016/j.scitotenv. 2016.06.235.

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[25] S. Kirschke, T. Avella´n, I. Ba¨rlund, J.J. Bogardi, L. Carvalho, D. Chapman, et al., Capacity challenges in water quality monitoring: understanding the role of human development, Environmental Monitoring and Assessment 192 (2020). Available from: https://doi.org/10.1007/s10661-020-8224-3. [26] N. Brauer, A.T. O’Geen, R.A. Dahlgren, Temporal variability in water quality of agricultural tailwaters: implications for water quality monitoring, Agricultural Water Management 96 (2009) 1001 1009. Available from: https://doi.org/10.1016/j.agwat.2009.01.011. [27] B. Shi, P.M. Bach, A. Lintern, K. Zhang, R.A. Coleman, L. Metzeling, et al., Understanding spatiotemporal variability of in-stream water quality in urban environments a case study of Melbourne, Australia, Journal of Environmental Management 246 (2019) 203 213. Available from: https://doi.org/10.1016/j.jenvman. 2019.06.006. [28] H.A. Shishaye, A.T. Asfaw, Analysis and evaluation of the spatial and temporal variabilities of river water quality parameters, Applied Water Science 10 (2020) 1 20. Available from: https://doi.org/10.1007/s13201-020-01222-2. [29] D. Fraisl, J. Campbell, L. See, U. Wehn, J. Wardlaw, M. Gold, et al., Mapping citizen science contributions to the UN sustainable development goals, Sustainability Science (2020). Available from: https://doi.org/10.1007/s11625-020-00833-7. [30] A. San Llorente Capdevila, A. Kokimova, S. Sinha Ray, T. Avella´n, J. Kim, S. Kirschke, Success factors for citizen science projects in water quality monitoring, Science of the Total Environment 728 (2020). Available from: https://doi.org/ 10.1016/j.scitotenv.2020.137843. [31] M. Derrien, S.R. Brogi, R. Gonc¸alves-Araujo, Characterization of aquatic organic matter: Assessment, perspectives and research priorities, Water Research 163 (2019) 114908. Available from: https://doi.org/10.1016/j.watres.2019.114908. [32] Y. Shi, L. Zhang, Y. Li, L. Zhou, Y. Zhou, Y. Zhang, et al., Influence of land use and rainfall on the optical properties of dissolved organic matter in a key drinking water reservoir in China, Science of the Total Environment 699 (2020) 134301. Available from: https://doi.org/10.1016/j.scitotenv.2019.134301. [33] W. Shi, W.E. Zhuang, J. Hur, L. Yang, Monitoring dissolved organic matter in wastewater and drinking water treatments using spectroscopic analysis and ultra-high resolution mass spectrometry, Water Research 188 (2021) 116406. Available from: https://doi.org/10.1016/j.watres.2020.116406. [34] G. Ferrero, K. Setty, B. Rickert, S. George, A. Rinehold, J. DeFrance, et al., Capacity building and training approaches for water safety plans: A comprehensive literature review, International Journal of Hygiene and Environmental Health 222 (2019) 615 627. Available from: https://doi.org/10.1016/j.ijheh.2019.01.011. [35] A. Davison, G. Howard, M. Stevens, P. Callan, L. Fewtrell, D. Deere, et al., Water Safety Plans Managing drinking-water quality from catchment to consumer Water, Sanitation and Health Protection and the Human Environment World Health Organization Geneva, Water (2005) 82 85. [36] M.J. Gunnarsdottir, S.M. Gardarsson, M.J. Figueras, C. Puigdome`nech, R. Jua´rez, G. Saucedo, et al., Water safety plan enhancements with improved drinking water quality detection techniques, Science of the Total Environment 698 (2020) 134185. Available from: https://doi.org/10.1016/j.scitotenv.2019.134185. [37] E.R. Kelly, R. Cronk, E. Kumpel, G. Howard, J. Bartram, How we assess water safety: A critical review of sanitary inspection and water quality analysis, Science of the Total Environment 718 (2020) 137237. Available from: https://doi.org/10.1016/j. scitotenv.2020.137237. [38] E.K. Bu¨nemann, G. Bongiorno, Z. Bai, R.E. Creamer, G. De Deyn, R. de Goede, et al., Soil quality a critical review, Soil Biology and Biochemistry 120 (2018) 105 125. Available from: https://doi.org/10.1016/j.soilbio.2018.01.030.

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[39] K. Godlewska, P. Stepnowski, M. Paszkiewicz, Pollutant Analysis Using Passive Samplers: Principles, Sorbents, Calibration and Applications: a review, Springer International Publishing, 2020. Available from: https://doi.org/10.1007/s10311-02001079-6. [40] N. Patel, Z.A. Khan, S. Shahane, D. Rai, D. Chauhan, C. Kant, et al., Emerging pollutants in aquatic environment: Source, effect, and challenges in biomonitoring and bioremediation- A review, Pollution 6 (2020) 99 113. Available from: https://doi.org/ 10.22059/POLL.2019.285116.646. [41] J. Causse, O. Thomas, A.V. Jung, M.F. Thomas, Direct DOC and nitrate determination in water using dual pathlength and second derivative UV spectrophotometry, Water Research 108 (2017) 312 319. Available from: https://doi.org/10.1016/j. watres.2016.11.010. [42] R. Dupas, J. Causse, A. Jaffre´zic, L. Aquilina, Flowpath controls on high-spatial-resolution water-chemistry profiles in headwater streams, in: Hydrological Processes, 35, Wiley, 2021, p. e14247. Available from: https://doi.org/10.1002/hyp.14247. [43] D. Costa, U. Aziz, J. Elliott, H. Baulch, B. Roy, K. Schneider, et al., The Nutrient App: Developing a smartphone application for on-site instantaneous community-based NO3 and PO4 monitoring, Environmental Modelling and Software 133 (2020). Available from: https://doi.org/10.1016/j.envsoft.2020.104829. [44] A. Friedrichs, J.A. Busch, H.J. van der Woerd, O. Zielinski, SmartFluo: a method and affordable adapter to measure chlorophyll A fluorescence with smartphones, Sensors (Switzerland) 17 (2017) 1 14. Available from: https://doi.org/10.3390/s17040678. [45] V.A.O.P. da Silva, R.C. de Freitas, P.R. de Oliveira, R.C. Moreira, L.H. MarcolinoJu´nior, M.F. Bergamini, et al., Microfluidic paper-based device integrated with smartphone for point-of-use colorimetric monitoring of water quality index, Measurement: Journal of the International Measurement Confederation 164 (2020) 108085. Available from: https://doi.org/10.1016/j.measurement.2020.108085. [46] J. Liao, F. Chang, X. Han, C. Ge, S. Lin, Wireless water quality monitoring and spatial mapping with disposable whole-copper electrochemical sensors and a smartphone, Sensors and Actuators, B: Chemical 306 (2020) 127557. Available from: https://doi. org/10.1016/j.snb.2019.127557. [47] K. Xu, Q. Chen, Y. Zhao, C. Ge, S. Lin, J. Liao, Cost-effective, wireless, and portable smartphone-based electrochemical system for on-site monitoring and spatial mapping of the nitrite contamination in water, Sensors and Actuators, B: Chemical 319 (2020). Available from: https://doi.org/10.1016/j.snb.2020.128221. [48] S. Lee, S.K. Thio, S.Y. Park, S. Bae, An automated 3D-printed smartphone platform integrated with optoelectrowetting (OEW) microfluidic chip for on-site monitoring of viable algae in water, Harmful Algae 88 (2019) 101638. Available from: https://doi. org/10.1016/j.hal.2019.101638. [49] A. Shahvar, M. Saraji, D. Shamsaei, Smartphone-based on-cell detection in combination with emulsification microextraction for the trace level determination of phenol index, Microchemical Journal 154 (2020) 104611. Available from: https://doi.org/ 10.1016/j.microc.2020.104611.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

2 The basis for good spectrophotometric UV
visible measurements Christopher Burgess Burgess Analytical Consultancy Limited, Barnard Castle, United Kingdom

O U T L I N E 2.1 Introduction

26

2.2 Interaction of light with matter 2.2.1 The electromagnetic spectrum 2.2.2 The origin of spectra, absorption of radiation by atoms, ions, and molecules 2.2.3 Quantitative laws of the attenuation of light 2.2.4 Presentation of spectral data 2.2.5 Nomenclature

26 26

2.3 Factors affecting the quality of spectral data 2.3.1 Good spectroscopic practice 2.3.2 Instrumental performance criteria 2.3.3 Use of certified reference materials 2.3.4 Procedures and best practices for assuring spectrophotometer performance

36 36 37 38

2.4 Sample presentation 2.4.1 Cuvettes 2.4.2 Cleaning procedures

49 49 51

2.5 Factors influencing spectral characteristics 2.5.1 Sample handling and storage

52 52

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00014-9

25

28 32 34 35

38

© 2022 Elsevier B.V. All rights reserved.

26

2. The basis for good spectrophotometric UV
visible measurements

2.5.2 2.5.3 2.5.4 2.5.5 2.5.6 2.5.7

Turbidity Solvent quality and polarity pH Ionic strength Temperature Data treatment

52 53 54 54 54 54

2.6 Data integrity and security

56

References

57

Further reading

58

2.1 Introduction This book is concerned with the application of UV
visible spectrophotometry to the identification and determination of materials in a variety of water samples. The spectra included in this book form a resource that enables users to apply the technique to their own samples. However, in order to apply UV
visible spectrophotometry data and information properly and reliably, we need to have an understanding of the principles and practices upon which it is based. The purpose of this chapter is to cover the essential elements. The reader who wishes to explore the subject further may turn to the more detailed and specialized works referenced in the bibliography.

2.2 Interaction of light with matter 2.2.1 The electromagnetic spectrum Spectroscopic processes rely on the fact that electromagnetic radiation (EMR) interacts with atoms and molecules in discrete ways to produce characteristic absorption or emission profiles. This is examined in more detail in Section 2.3. Before we can consider the origin of spectra, we have to look at some of the properties of EMR. Our own ability to perceive color is due to the human eye acting as a detector for EMR. The property of EMR that determines the range of color perceived is wavelength. The part of the electromagnetic spectrum that the eye can detect is known, unsurprisingly, as the visible region. EMR may be simply represented as a sine wave. Wavelength, λ, is the

UV-Visible Spectrophotometry of Waters and Soils

2.2 Interaction of light with matter

27

distance between adjacent peaks or troughs. This is illustrated in Fig. 2.1. Our ability to perceive color depends on many factors. However, the interaction mechanism of EMR with matter is of major importance. These optical processes will be discussed in more detail in the next section. From a visual detection point of view, our ability to perceive different colors is dependent on the optical process involved, for example, if the light is absorbed or reflected by the observed object. The wavelength, λ, of EMR can be expressed as a function of its frequency, ν, and the speed of light, c, by the following simple equation: v5

c λ

(2.1)

Our eyes are not uniform with response to EMR in the visible region of the spectrum. They are most sensitive in the region of 600 nm. Fig. 2.2 shows the relative sensitivity of the eye to visible light. This figure illustrates the importance of detector sensitivity and wavelength range for spectroscopic detectors. These and other instrument-related matters will be looked at in more detail in Section 2.3.

FIGURE 2.1 Sine wave representation of electromagnetic radiation.

FIGURE 2.2 The spectral sensitivity of the eye as a detector.

UV-Visible Spectrophotometry of Waters and Soils

28

2. The basis for good spectrophotometric UV
visible measurements

However, EMR behaves as a particle and as a wave (the dual nature of light); and the wavelength of such a particle, a photon, is related to energy as shown in the following equation: E5

hc 9 10 λ

(2.2)

where h is the Planck’s constant (6.63 3 10234 J-s), c is the speed of light in vacuum (2.998 3 108 ms21), E is the energy of the photon and is the wavelength in nm. The visible region of the electromagnetic spectrum constitutes a tiny part, as can be seen in Fig. 2.3. It is evident that there is an enormous span of energies, of more than 18 orders of magnitude. The equation linking energy to wavelength is of fundamental importance in spectroscopy and will be discussed further in the next section.

2.2.2 The origin of spectra, absorption of radiation by atoms, ions, and molecules When a photon interacts with an electron cloud of matter, it does so in a specific and discrete manner. This is in contrast to the physical attenuation of energy by a filter that is continuous. These discrete absorption processes are quantized and the energies associated with them relate to the type of transition involved. 2.2.2.1 Fundamental processes We can illustrate the process by the way of a simple calculation using Eq. (1.2). Assume that a photon of energy 8.254 3 10219 J interacts with the electron cloud of a particular molecule and causes promotion of an electron from the ground to an excited state. This is illustrated in Fig. 2.4.

FIGURE 2.3 The electromagnetic spectrum.

UV-Visible Spectrophotometry of Waters and Soils

2.2 Interaction of light with matter

29

The difference in the molecular energy levels, E2 2 E1, in the molecule corresponds exactly to the photon energy. Converting this energy into wavelength reveals that this excitation process occurred at a wavelength of 240 nm. This is an electronic transition and is in the ultraviolet part of the spectrum. If this were the only transition that the molecule was capable of undergoing, it would yield a sharp single spectral line. Molecular spectra are not solely derived from single electronic transitions between the ground and excited states. Quantized transitions do occur between vibrational states within each electronic state and between rotational sublevels. As we have seen, the wavelength of each absorption is dependent on the difference between the energy levels. Some transitions require less energy and consequently appear at longer wavelengths. Considering a simplified model of a diatomic molecule, we might expect that our spectra would be derived from the three transitions between the ground and first excited state illustrated in Fig. 2.5.

FIGURE 2.4

Photon capture by a molecule.

FIGURE 2.5 Idealized energy transitions for a diatomic molecule.

UV-Visible Spectrophotometry of Waters and Soils

30

2. The basis for good spectrophotometric UV
visible measurements

In practice, of course, even the simplest diatomic molecules have many energy levels resulting in complex spectra. These processes and their consequences in observed spectra are discussed in Section 2.3.4. 2.2.2.2 Optical processes in spectrophotometry Thus far we have only considered the absorption of energy by electronic and molecular transitions. When we make spectral measurements, it is necessary to consider other optical processes. This is particularly important for solution spectrophotometry. When light impinges on a cuvette containing our molecule of interest (solute) in a solution (solvent), other optical processes do or can occur: • • • • • •

transmission reflection refraction scattering luminescence chiro-optical phenomena.

All these processes, together with instrumental effects, distort or degrade the quality of the spectrum. The competent spectroscopist recognizes these dangers and seeks to minimize their impact. More details have been provided about good spectroscopic practice in Section 2.3.1. 2.2.2.3 Chromophores As we have seen from the previous sections, spectra are derived from quantized transition between energy states in atoms and molecules. The wavelengths at which these transitions occur are dependent upon the processes undergone. Hence, electronic transitions occur at higher energies (ultraviolet) than vibrational (infrared) or rotational ones (microwave). The molecular spectra observed in the UV
visible
Near InfraRed are a combination of these transitions. The intensity of the absorption is linked to the type of transition and the probability of its occurrence. Generally speaking, those transitions that are favored in quantum mechanical terms exhibit more intense absorption bands. Even simple molecules exhibit complex spectra in the UV portion of the spectrum. For example, benzene, as shown in Fig. 2.6A, exhibits part of the vapour-phase spectrum at a spectral bandwidth (SBW) of 0.1 nm between 230 and 270 nm, associated with an electronic transition at about 260 nm. The amount of fine detail observed is the result of different transitions between vibration modes overlaid with even finer rotational structure.

UV-Visible Spectrophotometry of Waters and Soils

2.2 Interaction of light with matter

(A) Vapor

31

(B) Solution in hexane

FIGURE 2.6 Spectrum of benzene in the region 230
270 nm.

FIGURE 2.7 Transitions between molecular orbitals: (A) vapor and (B) solution in hexane.

The part of the molecule involved in these absorption processes is known as the chromophore. The spectra arising from different chromophores are the “fingerprints” that allow us to identify and quantify specific molecules. These chromophores are the basic building blocks of spectra and are associated with molecular structure and the types of transition between molecular orbitals. There are three types of ground-state molecular orbitals—sigma (σ) bonding, pi (π) bonding, and nonbonding (n)—and two types of excited state—sigma star (σ*) antibonding and pi star (π*) antibonding—from which transitions are observed in the UV region. These are illustrated in Fig. 2.7.

UV-Visible Spectrophotometry of Waters and Soils

32

2. The basis for good spectrophotometric UV
visible measurements

TABLE 2.1 Examples of absorption maxima for isolated chromophores [1,2]. Chromophore

Transition

Approximate wavelength of maximum absorption (nm)

σ-σ*

150

n-σ*

185

n-σ*

195

n-σ*

195

π-π* n-π*

170 300

π-π*

170

These four transitions yield different values for ΔE and, hence, wavelength. Simple unconjugated chromophores can be characterized using these descriptors. Some examples are given in Table 2.1 [1,2]. In addition, in the visible region, ligand field and charge transfer spectra are also observed. For further information regarding the origin of UV
visible spectra and the effect of conjugation on chromophores, Rao [3], Dodd [4], and Jaffe´ and Orchin [5] should be consulted. Solution spectra, which are the concern of this book, are much less complex, particularly those involving polar solvents such as water or alcohols. Fig. 2.6B shows the solution spectrum of benzene in the nonpolar solvent hexane. Only the main vibrational fine structure is observed now due to solute
solvent interactions. This type of “fingerprint” is indicative of many benzenoid compounds, and the band intensities and positions are influenced by substituents. This topic will be discussed in more detail in Section 2.3 and illustrated in the spectral library. Hence, it is essential to control many parameters when measuring solution spectra, including: • • • • •

pH solvent purity and polarity solute concentration temperature ionic strength. See Section 2.5 for more details.

2.2.3 Quantitative laws of the attenuation of light Let I0 be the intensity of a parallel beam of radiation of wavelength λ incident on and passing through a cuvette containing a solution of

UV-Visible Spectrophotometry of Waters and Soils

2.2 Interaction of light with matter

33

A layer of solution db of total length b

I0

I

b

FIGURE 2.8 Attenuation of radiation by a cuvette containing a solution.

thickness b. Ignoring any losses from scattering or reflection, the emerging beam has been attenuated by the absorption process to an intensity I. This is illustrated in Fig. 2.8. Note that for work of the highest accuracy, a single cuvette should be used for both the sample and reference solutions to ensure that the scattering and reflection losses are compensated for and any effects minimized. The change in intensity of the incident beam dI caused by the thickness db of the absorbing solution is given by Lambert’s law: dl 5 2 kλ db

(2.3)

where kλ is a wavelength-dependent constant. Rearranging and integrating between the limits of intensity from I0 to I and pathlength from 0 to b, the equation becomes: ðb Ð I dI 5 2 k db λ I0 I 0 which becomes loge

I 5 2 kλ b I0

(2.4)

and upon taking logs to the base 10 log10

I0 kλ b 5 2:303 I

In a similar manner, the change in intensity of the incident beam, dI, caused by the concentration increment of an absorbing material, dM, in the solution thickness, db, is given by Beer’s law, where M is the concentration of the absorber in moles per dm3: dl 5 2 kλ dM where kλ is another wavelength-dependent constant.

UV-Visible Spectrophotometry of Waters and Soils

(2.5)

34

2. The basis for good spectrophotometric UV
visible measurements

These two laws may be combined to give the familiar Beer
Lambert law: A 5 log10

k0 bM l0 5 λ 2:303 l

(2.6)

where the constant term k0 λ/2.303 is called the molar absorptivity, and left-hand side of the equation is the absorbance, A. Expressed in the more usual form, the Beer
Lambert law is, for a single wavelength, λ, and a single component, as follows: Aλ 5 ελ bM

(2.7)

Hence, for a given wavelength and a single component, absorbance is a linear function of concentration of that component. However, this equation is based on some assumptions, including: • • • •

The radiation is perfectly monochromatic. There are no uncompensated losses due to scattering or reflection. The radiation beam strikes the cuvette at normal incidence. There are no molecular interactions between the absorber and other molecules in solution. • The temperature remains constant. These assumptions are not always met and can cause deviations from the ideal behavior of Beer
Lambert law.

2.2.4 Presentation of spectral data The measurement process, as shown in Fig. 2.8, illustrates that the actual measurement is one of transmittance, T 5 II0 , that is, the attenuation of radiation intensity by the sample concentration and the pathlength. However, as T is not linear with concentration, T is usually converted into absorbance, A 5 T1 , if Beer
Lambert law is obeyed. The usual practice is to plot the sample absorbance, A, against wavelength in nm. However, it can be advantageous to convert absorbance into a unit which makes comparisons between compounds easier. The most common is to calculate the absorbance of a 1% concentration, C in %m/v, with a pathlength, b, of 1 cm, A1% 1 cm . This can be useful when comparing compounds at a single wavelength. Its equivalent, the molar absorptivity, ε, which takes into account the molecular weight of the solute, M, may also be used. The interrelationships are shown in Eq. 2.8. A1% 1 cm 5

Aλ 10ε 5 M Cb

where the molar absorptivity, ε, is defined in Table 2.2.

UV-Visible Spectrophotometry of Waters and Soils

(2.8)

35

2.2 Interaction of light with matter

TABLE 2.2

Compilation of spectrophotometric nomenclature.

Accepted

Meaning

Alternatives

Symbol

Name

T

Transmittance

l l0

A

Absorbance Internal absorbance (IUPAC)

log10

A1% 1 cm

Extinction value

10ε M

E1% 1 cm

A

Absorptivity (c is concentration in g L21)

A bc

k

Extinction coefficient, absorbancy index

ε

Molar absorptivity Molar absorption coefficient

A bM

aM

Molar extinction coefficient

B

Pathlength

Pathlength

I or d

Absorption pathlength (IUPAC)

cm

Concentration

mol L21

M

l l0

Symbol

Name

τ (IUPAC)

Transmission factor transmittancy

OD, D, E

Optical density extinction

c (IUPAC)

The usefulness of A1% 1cm and ε can be simply illustrated. The UV spectra of acetone (molecular weight: 58.08) and 2 butanone (molecular weight: 72.11) in water are similar, in that they both have a single broad absorption band in the UV (see Chapter 11). Acetone has the maximum wavelength at about 266.1 nm and 2 butanone at about 268.1 nm. The acetone solution concentration was 0.1565% having an absorbance maximum of 1.195 and the 2-butanone solution concentration was 0.1037% having an absorbance maximum of 1.703. When we calculate the A1% 1cm , the values are 7.64 and 16.4, respectively, and which when converted to ε, they become 44.4 and 118.4 showing much clearer differences in their spectroscopic properties. If a comparison over a wide range of wavelengths is needed, then a plot of log10ε versus wavelength is preferred and is widely used in UV spectral atlases (see Ref. [6]).

2.2.5 Nomenclature The literature is full of conflicting and often confusing terminology. This book follows the accepted terminology given in Table 2.2, which is

UV-Visible Spectrophotometry of Waters and Soils

36

2. The basis for good spectrophotometric UV
visible measurements

a compilation of terms commonly found in the literature and the IUPAC “Orange Book” [7], and indicates the alternatives used.

2.3 Factors affecting the quality of spectral data Fascinating though they are, the primary concern of this book is not the theoretical aspects of UV spectrophotometry of materials in aqueous solution. It is concerned with the practical application of the technique and the production of reliable spectral data of known quality. Some of the requirements for solution spectra have been noted previously.

2.3.1 Good spectroscopic practice Good spectroscopic practice [8] is a set of pragmatic practical actions and operations that assist in ensuring accurate and reliable measurements for solution spectrophotometry. The following list includes some of the more important steps that should be ensured: 1. The spectrometer is in a proper state of calibration and is always well maintained. 2. The solution concentration is as free as possible from weighing, volumetric, and temperature errors. 3. The compound to be examined is completely dissolved; ultrasonic treatment as routine is highly recommended. 4. The solution is not turbid—filter if necessary—and there are no air bubbles on the cuvette windows. 5. Adsorption on the windows is not occurring. 6. The cuvettes are clean and oriented consistently in the light beam. 7. The reference solvent is subject to exactly the same procedures as the sample solution. 8. The SBW of the spectrometer is correct for the expected natural bandwidth if absorbance accuracy is important. 9. Important regions of the spectrum are measured with the sample absorbance lying between 0.8 and 1.5 A if absorbance accuracy is important. Adjust the cuvette length rather than concentration, if possible. See Section 2.3.4 for more details. 10. Ensure that stray light is not responsible for negative deviations from the Beer
Lambert law at high absorbance, particularly if the solvent absorbs significantly. 11. Make regular checks of absorbance and wavelength accuracy and check if the stray light is within specification. 12. The instrument manufacturer’s recommendations are observed.

UV-Visible Spectrophotometry of Waters and Soils

2.3 Factors affecting the quality of spectral data

37

13. The environment of the instrument is clean and free from external interference. Particular attention should be paid to electrical interference, thermal variations, and sunlight. 14. All persons operating the spectrometer and/or preparing samples are properly trained in following the requisite procedures and practices.

2.3.2 Instrumental performance criteria The role of the spectrometer in providing the integrity of data is fundamental to the end result. If the analytical practitioner cannot have faith in the reliability of the basic analytical signal within predetermined limits, then the information generated will be utterly useless. Reliability of the data quality should be linked to the performance standards for the spectrometer, in addition to having a regular maintenance program. Quality must be built into analytical procedures, based on the firm foundation of good measurement data and sample practices enshrined in good spectroscopic practice. This process is illustrated in Fig. 2.9. The key factors involved in ensuring good spectroscopic data from a UV spectrophotometer for solution measurements are wavelength accuracy and reproducibility, absorbance accuracy and reproducibility, and stray light. The resolution of the spectrometer is of great importance only if the chromophores contain absorption bands that are relatively sharp. For most purposes, a SBW of 1
2 nm will be suitable. For more information on the calibration of UV spectrometers see Clare [9] for example and Section 2.3.4. In addition, USP General Chapter ,1857. may be consulted [10].

FIGURE 2.9 Quality information and knowledge based on good spectroscopic practice.

UV-Visible Spectrophotometry of Waters and Soils

38

2. The basis for good spectrophotometric UV
visible measurements

2.3.3 Use of certified reference materials Many laboratories are required to operate to a regulatory standard from ISO (International Standards Organization), EPA (Environmental Protection Agency), or other regulatory bodies. In water testing, the standard most commonly adopted is based on the quality management system (QMS) described in ISO 9001:2015 and in 7.1.5.2 Measurement Traceability that states, When measurement traceability is a requirement, or is considered by the organization to be an essential part of providing confidence in the validity of measurement results, measuring equipment shall be: calibrated or verified, or both, at specified intervals, or prior to use, against measurement standards traceable to international or national measurement standards; when no such standards exist, the basis used for calibration or verification shall be retained as documented information.

In addition, the specific guideline for general requirements for the competence of testing and calibration laboratories, ISO/IEC 17025:2017 [11] specifies, 6.4.1 The laboratory shall have access to equipment (including, but not limited to, measuring instruments, software, measurement standards, reference materials, reference data, reagents, consumables or auxiliary apparatus) that is required for the correct performance of laboratory activities and that can influence the results. and A multitude of names exist for reference materials and certified reference materials, including reference standards, calibration standards, standard reference materials and quality control materials.

Therefore, for UV spectrophotometric standards, it is preferable to have certified reference materials that are traceable to a national or international metrology institute such as NIST (National Institute of Science and Technology, United States).

2.3.4 Procedures and best practices for assuring spectrophotometer performance 2.3.4.1 Wavelength accuracy and reproducibility For most routine purposes above 240 nm, a solution of holmium oxide in perchloric acid will provide a convenient method for routinely checking the calibration of the wavelength scale. Fig. 2.10 shows a typical spectrum. The values are known to be within 6 0.2 nm and are adequate for most solution work. If wavelengths 200
240 nm in the region are needed, then either atomic line sources such as a vapor discharge lamp or other rare earth solutions may be used. Fig. 2.11

UV-Visible Spectrophotometry of Waters and Soils

2.3 Factors affecting the quality of spectral data

39

FIGURE 2.10 Holmium perchlorate solution 5%m/v, 10 mm pathlength and 1 nm SBW. For all the accepted intrinsic values of the wavelength maxima, see [12]. SBW, spectral bandwidth.

shows a spectrum of a certified reference material which is commercially available [13]. 2.3.4.2 Absorbance accuracy and reproducibility For most routine purposes, a solution of potassium dichromate in dilute sulfuric or preferably perchloric acid will provide a convenient method of routinely checking the calibration of the absorbance scale at four wavelengths in the UV, at 235, 257, 313, and 350 nm. Fig. 2.12 shows a typical spectrum with the A1% 1 cm values plotted as a function of wavelength. Certified solutions containing perchloric acid are now widely available in sealed quartz cuvettes.

UV-Visible Spectrophotometry of Waters and Soils

40

2. The basis for good spectrophotometric UV
visible measurements

FIGURE 2.11 Commercial wavelength standard for 200
300 nm [13] with typical values.

For wavelengths below 235 m, materials such as nicotinic acid may be used as Fig. 2.13 shows. 2.3.4.3 Stray light Stray light causes deviations from the Beer
Lambert law and limits the upper limit of the absorbance scale. Fig. 2.14 illustrates the effect. The cutoff filter method is satisfactory for some routine applications. It must always be borne in mind that the observed instrumental stray light (ISL) is a function of the sample: the measurement of x% ISL with a cutoff filter does not mean that x% will again be present when a different absorber is in the beam. It is better to regard the filter method as one that detects stray light rather than measures it. The solutions and liquids listed below are recommended as standard cutoff filters. They are also the recommendations of the American Society for Testing and Materials (ASTM) and are generally accepted as industrial standards (Table 2.3). Compared to glass filters, they have the

UV-Visible Spectrophotometry of Waters and Soils

2.3 Factors affecting the quality of spectral data

41

21 FIGURE 2.12 A1% solution of potassium dichromate in dilute 1cm values for a 60 mg L sulfuric acid at 25 C.

advantages of reproducibility and freedom from fluorescence. It should be noted that the cells used must be clean, free from fluorescence, and with as high a transmission as possible in the region under investigation. Attention to these factors is particularly important when measuring stray light below 220 nm. The absorbance below 190 nm is strongly affected by dissolved oxygen. Pure nitrogen should be bubbled through for several minutes before use, and the water should be freshly distilled. Water purified by ion-exchange methods may contain significant amounts of organic impurities. Different concentrations or pathlengths may be used to displace the absorption edge of these filters so that they can be used in other regions. The absorbance of potassium chloride solution increases significantly with temperature by about 2% per  C. For most purposes, the apparent absorbance of these filters should be more than 2, giving an ISL value of ,1%.

UV-Visible Spectrophotometry of Waters and Soils

42

2. The basis for good spectrophotometric UV
visible measurements 1.3 1.2

A b s o r b a n c e v s r e fe r e n c e

1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

W a v e le n g th n m

FIGURE 2.13 Commercial nicotinic acid absorbance standard for 210 to 260 nm [13].

FIGURE 2.14 Effect of stray light on the Beer
Lambert law.

UV-Visible Spectrophotometry of Waters and Soils

300

295

290

285

280

275

265

270

260

255

250

245

240

235

230

225

215

220

210

205

0.0

2.3 Factors affecting the quality of spectral data

43

TABLE 2.3 Cutoff filters for stray light tests. Pathlength 10 mm (or 5 mm and 10 mm pathlength for the Mielenz method). Spectral range (nm)

Solution or liquid

190
210

Aqueous potassium chloride (12 g L21)

210
265

Aqueous sodium iodide or potassium iodide (10 g L21)

250
330

Acetone

300
395

Aqueous sodium nitrite (50 g L21)

However, there is a better and easier method for routinely monitoring stray light using these filters [14]. In this method, the same cutoff filter solution is used but using 2 cuvettes; one having a 10 mm path in the sample beam and the other a 5 mm path in the reference beam. Hence the 5 mm path cuvette containing the selected cut off solution is measured as a blank absorbance spectrum and then the 10 mm cuvette is measured against it. In this way, a differential absorbance spectrum is obtained. The maximum value for this differential absorbance, A’’, is obtained and the approximate value for s is calculated from: s5

1 2A00 10 4

(2.9)

For a limit of 1% stray light, any experimental value of Av greater than 0.7 will comply. The wavelength of the maximum differential absorbance, Av, is not relevant as this depends on s and the optical geometry of the spectrophotometer. Examples on three spectrophotometers using aqueous potassium chloride cutoff are shown in Fig. 2.15. All the instruments pass the 1% stray light criterion as the maximum differential absorbance, Av, is at or above 0.7. 2.3.4.4 Resolution The best performance of a spectrometer will only be attained, in terms of both absorbance and wavelength accuracy, if careful consideration is given to the resolution of the monochromator. Since resolution is a function of slit width as well as dispersion of the instrument, the choice of slit setting is a critical one. Most modern instruments use grating monochromators that provide constant dispersion with wavelength. The smaller the SBW, the greater the resolution, but the corresponding reduction in energy means that the signal-to-noise ratio falls. It is

UV-Visible Spectrophotometry of Waters and Soils

44

2. The basis for good spectrophotometric UV
visible measurements

1 .6

D iffe r e n tia l A b s o r b a n c e

1 .2 % m /v K C l ( 1 0 m m v s 5 m m p a th )

1 .5 1 .4

C a ry 5 0 0

1 .3

C a ry 5 0

1 .2

U n ic a m

1 .1 1 .0 0 .9 0 .8

C r it ic a l v a lu e f o r 1 % s t r a y lig h t

0 .7 0 .6 0 .5 0 .4 0 .3 0 .2 0 .1 0 .0 190

195

200

205

210

215

220

W a v e le n g th n m

FIGURE 2.15 Mielenz stray light method using potassium aqueous chloride (1.2%m/v) for three instruments.

therefore necessary to select the smallest possible slit width that gives an acceptable noise level. When measuring an absorbance band in a high-resolution instrument, it is recommended that the SBW should not exceed 10% of Natural Band Width (NBW) of the band. There is a simple check for the resolution of an instrument. Record the spectrum of a 0.02% v/v solution of toluene in hexane compared with a solvent blank. The ratio of the maximum at 269 nm and the minimum at 266 nm gives a measure of the resolution of the instrument. A set of spectra is shown in Fig. 2.16 and the observed ratios in Table 2.4. The ratio values are within 6 0.1 for temperatures between 15 C and 30 C, and concentrations of toluene between 0.005% v/v and 0.04% v/v. 2.3.4.5 Optimal spectrophotometric range For many measurements, we have to obtain the most accurate and precise value that we can, given the performance of the instrument. In order to do so, we need to operate in the optimal spectrophotometric range for both accuracy and precision. The majority of instruments

UV-Visible Spectrophotometry of Waters and Soils

45

2.3 Factors affecting the quality of spectral data

0.55

S p e c t r a l B a n d w id th 0 .2 n m

0.50

0 .5 n m 1 .0 n m 2 .0 n m

0.45

A b s o rb a n c e

5 .0 n m

0.40

0.35

0.30

0.25

0.20 265

266

267

268

269

270

271

W a v e le n g th n m

FIGURE 2.16 Variation of spectrum of 0.02% v/v toluene in hexane at 25 C with spectral bandwidth.

actually measure the apparent transmittance, T, of the sample, which is converted to the more useful absorbance, A, by log

k0 bC l0 1 5 log10 5 λ T 2:303 l

(2.10)

Ideally, the transmittance scale on a linear detector is fixed by a 0% T measurement (dark current measurement on the detector) and a 100% T measurement (total illumination of the detector by I0). A sample attenuates the I0 intensity signal and the sample transmittance; hence, the absorbance is obtained. All these individual measurements are subject to noise and drift errors and combine to give an overall measurement standard deviation, σT. This standard deviation is related to the

UV-Visible Spectrophotometry of Waters and Soils

46

2. The basis for good spectrophotometric UV
visible measurements

relative error of measurement, σc/C, and may be obtained by rearranging Eq. (1.7) and obtaining the partial derivative. Note that the molarity, M, in Eq. (2.5) has been replaced by C the concentration in g L21. The relative error function [15] is given by Eq. (2.11): σc 0:4343 σT 5 log10 T T C

(2.11)

Hence, the calculation of the relative error would be straightforward if it were not for the fact that the value of overall measurement standard deviation, σT, is not independent of the value for T. A detailed theoretical study of the sources and dependencies of the relative error has been made. Rothman et al. [16] were able to derive three expressions for σΤ (Table 2.5). These are given in Table 2.5. If we assign typical values for k1 and k2 of 6 0.3% T and 6 1.3% T for k3, then we are able to draw graphs of the three functions. This is shown in Fig. 2.17.

TABLE 2.4 Observed ratios and spectral bandwidth for 0.02% v/v toluene in hexane. Spectral bandwidth (nm)

Observed ratio

0.25

2.3

0.5

2.2

1.0

2.0

2.0

1.4

3.0

1.1

4.0

1.0

TABLE 2.5 Contributions to spectrophotometric precision (adapted from [16]). Standard deviation of a measurement σT σ T 5 k1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffi σ T 5 k2 T 2 1 T σT 5 k3T

Source of variability

Relative error function

Thermal detector, amplifier, and dark current noise [Independent of T]

σc C

5

0:4343 k1 log10 T T

Shot noise from the detector

σc C

5

0:4343 log10 T k2

Cell positioning, nonparallelism errors, and incident beam intensity fluctuations

σc C

5

0:4343 log10 T k3

UV-Visible Spectrophotometry of Waters and Soils

qffiffiffiffiffiffiffiffiffiffiffi 1 1 T1

2.3 Factors affecting the quality of spectral data

FIGURE 2.17

47

% Relative error plots for Table 2.5.

These are derived from theoretical considerations, but similar experimentally determined relative error curves are often observed in practice. The first plot is often found with older single-beam instruments where the error is at a minimum in the 0.4 to 0.6 A range. Modern double-beam instruments tend to have a broad minimum from 0.6 up to 1.5 A, depending on the stray light performance. For diode array spectrophotometers, generally the minimum tends to be extended to lower absorbance values. Fig. 2.18 gives an example of experimentally obtained relative error curves at three wavelengths, 240, 426, and 636 nm. Note that the visible wavelengths have a very similar broad minimum from about 0.2 to 2 A, where the relative errors are very small (,0.2%). However, this range is much smaller in the UV region, and, at 240 nm, this range is about 0.2 to 1 A.

UV-Visible Spectrophotometry of Waters and Soils

48

2. The basis for good spectrophotometric UV
visible measurements 6 .0 5 .5

240nm

5 .0

426nm

% r e la t iv e e r r o r

4 .5

636nm

4 .0 3 .5 3 .0 2 .5 2 .0 1 .5 1 .0 0 .5 0 .0 - 0 .5 0 .0

0 .5

1 .0

1 .5

2 .0

2 .5

3 .0

3 .5

4 .0

A b s o rb a n c e

FIGURE 2.18 Experimental relative error curves for an HP8450 DAS.

This range will become even smaller as the wavelength decreases. Particular care has to be taken when working below 220 nm, and accurate measurements are extremely difficult below 200 nm. Another approach for determining the optimal accurate absorbance range is to use the method of Vandenbelt, Forsyth, and Garrett (VFG) [17]. This is a very useful and simple method that is now not so well known. It includes making a series of solutions over a range of concentrations to cover the absorbance range. From the concentration and the observed absorbance value, the absorbance of a 1% solution in a 1 cm cuvette is calculated, A1% 1 cm . Ideally, all the values will be the same within an experimental uncertainty. However, in practice, this is not observed. At low values of absorbance, A1% 1 cm values tend to be high, and the converse is also true. The latter effect is usually attributed to stray light, whereas the former is a failure of Beer’s law for reasons that are not clearly understood. Fig. 2.19 [18] shows an experimental VFG study of a corticosteroid, betamethasone-17-valerate, in ethanolic solution at 25 C using a doublebeam spectrophotometer at 238 nm. The accepted value for the A1% 1 cm of this compound is 325 under the conditions of test. This value is observed if the absorbance range is between 0.6 and 1.9 A. If more concentrated solutions had been made and measured, the A1% 1 cm values above 2 A would have decreased, making the curve more sigmoidal. Note that A1% 1 cm values below 0.3 A yield significantly higher values.

UV-Visible Spectrophotometry of Waters and Soils

49

2.4 Sample presentation 370 365 360 355

A ( 1 % ,1 c m )

350 345 340 335 330 325 320 315

2 .0

1 .9

1 .8

1 .7

1 .6

1 .4

1 .5

1 .3

1 .2

1 .1

1 .0

0 .9

0 .8

0 .7

0 .6

0 .5

0 .4

0 .3

0 .2

0 .1

0 .0

310

M e a s u re d A b s o rb a n c e

FIGURE 2.19 VFG plot for betamethasone-17-valerate in ethanol at 25 C and 238 nm. VFG, Vandenbelt, Forsyth, and Garrett.

The conclusion is that, for the most accuracy, it is desirable to perform relative error or VFG experiments on the spectrometer used under the desired operating conditions. The importance of carrying out this work increases as the wavelengths approach the extremes of the operating range of the spectrophotometer, in particular, the ultraviolet.

2.4 Sample presentation The correct presentation of the sample in the spectrometer is critical for the validity of the acquired spectral data. The “fitness for purpose” of the instrument is only the first step in assuring data quality. The large majority of measurements in water analysis are conducted as transmittance measurements and therefore the sample cell or cuvette is of major importance.

2.4.1 Cuvettes The physical pathlength and parallelism of the measurement windows together with the orientation in the sample beam primarily

UV-Visible Spectrophotometry of Waters and Soils

50

2. The basis for good spectrophotometric UV
visible measurements

determine the accuracy and precision of the measurement. The material of construction of those windows is the limiting factor governing the operational wavelength range. The transmittance of an empty cuvette is affected by [19]: • • • • •

absorption of the window material scatter and reflection at all four window surfaces dispersion and deviation of the beam due to optical imperfections fluorescence of the window material itself cleanliness of the of the windows.

In addition, when the cuvette is filled with solvent, the transmittance of the solvent itself can be the limiting factor governing the operational wavelength range. Cuvettes are available in a range of materials and sizes. From a purely practical point of view, cuvette pathlengths of 2 to 50 mm are the usual limits. However, 1 to 100 mm cuvettes are obtainable if required. Typical materials of construction found in current practice are: 1. “plastics” (usually for disposable cuvettes and well plates); lower usable limit normally between 350 and 390 nm depending on the polymer used; 2. glass; lower usable limit about 330 nm; 3. optical glass; lower usable limit about 310 nm; 4. fused quartz; lower usable limit about 215 nm; and 5. synthetic fused silica; lower usable limit about 180 nm. Typical transmission curves of these materials are illustrated in Fig. 2.20. The operational wavelength range of the application will determine the cuvette material selection. However, for accurate work below 250 nm cuvettes made from synthetic fused silica (e.g., Suprasil quartz) are recommended. Cuvettes should always be handled by the nonoptical faces with the operator or technician wearing nonshedding particle-free gloves. The cleaning or polish of the optical faces should always be done with lens tissue or a specialist cloth such as Selvyt. The use of laboratory tissues is strongly discouraged as these may contain abrasive particles and fluorescent materials. The best practice for measuring solutions is to leave the clean cuvette in the spectrometer and transfer and remove the solvent and the solution in turn using transfer pipettes which should be nonabrasive and not contaminate the sample with extractives. Particular care should be taken to ensure that no drips intrude on the optical faces caused by overfilling or poor technique.

UV-Visible Spectrophotometry of Waters and Soils

51

2.4 Sample presentation

g r a d e w a t e r (1 0 m m ) a g a in s t a ir (t r a n s m is s io n )

% t r a n s m it t a n c e o f c u v e tt e c o n t a in in g s p e c tr o s c o p ic

100

90

80

70

60

50

S y n th e tic fu s e d s ilic a

40

F u s e d q u a rtz 30

B o ro s ilic a te o p tic a l g la s s G la s s

20

P o ly s ty re n e

10

390

400

380

370

360

340

350

320

330

300

310

280

290

270

250

260

240

230

220

210

200

190

0

W a v e le n g t h ( n m )

FIGURE 2.20 Transmission curves for cuvette window materials, 10 mm path with spectroscopic grade water against air reference.

2.4.2 Cleaning procedures In UV
visible spectroscopy, cleanliness of cuvettes is essential. Plastic cuvettes are easily scratched and can be difficult to clean. They should only be cleaned using mild nonalkaline detergents with lack of abrasion. Fused glass, quartz, or synthetic silica cuvettes are more resilient but alkaline solutions should only be used as a last resort. The hierarchy of cleaning should proceed from the mildest to the most drastic [19]: 1. 2. 3. 4. 5.

Rinse with spectroscopic grade water or miscible organic solvent. First cold then hot solutions of nonalkaline detergent solutions. Concentrated nitric acid first cold then hot. Concentrated nitric acid first cold then hot with ultrasonic treatment. Hot trisodium orthophosphate (80 C to 90 C) for 10 minutes followed by rinsing with multiple amounts of hot distilled water. This is alkaline and should only be infrequently employed when all milder cleaning methods have failed.

Note that methods 3 to 5 are hazardous and should only be carried out in a fume cupboard in conjunction with personal protection, goggles, gloves, etc. by properly trained operators.

UV-Visible Spectrophotometry of Waters and Soils

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2. The basis for good spectrophotometric UV
visible measurements

2.5 Factors influencing spectral characteristics 2.5.1 Sample handling and storage Samples should be stored in impervious containers (borosilicate glass is often preferred.) and sealed against evaporation and contamination which are clearly labeled. The container materials should not leach into the sample. Protection from light may also be required in some circumstances. In any event, all samples should be stored securely and under appropriate environmental conditions.

2.5.2 Turbidity The measured absorbance at any wavelength is the sum of the absorbance of all the components in the sample. This presupposes that all the absorbing components are known and or can be identified. This not always the case with water or wastewater samples which may contain suspended solids and/or materials of biological origin. These materials cause nonlinear irrelevant absorption backgrounds. Turbidity from colloidal materials is a particular problem in this respect. Filtration does not always remove these undesirable background effects. However, the application of nephelometric and turbidimetric measurements of physical and aggregate properties is discussed in detail in Chapter 7. Light scattering is the physical phenomenon in which a beam of light changes its direction of propagation (known as deflection) as a result of interaction with sufficiently small matter particles. It has been established from the Maxwell’s electromagnetic theory that a prerequisite for scattering to occur is that the refractive indices of the suspended particles must be different from those of the suspending liquid. The larger the difference, the more intense the scattering becomes. There are two types of light scattering: (1) elastic scattering, in which the wavelength of the scattered light and incident light are the same, and (2) inelastic light scattering, in which the wavelength of the scattered light and incident light are different. Only the first type of light scattering (elastic) is relevant to nephelometry and turbidimetry. In nephelometry, the intensity of the scattered light is measured at a 90 angle whilst, in turbidimetry, the intensity of the attenuated incident light, at 0 , transmitted through the sample is measured. Many turbidimetric methods require the use of a traceable standard Formazin is the only known primary turbidity standard. All other standards are secondary and must be traced to Formazin. The primary standard is defined in the IUPAC Compendium of Chemical Technology,

UV-Visible Spectrophotometry of Waters and Soils

53

2.5 Factors influencing spectral characteristics

second edition (the Gold Book) as one that is prepared by the user from traceable materials using well-defined methodologies and conditions. For details, see Ref. [20].

2.5.3 Solvent quality and polarity It is well known that solvent polarity has an effect on absorption spectra in the UV region. A solvatochromic shift refers to the dependence of an absorption spectrum with solvent polarity. If the shift is toward the blue, it is called a hypsochromic shift and correspondingly a bathochromic shift is a shift toward the red end of the spectrum. For more details, see Ref. [18]. Therefore care needs to be taken of solvent composition when making spectral comparisons. Less well known is the need to preserve solvent quality in the laboratory. This is particularly true for spectroscopic grade water and other solvents below 250 nm. Spectroscopic grade water for use in the low UV is very susceptible to contamination and leaching from polymeric storage bottles. For work below 230 nm, borosilicate glass storage is essential. Fig. 2.21 illustrates the contamination causing the reduction in transmittance for spectroscopic grade water stored in a Nalgene deionized water container. For work above 300 nm, this will not be a problem but in the UV, it most certainly is. 100

F r e s h S p e c tr o s c o p ic G r a d e W a te r

C u v e t t e t r a n s m it t a n c e v s a ir

95 90

S to r e d S p e c tr o s c o p ic G r a d e W a te r

85 80 75 70 65 60 55 50 45 40 35

W a v e le n g t h ( n m )

FIGURE 2.21

Spectroscopic grade water contamination from incorrect storage.

UV-Visible Spectrophotometry of Waters and Soils

400

390

370

380

360

340

350

330

320

310

300

290

280

270

250

260

240

230

220

210

200

190

30

54

2. The basis for good spectrophotometric UV
visible measurements

2.5.4 pH Many compounds, for example, dyes, have spectra which are highly sensitive to pH. For example, see Figure 3.8 for the dependence of alizarin dye spectra on pH. For this reason, it is essential to match sample and reference standard solutions.

2.5.5 Ionic strength Dye spectra also can be changed by changes in ionic strength of the solution. For example, Congo red’s ultraviolet
visible spectrum in aqueous solution main absorption band at about 498 nm shifts toward the blue while the molar absorptivity of this peak decreases predictably with increasing ionic strength [21]. Again, it is essential to match sample and reference standard solutions. This aspect will be particularly important for samples with high salinity.

2.5.6 Temperature Temperature impacts absorption measurements and thereby the photometric accuracy in several significant ways. Changes in temperature can induce expansion or contraction of the solvent, leading to lower/ higher concentrations and absorbance. While this is a particular issue with some organic solvents, careful temperature control, that is, within 6 2 C may be required where high-accuracy absorbance values are required for aqueous solutions. If temperature effects are known to be large, a cuvette thermostating system will be required. Some molecules show significant spectral changes with temperature [22,23]. This is particularly true where buffered solvents are used. Some observed temperature effects are due to the temperature dependence of the solvent. These effects are often associated with pH and ionic strength effects as they are also temperature dependent.

2.5.7 Data treatment This topic is covered in detail in Chapter 3. However, it is useful to introduce three main aspects of spectral treatment before more details are discussed. 2.5.7.1 Averaging and smoothing All spectroscopic measurements are subject to error. When multiple measurements are made at a single wavelength, the errors may be reduced by averaging to minimize the effect of noise. When the effect of noise is to be minimized across a single spectrum, this is known as smoothing. Many

UV-Visible Spectrophotometry of Waters and Soils

55

2.5 Factors influencing spectral characteristics

spectrometers have smoothing functions contained within the software. The most common of these smoothing functions is based upon polynomials and the work of Savitsky and Golay [24] (S
G). Care has to be taken not to “oversmooth” the data and cause distortion of the spectral band. For most work, values for n of 7 or 9 in the S
G algorithm are suitable. 2.5.7.2 Derivatives Savitsky and Golay also provided methodologies for generating derivative spectra. The technique is most useful in removing slow changing baselines or in revealing spectral features not readily observed in the traditional absorbance wavelength data presentation mode (zeroorder spectrum). Fig. 2.22 illustrates the application of the S
G derivative methodology to a portion of the holmium perchlorate spectrum between 320 and 380 nm containing three bands in the zero-order spectrum. For clarity the two derivative spectra on the right-hand axis have been offset to allow ease of comparison. The first-derivative spectrum contains nine readily identifiable peaks and troughs and hence is a better “fingerprint” for identity than the zero-order spectrum. The secondderivative spectrum has two distinct features. First, there are always 1 .0

0 .2 0 F ir s t d e r iv a t iv e s p e c t r u m o f f s e t b y 0 . 0 8

0 .1 5 0 .9

0 .0 5 0 .0 0

0 .7 - 0 .0 5 0 .6

S e c o n d d e r iv a t iv e s p e c t r u m o f f s e t b y - 0 . 2 0

- 0 .1 0 - 0 .1 5

0 .5 - 0 .2 0 0 .4

- 0 .2 5 - 0 .3 0

D e r iv a tiv e s p e c tr a

A b s o rb a n c e (z e ro o rd e r s p e c tru m )

0 .1 0 0 .8

0 .3 - 0 .3 5 0 .2

Z e ro o rd e r s p e c tru m

- 0 .4 0 - 0 .4 5

0 .1 - 0 .5 0 0 .0 320

- 0 .5 5 325

330

335

340

345

350

355

360

365

370

375

380

W a v e le n g th n m

FIGURE 2.22 Spectrum of holmium perchlorate solution, 1 m SBW and 0.05 data pitch; zero-order and first- and second-derivative spectra 320 to 380 nm. SBW, spectral bandwidth.

UV-Visible Spectrophotometry of Waters and Soils

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2. The basis for good spectrophotometric UV
visible measurements

negative peaks corresponding to the positive peaks in the zero-order spectrum and second, there is a much enhanced noise component. There is therefore no enhancement in the identification ability. Firstderivative spectra can be useful for quantitative purposes in multicomponent systems (see Ref. [25]). 2.5.7.3 Spectral correction Many spectral interpretation problems can be overcome using simple methodologies, including spectral subtraction (difference spectra) and spectral normalization, including isosbestic point evaluation in addition to derivative methodologies. Examples of these approaches are covered in Chapter 3 in some detail.

2.6 Data integrity and security Many laboratories concerned with the testing of water and wastewater operate under a regulated environment and are accredited to ISO norms such as ISO/IEC 17025; General requirements for the competence of testing and calibration laboratories. Data are routinely generated to show compliance with appropriate standards. In recent years, there has been a growing concern regarding the integrity of these data particularly from a legal perspective. Data integrity and security is concerned with more than just the generation and security of correct numbers. It is concerned with the totality of arrangements under a QMS to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data life cycle. “Data governance” is the term used for the overall control strategy to ensure data integrity and security. All works should be carried out within the data governance framework of the quality system. It is important to realize that there are other functional aspects within the laboratory and the quality function within overall organization that must be under control otherwise data integrity will be compromised, despite the best efforts of all staff. The foundation of data governance is the engagement and involvement of executive and senior management throughout any organization. Therefore there must be management leadership and data integrity policies that cascade down to laboratory data integrity procedures together with data integrity training. The ISO/IEC Guide 17025 2017
11 [11] specifies in Section 7.5 of technical records,

UV-Visible Spectrophotometry of Waters and Soils

57

References

Analycal Process Level 3: Analysis

Sample Generaon

Sample Collecon & Transport to Laboratory

Laboratory Receipt

Sample Storage & Use Logs

Sample Management

Sample Analysis

Level 2: Analycal Procedure

Level 1: Instruments & Systems

Foundaon: Corporate Level

FIGURE 2.23

Sample & Standard Preparaon

Sample & Standard Weights

Out of Specificaon Out of Expectaon Out of Trend Invesgaon

Sample Analysis Fitness for use check (e.g., SST), Analysis of Samples Data acquision, Processing and Calculaon

Soluon Preparaon, Weights and Records

Analycal Procedure: Validaon / Verificaon under actual condions of use, Insrument logs

Analycal Balance Qualificaon, Calibraon, Daily check & use log

Analycal Instrument / System Qualificaon, Validaon, Maintenance, Calibraon and Use Records

Reportable Result

CoA or Analysis Report

Maintenance and Use Logs

Corporate Data Integrity Culture, Ethics, Policy, Procedures and Training Audits Internal and Suppliers, Mechanisms for Raising and Invesgang Data Integrity Issues

An analytical process and the data integrity model [24].

7.5.1 The laboratory shall ensure that technical records for each laboratory activity contain the results, report and sufficient information to facilitate, if possible, identification of factors affecting the measurement result and its associated measurement uncertainty and enable the repetition of the laboratory activity under conditions as close as possible to the original. The technical records shall include the date and the identity of personnel responsible for each laboratory activity and for checking data and results. Original observations, data and calculations shall be recorded at the time they are made and shall be identifiable with the specific task. 7.5.2 The laboratory shall ensure that amendments to technical records can be tracked to previous versions or to original observations. Both the original and amended data and files shall be retained, including the date of alteration, an indication of the altered aspects and the personnel responsible for the alterations.

It is therefore a requirement that a suitable laboratory QMS is in place with adequate technical and procedural controls. An example of an analytical process and the data integrity model from the pharmaceutical industry is shown in Fig. 2.23 [26].

References [1] R. Keller, J.-M. Mermet, M. Otto, M. Valca´rcel, H.M. Widmer, Analytical Chemistry, 2nd Edition, Wiley
VCH, 2004. [2] I. Fleming, D.H. Williams, Spectroscopic Methods in Organic Chemistry, McGrawHill, 1966. [3] C.N.R. Rao, Ultraviolet and Visible Spectroscopy, 2nd Edition, Butterworths, 1967. [4] R.E. Dodd, Chemical Spectroscopy, Elsevier, Amsterdam, 1962.

UV-Visible Spectrophotometry of Waters and Soils

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2. The basis for good spectrophotometric UV
visible measurements

[5] H.H. Jaffe´, M. Orchin, Theory and Applications of Ultraviolet Spectroscopy, John Wiley, 1962. [6] H.-H. Perkampus, UV-VIS Atlas of Organic Compounds, 2nd edition, VCH, 1992. [7] IUPAC, Compendium of Analytical Nomenclature, Definitive Rules, 3rd Edition, Blackwell Science, 1997. [8] Standards and best practices in absorption spectrometry, in: C. Burgess, T. Frost (Eds.), Blackwell Science, 1999. [9] J.F. Clare, Calibration of UV-vis spectrophotometers for chemical analysis, Accreditation and Quality Assurance 10 (6) (2005) 283
288. [10] United States Pharmacopeia General Chapter , 1857 . Ultraviolet-visible spectroscopy
theory and practice. [11] ISO/IEC 17025:2017-11 General requirements for the competence of testing and calibration laboratories. International Organization for Standardization, Geneva, Switzerland. [12] J.C. Travis, J.C. Acosta, G. Andor, J. Bastie, P. Blattner, C.J. Chunnilall, et al., Intrinsic wavelength standard absorption bands in holmium oxide solution for UV/visible molecular absorption spectrophotometry, Journal of Physical and Chemical Reference Data 34 (1) (2005) 41
56. [13] Available from Starna Scientfic http://www.starna.com/. [14] K.D. Mielenz, V.R. Weidener, R.W. Burke, Heterochromatic stray light in UV absorption spectrometry: a new test method, Applied Optics 21 (18) (1982) 3354
3356. [15] D.A. Skoog, D.M. West, F.J. Holler, Fundamentals of Analytical Chemistry, seventh ed., Fort Worth: Saunders College Pub., 1996 (Chapter 24A). [16] L.D. Rothman, S.R. Crouch, J.D. Ingle Jr., Theoretical and experimental investigation of factors affecting precision in molecular absorption spectrophotometry, Analytical Chemistry 47 (8) (1975) 1226
1233. [17] J.M. Vandenbelt, J. Forsyth, A. Garrett, Industrial and engineering chemistry, Analytical Edition 17 (4) (1945) 235. [18] C. Burgess, in: C. Burgess, K.D. Mielenz (Eds.), Advances in Standards and Methodology in Spectrophotometry, Elsevier, Amsterdam, 1987, p. 307. [19] C. Burgess, A. Knowles (Eds.), Standards in Absorption Spectrometry, Chapman & Hall, 1981. [20] M. Sadar, Turbidity Standards Technical Information Series — Booklet No. 12 Technical Information Series: Turbidity Standards, https://www.hach.com. [21] T.E. Sladewski, A.M. Shafer, C.M. Hoag, The effect of ionic strength on the UV
vis spectrum of Congo red in aqueous solution, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 65 (3) (2006) 985
987. [22] V.A. Yarborough, J.F. Haskin, W.J. Lambdin, Temperature dependence of absorbance in ultraviolet spectra of organic molecules, Analytical Chemistry 26 (10) (1954) 1576
1578. [23] M. Ito, The effect of temperature on ultraviolet absorption spectra and its relation to hydrogen bonding, Journal of Molecular Spectroscopy 4 (1
6) (1960) 106
124. [24] A. Savitsky, M.J.E. Golay, Smoothing and differentiation of data by simplified least squares procedures, Analytical Chemistry 36 (1964) 1627
1639. [25] R. Bourne, C. Burgess, Effect of temperature on a multi-component ultraviolet spectrometric determination and the development of a temperature-independent assay procedure, The Analyst 120 (1995) 2075
2080. [26] European Compliance Academy Analytical Quality Control & IT Compliance Groups, Data Governance and Data Integrity for GMP Regulated Facilities, November 2016.

Further reading C. Reichardt, T. Welton, Solvents and solvent effects in organic chemistry, 4th edition, Wiley-VCH, Weinheim, Germany, 2010, p. 360.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

3 From spectra to qualitative and quantitative results Olivier Thomas1 and Jean Causse2 1

EHESP School of Public Health, Rennes, France, 2Transcender Company, Rennes, France

O U T L I N E 3.1 Introduction

59

3.2 Basic 3.2.1 3.2.2 3.2.3

61 61 68 71

handling of UV spectra One spectrum transformation Two-spectra comparison Evolution study from a spectra set

3.3 Concentration calculation 3.3.1 Ideal case: pure solution with no interference 3.3.2 Real samples: compensation of interferences 3.3.3 Real samples: pretreatment steps for improving UV response

74 75 80 87

3.4 Examples of application

88

Acknowledgments

90

References

90

3.1 Introduction The main objective of this chapter is to present the different methods for the exploitation of UV
visible absorption spectra of samples. If

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00011-3

59

© 2022 Elsevier B.V. All rights reserved.

60

3. From spectra to qualitative and quantitative results

quantitative methods, based on the use of the Beer
Lambert law, are well known (simple absorptiometry, multicomponents procedures, etc.), the qualitative exploitation of data is less employed. However, this missing step in spectrophotometric measurement is most often very useful for an understanding of the relationship between sample composition and spectra shape, or even for the “mathematical pretreatment” of the signal. Curiously, this approach, obvious for IR spectrophotometry with the Fourier transformation of the signal, for example, is rarely envisaged for UV
visible applications. Different methods, depending on the number of constituents to be determined and the complexity of sample, are presented. Before using one of the following methods, it is assumed that no saturation occurs when the spectrum is acquired, in order to be sure that the additive property of the Beer
Lambert law is available for one dissolved constituent [1] or a mixture of compounds [2]. For this purpose, good spectroscopic practices as described in Chapter 1 will be followed. In the first part, some basic tests or transformation methods can be proposed for qualitative exploitation of spectra. If only one spectrum is available, the user can extract some absorbance values at given wavelengths, calculate the derivative signals (the second one is of particular interest for peak or shoulder identification), or estimate a “shape factor,” (SF), this new parameter being useful for the treatability study of industrial wastewater. Where two spectra are to be studied, the most evident step is a comparison between them (their shape) either direct or after normalization, in a given window of wavelengths. This step must not overshadow the great interest of the arithmetical handling of spectra and particularly a simple differentiation. Moreover, while studying a set of spectra, the research of isosbestic point(s), direct or hidden (revealed after normalization), is very important in order to check an eventual conservation of composition, either quantitative or qualitative. Then, the different methods available for quantitative analysis will be reviewed. The problem of determining the concentration of one or two or more components can be solved with the usual methods based on the absorbance measurement at one or several wavelengths, if the optical response of the solution is free of interferences. Unfortunately, for water and wastewater analysis, there is always either physical (e.g., diffuse absorption of particles) or chemical interferences (e.g., overlapping peaks due to competitive absorbance of compounds), so that more robust methods have to be chosen for spectra exploitation. The simplest are probably the derivative techniques, because they offer to the user the possibility to check the sample quality in a more robust way. Multicomponent procedures based on the use of chemometric methods [multiple linear regression (MLR), principal component analysis (PCA), and partial least squares (PLS)] are also of great interest, like UV spectral deconvolution with a semideterministic approach specifically designed. The chapter

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3.2 Basic handling of UV spectra

61

concludes with a synthesis of some application of UV
visible spectrophotometric water for the study of the quality of water, wastewater, and related media (sewage sludge, soils, sediments, etc.).

3.2 Basic handling of UV spectra The different qualitative methods are presented in Fig. 3.1. All these procedures are easy to carry out either directly with the control software of the spectrophotometer, or with the use of any calculation commercial software (e.g., Microsoft Excel). They are classified with respect to the number of spectra to be considered and thus to the knowledge level to be reached [3]. Pure statistical procedures (e.g., factor analysis) are not presented in this part, because computation is more or less complex, and the user is diverted from the meaning of the raw data and the construction of its own experience.

3.2.1 One spectrum transformation Before considering more advanced methods, such as the use of derivative signals, for example, it can be interesting to present a simple transformation leading to the “visualization” of the UV response. 3.2.1.1 Colored scale A simple test can be proposed [4], based on the absorbance values in the UV region at three wavelengths chosen for their significance: 210 nm corresponding to the presence of nitrate, 240 nm allowing the discrimination between soluble organic matrix and suspended solids, and 320 nm for suspended solids. According to the absorbance values measured, a code value and a color can be proposed in order to give a simple

FIGURE 3.1 Qualitative methods for UV
visible spectra handling.

UV-Visible Spectrophotometry of Waters and Soils

TABLE 3.1 Colored scale for water and wastewater quality, based on absorbance values at 210 nm (λ1), 240 nm (λ2), and 320 nm (λ3). Absorbance values (a.u.)

Code

Colored scale

BOD5 ( mg L21)

COD ( mg L21)

TSS ( mg L21)

Nitrates ( mg L21)

λ1 , 0.5 λ2 , 0.2 λ3 , 0.05

1

Blue

, 10

,20

, 20

,1

Natural water without organic matter nor nitrate or treated and denitrified wastewater

λ1 . 0.5 λ2 , 0.2 λ3 , 0.05

2

Green

, 10

,20

, 20

.10

Natural water with nitrate or efficient biological wastewater treatment plant outlet

λ1 . 0.5 λ2 . 0.2 λ3 . 0.2

3

Yellow

, 50

,150

, 50

—

Biological wastewater treatment plant outlet

λ1 . 1.0 λ2 . 0.5 λ3 . 0.2

4

Red

.100

.200

.100

—

Raw wastewater

Example

This colored scale can be compared to codification systems for water quality mapping. BOD5, biological oxygen demand; COD, chemical oxygen demand; TSS, total suspended solids.

3.2 Basic handling of UV spectra

63

classification and an estimation of the main parameters (Table 3.1). Notice that this (very) simple classification takes into account the evolution of the organic pollution (biodegradation and nitrification). 3.2.1.2 Derivative spectra A priori, the derivatization of spectra does not increase the information content of the raw spectra, but it allows analyzing this information from a different point of view beyond a single absorbance value for a given wavelength. The study of the first derivative gives some information about the slope of the spectrum and enhances the shoulders and inflexion points, that is, gives better information about the spectrum structure. This allows a better characterization of a compound or shows the deformation due to the presence of a foreign compound. The use of derivatives allows removing the undesired contribution of turbid media. Fig. 3.2 shows the three successive derivatives of the UV spectrum of a uric acid solution. The first derivative corresponds to the slope evolution of the raw spectrum, with maxima corresponding to the increase of the absorbance with wavelength, and minima appearing after the maxima of the raw spectrum. On the other hand, the curve goes through a zero value, which corresponds to the maxima of the actual spectrum. The second derivative spectrum corresponds to the slope evolution of the first derivative spectrum.

FIGURE 3.2 Spectrum of uric acid, and derivative spectra (first, second, and third) calculated with several differentiation steps (2, 5, 10, 20, and 30 nm). For example: d2s10 is the second derivative spectrum for a diffferentiation step of 10 nm.

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3. From spectra to qualitative and quantitative results

In this case, there are several points where the curve has a zero value and minima corresponding to the maxima of the raw spectrum. The third derivative spectrum does not bring supplementary relevant information. Some generalizations can be drawn from these observations: • The spectra become more complex with the increase of the derivative degree. The derivative curve has as many occurrences of zero values as its derivative degree, having n 1 1 bands alternate between positive and negative values. • Among all these bands, those at the center are more intense. • The maximum of the raw spectra corresponds to a zero for the odd derivative spectra, and to a maximum or a minimum for the even derivatives. • The size (intensity) of the bands decreases with the derivative degree (note the ordinate values of the scales in the graphs of Fig. 3.2) • The intensity of the bands strongly depends on the original bandwidth of the direct spectrum. Derivatives make discrimination that benefits the narrower bands. Taking into account the previous considerations, the second derivative can be considered as a good compromise between the significance of the resulting spectra and the signal-to-noise ratio. Actually, the derivative computation is more a differentiation than a true mathematical calculation, since spectrophotometer control software often propose the choice of a differentiation step with a given derivatization of the initial spectrum. This choice has to be carefully made before further treatment of the UV signals, particularly for the second and eventually third derivative calculations (Fig. 3.2). A general recommendation can be proposed with a step value close to the quarter of the peak width, which corresponds approximately to 10 nm. An interesting point can be to understand the practical significance of the second derivative. First, peaks and shoulders can easily be located (corresponding to a minimal value) as well as inflexion points (corresponding to zero). Second, the second derivative value is related to the peak shape and height. Let us consider the estimation of the first “derivative”: Aλn 2 Aλðn2hÞ dAλn 5 dλn h

(3.1)

where h is the differentiation step, and Aλn and Aλ(n
h) are the absorbance values at the wavelengths λn and λ(n
h), respectively. For the second derivative, the relation is:

 dAλðn1hÞ =dλðn 1 hÞ 2 2dAλn =dλn Aλðn2hÞ 1 Aλðn1hÞ 2 2TAλn d2 Aλn 5 5 h dλn2 h2 (3.2)

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3.2 Basic handling of UV spectra

65

FIGURE 3.3 Significance of the second derivative signal. Example of uric acid: peak second derivative (288 nm) 5
0.005 (for a differentiation step of 10 nm), compared to Am
A*m 5 0.25.

If we consider for a peak, that the maximum of absorbance Am corresponds to Aλn, and that A*m is the absorbance value of the middle of the segment Aλ(n
h)
Aλ(n 1 h), the previous relation becomes: d2 Aλn 5 2 2 ðAm 2 ATmÞ=h2 dλn2



(3.3)

Thus the second derivative value is linked to the width at half height of the peak (Fig. 3.3). A last point is the limit of the use of the second derivative for real samples with optical interferences linked to the presence of suspended solids or other solutes. The basic assumption is that the derivative value resulting from interferences is close to zero inside the peak or shoulder wavelength window. This is possible when the corresponding spectra part can be considered to be linear as it is shown for water samples containing phenol (Fig. 3.4). The only case where the second derivative can be used for phenol estimation is for sample 2, around 290 nm where the raw spectrum is linear (and close to zero). Notice that in the 240-nm region, influence of the interferences (very strong on raw spectra) leads to the impossibility of considering the second derivative for other use than qualitative information. 3.2.1.3 Shape factor The characterization of spectra structure is very important as the existence of peaks or shoulders can be related to the presence of one or several absorbing compounds. In order to quantify this property, the ratio, for a peak wavelength, for example, between the value of the second derivative and the corresponding absorbance is considered to be of interest [5]. The choice of the use of the second derivative is obvious

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66

3. From spectra to qualitative and quantitative results

FIGURE 3.4 Standard addition of phenol to wastewater
spectra and second derivatives (raw samples: 1 and 2, samples with phenol: 1a and 2a).

and known to be a good compromise to reveal any signal perturbation [6]. The absorbance value weighting minimizes an eventual concentration effect. A SF is thus defined, at each wavelength corresponding to a peak or shoulder, by: SF 5 2

DðλÞ THT102 AðλÞ

(3.4)

where D(λ) is the value of the second derivative measured at the wavelength λ, A(λ) is the absorbance value measured at the same wavelength λ and H, the width at the half height of the peak (difference of wavelengths calculated from the second derivative spectrum; see Fig. 3.4). Considering the second derivative value and sign (negative for a peak), the initial ratio is transformed (*(
100)). According to the value of the SF, UV spectra can be classified into three groups (Fig. 3.5): 1. The first group, SF . 4, is composed of samples with UV spectra showing specific absorption peaks or shoulders revealing the presence of major absorbing component(s). In this case, the comparison of UV spectrum of a sample to a UV spectra library may allow the identification of pollutant.

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67

FIGURE 3.5 Typology of spectra according to the SF value. SF, shape factor.

2. The second group, 0.1 , SF , 4, includes samples giving monotonous UV spectra. In this case, there exists a large probability to consider the studied effluents as complex mixtures or due to the presence of colloids or suspended solids, and more complete investigations must be carried out for a better discrimination. 3. The last group, SF , 0.1, corresponds to nonabsorbing samples or mineral effluents. As said before, this phenomenon is actually rare for organic (industrial) wastewater. 3.2.1.4 Smoothing
denoising The following methods are used to increase the signal-to-noise ratio. They contribute to the smoothing of the signal by filtering the response. The use of these methods has been made possible by the significant advances in the field of developing instruments. The diode array and CCD (charge-coupled device) components, on the one hand, have eliminated the mechanical wavelength uncertainty of the spectrophotometers. On the other hand, the signal digitalization and the use of numerical exploitation have allowed substituting the optical and electronic systems with computational algorithms of derivation, like the Savitzky
Golay method [7], which simultaneously allows noise removal and signal smoothing. The principle of the Savitzky
Golay method is to fit low-frequency components of signal and smooth

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68

3. From spectra to qualitative and quantitative results

high-frequency components of noise, so it cannot effectively denoise low-frequency noise signal. One of the most important limitations of derivative spectroscopy is the background noise associated with any experimental measurement. This random noise usually has a higher frequency than the signal to be measured. This means that it gives weak, but narrow peaks. The use of derivatives strengthens this kind of peaks in front of the broader ones of the compounds, which are the most important in the raw spectrum. A solution to this problem is to use the Fourier transform (or better, the fast Fourier transform), which allows the use of filters for removing high-frequency contributions. The characteristic of Fourier transform is to analyze the spectrum of signal and then eliminate unwanted spectrum directly according to the filter requirements. In a recent work, Zhou et al. [8] confirm that a pretreatment method based on wavelet transform can be carried out for quantitative analysis of UV
vis spectroscopy. The proposed method solved the shortcomings of traditional threshold fixed at each scale and can flexibly set threshold to better preserve signal wavelet coefficients and eliminate noise wavelet coefficients. In any case, the sensitivity of the spectrophotometer is an important parameter in order to limit the measurement errors (distinction impossible between signal and noise) despite signal treatment.

3.2.2 Two-spectra comparison More than one spectrum characterization, a comparison of two spectra can often be of great interest. 3.2.2.1 Differential spectrum This operation is very simple and useful and can be performed with most laboratory spectrophotometers. By subtracting a spectrum by another (i.e., calculating the difference of absorbance values between the two spectra for each wavelength), the result can give relevant information such as a tentative of interferences’ elimination, the study of a treatment effect, as a filtration step, or the evolution of spectra with time. In Fig. 3.6, are presented wastewater samples (inlet and outlet of two treatment plants) and the difference between raw and treated wastewater corresponds to the efficiency of the treatment (for plant 1, a physical process with suspended solids removal only). The same result could be achieved with a filtration step, the difference between raw and filtered samples giving the same spectrum. The limit of the method is shown in the same figure (right), when absorbing compounds appear (like nitrate), leading to a negative difference (spectrum) between inlet and outlet. This is the case for the treatment plant 2, with a nitrification step very efficient.

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69

FIGURE 3.6 Spectra differences for wastewater treatment plants samples (inlet and outlet).

FIGURE 3.7 Revelation of organic matrix by spectra difference.

The previous examples deal with the spectra of real samples. Another way to proceed is to subtract, from the real spectrum, a spectral contribution corresponding to the presence of a given absorbing compound. For example, in natural water, the presence of nitrate can hide the optical response of organic compounds, even if the water was filtered with a very low cut-off membrane (Fig. 3.7). The determination of nitrate concentration (by conventional analytical methods or by UV method) allows subtracting the part of the spectrum related to nitrates from the raw spectrum. This basic handling makes possible the revelation of the UV response of the organic matrix. The presence of carboxylic acids at low concentration can explain the “denitrified filtrate” spectrum shape. 3.2.2.2 Direct comparison of two spectra This data exploitation is less used but interesting if a comparison between spectra is required, in order to check the general quality and origin of wastewater, for example. For each wavelength, the absorbance

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3. From spectra to qualitative and quantitative results

FIGURE 3.8 Direct comparison of absorbance values (the treated wastewater is considered the reference type).

value of one spectrum is plotted against that of the second spectrum. The initial spectra shape is lost, but the obtained graphical relation is relevant (Fig. 3.8). If the resulting graph is a straight line, the studied spectrum has the same shape and can be considered as homothetic. If the graph is a curve, the spectra shapes are thus different. Depending of the curve type, specific information on sample nature can be given [9]. Notice that the higher values of absorbance must often not be considered because of saturation risk, and that the slope of a straight line leads to the homothetic ratio. 3.2.2.3 Normalization In several cases, spectra normalization is a preliminary step to a further study [3]. This operation leads to give a same area (arbitrarily chosen) under the studied spectra. Considering that UV spectra of aqueous samples often relate to dilution phenomena, the normalization step tries to prevent this effect and make spectra comparable. In practice, the area of spectra is given by the sum of absorbances between two given wavelengths (e.g., 200 and 350 nm). More generally, if the absorbance values are acquired every h nanometers, the area can be calculated by: Area 5

350 X

AðλÞ 3 h

(3.5)

λ5200

For a chosen Norm, the corrected absorbance values A*(λ) must be calculated from the product of the measured absorbance values of each

UV-Visible Spectrophotometry of Waters and Soils

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71

FIGURE 3.9 Normalization of spectra of same shape (raw wastewater).

FIGURE 3.10

Normalization of spectra of different shapes.

spectrum by the ratio of the value of the Norm divided by the area of the spectrum. ATðλÞ 5 AðλÞ

 Norm Area

(3.6)

In the previous case (direct comparison of samples), two normalized spectra of a same sample would lead to a straight line with a slope equal to 1. Their corresponding normalized spectra are thus superposed (Fig. 3.9). In the general case, where the shape of spectra varies from one spectrum to another, the normalization step leads to crossing spectra, as shown in Fig. 3.10.

3.2.3 Evolution study from a spectra set The simplest method to study a spectra set evolution is to display the spectra on a single graph (e.g., Fig. 3.10). In some cases, isosbestic points can appear with time [3].

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3. From spectra to qualitative and quantitative results

3.2.3.1 Isosbestic points If all spectra, or at least several spectra of a same set, cross together at one point, this particular point is called isosbestic point (IP). Several isosbestic points may exist in a set of spectra. One classic example of such isosbestic points is, for instance, a set of spectra of one component in solution, at various pH values, which shows an equilibrium between acidic and basic forms, the proportion of which depends on pH value. Most of the studies on IP were made on reacting systems involving pure components artificially mixed in the laboratory (e.g., see [10]), but IP can be also observed for several samples taken in natural environments, as, for example, around the discharge of wastewater into a river (Fig. 3.11). In practice, because of some instrumental errors or environmental unknown effects, an IP is actually a small surface than a real point, and a procedure was proposed for its detection [11]. An isosbestic point is defined by the wavelength λIP as a point where the apparent coefficient of variation (CV*), explained in Eq. 3.7, is lower than a limit value (e.g., fixed at 2.5%, value obtained from a statistical study on repeatability): CVT 5

σTðλÞ 3 100 AðλÞ

(3.7)

where CV* is the apparent coefficient of variation (%), AðλÞ is the average of absorbance values at the wavelength λ (a.u.) and σ*(λ) is the standard deviation estimation of absorbance values at the wavelength λ (a.u.). The search for points called outliers, responsible for a coefficient of variation greater than the fixed value, is based on a statistical test (Dixon test). The UV spectra eliminated, following this test, are

FIGURE 3.11

Study of treated wastewater discharge into a river showing an isosbestic

point.

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3.2 Basic handling of UV spectra

73

considered as not representative of the studied flux. Then, a final statistical test is carried out (Rank test, for example) in order to check if the revealed point is a true isosbestic point. This final test is carried out at λIP 6 10 nm. The presence of at least one isosbestic point shows that there is a mass conservation between all samples, which can thus be considered as a mixture of compounds. More often, this indicates the presence of only two major mixtures (considered like pure compounds), the concentrations of which are linked in a way that the mass balance is conserved [12]. More precisely, there is a fixed linear relation between the concentrations of the two components (or mixtures of fixed mass composition) of the form: a1 C1i 1 a2 C2i 5 1;

’i

(3.8)

where C1i and C2i are the concentrations of components 1 and 2 in the mixture i, and a1 and a2 do not depend on the mixture i. The presence of an isosbestic point in Fig. 3.11 confirms the mass conservation between two mixtures characterized by the presence of anthropogenic organic matter (for treated effluent) and by nitrates (for river), the proportion of which varies according to the sampling place (river, effluent, or mixture between discharge and river). 3.2.3.2 Hidden isosbestic points Most often, no isosbestic point appears in a set of spectra of real samples. This can be explained by several factors as the occurrence of dilution or other physicochemical factors (sedimentation, precipitation, oxidation, etc.). However, in the case of quality conservation (simple dilution, for example), a normalization step can lead to the revelation of at least one isosbestic point in the resulting set of spectra. This isosbestic point is called hidden isosbestic point (HIP). If a normalization step is necessary before revealing one HIP, no mass conservation can be assumed, but the effluent quality remains constant [13]. In this case, the composition of the samples is characterized by the presence of the same components but in variable proportions. The global concentration is also variable. Indeed, the normalization step consists of making artificial conditions of mass conservation. Fig. 3.12 presents a set of spectra of wastewater sampled at different hours of a day, before and after normalization. The presence of a HIP after normalization proves the quality conservation of water. In this case, the samples (wastewater) are assumed to be composed of a mixture of two complex components, particles greater than 1.2 μm (total suspended solids) and matter smaller than 1.2 μm

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FIGURE 3.12 Raw (left) and normalized (right) spectra of wastewater showing hidden isosbestic point.

(“soluble” organic matter), the proportion of which varies according to time and weather [14]. The absence of direct IP can be explained by the infiltration of clear parasite (nonpolluted) water responsible of dilution, especially during night. This method is very interesting for the study of the qualitative variability of water and wastewater. 3.2.3.3 Application: variability estimation The variability is defined from the calculation of the ratio of the number of spectra crossing at the isosbestic point (Npi), divided by the total number of spectra (Nt) of the initial set [11]:   Npi 3 100 (3.9) V5 12 Nt The variability is estimated without any knowledge of the medium composition. An application is presented for industrial wastewater in Chapter 12.

3.3 Concentration calculation The main interest in using UV
visible spectrophotometry is for analytical purposes. Historically, a lot of analytical methods were based on the use or colorimetry, with a specific reagent and the visual or photometric detection of the color of the final solution. The analytical performances were improved with the final spectrophotometric detection, which is more sensitive and more accurate, with the user being able to check the working wavelength. All these methods, employed for the analysis of a single compound in solution, are based on the Beer
Lambert law, actually derived from the Bouguer
Beer
Lambert

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law [15]. From the spectrophotometric analysis of one compound to the simultaneous determination of specific or global parameters by using matricial procedures, this part reviews the main methods available for water and wastewater analysis.

3.3.1 Ideal case: pure solution with no interference 3.3.1.1 Simple absorptiometry for one analyte The Beer
Lambert law (presented in Chapter 1) has some limitations, and the main one is that it is only true for low analyte concentrations. The global relation used for the calculation of the concentration, C, from the absorbance value, A, at a given wavelength, is the following: C 5 fðAÞ

(3.10)

For higher concentrations, a number of corrective factors have to be introduced, as for example the refraction index variation related to the concentration of the analyte. One way to correct this effect is to substitute the value of ελ of the Beer
Lambert equation by ελn/(n2 1 2)2, where n is the value of the refraction index. Usually, this correction is negligible for concentrations lower than 0.01 M. Another effect that can distort the Beer
Lambert law linearity may be the use of a polychromatic radiation. This problem, encountered at the beginning of (spectro) photometry with instruments using filters (photometers), obliges the use of instruments allowing the selection of narrower wavelength ranges by means of monochromators (spectrophotometers). Usually, the absorbance measurements are taken at the spectrum maximum (peak, for example) due to several reasons. On the one hand, the maximum sensitivity (greater slope in the calibration curve) is obtained at this wavelength. On the other hand, the center of the maximum is where the absorbance gradient is minimum versus wavelength, which means lower probability of deviations from the Beer
Lambert law due to the polychromatism of the selected radiation. Finally, it will be a lesser variation of the method sensitivity due to the imprecision in positioning the wavelength. Measurements are sometimes performed not at the maximum, but in other places (shoulder, for example), in order to decrease the obtained values and, therefore, do not saturate the instrument response for a larger range of analysis. In quantitative determinations, a calibration curve is generally obtained with the use of standards. Although a great number of theoretical values of molar absorptivities may be obtained from the literature, a better option is to obtain oneself the calibration curve in exactly the same experimental conditions that will be later applied to the samples. Bibliographic data may, however, be valuable in order to know the

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sensitivity of the analytical method, determination limits, precision, etc. Several books such as Refs. [16,17] may be cited, in which a great number of tested photometric methods are described for a great number of elements. The molar absorptivity at the analytical wavelength is given for each method, together with the relative standard deviation, path length, interferences, etc. Sometimes, the matrix of the standards is quite different from the one of the samples to be analyzed. This may give big determination errors (due, for example, to the formation of binary or ternary complexes with other ligands present into the matrix). In this case, it is better to use the standard addition method, where the standards are not independently measured from the samples. In this method, n identical aliquots are taken for each sample, and increasing known quantities of the standard are added and diluted to the same final volume. Once the graph of absorbance values versus added concentration is represented, the extrapolation to zero absorbance will give the desired unknown concentration of the analyte. This method was recently improved [18] by using multiple repetitions of a single addition, rather than using a single repetition of multiple additions with incrementing of concentration. 3.3.1.2 Two analytes 3.3.1.2.1 Two wavelengths method

The spectral overlapping of the components of a mixture is one of the most important limitations of the spectrophotometric methods when one component has to be determined in the UV
visible range. When two analytes have to be determined in the same solution, two different wavelengths have to be chosen in such a way that one analyte does not interfere with the other. The general relation between the two concentrations to be determined and the two measured absorbance values is: C1; C2 5 fðA1; A2Þ

(3.11)

The practical calculation is explained hereafter, and one calibration curve for each analyte has to be drawn for its appropriate wavelength without any other complication. Another usual and undesired effect is the light scattering produced by the suspended or colloidal particles. This scattering is often considered as nearly constant or at least linearly decreasing in the overall UV
visible spectral range. Therefore an overlapping is produced with the spectrum of the analyte to be determined. In this case, the interference may be removed by subtracting to the absorbance values, the absorbance contribution of the particles. This one is measured in a spectral zone where the analytes do not absorb and where only the scattering particles are contributing. If the scattering is not constant, but the

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corresponding spectrum slope is, then derivative spectroscopy may be applied, as will be seen later. Since it is very often impossible to avoid the spectral overlapping of two analytes, it is necessary to use some mathematical procedure in order to make a discrimination of their spectrophotometric signals. The classical method is to find the same number of analytical wavelengths as the number of analytes to be determined, trying to select those where the difference between the molar absorptivities of the different compounds is maximum. In this way, the same number of linear equations as analytes is established and may be solved by means of traditional computing techniques (determinants, for example). So, if two species give two overlapped spectra, the following equations may be written: Aλ1 5 ελ1 1 bC1 1 ελ2 1 bC2 Aλ2 5 ελ1 2 bC1 1 ελ2 2 bC2

(3.12)

where the superindices λi correspond to the lectures made at the two analytical wavelengths i (1 and 2), whereas the subindices correspond to the compounds 1 and 2, respectively. The molar absorptivity values (ε) of both substances may be obtained from pure standards. Once these values are known, the absorbance values for each sample obtained at both analytical wavelengths allow computing the concentration values from the following equations: C1 5

λ1 λ2 Aλ2 ελ1 2 2 A ε2 λ1 λ1 λ2 ελ2 1 ε2 2 ε1 ε2

C2 5

λ2 λ1 Aλ1 ελ2 1 2 A ε1 λ2 λ1 λ1 λ2 ε1 ε2 2 ε1 ε2

(3.13)

This system may only be used if the Beer
Lambert law is applied and there is no mutual interference between the two components. 3.3.1.2.2 N wavelengths method

The spectrophotometric resolution of mixtures of two components on the basis of the extended Beer
Lambert law relies on absorbance measurements at two different wavelengths and the fulfilment of the law of the additivity of absorbances [19]. This methodology has several shortcomings, such as the use of only two experimental data obtained at two different wavelengths. While the method could in principle be used to resolve up to n components by making measurements at as many wavelengths, it has not been applied to more than two components because its accuracy

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decreases sharply as the number of involved determinants grows [20]. Performing measurements at more wavelengths can increase the precision of the above-described method in the resolution of multicomponent mixtures. Thus, for a given wavelength λi, Aλm1 AλS11

5

Aλi c2 c1 1 λSi2 c S1 A S1 c S2

(3.14)

where Aλm1 is the measured absorbance of the sample and Aλsii is the absorbance of the sample of the corresponding standard (S1 or S2) at wavelength λi . CS1 ; CS2 , are the concentrations of standards and C1 and C2 are the concentrations of components of the mixture. From the previous relation, it follows that, by plotting the ratio of the measured absorbances of the sample on the standard S1 against the ratio of the measured absorbances of the standards S1 and S2, for different wavelengths, one will obtain a straight line, the intercept and slope of which will provide the sought C1 and C2 values. Blanco et al. [20] compared these two procedures for the determination of binary mixtures with highly overlapped spectra, obtaining better results by the multiwavelength linear regression method. 3.3.1.3 Multicomponent method by mutlilinear regression In theory, if no interference exists in the solution, the generalization of the additive relation can be applied, providing matrix effects, and any chemical interactions involved are negligible. Aðλi Þ 5

p X

εjðλiÞ Cj 1 r

(3.15)

j51

where r is the residual value, that is, the difference between the measured and calculated values. This value must be minimized by using one of the following methods. For the whole spectrum, the previous relation must be extended: S5

n X i51

Aðλi Þ 5

p n X X

εjðλiÞ Cj 1 rðλi Þ

(3.16)

i51 j51

If the number of measurements (wavelengths, n) exceeds the number of components (p), then the above system will be overdimensioned and resolvable by MLR. Such a system can be expressed in a matrix form as:



L5K C which entails solving two analytical chemical problems, namely:

UV-Visible Spectrophotometry of Waters and Soils

(3.17)

3.3 Concentration calculation

79

First, one must determine the proportionality constants from matrix K by using standards of known concentration, and Once matrix K has been determined, one must resolve the unknown mixtures so as to determine the concentration matrix C from the following equation: C 5 ðK0 KÞ21 K0 L

(3.18)

where K0 denotes the transpose of K. Matrix K can be determined in a number of ways, including the following [21]. 3.3.1.3.1 Single or averaged standards

This is the simplest procedure and involves the use of a single or averaged standard of known concentration for each component and the calculation of the corresponding response factor from: km i 5

lm S c Si

(3.19)

3.3.1.3.2 Several standards of the components and their mixtures

This option entails calculating the different km i values by regression from standards of different concentrations of each component or mixtures of known composition. Mathematically, the procedure involves an equation system for each measuring channel of the form: m lm S 5z 1

n X

km i cS

’s 5 1. . .nS

(3.20)

i21

where lm S denotes the reading obtained for standards s in measuring channel m, zm the independent term of the fitting for each m value, and csi the concentration of component i in standard s. This equation system can be solved by MLR, provided that the number of standards used, ns is larger than that of components. On solving the system, one obtains m the km i and z values, as well as the deviation of the fitting for each measuring channel, dm, which can be calculated from: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP  2 u nS m lS;exp 2lm u S;calc t (3.21) dm 5 S21 nS 2 n The dm values can subsequently be used in resolving the unknown mixture so as to carry out a weighted fitting of the initial equation in such a way that the measuring channels with the

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greatest deviations will have a smaller weight than the rest. Thus the equation to be used is: n X km lm l i 5 k 1 ci 0 dm dm i21

’m 5 1. . .nm

(3.22)

3.3.1.3.3 Generalized (multiple) standard addition method

Eq. (3.20) can be applied to a data set obtained by spiking the unknown samples with known amounts of the components to be determined. In this case, the csi values will correspond to the added concentrations of each component in each standard. As mentioned above, the regressions performed for each measuring channel will provide the km i and zm values, which will represent estimates of the sensitivity of each component and the sample signal (in the absence of additive interferences), respectively. Finally, by using Eq. (3.20) with the zm values, one can calculate the concentration of each component in the unknown sample. In practice, MLR is often limited to five variables (components) for the regression, due to possible collinearity between spectra. However, MLR is the simplest method for multicomponent analysis because it is easy to understand and use, even for a nonexpert in matrix calculation. Multicomponent analysis techniques have opened up new prospects for the resolution of diverse analytical systems. Frequently, the application of these techniques requires some chemical or instrumental innovations with respect to the previous procedures. Among the referred algorithms, the multilinear regression method has often been used because of its easy implementation, and good results have been obtained in most cases. The chemical interferents are the main limitation of such techniques, because prior knowledge of each substance that contributes to the overall signal is needed. In this case, multiplicative interferences can easily be addressed using the multiple standard addition method. The elimination of additive interferences has not been achieved.

3.3.2 Real samples: compensation of interferences 3.3.2.1 Two wavelengths approach The use of a second wavelength for the compensation of turbidity was already proposed at the early time of UV spectrophotometry, without success. In the past decade, another two wavelengths method was proposed for the quantification of two aggregate components chosen for the characterization and quantification of dissolved organic matter

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(DOM) and dissolved organic carbon (DOC) in water [22,23]. Each of these components have distinct and constant spectrum. The first one was strongly absorbing and presumed to possess aromatic chromophores and hydrophobic character, whereas the second component absorbed weakly and could be assumed hydrophilic. The model applied for surface water samples gave good unbiased predictions of DOC content from absorbance data at 270 and 350 nm. However, it required a previous filtration for each sample, in order to avoid turbidity and suspended solids interferences. 3.3.2.2 Spectra slopes Another method based on the calculation of slopes of UV spectra was proposed at the same time. Two distinct spectral slope regions (275
295 nm and 350
400 nm) within log-transformed absorption spectra were used to compare DOM from contrasting water types, ranging from wetlands to photobleached oceanic water (Atlantic Ocean) [24]. On the basis of DOM size-fractionation studies (ultrafiltration and gel filtration chromatography), the slope of the 275
295-nm region and the ratio of these slopes (SR; 275
295-nm slope/350
400-nm slope) were related to DOM molecular weight, improving the characterization of DOM nature and fate in the aquatic environment. This approach was also proposed by Roccaro [25] on raw and treated drinking water. Experiments conducted over a period of 6 months at two full-scale treatment plants showed that the slopes of the logtransformed spectra were correlated with specific absorbance at 254 nm (SUVA, see Chapter 5), and that the spectral slopes determined for the range of wavelength 280
350 nm were also correlated with the yields of the main disinfection by-products (DBPs), such as trihalomethanes and haloacetic acids. 3.3.2.3 Derivative methods The interest of derivative signal, and particularly the second one, has already been presented before (Fig. 3.2). Different quantitative methods of analysis may be proposed from the use of derivative spectra: • The derivative value may be used at any wavelength (except in the wavelength where the derivative value is zero), and particularly where the second derivative is minimum (corresponding to the maximum of the normal spectrum). • In the peak
valley method, the difference between two maximum
minimum values is used, this method being more sensitive than the previous one. • In the tangent method, two lines are drawn between two consecutive maxima or minima, and the distance between this tangent and the

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intermediate maximum or minimum is calculated; this method allows correcting any variation of the base line due to a matrix effect. The derivative spectra allow knowing with better precision the position of spectrum maxima and minima in qualitative analysis. An increase in the derivative degree increases the number of peaks in the spectrum (Fig. 3.2) and allows a better characterization of the substances than the direct spectrum. By means of derivative spectroscopy, small distortions of the direct spectrum due to the presence of impurities can be revealed. Referring to quantitative analysis, derivatives spectra allow discriminating the presence of an overlapping compound in the foot of the peak of a major compound. In Fig. 3.13, spectra of polluted water and of pollutant (diuron) are presented. A minor compound (pollutant) appears as a shoulder of water sample characterized by the presence of a major compound (nitrate). The second derivative spectra of polluted water and of diuron are represented, and both signals are superimposed. An important application of derivative spectroscopy lies in the determination of analytes in turbid media. Turbid solutions usually present a continuous increase of the absorbance toward shorter wavelengths and, as a consequence, do not produce any sudden spectral change either in the first or in the second derivative spectrum. Phenol determination in wastewater was one of the first practical applications of the derivative spectroscopy in turbid media [26]. Another study deals with the evaluation of the second derivative determination of nitrate and total nitrogen

FIGURE 3.13 Interest of UV second derivative for the analysis of polluted water (presence of diuron at 1 mg L21).

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3.3 Concentration calculation

[27]. More recently, the simultaneous determination of nitrate and DOC in water was proposed by Causse [28], with the compensation of derivative interferences for DOC estimation. As nitrate absorbs (slightly) around organic matter shoulder (270
310 nm), the removal of its corresponding second derivative signal strongly improves the DOC determination. 3.3.2.4 Polynomial compensation of interferences Another way to compensate the interference effects is to modelize their optical response by a simple mathematical function, f (λ), explaining the measured absorbance value from the expected response of the p solutes: Aðλi Þ 5

p X

εjðλi Þ Cj 1 fðλi Þ

(3.23)

j51

Several functions can be proposed, but the most adapted seems to be a polynomial one [21]. fðλÞ 5

n X ai λi i51











(3.24)

fðλÞ 5 a0 1 a1 λ 1 a2 λ 1 a3 λ 1 ? 1 an λ 2

3

n

An interesting aspect of this choice is that, depending of the polynomial degree, several shapes are assumed for the restitution of the interferences effect: • A zero-degree function corresponds to a constant shift of absorbance, encountered when the spectrophotometric cell is dirty, for example. • A first-degree function is equivalent to a linear response of interferences very often used for the exploitation of chromatographic data (calculation of peak area). The correction procedures of Allen [29] (or Morton and Stubbs [30]) are based on the same assumption. This solution is equivalent to the use of the second derivative signal previously described, if we remember that a linear response gives a zero value for the second derivative. • For higher degrees, any interference response can be fitted with a polynomial response. However, the polynomial degree to be considered must not be too high for preventing the risk of the whole spectrum (interference and analytes) modelization. This is the reason why a polynomial of third degree was often proposed as the best compromise [21,31]. In some cases, this method can be simplified by neglecting the lower terms and keeping the only term of the third degree for compensation [32].

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3.3.2.5 Chemometric analysis: principal component analysis, principal component regression, and partial least squares The chief limitation of the above-described methods is that they require all the species contributing significantly to the measured signal to be known beforehand. Developing new chemometric procedures based on advanced multivariate analysis has lately circumvented this pitfall. Thus the last group of methods for the exploitation of spectra of real samples involves not only factor analysis procedures and related methods but also a semideterministic approach with spectral deconvolution. Both types of methods can be considered as statistical ones, but with a slight difference. Factor analysis and related methods are also described as “black boxes,” because they require no a priori information. They function in a manner that is quite opposite to that of MLR methods, for which the response of all compounds of the sample must be known (which is almost impossible for a real sample). On the other hand, the semideterministic approach is based on a “gray box” type of model, where only a part of the information is needed, the other part remaining stochastic. Starting from the matrix representation of data, these procedures tend to extract the relevant information with the aim of representing the synthesis of results within one or two simple graphs. This qualitative exploitation, leading to the decomposition of spectra in a first step, can be completed in a second step by a regression step for the estimation of component concentration (quantitative estimation). It allows the determination of the number of components significantly contributing to the analytical signal and then permits the spectrum of each individual component to be reconstructed, which finally allows the system to be analytically resolved. In practice, for spectra exploitation, the main procedure is the PCA, identifying a set of a few factors (the first eigenvectors of the matrix formed by the absorbance values for all wavelengths and for all samples to analyze) for the interpretation of data. Then, any spectrum can be explained as a linear combination of these factors (as a decomposition step), the coefficients of which are the PCA scores. For the estimation of components’ concentration, a second step is required, based on an MLR between the absorbance values and the PCA scores. This can be carried out automatically after the PCA step, within the principal component regression (PCR) procedure (including PCA). This methodology was firstly applied to analytical chemical problems 50 years ago by Lawton and Sylvestre [33] and has more recently been used in different models by other researchers for liquid chromatography [34,35]. The PCA procedure can also be coupled with cluster

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analysis, as described for the characterization of industrial wastewater samples [36]. Another method, very often used for more than 30 years in analytical chemistry, is the PLS regression [37]. This method is slightly different from PCR, because the two steps of the last method (decomposition and regression) are carried out at the same time and the decomposition process also includes concentration information. The results (eigenvectors and scores) are different and generally more relevant [38], because they are more related to the concentration data. In fact, both spectral and concentration data are considered and simultaneously decomposed by iteration, and the results depend on the selection of the chosen factor. The PLS method is more complex than the PCR method but seems to be more adapted for huge data sets as for spectroscopic application (e.g., Near-InfraRed (NIR) spectroscopy for petroleum products). A comparison of the efficiency between PCR and PLS is difficult because the quality of results is rather close (at least for a small set of data), but some specific advantages can be drawn for PLS. The intermediate results (eigenvectors) are related to the initial physical data (looks like specific spectra). Moreover, the calibration step is more robust if the data set is representative, and PLS can thus be used for the study of complex mixtures. Some known drawbacks are the computational time, the need for a large calibration set (representative) and some difficulties in understanding and explaining the resulting model. However, PLS and PCR were compared by Lin et al. [39], regarding the question of sufficient dimension reduction (SDR) with no loss of information. A simulation and validation on UV and NIR spectra data sets led to the conclusion that PLS and PCR were equivalent regarding SDR. These procedures are sometimes present in the built-in software of UV
visible spectrophotometers or online sensors. Factor analysis methods allow resolution of multicomponent mixtures when individual contribution of each component is unknown. In broad terms, this methodology yields to a solution set for each component, the width of which depends on the data supplied. Nevertheless, the complexity of the mathematical treatment has actually prevented the resolution of chemical mixture with more than three components. As mentioned previously, one of the main advantages of PLS is that the resulting spectral vectors are directly related to the constituents of interest. This is entirely unlike PCR, where the vectors merely represent the most common spectral variations in the data, completely ignoring their relation to the constituents of interest until the final regression step. 3.3.2.6 UV semideterministic method (UVSD) As for the factor analysis-based methods, this method aims at explaining any acquired spectrum through a deconvolution step, for the

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calculation of some parameters of interest. It assumes that each spectrum can be considered as a linear combination of a p reduced number of particular spectra, which are named “reference spectra” [6,40]: Su 5

p X

Srefi 1 Sres

(3.25)

i51

where Su is the spectrum of an unknown sample, and Srefi is ith reference spectrum (among p) and the Sres is the residual difference between the acquired and restituted spectra. The reference spectra (Fig. 3.14) are either spectra of specific compounds or of aggregate matrices (residual organics dissolved, colloids, and suspended solids). The first group of spectra (specific compounds) is the deterministic part of the model. It includes the compounds that may be likely found in the type of sample to be examined. The second one, being of experimental or mathematical nature (mixture, difference of spectra, for example), can be considered as the stochastic part of the model. Moreover, some of these spectra can be actually related to principal components calculated from the residual matrix. The selection is done between different spectra, which allows taking into account the effect of the main interferences. In Fig. 3.14, the spectrum of suspended solids is the difference between the spectrum of the raw sample and of the same sample previously filtered through a 1-μm filter. In the same way, the spectrum of the colloidal fraction is the difference between the raw spectrum and the spectrum of the sample after its filtration through a 1-μm filter, which obtained using a 0.025-μm filter. Finally, the interferences related to the organic matrix may be represented by the spectrum of the sample previously filtered through a 0.025-μm filter. The basis of reference

FIGURE 3.14

Example of reference spectra, normalized [ref [1]: dissolved organics, ref [2]: colloids, ref [3]: suspended solids, ref [4]: nitrates, ref [5]: surfactants (DBS)].

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spectra is completed by the spectra of nitrate and surfactant, considered as the deterministic part of the model. From a mathematical point of view, the coefficients of the linear combination are calculated from a system based in the following relationship established for each wavelength: Auλj 5

p X

ref

ai Aλj i 1 rj

(3.26)

i51

where Au and Aref are, respectively, the absorbance values of the sample spectrum and of the ith reference spectrum, for a given wavelength λi, ai is the coefficient of reference spectra contribution for the explanation of the sample spectrum, and rj is the error value. The validation of the model is given by the sum of the error values at each wavelength, which must be as low as possible. Moreover, the variation of the error value with wavelength must also be considered (a random distribution being waited). Starting from Eq. (3.26), any additive parameter (total organic carbon (TOC), for example) can thus be calculated by using the following equation: Pu 5

p X

ai Pi

(3.27)

i51

where Pu and Pi are, respectively, the parameter values of the sample and of the reference spectra, and ai the previously calculated coefficient. Finally, further ways of UV spectra exploitation could be found in reviews on chemometrics tools used in analytical chemistry [41,42].

3.3.3 Real samples: pretreatment steps for improving UV response Even if the above methods for the quantitative exploitation of UV presented are numerous, UV
visible spectrophotometry is limited by its lack of sensitivity and selectivity. Moreover, some compounds being nonabsorbant, their determination is not possible, at least without a specific reaction or pretreatment of sample. Some simple pretreatments are proposed by the literature for the improvement of UV
visible spectrophotometry. For example, the determination of biodegradable DOC is possible by following the variation of UV spectra during a 10-hour sand-filtration test [43]. The use of UV photoxidation of wastewater with a low-pressure mercury lamp led to the degradation of a wastewater sample and to its degradability assessment [44]. The same procedure (called UV/UV method) was used with adapted oxidants for the

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determination of N and P compounds in wastewater [45]. When the levels of micropollutants are too low for a UV spectrophotometric detection, their concentration is possible thanks to a multiple solid-phase extraction system before UV determination [46]. Pesticides and pharmaceuticals can be detected with this method. Finally, a last experiment using high-pressure size-exclusion chromatography and UV characterization of fractions was recently proposed [47] for the study of chlorination of drinking water and the detection of DBPs.

3.4 Examples of application Following the description of the different methods for the quantitative exploitation of UV spectra, different applications drawn from the literature are presented in Table 3.2. TABLE 3.2 Exploitation methods for UV
visible spectrophotometry and example of applications. UV
visible exploitation method

Sample type

Parameters

Reference (ex)

One wavelength (nm) Seawater

DOM

[48]

a

Wastewater

COD

[49]

a

A254

Water, wastewater

TOC

[50]

A280

Water

DBPs

[51]

A345 or 440

Wastewater

COD (CrVI)—low

A220 A254

A610

31

[52]

Wastewater

COD (Cr )—high

[53]

Soils

Water extract. OM

[54]

A254 compensed by A546

Water, wastewater

TOC

[55]

Model (e.g., A270 and A350)

Water

DOM, DOC

[22,23]

A250/A365

Water, wastewater

DOM

[56]

A254/A288

Soils

PAHs

[57]

225/250/275/350

Water

DOM

[58]

a

SUVA

Two wavelengths (nm)

Absorbances ratio (nm)

(Continued)

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3.4 Examples of application

TABLE 3.2

(Continued)

UV
visible exploitation method

Sample type

Parameters

Reference (ex)

Between 275 and 295

Water, wastewater

DOM

[24]

Ratio 275
295 350
400

Water

DBPs

[47]

Water, wastewater




[25]

ΔA272

Water

TOX

[59]

ΔA272

Water

DBPs

[60]

First derivative

Water

NO3

[61]

SDA 240
280

Water, wastewater

Phenols

[26]

SDA 216, SDA 223

Water

NO2, NO3

[62]

SDA 226, SDA 295

Water

NO3, DOC

[28]

Slopes (nm)

Absorbance slope index Log-transform absorbance (slope) ΔAbsorbance (nm, with time)

Second derivative

Polynomial comp. (third degree) Range 205
250

Water,

NO3

[63]

Range 250
450

wastewater

Cr(VI)

[64]

MLR

Water, wastewater

TOC

[65]

UVSD

Wastewater

DOC, NO3, TSS. . .

[6,40]

Sewage sludge

Humus index

[66]

Solid waste

Humus index

[67]

Soils/sediments

Wat. Extr. OC

[68]

Water, wastewater

DOM, NO3

[69]

COD, TSS

[70]

PLS

UV/sand filtration

Wastewater

BDOC

[71]

UV/UV

Industrial wastewater

Treatability

[44]

Wastewater

N and P compounds

[45] (Continued)

UV-Visible Spectrophotometry of Waters and Soils

90

3. From spectra to qualitative and quantitative results

TABLE 3.2 (Continued) UV
visible exploitation method

Sample type

Parameters

Reference (ex)

MSPE/UV

Water

Pesticides pharmaceuticals

[42]

HPSEC UV

Water

DBPs, NOM

[47]

a

The ratio of A254 on DOC content gives SUVA (standardized parameter). BDOC, biodegradable dissolved organic carbon; DBPs, disinfection by-products; DOC, dissolved organic carbon; DOM, dissolved organic matter; HPSEC, high-pressure size-exclusion chromatography; MLR, multiple linear regression; PLS, partial least squares; TOC, total organic carbon; COD, chemical oxygen demand; OM, organic matter; PAHs, polycyclic aromatic hydrocarbons; TOX, total organic halogen; NOM, natural organic matter; MSPE, magnetic solid-phase extraction; UVSD, UV SemiDeterministic method; SDA, second derivative absorbance; SUVA, ratio of A254 on DOC https:// doi.org/10.1016/S1462-0758(02)00019-5.

Acknowledgments The authors wish to thank V. Cerda for his contribution to the 1st edition: O. Thomas, V. Cerda, From spectra to qualitative and quantitative results, in UV-visible spectrophotometry of water and wastewater, O. Thomas, C. Burgess (Eds.), Elsevier, Amsterdam, (2007) 21
45.

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223. Available from: https://doi.org/10.1016/j.microc.2012.10.025. [47] G. Korshin, C.W.K. Chow, R. Fabris, M. Drikas, Absorbance spectroscopy-based examination of effects of coagulation on the reactivity of fractions of natural organic matter with varying apparent molecular weights, Water Research 43 (2009) 1541
1548. Available from: https://doi.org/10.1016/j.watres.2008.12.041. [48] N. Ogura, T. Hanya, Ultraviolet absorption of the sea water, in relation to organic and inorganic matters, International Journal of Oceanology and Limnology 1 (1967) 91
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443. Available from: https://doi.org/10.1016/j.geoderma.2010.10.014. [55] S.M. Gerchankov, J.S. Mattson, C.A. Smith, T.T. Jones, Continuous monitoring of dissolved organic matter by UV-visible photometry, Limnology and Oceanography 19 (1974) 530
535. Available from: https://doi.org/10.4319/lo.1974.19.3.0530. [56] K.E. Frey, W.V. Sobczak, P.J. Mann, R.M. Holmes, Optical properties and bioavailability of dissolved organic matter along a flow-path continuum from soil pore waters to the Kolyma River mainstem, East Siberia, Biogeosciences 13 (2016) 2279
2290. Available from: https://doi.org/10.5194/bg-13-2279-2016. [57] E. Touraud, M. Crone, O. Thomas, Rapid diagnosis of polycyclic aromatic hydrocarbons (PAH) in contaminated soils with the use of ultraviolet detection, Field Analytical Chemistry Technology 2 (1998) 221
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[59] G.V. Korshin, C.W. Li, M.M. Benjamin, The decrease of UV absorbance as an indicator of TOX formation, Water Research 31 (1997) 946
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1079. Available from: https://doi.org/10.1039/an9871201077. [63] O. Thomas, S. Gallot, Ultraviolet multiwavelength absorptiometry (UVMA) for the examination of natural waters and wastewaters. Part II. Determination of nitrate, Fresenius’ Journal of Analytical Chemistry 338 (1990) 238
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867. Available from: https:// doi.org/10.1016/j.talanta.2006.05.067. [69] G. Langergraber, N. Fleischmann, F. Hofsta¨dter, A multivariate calibration procedure for UV/VIS spectrometric quantification of organic matter and nitrate in wastewater, Water Science and Technology 47 (2003) 63
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588. Available from: https://doi.org/10.2166/wst.2008.131. [71] C. Massiani, O. Thomas, N. Mazas, Determination of biodegradable DOC, Environmental Technology 73011 (1992) 487
493. Available from: https://doi.org/ 10.1080/09593339309385317.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

4 Organic constituents Olivier Thomas1 and Marine Brogat2 1

EHESP School of Public Health, Rennes, France, 2School of Public Health, Ecole des Hautes Etudes en Sante´ Publique (EHESP), Rennes, France

O U T L I N E 4.1 Introduction

96

4.2 Colored organic compounds 4.2.1 Dyes 4.2.2 Colored reagents

96 97 107

4.3 UV-absorbing organic compounds 4.3.1 Aldehydes and ketones 4.3.2 Amines 4.3.3 Benzene and related compounds 4.3.4 Pesticides 4.3.5 Pharmaceuticals 4.3.6 Phenols 4.3.7 Phthalates 4.3.8 Polycyclic aromatic hydrocarbons 4.3.9 Sulfur organic compounds 4.3.10 Surfactants

112 113 114 117 118 122 126 135 136 146 148

4.4 Solid-phase extraction and UV
visible spectrophotometry

151

4.5 Nonabsorbing organic compounds 4.5.1 Carbonyl compounds: use of absorbing derivatives 4.5.2 Aliphatic amines and amino acids: photooxidation 4.5.3 Carbohydrates: photodegradation

154 155 155 155

Acknowledgments

158

References

158

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00006-X

95

© 2022 Elsevier B.V. All rights reserved.

96

4. Organic constituents

4.1 Introduction Since the beginning of last century, synthetic organic compounds have been produced either for domestic (detergents, plastics, etc.); industrial (solvents, additives, dyes, etc.); agricultural (pesticides, etc.); or health [pharmaceuticals and personal-care products (PPCP)] uses. These compounds are widely used in human activities and could be found in the environment, especially in water (surface water, groundwater, and industrial or urban wastewater) or polluted soils. The importance of identifying organic constituents in water and wastewater is related to their occurrence and fate and more precisely to their potential toxicity, biodegradability, and availability. Contrary to mineral constituents, the number of which is less important, organics in water are more and more considered for their environmental impact and health effects, as underlined by the increasing number of works on pesticides and pharmaceuticals residues in water. This evolution is explained by a better understanding of health effects of emerging micropollutants (e.g., endocrine disruptors), and the regulation pressure for priority and emerging substances [1]. Another study has reported a screening by gas chromatography-mass spectrometry (GC-MS) for over 1550 organic micropollutants in the environment [2], while pharmaceuticals, personal care products, and endocrine disruptors are nowadays regularly monitored in water and wastewater [3,4]. Although some aggregate parameters may be used for the estimation of pollution effects and treatment (see Chapter 5), the determination of specific organic compounds is necessary, namely, in the frame of regulation compliance. Most of them present a conjugated structure (aromatic rings), which induces UV absorption, sometimes in the visible range, if the conjugation in the molecule is extended (dyes). Moreover, aliphatic compounds that have a potential chromophore in their structure (carbonyl group, for example) show poor absorption in the UV region. As for colorimetry in the visible region, a derivatization step may enhance their absorption in UV, with the use of a specific reagent. Finally, some of them, such as carbohydrates, which do not absorb in UV
visible range, can be indirectly detected thanks to their degradation by-products after a photooxidation step, for example. In the following sections, all spectra are acquired with a 10 mm pathlength cell (except when noted).

4.2 Colored organic compounds Colored organic compounds absorb UV
visible light, generally with a strong absorptivity in the visible range (ε . 103 Lmol21cm21). In this

UV-Visible Spectrophotometry of Waters and Soils

4.2 Colored organic compounds

97

part, are presented some dyes possessing acid
base properties, and some usual colored reagents such as pH, redox, or complexometry indicators. A careful spectroscopic study, dealing with the nature of the electronic transitions and the location of the corresponding absorption bands, is proposed.

4.2.1 Dyes Organic colorants or dyes often contain two or more cyclic rings that may or may not be aromatic and condensed. From a chemical point of view, a dye molecule can be characterized, on the one hand, by the basic structure, which is related to a dye family and contains chromophores (conjugated double bonds, and aromatic rings), which induce the dye solution coloration, and, on the other hand, by the substituents or auxochromic groups, which infer aqueous solubility by ionization (NH2, OH, COOH, SO3H, etc.) and can enhance conjugation in the dye molecule. The most important families of dyes are azoic and anthraquinonic ones. Azoic dyes are characterized by an azo bond (NQN) connected to aromatic rings or heterocycles, meanwhile anthraquinonic dyes are derivatives of substituted anthraquinone and have two carbonyl groups (CQO) in their structure. Various substitutes can be found, such as alkyl, amino, hydroxy, halogeno, sulfonate, or more complex groups. The following dye solutions have been prepared in water at a concentration of 50 mgL21. The effect of pH on UV
visible spectra is pointed out. 4.2.1.1 Azoic dyes Two isomeric phenylazonaphthols (called Orange 1 and Orange 2) and one aminobenzene derivative (Orange 3 or methyl orange) are presented. The chemical structure of these phenylazonaphthols shows that these compounds give rise to a tautomerism between the azo and hydrazone forms by a proton-exchange effect (Figs. 4.1 and 4.2). In aqueous medium, the hydrazone form is preponderant. Orange 1 and Orange 2 are pH-dependent compounds. In Fig. 4.3, UV
visible spectra are displayed according to different pH values. In acidic medium, the absorption band in the visible region, imputed to the π
π* transition of the hydrazone form, is observed, respectively, at 475 nm (ε 5 13,950 Lmol21cm21) for Orange 1, and at 483 nm (ε 5 17,970 Lmol21cm21) for Orange 2. The other peaks, located around 230, 240, and 270 nm, are assigned to the π
π* transitions of aromatic rings (benzene and naphthalene rings). Moreover, for Orange 2 dye, a slight

UV-Visible Spectrophotometry of Waters and Soils

98

4. Organic constituents

FIGURE 4.1 Orange 1 dye azo-hydrazone tautomerism.

FIGURE 4.2 Orange 2 dye azo-hydrazone tautomerism.

FIGURE 4.3

Orange 1 (left) and Orange 2 (right) dyes UV
visible spectra (water, path-

length: 2 mm).

shoulder is noticed around 400 nm due to the n
π* transition of NQN azo group. In both cases, the coloration is orange. In basic medium, a bathochromic shift (λ 5 513 nm, ε 5 14,100 Lmol21cm21) can be seen for the Orange 1: the color turns then from orange to red. The phenate form is predominant (Fig. 4.4) and the presence of an isosbestic point (λ 5 493 nm) confirms this phenomenon.

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4.2 Colored organic compounds

99

FIGURE 4.4 Acido
basic equilibrium of Orange 1 dye.

FIGURE 4.5 Ammonium
azonium equilibrium of methyl orange dye.

In basic medium, behavior of the Orange 2 dye is a little different. The phenate ion is not easily formed because of the stabilization of the hydrazone form, by a hydrogen bond between the oxygen of the carbonyl group and the hydrogen connected to the β nitrogen of the azo group (Fig. 4.2). Nevertheless, a change of coloration (from orange to red) is observed in strong basic medium (pH 5 12.0). The shoulder around 400 nm disappears, and the absorption band in the visible region extends (aggregation process, dimerization). Orange 3 or methyl orange has not the same behavior as the previous dyes, no azo-hydrazone tautomerism being possible. Nevertheless, in strong acid medium, an ammonium
azonium equilibrium occurs, as shown in Fig. 4.5. This phenomenon has been pointed out on UV
visible spectra (Fig. 4.6). The maximum absorption of Orange 3 under its molecular form is located at 463 nm (ε 5 23,560 Lmol21cm21) and can be imputed to the π
π* transition of the azo group and the slight shoulder around 400 nm to the n
π* transition of the same group (pH 5 6.3). At pH 5 2.0, the azonium tautomeric forms become preponderant and absorb strongly in the visible region (λ 5 506 nm, ε 5 39,180 Lmol21cm21). The color of the solution becomes red.

UV-Visible Spectrophotometry of Waters and Soils

100

4. Organic constituents

FIGURE 4.6 Orange 3 dye UV
visible spectra (water, pathlength: 2 mm).

A shoulder can be observed at 319 nm (ε 5 6760 Lmol21cm21), related to the ammonium form. In a strong acid medium, it can be expected that a diprotonated form [
1NH
(CH3)2,
NαQ1Nβ H
] may exist, showing an absorption at 410 nm [5]. At pH 5 1.2, an absorbance decrease at 506 nm and a very slight absorbance increase around 400 nm are observed. As expected, no bathochromic effect is observed between neutral and basic media. 4.2.1.2 Anthraquinonic dyes Four anthraquinonic dyes, namely, Alizarin red S, Alizarin violet R, Acid green 25, and Acid blue 129 are studied. The simplest one, the Alizarin red S, is derived from Alizarin by the introduction of a sulfonate group in the alizarin structure in position 3 (Fig. 4.7). UV
visible spectra of Alizarin (Fig. 4.8) and Alizarin red S (Fig. 4.9) are acquired according to different pH. Alizarin is slightly soluble in water for acid pH. Sulfonic acid group induces aqueous solubility for Alizarin red S. Dealing with the solution color, two changes can be observed for Alizarin: the color turns progressively from pale yellow (pH 5 5.2) to pale rose (pH 5 7.1) and then to violet (pH 5 10.1). Alizarin exhibits two ionizable enolic functions: it can be supposed that they are ionized in strong basic medium. The bathochromic shift is then imputed to the extended conjugation of the molecule. A fine structure, imputed to the n
σ* transition of the hydroxy groups, is noticed for pH 5 12.0 (λ 5 566 nm, ε 5 7000 Lmol21cm21 and λ 5 608 nm, ε 5 6500 Lmol21cm21). In the same time, the absorption peak located at 326 nm decreases.

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101

FIGURE 4.7 Alizarin and Alizarin red S monohydrate chemical structures.

FIGURE 4.8

Alizarin UV
visible spectra according pH medium (water, pathlength:

10 mm).

FIGURE 4.9

Alizarin red S monohydrate dye UV
visible spectra (water, pathlength:

2 mm).

At pH , 10.0, only one phenol function is ionized (probably in position 1): the two absorption peaks (λ 5 326 nm and λ 5 520 nm) can reasonably be assigned to the n
σ* transition of the protonated and deprotonated forms of hydroxy groups, respectively.

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4. Organic constituents

The absorption in the UV region is imputed to the π
π* transitions of the anthraquinonic structure (benzenoid and quinonoid bands). A similarity is observed with Alizarin red S in basic medium (Fig. 4.9) because of the same fine structure observed (λ 5 556 nm, ε 5 11,600 Lmol21cm21 and λ 5 596 nm, ε 5 10,450 Lmol21cm21). In comparison with Alizarin, a slight hypsochromic effect occurs, probably due to the sulfonic acid function. The solution is violet. Between pH 5 9.0 and 11.0, the color turns from red (λ 5 514 nm) to violet: an isosbestic point, located around 510 nm, shows that we are faced with two different forms (monoionized and deionized) of the molecule. Moreover, a hyperchromic effect can be noticed at pH 5 12.0. In acidic medium, two isosbestic points are pointed out at 375 and 450 nm: the color turns progressively from pale yellow (λ 5 425 nm, pH 5 2.0) to yellow
orange (pH 5 4.9) and then to red (λ 5 510 nm, pH 5 6.2). At pH 5 2.0, only the molecular form is predominant (λ 5 426 nm, ε 5 4400 Lmol21cm21). The absorption band located at 261 nm can be assigned to the π
π* transition of quinonic structure. With the two disubstituted anthraquinonic dyes (Green acid 25 and Violet alizarin R), the influence of the position of the substituents on the UV
visible absorption can be discussed. Green acid 25 is a 1,4 disubstituted anthraquinone, while Violet alizarin R is a 1,5 one (Fig. 4.10). According to their structure, these isomeric dyes must be nonsensitive to pH. UV
visible spectra confirm this hypothesis (Fig. 4.11). The absorption bands in the visible region of the spectra are attributed to a resonance effect between the quinonic ring and the substituents. In 1,4 disubstituted anthraquinones, an interaction between the two chromophoric systems occurs, giving a resonance effect between two extreme structures. This difference in behavior has been pointed out: 1,4 dihydroxy and diamino compounds show a “double-headed” peak that is not shown for 1,5 disubstituted compounds. The latter ones behave as twice the corresponding monosubstituted compound (Fig. 4.12).

FIGURE 4.10 Chemical structures of Acid green 25 (right) and Violet alizarin R (left).

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FIGURE 4.11 UV
visible spectra of Acid green 25 (left) and Alizarin violet R (right) dyes in acid and basic media (water, pathlength: 2 mm for Acid green 25, 10 mm for Alizarin violet R).

FIGURE 4.12

1,4 and 1,5 disubstituted anthraquinones behavior.

A marked bathochromic effect is observed for Acid green 25 but the usual double-headed peak is mitigated, probably because of the benzoylation of amino groups. The decreasing of the molecule’s quinonoid character is associated with the visible band’s weakness (Table 4.1). In the UV range, benzenoid and quinonoid bands are more intense for Violet alizarin R. Finally, the case of a 1,2,4 trisubstituted anthraquinone derivative (Acid blue 129) is presented in Fig. 4.13. The typical double-headed peak of 1,4 disubstituted derivatives is present on Acid blue 1,2,5 visible range of UV spectra (λ 5 588 to 628 nm). In strong acid medium (pH 5 1.0), a protonation can occur on the nitrogen atom: it seems to be easier with Acid blue 1,2,9 than with Acid green 2,5 and Alizarin violet R dyes, where the steric hindrance around nitrogen atoms is more important.

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TABLE 4.1 Visible absorption of disubstituted anthraquinones. Dye

Wavelength (nm)

εmax (Lmol21cm21)

Acid green 25 (pH 5 4.9)

610/645

2480/2480

Alizarin violet R (pH 5 5.0)

557

6520

FIGURE 4.13

Acid blue 129 chemical structure and UV
visible spectra (water, path-

length: 10 mm).

4.2.1.3 Other dyes There exist other dyes, among which derivatives of triphenylmethane. These dyes usually possess acid
base indicator properties. Phenol red belongs to this family. Its structure gives rise to different equilibria according to pH (Fig. 4.14). In strong acid medium (pH 5 1.0), there is a protonation of the tautomeric forms Ia and Ib to give form II. In more basic medium, the equilibrium phenol (form I)/phenate (form III) is observed (pKa 5 8.0). UV spectrophotometry allows pointing out these different forms (Fig. 4.15). In acid medium, forms I and II coexist, form II prevails at pH 5 1.0 and presents a strong absorption in the visible region (λ 5 504 nm, ε 5 42,300 Lmol21cm21). At pH 5 3.6, the color of the solution turns from orange to yellow and the main absorption of form Ia and Ib is located at 432 nm (ε 5 20,640 Lmol21cm21). An isosbestic point can be noticed at 466 nm. In basic medium, the phenate form is predominant (pKa 5 8.0); both bathochromic and hyperchromic effects are observed for the transition n
σ* of the hydroxy group (λ 5 558 nm, ε 5 56,540 Lmol21cm21). The coloration turns red. Another specific isosbestic point appears at 481 nm. In the UV range, absorption bands around 270 nm can be reasonably imputed to π
π* transition of aromatic rings.

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FIGURE 4.14

Phenol red equilibria according to pH medium.

FIGURE 4.15

Phenol red UV
visible spectra according to pH solution (water, path-

length: 2 mm).

Crystal violet is another triphenylmethane derivative for which a tautomeric and pH-dependent equilibrium exists between the triphenylmethyl cation and its quinoidal form (Fig. 4.16). In acid medium, the quinoidal form leads to mono and dicationic forms by the protonation on nitrogen atoms (Fig. 4.17). The pH range for Crystal violet is 0.0
1.8. UV spectra point out the three different forms (Fig. 4.18). At pH 5 5.3, the neutral form is predominant and absorbs strongly in the visible range (λ 5 590 nm, ε 5 81,000 Lmol21cm21). The solution is dark violet. No change occurs in basic medium, but at pH 5 12.0, the

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4. Organic constituents

FIGURE 4.16 Crystal violet tautomerism.

FIGURE 4.17 Crystal violet mono and dicationic forms in acid medium.

solution loses its color within a few minutes. In strong acid medium (pH 5 1.0), the monocationic (λ 5 425 nm, ε 5 13,500 Lmol21cm21) and dicationic forms (λ 5 630 nm, ε 5 20,100 Lmol21cm21) coexist; the color

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FIGURE 4.18

107

Crystal violet UV
visible spectra according to pH medium (water, path-

length: 2 mm).

of the solution is green, between yellow and blue. At pH 5 1.8, a mixture of the neutral and monocationic forms is present.

4.2.2 Colored reagents The studied reagent solutions were prepared in water at a concentration of 50 mgL21. 4.2.2.1 pH indicators Three pH indicators, the pH range of which varying from 4 to 12, are presented: Methyl red, Alizarin yellow R, and Bromothymol blue. Methyl red (pH range: 4.4
6.2) is an azo compound, the structure of which differs from methyl orange (Orange 3) one by the substitution of the sulfonic acid function by a carboxylic acid function (Fig. 4.19). UV
visible spectra are displayed according to pH (Fig. 4.20). As for Orange 3, it can be supposed that, in strong acid medium, an ammonium
azonium equilibrium occurs. At pH 5 1.0, the azonium forms are predominant and strongly absorb in the visible range (λ 5 515 nm, ε 5 23,000 Lmol21cm21). Between pH 5 2.0 and 7.0, the aqueous solubility of the compound is incomplete, indicating a partial ionization of the carboxylic function (pKa 5 5.0). At pH . 7.0, the maximum of absorption is located at 431 nm (ε 5 19,600 Lmol21cm21 at pH 5 12.0) and the coloration of the solution is yellow. The chemical structure of Alizarin yellow R (pH range 5 10.1
12.0) is given in Fig. 4.21. This azo compound is very slightly soluble in water in strong acid medium (pH 5 1.0 or 2.0) where carboxylic acid and phenol functions are not ionized. At pH 5 4.4, the aqueous solubility

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4. Organic constituents

FIGURE 4.19 Methyl red chemical structure.

FIGURE 4.20 Methyl red UV
visible spectra according to pH medium (water, pathlength: 2 mm at pH 5 1.0 and 12.0, 10 mm at pH 5 2.0, 4.2, 6.0, and 7.0).

FIGURE 4.21 Alizarin yellow R chemical structure.

increases due to the ionization of the carboxylic group. In basic medium, the phenate form is predominant. UV
visible spectra of Alizarin yellow R, according to pH (after filtration at pH 5 4.4), are shown in Fig. 4.22. A strong bathochromic effect can be noticed at pH 5 12.0. At pH 5 8.5, the color of the solution turns from yellow (λ 5 373 nm, ε 5 20,500 Lmol21cm21) to red at pH 5 12.0 (λ 5 493 nm, ε 5 27,000 Lmol21cm21). Bromothymol blue (pH range: 6.2
7.6) is a triphenylmethane derivative, the chemical structure and spectral behavior of which are close to the ones of phenol red (Fig. 4.23). In basic medium, a strong bathochromic and hyperchromic effect can be observed (λ 5 615 nm, ε 5 17,800 Lmol21cm21). The color turns from yellow (λ 5 433 nm, ε 5 5900 Lmol21cm21, pH 5 5.0) to blue (pH 5 12.0). The bromine atom located on the phenolic ring induces a more intense bathochromic shift than for Phenol red. 4.2.2.2 Redox indicator Ferroin or tri (1,10-phenanthrolin) iron II is not pH dependent, as shown by UV visible spectra (Fig. 4.24).

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FIGURE 4.22 Alizarin yellow R UV
visible spectra according to pH medium (water, pathlength: 10 mm at pH 5 4.4, 2 mm at pH 5 8.5 and 12.0).

FIGURE 4.23 Bromothymol blue chemical structure and UV
visible spectra (water, pathlength: 10 mm).

The extended conjugation in the molecule due to the phenanthrolin structure induces an absorption in the visible range: the solution coloration is orange. 4.2.2.3 Complexometry indicators Three indicators used in complexometric determinations of metals are presented (4-(2-pyridylazo) resorcinol (PAR), Eriochrom Black T, and Dithizone or diphenylthiocarbazone). The 4-(2-pyridylazo) resorcinol, monosodium salt hydrate (PAR, monosodium salt) is a reagent for the selective determination of metallic compounds such as Cr (III). Its chemical structure and UV
visible absorption are given in Fig. 4.25. As for phenylazonaphthols, the PAR molecule gives rise to a tautomerism between the

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4. Organic constituents

FIGURE 4.24 Ferroin UV
visible UV spectra in acid and basic media (water, pathlength 5 10 mm).

FIGURE 4.25 PAR monosodium salt hydrate UV
visible spectra according to pH (water, pathlength: 2 mm).

azo and hydrazone forms, the latter being predominant in water. In acid medium (pH 5 2.0), a large absorption band occurs in the visible range (yellow
orange). In neutral and basic media, the maximum of absorption is located at 413 nm (ε 5 31,000 Lmol21cm21, pH 5 10.0). At pH 5 12.0, the coloration turns from yellow to orange (λ 5 482 nm, ε 5 14,000 Lmol21cm21); the two phenol functions of resorcinol are ionized. The Eriochrom Black T is an azo compound, the tautomerism equilibrium of which is presented in Fig. 4.26. UV
visible spectra according to pH are shown in Fig. 4.27. In the visible range, the absorption bands are rather large with slight shoulders. A maximum of bathochromic shift can be noticed at pH 5 9.0
10.0. Dithizone is also used for the determination of metallic compounds such as copper (II) (Fig. 4.28). Because of the slight aqueous solubility of

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FIGURE 4.26

111

Eriochrome Black T azo-hydrazone tautomerism.

FIGURE 4.27 Eriochrome Black T UV
visible spectra according to pH (water, pathlength: 10 mm).

FIGURE 4.28

Dithizone chemical structure.

this compound, UV
visible spectra have been acquired in methanol/ water (50/50) solution (Fig. 4.29). In strong acid medium (pH 5 2.0), the maximum of absorption in the visible region is located at λ 5 588 nm (ε 5 13,700 Lmol21cm21). A protonation may occur on the sulfur atom. The coloration of the solution is dark blue. In basic medium (pH 5 12.0),

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4. Organic constituents

FIGURE 4.29 Dithizone UV
visible spectra according to pH (methanol/water: 50/50, pathlength: 10 mm).

the color turns to orange (λ 5 472 nm, ε 5 8100 Lmol21cm21). Two isosbestic points, located at λ 5 425 nm and λ 5 517 nm, confirm this phenomenon.

4.3 UV-absorbing organic compounds Organic constituents, or micropollutants, are found from traces in freshwater up to mgL21 in wastewater. Micropollutants are often UVabsorbing organic compounds [6] and known for their environmental and health effects. According to their toxicity, international legislation has established several lists for the limitation of their use. For example, a priority pollutant list of around 130 substances was established in the United States in the 1980s, and the European Union planned different lists of substances at high risk to be prioritized in its 2000 Water Framework Directive. A list of 33 priority substances/groups of substances was proposed in 2008, and a complementary list of 45 substances to be monitored was published in 2013. In 2015 a first watch list of 10 substances/groups of substances was defined, including pharmaceuticals for the first time [2]. The groups of organic substances considered in this section are the following: • • • • •

aldehydes and ketones amines benzene and related compounds (BTEX) pharmaceuticals pesticides

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4.3 UV-absorbing organic compounds

• • • • •

phenols phthalates polycyclic aromatic hydrocarbons (PAHs) sulfur organic compounds surfactants.

All these compounds are considered in the final library of UV spectra (see Chapter 15).

4.3.1 Aldehydes and ketones In general, the carbonyl compounds (aldehydes, ketones) present a poor absorption in the UV region, except benzaldehyde (Table 4.2). 4.3.1.1 Aldehydes Formaldehyde does not present a significant absorption spectrum without derivatization. On the contrary, acetaldehyde, butyraldehyde, and benzaldehyde show absorption maxima of different intensities (Fig. 4.30). For benzaldehyde, the peak position (248 nm) [7] is close to the one of the aromatic rings (250 nm). 4.3.1.2 Ketones Like aldehydes, ketones generally present an absorption in the UV region due to the carbonyl group. For example, acetone and butanone have an absorption maximum at 266 and 268 nm, respectively (Fig. 4.31). The difference between the absorption peak position of diisobutylketone (ramified ketone) and butanone is approximately 20 nm. The presence of an aromatic ring like acetophenone lowers the absorption peak (242 nm) [7]. The existence of two benzenic rings in benzophenone and derivatives (hydroxybenzophenones) leads to a an TABLE 4.2

Absorptivities of some carbonyl compounds.

Compounds

Wavelength (nm)

ε (Lmol21cm21)

Formaldehyde

200

0.2

Acetaldehyde

277

6.0

Butyraldehyde

283

47

Benzaldehyde

251

12,571

Acetone

266

20

2-Butanone

268

17

Methyl isobutyl ketone

248

27

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4. Organic constituents

FIGURE 4.30 UV spectra of formaldehyde (300 gL21), acetaldehyde (12 gL21), butyraldehyde (10 gL21), and benzaldehyde (10 mgL21).

FIGURE 4.31 UV spectra of acetone (4 gL21), 2-butanone (6 gL21) and diisobutylketone

(500 mgL21).

absorption peak between 252 and 260 nm [8]. These last ones are considered as UV-filters.

4.3.2 Amines In the amine group of substances, aromatic amines (aniline and substituted anilines) are used as intermediates in industrial and pharmaceutical chemistry [9]. Because of their aromatic character, they strongly absorb in the UV range. On the opposite, aliphatic amines do not absorb directly (see Section 4.3.5.2).

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4.3.2.1 Aniline Aniline dissociation equilibrium shows that the dissociated form prevails for pH lower than the pKa value, and the undissociated form for pH greater than the pKa value (Fig. 4.32). The UV spectrum of a basic aniline solution presents two maxima (230 and 280 nm), whereas in acidic conditions, the spectrum shape does not show any specific absorption band and is not exploitable. For a pH of 4.3, close to the pKa value (4.6), both dissociated and undissociated forms are present (Fig. 4.33). 4.3.2.2 Chloroanilines Compared to chlorophenol (see Section 4.3.6), the chlorine position on the aromatic ring of chloroaniline seems to have a limited influence on the position of the absorption maximum (bathochromic shift of approximately 10 nm for the 230-nm peak, see Fig. 4.34). Concerning disubstituted chloroaniline, the position of the absorption maximum is of the same order than the one of para-monochloroanilines. 4.3.2.3 Toluidine and anisidine No significant bathochromic shift is observed between spectra of aniline and toluidine (methyl-substituted aniline) (Fig. 4.35). When aniline

FIGURE 4.32

Dissociation equilibrium of aniline.

FIGURE 4.33

pH effect on the UV spectrum of aniline (15 mgL21).

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4. Organic constituents

FIGURE 4.34 UV spectra of analine and chlorinated anilines (10 mgL21).

FIGURE 4.35 UV spectra of aniline, toluidine, and para-anisidine (10 mgL21).

is substituted by a methoxy group (anisidine), a bathochromic shift of 15 nm is observed for the peak at 280 nm. Thus the methoxy group has a greater influence than the methyl group on the position band of substituted aniline. 4.3.2.4 Other aromatic amines Among the other aromatic amines, the 4,40 -diaminodiphenylmethane or MDA is a suspected carcinogen. Despite its dianiline structure, the position of the main absorption peak at 241 nm is very close to the one of para-monochlorinated or dichlorinated anilines (Fig. 4.36).

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4.3 UV-absorbing organic compounds

FIGURE 4.36

117

UV spectrum of 4,40 diaminodiphenylmethane (10 mgL21).

4.3.2.5 Applications As aromatic amines strongly absorb in the UV region, a procedure was proposed for the estimation of aniline derivative concentrations in industrial wastewater, based on the use of the UV spectral deconvolution (UVSD) method [10]. For the purpose, a basis of reference spectra was defined by including characteristic average spectra for global and chlorinated aniline mixtures. Another work on aromatic amines from azo dye reduction gave a complete overview on the use of direct UV spectrophotometric detection in the monitoring of textile industry wastewater [11].

4.3.3 Benzene and related compounds 4.3.3.1 BTEX The term “BTEX” concerns benzene and its alkyl derivatives such as toluenes, ethylbenzenes, and xylenes. These compounds are semivolatile and, therefore, solutions must be prepared with great attention in order to minimize evaporation loss. The shape of benzene and all alkylbenzenes UV spectra is characterized by a relatively fine structure. A general bathochromic effect can be observed according to the nature and position of the substituents with regard to benzene UV spectrum. The latter has a maximum at 256 nm, whereas the maximum is around 262 nm for toluene and ethylbenzene (Fig. 4.37) and around 266 nm for xylenes (Fig. 4.38). Concerning xylenes, the bathochromic shift is more important for the parasubstituted isomer (4 and 3 nm, respectively, compared to the spectra of 2-xylene and 3-xylene). In this case, a hypochromic effect is added to the bathochromic shift.

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FIGURE 4.37 UV spectra of benzene, toluene, and ethylbenzene (40 mgL21).

FIGURE 4.38 Substitution effect of a methyl group on the aromatic ring: 2-xylene, 3-xylene, 4-xylene (30 mgL21).

4.3.3.2 Chlorobenzene Benzene could also be substituted by a chlorine atom (chlorobenzene) or a nitro group (nitrobenzene). The effect of the addition of one chlorine atom on the aromatic ring leads to a bathochromic shift of 5 nm (Fig. 4.39).

4.3.4 Pesticides Pesticides are often present in the lists of priority pollutants and thus are one of the main group of substances monitored in the last decades.

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4.3 UV-absorbing organic compounds

FIGURE 4.39

119

Benzene and chlorobenzene (40 mgL21).

They are usually classified according to their use and chemical type [12,13]: • herbicides (chloroacetanilids, phenylureas, triazins, ammonium quaternary, etc.) • insecticides (organophosphorous, carbamates, etc.) Like other organics, such as PAHs, these compounds are slightly soluble in water. Therefore solvent (methylene chloride, for example) can be used for their spectroscopic study. 4.3.4.1 Herbicides The first group is composed of organochloride molecules with a chloroacetanilid structure. Despite the presence of an aromatic ring, their spectra do not show any absorption peak, but only a shoulder around 220 nm (Fig. 4.40). On the contrary, the spectra of phenylurea pesticides (nitrogeneous herbicides) are characterized by two strong absorption peaks, one around 210 nm, and the other one around 240
250 nm (Fig. 4.41). The group of atrazine (also nitrogeneous herbicides) constitutes the triazine group and their spectra present a principal absorption peak at 222
224 nm. Hexazinone, which differs from triazines by the presence of a ketonic group, shows a bathochromic and hypochromic shift of its main UV peak, at 246 nm (Fig. 4.42). Phenoxy herbicides are characterized by two absorption peaks less absorbing than the cyclic molecules, one at 229 nm and the other one at 284 nm (Fig. 4.43). A fourth group of herbicides (ammonium quaternary) are dipyridil substances which present one main large absorption peak at 257 nm for paraquat and 309 nm for diquat (Fig. 4.44). Other herbicides can be considered as dinoterb (dinitro phenol), which is characterized by a continuous absorbance with three peaks at 214, 270, and 373 nm (Fig. 4.45).

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4. Organic constituents

FIGURE 4.40 UV spectra of chloroacetanilide herbicides: alachlor (5 mgL21), metazachlor (5 mgL21), and metolachlor (5 mgL21).

FIGURE 4.41 UV spectra of phenylurea herbicides: chlortoluron (5 mgL21), diuron (2.5 mgL21), isoproturon (5 mgL21), and linuron (5 mgL21).

FIGURE 4.42 UV spectra of triazine/triazinone herbicides: atrazine (2.5 mgL21), hexazi-

none (6.25 mgL21), simazine (4 mgL21), terbuthylazine (2.5 mgL21), and terbutryn (2 mgL21).

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4.3 UV-absorbing organic compounds

FIGURE 4.43 21

121

UV spectra of phenoxy herbicides: 2,4-D (2.5 mgL21), dichlorprop

(5 mgL ).

FIGURE 4.44 UV spectra of quaternary ammonium herbicides: diquat (5 mgL21), para-

quat (7.5 mgL21).

FIGURE 4.45 UV spectra of organothiophosphates insecticides: chlorpyrifos* (1 mgL21), diazinon (12.5 mgL21), malathion (25 mgL21), and parathion (10 mgL21). (*pathlength: 40 mm).

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4. Organic constituents

4.3.4.2 Insecticides Organophosphorous compounds are the main group of insecticides. Diazinon and parathion have one main absorption peak (at 247 and 277 nm, respectively), and chlorpyrifos presents two peaks at 228 and 288 nm. On the contrary, malathion, which does not have an aromatic ring in its structure, shows a UV spectrum without specific figures except a slight shoulder around 220 nm (Fig. 4.46). Other insecticides can be considered as carbamates group, with carbaryl as representative compounds (Fig. 4.45). This pesticide is the more absorbing compound of the selection, with a sharp peak at 220 nm and an absorptivity value twice than the others (68,000 Lmol21cm21). Finally, Table 4.3 presents the main absorption peaks of the selected pesticides, with the corresponding absorptivity values.

4.3.5 Pharmaceuticals PPCP are more and more considered in water quality monitoring schemes [14,15]. Several of them are included in the EU Watch List of contaminants of emerging concern published in the decision 2015/495/ EU (2015). These so-called contaminants of emerging concern comprise two natural hormones (estrone, E1 and 17-β-estradiol, E2); a synthetic estrogen (17-α-ethinylestradiol, EE2); a nonsteroidal antiinflammatory drug (diclofenac); three macrolide antibiotics (azithromycin, clarithromycin, and erythromycin); an antioxidant (2,6-ditert-butyl-4-methylphenol, BHT); and a UV filter (2-ethylhexyl 4-methoxycinnamate, EHMC), commonly used in personal protection products [16]. The first main group of PPCP is antibiotics. Spectra of ciprofloxacin, erythromycin, sulfamethoxazole, and trimethroprim are shown in

FIGURE 4.46 UV spectra of other pesticides: carbaryl (4 mgL21), dinoterb (10 mgL21),

and warfarin (5 mgL21).

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4.3 UV-absorbing organic compounds

TABLE 4.3

Main absorption peaks of some pesticides. Wavelength (nm)

ε (Lmol21cm21)

2,4-D

229/284

6900/1700

Alachlor

n/a




Atrazine

222/264

37,300/3500

Carbaryl

220/279

68,000/4890

Chlorpyrifos

228/288

1110/580

Chlortoluron

210/241

29,400/14,100

Diazinon

247

3700

Dichlorprop

229/284

7100/1520

Dinoterb

214/270/373

9000/6950/7500

Diquat

309

18,100

Diuron

211/248/284

24,100/14,600/990

Hexazinone

246

16,400

Isoproturon

202/239

23,600/13,100

Linuron

210/246/283

26,500/15,800/990

Malathion

303

300

Metazachlor

n/a




Metolachlor

n/a




Paraquat

257

15,400

Parathion

277

7300

Prophenofos







Simazine

222/263

25,800/2210

Terbuthylazine

224/261

33,500/2700

Terbutryn

224

32,000

Pesticide

Fig. 4.47. Except erythromycin, three antibiotics present a main absorption peak at 277, 265, and 203 nm, respectively, with shoulders or minor peaks. The absorptivity value of the first peak of trimethoprim is the highest of the selected PPCP (Table 4.4). Analgesics and antiinflamatories constitute another group of PPCP (Fig. 4.48). Acetaminophen, diclofenac, and ibuprofen show one main peak at 243, 276, and 221 nm, respectively, with absorptivity values lower than those of antibiotics. The spectra of a third group of PPCP—atelonol (beta-blocker),

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4. Organic constituents

UV spectra of antibiotics: ciprofloxacin (2.5 mgL21), erythromycin (1 mgL ), sulfamethoxazole (5 mgL21), and trimethoprim (2.5 mgL21).

FIGURE 4.47 21

TABLE 4.4 Main absorption peaks of some pharmaceuticals and personal care products. Pharmaceutical/personal care product

Wavelength (nm)

ε (Lmol21cm21)

1,7-Ethinylestradiol

278

1900

Acetaminophen

243

10,200

Atenolol

224/274

7200/1,300

Caffeine

205/273

24,300/8960

Carbamazepine

221/285

28,700/11,700

Ciprofloxacin

207/277/315/328

15,800/39,900/12,300/11,000

Clofibric acid

227/279

9100/930

Cyclophosphamide

n/a




Diatrizoate

238

25,300

Diclofenac

276

10,500

Erythromycin

299

3300

Ibuprofen

221

8700

Methylparaben

255

13,800

Sulfamethoxazole

265

15,200

Trimethoprim

203/273

48,500/5090

Warfarin

204/284/305

37,900/10,000/11,300

carbamazepine (antiepileptic), clofibric acid (lipid lowering), and warfarin (anticoagulant)—are shown in Fig. 4.49. They present two peaks, one between 204 and 227 nm and the other one between 274 and

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4.3 UV-absorbing organic compounds

FIGURE 4.48 UV spectra of analgesics and antiinflamatory: (2.5 mgL21), diclofenac (5 mgL21), and ibuprofen (5 mgL21).

125

acetaminophen

FIGURE 4.49 UV spectra of atelonol (5 mgL21), carbamazepine (2.5 mgL21), clofibric acid (5 mgL21), and warfarin (5 mgL21).

285 nm. Warfarin shows a third peak at 305 nm and high absorptivity values are found for the first peak of carbamazepine and warfarin. Finally, other different PPCP are grouped in Fig. 4.50, among which 1,7 ethinylestradiol (hormone), caffeine (stimulant), diatrizoate (radiocontrast agent), and methylparaben (PCP preservative). Except EE2, the three other substances present at least one peak between 238 and 273 nm. Redasani et al. published a review on derivative UVspectrophotometry analysis of drugs in pharmaceutical formulations and biological samples [17]. They examined the derivative analysis of the mixture of around 45 pharmaceutical products and 50 drugs. Finally, a recent spectrophotometric method was proposed for the

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4. Organic constituents

FIGURE 4.50 UV spectra of other pharmaceuticals and personal care product: 1,7

ethinyl-estradiol (5 mgL21), caffeine (5 mgL21), diatrizoate (5 mgL21), and methyl paraben (2.5 mgL21).

FIGURE 4.51 Phenol dissociation equilibrium.

analysis of mixtures of lesinurad and allopurinol, prescribed to prevent gout flares or attacks [18]. The lesinurad concentration was quantified at 290 nm where allopurinol does not absorb. Then the method proposed to use a ratio difference and a ratio derivative to determine the allopurinol concentration from calibration graph. Given the existence of two absorbing substances, with different UV spectra, a deconvolution procedure (see Chapter 3) could have been tested.

4.3.6 Phenols Several industries produce or use phenolic compounds such as alkylphenols, chlorophenols, nitrophenols, aminophenols, polyphenols, or polyaromatic phenols. Chlorophenols and nitrophenols are known for their toxicity and also for their organoleptic properties. Owing to their acidic character, they could exist as undissociated or dissociated forms (Fig. 4.51). The undissociated form is observed for pH values lower than the pKa, and the ionized form is predominant for pH values higher than the pKa. In these conditions, a bathochromic shift generally appears

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FIGURE 4.52

127

UV spectra of phenol (30 mgL21) in acidic and basic media.

between acidic and basic media and can be used for the detection of phenolic compounds (Fig. 4.52). The bathochromic shift, according to acidic and basic conditions, is observed for alkylphenols and chlorophenols around 19 nm (Table 4.5). For nitrophenols, the shift depends on the number and position of substituents. On the other hand, UV spectra (shape, bands’ position, and absorptivities) depend on the phenolic family and substitution (number and position). 4.3.6.1 Alkylphenols In this case, phenol is substituted, for example, by a methyl group (cresols, dimethylphenol, and trimethylphenol), or by a tributyl group (tributyl-4-methylphenol). The effect of an alkyl substituent induces a bathochromic shift of the characteristic phenolic band (λmax 5 270 nm, see Fig. 4.52), for example, to 277 nm for 4-cresol. The para position induces the more important shift between the substituted compound and unsubstituted phenol. The previous shift is nevertheless independent of the number of methyl groups (dimethylphenol, trimethylphenol) and of the nature of the predominant forms (thus of the pH value). Considering the close values of the shifts (with regard to phenol) of tributyl 4-methylphenol and of 4-cresol (4-methylphenol), it is obvious that the main shift factor in this case is the para position of the methyl substituent. Table 4.6 displays the values of the different shifts corresponding to the studied alkylphenols.

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4. Organic constituents

TABLE 4.5 Bathochromic shift between undissociated and dissociated forms of phenolics. Compound

Undissociated forms λmax (nm)

Dissociated forms λmax (nm)

Bathochromic shift Δ (λmax) nm

Phenol

270

286

16

2-Cresol

271

287

16

3-Cresol

272

288

16

4-Cresol

277

294

17

2,5-Dimethylphenol

274

290

16

2,4,6-Trimethylphenol

277

295

18

2-Terbutyl 4-methyl-phenol

278

297

19

2-Chlorophenol

274

292

18

3-Chlorophenol

274

291

17

4-Chlorophenol

279

298

19

2,3-Dichlorophenol

277

297

20

2,4-Dichlorophenol

283

306

23

2,4,6-Trichlorophenol

286

312

26

Pentachlorophenol

302

320

18

4-Chloro 3-methyl phenol

279

297

18

4.3.6.2 Chlorophenols For monochlorophenols, the parasubstituted isomer (4-chlorophenol) presents an absorption maximum at a wavelength higher than those of the other isomers (Fig. 4.53). Moreover, contrary to alkylphenols, the number of substituents has an influence on the position of the bands, increasing the shift with phenol (Table 4.5). Thus pentachlorophenol leads to the greater shift value, whatever its form (undissociated or dissociated). For 4-chloro 3-methyl-phenol, the substituted methyl group seems to have no influence on the position of the band compared to the one of 4-chlorophenols. 4.3.6.3 Nitrophenols As with alkyl and chlorophenols, the position of the band of parasubstituted nitrophenol isomers is different from the other isomers (Fig. 4.54).

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4.3 UV-absorbing organic compounds

TABLE 4.6 Position of the phenol band shift calculated from phenol band, for undissociated and dissociated forms of some phenolics compounds. Compound

Undissociated form λmax (nm)

Shifta (nm)

Dissociated form λmax (nm)

Shiftb (nm)

2-Cresol

271

1

287

1

3-Cresol

272

2

288

2

4-Cresol

277

7

294

8

2,5-Dimethylphenol

274

4

290

4

2,4,6-Trimethylphenol

277

7

295

9

2-Terbutyl-4-methylphenol

278

8

297

11

2-Chlorophenol

274

4

292

6

3-Chlorophenol

274

4

291

5

4-Chlorophenol

279

9

298

12

2,3-Dichlorophenol

277

7

297

11

2,4-Dichlorophenol

283

13

304

18

2,4,6-Trichlorophenol

286

16

312

26

Pentachlorophenol

302

32

320

34

4-Chloro 3-methylphenol

279

9

297

11

a

Shift calculated from phenol band obtained for undissociated form (270 nm). Shift calculated from phenol band obtained for dissociated form (286 nm).

b

UV spectra of monochlorophenol isomers (15 mgL21): 2-chlorophenol, 3chlorophenol, and 4-chlorophenol.

FIGURE 4.53

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4. Organic constituents

FIGURE 4.54 UV spectra of nitrophenol isomers (20 mgL21): 2-nitrophenol, 3nitrophenol, and 4-nitrophenol.

4.3.6.4 Polyphenols The presence of a second hydroxy group on the aromatic ring (e.g., catechol) does not modify the position of the absorption maximum with regard to phenol, if the two hydroxy groups are contiguous (Fig. 4.55). Like chlorophenols, the presence of chlorine atoms linked to the aromatic ring induces a bathochromic shift. Polyphenols can also be molecules with two or more phenolic structures as for example in natural substances (coumarins, flavonoids, anthocyanins, etc.). Polyphenols are also found as synthetic substances as bisphenol A in the environment, used in the fabrication of plastics and epoxy resins. Bisphenol A is considered as an endocrine disruptor and banned by several countries. The UV spectrum of bisphenol A is shown in Fig. 4.56. Like all other phenols, the spectrum is characterized by two peaks, with a bathochromic shift of 6 nm with regard to phenol (acidic form, Fig. 4.52). Finally, several theoretical works based on timedependent density functional theory were proposed for the prediction of peak position of the π
π* transition (above 250 nm) of phenolic compounds, among which one study was on natural polyphenols [19]. 4.3.6.5 Phenol index Besides the examination of specific phenolic compounds and their individual analysis, for example by LC (liquid chromatography)-MSMS, the estimation of their global concentration can be useful for the characterization of an industrial wastewater discharge. In this context, the “phenol index” has been proposed (ISO 6439:1990). This method consists of two main steps. A steam distillation is carried out in acidic medium, and then a colorimetric measurement is used with amino-4

UV-Visible Spectrophotometry of Waters and Soils

4.3 UV-absorbing organic compounds

FIGURE 4.55

UV spectra of catechol and 4,5 dichlorocatechol (20 mgL21).

FIGURE 4.56

UV spectrum of bisphenol A (20 mgL21).

131

antipyrine as reagent. This analytical procedure is time-consuming and not quantitative, since some phenolic compounds as nitrophenols, aminophenols, or polyphenols (catechols) do not fully react. According to their structure (aromatic ring), phenolic compounds are easily detected by UV spectrophotometry (as, e.g., in HPLC (high performance liquid chromatography) procedures). Moreover, a characteristic bathochromic shift in basic medium can also be used for their determination. UV spectrum exploitation can be improved with the use of a second derivative [20]. As illustrated in Fig. 4.57, the second derivative of UV spectra corresponding to a standard phenol solution and a spiked wastewater sample shows a good similarity to the phenol-specific wavelength (close to 270 nm). However, this method needs to take into account the target phenolic compound and to have a zero value for the corresponding second derivative value of

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4. Organic constituents

FIGURE 4.57 Spectra and second derivative of phenol standard solution and wastewater sample UV spectra (phenol concentration close to 5 mgL21).

FIGURE 4.58 UV spectra of industrial-wastewater-containing phenol (14.2, 16.6, and 18.2 mgL21, respectively).

interferences matrix. Fig. 4.58 shows some examples of chemical- and petrochemical-wastewater-containing phenols. The UVSD (UV spectral deconvolution) method [21] can be used as an alternative method for the rapid estimation of the “phenol

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133

index.” UV spectra are acquired from raw samples and a pH correction (pH 5 12) is carried out, in order to exploit both the bathochromic and hyperchromic effects observed for the dissociated forms (phenates). This procedure is particularly useful for nitrophenols, the spectra of which present a specific adsorption between 360 and 500 nm (Fig. 4.59). On the other hand, phenol and 2-chlorophenol have their adsorption peak range from 250 to 360 nm. It should be noticed that below 250 nm, the adsorption band is not specific to phenolic compounds, because a lot of organic compounds have an absorption in this UV spectral window. The proposed method for the quantification of phenolic compounds includes mainly two deconvolution steps (Fig. 4.60). The first step is carried out for a spectral window range of 360 to 500 nm and allows the quantification of nitrophenol compounds (C1). Then the second deconvolution is performed on the residual spectrum for a spectral window range of 250 to 360 nm and leads to estimating the concentration of the other phenolic compounds (C2). The total concentration (C1 1 C2) can be considered as a good estimation of global phenolic compound concentration. According to the principle of the UVSD method, the difference between the sample spectrum and the restituted one is defined by the quadratic error (see Chapter 3). If the quadratic error is higher than 5%, it means that other compounds are present and could interfere with the estimation of phenolic compound concentrations. In this case, no quantification is possible, but a qualitative result can be displayed (i.e., presence of unknown compounds). A comparison between expected concentrations (from ultrapure water spiked with phenol mixtures) and UV
visible estimated

FIGURE 4.59

Specific UV-visible absorption range of phenolic compounds (pH 12).

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4. Organic constituents

FIGURE 4.60 Deconvolution procedure for the estimation of the concentration of global phenolic compounds.

concentrations shows a good agreement (Fig. 4.61). Phenolic compounds used for the purpose are listed in Table 4.7 and were selected according to EPA list of priority pollutants. The same comparison with “phenol index” measurement points out the limits of the standard method. In order to check the potential interference of aqueous matrix, an industrial wastewater sample was spiked with phenol mixtures (Fig. 4.62). The correlation between concentrations estimated by the proposed UV
visible method and expected concentrations gives satisfactory results and reveals the presence of phenolic compounds in the raw industrial wastewater sample (around 15 mgL21). In conclusion, the UVSD procedure seems to be a suitable method for the rapid and simple estimation of global phenolic compound concentration. However, samples have to be filtered in order to avoid interference due to the high total suspended solids (TSS) level in UV spectrophotometry measurement. Finally, this method could

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4.3 UV-absorbing organic compounds

FIGURE 4.61 Comparison between expected concentrations and estimated concentrations by UV-visible (deconvolution) method (R2 5 0.98) and standard method (phenol index). TABLE 4.7

Selected phenolic compounds.

Nonsubstituted phenols

Methylphenols

Chlorophenols

Nitrophenols

Phenol

4-Cresol

2-Chlorophenol

2-Nitrophenol

Naphtol

2,4-Dimethylphenol

4-Chlorophenol

4-Nitrophenol







2,4-Dichlorophenol

2,4-Dinitrophenol







Trichlorophenol










Pentachlorophenol




be extended to other phenolic families such as aminophenols or polyphenols.

4.3.7 Phthalates Phthalates are often used as plasticizers for polyvinyl, polyvinyl chloride, or cellulose resins. They are esters of phthalic acid. Diethylphthalate, dibutylphthalate, and butylbenzylphthalate show an absorption peak around 275 nm (Fig. 4.63). One phthalate is of primary interest regarding its endocrine disrupting impact, Di(2-ethylhexyl)phthalate (DEHP). This substance is one of the priority substances in the field of water policies. Its UV spectrum (Fig. 4.64) shows three absorption peak more or less marked at 204, 230, and 277 nm, like the other phthalates.

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4. Organic constituents

FIGURE 4.62 Correlation between expected and estimated concentrations for spiked wastewater sample (R2 5 0.91).

FIGURE 4.63 UV spectra of phthalates (10 mgL21).

4.3.8 Polycyclic aromatic hydrocarbons PAHs are highly toxic pollutants (LC50 , mgL21 for aquatic organisms), and some of them have proven to be carcinogenic [22,23]. They are monitored in potable water and wastewater, but PAHs are also often present at high concentrations in polluted soils, that is, on sites previously occupied by gas works and coking plants (see Chapter 13). In all cases, as they are very few soluble in water, a liquid/solid extraction step is necessary and UV spectra are performed in organic medium. PAHs are constituted by two or more aromatic rings joined together or separated by a five-membered cycle. The studied PAHs and their

UV-Visible Spectrophotometry of Waters and Soils

4.3 UV-absorbing organic compounds

FIGURE 4.64

137

UV spectrum of DEHP (5 mgL21).

structures are shown in Fig. 4.65. In addition to these 16 PAHs, benzo [b]fluorene and benzo[e]pyrene are also studied. 4.3.8.1 Solvent effect Only light PAHs (2 or 3 rings) are soluble in water at the mgL21 level. Fig. 4.66 shows UV spectra of acenaphthylene in water and in acetonitrile. The shape of the two spectra is similar, and no solvent effect is observed on the absorption wavelengths. Consequently, all PAH UV spectra have been acquired in acetonitrile solutions. 4.3.8.2 Influence of the number of aromatic rings The shape of UV spectra of PAHs is related to the number of aromatic rings and their arrangement (linear, angular, or clusters) in the PAH molecule. A general bathochromic effect is observed as the number of aromatic rings increases in the PAH molecule. For example, Fig. 4.67 shows UV spectra of naphthalene (two aromatic rings), phenanthrene (three aromatic rings), pyrene (four aromatic rings), benzo[a]pyrene (five aromatic rings), and benzo[g, h,i]perylene (six aromatic rings). However, the bathochromic effect weakens as the ring number increases, especially for high ring numbers as shown for benzo[a]pyrene and benzo[g,h,i]perylene spectra. Dealing with the arrangement of aromatic rings in the PAH molecule, this bathochromic effect is especially important for PAHs with a linear general structure (Fig. 4.68). This phenomenon can be explained by an important delocalization of π electrons promoted by the linear annelation. For an angular arrangement, the observed bathochromic shift is weaker (Fig. 4.69).

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4. Organic constituents

FIGURE 4.65 USEPA list of PAHs (*: PAH abbreviated name). PAH, polycyclic aromatic hydrocarbons.

FIGURE 4.66 UV spectra of acenaphthylene in water and acetonitrile (5 mgL21).

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FIGURE 4.67 UV spectra of naphthalene, phenanthrene, pyrene, and benzo[a]pyrene and benzo[g,h,i]perylene (5 mgL21).

UV spectra of naphthalene and anthracene (5 mgL21) with reference to benzene (400 mgL ).

FIGURE 4.68

21

The main absorption peak wavelengths and the corresponding absorptivities for the studied PAHs are given in Table 4.8. 4.3.8.3 Isomeric polycyclic aromatic hydrocarbon UV spectra Pure aromatic PAH isomers have different UV spectra, as shown in Fig. 4.70 (anthracene and phenanthrene) and in Fig. 4.71 (benzo[a]pyrene and benzo[e]pyrene). A hypsochromic shift and a hypochromic effect are observed for phenanthrene (angular arrangement) in comparison with anthracene (linear arrangement).

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140

FIGURE 4.69 (5 mgL21).

4. Organic constituents

UV spectra of phenanthrene and chrysene in reference to naphthalene

TABLE 4.8 Polycyclic aromatic hydrocarbons’ (PAH) main absorption peak characteristics. PAH name

Ring number (aromatic 1 nonaromatic)

Wavelength (nm)

ε (Lmol21cm21)

Acenaphthene

3 (2 1 1)

289

7500

Acenaphthylene

3 (2 1 1)

322

10,000

Anthracene

3 (3)

357

6400

Benzo[a]anthracene

4 (4)

286

83,300

Benzo[a]pyrene

5 (5)

295

53,500

Benzo[b]fluoranthene

5 (4 1 1)

300

37,100

Benzo[g,h,i]perylene

6 (6)

299

53,400

Benzo[k]fluoranthene

5 (4 1 1)

307

55,800

Chrysene

4 (4)

268

106,000

Dibenzo[a,h] anthracene

5 (5)

294

110,200

Fluoranthene

4 (3 1 1)

286

27,600

Fluorene

3 (2 1 1)

263

22,700

Indeno[1,2,3-c,d] pyrene

6 (5 1 1)

302

31,200

Naphthalene

2 (2)

275

5700

Phenanthrene

3 (3)

252

63,000

Pyrene

4 (4)

333

48,300

UV-Visible Spectrophotometry of Waters and Soils

4.3 UV-absorbing organic compounds

FIGURE 4.70

UV spectra of anthracene and phenantrene (4 mgL21).

FIGURE 4.71

UV spectra of benzo[a]pyrene and benzo[e]pyrene (5 mgL21).

141

For benzene clusters such as benzo[a]pyrene and benzo[e]pyrene, UV spectra also reveal a hypsochromic shift and a hypochromic effect for benzo[e]pyrene which is more condensed. The difference is not so marked for benzo[b]fluoranthene and benzo [k]fluoranthene, which have a five-membered cycle in their structure. Nevertheless, a slight bathochromic effect can be seen for the less condensed isomer (Fig. 4.72). 4.3.8.4 Introduction of a five-membered cycle in the polycyclic aromatic hydrocarbon structure Some PAH molecules include a five-membered cycle in their structure. When an aromatic ring is added to the linear chain containing the

UV-Visible Spectrophotometry of Waters and Soils

142

FIGURE

(5 mgL21).

4. Organic constituents

4.72 UV spectra of benzo[b]fluoranthene and benzo[k]fluoranthene

FIGURE 4.73 UV spectra of fluorene and benzo[b]fluorene (5 mgL21).

five-membered cycle, both bathochromic shift and hyperchromic effect are observed. The following spectra illustrate this phenomenon for fluorene and benzo[b]fluorene (Fig. 4.73) and for fluoranthene and benzo[k]fluoranthene (Fig. 4.74), respectively. When the aromatic ring is not added to the linear chain, these effects are less significant (benzo[b]fluoranthene and indeno [1,2,3,cd]pyrene). Fig. 4.75 presents some UV spectra of nonaromatic PAHs, with an increasing ring number (from 3 to 6). In comparison with aromatic PAHs, a bathochromic effect can be seen as the ring number increases, but a general hypochromic effect is observed. Fig. 4.76 gives the absorption range of the 16 PAHs, showing the main and secondary peaks for each of them.

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FIGURE 4.74

UV spectra of and fluoranthene and benzo[k]fluoranthene (5 mgL21).

FIGURE 4.75

UV spectra of nonaromatic PAHs (5 mgL21). PAH, polycyclic aromatic

hydrocarbons.

In conclusion, according to the aromatic character of PAH molecules, some general tendencies can be pointed out from the study of UV spectra of PAHs: • A bathochromic shift is observed as the length of the aromatic chain increases in the PAH molecule. • A hyperchromic effect is noted when aromatic rings are less condensed. • The presence of a five-membered cycle in the PAH molecule induces a bathochromic effect. 4.3.8.5 Polycyclic aromatic hydrocarbon (index) Soil contamination is one of the main environmental problems, mainly in industrial countries, and is very often linked to water resource degradation. The diagnosis of potentially polluted sites is often

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144

4. Organic constituents

FIGURE 4.76 Absorption range of the 16 USEPA PAHs. PAH, polycyclic aromatic hydrocarbons.

difficult due to the lack of simple procedures, and the treatment scheme must be defined and carried out with respect to soil characteristics and future use. The classical approach includes a sampling phase before laboratory analysis, generally with HPLC or GC analysis of organic extracts, leading to a good selectivity [24]. However, the knowledge of the global concentration of PAH can be a good way for diagnosis and treatment monitoring. A PAH UV index was developed, based on the UV spectrophotometric analysis of a soil organic extract and gives a global PAHs estimation in reference to the 16 USEPA PAHs (see Fig. 4.65). The extraction step is carried out by acetonitrile [25] and can be improved with a solid-phase extraction (SPE) step [26]. The soil organic extract corresponding to a concentrated and purified soil PAH solution is obtained through the simple procedure described in Fig. 4.77. This procedure has been applied to nearly 80 samples of soils from various industrial origins. After solvent extraction, two types of UV organic extracts spectra are presented in Fig. 4.78. Each UV spectrum shows a structured shape with high absorbance at the beginning of the spectrum, which decreases after 300 nm. It can be noticed that absorbance value over 350 nm is more important for the soil/type 2, according to the bathochromic effect observed for heavy PAHs. Indeed, it has been shown that UV spectrum of soil/type 1 corresponds to soils mainly contaminated by light PAHs (2 or 3 cycles) and UV spectrum of type 2 to soils mainly contaminated by heavy PAHs (4 or more cycles) [27]. Two specific peaks, located at 254 and 288 nm, respectively, are always present on UV spectra profiles. The first one is characteristic of

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145

FIGURE 4.77 PAH-contaminated soils: diagnosis procedure. PAHs, polycyclic aromatic hydrocarbons.

the presence of the 16 USEPA PAHs. Thus, for a quantitative application, the measurement of the absorbance value at this wavelength is proposed as a PAH index for a simple estimation of global PAHs concentration in contaminated soil. A validation of this approach is given by HPLC analysis of mixtures of the 16 USEPA PAHs (Fig. 4.79). Concerning the absorption peak at 288 nm, it has been observed that the ratio between the absorbance values at 254 and 288 nm, A254 nm/ A288 nm, varies from one soil to another (between 1 and 4) and appears to be related to the proportion of light and heavy PAHs in contaminated soil. By the way, a correlation was found between this ratio value and the relative proportion of three-cycle PAHs (light PAHs), measured by HPLC (Fig. 4.80). It must be specified that naphthalene (two cycles) was never been found in the studied PAHs-contaminated soils. This simple method of UV spectra exploitation (mono wavelength correlation) was proposed for a rapid diagnosis of PAHs-contaminated soils [28]. Moreover, the absorbance value ratio of the two main characteristic peaks gives information about the PAHs distribution in terms of

UV-Visible Spectrophotometry of Waters and Soils

146

FIGURE 4.78

4. Organic constituents

PAH-contaminated soils extract UV spectra. PAHs, polycyclic aromatic

hydrocarbons.

FIGURE 4.79 Correlation between HPLC measurement and absorbance value at 254 nm. HPLC, high performance liquid chromatography.

light and heavy PAHs. These tools appear to be relevant with regard to the management of contaminated soils.

4.3.9 Sulfur organic compounds Organic sulfur compounds are volatile. Some of them are a few soluble in water and are associated with bad odor. They are essentially

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147

FIGURE 4.80 Correlation between A254 nm/A288 nm ratio and presence of three-cycle PAH. PAHs, polycyclic aromatic hydrocarbons.

FIGURE 4.81 UV spectra of ethanethiol (Et-SH) 70 mgL21, thiophenol (Ph-SH) 10 mgL21, ethyl disulphure (EtS)2 290 mgL21, and phenyldisulphure (PhS)2 20 mgL21.

studied in gazeous phase, but they can be present in wastewater because of their solubility in water-miscible solvents such as alcohols. In aqueous solution [29], they present a characteristic UV absorption (Fig. 4.81). Sulfur organic compounds, when dissolved in water, are very sensitive to pH (Fig. 4.82). As the acidic form absorbs differently from the basic one, it is possible to calculate the pKa value of these compounds. The example of thiophenol is given, and the estimated pKa value is close to 6.2 (Fig. 4.82). In basic media, bathochromic and hyperchromic

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4. Organic constituents

FIGURE 4.82 UV spectra of thiophenol according to pH values (A) and predominance diagram (B).

shifts are observed in all cases. These effects are particularly marked with thiophenate ion because of a stabilization of the negative charge with the π electrons of the aromatic ring. By adjusting the pH of sample to 11 after addition of sodium hydroxide solution 2.5 mol L21, the spectra show a well-defined peak of absorbance at 238 nm for the alkylthiols or 263 nm for the thiophenols. The application of the deconvolution method allows the estimation of the global concentration of mercaptans in wastewater. A major point is related to the need to adjust the sample pH to 11. At this value, all mercaptans are supposed to be under the thiolate form, which is known to be readily oxidized at high pH value in the presence of dissolved oxygen. Thus the spectra acquisition must be carried out less than 15 minutes after pH adjustment [29].

4.3.10 Surfactants Surfactants are the active substance of detergents always present in wastewater and are used as cleaning agents for domestic or industrial applications. Their occurrence in the environment is often linked to the presence of foam downstream of a treated wastewater discharge, and also to some toxic effects depending on surfactant nature and concentration [30]. It is the principal reason why the remaining surfactants responsible for this phenomenon are often used as a fingerprint of urban pollution. Commonly, these molecules are made up of two parts with very different characteristics. The long hydrocarbon chain forms the hydrophobic tail, and the polar group (carboxylic, sulfate, phosphate, ammonium, and ethoxylate) forms the hydrophilic head. The tail group would rather dissolve in nonpolar material such as grease, whereas the head group has a great solubility in water. Therefore surfactants are oriented toward interfaces such as oil
water and form a surfactant monolayer that greatly reduces the surface tension.

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149

Surfactants are classified into four categories according to the nature of the charge of the hydrophilic part of the molecule: 1. 2. 3. 4.

anionic surfactants cationic surfactants nonionic surfactants amphoteric surfactants.

The anionic category represents the major part of surfactant consumption. Anionic surfactants include linear alkylbenzene sulfonates (LASs), alcohol sulfates, or alcohol ethersulfates. The latter compounds have a poor absorption in the UV range, whereas the LASs, often considered as the most common class of surfactants existing in wastewater [31], show a significant absorption (Fig. 4.83). Dodecylbenzene sulfonate (DBS) is used as a reference for LAS measurements (standard method) and shows an absorption band at 225 nm (Fig. 4.84). Other anionic surfactants, such as nonyl phenol ethoxy phosphate (commercial term) with an aromatic structure, also absorb in the UV range at 225 and 275 nm. The presence of two aromatic rings in the molecular structure of alkyl diphenyloxide disulfonate (commercial term) leads to a bathochromic shift due to an extended conjugation in the molecule. In this case, the absorption band is observed at 237 nm. Nonionic surfactants such as polyethylene glycol p-isooctylphenyl ester (octoxynol-9) also present two absorption maxima (225 and 275 nm). Fatty alcohol ethoxylates do not absorb in the UV region. Cationic surfactants (quaternary ammonium group) and amphoteric surfactants with a long alkyl chain present a poor absorption in UV.

FIGURE 4.83

UV spectra of aromatic surfactants.

UV-Visible Spectrophotometry of Waters and Soils

150

4. Organic constituents

FIGURE 4.84 Surfactant spectrum of octynol-9 (50 mgL21), alkyl diphenyloxide disul-

fonate (50 mgL21), nonyl phenol ethoxy phosphate (50 mgL21), and DBS (30 mgL21).

Besides the specific identification of surfactants, the MBAS (methylene blue active substances) determination is often used [32]. However, this method is nonspecific and time-consuming (due to the extraction step) and is sensitive to many interferences. Considering the UV absorption of aromatic surfactants, different methods for the UV estimation of these compounds have been proposed. The simple UV absorptiometric methods using absorbance measurement at a few wavelengths (1 to 3) or the derivative signal of the spectrum [33] have their limitation (existence of a spectral background, poor quantification limit, nonlinear interference signal, etc.) and are not relevant for all types of samples. Another way is the use of the UVSD approach for the spectra exploitation [21], taking into account the existence of a shoulder near 220
225 nm, commonly seen for the major part of urban wastewater samples, and often associated with the existence of highly absorbing surfactants such as LAS [34]. Several defined reference spectra, and different combinations of them, were selected for the constitution of three bases of reference spectra used for the restitution of spectra of natural and urban wastewater samples [25]. These three bases allowed to restitute raw or biologically treated sewage, physicochemically treated sewage, and natural water, respectively. In this approach, the type of reference spectra is not all related to specific compounds. Notice that the spectrum related to LAS appears in each basis, as these compounds can be present in natural water or wastewater. Such a method has taken into account that LASs are a mixture of various alkyl homologs (ranging from C10 to C14) and phenyl positional isomers, the composition of which varies from the

UV-Visible Spectrophotometry of Waters and Soils

4.4 Solid-phase extraction and UV
visible spectrophotometry

151

commercial products to the wastewater treatment. This procedure has been validated on different samples of wastewater (inlets and outlets of biological and physicochemical wastewater treatment plants) to which standard amounts of DBS were added (Fig. 4.85). The standard addition method is used, taking into account the poor precision of the reference procedure (MBAS). The concentration of the added DBS was between 0 and 25 mgL 21 , which is the common range encountered in such samples. The values of the recovered addition of DBS, calculated as the difference between the concentration after addition (i.e., taking into account initial and added detergents) and the initial one, determined with UV method, were compared with the real added concentration. The relative error calculated between the measured and theoretical values showed that the results were very close. Fig. 4.86 shows the regression line resulting from the comparison with a very good determination coefficient (R 2 5 0.99).

4.4 Solid-phase extraction and UV
visible spectrophotometry The application of UV spectrophotometry for the identification of organic micropollutants is limited by the sensitivity of the technique. For example, the limit of detection for one of the most absorbing pharmaceuticals, carbaryl in aqueous solution, is around 100 μgL21 for a 10 mm pathlength cell.

FIGURE 4.85 Spectra of DBS (dodecylbenzene sulfonate) solution and urban wastewater with and without standard addition.

UV-Visible Spectrophotometry of Waters and Soils

152

4. Organic constituents

FIGURE 4.86 Regression line resulting from the comparison between estimated and added DBS concentration in a water sample.

In order to improve the sensitivity and to simplify the detection of substances in mixtures, an solid phase extraction (SPE) step can be used [35]. The technique is based on the use of an automatic multiple SPE device (MSPE), including a UV spectrophotometer (MSPE/UV) for the detection of organic micropollutants in water (pesticides and pharmaceuticals namely). After a comparison of the capacity of retention of sorbents for some pharmaceuticals (Table 4.9), two different extraction sorbents were chosen. These sorbents and several eluting solvents allow a separation of the compounds based on the physicochemical properties of each substance (pKa, log Kow) and on the specific interactions with the sorbent (Fig. 4.87). Then, a UV analysis of each fraction, either at the maximum of absorbance using a calibration curve, or from the whole spectrum using the UVSD method [21], allows a determination and a quantification of each compound. The method is rather rapid (less than 2 hours), sensitive enough for an accidental/intentional contamination (between 5 and 40 μgL21 of limit of quantification (LOQ) according to the substances) and with a good precision (between 4% and 14%) [36]. A same approach was chosen for the determination of atrazine (ATZ) and its main metabolites desethylatrazine (DEA) and deisopropylatrazine (DIA) [37]. After an SPE step, the PLS exploitation of UV spectra of fractions was satisfactory with mean errors between 0.33% and 10% in the simultaneous determination of ATZ, DIA, and DEA in aqueous solution.

UV-Visible Spectrophotometry of Waters and Soils

TABLE 4.9 Capacity of retention of sorbents (pH 6.2, 10 mL at 2 μgmL21). OasisHLB

Sep-Pak Plus PS2

LichrolutEN

OasisMAX

OasisMCX

StrataX-C

StrataSAX

StrataPAH

SupelMIP Triazine

SupelMIP NSAID

Targeted compounds

Ac, Neut., Bas.

Pest.

Ac, Neut., Bas.

Polar

Ac.

Bas.

Strong cation

Strong anion

PAH

Triazine

NSAID

Diclofenac

.75%

.75%

25%
50%

.75%

.75%

.75%

.75%

.75%

.75%

50%
75%

.75%

Sulfamethoxazole

.75%

.75%

.75%

.75%

.75%

.75%

.75%

.75%

.75%

.75%

.75%

Ibuprofen

.75%

.75%

.75%

.75%

.75%

.75%

.75%

.75%

25%
50%

.75%

.75%

Caffeine

.75%

.75%

.75%

.75%

.75%

.75%

.75%

,25%

.75%

,25%

.75%

Trimetoprim

.75%

.75%

.75%

.5%

, 25%

.75%

.75%

,25%

.75%

.75%

50%
75%

Carbamazepin

.75%

.75%

.75%

.75%

.75%

.75%

.75%

,25%

.75%

.75%

.75%

Atrazine

.75%

.75%

.75%

.75%

.75%

.75%

.75%

,25%

.75%

.75%

.75%

Diazinon

.75%

.75%

. 75%

.75%

.75%

.75%

.75%

,25%

.75%

.75%

.75%

1,7 Ethinylestradiol

.75%

.75%

.75%

.75%

.75%

.75%

.75%

,25%

,25%

.75%

.75%

Strata-X

Ac., acidic; Neut., neutral; Bas., basic; Pest., pesticides. Adapted from M. Brogat, A. Cadiere, A. Sellier, O. Thomas, E. Baures, B. Roig, MSPE/UV for field detection of micropollutants in water. Microchemical Journal 108 (2013) 215
223.

154

4. Organic constituents

FIGURE 4.87 Methodology used for the separation and concentration of five micropollutants: After a preconcentration step of substances by using chemical sorbents with different properties, the separation of substances based on their physicochemical properties is possible thanks to different eluting solvents followed by a UV analysis of each fraction. Source: Adapted from M. Brogat, A. Cadiere, A. Sellier, O. Thomas, E. Baures, B. Roig, MSPE/ UV for field detection of micropollutants in water. Microchemical Journal 108 (2013) 215
223.

4.5 Nonabsorbing organic compounds The absorbance of some organic substances in the UV
visible range is very weak for the concentrations usually found in water and wastewater. Some of them do not absorb, even at high concentration. However, sometimes it is possible to detect or reveal their presence with the use of a pretreatment, such as a specific reaction of derivatization or a photooxidation step.

UV-Visible Spectrophotometry of Waters and Soils

4.5 Nonabsorbing organic compounds

155

4.5.1 Carbonyl compounds: use of absorbing derivatives As seen previously, some carbonyl compounds, such as formaldehyde, do not have any specific absorption band. Therefore their spectrophotometric determination is not easy. In this case, the use of a derivative from 2,4-dinitrophenylhydrazine, namely, 2,4-dinitrophenylhydrazone [38], is advisable since the resulting UV spectrum shows an absorption maximum at 360 nm (Fig. 4.88). Aldehyde and acetone derivatives show an absorption peak around 370 nm. The absorptivities of derivatives (2,4-dinitrophenylhydrazone) increase notably as compared to carbonyl compounds, making their detection easier (Table 4.10).

4.5.2 Aliphatic amines and amino acids: photooxidation Aliphatic amines, such as diethylamine, and amino acids, such as glycine and glutamic acid, do not show any specific absorption (Fig. 4.89). In this case, a photooxidation (using a low-pressure mercury lamp and a potassium peroxodisulphate solution as oxidant) could lead to nitrate formation (Fig. 4.90), easily determined by UV spectrophotometry (see Chapter 6). In contrast with the previous case, this procedure is not specific and only shows that the photooxidized substance contains nitrogen.

4.5.3 Carbohydrates: photodegradation Carbohydrates do not absorb in the UV
visible range, but a photodegradation step is able to reveal their presence in an aqueous

FIGURE 4.88 (9 mgL21).

Use of a formaldehyde derivative from 2,4-dinitrophenylhydrazone

UV-Visible Spectrophotometry of Waters and Soils

156 TABLE 4.10

4. Organic constituents

Absorptivities of some carbonyl compounds and their derivatives.

Carbonyl compounds

Wavelength (nm)

ε (Lmol21cm21)

Acetone

266/371a

20/5470b

Acetaldehyde

277/370

6.0/20,680

Butyraldehyde

283/367

47/22,290

Benzaldehyde

251/367

12,571/22,750

Formaldehyde

200/360

0.2/19,350

a

Wavelength of absorption peak of compound and derivative. Molar extinction coefficient of absorption peak of compound and derivative.

b

FIGURE 4.89

UV spectra of diethylamine (50 mgL21) and amino acids (500 mgL21).

solution [39]. Contrary to the previous case where the association of UV irradiation with a chemical oxidant (peroxodisulfate) has demonstrated its efficiency, this procedure is not adapted for carbohydrate determination. The oxidation rate of this method is too fast, and absorbing intermediates (carbonyl compounds) are only weakly observed, because they are quickly oxidized into carboxylic acids. Thus milder oxidation conditions (such as a simple UV lamp) should be used in order to provide an appropriate oxidation reaction that allows the formation of an intermediate compound with a stable absorption peak (Fig. 4.91). This method, called UV/UV procedure [39], needs the addition of a pH 9.0 buffer solution, before sample irradiation for 10 minutes. Under the influence of UV radiation, sugars are oxidized into UV-absorbing carbonyl compounds

UV-Visible Spectrophotometry of Waters and Soils

4.5 Nonabsorbing organic compounds

157

FIGURE 4.90

UV spectra of glycine (27 mgL21) before and after photooxidation.

FIGURE 4.91

UV spectra of sugar solution (glucose, 150 mgL21) before and after

photodegradation.

characterized by a maximum absorbance at 268 nm (Fig. 4.92). The formation of these compounds may be monitored by UV absorption spectrophotometry at the wavelength of maximum absorbance. Contrary to N compounds, the use of a chemical oxidant (peroxodisulfate) is not needed because carbonyl compounds are too quickly oxidized.

UV-Visible Spectrophotometry of Waters and Soils

158

4. Organic constituents

FIGURE 4.92 UV spectra of sugar solution (glucose, 320 mgL21) after photodegradation: influence of pH.

Acknowledgments The authors wish to thank Catherine Gonzalez, Evelyne Touraud, and Sylvie Spinelli for their contribution to the first-edition chapter (C. Gonzalez, E. Touraud, S. Spinelli, O. Thomas, Organic constituents, in: UV-visible spectrophotometry of water and wastewater, O. Thomas, C. Burgess (Eds.), Elsevier, Amsterdam (2007) pp. 47
87).

References [1] M.O. Barbosa, N.F.F. Moreira, A.R. Ribeiro, M.F.R. Pereira, A.M.T. Silva, Occurrence and removal of organic micropollutants: an overview of the watch list of EU Decision 2015/495, Water Research 94 (2016) 257
279. Available from: https://doi.org/ 10.1016/j.watres.2016.02.047. [2] A.J. Bergmann, G.L. Points, R.P. Scott, G. Wilson, K.A. Anderson, Development of quantitative screen for 1550 chemicals with GC-MS, Analytical and Bioanalytical Chemistry 410 (2018) 3101
3110. Available from: https://doi.org/10.1007/s00216-0180997-7. [3] Y. Yang, Y.S. Ok, K.H. Kim, E.E. Kwon, Y.F. Tsang, Occurrences and removal of pharmaceuticals and personal care products (PPCPs) in drinking water and water/sewage treatment plants: a review, Science of the Total Environment, 596
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320. Available from: https://doi.org/10.1016/j.scitotenv.2017.04.102. [4] C.L.S. Vilela, J.P. Bassin, R.S. Peixoto, Water contamination by endocrine disruptors: impacts, microbiological aspects and trends for environmental protection, Environmental Pollution 235 (2018) 546
559. Available from: https://doi.org/ 10.1016/j.envpol.2017.12.098. [5] K.M. Tawarah, H.M. Abu-Shamleh, A spectrophotometric study of the tautomeric and acid-base equilibria of methyl orange and methyl yellow in aqueous acidic solutions, Dyes and Pigments 16 (1991) 241
251. Available from: https://doi.org/10.1016/01437208(91)85014-Y.

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1324. Available from: https://doi.org/10.1007/s10562-019-03045-6. [8] M. Basu, S. Sarkar, S. Pande, S. Jana, A. Kumar Sinha, S. Sarkar, et al., Hydroxylation of benzophenone with ammonium phosphomolybdate in the solid state via UV photoactivation, Chemical Communications (2009) 7191
7193. Available from: https://doi.org/10.1039/b905718h. [9] S. Gorog, Ultraviolet-Visible Spectrophotometry in Pharmaceutical Analysis, CRC press, 2018. [10] F. Perez, Etude spectrophotometrique d’effluents industriels: application a l’estimation de parametres, University of Provence, Marseille, France, 2001. [11] H.M. Pinheiro, E. Touraud, O. Thomas, Aromatic amines from azo dye reduction: status review with emphasis on direct UV spectrophotometric detection in textile industry wastewaters, Dyes and Pigments 61 (2004) 121
139. Available from: https://doi.org/10.1016/j.dyepig.2003.10.009. [12] D.W. Connell, Basic Concepts of Environmental Chemistry, CRC Press, 2005. [13] H.S.H. Beitz, D.W. Bewick, C.N. Gouyot, M. Hafner, F. Herzel, M.O. James, Pesticides in Ground and Surface Water, Vol. 9, Springer Science & Business Media, 2012. [14] J.O. Tijani, O.O. Fatoba, O.O. Babajide, L.F. Petrik, Pharmaceuticals, endocrine disruptors, personal care products, nanomaterials and perfluorinated pollutants: a review, Environmental Chemistry Letters 14 (2016) 27
49. Available from: https:// doi.org/10.1007/s10311-015-0537-z. [15] S. Mompelat, B. Le Bot, O. Thomas, Occurrence and fate of pharmaceutical products and by-products, from resource to drinking water, Environment International 35 (2009) 803
814. Available from: https://doi.org/10.1016/j.envint.2008.10.008. [16] J.C.G. Sousa, A.R. Ribeiro, M.O. Barbosa, C. Ribeiro, M.E. Tiritan, M.F.R. Pereira, et al., Monitoring of the 17 EU Watch List contaminants of emerging concern in the Ave and the Sousa Rivers, Science of the Total Environment 649 (2019) 1083
1095. Available from: https://doi.org/10.1016/j.scitotenv.2018.08.309. [17] S.S.V. Redasani, P.R. Patel, D.Y. Marathe, S.R. Chaudhari, A.A. Shirkhedar, A review on derivative UV-spectrophotometry analysis of drugs in pharmaceutical formulations and biological samples review, Journal of the Chilean Chemical Society 63 (2018). Available from: https://jcchems.com/index.php/JCCHEMS/article/view/ 774. [18] A.A. Mohamed, A. El-Olemy, S. Ramzy, A.H. Abdelazim, M.K.M. Omar, M. Shahin, Spectrophotometric determination of lesinurad and allopurinol in recently approved FDA pharmaceutical preparation, Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 247 (2021) 119106. Available from: https://doi.org/ 10.1016/j.saa.2020.119106. [19] E.H. Anouar, J. Gierschner, J.L. Duroux, P. Trouillas, UV/Visible spectra of natural polyphenols: a time-dependent density functional theory study, Food Chemistry 131 (2012) 79
89. Available from: https://doi.org/10.1016/j.foodchem.2011.08.034. [20] R.G.A.R. Hawthorne, S.A. Morris, R.L. Moody, Duvas as a real-time, field-portable wastewater monitor for phenolics, Journal of Environmental Science & Health Part A 19 (3) (1984) 253
256. Available from: https://doi.org/10.1080/10934528409375156. [21] O. Thomas, F. Theraulaz, M. Domeizel, C. Massiani, UV spectral deconvolution: a valuable tool for waste water quality determination, Environmental Technology

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[33]

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183. P. Howard, volume IV Handbook of Environmental Fate and Exposure Data for Organic Chemicals, CRC Press, 1993. ISO, ISO standard 13877: Soil quality, PAH determination, 1995. E. Touraud & O. Thomas, Rapid diagnosis of hydrocarbons contaminated soils using UV detection, F. Screen. Eur Springer, (1997) pp. 363
366. O. Cloarec, C. Gonzalez, E. Touraud, O. Thomas, Improvement of UV spectrophotometry methodology for the determination of total polycyclic aromatic compounds in contaminated soils, Analytica Chimica Acta 453 (2002) 245
252. M. Crone, Diagnostic de Sols Contamine´s Par Des Hydrocarbures Aromatiques ` l’aide de La Spectrophotome´trie UV. PhD Thesis, Institut Polycycliques (HAP) A National des Sciences Appliquees (INSA), Lyon, France, 2000. E. Touraud, M. Crone, O. Thomas, Rapid diagnosis of polycyclic aromatic hydrocarbons (PAH) in contaminated soils with the use of ultraviolet detection, Field Analytical Chemistry & Technology 2 (1998) 221
229. B. Roig, E. Chalmin, E. Touraud, O. Thomas, Spectroscopic study of dissolved organic sulfur (DOS): a case study of mercaptans, Talanta 56 (2002) 585
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206. S. Suryani, F. Theraulaz, O. Thomas, Deterministic resolution of molecular absorption spectra of aqueous solutions: environmental applications, TrAC Trends in Analytical Chemistry 14 (1995) 457
463. M. Brogat, Developpement d’une methode d’extrcation et de fractionnement sur phases solides multiples (MSP2E) de micropolluants organiques, University de Rennes, France, 2014. M. Brogat, A. Cadiere, A. Sellier, O. Thomas, E. Baures, B. Roig, MSPE/UV for field detection of micropollutants in water, Microchemical Journal 108 (2013) 215
223. Available from: https://doi.org/10.1016/j.microc.2012.10.025. B. do Amaral, J.A. de Araujo, P.G. Peralta-Zamora, N. Nagata, Simultaneous determination of atrazine and metabolites (DIA and DEA) in natural water by multivariate electronic spectroscopy, Microchemical Journal 117 (2014) 262
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329. Available from: https://doi.org/10.1016/S0003-2670(02)01427-7.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

5 Aggregate organic constituents Olivier Thomas1, Jean Causse2 and Marie-Florence Thomas1 1

EHESP School of Public Health, Rennes, France, 2Transcender Company, Rennes, France

O U T L I N E 5.1 Introduction

162

5.2 Dissolved organic matter

166

5.3 Specific UV absorbance as a proxy for dissolved organic matter characterization and formation potential

167

5.4 Assistance of reference methods 5.4.1 Explanation of total organic carbon and dissolved organic carbon 5.4.2 Biological oxygen demand measurement 5.4.3 Final determination of chemical oxygen demand

169 169 172 174

5.5 Biodegradable dissolved organic carbon

175

5.6 Water quality indices and UV spectrophotometry

176

5.7 UV estimation of total organic carbon, dissolved organic carbon, chemical oxygen demand, and BOD5 5.7.1 UV spectra exploitation from a limited number of wavelengths 5.7.2 UV spectra exploitation from a multiwavelengths approach 5.7.3 Validation

178 178 179 184

5.8 UV recovery of organic pollution parameters

186

References

188

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00001-0

161

© 2022 Elsevier B.V. All rights reserved.

162

5. Aggregate organic constituents

5.1 Introduction Water quality is mainly chemically defined through its dissolved constituents which are very numerous. Since their individual determination is not possible, especially for organic compounds, aggregate parameters are often considered. Appeared in the early 20th century for the characterization of water pollution, these type of parameters were developed with the improvement of analytical techniques particularly during the second half of the last century. There exist a huge number of analyses for organic constituents in water and wastewater. They can be classified into two general classes: those that quantify an aggregate amount of organic substances of a same chemical family or presenting a common characteristic and those that quantify individual organic compounds. Parameters used for the analysis of aggregate organic constituents are grouped in the Part 5000 of standard methods [1] for examination of water and wastewater. In this part, several types of parameters are considered (Table 5.1): • oxygen-demand methods [biological oxygen demand (BOD) and chemical oxygen demand (COD)]; TABLE 5.1 Aggregate organic parameters. Standard methods

Parameter

Principle

Biochemical oxygen demand

Dilution of sample by seeding solution and O2 consumption

5210

Chemical oxygen demand

Quantity of strong oxidant needed

5220

TOC and DOC

CO2 formed after combustion or photooxydation

5310

Dissolved organic halogen (DOX)

Adsorption pyrolysis titration

5320

Aquatic humic substances

Separation, concentration isolation by sorption on a macroporous resin or by anion exchange

5510

Oil and grease

Separation with solvent or solid phase and infrared detection or gravimetry

5520

Phenols

Colorimetry

5530

Surfactants

Colorimetry after solvent extraction

5540

Tannin and lignin

Colorimetry

5550

Organic and volatile acids

Adsorption and gas chromatography

Formation of THMa and other DBPsb UV-absorbing constituents

5560 5710

Absorbance at 254 nm (1 cm pathlength) Needed for SUVA calculation (with TOC value)

a

THM: trihalomethanes. DBPs: disinfection by-products.

b

DOC, dissolved organic carbon; SUVA, specific UV absorbance; TOC, total organic carbon.

UV-Visible Spectrophotometry of Waters and Soils

5910

5.1 Introduction

163

• organic carbon and halogen-bound content [total organic carbon (TOC) and dissolved organic halogen (DOX)]; • family of organic compounds (humic substances, oil and grease, phenols, surfactants, tannin and lignin, and organic and volatile acids); • compounds with other properties [trihalomethane (THM) precursors and UV absorbing]. Actually, there exist some other aggregate parameters related to organic compounds with heteroatoms-bound different than halogens (nitrogen, phosphorous, and sulfur). For example, total Kjeldahl nitrogen (TKN) for all organic nitrogen forms (with ammonia) or total phosphorus (TP) will be presented in Chapter 6. As for a lot of analytical methods for water analysis, aggregate organic parameters have a corresponding ISO standard (or several, each procedure for a same parameter being described in one standard). A recent ISO standard [2] described a multiparameter method or the determination of TOC, total nitrogen (TN), and TP, extending the concept of aggregate organic parameters to almost all organic compounds. Dissolved bound sulfur and other organometallic compounds are not included in the above standards but will be briefly considered in Chapter 6. It is difficult to show the relations between aggregate parameters precisely. If one can agree that TOC include all organic constituents, there are some common parts between other parameters (Fig. 5.1). The analysis of specific organic compounds or family of compounds in water or wastewater gives a useful but partially insight of the water quality [3]. For example, the presence of dissolved natural organic matter, which is a mixture of molecules coming from biomass and soil degradation, and the impact of organic pollution on dissolved oxygen depletion are not addressed by specific analysis. This is the reason why the measurement of

FIGURE 5.1 Relations between aggregate parameters (tentative).

UV-Visible Spectrophotometry of Waters and Soils

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5. Aggregate organic constituents

aggregate organic parameters such as TOC or dissolved organic carbon (DOC) and COD and BOD for water and wastewater, and the characterization of dissolved organic matter (DOM) particularly in freshwaters, is more considered than specific analysis for pollution assessment. Historically, the measurements of BOD and COD were the only ways for the assessment of organic pollution of (waste)waters. At the beginning of the 20th century, a British royal commission proposed the measurement of dissolved oxygen depletion of a dilute wastewater after 5 days (flow time of the Thames river from London to the sea). Ten years after, the use of potassium dichromate in acidic solution, instead of sodium permanganate, allowed shortening the chemical pollution test to 2 hours of oxidation time. This improvement for reducing the analysis time between BOD and COD was then completed in the 1960s with the measurement of TOC after combustion or photochemical oxidation of the sample, and the measurement of CO2 produced after removal of inorganic carbon. More recently, the characterization of DOM has completed the knowledge of freshwaters and seawater quality in particular. The main effect of organic pollution is the oxygen depletion in water. At the end of the 19th century, this type of chronic pollution was historically the starting point of developing for wastewater measurement and treatment. Fig. 5.2 represents the schematic principle of the biological phenomenon (aerobic biodegradation), used for the characterization and treatment of organic pollution. Organic matter can be considered as a carbon source for microorganisms present in natural water and wastewater. Obviously, the presence of oxygen and nutrients is required. The residual compounds can be either simple mineral ions or molecule if the mineralization process is completed or residual degradation by-products. As oxygen depletion is the main ecological impact of organic pollution, oxygen demand-based methods (BOD—biological and COD— chemical) are very important. A more complete approach is shown in Fig. 5.3, with details on other organic matter forms (natural, dissolved), evolution factors (chemical and physicochemical reactions), and residual by-products (organic acids, refractory organics, etc.).

FIGURE 5.2 Principle of organic matter degradation and measurement.

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FIGURE 5.3 Evolution of organic matter in water.

The measurement of BOD and COD requires at least 5 days and 2 hours, respectively, and considering the other limits related to their principle, the use of rapid techniques for a direct characterization of organic matter has been developed, among which are TOC measurement and UV spectrophotometry. Notice that an initiative to propose a rapid-oxygen-demand technique called total oxygen demand based on the measurement of the quantity of oxygen needed for the total combustion of water sample was designed but abandoned in the 1980s due to the fragility of the instruments. Besides these parameters or techniques, fluorescence spectroscopy is largely used for DOM characterization since the 1990s. Contrary to spectrophotometric methods for which the light absorption is detected in the same axis than the emission source, spectrofluormetric ones work perpendicularly. When a water sample is excited by a given wavelength emission, dissolved substances can absorb a part of the light intensity and reemit a fluorescent light at another (higher) wavelength. This property appears for example for complex molecules with aromatic rings, explaining why fluorescence is often proposed for DOM characterization. There exists a great complexity of organic compounds in freshwaters as well as in effluents, from natural and/or anthropogenic origins. Most of these compounds are susceptible to be degraded by chemically or biologically processes, depending on the medium. For surface water, for instance, natural biodegradation processes require a long period of time in environment with the presence of dissolved oxygen. In contrast, for urban wastewater, an engineered biodegradation process can be carried out inside a global treatment scheme, within a shorter time (a few hours in a wastewater treatment plant). However, in both cases, the global quality of water depends on the concentration of the aggregate organic constituents, proxies of organic matter. This control is important both to prevent a eutrophic sate evolution of natural water and to determine

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the elimination rate of the process for wastewater treatment plant. Before considering the main parameters used for the quantification of organic matter for water and wastewater quality control (BOD, COD, TOC, or DOC), the characterization of DOM, as such, is first presented.

5.2 Dissolved organic matter DOM is considered as a complex mixture of low-molecular-weight (MW) substances and larger MW biomolecules, for example, proteins, polysaccharides, and exocellular macromolecules [4]. DOM present in the environment is introduced either naturally or anthropogenically. Natural organic matter (NOM) is generated by the breakdown and degradation of organisms, vegetables, and soil constituents through various biological and physical mechanisms. Anthropogenic OM, on the other hand, is introduced in the environment through human activities and by-products. A more or less fraction of DOM interacting with light is defined as the chromophoric or colored DOM (CDOM) fraction. CDOM is thus responsible for the color of freshwaters, preventing the light penetration in natural water bodies (ponds or lakes, for example). The characterization of CDOM is often studied by fluorescence [5]. However, UV visible spectrophotometry was proposed in different works with an original approach based on spectral slopes (between 275 and 295 nm and between 350 and 400 nm) and their slopes ratio (SR), within the log-transformed absorption spectra [6]. Different spectral parameters such as spectral slopes, the ratio of absorbance values (A250/A365), and specific UV absorbance (SUVA) were considered for CDOM assessment and the results confirmed the interest of using UV visible spectrophotometry for the monitoring of the quality and quantity of DOM. On the other hand, a simple method based on a two-component model of UV absorbance for the DOM quantity and quality of freshwater was proposed [6]. The first component absorbs UV light strongly and is presumed to possess aromatic chromophores and hydrophobic character, whereas the second one absorbs weakly and can be assumed hydrophilic. The model was parameterized with DOC and corresponding UV spectra for 1700 filtered surface water samples from North America and the United Kingdom. A good estimation of DOC was obtained from absorbance data at 270 and 350 nm, giving also the relative amounts of the two components of DOM. The coupling of UV absorbance and fluorescence measurement was recently proposed for the study of relationships between CDOM sources and light attenuation in shallow estuaries [7]. The ratio between CDOM (estimated by the absorbance value at 340 nm) and fDOM (fluorescence intensity measured at 460 nm for an excitation wavelength of 365 nm)

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varied depending on the nature and sources of DOM and spectral slope (between 340 and 440 nm) can be used of as a complementary parameter for light absorption modeling. A recent review of characterization of aquatic organic matter showed that although the organic matter topic was extensively explored over the last decades, several research gaps still need to be filled and improvements and/or new ideas are required [8]. Future studies should be done at various structural levels and from diverse perspectives. Simultaneous studies of both particulate and dissolved OM fractions should be prioritized. For wastewater and water treatment processes (WWTP), DOM can be characterized by molecular size (HPLC-SEC) and fluorescence analysis. In a recent study investigating changes of DOM characteristics during WWTP [9], three peaks were found on the high-performance liquid chromatography - size exclusion chromatography (HPLC-SEC) chromatogram based on TOC detection. The results of fluorescence analysis, improved by a parallel factor (PARAFAC) analysis step for the identification of independent fluorescence signals (fluorophores), showed that five fluorescent components were identified with a different fate according to the WWTP scheme. In another recent study on the characterization of groundwater DOM using liquid chromatography (LC) and organic carbon detection [10], a large number of surface and groundwater samples from Australian sites have been analyzed. The results show that the composition of DOM can be very variable, with implications on the treatment needed for DOM removal. Following another analytical approach, a study on the spectral and mass spectrometric characteristics of different MW fraction of DOM [11] identifies several hundred of molecules (840) accounting for almost 60% of fluorescent peak intensities of raw water. These recent studies show that further research works are needed for improved analytical ways and a deeper understanding of fluorescence mechanism for a complex matrix like DOM [5]. Finally, considering the complex mixture constituting DOM, its estimation by the DOC value is questionable as carbon-based results vary from one group of OM to another [7] as well as UV visible parameters depending on the intrinsic optical properties of compounds. Further works are thus needed for a better characterization of DOM or CDOM with optical methods.

5.3 Specific UV absorbance as a proxy for dissolved organic matter characterization and formation potential Before presenting some applications of UV spectrophotometry for the improvement of the measurement of aggregate parameters, let us consider the use of SUVA as a full parameter for DOM characterization.

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The measurement of absorbance value at 254 nm (main emitting wavelength of the low-pressure mercury lamp), as surrogate for water quality estimation (COD, TOC, and phenol), was known since the mid20th century [12], but its use was not adopted by technicians and scientists regarding the weak correlations obtained. However, the proposal of new parameter such as SUVA and new instruments and methods of spectra exploitation contributed to the success of UV spectrophotometry for water quality monitoring at the end of the last century. SUVA, proposed by Weishaar, was defined as the ratio between the absorbance value measured at 254 nm (main emitting wavelength of the low-pressure mercury lamp), for a 1 cm pathlength Suprasil quartz cell, and the concentration of TOC. This ratio was multiplied by 100 in order to get the SUVA value. SUVA can also be calculated at 280 nm. SUVA254 and SUVA280 are the parameters used for the assessment of aromaticity of organic matter or humic substances in water [13]. Considering that the number of works based on SUVA254 is increasing (2670 citations between 2015 and 2020 against 1460 between 2000 and 2014), it is interesting to explain the use of this parameter for water and soil quality monitoring. Fig. 5.4 displays the three main topics addressed by SUVA254. 1. The first is the DOM characterization with the presence of aromatic compounds [13].

FIGURE 5.4 SUVA measurement and significance. SUVA, specific ultraviolet absorbance.

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2. A relation between SUVA254 and disinfection by-products’ formation potential for water treatment (drinking water treatment plant and swimming pools) [14,15]. 3. The third topic is the characterization of soil organic matter [16].

5.4 Assistance of reference methods As shown in Fig. 5.2, the main methods for the quantification of organic matter, potentially considered as a pollution source, are organic carbon content (TOC or DOC) and oxygen demands (BOD and COD). UV absorbance at 254 nm and SUVA (ratio of A254/DOC) are also used as surrogate parameters. These methods are standardized (Table 5.2) but UV spectrophotometry can give useful complementary information by considering the whole UV spectrum and not only a single wavelength (254 nm). Concerning the measurement of organic content, either dissolved (DOC) or total (TOC), the obtained result for a given sample does not give any information about the nature of organics responsible for the DOC or TOC value. The two techniques proposed (persulfate oxidation and high-temperature combustion) convert the organic carbon of the sample into CO2 globally, which is detected and quantified. For the oxygen demand-based methods, the BOD gives a representation of the quantity of oxygen consumed by microorganisms in the conditions of natural biodegradation of organic matter. On the other side, the COD uses a chemical oxidation that is more rapid but does not address the same oxidizable compounds as BOD. This method concerns all compounds that can be oxidized by potassium dichromate in acidic medium (the major part of organic compounds), and oxidizable mineral salts (sulfide, sulfite, etc.).

5.4.1 Explanation of total organic carbon and dissolved organic carbon TOC or DOC is the most relevant parameter for the global determination of organic pollution of water and wastewater. It has been proposed in the 1970s for an automatic survey of water and wastewater quality, because of the problems related to the use of BOD and COD (imprecision and time). Organic carbon measurement is used for the global quantification of organic carbon content linked to organic compounds, contrary to the other parameters which quantify its main effect, the oxygen consumption. It is the reason why TOC or DOC is often considered as the “true” parameter, even if its measurement is not easy [17].

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TABLE 5.2 Standard methods and ISO standards for aggregate organic parameters. Parameter

Standard method [1]

ISO standard

TOC/DOC

5310 (2017)

8245:1999 (guidelines TOC/DOC) 20236:2018 (TOC/DOC/TN/DN/TP/DP after high temp. oxidative combustion) 21793:2020 (det. TOC/DOC/TN/DN/TP/DP) after wet catalyzed ozone hydroxyl radical oxidation (COHR)

COD

5220 (2017)

BOD

5210 (2017)

AOC or BDOC

9217 (2017)

DOX dissolved organic halogen

5320 (2017)

6060:1989 15705:2002 (small scale sealed tube method) 5815-1:2019

9562:2004 10301:1997 highly volatile hal. hydrocarbons

Aquatic humic substances

5510 (2017)

Oli and grease

5520 (2017)

9377-2:2000

Phenols

5530 (2017)

6439:1990 14402:1999 (FIA and CFA)

Surfactants

5540 (2017)

7875-1:1996/2003 anionic surfactants (MBAS) 16265:2009 (MBAS/CFA) 7875-2:1996 nonionic surfactants

Tannin and lignin

5550 (2017)

Organic and volatile acids

5560 (2017)

17943:2016 volatile organic compounds (GC-MS) 20595:2018 highly volatile compounds (head space)

THM precursors and other DBP

5710 (2017)

UV-absorbing organic constituents

5910 (2017)

7887:2011

UV254 Examination of color (3.3 DOC) BDOC, biodegradable dissolved organic carbon; BOD, biological oxygen demand; COD, chemical oxygen demand; DBP, disinfection by-product; DOC, dissolved organic carbon; THM, trihalomethanes; TOC, total organic carbon.

The main input of UV spectrophotometry for TOC or DOC explanation is to give information on the risk of global organic pollution. Figs 5.5 to 5.7 present spectra of wastewater samples with the same TOC value.

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The first case (Fig. 5.5) shows the effect of a wastewater dilution, leading to the same value of TOC than the treated sample. Obviously, the nature of organic matter is different as the treated effluent contains small oxygenated organic molecules, confirmed by the presence of nitrate (high Gaussian absorbance for short wavelengths; see Chapter 6). From the environmental point of view, the impact of both effluents is obviously not similar. The second example (Fig. 5.6) shows a difference between the shape of wastewater spectra due to the distribution of suspended and colloids matters (see Chapter 7) and to the nature of DOM. For example, the importance of the absorption peak at 225 nm (related to benzenic surfactants) of the samples is variable.

FIGURE 5.5 UV spectra of raw (dilution 3) and treated (biological process) urban wastewater with the same TOC value (17.7 mg L21). TOC, total organic carbon.

FIGURE 5.6 UV spectra of three samples of raw urban wastewater (TOC 5 57 mg L21). TOC, total organic carbon.

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FIGURE 5.7 Spectra of urban wastewater and phenol (pH 7), with the same value of TOC (60 mg L21). TOC, total organic carbon.

The last example (Fig. 5.7) displays two spectra, one corresponding to urban wastewater, with a mixture of organic compounds, and the other one to a phenol solution of 60 mg L21. The TOC value is the same for the two samples, but as in the first example (Fig. 5.5), the environmental impacts are very different. In case of the presence of a major pollutant, represented here by phenol, the diagnosis for potential toxicity has to be envisaged. Chapter 12, dealing with industrial wastewater, shows several examples illustrating this problem. The interest in the explanation of TOC or DOC by UV spectrophotometry is thus evident, most of the time, both to check the stability degree of organic matter in wastewater (raw diluted or treated) and to evaluate the potential toxicity of a polluted freshwater or a wastewater in the case of the presence of a major pollutant, strongly absorbing (here phenol in Fig. 5.7).

5.4.2 Biological oxygen demand measurement The principle of the BOD [1] is very close to the basic principle of organic matter degradation (Fig. 5.2). A sample is introduced in a flask, diluted with a feeding solution (nutrients) saturated with oxygen, and in the presence of microorganisms. The flask is placed in the dark, and the temperature is controlled at 20 C. The consumption of oxygen is followed during 5 days or more, either with a manometric device (including a CO2 trap) or by direct oxygen measurement (e.g., using a potentiometric or a bioluminescent electrode). The BOD5 value is deduced from the oxygen consumption (minus the value corresponding to a blank), taking into account the dilution factor. One important point

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is to be sure that the dilution is adapted to both the reagent concentration and the sample demand. A good result is obtained when the oxygen consumption is between 40% and 60% of the initial concentration (about 9 mg L21 oxygen at 20 C). Thus several dilutions (calculated from the COD value) are planned for the purpose. Regarding the analytical constraints with a 5-day test and a variability of the final result of about 20%, depending of the experimental conditions (namely, feeding) [18], several techniques that were less time-consuming and more reliable were recently proposed. The first one is based on real-time monitoring of oxygen uptake using a novel optical biogas respirometric system [19]. Micro fuel cell (MFC)-based biosensors developed during the last 20 years have gained in maturity [18] The continuous development from double-cell MFC to single-cell MFC and the recent design of multistage MFC for powering the performances have to be underlined. The evolution of the BOD value with time during a classical test is typically the one in Fig. 5.8 and shows two plateaux. The first one, occurring between 3 and 6 days, corresponds to the carbonaceous degradation, and the second one, slower to appear, to the nitrification phenomenon. As the first step concerns only organic pollution characterization, a nitrification inhibitor (allylthiourea) can be added to the flask.

FIGURE 5.8 Evolution of the UV spectrum of an urban wastewater sample (dilution 5), with BOD measurement. BOD, biological oxygen demand.

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The study of UV spectrum evolution of the sample showed clearly the sequence of the different oxygen consumption phases, with a degradation of the signal related to raw organic matter (on the whole spectrum) in the first 5 days leading to the BOD5. Then, the UV spectrum shape is characterized by the increase of nitrate response between 200 and 230 nm (see Chapter 6) up to the end of the test at 21 days (ultimate BOD or BOD21). In some cases the nitrification process may occur before 5 days, leading to a wrong result of the BOD5. This is the reason why a nitrification inhibitor is introduced into the flask before the beginning of the test. Thus UV spectrophotometry avoids the use of the inhibitor by monitoring the nitrification beginning. Another use of UV spectrophotometry for BOD assistance is for helping in the choice of the right dilution, the knowledge of the COD value being necessary for the dilution calculation. With the aim of a BOD5 value of the diluted sample around 5 mg L21, it is possible to determine which dilution is to be applied, from the UV spectrum of the raw sample, if a calibration model for BOD or COD estimation is available.

5.4.3 Final determination of chemical oxygen demand COD measurement is based on the use of an oxidizing solution (potassium dichromate in concentrated sulfuric acid) in contact with a water sample during 2 hours of mineralization in hot conditions. The oxidant consumption is determined by the difference between its initial and final concentrations thanks to a redox titration [1]. Since this method is rather tedious and considering the optical properties of dichromate and trivalent chromium ions, CR2O72 and Cr31 (this last form resulting in the reduction of dichromate during the oxidation of organic matter), several UV visible spectrophotometric methods were proposed for the final determination [20,21]. Fig. 5.9 presents the spectra of COD test tube solutions (range: 50 1500 mg L21), corresponding to three trials (blank, industrial wastewater, and concentrated ethanol solution). The absorption peak of the dichromate (at 440 nm) decreases with COD, while, at the same time, the one corresponding to the reduced form (Cr31) increases. Compared to the corresponding spectra of hexavalent chromium (dichromate) and Cr31 in neutral conditions (see Chapter 5), the peaks are more intense in sulfuric acid (hyperchromic effect) and are accompanied with a decrease of corresponding wavelengths (hypsochromic effect). These optical properties are used for the final determination of residual hexavalent chromium or of the Cr3 1 formed for COD determination (Table 5.3). The choice of wavelengths (and obviously of the oxidant concentration) depends on the COD range. The final determination of COD at 610 (600) or 440 (435) nm is widely used, with filter photometers for

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5.5 Biodegradable dissolved organic carbon

FIGURE 5.9 UV visible spectra of COD test tube solutions (blank, sample corresponding to 1107 mg L21 of COD and saturated sample of 1500 mg L21). COD, chemical oxygen demand. TABLE 5.3 Absorptiometric methods for the final determination of chemical oxygen demand (COD) [21,22]. COD range (mg L21)

Chromium form detected

Wavelength (nm)

K2Cr2O7 conc.a (mol L21)

2 30

Dichromate

345 (350)

2.50 3 1023

10 150

440 (435)

8.33 3 1023

5 150

405 440 (435) 520b

8.33 3 1023

610 (600)

4.17 3 1022

50 800

Cr31

8 3 1022

50 1500 a

In the reagent (diluted six times for the test). Preferable if suspended solids.

b

commercial tube test methods. However, some problems in reading can exist in the case of suspended solids present in industrial samples, for example, or, more often, in the case of precipitation occurring during COD test. If so, a tri-wavelengths method can be used for interference compensation [3] (see also Chapter 3).

5.5 Biodegradable dissolved organic carbon As the measurement of TOC or DOC is not sufficient for the determination of the biodegradable fraction of organic matter, the measurement of the biodegradable DOC (BDOC) can be carried out with the assistance of UV spectrophotometry [22]. Fig. 5.10 shows how UV spectrophotometry can be helpful for BDOC determination.

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FIGURE 5.10 Evolution of COD value and UV spectra during BDOC determination of an urban wastewater sample (RDOC, refractory dissolved organic carbon). BDOC, biodegradable dissolved organic carbon; COD, chemical oxygen demand.

The BDOC represents the fraction of DOC assimilated by the biomass (fixed on a sandy bed) for the conditions of the test [23,24] corresponding, in this example, to more than 50% of the initial DOC. UV spectrophotometry can thus be proposed to check the activated sand preparation, to confirm the progress of the degradation process, and to estimate the DOC evolution during the test as well as the final result [16].

5.6 Water quality indices and UV spectrophotometry The notion of water quality index was known since the 1970s. A few works were published before 2000, but the number strongly increased after the adoption of the European Water Framework Directive (WFD) in 2000 and the proposal of the Canadian Council of Ministers of Environment Water Quality Index (CCME-WQI) in 2001 (Fig. 5.11). Water quality of water bodies can be evaluated thanks to its chemical status as defined in WFD or by the CCME. The CCME-WQI is stricter than the WFD values (defined in Article 4 and Annex V) because it includes toxic pollutants not considered in the evaluation of the WFD chemical status [25]. The CCME-WQI provides a convenient means of summarizing complex water quality data and facilitating its communication to a general audience. The index incorporates three elements: scope—the number of parameters not meeting water quality guidelines; frequency—the number of times these guidelines are not met; and amplitude—the amount by which the guidelines are not met. The index produces a number between 0 (worst water quality) and 100 (best water quality) [26]. The CCME-WQI has been adopted for use under the United Nations Environment Programme in three forms: the global drinking water quality index, health water quality index, and acceptability water quality index, each with specific parameters selected.

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FIGURE 5.11 Evolution of works published on “water quality index” for 50 last years. Source: Adapted from Science-direct database.

In a recent review Banda and Kumarasamy [27] concluded that the most challenging aspect is that water quality indices are developed for a particular region and are source-specific as there is no single water quality index that has been globally accepted. However, the same authors demonstrated recently that a multivariate statistical approach could be successfully envisaged for the development of a universal water quality index at a regional scale [27]. Finally, the use of UV visible spectrophotometry associated with artificial neural networks was recently proposed as an alternative for determining a water quality index [28]. The estimated WQI in this study was the first historically proposed [29] by the National Sanitation Foundation (NSF) [30]. Like all WQI, the NSF-WQI is a 100-point scale that integrates results from a total of nine different parameters (dissolved oxygen, fecal coli, pH, BOD, temperature, TP, nitrate, turbidity, and total solids). Its range varies from 0 24 (very bad quality) to 90 100 (excellent quality). The online calculation is free, simple, and well documented. Besides WQI, some other indices based on remote measurements will be discussed in Chapter 9. A last index was designed in 1977 for the evaluation of lake water quality by Carlson [31]. The trophic state index gave a result between 0 and 100 calculated from different parameters (transparency, chlorophyll, and TP), defining three classes of lake’s quality (oligotrophic, mesotrophic, and eutrophic). The application of trophic state index and the relation with optical properties such as the shape and importance of UV visible absorbance spectra will be presented and discussed in Chapter 8.

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5.7 UV estimation of total organic carbon, dissolved organic carbon, chemical oxygen demand, and BOD5 The estimation of the main aggregated organic parameters for water and wastewater is one of the first applications of UV spectrophotometry [32]. As a lot of organic compounds absorb in the UV region (see Chapter 4), the exploitation of the UV spectrum is interesting for a quick estimation of COD, BOD, or even TOC (keeping in mind that the corresponding methods are time-consuming or expensive). Two main approaches can be proposed, depending on the number of wavelengths to be considered (Fig. 5.12).

5.7.1 UV spectra exploitation from a limited number of wavelengths The first way is very simple as it is the research of a “useful” correlation between one wavelength absorbance measurement and the corresponding parameter value. This simple absorptiometry has been historically carried out from the use of a low-pressure mercury lamp emitting principally at 254 nm [33 35]. This method was proposed more for economic reasons than for scientific ones, (cheap UV light source). The use of a second wavelength (in the visible range) was sometimes proposed for interference compensation (due to colloids and suspended solids). The numerous works reporting the interest of the measurement of the absorbance at 254 nm as a surrogate parameter for absorbing organic constituents are at the origin of a standardized method [1], completed by a related parameter, SUVA, previously discussed in this chapter. A huge trend of UV visible spectrophotometry applications is the monitoring of DOC in water at high frequency thanks to in situ or on-

FIGURE 5.12

Main approaches for aggregate parameters’ estimation from UV spectra

exploitation.

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site measurement with simple optical sensors. In a recent review, the complementarity of UV visible spectrophotometry with fluorimetry was demonstrated in the study of DOC dynamics in streams and watersheds [36]. Simple parameters were proposed such as SUVA, spectral slope ratio [37], or fluorescence index for DOC concentration and composition properties. UV absorbance measurement can also be carried out at some wavelengths coupled with fluorescence measurement, for prediction of pollution parameters such as BOD, COD, or total nitrogen [38]. In this work, a linear regression (LR) model with UV absorption values at 220 nm (UV220) and 254 nm (UV254) as well as fluorimetry data from PARAFAC analysis gave interesting results for an urban river receiving treated wastewater. Among the three components revealed by fluorimetry, microbial humic-like (C1) and protein-like organic substances (C3), coupled with UV220, led to the best results for BOD, COD, and TN estimation. The use of a few absorbance values being limited from an analytical point of view, the exploitation of several wavelengths from half a dozen to the whole spectrum, coupled with multivariate procedures, gives more robust results for UV visible estimation of TOC, DOC, COD, or BOD. Adapted for their implementation in existing commercial UV sensors, these procedures, based on chemometrics methods, are the main way for the exploitation of absorbance values at several wavelengths.

5.7.2 UV spectra exploitation from a multiwavelengths approach Developed in the 1990s, the multiwavelengths methods can be shared between two statistical approaches, either a “gray box” type modeling (semideterministic procedure) or a “black box” one with classical chemometric methods such as principal components analysis (PCA), partial least square (PLS) regression (PLSR), or multiple LR (MLR) (see Chapter 3). As it is not possible to know the optical response of all components of a real water sample, dissolved or not, a whole deterministic method based on a multilinear regression from UV spectra of all compounds (white box modeling) is not realistic. A review and comparison of different methods used for the estimation of TSS (total suspended solids) and COD in freshwaters and wastewaters [39] presented a study on five methods among which are the methods based on LR, PLS, multiobjective evolutionary strategies, and support vector machine regression. Spectral methods shortly presented in the study are not considered for the comparison. However, these methods are largely chosen for their implementation in UV visible sensors for online or high-frequency onsite measurements.

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PLSR is the main chemometric method used for UV visible data exploitation for sensors and field portable devices. Numerous applications for streams and watersheds, as well as urban drainage systems and wastewater were published in the last decades, integrating PLSR for calibration and parameter estimation [40 43]. This widespread method is very interesting because it only needs the acquisition of UV visible spectra and target parameters for the corresponding samples. This is a “statistically” simpler approach than spectral methods but requires a chemometric experience for the design of the estimation model. An optical device measuring absorbance values at six wavelengths (240, 250, 254, 260, 275, and 365 nm) was designed for wastewater quality monitoring [44]. Coupled with the measurement of physicochemical parameters, data are exploited by PCA or PLSR, namely, for TOC estimation. This method gave interesting results for the monitoring of spatial and temporal changes in wastewater quality. Another work on the application of high-resolution spectral absorbance measurements dealt with the determination of DOC concentration of surface and soil pore water in remote areas [40] with a field portable UV visible sensor. The comparison of different multivariate models from selected wavelengths showed that site-specific calibration models are needed to get the optimal accuracy of the proxy-based DOC quantification. The development of variable pathlength UV vis spectroscopy, combined with PLSR for wastewater COD monitoring, was recently proposed by Chen et al. [45]. PLSR models built from two data fusion strategies gave an interesting COD prediction, and the use of slopederived spectra improved the results for a large range of measurement (around 100 to 1800mgO2 L21). Monitoring of multiparameters (nitrogen, carbon, phosphorus, and suspended solid concentrations) in a brackish tidal marsh was carried out with a commercially UV visible sensor [46]. The high-frequency acquisition of absorbance values was exploited with different chemometric methods such as PLSR, Lasso, and stepwise regression. The results were satisfactory for nitrate, total Kjeldhal nitrogen, DOC, and suspended solids. A method based on the UV absorbance measurement between 250 and 300 nm was designed for TOC estimation [47]. Based on an MLR model, the method was successfully applied for real-time monitoring of tap water, freshwater, and seawater. The spectra deconvolution with the use of a semideterministic procedure must also be considered regarding the knowledge of water or wastewater composition [48 50]. The mathematical principle of the procedure has been explained in Chapter 3, but its application to aggregated organic parameter estimation is presented hereafter. Two main

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steps are carried out during the procedure. The first one is the mathematical deconvolution (modeling) with a given basis of reference spectra, completed by the checking of the restitution quality with the study of the error between the actual spectrum and the restituted one after deconvolution. The second step is the parameter calculation from a calibration file. Starting from the study of several thousands of urban wastewater samples, a first “universal” basis of reference spectra has been proposed for one application concerning the wastewater quality monitoring [48,51]. The selected reference spectra are displayed in Fig. 5.13. The choice of reference spectra is carried out, on the one hand, “manually” from real (deterministic) spectra, with the consideration of determined spectra of specific compounds (nitrate, nitrite, and surfactants) likely to be present in wastewater. It is completed automatically, on the other hand, with a mathematical procedure [52] allowing the selection of the more relevant real spectra for the model, related to mixture of compounds (colloids, suspended solids, etc.) and able to explain by a linear combination integrating the specific reference spectra, the shape of UV spectra of water or wastewater. This semideterministic approach is interesting because the basis of reference spectra used for the modeling of real spectra is made up of spectra mixtures and specific mineral or organic compounds, the optical properties of which often explain a part of the UV spectrum. The reference spectra of mixtures are statistically representative of the different heterogeneous fractions of wastewater, because they are selected from wastewater fractionation. For each wastewater sample, several filtrations are carried out (1 and 0.025 µm), and the spectra of the filtrate are acquired. The differential spectra are

FIGURE 5.13 Normalized reference spectra of the universal basis of the semideterministic method (DBS: dodecylbenzene-sulfonate).

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182

5. Aggregate organic constituents

then considered for the basis constitution (Table 5.4). This procedure can be generalized for the study of surface or seawater, for example. In these cases, some reference spectra (suspended solids, colloids, and surfactants) are replaced by more relevant spectra for the medium, such as humic substances, mineral-suspended solids, or chloride [49], in order to constitute a specific rather than “universal” basis of reference. After the deconvolution step, giving the contribution coefficients of reference spectra (see Chapter 3), calculation of the parameters is possible by using the coefficients calculated from the deconvolution step and a corresponding calibration file (Fig. 5.14). This calibration

TABLE 5.4 Origin of reference spectra of mixtures (see Fig. 5.13). Name of reference spectrum

Origin (urban wastewater)

Total suspended solids

Difference between spectra of raw and filtered (1 µm) sample

Colloids

Difference between filtered samples (at 1 and 0.025 µm)

Dissolved organic matter

Spectrum of filtrate (filtration at 0.025 µm)

FIGURE 5.14 General procedure for aggregate organic parameter estimation (e.g., chemical oxygen demand) with a model of p reference spectra of mixture and q of specific compounds (r is the restitution error, which must be minimal; see Chapter 3).

UV-Visible Spectrophotometry of Waters and Soils

5.7 UV estimation of total organic carbon, dissolved organic carbon, chemical oxygen demand

TABLE 5.5

183

Reference spectra contribution for urban wastewater calibration file.

Reference spectra

COD

BOD

TOC

Surfactants

Nitrate

TSS

TSS

ü

ü

ü

—

—

ü

Colloids

ü

ü

ü

—

—

—

Dissolved OM

ü

ü

ü

—

—

—

Surfactants

ü

ü

ü

ü

—

—

Nitrate

—

—

—

—

ü

—

Note: For surfactants, see next section; for nitrate and TSS, see Chapters 5 and 6. BOD, biological oxygen demand; COD, chemical oxygen demand; TOC, total organic carbon; TSS, total suspended solids.

file includes the corresponding concentrations of specific compounds (nitrate, nitrite, anionic surfactants, etc.) and the values related to the reference spectra of mixtures (Table 5.5). These values are statistically calculated for the purpose, through a preliminary stepwise regression study, from a set of at least 30 samples (with 30 corresponding values of parameters and 30 sets of contribution coefficients). Actually, a few models (basis of reference spectra 1 calibration file) are sufficient for the main applications concerning water and wastewater quality monitoring. Besides multiwavelengths methods (PLS or semideterministic), other methods of UV spectra exploitation for the estimation of aggregate parameters are available. For example, a simple method for DOC estimation was recently proposed by Causse et al. [53]. This method is based on UV spectrophotometric measurement of raw samples (without filtration), coupling a dual pathlength for spectra acquisition and the second derivative exploitation of the signal around 295 nm. Considering that nitrate slightly absorbs around 300 nm (see Chapter 3), a correction of the second derivative absorbance (SDA) related to nitrate is proposed before DOC estimation. The nitrate concentration of a water sample is calculated from SDA at 226 nm in a first step of the procedure. The method was tested on several hundred of samples from small rivers of two agricultural watersheds located in Brittany, France, taken during dry and wet periods. The comparison between the proposed method and the standardized procedures for DOC measurement gave a good adjustment for a range of 1 30 mg L21 DOC (Fig. 5.15). Another study [54] proposed to compensate the turbidity effect for COD estimation, from the normalization of standard formazine turbidity solutions. After considering the optical shifts related to turbidity variations and the chemical interaction between turbidity and standard

UV-Visible Spectrophotometry of Waters and Soils

184

5. Aggregate organic constituents

FIGURE 5.15 Relation between measured and estimated (by SDA*295) DOC concentrations for580 freshwater samples, after nitrate compensation [53]. DOC, dissolved organic carbon; SDA, second derivative absorbance.

solutions of potassium hydrogen phthalate (COD standard), a model was designed for COD estimation improvement. Further validation with real samples of water and wastewater is however needed for this method.

5.7.3 Validation Several validation experiments have been carried out for water and wastewater quality monitoring. Table 5.6 presents the general conditions and the results of UV estimation of some aggregate organic parameters. The deconvolution method has been tested from a lot of real samples corresponding to different treatment plants and rivers or lakes for each water type (at least 30). The results have been compared with the ones obtained by reference methods. The comparison is carried out for each parameter by calculating the determination coefficient (R2) and the parameters of the regression line (slope and intercept). The adjustment between the measured and estimated values of aggregate parameters is quite satisfactory, with a slope and intercept close to 1 and 0, respectively. The raw numerical results related to the regression study (R2) for each aggregated parameter comparison is not sufficient. On the one hand, the distribution of the results must be carefully checked, for example, with the study of the residues variation, corresponding to a residual standard deviation lower than 20%. In this case, no particular distribution shape is noticed, indicating that an LR model can be envisaged for the estimation. On the other hand, a statistical

UV-Visible Spectrophotometry of Waters and Soils

TABLE 5.6 Validation experiments for UV estimation of aggregate organic parameters. Parameter BOD5

Water Urban ww.

Samples number a

Freshwater

COD

21

1 15 mgO2 L

21

Method

References

0.89

Deconvolution

[56]

0.92

Deconvolution

[56]

B100

0 1000 mgO2 L

0.79

One wavelength (280 nm)

[57]

140

10 500 mgO2 L21

0.91

Deconvolution

[56]

0.82

PLS

[44]

0.93

Deconvolution

[56]

163 55

21

100 2000 mgO2 L 21

2 50 mgO2 L

21

Rural sewage

B80

0 250 mgO2 L

0.82

Three selected wavelengths

[58]

Treated ww.

B100

0 60 mgO2 L21

0.95

PLSR (240-400 nm)

[59]

0.77

MLR (380-700 nm)

[60]

0.96

Deconvolution

[56]

0.93

Deconvolution

[56]

0.96

Deconvolution

[49]

0.98

Deconvolution

[56]

Urban ww. Urban ww.

100 75

21

500 1600 mgO2 L 21

5 150 mgC L

Freshwater

40

0.5 10 mgC L

Seawater ww.

35

1 12 mgC L21

Urban ww.

75

1

21

5 150 mgC L

21

Urban ww.

336

20 100 mgC L

0.95

Deconvolution

[50]

Freshwater

580

1 20 mgC L21

0.99

Second derivative

[53]

0.98

PLS

[40]

0.94

PLS

[46]

41 94

a

5 250 mgO2 L

R2

Urban ww.

Freshwater

DOC

55

21

Industrial ww. (paper)

Food ww.

TOC

120

Range

21

1 80 mgC L

21

5 22 mgC L

21

40

0.5 35 mgC L

0.99

Two components/wavelengths

[61]

251

1.5 15 mgC L21

B0.8

Two components/wavelengths

[6]

ww.: wastewater (raw or treated).

COD, chemical oxygen demand; DOC, dissolved organic carbon; MLR, multiple linear regression; PLS, partial least square; PLSR, partial least square regression; TOC, total organic carbon.

186

5. Aggregate organic constituents

comparison can be used in order to test the validity of the linear model (slope and intercept not different from 1 and 0, respectively). For the previous results, all validation experiments have led to the acceptance of the UV procedure as an alternative method for the estimation of the corresponding aggregated parameter, except for the BOD5 estimation of urban wastewater.

5.8 UV recovery of organic pollution parameters At the end of this chapter, it can be interesting to qualitatively compare the different parameters with the UV response of families of organic compounds. Fig. 5.16 presents different domains (families of organic and mineral compounds) that are related to the aggregate organic parameters and to their UV sample’s response. The common part of UV and classical parameters (TOC, COD, and BOD) is the biodegradable fraction of organic matter. The most comparable parameter is certainly the TOC, which also includes carbohydrates (sugars) and aliphatic (saturated) hydrocarbons not absorbed in the UV region. Some specific compounds as nitrates are strongly associated with the UV response. More precisely, the main organic compound families are more or less recovered by the aggregate parameters and by UV spectrophotometry (Table 5.7). Additional comments can be made concerning organic compounds containing heteroatoms (N or S). They are at best partially oxidized by COD or BOD, or totally for aromatic amines and N heterocycles by TOC. For these compounds, UV spectrophotometry is very relevant.

FIGURE 5.16 Significance of aggregate organic parameters and UV response.

UV-Visible Spectrophotometry of Waters and Soils

187

5.8 UV recovery of organic pollution parameters

TABLE 5.7

COD, BOD, TOC, and UV responses of some organic compounds.

Compounds

COD

BOD

TOC

UV

Saturated compounds

P

P

Y

N

Aliphatic unsaturated hydrocarbons

Y

Y

Y

P

Aromatic compounds

P

N

Y

Y

Acids

Y

P

Y

P

Aldehydes, ketones

P

P

Y

P

Alcohols

Y

P

Y

N

Phenolic compounds

Y

P

Y

Y

Aliphatic amines

P

P

P

N

Aromatic amines

P

P

Y

Y

N unsaturated heterocycles

N

N

Y

Y

S unsaturated heterocycles

P

N

P

Y

Humic-like substances

P

P

P

Y

N: nonabsorbing compounds. Y: 90% 100% of conversion or high absorption or absorption after photooxidation. P: partially converted or some absorbing compounds. COD, chemical oxygen demand; BOD, biological oxygen demand; TOC, total organic carbon.

FIGURE 5.17 Theoretical evolution of aggregate organic parameter and UV response with degradation time of urban wastewater (UV* expressed, for example, as the area under spectrum for wavelength .230 nm).

UV-Visible Spectrophotometry of Waters and Soils

188

5. Aggregate organic constituents

If we consider the variation of these parameters during a biodegradation test [55] (Fig. 5.17), the evolution is very close, with some slight differences. The BOD decrease is obviously faster than for the other parameter, and TOC presents at the beginning, a latent period related to the production of by-products containing organic carbon. The UV decrease response is very close to the one of COD but tends to zero toward the end, even if some small end products are present, like simple carboxylic acids. The latter absorbs in the far UV region (,200 nm), showing only the tail of their spectrum when their concentrations are high.

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UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

6 Mineral constituents Olivier Thomas1 and Benoit Roig2 1

EHESP School of Public Health, Rennes, France, 2University of Nıˆmes, UPR CHROME, Nimes, France

O U T L I N E 6.1 Introduction

193

6.2 Inorganic nonmetallic constituents 6.2.1 N compounds 6.2.2 P compounds 6.2.3 S compounds 6.2.4 Cl compounds

195 195 205 209 213

6.3 Metallic constituents 6.3.1 Chromium (direct measurement) 6.3.2 Metallic constituents determination by complexometry

217 218 221

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226

6.1 Introduction After considering the UV
visible applications for several organic compounds and related aggregate parameters, this section shows that some inorganic constituents can also be studied either directly or indirectly with a UV
visible detection. Fig. 6.1 presents the different species studied, present under ionic forms in water and wastewater. Among the different mineral constituents potentially dissolved in water, several groups can be considered with regard to their environmental interest and nature:

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00010-1

193

© 2022 Elsevier B.V. All rights reserved.

194

6. Mineral constituents

FIGURE 6.1 Mineral constituents studies in this section (dark box) and in the library (pale box).

• The major minerals coming from the geochemical history of water include cations (Na1, K1,
Ca21, and Mg21) and anions 22 2 2 HCO2 3 ; SO4 ; CI ; NO3 : These constituents are generally present in all water with concentration from 1 milligram per liter to several grams per liter (seawater). Notice that all these ions have a natural origin, except nitrate. • The nonmetallic minerals, associated with water pollution, are N, P, S (except sulfate) compounds. The nature and concentration of these constituents is highly dependent on the origin of the pollution (urban, agriculture, industries, etc.) and its importance and type (chronic or accidental). The concentrations corresponding are obviously variable but can reach several hundred milligrams per liter. • The metallic constituents are from natural origin (ore) or more frequently associated with an anthropogenic pollution. Among them, toxic and heavy metals including hexavalent chromium, cadmium, and mercury must be monitored. Almost all these ions can be determined with a colorimetric method [1].

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6.2 Inorganic nonmetallic constituents In a recent paper, Birkmann et al. [2] studied the UV spectroscopic properties of principal inorganic ionic species in natural waters in the wavelength range of 195 to 280 nm. All absorbing species were identified and the corresponding molar absorptivities were determined experimentally. No cations were found to influence the UV spectrum. Relatively high molar absorptivities were observed for iodide, bromide, and nitrate. Actually, N and P compounds are the most important inorganic nonmetallic constituents with regard to their environmental effects such as eutrophication of water bodies (lakes and rivers). Some of them are directly considered as nutrients (nitrate or phosphate), while others are nutrient precursors (ammonia, organic nitrogen, and organic phosphorous). Other constituents must also be considered, such as S compounds, mainly, because of specific environmental odor or toxicity concerns.

6.2.1 N compounds Among water quality parameters, nitrogen is probably the most known and monitored, particularly under is more stable form nitrate. N is present in water in reduced form (organic and ammonium nitrogen) and oxidized form (nitrites and nitrates). The evolution of the N compounds depends on physicochemical and biological mechanisms occurring in natural water or all along treatment processes (Fig. 6.2). The presence of the reduced N compounds in surface water is due to natural organic matter decomposition and, in urbanized area, to discharges coming from raw wastewater or from biological wastewater treatment plants for the ammoniacal form. These reduced compounds increase

FIGURE 6.2 Main nitrogen forms in water.

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the oxygen demand, resulting in the formation of final stable products as nitrate, and are toxic for fishes in rivers. Oxidized forms are one of the main eutrophication factors with phosphorous compounds and can also be toxic to human beings, through tap water. Nitrate can be chemically reduced in nitrite, which is responsible for the lack of oxygenation of cells, more particularly for babies (methemoglobinemia). The determination of N compounds in water has given a lot of standardized analytical methods (Table 6.1), including different standard methods for nitrate determination (SM 4500 nitrogen-nitrate) [3]. Among these methods, the colorimetric and spectrophotometric ones are the most widely used for field or lab monitoring. If colorimetric methods based on continuous flow analysis (CFA), flow injection analysis (FIA), or sequential injection analysis (SIA) are available as standard methods [2], UV procedures are also standardized and largely implemented in online or field-portable instruments mainly for nitrate monitoring

TABLE 6.1 Selection of standard methods for nitrate determination in water. Method

Principle

Range

Observations/interference 21

4500NO32 B

UV spectrophotometry

0.2
11 mg L NO32

Organic matter and colloids interference

4500NO32 C

Second-derivative UV

0.5
2.5 mg L21 NO32

Pathlength 10 mm screening method

4500NO32 D

Nitrate electrode

0.14 to 1400 mg L21

3 , pH , 9 constant

4500NO32 E

Colorimetry Cadmium reduction

0.01
1.0 mg L21 NO32

Suspended matters, metals, oils grease, color

4500NO32 F

Automated Cd reduction

0.05
10 mg L21 NO32

Turbidity, color

4500NO32 H

Automated hydrazine reduction

0.05
10 mg L21 NO32

Sulfide, color

4500NO32 I

Cd reduction Flow injection

0.01
2.0 mg L21 NO32

Particles, residual chlorine, metals

4500NO32 J

Enzymatic reduction

0.05
10 mg L21 NO32

Particles

4110NO32 B

Ion chromatography

0
12 mg L NO32

Particles

4140NO32 B

Capillary electrophoresis

0.1
50 mg L21 NO32

Buffering Particles

ISO 7890

Colorimetric method

0.03
0.2 mg L21 NO32

Colored sample

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in water and wastewater. Ion chromatography or capillary electrophoresis can also be used for nitrate determination. In their review, Azmi et al. [4] proposed some membrane techniques for the improvement of the capabilities of various nitrate detection methods. A recent review [5] on spectroscopic methods for the determination of nitrite and nitrate in environmental samples concluded that spectroscopic methods were successful if water sample contains little or no organic matter. Another work on recent progress in sensing nitrate, nitrite, phosphate, and ammonium in aquatic environment [6] recommended that direct nitrate detection is beneficial and can be achieved by a UV spectrophotometric-based sensor. For other forms of nitrogen (ammonia or organic nitrogen), CFA, FIA, and SIA as well as other methods based on the Kjeldahl method can be considered [7
9]. For total Kjeldahl nitrogen (TKN), including organic and ammoniacal nitrogen, the reference procedure [10] needs mineralization and distillation steps and the use of a final colorimetric or titrimetric determination. In this context, an alternative approach using UV spectrophotometry, either directly or after some simple treatment steps for the final detection, was proposed. 6.2.1.1 General procedure This simple methodology, called UV/UV method, was designed for the quick determination of the different forms of nitrogen in water, nitrite, nitrate, ammonium, and TKN [11]. The general procedure is based on several steps (Fig. 6.3) but includes a UV determination of the oxidized forms and one or two UV photooxidation step(s) for the

FIGURE 6.3 General UV/UV procedure for N compound determination [11].

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mineralization of the reduced forms. Phosphorus determination is also possible with this method. Neither filtration nor acidification of the sample is needed. First of all, the oxidized forms of inorganic nitrogen (nitrate, nitrite) are detected by direct UV spectrophotometry (way A). Second, the sample is photooxidized with a photo-digester (way B), in the presence of an alkaline oxidant solution (potassium peroxodisulfate buffered to pH 9). Under the influence of UV radiation (in this case, a simple lowpressure mercury lamp emitting at 254 nm), the global nitrogen is converted into nitrate. Then, TKN can be calculated from the difference between the concentration of global nitrogen and the one of the oxidized forms previously determined. In the third part of the general procedure (way C), the sample is photooxidized in the presence of an acidic oxidant solution (pH 2 with sulfuric acid 20%). In these conditions, only organic nitrogen is converted into nitrate. The difference between the concentration of TKN previously calculated and that of organic nitrogen leads to the calculation of ammonium nitrogen concentration. Thus all nitrogen forms (Nox, Norg, NH1 4 ; and TKN) present in water and wastewater can be rapidly and simply determined by UV spectrophotometry. 6.2.1.2 Nitrate measurement The determination of nitrate is certainly the most important application of UV spectrophotometry for water quality monitoring. The reason is that the spectra of nitrate solutions are very characteristic between 200 and 400 nm in function of the concentration (Fig. 6.4). Nitrate ion is rather sensitive to UV absorption with a half Gaussian shape for low concentration (between 0.5 and 15 mg NO3- L21 without dilution for 10 mm pathlength).

FIGURE 6.4 UV spectra of nitrate solutions.

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When the concentration increases, an absorption peak appears around 310 nm from 0.2 g NO3- L21, without dilution for 10 mm pathlength. In between, no particular shape exists, except the saturation wall for short wavelengths. This typical response is exploited for nitrate determination with different wavelength ranges in function of the expected concentration. Actually, Simeon et al. [12] reported the existence of two forms of nitrate ions in relatively concentrated solutions (around 5 g L21), with a very slight difference in their UV spectra. They also showed that depending on the associated cation, nitrate ion associated as ion pairs can give different UV response at high salt concentrations for transition-metal ions compared to that of nontransition cations. That could be a consequence of different types of cation
anion interactions [13]. A lot of works have been published for more than 50 years on the use of UV spectrophotometry for nitrate determination in water, with more than 35,000 papers since 2017 (Google Scholar database). The proposed methods can be classified according to the exploitation method used, among those presented in Chapter 2: • The earlier methods are based on simple absorptiometry at one or two wavelength(s) [14
17]. Generally, the use of one absorbance value between 205 and 220 nm, compensated by another one between 250 and 300 nm, is proposed. For example, a screening method based on the measurement of absorbance at 220 nm was standardized [2], with a correction for dissolved organic matter from a second measurement at 275 nm. For high concentrations of nitrate (in the case of industrial application), a simple absorptiometric method can be envisaged at 310 nm. • The second group focuses on the interest of the calculation of the second derivative or its estimation with a three-wavelengths measurement, around 220
225 nm [18
21]. A standardized method using second derivative was also proposed [2]. These methods are less sensitive to interferences than the first group (supposing that the slope of the UV spectrum of interferences is constant) and can be run on most UV sensors and lab spectrophotometers. • Other efficient methods exploit the entire UV spectrum between 200 and 350 nm [22
25], through multiwavelengths procedures. They are based either on partial least squares (PLS) algorithm [25] or on the semideterministic deconvolution procedure or UVSD (UV spectral deconvolution) method [23] described in Chapter 2. These methods give excellent and very rapid results compared to classical analysis, such as colorimetry, but need a specific software for data processing. An example of the comparison of a second-derivative UV method, with a standard one (NF EN ISO13395) using CFA and colorimetry at 543 nm, is given in Fig. 6.5 [18]. The validation was carried out on 580 freshwater samples. The adjustment between the measured

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6. Mineral constituents

concentration of nitrate and the estimated values [by second-derivative absorbance (SDA) at 226 nm] gave a coefficient of determination R2 greater than 0.99, without filtration of samples. 6.2.1.3 Nitrite measurement As for nitrate, nitrite concentration can be measured by FIA or SIA [7,8]. Concerning its UV absorption characteristics, nitrite ion also presents a specific absorption in the far UV region of spectrum, slightly different from the one of nitrate (Fig. 6.6). A first peak appears, as for nitrate, at 210 nm for relatively low concentrations (actually, rather high with respect to regulation limits). This first peak is shown from concentrations greater than 0.5 mg NO22 L21 for a 10 mm pathlength. A second peak appears at

FIGURE 6.5 Relation between measured and estimated (from SDA226) NO3 concentrations for 580 freshwater samples [18].

FIGURE 6.6 UV spectra of nitrite solutions.

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355 nm when concentration increases from 0.2 g NO22 L21, without dilution for a 10 mm pathlength, as for nitrate. However, contrary to the nitrate spectrum, a shoulder at 280
300 nm accompanies the second peak. This difference can be used for the analysis of nitrate and nitrite in mixture. Actually, as nitrite is very unstable and is easily oxidized into nitrate, its concentration in water is often very low. Thus UV spectrophotometry seems to be useful, for example, for industrial applications where nitrite concentration may be higher than 0.5 mg NO22 L21 (in agrofood industry wastewater, for example). Based on the spectra characteristics of nitrite solutions (Fig. 6.6), the expected figures of the UV method are a measuring range of 1 to 30 mg NO22 L21 with a detection limit of 1 mg NO22 L21. 6.2.1.4 Total Kjeldahl nitrogen measurement Concerning the determination of nitrogenous organic compounds, several methods are available often coupled with phosphorous organic compounds [26], but the measurement of the TKN remains the main procedures. Several methods using UV radiation for the mineralization of samples have been proposed in order to simplify the reference Kjeldahl procedure [27]. These methods generally require a classical low-pressure mercury lamp emitting at 254 nm. The use of the far UV radiation of the Hg lamp (185 nm) is made possible by using transparent Suprasil quartz and leads to the improvement of the photodegradation process as compared to the Kjeldahl method (5 to 10 hours for Kjeldahl method; 2 to 3 hours for direct UV). The use of a strong oxidant with a far UV radiation leads to the drastic decrease of the reaction time (5 to 10 minutes) [28]. The reaction can be followed by UV spectrophotometry (Fig. 6.7) with the disappearance of UV spectra of initial compounds and the appearance of that of final compound (nitrite and

FIGURE 6.7

Initial and final spectrum during photodegradation of an urban wastewa-

ter sample.

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6. Mineral constituents

nitrate). The quantification is possible with the semideterministic approach (Chapter 3) by using two sets of reference spectra for the restitution of spectra acquired during the photodegradation. The first basis is the one defined for the estimation of aggregated parameters (see Chapter 5) in water and wastewater. The second basis is simpler as it can include only nitrate and nitrite spectra. For TKN determination, the photooxidation is carried out in the presence of an oxidant solution (potassium peroxodisulfate K2S2O8 buffered to pH 9). In this case, both organic and inorganic nitrogen compounds are converted into nitrate. The concentration of TKN is equal to the difference between nitrate measured after photooxidation ([NOX]f), and nitrate measured before ([NOX]i): ½TKN 5 ½NOXf
½NOXi The procedure was first applied on model compounds. Table 6.2 displays the results of the determination of the nitrogen concentration of various N-containing compounds. The conversion into nitrate from all compounds is quantitative, whatever the concentration, and the conversion times are very short, around 5 minutes of irradiation time [28]. The conversion into nitrate is possible under UV irradiation without the presence of chemical oxidant, with an increase of the reaction time (around 2 hours). The validation of the procedure has been carried out from the UV/UV estimation of TKN concentration of 80 samples of urban and industrial wastewater. TKN standard method was used as reference method, and the results are shown in Fig. 6.8. A good adjustment can

TABLE 6.2 Percentage of conversion into nitrate from N compounds. Compoundsa

UV methodb (percentage recovery)

Compoundsa

UV methodb (percentage recovery)

Urea

95
100

Glycine

90
95

N-acetylglucosamine

95
100

EDTA

80
90

4-aminophenol

95
100

m-toluidine

80
90

2-nitrophenol

90
100

Atrazine

80
90

4-nitrophenol

90
100

Aniline

70
80

3-aminophenol

90
100

Glutamic acid

70
80

a

Concentration between 5 and 50 mg N/L, pH 9. 5 min irradiation time.

b

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FIGURE 6.8 TKN measurement comparison between the Kjeldahl and the UV/UV methods [28]. TKN, total Kjeldahl nitrogen.

be observed between the results for a wide concentration range. From few mg N L21 to several hundred mg N L21, the comparison between the two methods is good, as well as the precision. Thus the UV/UV method can be used as an alternative way for the reference TKN method. 6.2.1.5 Ammonium measurement Ammonium determination is usually carried out with CFA, FIA, or SIA [8,9]. With the UV/UV procedure two steps of photooxidation are needed [29]. The first one allows the measurement of TKN as shown above. Then, a second one is carried out in the presence of an oxidant solution (potassium peroxodisulfate K2S2O8), without buffer. In these particular conditions (acidic medium), only organic nitrogen compounds are converted into nitrate, as will be explained afterward. The concentration of ammonium can be calculated by the difference between the TKN-estimated concentration ([TKN]) and the one of organic nitrogen ([NORG]), taking into account an eventual dilution. The relation is the following, where all concentrations must obviously be expressed in the same unit (mg N L21): ½NH1 4  5 ½TKN 2 ½NORG 

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6. Mineral constituents

A validation was carried out for 80 industrial wastewater samples analyzed with both a standard method (capillary electrophoresis) and the UV/UV one (Fig. 6.9). A good adjustment can be observed between the results for a wide concentration range (1
100 mg N L21) and the detection limit is 1.2 mg NH41 L21, confirming that this method is only suitable for high ammonium concentrations. The presented UV/UV method allows the simple and reliable determination of all nitrogen forms by using the same technique. Moreover, the total reaction time is rather short as a maximum of 40 minutes is required for all measurements (Table 6.3). Some comments can be made concerning the process, and more precisely the impact of the pH on the photodegradation results. Actually, the photooxidation of nitrogenous organic compounds leads to the cleavage of C
N bond resulting in the formation of ammonium radical

FIGURE 6.9

Comparison of ammonium determination for industrial wastewater (reference method is capillary electrophoresis).

TABLE 6.3 Time analysis (in minutes) for the determination of nitrogen forms. NOx

Norg

Nglobal

TKN

NH1 4

Photooxidation

no

10
15

10
15

no

no

Measurement

1

1

1

no

no

Exploitation

1 (dec)

1 (dec)

1 (dec)

1 (cal)

1 (cal)

Total

2

10
15

10
15

1

1

Note: dec, deconvolution; cal, calculation. TKN, total Kjeldahl nitrogen.

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205

NH1 [30]. A hydrolysis step follows this reaction in order to obtain 4 first ammoniacal nitrogen and second the final oxidized forms of nitrogen, that is, nitrite and nitrate. Moreover, the pH value allows the specific reactivity of organic and inorganic nitrogen. The pH effect can be studied from the comparison of the photooxidation of urea (as organic nitrogen) and ammonium chloride (as inorganic nitrogen). It appears that the conversion of urea is weakly modified by the pH and varies between 80% and 95%, whatever the pH value, whereas the conversion yield from the ammonium nitrogen is very weak in acidic medium and becomes quantitative for basic conditions [31]. The presence in wastewater of several organic (solvent) and inorganic (carbonates) compounds may interfere with the photooxidation because they are scavengers, inhibiting the action of oxidant radicals. Their action can decrease the conversion yield to 50% in function of the concentration [26].

6.2.2 P compounds Two reviews about phosphorus determination in natural waters were published recently [32,33]. Considering the role of excess phosphorus in eutrophication and algae blooms, the monitoring of dissolved phosphorus in water and wastewater is important. The main analytical methods for the determination of phosphorus are spectrophotometric ones with the formation of a phosphomolybdate complex. The phosphorus compounds in water and wastewater concern not only some organic forms (natural or anthropic) but also orthophosphate ion and acid hydrolyzable phosphate (condensed phosphate) (Fig. 6.10). Standard methods [2] used for the determination of total phosphorus
include several procedures. Orthophosphates PO32 4 and associated forms are determined by colorimetry, ion chromatography, or spectrophotometry (UV
visible). Acid hydrolysis allows the transformation of polyphosphates

FIGURE 6.10

Phosphorus forms in water.

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6. Mineral constituents

in orthophosphates, which are measured by one of the mentioned methods. Finally, a chemical digestion, followed by the determination of resulting spe
allows the measurement of organic phosphorus forms. cies PO32 4 Procedures are time-consuming (more than 5 hours) and require strong conditions (acidic medium, high temperature, and catalyst). Some improvements have been proposed, such as the use of photooxidation as an alternative to chemical digestion, as it has been successfully used for similar applications [32,34,35] particularly for flow analysis. In order to minimize the photooxidation time, the UV light source (high-, medium-, or low-pressure mercury lamp) can be associated with oxidants (hydrogen peroxide or potassium peroxodisulfate). Generally, the converted forms (orthophosphates) are determined by off-line analysis but can also be measured by CFA, FIA, or SIA. In this section, a simple procedure, based on the use of a UV photooxidation module (previously described) and a UV
visible measurement, is presented [36]. Another procedure, based on the use of the alternative vanadomolybdophosphoric acid method with a UV-LED detection at 380 nm can also be envisaged [37]. 6.2.2.1 General procedure As previously mentioned, phosphorus compounds are commonly
classified into orthophosphates PO32 ; acid-hydrolyzable (condensed) 4 phosphates, and organic phosphates. It must be noticed that acidhydrolyzable phosphates (as pyrophosphates) are negligible in sewage [38]. The general procedure illustrated in Fig. 6.11 includes
two main and a steps: an indirect UV
visible measurement for PO32 4

FIGURE 6.11

Speciation of phosphorus in wastewater.

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207

photooxidation step followed by a UV
visible measurement for global phosphorus determination (PGL). First, orthophosphates are determined by the spectrophotometric measurement of the phosphomolybdate complex (formed with addition of ammonium molybdate 40 g L21) using the spectrum deconvolution method (Chapter 3). Then, an oxidant solution (40 g L21 potassium peroxodisulfate K2S2O8) is added to the sample for the photoconversion (15-minute irradiation times) of the organic phosphorus forms into orthophosphates. Orthophosphates are then determined by UV
visible spectrophotometry.
The procedure allows the measurement of orthophosphate PO32 4 and global phosphorus (PGL). The determination of the sum
of organic is then and hydrolyzable phosphorus PORG 1 Phyd 5 PGL 2 PO32 4 possible. 6.2.2.2 Orthophosphates The measurement of orthophosphates is generally assumed thanks to the phosphomolybdate complex formation produced and detected in a flow analysis system (CFA, FIA, or SIA). For UV determination, the deconvolution procedure [23] can be used with a basis of reference spectra corresponding to the molybdate solution (2.4 g L21) and to the phosphomolybdate complex obtained from 10 mg L21 of orthophosphate. Notice that in this case the use of a simple multicomponent procedure may be sufficient. The quantification is carried out between 380 and 450 nm. The orthophosphate concentration is given by the product of the phosphomolybdate complex coefficient by the concentration of the corresponding reference spectrum. Fig. 6.12 shows the comparison between the orthophosphate concentration estimated by deconvolution and the concentration measured by ascorbic

FIGURE 6.12 UV estimation of orthophosphate concentration [9].

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6. Mineral constituents

acid colorimetry as a reference method. The UV-estimated concentrations are in good agreement with the expected ones and then can be used as an alternative to the standardized method. Even if the characteristics of the method are interesting with a detection limit of 10 μg PO4 L21, this must be lowered up to 5 μg PO4 L21 for trace analysis. Classical methods based on flow analysis techniques are more adapted in this case. 6.2.2.3 Total phosphorus As orthophosphates, total phosphorus is generally determined by flow analysis (CFA, FIA, or SIA). Inductively coupled plasma with optical emission spectroscopy (ICP-OES) can also be used for detection if concentrations are sufficiently high [32] as well as atomic absorption spectroscopy (AAS). The determination of total phosphorus needs a photooxidation step in the presence of an oxidant (potassium peroxodisulfate, for example) allowing the transformation of all phosphorus forms into orthophosphates that are complex (with a molybdate solution) when they are formed [36]. Phosphorus forms

hv=S2 O22 8 =Molybdate


!

PO32 4

Table 6.4 displays the results for the determination of the orthophosphate concentration from the photooxidation of various P-containing compounds. The molybdate solution (1.5 mL) is introduced into the reactor in order to follow the phosphomolybdate complex formation. The conversion yields are quantitative using irradiation time no longer than 15 minutes. The UV/UV
visible method was applied for the determination of total phosphorus from raw effluents. The results were compared with those obtained by atomic absorption spectrometry (AAS) as the reference method (Fig. 6.13) and the comparison showed a good correlation TABLE 6.4 Conversion yields obtained from organic phosphorus compounds. Conversion yieldsb

Compoundsa

Compoundsa

Conversion yieldsb

N (phosphonomethyl) glycine

100

Glucose-1phosphate

100

Mevinphos

100

AMP

90

Dichlorvos

100

ADP

90

Dibrom

100

ATP

85

Tris (2-chloroethyl) phosphate

90







Concentration between 1 and 10 mg L21. 10
15 min irradiation time.

a

b

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209

FIGURE 6.13 Comparison of total phosphorus measurement by the atomic absorption and UV/UV
visible methods.

(R2 5 0.979). The detection limit was 0.05 mg P L21 for a range of 0.05 to 10 mg P L21. The UV/UV system was described as an efficient method for the measurement of phosphorus in wastewater. According to the experimental conditions of photooxidation or UV measurement, it is possible to evaluate the major part of phosphorus forms (oxidized, organics, inorganics, and hydrolyzable) and total phosphorus. Finally, a recent method for the simultaneous determination of total dissolved nitrogen and total dissolved phosphorus in natural waters with an online UV and thermal digestion was proposed [27].

6.2.3 S compounds Sulfur occurs in wastewater in various forms. Most of the time, anoxic conditions in urban sewer lead to the production of hydrogen sulfide, but the presence of sulfur compounds is more often related to industrial discharges, mainly from refineries or petrochemical plants. Some petroleum contains elemental sulfur, which can occur as hydrogen sulfide (H2S) and carbonyl sulfide (COS). Sulfur is also present in a wide range of hydrocarbons, largely as mercaptans, organic sulfides, and thiophene derivatives [39]. Sulfur compounds tend to be concentrated in the higher boiling fractions of petroleum, are generally corrosive to metals, and may poison various catalysts. Stripping water is responsible for the presence of sulfide and mercaptans into crude oil refinery wastewater. Sulfide under dissolved H2S form is toxic for fish and other aquatic organisms and can be responsible for the decrease of wastewater

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6. Mineral constituents

treatment plants’ efficiency. Therefore its concentration needs to be controlled, especially in wastewater from crude oil refineries. Inorganic sulfur compounds are numerous and can be classified, as for nitrogen, between reduced and oxidized forms. Table 6.5 lists these compounds and their main origin areas, when they are found in water or wastewater. The reduced forms of sulfur are also called total sulfides, as they are all associated with the acido-basic equilibrium of hydrogen sulfide (at 25 C):
pKa H2 S=HS
5 7:05
pKa HS
=S2
5 12:92 Considering the pKa values, the predominant form is the bisulfide ion (HS2), since the molecular form of hydrogen sulfide (H2S) is volatile. This observation is true if the pH value of water or wastewater is about 7. The corresponding UV spectra, the shape of which depends on the pH value, are shown in Fig. 6.14. An important peak can be noted at 231 nm, related to the presence of hydrogen sulfide and corresponding to the bathochromic shift between the two forms of first acidity (notice that the spectrum of acidic solutions are much less absorbing because of the hypochromic effect and volatility of hydrogen sulfide). From an analytical point of view, total sulfide includes dissolved H2S and bisulfide ion HS2, which are in equilibrium with hydrogen ions. For pH values of wastewater, the S22 form is generally negligible, less than 1% of the dissolved sulfide under pH 10.

TABLE 6.5 Main sulfur species related to water and wastewater. Sulfur species Sulfide ion

Formula 22

S

2

Origin or use Industrial

Bisulfide ion

HS

Industrial/OM reductive degradation

Hydrogen sulfide

H2S

OM reductive degradation (gas)

Thiosulfate ion

S2 O22 3

Chemical reagent (titration)

Tetrathionate ion

S2 O22 5

Industrial

Sulfate ion

SO22 4

OM oxidative degradation

Peroxodisulfate ion

S2 O22 8

Chemical reagent (digestion)

OM, organic matter.

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6.2 Inorganic nonmetallic constituents

FIGURE 6.14

211

UV spectra of a sulfide solution (HS2 10 mg L21).

Sulfide can be determined in different media, using various techniques [40,41], including flow analysis systems (FIA and SIA). Three main methods are commonly used for the determination of sulfide in solution: 1. the colorimetric method, with methylene blue, based on the reaction of sulfide, ferric chloride and dimethyl-p-phenylenediamine to produce methylene blue, which absorbs at 664 nm [2]; 2. the iodometric method based on the oxidation of sulfide by iodine in acidic solution followed by a back titration with sodium thiosulfate solution; 3. the potentiometric method using a selective silver electrode. Fig. 6.15 shows UV spectra of refinery wastewater containing mineral sulfide (with corresponding dissolved organic carbon (DOC) around 1000 mg C L21). The value of samples’ pH is around 9. The characteristic peak of bisulfide ion appears clearly on the UV spectra (231 nm), despite the matrix sample. The intensity of the UV band is related to the concentration of sulfide. The use of a UV spectrophotometric procedure can also be proposed as an alternative method for the determination of inorganic sulfide in water and wastewater. A first method, based on the use of a multiwavelengths procedure, has been proposed for natural water [42]. The interferences are modeled from an exponential function and the simultaneous determination of total sulfide and iodide is possible. A second method integrates the semideterministic deconvolution procedure or UVSD method [23,36]. The potentiometric method will be chosen as reference for the validation of this last procedure. The UV quantification is carried out by deconvolution (see Chapter 3) between 205 and 320 nm. Raw samples were diluted four times

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6. Mineral constituents

FIGURE 6.15 Refinery wastewater UV spectra (dilution 25), with sulfide (88, 39, 23, and 45 mg S L21, respectively).

FIGURE 6.16 Validation of sulfide UV determination.

to prevent the UV signal saturation for a 10-mm quartz cell. Sulfide concentration is given by the product of the contribution coefficient of sulfide reference spectrum (replacing nitrate in the previous set) and the corresponding concentration, affected by the dilution factor. About 40 wastewater samples from refineries were used for the validation of the UV method [36]. Fig. 6.16 shows a good linear adjustment between the measured concentration by the potentiometric method and UV determination of sulfide. The characteristics of the UV method (without dilution) are a concentration range between 0.5 and 15 mg S L21 and a detection limit of 0.5 mg S L21. Some compounds absorbing close to 231 nm sometimes present in raw wastewater samples can interfere with sulfide spectra. Several

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213

compounds, for example, p-chlorophenol (absorption at 227 nm), anionic surfactant, RBS commercial product (absorption at 223 nm), and 1-propanthiol (absorption at 239 nm), were tested, and the results show low interference values to the studied compounds [36]. The error of restitution by deconvolution was 5% at maximum with the highest interference associated with anionic surfactant (leading to an error of 4.3%). The UV method has been described as a simple and reliable procedure for the determination of sulfide in wastewater. Compared with some reference methods, it is less sensitive but do not need any sample preparation (pretreatment, filtration, etc.) and is unaffected by interferences (salinity, suspended matter, organics compounds, etc.). Recently, two UV-based methods were proposed. The first one [43] is a fast determination of the main reduced sulfur species (sulfides, sulfites, and thiosulfates) in aquatic systems by a direct and second-derivative spectrophotometric method, based on the comparison of SDA spectra of the same sample at different pH (9.2, 4.7, and 1.0) and selected absorption wavelengths (250 and 278 nm). The second method is a field method for the determination of trace levels of sulfite in natural waters [44], based on automated dispersive liquid
liquid microextraction (DLLME) coupled with ultraviolet
visible spectrophotometry. The automated DLLME system was designed around a single syringe pump coupled with a multiposition valve. The method allows the precise determination of trace levels of sulfite with a detection limit of 1 μg L21. Concerning the determination of sulfate ion, there exist several standard methods [45], such as the ion chromatographic method, capillary ion electrophoresis (for low concentrations), and gravimetric methods for concentration above 10 mg L21. Moreover, two procedures used a final optical detection, one based on turbidimetry after precipitation with Baryum chloride (measurement of the absorbance at 420 nm) and the other based on the automated methylthymol blue method (final detection at 460 nm). Simple colorimetric reactions are not available for sulfate analysis and sulfate ion does not absorb in the UV
visible range. However, a recent spectrophotometric method was proposed by Salem and Draz [46] thanks to the synthesis of PEG-25 stabilized silver nanorods used as sensing probes for the colorimetric determination of sulfate from the decrease of the peak around 460 nm.

6.2.4 Cl compounds Chlorine compounds are potentially numerous, as sulfur ones, with several oxidation states. Nevertheless, there is a great difference between the two elements, because all oxidized forms of chlorine compounds are generally unstable in solution contrary to sulfate ion. Some of them, for

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6. Mineral constituents

example, chlorine itself (Cl2), hypochlorite ion (ClO2), and chlorine dioxide (ClO2), are used as oxidant agents for tap water production or swimming pool treatment. 6.2.4.1 Chloride Chloride ion is one of the major inorganic anions in water and wastewater because it is the more stable form of chlorine in solution. Chloride concentration is very variable and can be high not only in seawater. It is used for domestic purpose, but rather often for industrial processes. In some applications, the resulting concentration in wastewater can be important and greater than 1 g L21 (food and chemical industry, for example). Fig. 6.17 presents several spectra of industrial water containing chloride as compared to seawater. In all cases, the shape of chloride signal can be observed, characterized by a very high increase of absorbance values below 210
220 nm, always convex, in contrast to nitrate. The difference between industrial cooling water UV spectra and seawater spectrum is that the first ones show a residual diffused absorption for wavelengths greater than 220 nm due to the presence of organic matter at a higher concentration than in seawater. Several methods can be used for the determination of chloride in water [2]. The argentometric and mercuric nitrate methods are based on the titration of chlorine in the presence of an indicator. Experimental procedures are easy, but many substances may interfere with the results. There are also other methods such as potentiometry, capillary electrophoresis and other automated colorimetric methods implemented in flow analysis systems (CFA, FIA, or SIA).

FIGURE 6.17

UV spectra of cooling and seawater (dilution 5). Chloride concentrations are between 15 and 22 g L21.

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215

Taking into account its UV absorption signal, chloride concentration can be quickly estimated by UV for high concentrations (above 500 mg L21). The semideterministic method (Chapter 3) used for UV spectra exploitation can be applied by integrating the spectrum of a chloride solution on the basis of reference spectra. The concentration is then calculated by multiplying the corresponding chloride concentration with the contribution coefficient of the chloride spectrum. For a wavelength window between 200 and 320 nm, the method can be applied for a concentration range of 0.5
6 g L21 with a detection limit of 0.5 g L21. A comparison between this UV procedure and the reference potentiometric method has been carried out on 110 industrial samples. Fig. 6.18 shows the quality of the adjustment between the two sets of results. 6.2.4.2 Hypochlorite Chlorine may also be found in water under the hypochlorite ion form, which is a strong oxidant used for water and housing disinfection. It is used for tap water production as well as for swimming pool water treatment or, in some cases, for the oxidation of odorous compounds. This product (commonly found as “Eau de Javel” or bleach solution) also has a persistent disinfecting action as long as hypochlorite remains in the solution. The residual concentration must thus be sufficient, but not in large excess, because of the formation of organohalogeno compounds such as chloramines, which are responsible for eye irritations and can even be toxic. The presence of hypochlorite ion can easily be monitored by UV spectrophotometry for relatively high concentrations, because its UV spectrum presents, contrary to the one of chloride ion, an important

FIGURE 6.18 Comparison of UV estimation of chloride in industrial water with the potentiometric method.

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6. Mineral constituents

Gaussian-like peak centered on 290 nm. Applications for oxidation process control using hypochlorite, gaseous chlorine, or chlorine dioxide are thus possible using UV spectrophotometry. The spectrum exploitation is possible with the semideterministic method (by including the corresponding reference spectrum, as previously) but a simpler absorptiometric procedure can be used at 290 nm, if interferences (organic compounds, suspended solids, etc.) are negligible. Fig. 6.19 shows some examples of samples of swimming pool and deodorization water. A comparison has been carried out between the concentration obtained by UV spectrophotometry, either simple absorptiometry at 290 nm or with the semideterministic approach. Fig. 6.20 shows the right adjustment for the results for 10 samples of real water with standard additions of hypochlorite. For a wavelength window between 220 and 325 nm or a single wavelength at 290 nm if no interferences are present, the method can be applied for a concentration range of 0 to 400 mgCLO2 L21 with a

FIGURE 6.19

UV spectra of water (swimming pool and deodorization) containing

hypochlorite.

FIGURE 6.20

Hypochlorite detection by absorptiometry (A) or by semideterministic

method (B).

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6.3 Metallic constituents

217

detection limit of 5 mg L21. Using a 100-mm pathlength, this last can be lowered to 0.5 mg L21, a value acceptable for some regulation applications. 6.2.4.3 Organochlorine compounds The use of UV spectrophotometry for the estimation of monochloramine in water distribution systems was recently reported [47], coupled with the determination of nitrate and nitrite. A model based on the absorbance values measured at 220, 245, 280, and 320 nm gave an estimate of monochloramine concentration. Implemented in an online UV
visible spectrophotometer, this method was applied to a given water distribution system and further experiments are needed to check the interest of the method on other sites, regarding potential interferences with organic matter and pH. Finally, the monitoring of total organochlorine compounds (TOX) during chlorination was proposed from the decrease of the absorbance value at 272 nm [48]. This approach is detailed in Chapter 10.

6.3 Metallic constituents The constant evolution of water quality standards implies the development of faster and cheaper analytical procedures. These are more particularly adapted for online measurement, for the frequent analysis of metallic constituents or for the estimation of global parameters. The cheapest and simplest methods for the determination of metallic constituents are colorimetric ones, which are less precise and selective than instrumental reference methods such as graphite furnace (GF)-AAS or ICP-OES. Other instrumental techniques based on electrochemical principles are also proposed for water quality monitoring, but they are relatively expensive, sensitive to interferences, and, thus, rather inappropriate for wastewater monitoring. The improvement of UV
visible spectrophotometers has led to a renewal in the colorimetric procedures, actually absorptiometric, with the possibility of simultaneous metallic compound determination after selective complexation. Based on the reaction between a ligand and some metallic elements, these complexometric procedures use absorptiometry for final detection generally through flow analysis systems (CFA, FIA, or SIA). Before considering some of these complexometric procedures with final UV
visible detection, designed for the simultaneous determination of metallic constituents, a more simple approach must be considered. In the case of UV
visible absorption for some metallic constituents without any reagent, it is interesting to check the feasibility of a direct absorptiometric

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6. Mineral constituents

measurement. This is studied in the first part with the determination of chromium in water and wastewater.

6.3.1 Chromium (direct measurement) 6.3.1.1 Hexavalent chromium Chromium VI is one of the metallic contaminants often encountered in industrial wastewater, particularly for metal processing activities such as electroplating, for example. Because of its toxicity, its maximum concentration allowed in treated wastewater must generally be 50 μg L21 before being discharged into a receiving medium. There exist several analytical methods for chromium VI determination, often very complex [49,50]. One of the most simple and widespread methods is the former diphenylcarbazide colorimetric one. Its detection limit is about 1 μg L21, but the procedure is time-consuming. Another type of method is based on the spectrophotometric properties of Cr (VI), sometimes used as standard solution [51]. Moreover, the spectrophotometric determination of Cr (VI) is well known in the field of water examination, as it can be used for alternative measurement of chemical oxygen demand (COD) [52] (see Chapter 5). Two other UV
visible spectrophotometric methods have been proposed. The first one [53], designed for natural water, uses the peak height measurement at 372 nm, for a basified sample (pH . 9). The peak height is calculated from the absorbance values at three wavelengths (310, 372, and 480 nm), taking into account a very simple third-degree polynomial interference signal. The second UV
visible spectrophotometric method [22] was proposed for natural and urban wastewaters with a more general mathematical compensation of interferences. Both UV
visible spectrophotometric methods are rapid and simple with a detection limit of around 5 μg L21 for a 50-mm optical pathlength. Unfortunately, they cannot be applied for industrial wastewater survey, as the presence of specific compounds cannot be modeled by the mathematical tools used for the removal of interferences. The application of the semideterministic approach, described in Chapter 3 and applied before in this chapter, can be used for the determination of hexavalent chromium. Exceptionally, the optical pathlength is 50 mm for this application because of the regulation compliance constraint. Contrary to the previous determination (nitrate, sulfide, hypochlorite, etc.), this application must take into account the pKa value (about 5.8) of the dichromate in equilibrium with the chromate form: 22 1 Cr2 O22 7 1 3H2 O$2CrO4 1 2H3 O

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6.3 Metallic constituents

219

As the pH of industrial wastewater must be around the pKa value (before neutralization), both chromate and dichromate spectra must be chosen in the basis of reference spectra, taking into account the influence of pH on chromium VI speciation (Fig. 6.21). The two first reference spectra correspond to chromium VI solutions of pH 3.5 and 9.5, respectively. Fig. 6.22 shows real spectra from industrial wastewater and natural sample containing hexavalent chromium at different concentrations. The spectra of Fig. 6.21 show two absorption peaks in the UV region, depending on pH, and a slight residual absorbance in the visible one. Bathochromic and hyperchromic effects can be noticed between the

FIGURE 6.21 Absorption spectra of a K2Cr2O7 solution (1 mg L21 of Cr, pathlength 50 mm) for different pH.

FIGURE 6.22

UV
visible spectra of wastewater containing hexavalent chromium.

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220

6. Mineral constituents

acidic and basic media. The resulting visible color in the sample containing hexavalent chromium for neutral pH is orange. A comparison between the diphenylcarbazide and UV
visible method for Cr (VI) determination was carried out for more than 50 samples of different origins (44 industrial wastewaters, 8 urban wastewaters, and 4 natural waters), the concentration of which varied between 0 and almost 1 mg L21. Fig. 6.22 shows some examples of the sample spectra. The results of the comparison are shown in Fig. 6.23, and the adjustment is quite good. For a wavelengths window between 300 and 450 nm, the method can be applied for a concentration range of 5 to 1000 μgCr L21 with a detection limit of 5 μg L21. As the method is designed for industrial applications, the interference of some metallic compounds must be checked. Indeed, different ions, such as copper (II), iron (III), lead (II), and mercury (II), for example, potentially existing in electroplating wastewater, may interfere with the determination of Cr (VI). Except in the presence of Fe31, the measurement of Cr (VI) gives an error generally lower than 3% for 0.5 mg L21 of metallic ion, and for greater concentrations (up to 10 mg L21), the error is negligible for Cu21, and lower than 15% for the other ions [54]. The reason why Fe31 interferes is shown in Fig. 6.24. Even for a concentration of 1 mg L21, Fe31, which is the most probable form of dissolved iron in water which absorbs around 300 nm in acidic medium. In this case, the quadratic error of the deconvolution is too high. An increase of the pH value up to 9 (with some drops of NaOH 1M, for example) leads to the precipitation of the hydroxide form, the spectrum of which is very close to the reference of suspended solids. With this simple pretreatment, the error in Cr (VI) determination between the diphenylcarbazide and the UV
visible methods becomes lower than 2.5%.

FIGURE 6.23 Comparison of the diphenylcarbazide and UV
visible method for Cr (VI) determination, for water and wastewater samples (pathlength 50 mm).

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6.3 Metallic constituents

221

FIGURE 6.24 Absorption spectra (pathlength 50 mm) of synthetic solutions containing 1 mg L21 of iron [a—pH 3, b—pH 9, and c—pH 9 and addition of Cr (VI)].

6.3.1.2 Trivalent chromium Trivalent chromium (Cr31 ion) is not as toxic as the hexavalent form and can be directly detected by visible spectrophotometry, its UV absorption being nonspecific. The UV
visible spectra of the trivalent form shows a great difference with the one of the hexavalent forms. At pH 1 and 5, two peaks of absorbance are noted at 433 and 600 nm, with very close absorbance values and giving a green color to the solution. The main application of the optical properties of Cr31 ion is the final colorimetric determination of COD with the measurement of absorbance at 600 nm, maximum of absorbance in acidic medium (see Chapter 5). As a consequence, the direct spectrophotometric determination of Cr31 ion in water and wastewater is limited to highly concentrated samples. Some methods for the indirect determination of Cr31 ion in water or wastewater are proposed in the following section.

6.3.2 Metallic constituents determination by complexometry Several procedures for the simultaneous determination of metallic constituents have been proposed at least for 25 years (Table 6.6). These analytical methods published since the 1990s show the evolution of the nature of the reagents and the spectrophotometric detection method. The majority of methods was implemented in flow analysis systems (CFA, FIA, or SIA), but the lack of substantial validation on real samples explains why these methods are actually not used for water or wastewater monitoring, for which reference methods such as ICP-OES are preferred.

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222

6. Mineral constituents

TABLE 6.6 Complexometric procedures for the determination of metallic constituents. Metallic constituents

Complexant/ reagent

Exploitation method

References

Ca, Mg

PAR

Multicomponent analysis

[52]

Cu, Co

MEDTA 5-BSAT

Derivative spectrophotometry PLS

[53,55]

Cd, Co, Cu, Pb, Mn, Ni, Zn

PAR

Multiple linear regression

[56]

Hg

Dithizone

Peak valley (480
585 nm)

[57]

Hg

Specific chemosensor

460 nm

[58]

Co, Cu, Pb, Mn, Ni, Zn, Fe

PAR

First-, second-, and thirdderivative spectrophotometry

[59]

Cu, Fe

Bathocuproin/ LLE

Second-derivative spectrophotometry

[60]

Cr, Cu

MEDTA

Derivative spectrophotometry

[61]

Cu, Pd

Oxazolidine

Spectrum and first-order derivative spectrophotometry

[62]

Fe, Ag, Mn

Rhodamin B

Kalman filter

[63]

Ni, Co, Cu, Fe

PAR

Derivative spectrophotometry

[64]

Fe, Ru

Diphenyl phenantholin

Second-derivative spectrophotometry

[65]

Cu, Fe, Hg

Dithizon, EDTA, BHA

Deconvolution

[66]

Cu, Zn

Zincon

PLS

[67]

Cu, Ni, Co, Zn

Zincon Nitroso-R salt

PLS Kalman filter

[68,69]

Al, Fe

Hematoxylin Chrome Azurol/ SPE

PLS Measurement at 620 nm

[70,71]

U, Th, Zr

Arseno III/SPE

PLS

[72]

Zn, Cd, Pb

TAR

PLS

[73]

Cd, Zn, Co

Br-PADAP

PLS

[74]

Pb, Cu, Ni

MEDTA

Multiwavelength and derivative measurement

[75] (Continued)

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223

6.3 Metallic constituents

TABLE 6.6

(Continued) Complexant/ reagent

Exploitation method

References

Cu, Pd

Mesoporous adsorbent

Measurement at 425 (Cu) and 407 nm (Pd)

[76]

Fe

2,4,6-tripyridyl-striazine

Led spectrophotometer (600 nm)

[77]

Se

Composite adsorbent

Measurement at 430 nm

[78]

Cu, Ni, Co

SPE

PLS

[79]

Co

Composite adsorbent

Measurement at 420 nm

[80]

Ni, Mn, Zn, Ag, Cd, Fe, Hg, Cu, Sn, Co, Pb

Thiols, urease, bromothymol blue

Linear discriminant analysis

[81]

Metallic constituents

LLE, liquid
liquid extraction; PLS, partial least squares; SPE, solid-phase extraction.

All methods are based on the main complexometric reaction between a reagent (ligand) and some metallic elements, after a liquid
liquid or solid-phase extraction step if needed, and the resulting signal is exploited in order to determine the corresponding concentrations. The exploitation methods are different according to the methods, based on simple absorptiometry, derivative spectrophotometry, spectral deconvolution, or chemometrics methods (namely PLS). The choice of the different reagents (complexant, buffer, etc.) is obviously dependant on the metallic ion to be determined, but also on their optical properties (principally in the visible region). Several complexant mixtures are proposed in Table 6.6. Concerning the choice of the metallic constituents, the majority of studies deal with the main metallic contaminants in water and wastewater, but some other elements found at ultratrace level are also considered (e.g., Co, Pd, and Ru). As an example, a complexometric method for the simultaneous determination of Cu, Fe, and Hg in water and wastewater is presented more in details [66]. The complexant solution is based on the use of dithizone (diphenyl-1,5-thiocarbazone) widely used for metal analysis up to the seventies [82,83]. The dithizone solution is mixed with EDTA employed as masking agent, BHA (butylhydroxyanisol), glycine, ethanol, and distilled water. Taking into account the possible metallic hydroxide precipitation and the potential presence of natural complexes (with humic substances in

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6. Mineral constituents

natural waters, or with specific compounds in industrial wastewater) [84], the working pH is fixed at about 2.8, with a glycine solution for the stabilization of the dithizone spectrum. Notice that the glycine and its eventual metallic complexes are almost transparent in the UV region and do not change the general shape of the spectra of the final mixture [85]. Similarly, EDTA (in all the wavelength range) and BHA (transparent above 310 nm), alone or complexed, do not interfere with the UV signal except for copper and iron complexes, responsible for a great absorbance in the UV region. The quantification of the metallic constituents studied [Cu (II), Hg (II), and Fe (II)] is carried out by the spectral semideterministic method or by any multicomponent procedure (see Chapter 3). The choice of the wavelength range depends on the studied element and on the corresponding basis of reference spectra. Moreover, the optical pathlength is either 10 or 50 mm for high or low concentration, respectively, with two reactive solution concentrations [85]. The UV
visible spectra of the metallic complexes constitute the basis of reference spectra used for the calculation of Cu (II), Hg (II), and Fe (II) concentrations. Fig. 6.25 shows these reference spectra (completed by a blank of the reagent) and also shows that the UV region is of poor interest for the purpose, taking into account the optical properties of the chosen reagents. Each of these spectra corresponds to a given concentration of a metallic constituent, which serve for the final concentration calculation of a sample. This value is dependant on several parameters, among which the concentration of the studied metal, related to the stoichiometric equilibrium (with dithizone) and the dilution rate of the

FIGURE 6.25 Reference spectra basis for the computation of Cu (II), Hg (II), and Fe (II) concentrations for the high-concentration mixture (10 mm cell pathlength) (SR1: Cu (II)dithizonate, SR2: blank of the reagent, SR3: Hg (II)-dithizonate, and SR4: Fe (II)-EDTA complex) [54].

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6.3 Metallic constituents

sample in the sample
reagent mixture. The metallic constituent concentration of an unknown sample corresponds to the product of this value by the contribution coefficient related to the corresponding dithizonate spectrum in the linear combination. For natural waters and/or effluent applications, some interference may occur with the presence of natural or anthropogenic chelating agents. For example, humic substances can compete with dithizone for metallic constituent complexation in natural waters [86]. In this case, the degradation of organometallic complexes must be effective before the analytical determination. A photodegradation step, with a simple device as the one already proposed in this chapter for the determination of N and P compounds, can be used for the purpose. However, contrary to the previous procedure using an oxidative reagent, the decomplexation can be carried out without the reagent [73]. The characteristics of the method for high- and low-concentration mixtures are presented in Table 6.7. The precision values are calculated for the middle of their respective range, and the sensitivities correspond to the smallest concentration difference data that can be obtained, depending both on the spectrophotometer resolution (1023 a.u.) and on the precision for the calculation of the contribution coefficient values. The detection limit values are three times the standard deviation of the blank. Several experiments were carried out on samples of natural waters and industrial wastewaters (12 for Cu, 9 for Hg, and 14 for Fe), with standard additions for some samples [56]. The analysis of the samples was performed both by a reference method (GF-AAS for copper and iron, cold vapor-AAS for Hg) and by the proposed spectrophotometric method. Fig. 6.26 shows the comparison results between the two methods for each metallic constituent. The correlation coefficients of the related

TABLE 6.7

Copper

Mercury

Iron

Characteristics of the proposed method. Wavelength (nm)

Working range (µg.L21)

Detection limit (µg.L21)

Precision (%)

300
650

0
30

3

3.0

0
600

100

5.6

0
100

2

3.2

0
5000

80

5.0

0
1500

45

0.5

0
12,500

1200

0.4

300
650

300
650

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6. Mineral constituents

FIGURE 6.26 Comparison between the UV
visible spectrophotometric method and the corresponding reference methods for (A) copper, (B) mercury, and (C) iron determination, with or without standard additions of metals in sample.

regression lines are satisfactory and greater than 0.95 for the three studied metallic constituents, and the intercept values are very small compared to the concentration ranges. Finally, despite the interest of this spectrophotometric approach, performance analytical techniques, such as ICP-OES or ICP-MS depending on the matrix, described as standard methods [2], are more adapted for water and wastewater quality monitoring.

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UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

7 Physical and aggregation properties Marie-Florence Thomas1, Christopher Burgess2 and Olivier Thomas1 1

EHESP School of Public Health, Rennes, France, 2Burgess Analytical Consultancy Ltd, Barnard Castle, United Kingdom

O U T L I N E 7.1 Introduction

234

7.2 Color 7.2.1 Determination of color 7.2.2 Relationship between color and visible absorbance

235 235 237

7.3 Physical diffuse absorption 7.3.1 Some elements on the diffusion of light by particles 7.3.2 Methods for the study of heterogeneous fractions 7.3.3 UV
visible responses of mineral suspensions 7.3.4 UV responses of nanoparticles 7.3.5 Interactions of dissolved organic matter with natural and engineered colloids 7.3.6 UV responses of microorganisms 7.3.7 UV responses of wastewater

238 238 241 241 243

7.4 Total suspended solid estimation 7.4.1 Turbidimetry 7.4.2 UV estimation of total suspended solids

250 251 253

References

255

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00004-6

233

244 244 246

© 2022 Elsevier B.V. All rights reserved.

234

7. Physical and aggregation properties

7.1 Introduction Parameters previously described in Chapters 4 to 6 are related to specific compounds or groups of compounds, always dissolved in water. However, other parameters must be considered to complete the physicochemical characterization of water and wastewater. These parameters can be quantified either by simple sensors such as temperature, redox potential, electrical conductivity, dissolved oxygen or by optical methods for the determination of color, turbidity, or suspended solids. The latter applications, using UV
visible light, are presented in this chapter. The pollution load of water and wastewater is often associated with the presence of floating or suspended matters of different nature and size. This type of pollution is particularly important because of its consequences in terms of deposition, clogging or anaerobic degradation, and its adsorption potential for metallic, organic compounds, or microorganisms. Moreover, colloidal and particulate matters in water and wastewater have a great influence on the performance of treatment plants. Each operation unit, such as settling, biological or chemical treatment, is affected by the phenomena of agglomeration or dispersion of colloids or suspended solids [1]. Therefore an understanding of these different phenomena and the development of methods allowing the characterization and quantification of related parameters in sewage is necessary for the optimization of treatment processes and evaluation of their performances [2]. Water and particularly wastewater can be considered as a mixture of organic and mineral pollutants released from different sources (households, farms, industries, etc.). For example, urban sewage contains both anthropogenic and natural contaminants, the size distribution of which (Fig. 7.1) can be characterized by four fractions: dissolved or soluble (,0.001 µm), colloidal (0.001
1 µm), supracolloidal (1
100 µm), and settleable (.100 µm) [3]. Actually, the size of the smallest contaminants is often expressed in Daltons, unified atomic mass unit, largely used for the characterization of biomolecules (e.g., proteins). For example, particles of 0.005 µm (5 nm) of diameter correspond to spherical protein macromolecules of 500 kDa [4] In a recent study on urban wastewater characterization by combining fractionation, chemical composition, and biodegradability, Ravndal et al. [5] showed that carbohydrates, possibly from toilet paper, were dominant in larger particulate size fraction (.100 µm). The biodegradation rates of particles in wastewater were controlled, on the one hand, by particle size, the smaller particles having higher degradation rate, and, on the other hand, by their chemical composition for the colloidal and supracolloidal fractions. With regard to this size distribution, the measurement of total suspended solids (TSS), which is one of the main parameters for water and wastewater quality characterization, includes the coarse fractions, that is,

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7.2 Color

235

FIGURE 7.1 Size distribution of contaminants in urban wastewater [54]. Source: Adapted from R.B. Baird, A.D. Eaton, E.W. Rice, Standard methods for the examination of water and wastewater, 2019.

supracolloids and settleable matter. Before considering the TSS measurement, the problem of the border between dissolved and solid phases has to be discussed. Depending on the method used for TSS quantification, the result can vary depending on the cut-off size of the separation systems. For example, a part of the colloidal fraction (between 0.001 and 1 µm) can be included in TSS measurement when using a filtration membrane of 0.7-µm pore size proposed in the US standard method. Even if the results obtained from different TSS measurement methods are variable, depending on the separation system used, the main source of error is the subsampling step between on-site sampling and final analysis [6]. Finally, UV
visible spectrophotometric responses are influenced as a result of different phenomena such as physical absorption (particle absorption, diffusion, refraction, and diffraction) and chemical absorption. This is the reason why optical responses of water and wastewater are complex and difficult to interpret. Before considering the diffuse absorption of suspended particles and colloids, the global optical response perceived by an observer, the color of water, is briefly considered in the following section.

7.2 Color 7.2.1 Determination of color Color of water and wastewater is related to the presence of dissolved compounds, including chromophores in their structure. Its perception is very important as emphasized in Chapter 1. Table 7.1 presents the wavelength regions corresponding to the different colors of the visible

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236

7. Physical and aggregation properties

TABLE 7.1 Colors and their complements. Wavelength (nm)

430

480

540

580

620

650

Color

Violet

Blue

Green

Yellow

Orange

Red

Complement

Yellow

Orange

Red

Violet

Blue

Green

TABLE 7.2 Some methods for the determination of color. Method

Principle

Result

Comments

Standard Method 2120-B

Visual comparison with acidic solutions of potassium chloroplatinate

Color units

Use Nessler tubes for comparison

Standard Method 2120-C

Calculation of tristimulus values from transmittance values

Dominant wavelength (nm) Hue, luminance (%) Purity (%)

Use of a set of wavelengths (between 410 and 670 nm)

Standard Method 2120-D

Tristimulus filter method

Dominant wavelength (nm) Hue luminance (%) Purity (%)

Use of three special tristimulus light filters (corresponding to the following wavelengths: 590, 540, and 438 nm)

ISO 7887B

Absorbance measurement

Coefficient of spectrum absorption

Use of three wavelengths: 436, 525, and 620 nm

spectrum. The color of a solution seen by the human eye is in fact the result of the absorption by the solution’s compounds of the complementary color, in the presence of a polychromatic source light (solar or ambient light). Colored samples contain dissolved substances showing a strong absorption in the visible region (ε . 103 mol L21 cm21). The lower sensitivity limit of the human eye is approximately 380 nm, between the near UV and visible regions. As for the color of paints or materials, the color of an aqueous sample is defined by its hue (the name of the color), luminance (the relative lightness or darkness of the color), and saturation (purity of the color). Color determinations can be carried out by several reference methods, generally based on the sample optical properties in the visible region (Table 7.2). Historically, comparison methods, as platinum-cobalt (or chloroplatinate) method (Standard Method 2120-B) for the color determination of

UV-Visible Spectrophotometry of Waters and Soils

7.2 Color

237

potable or ocean water, used a colored scale for a rapid characterization of samples. This method can be complemented by the well-known Forel-Ule [7] comparison already presented in Chapter 2. For the spectrophotometric methods, the examination of the different procedures leads to the conclusion that, except for the Standard Method 2120-C, which uses several sets of three wavelengths, the others are limited to the choice of the wavelengths to be considered and give less useful results. Almost all methods can be automatically performed by a PC-controlled spectrophotometer, provided the bandwidth of the instrument is adapted for the measurement. For example, an online spectrophotometric method was proposed for the monitoring of a discoloration process of industrial wastewater (paper industry), based on the calculation of color differences from 10 wavelengths in CIELAB color space [8].

7.2.2 Relationship between color and visible absorbance Fig. 7.2 shows the UV spectrum of an industrial chemical product used for the diagnosis of metallic surface defects. In the case of a small crack, for example, the defect remains colored in red. The UV
visible spectrum shows an important peak at 552 nm, probably due to the presence of an unknown azo-dye. The origin of the color can be related to one-colored product or pollutant, or to natural constituents (humic substances, metallic ions or complexes, for instance). The color is well defined when only one-colored solute is present (Fig. 7.2), but, generally, several compounds are responsible for a mixture of colors giving a broad visible spectrum (Fig. 7.3).

FIGURE 7.2 UV
visible spectrum of an industrial product used for metallic surface checking (dye penetrant inspection, DPI).

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238

7. Physical and aggregation properties

FIGURE 7.3 Spectra of colored solutions and waters. TABLE 7.3 Results of samples of Figs. 7.2 and 7.3 (according to Standard Method 2120-C). Tristimulus coordinates

Sample Industrial producta Natural water

b b

Urban wastewater

b

Amax. λ

Purity

Color hue

0.196

540

55

Reddish-purple

0.329

0.335

580

,10

Yellowish-orange

0.354

0.375

574

28

Greenish

X

Y

0.341

0.352

0.350

580

21

Yellowish-orange

b

0.332

0.340

577

12

Yellow

b

0.329

0.331

581

,10

Yellowish-orange

Textile wastewater

Treated wastewater 1 Treated wastewater 2 a

Fig. 7.2. Fig. 7.3.

b

Tables 7.3 and 7.4 present the tristimulus coordinates and the corresponding absorption coefficients of the different samples presented in Figs. 7.2 and 7.3. Some conclusions can be drawn. Except for the industrial product, the corresponding color characteristics are rather similar, even if the visible spectra show some differences. This is particularly true for the tristimulus method, which gives an integrated response more adapted for dyes than for water or wastewater. The Standard Method 2120-C seems to discriminate more efficiently between the colors but needs a more complete comparative study for each absorption coefficient.

7.3 Physical diffuse absorption 7.3.1 Some elements on the diffusion of light by particles When a beam of light illuminates a particle in water, it scatters light in all directions. This is the diffusion phenomenon, involving

UV-Visible Spectrophotometry of Waters and Soils

239

7.3 Physical diffuse absorption

TABLE 7.4 Spectral absorption coefficients1 results of samples of Figs. 7.2 and 7.3 according to ISO 7887-B. Sample a

Industrial product b

Natural water

Urban wastewater

b

Textile wastewater

b

α (436 nm)

α (525 nm)

A (620 nm)

12.1

84.4

4.8

20.5

13.0

8.2

29.8

4.8

3.0

64.4

49.2

38.8

b

35.4

24.8

21.1

b

23.5

16.9

11.1

Treated wastewater 1 Treated wastewater 2 1

Ratio between the absorbance value and the pathlength. Fig. 7.2. b Fig. 7.3. a

FIGURE 7.4 Main mechanisms of light diffusion.

four mechanisms: absorption, refraction, reflection, and diffraction (Fig. 7.4) The interactions between a light beam and a given particle depend mainly on the ratio between the particle size and the wavelength of the beam of light. In order to characterize the domain of each phenomenon, a size parameter α is defined [9]: α5

πd λ

where d is the particle diameter and λ the wavelength.

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7. Physical and aggregation properties

According to the value of the parameter α, and considering the light sources usually used [UV, visible, near-IR (NIR)], three domains are considered [9]. • Particles that are greatly submicronic (α , 0.3). For α , 0.3, the optical model of the Rayleigh scattering is used. Particles diffuse light as much forward as backward. • Particles’ size greater than several microns (α . 30). Laws of optical geometry and diffraction are used. In this case, the light is diffracted, meaning that the diffusion is mainly concentrated in front of the particles. • Micronic particles (0.3 , α , 30). This intermediate domain corresponds to the validity limit of the last models (diffusion and diffraction) because diffusion is also influenced by reflection, refraction, and absorption phenomena. These can be taken into account by the complex theory of Lorenz
Mie. There are, however, some limitations to this presentation. The diffusion is only valid for spherical particles and single diffusion. The shape of the particles (spherical in Mie model) has a strong influence on optical properties [15], as well as the orientation of particles [9]. Thus other approaches have been proposed for nonspherical particles [10,11]. However, extinction observations are used for estimating the size of scattering particles in granulometric methods. Finally, if the particle size is comparable to the wavelength of light, the extinction will depend on the particles’ shape. For aggregate particles with a size comparable to the wavelength, the spectral dependence of extinction efficiency is less steep than that for equivalent spheres, and its maximum is shifted to larger size parameters, that is, smaller wavelengths [12]. Actually, there are very few studies concerning the UV
visible responses of particles. One study [13] on organic pigments in aqueous dispersions has clearly shown that the optical response is the result of absorption and scattering of light by the pigment particles. The intensity of the two phenomena (absorption and scattering) is used to determine the dispersion degree of organic pigments. Another study [14] proposed a model for the quantitative interpretation of UV
visible spectra of microorganism suspensions (see Section 7.3.4). The model is mainly based on light scattering theory and spectral deconvolution techniques, initially proposed by Thomas et al. [15,16] with reference spectra of suspended solids and colloids for the modeling of the interferences of UV spectra of wastewater and natural waters.

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7.3 Physical diffuse absorption

241

7.3.2 Methods for the study of heterogeneous fractions A variety of techniques are available for the characterization of particle size distributions, but, because of the large size distribution of solids in water and wastewater, no single analytical method can be used. Besides their physicochemical properties (stability, settleability, etc.), heterogeneous fractions have intrinsic optical properties. This is the reason why optical methods take an important place among granulometric methods with dynamic light scattering, laser diffraction, laser diffusion, or UV
visible spectrophotometry. Image analysis by microscopy, sieves, centrifugation, field flow fractionation, or membrane techniques are also used for the characterization or separation of solids in waters. The principles of optical granulometric methods are more often based on the interaction between spherical particles and light or other physical resistance. This constraint is not a problem for industrial applications where the suspension granulometry is generally well controlled with homogeneous conditions. Unfortunately, wastewater suspensions are very variable in nature, size, and properties. This is the reason why the application of granulometric methods is limited in this field.

7.3.3 UV
visible responses of mineral suspensions A study has shown the relation between UV
visible response and size of some mineral suspensions in water [15]. After filtration of commercial suspensions of talc, kaolin, and carbonate (Table 7.5), the UV
visible spectra of the different granulometric fractions are normalized (see Chapter 3) and compared (Fig. 7.5). • For particles, the diameter of which is larger than 10 µm, the diffusion domain is the diffraction one. This phenomenon is characterized by UV
visible spectra with absorbance values slightly dependent on wavelength. The ratio between absorbance values at 200 and 800 nm is about 2. TABLE 7.5

Main granulometric characteristics of the studied suspensions [16].

Suspension

Main modes

Talc

10 µm

Kaolin (slurry)

0.6 and 2 µm

Carbonate (slurry)

0.5 and 2 µm

Kaolin (powder)

5 and 50 µm

Carbonate (powder)

10 µm (broad granulometric spectrum)

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FIGURE 7.5 UV
visible responses (normalized spectra) of mineral particles according to their size [16].

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• For particles measured as TSS ( . 1.2 µm), the diffusion domains are both Mie and diffraction ones. TSS present the same optical response as particles, the size of which is greater than 10 µm even if a marked slope is noticed for kaolin. The ratio between absorbance values at 200 and 800 nm is around 2, except for kaolin, greater, because of organic contaminant responsible for a shoulder around 250 nm. • For colloids, the diffusion domain is the Mie one. UV
visible response depends strongly on wavelength. The ratio between absorbance values at 200 and 800 nm ranges from 10 to 24. The difference observed between UV spectra of particles in slurry form and in powder form can mainly be explained by the presence of organic compound(s) used in the formulation that may absorb in the UV region (particularly for kaolin). In their study, Alin et al. [17] applied UV
visible spectroscopy for the characterization of clay particle size in aqueous suspensions (organo-modified and unmodified montmorillonite). The average particle size of the organo-modified clay in suspension was significantly larger and pH and salinity variations were responsible for particles’ size decrease contrary to the unmodified montmorillonite.

7.3.4 UV responses of nanoparticles Among the characterization techniques used for nanoparticles, size and shape are the main parameters studied. Besides X-ray-based techniques often used, additional optical methods are applied among which UV
visible spectroscopy, considered as relatively simple and low cost for the study of nanoscale materials [18]. Gold, silver, and copper nanostructure sols exhibit characteristic UV
vis extinction spectra due to the existence of a localized surface plasmon resonance (LSPR) signal in the visible part of the spectrum. The LSPR is an optical phenomena generated by light when it interacts with conductive nanoparticles (metallic) that are smaller than the incident wavelength [18]. It leads to well-structured visible spectra exploited for the determination of their size distribution from the whole spectrum between 200 and 800 nm [19]. In this study, the size of silver nanoparticles is controlled using a reducing agent (sodium borohydride) and UV
visible resulting spectra present a sharp absorption peak at 400 nm. In a recent review, Sui et al. [20] also used this complementary technique for the characterization of engineered nanoparticles (Pt and AuPt NPs). Zook et al. [21] used the decrease in area under the UV
visible spectrum between 325 and 395 nm, for the measurement of silver nanoparticle dissolution in complex biological and environmental matrices. UV-Visible Spectrophotometry of Waters and Soils

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7.3.5 Interactions of dissolved organic matter with natural and engineered colloids In natural water as well as in wastewater, the presence of colloids and dissolved organic matter (DOM) gives rise to aggregation through adsorption mechanisms, the strength of which depending on the physicochemical nature of constituents. In their critical review, Philippe and Schaumann [22] examine the principal effects of DOM on the colloidal stability, with no adsorption, different mechanisms of adsorption, and disaggregation of aggregate DOM
colloids. Recently, the review of Ly et al. on the characterization of DOM for understanding the adsorption on nanomaterials in water [23] presented the up-to-date analytical methods to characterize the DOM for the study of adsorption mechanisms. Among the different methods, the UV
visible spectroscopic measurements stand as a good choice, with basic parameters (UV254, SUVA254) and absorbance ratios (A250/A365 and A465/A665) showing good correlations with molecular weight (MW), aromaticity, or polarity [24]. The change in DOM MW after adsorption on nanomaterials can also be studied using the UV spectrum slope between 275 and 295 nm as it was proposed by Helms et al [25]. Finally, a recent review highlights different strategy assessing biofunctionalized inorganic nanoparticles toward the detection of pathogens in food and water samples [26].

7.3.6 UV responses of microorganisms The use of UV
visible spectrophotometry for mineral suspensions (including suspended solids’ measurement and turbidimetry) has been known from many years. However, its application to microorganisms is more recent. A review of optical methods used for microbial contamination detection in water resources [27] underlined the limits of the use of turbidimetry for sanitary control and the interest and need for further developments of UV-based systems. A model was proposed for the interpretation of UV
visible spectra of Escherichia coli and Bacillus globigii based on light scattering theory, spectral deconvolution techniques, and the approximation of the frequency-dependent optical properties of the basic constituents of living organisms [14]. Another study on the characterization of E. coli suspensions using UV/Vis/NIR absorption spectroscopy [28] demonstrated its feasibility for the online and in situ monitoring of processes for the inactivation of microbiological organisms. However, microorganisms may have a different optical response, according to species, from scattering to specific absorption given the presence or not of chromophores (e.g. Chlorella). A well-documented

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work [29] provided a rational approach for improving the accuracy of absorbance measurements for the study of microorganisms’ solutions, by choosing the best wavelength depending on the microorganisms’ optical response. More than the presence of microorganisms, the knowledge of their viability is important for sanitary reasons. A rapid assessment method was proposed [30] based on the ratio of absorbance values at 230 and 670 nm. A strong linear correlation was found between this ratio and the bacterial viability of E. coli, Bacillus subtilis, and Staphylococcus epidermidis. A first application was described for the study of E. coli in bioaerosols. Recently, a rapid detection of live and dead E. coli in a suspension using spectroscopy and chemometrics was proposed [31]. Live bacterial suspension showed absorption peak at 260 nm with decreasing amplitude as the proportion of live bacteria was reduced in the suspension and vice versa. The percentage of live and dead bacteria in a suspension could be predicted with coefficient of determination (R2) of 0.980 and 0.977 for calibration and validation sample sets, respectively, in the range of 259
261 nm using multiple linear regression (LR). The interest approach is confirmed by Hu et al. [32]. The previous studies were carried out under laboratory conditions with standard solutions. Another study compared the feasible methods for microalgal biomass determinations during tertiary wastewater treatment [33]. Among the available methods, the absorbance measurement at 680 nm was recommended as a simple and reliable method for biomass determinations for green (Chlorella vulgaris) and blue-green (Phormidium sp.) algae. Other biological applications of UV
visible spectrophotometry were proposed for the study of harmful algal bloom species as for example those responsible for frequent red-tide blooms in Florida coastal waters. The exploitation of optical properties of natural pigments using their absorption signature in the visible region was applied for the study of dinoflagellate Gymnodinium breve [34]. This method was improved with the use of multiwavelength spectroscopy in the UV
visible range for the detection and quantification of Karenia brevis [35] by designing a model-based interpretation of UV
vis spectra of this microorganism. The parameters for the interpretation model were based upon both reported literature values and experimental values obtained from live cultures and pigment standards. An one more application of the microbiological use of UV spectrophotometry was for monitoring a yeast-based deoxygenation process proposed to treat ships’ ballast water [36] for preventing the transfer of aquatic invasive species. During the treatment, the UV absorbance spectra values were strongly correlated (R2 5 0.96) to yeast cell density in treated waters and the second derivative absorbances at 215 and 225 nm

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7. Physical and aggregation properties

were strongly correlated to ammonia and dissolved oxygen concentrations, respectively. These findings confirmed the interest of UV spectrophotometry for the direct monitoring of the treatment performance onboard ships, with the proposal of a water quality index of the treated water related to the production of ammonian.

7.3.7 UV responses of wastewater UV absorption spectra of wastewater are not easy to understand in view of their featureless shapes, partly due to the effect of suspended matters, the diameters and geometry of which being very variable [5]. The latter are very heterogeneous and responsible for a scattering effect more intense in the UV region than in visible. Actually, UV responses of particulate and colloidal matter are often the result of both chemical and physical responses related to their nature (organic for a great part and able to adsorb soluble compounds such as surfactants). For example, in the case of slate particles (mineral) of a few micrometers, the spectrum is flat. The absorbance is uniform and higher for smaller particles. The chemical absorbance related to the presence of suspended solids of organic nature seems to emphasize spectrum slope and to create shoulders (Fig. 7.6). The role of settleable matter and supracolloids in the absorbance of suspended solids of wastewater has been studied more precisely from a simple experiment [37]. Raw wastewater was introduced into an Imhoff cone and left over 1 hour before collecting carefully four granulometric fractions from the surface (fraction 1) to the bottom (fraction 4). These fractions were then analyzed both with laser granulometry (laser diffraction) and with UV spectrophotometry (deconvolution method, see Chapter 3). With laser diffraction technology, the particle size distribution is determined on the basis of the scattered monochromatic light at 750 nm.

FIGURE 7.6 Diffusion of UV light (left: slate particles of different size distributions, right: normalized spectra of slate particles and urban TSS). TSS, total suspended solids.

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The granulometric analysis of fractions 1, 2, and 3 clearly shows that particles of these fractions are mainly of supracolloids nature, between 1 to 100 µm, with a mode value close to 30 µm (Fig. 7.7). The fraction 4 (at the bottom of the Imhoff cone) presents the widest range size with particle larger than 100 µm, and a multimodal distribution. After 1 hour of settling, the separation of settleable matter is achieved, as fractions 1 to 3 do not contain particles of size above 100 µm, even if, in the first fraction, some millimetric floating particles are present. One can note that no colloidal population is detected by laser granulometry. Fig. 7.8 shows that UV spectra of the fractions 1, 2, and 3 are superposed, confirming the granulometric results. Fraction 4 presents a higher

FIGURE 7.7 Granulometric size distribution study (left: separation device, right: laser granulometry results) [37].

FIGURE 7.8 UV spectra (right) and repartition of TSS (total suspended solids) and colloids contribution (left) of the four fractions of Fig. 7.7 (coefficients estimated by the deconvolution method, see Chapter 3).

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absorbance due to the presence of suspended solids. Indeed, the quantity of colloids (quantified by the calculation of the coefficient’s contribution by deconvolution, see Chapter 3) appears to be the same for the four fractions. The absorbance is not proportional to the concentration and can be explained by the fact that settleable particles, the sizes of which are above 100 µm, scatter the light slightly by diffraction. Thus light scattering by TSS is mainly due to the presence of supracolloids. The normalization of the set of spectra (Fig. 7.9) allows comparison of the quality of each fraction. The slope break of the UV spectrum of fraction 4, around 240 nm, is flattened, confirming the presence of large particles [37]. If the discrimination between supracolloids and settleable matter is not possible by UV spectrophotometry, the laser granulometry results confirm the major role of supracolloids in the scattering response. Thus it clearly appears that the advantage of using of UV spectrophotometry for the study of suspended solids is that the UV response integrates the effect of the different solid classes and gives an average signal of the solid mixture. Before considering the quantification of TSS of water and wastewater, the relevance of UV spectrophotometry should be investigated for the study of different types of wastewater. The comparison of the behavior toward UV light of various size ranges studied for different water and wastewater types is reported in Fig. 7.10. Some conclusions related to UV spectra shape corresponding to different types of water and wastewater can be made [38]: • The smaller the particles are, the higher is the absorbance value. • The spectrum of settleable matter is generally flat. The UV spectrum of suspended solids of natural water, mainly of mineral nature, is

FIGURE 7.9 Normalized UV spectra of Fig. 7.8.

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249

FIGURE 7.10 Spectra of size range fractions according to the water type (normalized spectra) [38].

essentially related to the diffusion of light. Some slight shoulders may appear for urban wastewater. For supracolloidal matter, the spectra shape can be modified for urban wastewater for short wavelengths (,250 nm) with the presence of organics absorbed on particles. • For smaller particles, the spectrum is close to the one of the reference spectrum of dissolved substances (see Chapter 4). Chemical absorbance induces a shoulder around 225 nm, often associated with the presence of surfactants in wastewater [39]. This shoulder is

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7. Physical and aggregation properties

especially marked on UV spectra of fine colloids in raw urban wastewater. This tends to show the general affinity of soluble compounds for fine particles and confirms that surface phenomena are more important for finer particles than for the larger ones. This behavior can also be explained by the presence of macromolecules such as humic-like substances. • For the dissolved substances of low MW, the resulting spectra show a steeper slope for short wavelengths due to degradation products such as carboxylic acids. A comparison can be made between Fig. 7.5 (mineral suspensions) and Fig. 7.9 (wastewater). In both cases, the presence of coarse particles is characterized by a diffusion spectrum with a relatively flat shape due to the very light dependence of absorbance versus wavelength (diffraction). On the contrary, the presence of colloids is responsible for a high variation of absorbance with wavelength (Mie diffusion). The main difference between the two sets of spectra is the presence of structured elements (i.e., shoulders) on the spectra of wastewater, related to the chemical absorption of organic compounds bound to the particles (i.e., surfactants). It is also interesting to study the global response of the main fractions (dissolved and solids) of a wastewater. As the deconvolution method of spectra exploitation uses reference spectra (see Chapter 3), the theoretical contribution of the main fractions can be calculated at each wavelength from the corresponding absorbance values. Assuming that a theoretical spectrum is the weighted sum of reference spectra, the contribution of the dissolved phase can thus be calculated at each wavelength from the sum of the dissolved fraction, nitrate and surfactants if any, divided by the sum of absorbance of all other reference spectra. The contribution of the solid phase is calculated from the sum of spectra of suspended solids and colloids. Fig. 7.11 displays the evolution of this global response and shows that the dissolved fraction is predominant with regard to the UV absorbance below 225 nm, while the solid one has a more important contribution above 245 nm. Between the two phases, the same theoretical influence on UV responses can be noted. This observation could explain that, in numerous cases, UV spectra present a break in their shape at around 235
240 nm.

7.4 Total suspended solid estimation TSS measurement can be carried out by gravimetry after separation by filtration or centrifugation, according to standard methods. For

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7.4 Total suspended solid estimation

FIGURE 7.11

251

Theoretical contribution of solids and solutes in UV response of water.

online measurement, these techniques cannot be applied without expensive devices. This is the reason why optical methods are often used, especially in industry. Among the different systems, turbidimetry, which is widely used for water quality control as an alternative method for suspended solids [40], is probably the simplest way to hope for a coarse estimation of both suspended solids and a part of the colloidal fraction. It is particularly used for sanitary control in water resources and drinking water treatment plants.

7.4.1 Turbidimetry Among the various available techniques for turbidity measurement, the main one called nephelometry is based on the reflection of light induced by particles in water. The angle between the source (a red LED, for example) and the detector is 90 degrees. Another principle used for turbidimetry is backscattering, often used for higher concentrations of solids. The intensity of the detected light is a function of the number of particles and their apparent size. Turbidity measurement depends on concentration, size, and surface properties of particles [41]. It mainly considers the physical response of non-dissolved matter, among which are particulate and colloidal fraction [37], which limits its use to wastewater of constant quality. The turbidity response is thus affected by suspended solids and colloidal matter, and by the distribution between colloids and supracolloids. The latter point implies that the estimation of suspended solid concentration is only possible if the solids to be characterized have a very simple granular spectrum with a single class of particles (distribution mode). In this case, a calibration between

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7. Physical and aggregation properties

turbidity and suspended solid concentration will roughly lead to the estimation of TSS. Nevertheless, a broad granular spectrum can sometimes have a rather constant distribution, and a coarse relation can also be established between turbidity and suspended solids. Such a relation needs to be checked regularly. This can be the case for raw wastewater coming from a separate sewer system at the inlet of a treatment plant. On the contrary, it is more difficult to find a useful relation between these two parameters for treated wastewater since, on the one hand, the calibration range is weak, and on the other hand, the spectrum is related to one functioning point of the treatment plant. This consideration limits the use of turbidity for the control of chemically treated wastewater. However, a method to extract particle concentration and characteristic particle size from turbidimeter readings was proposed [42]. The method used a turbidimeter capable of measuring the forward (12 degrees) and sideways (90 degrees) scattered light simultaneously. Tested on the calibration of filter aids, an industrial pigment and yeast, the method is limited to the range of particle concentration (1
200 ppm) and characteristic particle size (1
100 µm) used in the calibration. A second limitation arises from the fact that the other parameters influencing scattering (optical properties, particle shape and porosity, width of the size distribution) are not considered. In conclusion, the estimation of suspended solids from turbidity measurement is hazardous and has to be considered only for water and wastewater of constant quality with regard to the particulate fraction. In any case, the calibration must be carefully established and regularly checked. Before considering the use of UV spectrophotometry, the measurement of a simple absorbance value (generally around 700
800 nm) for suspended solid measurement should be mentioned. Even if this technique presents the same drawbacks as nephelometry, it can be used when the colloidal fraction is negligible, for example, for the estimation of sludge concentration. A better result is obtained if the sample is first dispersed by ultrasonic or mechanical means [43]. In the case of low concentration of suspended solids in water (a few mg L21), turbidity can be responsible for more or less important diffuse absorbance preventing the exploitation of UV
visible spectra for parameters such as nitrate or dissolved organic carbon. If sample filtration can be envisaged for suspended solids and supracolloidal fraction removal, spectrophotometric methods for turbidity compensation are more appropriate for on-site/online measurement. One of the first methods proposed almost 50 years ago [44] was the use of the absorbance value at 545 nm for the compensation of particle effect on absorption (light scattering). In this study, a first dissolved organic carbon analyzer was designed for the absorbance measurement at 254 and 545 nm.

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Recently, experimental research of turbidity influence on COD estimation were carried out. Spectra from formazin solution as reference compound for turbidity measurement, on the one hand, and of potassium hydrogen phthalate for COD, on the other hand, were used to propose a turbidity compensation method [45]. In the same way, a normalization technique for turbidity compensation was proposed for a better COD estimation [46], from the acquisition of the UV
visible spectrum of a given sample between 220 and 750 nm. Hu et al. [47] proposed recently a direct derivative absorption spectroscopy for the correction of turbidity effects on COD measurements without baseline required. Starting from UV
visible spectra of standard solutions and their mixture, a normalization technique was proposed to estimate the turbidity and dynamically simulate the absorption spectra of the turbidity. The root mean square errors of the COD predictions were very small after the process of turbidity compensation. These techniques were designed from laboratory experiments and should be validated on real samples. Considering the shape of the UV spectrum of suspended solids and colloids (from a quasilinear to a decaying exponential curve), the use of second derivative was proposed in order to reduce the corresponding interferences for the determination of nitrate and DOC in freshwaters [48]. In order to prevent fouling or clogging of immerged optical sensor, rather frequent for wastewater in situ monitoring, a non-contact method was proposed [49] to measure simultaneously the COD and turbidity in water. Based on the measurement of diffuse reflectance UV
vis
NIR spectroscopy between 200 and 1100 nm, a model based on a PLS (partial least squares) algorithm gave R2 values of 0.85 and 0.96 for COD and turbidity, respectively.

7.4.2 UV estimation of total suspended solids A review and comparison of the different methods to estimate TSS and COD in water and wastewaters from UV
vis spectra acquired by online sensors [50] demonstrated that chemometrics methods using LR and evolutionary algorithm (EVO) appeared robust and sustainable solutions for concentration estimation, according to uncertainties in laboratory analysis and ranking of methods. However, TSS estimation can also be estimated from the use of the deconvolution method of UV spectra exploitation (see Chapter 3). Because of the complexity of the light diffusion phenomena according to the type of water or wastewater, the TSS estimation depends on either SS coefficient alone or both colloids and SS coefficients.

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The following relation is used for TSS estimation after UV spectrum deconvolution:





½TSS 5 αSS CSS 1 αColl CColl 1 r where αSS and αColl are, respectively, the values of the contribution coefficients of the reference spectra corresponding to suspended solids and to colloids, CSS and CColl are the equivalent concentrations of TSS (statistically determined) associated with these reference spectra, and r is the error on the computation of the parameter value. Fig. 7.12 presents the results of a comparison between the UV deconvolution method and a standard one (EN 872) obtained from urban treated wastewater (physicochemical treatment). This method has been applied for several wastewater types and gives relatively good results except for raw industrial wastewater where the quality of suspended solids is highly variable [51,52]. However, the application of the UV deconvolution method on natural water is also difficult because of the solids’ nature and of the concentration range (from a few mg L21 to several g L21 in the case of floods). Finally, other simpler methods can be found for specific TSS measurement as for polyacrylamide polymers used for sludge dewatering and for coagulation and flocculation of particles in water and wastewater treatment using an in-line UV
vis spectrophotometer [53]. The acquisition of UV
visible spectra between 191.5 and 750 nm of seven polymers showed a strong correlation (R2 . 0.97) between polymer concentration and absorbance values at 191.5, 200, and 210 nm. The limits of detection were depending on the polymer type and of the water

FIGURE 7.12 Comparison between reference and UV deconvolution alternative method (urban wastewater treated by physicochemical process).

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[37]

[38] [39] [40]

[41]

[42]

[43]

[44] [45]

257

spectroscopy analysis including application to bioaerosols, Aerosol and Air Quality Research 12 (2012) 395
404. Available from: https://doi.org/10.4209/aaqr.2011.08.0129. J. Pranita, L. Shweta, S. Neha, Rapid detection of live and dead Escherichia coli in a suspension using spectroscopy and chemometrics, Journal of Agricultural Engineering 56 (3) (2019). Y. Hu, N. Zhao, T. Gan, J. Duan, H.J. Yu, D. Meng, et al., Analytic method on characteristic parameters of bacteria in water by multiwavelength transmission spectroscopy, Journal of Spectroscopy (2017). Available from: https://doi.org/10.1155/2017/4039048. Y. Su, A. Mennerich, B. Urban, A comparison of feasible methods for microalgal biomass determinations during tertiary wastewater treatment, Ecological Engineering 94 (2016) 532
536. Available from: https://doi.org/10.1016/j.ecoleng.2016.06.023. D.F. Millie, O.M. Schofield, G.J. Kirkpatrick, G. Johnsen, P.A. Tester, B.T. Vinyard, Detection of harmful algal blooms using photopigments and absorption signatures: a case study of the Florida red tide dinoflagellate, Gymnodinium breve, Limnology and Oceanography 42 (1997) 1240
1251. Available from: https://doi.org/10.4319/ lo.1997.42.5_part_2.1240. A.H. Spear, K. Daly, D. Huffman, L. Garcia-Rubio, Progress in developing a new detection method for the harmful algal bloom species, Karenia brevis, through multiwavelength spectroscopy, Harmful Algae 8 (2009) 189
195. Available from: https:// doi.org/10.1016/j.hal.2008.05.001. E´. Veilleux, Y. de Lafontaine, O. Thomas, UV spectrophotometry for monitoring the performance of a yeast-based deoxygenation process to treat ships’ ballast water, Environmental Monitoring and Assessment 188 (2016) 207. Available from: https:// doi.org/10.1007/s10661-016-5209-3. N. Azema, M.-F. Pouet, C. Berho, O. Thomas, Wastewater suspended solids study by optical methods, Colloids and Surfaces A: Physicochemical and Engineering Aspects 204 (2002) 131
140. Available from: https://doi.org/10.1016/S0927-7757(02)00006-7. S. Vaillant, La matie`re organique des eaux re´siduaires urbaines: caracte´risation et e´volution. PhD Thesis, University of Pau et Pays de l’Adour, 2000. F. Theraulaz, L. Djellal, O. Thomas, Simple LAS determination in sewage using advanced UV spectrophotometry, Tenside, Surfactants, Detergents 33 (1996) 447
451. A. Hannouche, G. Chebbo, G. Ruban, B. Tassin, B.J. Lemaire, C. Joannis, Relationship between turbidity and total suspended solids concentration within a combined sewer system, Water Science and Technology 64 (2011) 2445
2452. Available from: https:// doi.org/10.2166/wst.2011.779. C. Joannis, G. Ruban, M.C. Gromaire, J.L. Bertrand-Krajewski, G. Chebbo, Reproducibility and uncertainty of wastewater turbidity measurements, Water Science and Technology 57 (2008) 1667
1673. Available from: https://doi.org/10.2166/wst.2008.292. H.H. Kleizen, A.B. de Putter, M. van der Beek, S.J. Huynink, Particle concentration, size and turbidity, Filtration and Separation 32 (1995) 897
901. Available from: https://doi.org/10.1016/S0015-1882(97)84175-4. C. Berho, M.F. Pouet, O. Thomas, Study of the impact of mechanical treatments on wastewater solids by UV spectrophotometry, Environmental Technology (United Kingdom) 24 (2003) 1545
1551. Available from: https://doi.org/10.1080/09593330309385700. T.T.J.J. Masson, C.A. Smith, Continuous monitoring of dissolved organic matter by UV-visible photometry, Limnology and Oceanography 19 (1974) 530
535. B. Tang, B. Wei, D.C. Wu, D.L. Mi, J.X. Zhao, P. Feng, et al., Experimental research of turbidity influence on water quality monitoring of COD in UV-visible spectroscopy, Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 34 (2014) 3020
3024. Available from: https://doi.org/10.3964/j.issn.1000-0593(2014) 11-3020-05.

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7. Physical and aggregation properties

[46] Y. Hu, Y. Wen, X. Wang, Novel method of turbidity compensation for chemical oxygen demand measurements by using UV-vis spectrometry, Sensors and Actuators, B: Chemical 227 (2016) 393
398. Available from: https://doi.org/10.1016/j. snb.2015.12.078. [47] Y. Hu, D. Zhao, Y. Qin, X. Wang, An order determination method in direct derivative absorption spectroscopy for correction of turbidity effects on COD measurements without baseline required, Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 226 (2020) 117646. Available from: https://doi.org/10.1016/j. saa.2019.117646. [48] J. Causse, O. Thomas, A.V. Jung, M.F. Thomas, Direct DOC and nitrate determination in water using dual pathlength and second derivative UV spectrophotometry, Water Research 108 (2017) 312
319. Available from: https://doi.org/10.1016/j. watres.2016.11.010. [49] J. Agustsson, O. Akermann, D.A. Barry, L. Rossi, Non-contact assessment of COD and turbidity concentrations in water using diffuse reflectance UV-Vis spectroscopy, Environmental Science: Processes & Impacts. 16 (2014) 1897
1902. Available from: https://doi.org/10.1039/c3em00707c. [50] M. Lepot, A. Torres, T. Hofer, N. Caradot, G. Gruber, J.B. Aubin, et al., Calibration of UV/Vis spectrophotometers: a review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers, Water Research 101 (2016) 519
534. Available from: https://doi.org/10.1016/j. watres.2016.05.070. [51] O. Thomas, N. Mazas, C. Massiani, Determination of biodegradable dissolved organic carbon in waters with the use of UV absorptiometry, Environmental Technology 14 (1993) 487
493. [52] O. Thomas, F. Theraulaz, C. Agnel, S. Suryani, Advanced UV examination of wastewater, Environmental Technology 17 (1996) 251
261. ¨ rmeci, Measurement of polyacrylamide polymers in water and [53] F.A. Al Momani, B. O wastewater using an in-line UV-vis spectrophotometer, Journal of Environmental Chemical Engineering 2 (2014) 765
772. Available from: https://doi.org/10.1016/j. jece.2014.02.015. [54] R.B. Baird, A.D. Eaton, E.W. Rice, Standard methods for the examination of water and wastewater, 2019.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

8 Natural water Olivier Thomas1, Jean Causse2 and Marie-Florence Thomas1 1

EHESP School of Public Health, Rennes, France, 2Transcender Company, Rennes, France

O U T L I N E 8.1 Introduction

260

8.2 Significance of UV spectra of natural water

262

8.3 Quality of natural water 8.3.1 Water quality variation along a river 8.3.2 Rain influence on river water quality 8.3.3 Small tributaries quality 8.3.4 Wetland water quality 8.3.5 Lakes water quality 8.3.6 Groundwater quality

266 266 268 270 272 274 281

8.4 Point 8.4.1 8.4.2 8.4.3

285 286 287 288

source and accidental discharge Discharge in river Discharge in sea Accidental discharge

8.5 Different freshwaters but some common fate 8.5.1 About hidden isosbestic point 8.5.2 Relation between parameters (DOC/NO3)

291 291 292

8.6 Second derivative of UV spectra, a key parameter

293

Acknowledgments

294

References

294

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00007-1

259

© 2022 Elsevier B.V. All rights reserved.

260

8. Natural water

8.1 Introduction The composition of natural water depends, on the one hand, on the hydrometeorological and geochemical context and, on the other, on the different inputs (natural and anthropogenic) such as storm runoff or discharge of treated wastewater, carrying biological or chemical pollution loads (Fig. 8.1). From a physical point of view, natural water is characterized by the presence of dissolved constituents (of mineral or organic forms), including gas (oxygen, carbon dioxide, etc.) and heterogeneous fractions (from suspended solids with particles greater than 1 µm, to colloids, between 1 and 0.1 µm). This composition was discussed in Chapter 7. The expression “natural water” refers to water from a large array of sources. From drinking water, including tap and mineral water, to seawater, a large scale of mineralization is covered, from a few milligrams per liter to several tenths of grams of dissolved solids per liter. In between, groundwater and surface water, including rivers and lakes, ponds, and wetland, can be found. Except for these last media, either from natural (ponds and wetlands) or anthropogenic (polluted water) origin, the concentration of organic compounds is generally much lower than the one of inorganic constituents, with only a few milligrams per

FIGURE 8.1 Factors influencing the composition of natural water.

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261

liter or less. Natural organic matter (NOM) or dissolved organic matter (DOM) is composed of all organic compounds mainly issued from the degradation of vegetal or animal biomass. It includes a lot of degradation by-products, from leaves and wood decomposition, and also humic-like substances (HLS). A fraction of HLS can then be mineralized and used as nutrients for fresh biomass synthesis. The rest of HLS is condensed. Anthropogenic organic matter (AOM) occurs when wastewater, generally treated, is discharged into water. Agricultural practices are also responsible for AOM introduction with the runoff of manure pile on field (if not buried) or cattle having access to river. AOM is composed of biodegradable organic compounds from animal dung, domestic activities (proteins, carbohydrates, fats, surfactants, etc.) but can be completed by biorefractory organic compounds. As a result of the ultimate biodegradation of AOM in water, in the presence of microorganisms and oxygen, organic and mineral compounds, such as carboxylic acids, phosphate, and nitrate, are produced. These organic and mineral compounds are considered as nutrients or as inorganic mineral matter. Fig. 8.2 presents schematically the evolution of organic matter in water. Before considering the use of UV spectrophotometry for natural water quality characterization and control, it is important to underline that the visual observation of the aqueous media (river, lake, wetland, etc.) can be of great interest (see Chapter 1). Actually, several indicators can reveal the level of pollution (present or past) of water. River water may have deposed some solid wastes on the banks (plastics debris, glass, metallic wastes, etc.). The aspect and color of the interface between water and air can also be significant: brown mud or bacteria aggregates related to existing pollution, green to a past one (presence of nutrients), orange to the presence of iron in peatlands, etc. Moreover, the absence of plants on the banks can also be a sign of

FIGURE 8.2 Evolution of organic matter in water. AOM, Anthropogenic OM; IM, inorganic matter; NOM, natural OM; OM, organic matter.

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8. Natural water

pollution (except for rocky sites) and a proliferation of them may be related to a high concentration of nutrients. Water bodies can be turbid, slightly brown, or green, with aquatic plants such as algae or macrophytes (duckweed, reeds, etc.) in the case of eutrophication. Living species such as fishes or small animals (proto and metazoaires) can be associated with a specific pollution level . Further biological consideration can be found in the literature.

8.2 Significance of UV spectra of natural water The use of UV spectrophotometry for the characterization of natural water quality has led to several quantitative procedures, and some examples are presented in Table 8.1. The first UV measurement for an environmental parameter (nitrate ion) was proposed more than 60 years ago [2]. Based on the simple exploitation of UV spectrum, the measurement of absorbance at one (or two) wavelengths has also been applied to other parameters such as chemical oxygen demand (COD) and followed by multiwavelength procedures (see Chapter 3). This quick literature survey in Table 8.1 shows that the direct quantitative exploitation of UV spectra of water concerns only a few parameters or compounds, such as dissolved organic carbon (DOC) and nitrate. Considering the huge number of UV spectra acquired in the frame of our research, it is possible to propose a classification of natural water, according to the shape of spectra into four groups, including suspended solids and chloride response (Fig. 8.3). The first type of spectrum is characterized by the presence of nitrate at a relatively high concentration (around 10 mg L21 on Fig. 8.3). As nitrate is the most stable form of nitrogen in water, resulting from the oxidation of all other dissolved N compounds (nitrite, ammonia, or organic nitrogen), the majority of natural water contains nitrate. Taking into account the UV spectrum shape of nitrate (see also Chapters 6 and 15), the presence of nitrate is easy to recognize if the concentration is greater than 5 mg L21 (of N 2 NO2 3 ). Even in the presence of other compounds constituting the organic matter matrix, a convex form in the 205 220 nm region is most of the time related to the presence of nitrate. The second type of UV spectrum often encountered for natural water is characterized by a general decreasing concave shape on a wide range of wavelengths, with a slight shoulder around 260 270 nm. The absorbance values are rarely close to zero, even in the higher wavelengths. This is due to the presence of dissolved NOM and, more precisely, HLS composed of humic acids, fulvic acids, and related substances. Humic acids have the property of precipitating in acid solution (pH , 2), contrary to fulvic acids, representing the major part of HS (between 80%

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8.2 Significance of UV spectra of natural water

TABLE 8.1

Use of UV visible spectrophotometry for natural water quality study.

Parameter

Wavelength (nm)

Procedure

DOM

220 254

COD

DOC

Nitrate

263

Application

Refs.

Absorptiometry

Sea

[1]

Absorbance ratio

Polluted rivers

[2]

190 400

Contour diagrams (e.g., 330 nm)

Lake

[3]

200 600

Spectral slope

Cave and spring water

[4]

Two (e.g., 270 350)

MLR

Different freshwaters

[5,6]

275 295

Spectral slope

Wetlands, sea water

[7]

254, 350, 275 295

Absorptiometry, spectral slope

Lake

[8]

254, 290 350

Spectral slope

Rivers (storms)

[9]

254

Absorptiometry

Surface water

[10]

260 320

PLS

Synthetic mixtures

[49]

205 330

Deconvolution

Surface water

[11 13]

250 270

MLR

Sea water

[14]

200 750

MLR, PLS

Bog, fen, rivers

[15]

220 720

PLS

River (storms)

[16]

250 350

Different methods

Peatland

[17]

254

SUVA

Freshwaters

[18]

295

Second derivative

Rivers

[19]

210/275

Absorptiometry

Surface water

[20]

205 250

Polynomial mod.

Surface water

[22]

225 260

PLS

Synthetic solutions

[12]

205 330

Deconvolution

Surface water

[12,13]

205 750

PLS

Groundwater

[24]

200 750

PLS

Rainfall runoff

[23]

226

Second derivative

Rivers

[19]

DOC, Dissolved organic carbon; DOM, dissolved organic matter; SUVA, specific UV absorbance; PLS, partial least square; MLR, multiple linear regression.

and 85% in weight), which are more soluble and extractable in basic condition (pH . 10). These compounds correspond to the major part of NOM and DOC (between 50% and 70%, on average). Even if some study advances a less complex composition [25], HS or HLS can be

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8. Natural water

FIGURE 8.3 Main types of UV spectra of natural water.

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265

considered as macromolecules of high molecular weight, including several chromophore groups due to unsaturated binding sites susceptible to absorb significantly in the UV, as phenolic or carboxylic functions, for example. More recently, Gerke [26] underlined the relevance of a physicochemical biochemical model for the formation of humic substances and their transformation (humification). This is the reason why no specific defined shape of UV spectrum, such as peaks or marked shoulders, is associated with this organic HS matrix. The resulting shape is rather constant and seems to come from the overlapping of a huge number of spectra of simpler organic compounds, with more often aromatic structure. The slight shoulder around 260 270 nm, which can be accompanied by a smaller one at 220 230 nm, is due to the presence of phenolic-type chromophores. In some cases, it is possible to associate a particular concave absorbance evolution between 200 and 220 nm due to the presence of carboxylic compounds (without nitrate). The third type of UV spectrum of natural water is rather simple to recognize as the major part of the spectrum is more or less linear and almost horizontal, with absorbance values relatively high. This is due to the physical diffuse absorption response of suspended solids, as explained in Chapter 7. Depending on the nature of solids and on the presence of high colloid concentration, the absorption level and the general mean slope of the spectrum in the 250 350 nm region can vary. Samples must often be diluted in this case, to prevent the saturation of absorbance values and, considering that natural water often contains nitrates, the resulting spectrum shows the convex characteristic signal. The fourth main type of UV spectrum of natural water is very different from the previous ones with a strong concave shape close from 200 to 220 nm. This shape is related to seawater with a high content of chloride. As shown in Chapter 6, chloride is responsible for an absorption wall under 220 nm, at high concentration. As no other constituent is present in high concentrations in seawater, the spectrum is generally close to zero for wavelengths greater than 225 nm. In addition to these four main types of UV spectra of natural water, many more can be found. In the case of pollution the UV spectrum shape is obviously dependent on the pollutant nature and concentration if it is absorbing. On the contrary, if the UV spectrum of natural water is flat and close to zero, the pollution is likely to be rare, except if pollutants do not absorb. On the contrary, a more or less important UV spectrum is always related to the presence of dissolved absorbing compounds, colloids, or suspended solids. Several real case studies are presented in the next sections, coming from different water bodies (river, lake, groundwater, etc.), which show the interest of UV spectrophotometry for qualitative (quality characterization

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and evolution) and quantitative (concentration and parameters estimation) applications.

8.3 Quality of natural water 8.3.1 Water quality variation along a river The first example proposed is the study of the water quality of the Gardon de Saint-Jean River, located in South France in the Cevennes area. This 50-km-long river is of Mediterranean type, with a very low flow rate in Summer and some frequent flash floods the rest of the time. From the pollution point of view, this river is characterized by the presence of two main villages (of 2000 and 5000 inhabitants) with two wastewater treatment plants (WWTPs). It is also the heart of a touristic area with an estimated overpopulation of 50,000 people in the summertime. A sampling campaign was carried out in Summer 2000, with nine points along the river (Fig. 8.4). For each station the UV spectrum and the measurement of dissolved oxygen, conductivity, and pH were acquired on fresh samples. A field-portable UV spectrophotometer (Secomam) and handheld instruments for the other parameters were used. The evolution of the physicochemical parameters is presented in Fig. 8.5. Close to the source, the dilution of the river by the upstream WWTP discharge was accompanied between sampling points 2 and 3 by a slight increase in conductivity. Inversely, the discharge of the second WWTP between stations 6 and 7 could be responsible for an increase in pH values downstream.

FIGURE 8.4 Sampling locations on the Gardon de Saint-Jean River, South France. WWTP, Wastewater treatment plant.

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267

FIGURE 8.5 Evolution of physicochemical parameters (average water temperature 5 25 C).

FIGURE 8.6 Evolution of UV spectra.

Fig. 8.6 displays the evolution of UV spectra along river. As expected, nitrate ion is present in most of the samples, and the organic matter content is rather low due to geological context and low flow conditions. The study of normalized spectra (see Chapter 3) shows the existence of two isosbestic points (IPs) related to two groups of spectra (Fig. 8.7). The first group concerns the upstream points under the influence of the discharge of the main WWTP (WWTP1). The second group of spectra is related to the consumption of organic matter and nitrate in the second part of the river. This phenomenon is confirmed in Fig. 8.8, by the evolution of pollution parameters, total organic carbon (TOC), total suspended solids (TSS), and NO2 3 , estimated by UV (see Chapters 3 and 4). The conclusion of this experiment is that, even if the pollution level of the river is low, due to high performances of the WWTP, the use of

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8. Natural water

FIGURE 8.7 Isosbestic points revelation after normalization (at right, zoom of isosbestic point).

FIGURE 8.8 Evolution of UV-estimated pollution parameters.

UV spectrophotometry simplifies the field (and laboratory) work by bringing complementary information on the evolution of water quality.

8.3.2 Rain influence on river water quality Another example is related to the study of the influence of rain on river water quality. Given that the main effect of rain on water quality is the increase of water turbidity after a rain event, due to soils runoff, a first experiment was carried out on a small river of Southern France, with samples filtration of suspended solids and examination of fractions. Fig. 8.9 shows the difference between UV spectra shapes, before and after a rain event [27]. The river water during dry weather is characterized by the presence of nitrate (around 3 mgNO32 L21) and a low concentration of organic matter (around 1.8 mg L21 of TOC).

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269

FIGURE 8.9

Fractionation of natural water sampled in dry weather conditions (left) and under rain conditions (right) [27].

FIGURE 8.10 Differential spectra resulting in a wet weather sample fractionation [27].

The filtration of the water is very easy (the TSS concentration being close to zero), and the spectrum of the filtrate (after filtration at 10 kDa, i.e., around 0.001 µm) is very similar to the one of raw water, indicating a very low quantity of colloids and suspended solids. The main difference after the flow rate peak is due to the presence of suspended solids and colloids brought in water by soils runoff, giving a strong and linear diffuse absorption above 240 nm. The TSS concentration was 276 mg L21 (measured by a gravimetric reference method), principally of mineral composition as organic fraction represented only 18%. The fractionation of sample shows that the main absorption is linked to suspended matter and supracolloids with a size greater than 0.1 µm, the nitrate concentration being slightly diluted (absorbance below 240 nm). Fig. 8.10 shows the spectra corresponding to the compounds retained on the filtration membranes, calculated from the difference of the corresponding spectra (e.g., settling matter spectrum 5 raw water

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270

8. Natural water

spectrum 2 settled water spectrum). The differences between spectra are important, showing a large distribution of suspended solids and colloids. The responses are related to the diffuse absorption of solids and colloids. The general tendency is that the slope of the spectrum generally decreases as the particle size increases. Spectra corresponding to suspended solids are flat, confirming a mineral nature. Adsorption phenomenon can mainly be seen on fine colloids, and the spectral shape of soluble fractions (deduced from nitrate contribution) is related to the probable presence of HLS. The evolution of UV spectra during a rain event can give useful information about the relation between flow rate and water quality. An experiment was led on a small river in Brittany (France) for 6 days, around a heavy rain event [28]. Fig. 8.11 shows the evolution of rain intensity, river flow rate, and dissolved oxygen and the corresponding UV spectra before, during, and after the rain peak. The main rain intensity occurred between 70 and 90 hours after the beginning of the monitoring period, with around 20 mm of rain, and the flow rate peak was slightly shifted at the end of the rain, because of the runoff lag time. The dissolved oxygen percentage decreased just after the flow rate peak and the pollution load continued to increase up to 2 days after the flow rate peak as can be seen in the evolution of UV spectra. The variation of DOC concentration confirmed this observation, with a maximum range for sample 4 (14.9 mgC L21 vs 5.5 at the beginning of the period). Similar findings were reported [10] with a peak of DOC occurring after the peak discharge explained by terrestrially derived sources of DOM during the storm.

8.3.3 Small tributaries quality The acquisition of UV spectra of small brooks can give useful information on the influence of watersheds. An experiment was conducted

FIGURE 8.11 Evolution of intensity rain, river flow rate, and dissolved oxygen (left) and UV spectra of filtered samples (right) [28]. Absorbance values are given for a pathlength of 100 mm, without dilution.

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8.3 Quality of natural water

271

FIGURE 8.12 Spectra of samples of River Magog during Summer 2006. (Juillet 5 July; Aouˆt 5 August; Septembre 5 September).

in 2005 and 2006 on the 259 km2 area watershed of River Magog located between the Massawippi Lake and the River Saint Franc¸ois (South Quebec) in the frame of its watershed analysis [29]. Fifteen grab samples were collected between the origin of River Magog (Lake Massawippi) and its confluence with River Saint-Franc¸ois in Summer 2006 (Fig. 8.12). The difference between the two sets of spectra is explained by a rainy period before the 2005 campaign and the existence of colloids and suspended solids for upstream stations under tributaries’ runoff influence. The four sets of results are characterized by a majority of spectra with a slight decreasing absorbance and without specific shape, except a few of them showing moderate concentrations of nitrate with a convex form between 200 and 225 nm. These last corresponded to the discharge area of treated wastewater one of the treatment plants of Sherbrooke city, located in Rock Forest. In May 2005 grab samples were collected from the 12 main tributaries of River Magog, with subwatersheds area between 4 and 42 km2. The shape of UV visible spectra and the values of physicochemical parameters analyzed led to the proposal of a typology of the studied brooks, from rural type to urban one (Fig. 8.13). Urban brooks (A) were located downstream River Magog, near Sherbrooke (brooks “Vignobles,” “Mivallon,” “Pare´,” “Fontaine,” “Lyon”). Their spectra are characterized by

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8. Natural water

FIGURE 8.13 Proposal of brooks typology (River Magog watershed): (A) urban brooks, (B) rural brooks, and (C) mixed brooks.

a slight influence of nitrate ( . 1 mg L21), and physicochemical parameters showed higher values of conductivity (close to 1000 µS cm21) and fecal coliforms ( . 1000 u) than the ones for the two other groups. Rural brooks (B) are located close to the origin of Magog River, around Lake Magog (brooks “Noir,” “Or,” “Gordon,” “Labrecque,” “Lamontagne”). Their spectra are more shapeless and quality parameters showed relatively low values of conductivity (250 µS cm21) and fecal contamination (,10 µm) but high concentrations of total phosphorus (45 µg L21). The spectra of the third group (C) presented a slightly higher absorbance background than for the two other types, probably linked to the presence of colloids. The corresponding brooks are located in the middle of the watershed (brooks “Venise,” “Rouge,” “Gagnon,” “D”) and they can be qualified or mixed.

8.3.4 Wetland water quality Waters from wetlands and lakes may have relatively high concentrations of NOM. A study of surface water quality of wetland was carried out on one of the Seine estuary, the Hode Marsh. This littoral wetland is located in the northern part of the Seine floodplain in France. Two different bridges delimit the Hode Marsh: Tancarville Bridge to the East and the Normandy Bridge to the West. For over a century the Seine estuary has been highly impacted by human activities with the development of seaport installations and industries, polders for agriculture, the construction of the fluvial navigational canal, etc. All these human actions have severely altered the physical landscape, resulting in the reduction of wetland habitat. Fig. 8.14 presents the location of sampling stations. Sampling stations 1, 2, and 3 were located on mudflats and reed beds. Sample 4 represented the water quality of the Tancarville canal, while sampling stations 5 and 6 were located in wet meadows. UV spectra of the different samples, taken in Summer 2000, are shown in Fig. 8.15, and the results of physicochemical parameters are given in Table 8.2. The results obviously confirmed the great difference in water quality between seawater and freshwater.

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273

8.3 Quality of natural water

FIGURE 8.14

Location of sampling stations in the Hode Marsh.

FIGURE 8.15 UV spectra of the Hode Marsh samples (Fig. 8.14). Raw spectra (left) and normalized (right).

TABLE 8.2

Physicochemical characteristics of samples (Fig. 8.14).

St.

pH (20 C)

Cond (mS cm21)

Pt (mg L21)

PO32 4 (mg L21)

NH1 4 (mg L21)

COTa (mg L21)

a NO2 3 (mg L21)

1

8.6

3.02

0.4

0.2

,0.1

9

,0.5

2

7.9

2.11

0.4

0.4

0.7

10

,0.5

3

7.8

1.08

0.5

0.47

0.68

9

,0.5

4

7.9

18.7

,0.1

,0.1

,1

,0.5

5

8.9

0.43

,0.1

,0.1

,0.1

,1

33

6

7.5

0.42

0.4

,0.1

,0.1

,1

36

a

Estimated by UV.

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8. Natural water

According to the shape of UV spectra and physicochemical results, different types of water can be identified. The northern part of the marsh (wet meadows, stations 5 and 6) was only influenced by freshwaters. UV spectra were typical of natural water without organic matter (the absorbance above 240 nm is close to zero). The Gaussian shape around 210 nm indicated the presence of nitrates. UV spectra are superposed, meaning that water quality was very close for these two stations. The conductivity value of 420 µS cm21 showed the direct influence of the chalk aquifer. Sample 4 presented a low concentration of organic matter, giving a residual absorbance on the entire spectrum, except for the shortest wavelengths related to the presence of chloride, the mouth of the canal directly opening into the channel. This direct influence of saltwater explained the high value of conductivity. UV spectra of samples 1 and 2, similar and superposed, were characteristic of water containing NOM. The diffuse absorbance on the entire spectral domain could be explained by compounds close to HLS. UV spectrum of sample 3 presented an important residual absorbance, showing that this sample was characterized by the presence of suspended solids and organic matter. The presence of nitrate could also be suspected. These results, in addition to chemical characteristics [30], confirmed the influence of estuarine water in the south of the marsh. Surface waters were brackish to salt-enriched, with organic matter and nutrients (Table 8.2).

8.3.5 Lakes water quality Lakes and ponds may have very variable water quality. Fig. 8.16 shows some examples of UV spectra from samples taken in Summer 1998 in French lakes and ponds, mainly located in the Alps. The concentration of organic matter expressed as DOC was variable from one

FIGURE 8.16 Examples of French lakes and ponds: raw spectra (left) and normalized spectra (right) after nitrate removal.

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275

water body to another, between 0.8 mg L21 for Realtor to 3.4 mg L21 for Helene and even 5.4 mg L21 for the studied pond. Nitrate content was low, with 2.4 mg L21 as a maximum, for Realtor. This last was characterized by a TSS concentration of 7.4 mg L21. After nitrate removal (on UV spectrum; see Chapter 2) and normalization, two types of water composition appeared clearly, as the three corrected spectra seem to be similar. On the one hand, mesotrophic or even eutrophic water was characterized by the presence of NOM with a majority of HLS, and on the other hand, water was of good quality (oligotrophic medium) with a very few organic matters (but a small amount of suspended solids). The considered lake was actually used as a reservoir for a drinking water treatment plant. Another example is presented in Fig. 8.17. A total of 21 lakes from Southern Que´bec, Canada, were sampled in Summer 2004, and the corresponding UV spectra were different from one lake to another. The main explanation was related not only to the nature and content of NOM but also to the trophic state of the water body. The spectra shapes were different, depending on water composition and trophic state. After normalization the spectra shapes were rather similar without IPs. This was probably explained by the very low concentrations of nitrate in water and the variability of NOM composition. The trophic classes are given from the raw values of lake’s water quality or after the calculation of the trophic state index (TSI), from phosphorous concentration, chlorophyll-A concentration, and Secchi disk depth (Z) [31]. The UV spectra of 15 lakes on the 21 considered previously can be used for the rapid identification of their trophic state (oligotrophic mesotrophic and eutrophic) (Fig. 8.18). Given the concentration of nitrate close to zero for these lakes, their shapes presented no specific signals except a slight shoulder around 270 nm for some. The absorbance values were higher for eutrophic lakes and the three groups of spectra could be characterized by the mean values of some important

FIGURE 8.17 Spectra of 21 lakes from Southern Quebec (left, raw and right after normalization).

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8. Natural water

FIGURE 8.18 UV spectra of 16 lakes of Southern Que´bec grouped in the function of their trophic state.

UV-Visible Spectrophotometry of Waters and Soils

277

8.3 Quality of natural water

TABLE 8.3

Characteristics (mean values) of the three groups of lakes (Fig. 8.18). P total (µg L21)

N total (mg L21)

Chloro A (µg L21)

Z Secchi (m)

DOC (mg L21)

SUVA

TSI (mean)

10.4

0.21

4.0

3.3

4.4

24.3

36

Mesotrophic 11.8 (5 lakes)

0.27

4.4

2.8

5.6

31.0

44

Eutrophic (5 lakes)

0.4

43.7

0.9

15.6

34.2

61

Oligo-meso trophic (6 lakes)

55.5

DOC, Dissolved organic carbon; SUVA, specific UV absorbance; TSI, trophic state index.

physicochemical parameters (Table 8.3). Besides parameters usually monitored for water quality assessment (phosphorous, nitrogen, DOC, transparency, and chlorophyll-A), the values of the specific UV absorbance (SUVA), ratio between the absorbance value at 254 nm and DOC, and the TSI were calculated. The values of total phosphorous, DOC, chlorophyll-A, SUVA, and TSI were higher for the group of eutrophic lakes. The values correspond to the usual limits of the trophic states definition for the concentrations of total phosphorous, chlorophyll-A, and the measurement of transparency. Moreover, the results of DOC and SUVA showed a rather better discrimination of the three classes, even if the limits of the intermediate class (mesotrophic lakes) seemed to be fuzzy. The reason why there was a relation between these two approaches was probably linked to the significance of the results. On the one hand, the classical procedure, based on the measurement of the previous parameters, gives information on the conditions and/or the presence of phytoplankton. On the other hand, the use of UV spectrophotometry gives qualitative information (from the spectra shape), as well as quantitative estimation of DOC and nitrate. These complementary parameters of water composition are linked to the conditions and/or presence of microorganisms and plankton. Relatively high concentrations of DOC and nitrate give rise to the increase of biomass in the water body. Another experiment was conducted in the frame of the ERA-NET CSA Coordination Action project “Environmental change and rising DOC trends: Implications for Public Health (2007 2013).” In their study on temporal variations in DOC in upland and lowland lakes in North Wales, Hugues et al. [32] monitored monthly four lakes in different environments (Cefni, Cwellyn, Conwy, and Teyrn). The UV spectra of some water samples were acquired during 10 12 days for each lake in October 2011 (Fig. 8.19). The shape of spectra of Lake Cefni was rather

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278

FIGURE 8.19

8. Natural water

UV spectra of 4 lakes of Northern Wales (left, raw and right after

normalization).

different from the other three. Nitrate concentration was relative high (mean 4.5 mg L21 compared to means ,1 mg L21 for the other 3) as well as DOC (mean 7.6 vs , 4 mg L21). Lakes Cwellyn and Teyrn presented

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279

spectra with rather low shapes, without specific signal. The spectra of Lake Conwy samples were also without specific signal, except a slight shoulder around 250 280 nm, but their absorbance values were generally higher than the other three for wavelength upper than 230 nm. After normalization (with area under spectra 5 100), all spectra sets presented a hidden IP (HIP), around 225 nm except for Conwy Lake. Finally, these results were completely in correspondence with the environment of the four lakes of small surface (less than 1 km2) [32]. Two streams among one flowing through an agricultural subwatershed fed Lake Cefni, Lake Cwellyn is considered as oligotrophic, Lake Conwy is surrounded by a blanket bog, and Lake Teyrn by bog and peat soils. A last experiment was carried out with 26 lakes of Brittany (France), with grab sampling and UV spectra acquisition (Fig. 8.20) in the frame of looking for geographical patterns in cyanobacteria distribution [33]. Around half of spectra were characterized by the presence of nitrate signal, without other specific shapes. After normalization (area under spectra 5 100 a.u.), all spectra crossed together, with an isobestic point at 235 nm. A last example concerns the study of a large lake and its tributaries. In summer 2004, five sampling campaigns were carried out for the study of Lake Brome watershed in Southern Que´bec, Canada. Its area is 14.5 km2 and it is the source of the Yamaska River, tributary to the Saint Lawrence River. Fig. 8.21 shows the sampling stations on the lake and its tributaries. Four sampling stations were located on the Lake (2 5), one at the outlet (1), Yamaska River, and eight on the main tributaries (6 13). Some physicochemical parameters were acquired, as well as UV spectra of samples. Table 7.4 presents the synthesis of the results. The main point is that the water quality of the Lake was more constant during Summer for temperature, conductivity, and pH than the one of tributaries. Obviously, temperature and pH values were higher in the Lake. On the contrary, except for three tributaries, the concentration of dissolved organic carbon was more variable in the Lake, even if the

FIGURE 8.20

UV spectra of 26 lakes of Brittany (Western France) (left, raw and right after normalization).

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280

8. Natural water

FIGURE 8.21 Lake Brome, Southern Que´bec: sampling stations.

concentrations were lower. The characteristics of Lake Brome, with a residence time of about 2 years and a maximal depth of 13 m, explain these observations. The set of spectra corresponding to the last campaign of July (Fig. 8.22) showed a great difference in shape, with spectra characterized by high absorbance values for tributaries (samples 6, 7, 12, and 13), and by low ones for water of the Lake (samples 2 5).

UV-Visible Spectrophotometry of Waters and Soils

8.3 Quality of natural water

FIGURE 8.22

281

UV spectra of samples of Lake Brome and tributaries.

The spectrum of one sample (9) was very different from the others, because of a higher concentration of nitrate (see Table 8.4). In contrast to the other tributaries characterized by the presence of NOM, more often colored, the tributary corresponding to sample 9 was a small one, coming from a golf course and thus probably leaching the excess of nutrients used for greens. Despite the low values of total phosphorus (analyzed once), the trophic status of Lake Brome must be considered mesotrophic, taking into account the average measurement of transparency (around 2.5 m) and the concentration of DOC. Fig. 8.23 presents the variation of UV spectra with time for the lake and some tributaries. The maximum absorbance was measured at the beginning of September for the Lake, contrary to the tributaries where the maxima were in August. This can be explained by the beginning of algae bloom at the end of August, when the total nitrogen concentration becomes limiting, followed by a rainy period. The biomass production was still high in Lake during the September campaign. Before leaving this section, one question could be asked about UV visible characterization of pigments. Obviously, these compounds are absorbing substances, but taking into account their potential concentration in water, spectrophotometry seems to be limited for the purpose. However, an application for chlorophyll-A is presented in the next chapter.

8.3.6 Groundwater quality In contrast to the previous applications the study of groundwater quality is always more difficult because of sampling. In general, a sample must be obvious representative of the characteristics of the studied

UV-Visible Spectrophotometry of Waters and Soils

TABLE 8.4 Characteristics of water and tributaries of Lake Brome (N total and P total have been analyzed once). Cond. (µS cm21)

Temp. (C)

DOC (mg L21)

pH (u. pH)

Sample

Mean

C.V. (%)

Mean

C.V. (%)

Mean

C.V. (%)

Mean

C.V. (%)

N total (mg L21)

P total (µg L21)

1

21.8

6.2

116

12.5

7.3

5.8

5.9

25.4

0.22

4

2

21.5

9.1

112

5.4

7.5

1.3

6.0

27.8

0.28

4

3

21.3

6.6

110

6.0

7.5

1.8

5.6

27.8

0.25

3

4

21.3

8.5

110

5.1

7.6

1.8

5.6

28.6

0.29

5

5

21.1

6.9

111

7.6

7.6

1.4

5.7

28.7

0.30

3

6

16.9

2.5

128

20.2

7.2

2.4

17.0

3.2

0.49

15

7

19.2

11.6

71

26.3

7.0

2.7

15.3

11.3

0.47

22

8

18.3

12.3

64

16.5

7.2

3.8

7.5

11.3

0.41

7

9

16.2

14.1

212

25.4

7.2

2.8

7.6

31.3

1.18

4

10

18.0

13.4

110

28.7

7.2

2.9

5.2

30.2

0.31

3

11

19.6

16.8

147

12.5

7.1

1.7

10.9

42.1

0.59

13

12

17.4

18.7

124

22.2

7.1

2.6

14.3

16.1

0.56

14

13

15.9

16.0

220

33.4

7.1

2.0

23.7

0.8

0.46

22

DOC, Dissolved organic carbon.

8.3 Quality of natural water

FIGURE 8.23

283

UV spectra of samples of Lake Brome and tributaries: variation with time.

water body. For surface water a sampling program can include several samples depending on spatial or time water quality variations. For groundwater, often pumped through wells or boreholes, the local water quality around the pumping point may be different from the one of the aquifer. Moreover, the material can bring some interference in water quality as, for example, metallic compounds. To be confident with the sample quality, it is recommended to pump before sampling during a given time (e.g., at least 1 3 hours). Considering that the monitoring of water quality is preferable to an arbitrary pumping time, a procedure based on UV spectra evolution has been chosen [34]. The sampling operation can be decided when the UV spectrum shape no longer varies. The conductivity measurement can also be followed during pumping. The first experiment on groundwater quality study was conducted in a karstic site in the south of France. Physicochemical parameters and UV spectra were acquired for the general study of the aquifer [35]. Figs. 8.24 and 8.25 showed the location and UV spectra of samples from two close boreholes, at different heights, for one of the wells. The distance between the two boreholes B1 and B2 is 5 m, and their depth was 60 m. The water table was at a depth of 43.5 m below the topographic surface. The area was under the influence of a small river where treated wastewater of a small village (1500 inhabitants) is discharged. The sampling height in B1 was 45 m, while three heights of sampling were considered for B2: 45, 49, and 58 m.

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284

8. Natural water

FIGURE 8.24 Sampling location for two boreholes.

FIGURE 8.25 UV spectra of groundwater samples of Fig. 8.24.

Fig. 8.25 clearly shows the existence of a relationship between the two boreholes, B1 near the water table and B2c near the bottom. Moreover, water pumped from B2 was characterized by a higher concentration in organic matter, probably due to the drainage of the abovementioned river. Another experiment was carried out near Rouen in the North of France, in an alluvial plain bordered by chalk cliffs. Four sampling points have been chosen to study the water quality relation between the cliffs and the aquifer of the plain (Fig. 8.26). Fig. 8.27 shows that the water quality was close for the different samples with an increase in nitrate concentration during its infiltration into the karst (stations 2, 3, and 4). The presence of fine particles in sample 4 (borehole) suggested the drainage of the alluvial deposits. Table 8.5 presents some chemical characteristics

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285

8.4 Point source and accidental discharge

FIGURE 8.26

Sampling site near Rouen.

FIGURE 8.27

UV spectra of water samples of Fig. 8.26.

TABLE 8.5

Physicochemical characteristics of samples (Fig. 8.26). Doline

Borehole

Source

Pumping

DOC (mg L )

,1

3

,1

2.5

NO2 3

13

21

30

26

,5

29

,5

,5

21

21 a

(mg L ) 21 a

TSS (mg L ) a

Estimated by UV. DOC, Dissolved organic carbon.

of the samples. The influence of the doline water composition on the source quality is visible only during wet weather conditions because of important infiltrations due to the karstic drainage [36]. Normalized spectra presented an IP that confirmed the quality conservation of water.

8.4 Point source and accidental discharge This part is related to studies of wastewater discharges, generally treated, into rivers and seas.

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286

8. Natural water

8.4.1 Discharge in river One of the main applications of UV spectrophotometry is the study of polluted water, in case of wastewater discharge, for example. The first experiment presented in Fig. 8.28 concerned a local study close to the discharge of treated wastewater of a 150,000-inhabitant biological treatment plant (Aix en Provence, Southern France) into a river (Arc) with a dilution factor of about 10. The sampling of river water was carried out at 10 m upstream and downstream of the discharge. UV spectra of river water compared to that of the effluent showed a direct IP, witness of quantity, and quality conservation of the main groups of compounds, principally nitrate and AOM (see Chapter 3). The existence of a direct IP is not always observed in case of the study of wastewater discharge. Another experiment has been carried out from the study of the evolution of the Calavon river quality (Southern France), between Apt and Cavaillon (25 km). This part of the river receives the discharge of a 20,000-inhabitant biological treatment plant, with a small dilution factor (5) in summertime. Fig. 8.29 shows the evolution of UV spectra, between the upstream (sampling points 1 and 2) and downstream (sampling points 4 and 5) areas of the discharge (sample 3). From the discharge point, the distances were, respectively, 23 km, 2100 m, 1100 m, and 115 km. The treatment efficiency of the treatment plant was quite good, as the pollution parameter values were low (TOC 5 14.5 mg L21, COD 5 67 mg L21, BOD5 5 13 mg L21). Considering the shape of UV spectra of the river water and treated wastewater, a HIP can only be found after normalization. In this case the spectra set evolution clearly shows a dilution of anthropogenic

FIGURE 8.28 UV spectra of water samples at a wastewater discharge point.

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287

FIGURE 8.29 UV spectra (raw and normalized) of the Calavon River around a discharge of treated wastewater (station 3).

matter discharged (and the partly self-purification of river water) and of nitrate concentration of the upstream river water. The presence of an HIP is related to the quality conservation of water (see Chapter 3). Another study based on the use of UV spectrophotometry for the evaluation of the impact of treated wastewater discharge in rivers [37] has shown that not only the qualitative and quantitative evolution of river water quality was possible but also some hydraulic parameters such as the dilution factor of discharge or confluences. The dilution factor (F) of a mixture can generally be calculated as follows: F5

Qm Cd 2 Ci 5 Qd Cm 2 Ci

where Qd and Qm are the flow rates of the treatment plant discharge and of the mixture downstream of the discharge, respectively, and Cm, Cd, and Ci are the respective concentrations of any conservative parameter in the mixture, in the discharge, and in the river upstream of the discharge. The calculation of dilution factors has been validated in this same study [37] from data of TOC, nitrate, and anionic surfactants, either measured by reference methods or UV-estimated.

8.4.2 Discharge in sea In seawater the spatial impact of treated wastewater discharge is limited, considering the high dilution potential of the sea. Two experiments were carried out on the Mediterranean Sea, near Marseille in the south of France, with UV spectra acquisition. The first concerned the study of treated wastewater discharge of the Marseille urban area treatment plant (2,000,000 inhabitants). Fig. 8.30 presented the evolution of UV spectra of seawater, sampled (at the surface of the sea, from a boat) from the discharge, along the direction of the main current.

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288

8. Natural water

FIGURE 8.30 Dilution of treated wastewater of Marseille area in the Mediterranean Sea.

The influence of the discharge was perceptible up to 30 m, mainly because of the presence of relatively high residual organic matter in treated wastewater (COD around 100 mg L21), due to the physicochemical process of the treatment plant. The TSS concentration (around 30 mg L21) contributed to the turbidity effect in the mixture. The treatment plant extension that should include a biological process would lead to the reduction of wastewater impact in this sea area. The second experiment was conducted not far from the previous one, on a petrochemical site (Naphtachimie, Lave´ra), integrating one of the largest cracking unit of the world, producing almost 1,000,000 tons of ethylene a year. A very efficient biological WWTP treated all wastewater of the site plus some external wastes (see Chapter 12). Several samples were taken from a boat in the discharge area [38]. As for the last study, spectra presented a high absorption below 220 nm due to the presence of a high concentration of chloride in seawater (Fig. 8.31). Fig. 8.32 shows the evolution of the absorbance value at 230 nm with distance for the two examples. The organic and particle load of the treated wastewater discharged at sea is lower than the one of Marseille urban areas. But the dilution of wastewater remains always visible at a distance of about 100 m. Actually, in contrast to the previous case, the discharge site is not located in an open sea, but in a relatively small bay.

8.4.3 Accidental discharge Before studying drinking water quality, two last applications are presented for the diagnosis of accidental pollution in rivers.

UV-Visible Spectrophotometry of Waters and Soils

8.4 Point source and accidental discharge

FIGURE 8.31

289

Dilution of treated wastewater of a petrochemical site in the Mediterranean

Sea.

FIGURE 8.32 Evolution of absorbance at 230 nm with the distance from two discharges.

UV spectra of several samples collected in one industrial site (samples 1 and 2) and in the polluted river (samples 3, 4, and 5) have been acquired (Fig. 8.33) further to the death of a lot of fishes (for the sake of confidentiality, no precise indication will be given). The presence of organic pollutants of phenolic nature has been suspected from the specific signal at 278 nm (Chapters 3 and 10). This hypothesis has been confirmed by a bathochromic shift in basic medium. It can be observed that a dilution factor occurs for samples 3 and 4. Sample 5 has been taken 2 days after the incident, and it can be noticed that the pollutant concentration has clearly diminished. Another observation can be made related to the high absorbance values below 250 nm, probably associated with a

UV-Visible Spectrophotometry of Waters and Soils

290

FIGURE 8.33

8. Natural water

UV visible spectra acquired during accidental discharge into a river

with fishes’ death.

FIGURE 8.34 Surface water spectrum containing a mixture of anthropogenic and natural organic matter with an important concentration of surfactants.

high mineralization of water. The conductivity measurement of samples has confirmed this fact with values between 3 and 8 mS cm21, the higher values corresponding to industrial samples. This real experiment has been followed by the diagnosis of the pollution origin. The last example is given in Fig. 8.34. The spectrum of a polluted river water showed a shape close to the one of a diluted raw wastewater with a significant concentration of surfactants giving an important shoulder near 220 230 nm (due to the aromatic ring, see Chapters 4 and 5). Surfactants are usually an interesting fingerprint of domestic pollution, responsible for the relatively high values of aggregate parameters (TOC, BOD, and COD). In this case a simpler visual characteristic can be used for pollution diagnosis, the presence of white persistent foam, particularly in aerated areas, related to the presence of surfactants.

UV-Visible Spectrophotometry of Waters and Soils

8.5 Different freshwaters but some common fate

291

Even if its interest has been demonstrated for these applications, UV spectrophotometry cannot show the presence of nonabsorbing species or of absorbing compounds with too low concentration.

8.5 Different freshwaters but some common fate 8.5.1 About hidden isosbestic point The presence of an IP, when spectra cross together at the same wavelength is of primary importance. The existence of a direct IP is not frequent for raw spectra except in case of water mixing (see Fig. 8.27), when the flow of the discharge is of the same order as the one of the receiving water bodies. However, the normalization of spectra with the same area under the spectrum, (see Chapter 2) gives frequently HIP. An example of the importance of HIP is shown in Fig. 8.35 where more than 650 normalized spectra of rivers and lakes samples from France and Canada (Quebec) are displayed. All samples were collected during a dry weather period. The presence of HIP is linked to the existence of two major absorbing components, nitrate and DOM, the proportion of which is varying from one sample to another. In the case of sampling during a wet weather period, it is likely that no HIP occurs because of the presence of a third major component in water (mixture of suspended solids and colloids). If nitrate absorption gives a specific spectrum (see Chapter 6), the case of DOM is different. Indeed, it is not a single component chemically speaking, but the mixture of organic substances, aggregated HLS or not (degradation products with aromatic phenolic like structure), gives a uniform response with small variations around a slight shoulder between 250 and 290 nm. These variations in

FIGURE 8.35

Normalized spectra of freshwater samples showing an isosbestic point.

UV-Visible Spectrophotometry of Waters and Soils

292

8. Natural water

shape lead to the fact that a HIP is not actually a single point, but a small surface around 225 nm (Fig. 8.35). The great number of spectra gives the illusion that they all cross in a single point.

8.5.2 Relation between parameters (DOC/NO3) The above observation led to the existence of a strong nonlinear relationship between nitrate and organic matter (assessed by DOC) as demonstrated by Taylor and Townsend [39], namely for freshwaters. Considering the presence of a HIP, a simple linear model can be proposed for nitrate DOC relationship: (a 3 NO3 1 b 3 DOC 5 1) [37]. Fig. 8.36 shows the graphical relationship drawn from 55 spectra of different Brittany rivers and lakes, presenting a HIP around 225 nm after normalization. Concerning a possible relation between DOC and NO3 concentrations, raw data showed a coarse linear tendency, which is strongly improved after the normalization of data (using the same normalization coefficient as for spectra). Actually, the relation coefficients (a and b) are varying with water bodies’ environment and sampling conditions. As for Fig. 8.33, samples taken during a wet period cannot be considered. Finally, the existence of such a relationship means for water sample that the lower the DOC concentration, the higher the nitrate one.

FIGURE 8.36

Raw and normalized spectra of Brittany’s freshwaters and corresponding relations between nitrate and DOC concentrations. DOC, Dissolved organic carbon [40].

UV-Visible Spectrophotometry of Waters and Soils

8.6 Second derivative of UV spectra, a key parameter

293

These results are fully comparable to the ones of Taylor and Townsend’s findings [33], showing a consistent and negative nonlinear relationship between nitrate and DOC concentrations occurring along a hydrological continuum from soils to water bodies (e.g., streams and lakes). On the other hand, the significant contribution of this work is the demonstration of a linear model between nitrate and DOC, thanks to the exploitation of UV spectra showing an IP after normalization. The linear model is simple (a 3 NO3 1 b 3 DOC 5 1) and has been tested on more than 150 samples of different Brittany rivers and lakes. Further investigation of other freshwater samples is needed to determine more accurate limits of this linear model, particularly for natural environments with no allochtonous nitrate inputs (forest, peatland, etc.). This important finding could help to predict the concentration of the two main absorbing components (nitrate and DOC) from the exploitation of UV spectra that may constitute a potentially interesting tool for the survey of water quality.

8.6 Second derivative of UV spectra, a key parameter The use of second derivative UV spectrum is a key step for nitrate and DOC determination [41]. It can also be envisaged for the qualitative assessment of the DOM by explaining the shoulder rather frequent in UV spectra around 270 330 nm. A first example is shown in Fig. 8.37 for the experiment reported in Section 8.3.2 (Fig. 8.11). The evolution of the second derivative around the rain event shows that water sampled before the heavy rain (samples 1 and 2) showed two main slight peaks at 295 and 330 nm, contrary to samples taken during and after rain (samples 3, 4, and 5). A redshift of 5 nm is observed for the two main peaks with a hyperchromic effect for the first one (at 300 nm). This

FIGURE 8.37

Second derivative spectra during a rainy event.

UV-Visible Spectrophotometry of Waters and Soils

294

8. Natural water

means that rain brings DOM of a different nature from soils runoff and that DOC increases during the flood while nitrate is diluted [42]. Further investigation is needed for a better understanding of DOM nature from UV second derivative [43].

Acknowledgments The authors wish to thank Valerie Mesnage, for their contribution in the first and second editions as well as Joelle Muyldermans and Guy Labbe´ for their participation in new studies carried out in Sherbrooke area (Quebec) and included in this new edition.

References [1] N. Ogura, Ultraviolet absorption of sea water in relation to organic and inorganic matter, International Journal of Oceanology and Limnology 1 (1967) 91 201. [2] R.A. Dobbs, R.H. Wise, R.B. Dean, The use of ultra-violet absorbance for monitoring the total organic carbon content of water and wastewater, Water Research 6 (1972) 1173 1180. Available from: https://doi.org/10.1016/0043-1354(72)90017-6. [3] R. Aryal, A. Grinham, S. Beecham, Tracking inflows in Lake Wivenhoe during a major flood using optical spectroscopy, Water 6 (8) (2014) 2339 2352. Available from: https://doi.org/10.3390/w6082339. [4] J.E. Birdwell, A.S. Engel, Characterization of dissolved organic matter in cave and spring waters using UV Vis absorbance and fluorescence spectroscopy, Organic Geochemistry 41 (3) (2010) 270 280. Available from: https://doi.org/10.1016/j. orggeochem.2009.11.002. [5] H.T. Carter, E. Tipping, J.F. Koprivnjak, M.P. Miller, B. Cookson, J. Hamilton-Taylor, Freshwater DOM quantity and quality from a two-component model of UV absorbance, Water Research 46 (14) (2012) 4532 4542. Available from: https://doi.org/ 10.1016/j.watres.2012.05.021. [6] E. Tipping, H.T. Corbishley, J.F. Koprivnjak, D.J. Lapworth, M.P. Miller, C.D. Vincent, et al., Quantification of natural DOM from UV absorption at two wavelengths, Environmental Chemistry 6 (6) (2009) 472 476. Available from: https://doi. org/10.1071/EN09090. [7] J.R. Helms, A. Stubbins, J.D. Ritchie, E.C. Minor, D.J. Kieber, K. Mopper, Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter, Limnology and Oceanography 53 (3) (2008) 955 969. Available from: https://doi.org/10.4319/lo.2008.53.3.0955. [8] P.E. Kolic, E.D. Roy, J.R. White, R.L. Cook, Spectroscopic measurements of estuarine dissolved organic matter dynamics during a large-scale Mississippi River flood diversion, Science of the Total Environment 485 (2014) 518 527. Available from: https:// doi.org/10.1016/j.scitotenv.2014.03.121. [9] J.F. Saraceno, B.A. Pellerin, B.D. Downing, E. Boss, P.A. Bachand, B.A. Bergamaschi, High-frequency in situ optical measurements during a storm event: assessing relationships between dissolved organic matter, sediment concentrations, and hydrologic processes, Journal of Geophysical Research, Biogeosciences 114 (G4) (2009). Available from: https://doi.org/10.1029/2009JG000989. [10] R. Briggs, K.V. Melbourne, Recent advances in water quality monitoring, Water Treatment and Examination 17 (1968) 107 120. [11] O. Thomas, F. The´raulaz, C. Agnel, S. Suryani, Advanced UV examination of wastewater, Environmental Technology 17 (1996) 251 261.

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[12] X. Chen, G. Yin, N. Zhao, T Gan, et al., Simultaneous determination of nitrate, chemical oxygen demand and turbidity in water based on UV Vis absorption spectrometry combined with interval analysis, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 244 (2021). Available from: https://doi.org/10.1016/j. saa.2020.118827. [13] O. Thomas, F. The´raulaz, M. Domeizel, C. Massiani, UV spectral deconvolution: a valuable tool for waste water quality determination, Environmental Technology 14 (1993) 1187 1192. [14] O. Thomas, F. The´raulaz, V. Cerda`, D. Constant, P. Quevauviller, Wastewater quality monitoring, Trends in Analytical Chemistry 16 (1997) 419 424. [15] E. Asmala, C.A. Stedmon, D.N. Thomas, Linking CDOM spectral absorption to dissolved organic carbon concentrations and loadings in boreal estuaries, Estuarine, Coastal and Shelf Science 111 (2012) 107 117. Available from: https://doi.org/ 10.1016/j.ecss.2012.06.015. [16] A. Avagyan, B.R. Runkle, L. Kutzbach, Application of high-resolution spectral absorbance measurements to determine dissolved organic carbon concentration in remote areas, Journal of Hydrology 517 (2014) 435 446. Available from: https://doi.org/ 10.1016/j.jhydrol.2014.05.060. [17] E.J. Lee, G.Y. Yoo, Y. Jeong, K.U. Kim, J.H. Park, N.H. Oh, Comparison of UV VIS and FDOM sensors for in situ monitoring of stream DOC concentrations, Biogeosciences 12 (10) (2015) 3109 3118. Available from: https://doi.org/10.5194/ bg-12-3109-2015. [18] M. Peacock, C.D. Evans, N. Fenner, C. Freeman, R. Gough, T.G. Jones, et al., UVvisible absorbance spectroscopy as a proxy for peatland dissolved organic carbon (DOC) quantity and quality: considerations on wavelength and absorbance degradation. 2014, Environmental Science: Processes & Impacts 16 (6) (2014) 1445 1461. [19] J.L. Weishaar, G.R. Aiken, B.A. Bergamaschi, M.S. Fram, R. Fujii, K. Mopper, Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon, Environmental Science and Technology 37 (20) (2003) 4702 4708. Available from: https://doi.org/10.1021/es030360x. [20] J. Causse, O. Thomas, A.V. Jung, M.F. Thomas, Direct DOC and nitrate determination in water using dual pathlength and second derivative UV spectrophotometry, Water Research (2016). Available from: https://doi.org/10.1016/j.watres.2016.11.010. [21] R.C. Hoather, Application of spectrophotometry in the examination of water, Water Treatment and Examination 2 (1952) 9 19. [22] O. Thomas, S. Gallot, N. Mazas, Ultraviolet multiwavelength absorptiometry (UVMA) for the examination of natural waters and wastewaters— Part II. determination of nitrate, Fresenius’ Journal of Analytical Chemistry Volume 338 (1990) 238 240. [23] J. Dahle´n, S. Karlsson, M. Ba¨ckstro¨m, J. Hagberg, H. Pettersson, Determination of nitrate and other water quality parameters in groundwater from UV/Vis spectra employing partial least squares regression, Chemosphere 40 (1) (2000) 71 77. Available from: https://doi.org/10.1016/S0045-6535(99)00242-8. [24] M. Schwab, J. Klaus, L. Pfister, M. Weiler, Understanding the relationship between DOC and nitrate export and dominant rainfall-runoff processes through long-term high frequency measurements, in: EGU General Assembly Conference Abstracts, April 2016, vol. 18, p. 5623. [25] R. Sutton, G. Sposito, Molecular structure in soil humic substances: the new view, Environmental Science & Technology 39 (23) (2005) 9009 9015. Available from: https://doi.org/10.1021/es050778q. [26] J. Gerke, Concepts and misconceptions of humic substances as the stable part of soil organic matter: a review, Agronomy 8 (5) (2018) 76. Available from: https://doi.org/ 10.3390/agronomy8050076.

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[27] S. Vaillant, Organic Matter of Urban Wastewater: Characterization and Evolution. Ph.D. Thesis, University de Pau et Pays de l’Adour, France, 2000. [28] I. Delpla, Changements climatiques et de´gradation de la qualite´ des eaux de surface destine´es a` la consommation humaine en zone agricole. PhD Thesis , University of Rennes 1, France, 2011. [29] M-F Pouet, O. Thomas, J. Muyldermans, Diagnosis and Monitoring Procedure for the Magog River Water Quality, Report for the City of Magog, Canada, 2005. [30] V. Mesnage, S. Bonneville, B. Laignel, D. Lefebrre, J.-P. Dupont, D. Mikes, Filling of a wetland (Seine estuary, France): natural eutrophication or anthropogenic process? A sedimentological and geochemical study of wetland organic sediments, in: E. Orive, M. Elliott, V.N. de Jonge (Eds.), Nutrients and Eutrophication in Estuaries and Coastal Waters, Kluwer Academic Publishers, The Netherlands, 2002, pp. 423 435. [31] R.E. Carlson, A trophic state index for lakes, Limnology and Oceanography 22 (1977) 361 369. [32] D.D. Hughes, P.J. Holliman, T. Jones, C. Freeman, Temporal variations in dissolved organic carbon concentrations in upland and lowland lakes in North Wales, Water and Environment Journal 27 (2) (2013) 275 283. Available from: https://doi.org/ 10.1111/wej.12025. [33] F. Pitois, O. Thomas, I. Thoraval, et al., Learning from 8 years of regional cyanobacteria observation in Brittany in view of sanitary survey improvement, Environment International 62 (2014) 113 118. Available from: https://doi.org/10.1016/j.envint. 2013.09.018. [34] O. Thomas, F. The´raulaz, Analytical assistance for water sampling, Trends in Analytical Chemistry 13 (1994) 344 348. [35] T. Winiarski, O. Thomas, C. Charrier, Analysis of the spatial and temporal variations in the water quality of a karstic aquifer using UV spectrophotometry, Journal of Contaminant Hydrology 19 (1995) 307 320. [36] N. Massei, M. Lacroix, H.Q. Wang, J.P. Dupont, Transport of particulate material and dissolved tracer in a highly permeable porous medium: comparison of the transfer parameters, Journal of Contaminant Hydrology 57 (1 2) (2002) 21 39. [37] H. El Khorassani, F. The´raulaz, O. Thomas, Application of UV spectrophotometry to the study of treated wastewater discharges in river, Acta Hydrochimica et Hydrobiologica 26 (1998) 296 299. [38] H. El Khorassani, Characterization of Industrial Effluents by UV Spectrophotometry: Application to Petrochemical Industry (Ph.D. thesis), University Aix-Marseille 1, 1998. [39] J. Causse, Temporalite´ des transferts de nutriments dans les bassins versants a` algues vertes (Doctoral dissertation), University Rennes 1, France, 2015. [40] O. Thomas, A.V Jung, J. Causse, et al., Revealing organic carbon-nitrate linear relationship from UV spectra of freshwaters in agricultural environment, Chemosphere 107 (2014) 115 120. Available from: https://doi.org/10.1016/j.chemosphere.2014. 03.034. [41] J. Causse, M.-F. Thomas, A.-V. Jung, O. Thomas, Direct DOC and nitrate determination in water using dual pathlength and second derivative UV spectrophotometry, Water Research 108 (2017) 312 319. [42] E. Baure`s, I. Delpla, S. Merel, M.F. Thomas, A.V. Jung, O. Thomas, Variation of organic carbon and nitrate with river flow within an oceanic regime in a rural area and potential impacts for drinking water production, Journal of Hydrology 477 (2013) 86 93. Available from: https://doi.org/10.1016/j.jhydrol.2012.11.006. [43] P.G. Taylor, A.R. Townsen, Stoichiometric control of organic carbon nitrate relationships from soils to the sea, Nature 464 (7292) (2010) 1178 1181.

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C H A P T E R

9 Remote sensing and highfrequency monitoring Olivier Thomas1 and Jean Causse2 1

EHESP School of Public Health, Rennes, France, 2Transcender Company, Rennes, France

O U T L I N E 9.1 Introduction

298

9.2 Satellites applications

299

9.3 Other airborne applications 9.3.1 Aircrafts 9.3.2 Drones

302 302 303

9.4 Surface applications 9.4.1 Boats and buoys 9.4.2 High-frequency grab sampling 9.4.3 Handheld devices 9.4.4 On-site systems

304 304 306 307 308

9.5 Underwater applications

311

9.6 Wireless sensor networks

312

9.7 Remote sensing techniques appraisal

313

References

315

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00003-4

297

© 2022 Elsevier B.V. All rights reserved.

298

9. Remote sensing and high-frequency monitoring

9.1 Introduction Remote sensing measurement for water quality monitoring is known for 50 years and has been developed with new satellites and improved onboard sensors. Since then, a lot of works have been published, among which a recent review by Chawla et al. [1]. In the same time, new handheld or online devices were proposed for a better knowledge of water quality as the fine structure of water quality dynamics [2]. A few years ago, Rode et al. presented in their review [3] the advancement of technologies for in situ water quality measurement with some important applications. However, some recent works as the one by O’Grady et al. [4] linked both approaches (remote and on-site) within a tiered framework of integrated sensing technologies. They assume that the future of monitoring will involve satellite, in situ and airborne devices with data analytics playing a key role in providing decision support tools. However, monitoring applications are still grouped either under remote sensing from satellites or under on-site high-frequency measurements, carried out under different ways. Fig. 9.1 represents the different levels of water monitoring, form airborne systems (satellites, aircrafts, and drones) to underwater ones, with floating devices (boats and buoys) and wireless sensor network (WSN) if any. The scene of monitoring is completed by in situ or online measurements of water quality. This chapter is organized from the top-down gradients of Fig. 9.1, from satellites applications to underwater observations, and considers

FIGURE 9.1 Remote and high-frequency systems for water quality monitoring. Credit: O. Thomas, M-F. Thomas and J. Causse.

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only works carried out with optical sensors, which are the great majority of remote sensing at large applications.

9.2 Satellites applications Water has specific reflectance characteristics, which can be measured at different wavelengths, based on the scattering response of suspended solids and on the absorbing properties of some of its dissolved components [chlorophyll, colored dissolved organic matter (DOM) (CDOM), etc.]. With the knowledge of their optical characteristics, it is possible to retrieve quantitative values of the concentrations for these water constituents, solely based on the reflectance of light measured by satellite sensors. Thus selective sensors boarded on satellites can remotely carry out the measurement of radiation reflected by the Earth’s components, including water surface. Remote sensing measurement for water quality monitoring is known for 50 years. It was first proposed with the observation satellites launching around the 1970s. In a recent review, Topp et al. [5] showed that a huge number of scientific and technical papers have demonstrated the interest of remote sensing for the estimation of biological, chemical, and physical properties of inland water bodies and seawater in addition to soil, territory, and vegetation data. This review showed the progression of inland water quality remote sensing from methodological development to scientific applications. At the beginning, the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early 2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Works in the past decade showed that researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters [5]. In a recent study, Du et al. [6] reported the evolution of total suspended matter (TSM) concentrations in waters using Landsat images during almost two decades across the Songnen Plain, China. From 2012 to 2015, a total of 142 water samples were collected from 22 water bodies for the model validation. The sampling point number for each water body was determined according to its water area with a minimum distance of 500 m observed between points. Different sensors were available according to Landsat flights: TM/ Thematic Mapper instruments for Landsat 5, ETM/Enhanced Thematic Mapper Plus scanner for Landsat 7, and OLI/Operational Land Imager

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9. Remote sensing and high-frequency monitoring

for Landsat 8. Images without clouds or snow from April to October were only selected from 1984 to 2018. This study demonstrated the potential of using historical Landsat imagery combined with field survey data to map the spatiotemporal patterns of TSM in inland waters. A model was established for TSM concentration between band combinations from Landsat’ sensor images and field-measured TSM concentrations. The results showed that TSM concentrations in most of waters were decreasing gradually from 1984 to 2018 and that of all water bodies generally decreased from April to the minimum level in July and then increase from September. Moreover, high spatial heterogeneities of the TSM across the study area were found with TSM in reservoirs generally lower than in lakes. TSM concentrations in the larger lakes were usually lower than those in the smaller lakes. Finally, the study also revealed the potential role of wind speed and accumulated precipitation in regulating TSM changes. In addition, increasing of vegetation can strongly reduce the TSM in waters. Among sensors used for remote measurement of water quality, MODIS (Moderate Resolution Imaging Spectroradiometer) is one major instrument often chosen for the understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is viewing from 2000 the entire Earth’s surface every 1 or 2 days. A recent study published by Feng et al. [7] used long-term MODIS observations for the monitoring of the water transparency of 50 large lakes on the Yangtze Plain (China). A semianalytical algorithm for water clarity estimation proposed by Lee et al. [8] was used for water transparency calculation from MODIS observations between 2003 and 2016, expressed as the Secchi disk depth (Zsd) estimation. The results showed significant seasonal and interannual changes in water clarity and differences between lakes, with human activities influencing more lakes than natural variability. The Zsd results showed different changing patterns from those described in previous turbidity studies, as water turbidity only reflects concentrations of total suspended solids (TSS), while the Zsd also takes into account the compound effects of optically active substances (chlorophyll-a, TSS, and CDOM). This study highlights the importance of satellite observations and robust algorithms in obtaining large-scale water quality information. A multidate comparison of remote sensing data was proposed by Carstens and Amer [9] who showed changes in urban development and water quality of the Pontchartrin Basin (Lousiana) within 30 years. A Landsat Thematic Mapper image from 1985 and a Landsat Operational Land Imager from 2015 were processed and compared. Additional in situ water quality data were considered. The results indicated that urbanization has a negative impact on water quality.

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The development of impervious surfaces and related urbanization area has led to increasing water pollution with some water quality parameters higher (fecal coliform, phosphorus) and some lower (dissolved oxygen). A recent report by Ledang et al. [10] presented the results of tests on the application of remote sensing data for selected lakes included in the lake monitoring programs run by the Norwegian Environment Agency. Monitoring of lake water quality is challenging in Norway due to the large number of lakes, and their remote location. The data of three satellites were considered (1) Envisat, launched in 2002 with the MERIS sensor (Medium Resolution Imaging Spectrophotometer) operating between 390 and 1040 nm. Its revisiting time for Norway is 2
3 days and its resolution is around 300 m on the Earth. (2) Sentinel2 launched in 2015 in the frame of program Copernicus, with a multispectral instrument (MSI) working at 13 bands and giving a resolution of 60 m for water bodies. Like Envisat, it is available for Norway every 2
3 days. (3) Sentinel-3 launched for sea surface, temperature, and color observation boarding four main instruments, including the Ocean and Land Color Instrument based on the MERIS design. Its spatial resolution was 300 m. The data acquired by these three satellites were used for the estimation of some water quality parameters (chlorophyll-a, TSM, and CDOM) after atmospheric correction of sensors signals. Five different lakes have been studied in this work. Data from MERIS, Sentinel-2, and Sentinel-3 showed general good results for chlorophyll-a, TSM, Secchi disc depth (zSD), and turbidity compared to in situ measurements. However, an overestimation of chlorophyll-a was observed when the reflectance signal of water is very low. In this case, some ways of signal improvement should be searched (better atmospheric compensation, better estimation tools). In a recent paper by Erena et al. [11], the water quality of a 20 km length coastal lagoon (Mar Menor, Southern-East of Spain) was investigated between May 2015 and October 2017. Remote sensing data from Landsat 8 and Spot 7 and four different sensors (TM, ETM, OLI, and MERIS) were acquired with complementary weekly field sample analysis and measurements of chlorophyll-a, phycocyanin (for the assessment of algae blooms), and turbidity. Spatial remote sensing has detected that turbidity in the Mar Menor is not caused only by phytoplankton; there are also chemical compounds involved, interacting with phytoplankton and leading to changes in color. Besides the abovementioned works for inland water bodies, the observation of seawater quality has given numerous applications that are presented in Table 9.1.

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TABLE 9.1 Some applications of remote sensing from satellites for seawater quality monitoring. Location

Date

Satellite/sensora

Parameters

References

Estuarine waters, Australia

1990
91

Landsat/TM

Chlorophyll-a, transparency (Zsd) salinity

[12]

Gulf of Finland

1997

Landsat/TM, ERS2/SARdata

Turbidity, transparency (Zsd), suspended solids

[13]

Gulf of Gabes, Tunisia

2009

Aqua/MODIS

Chlorophyll-a, Turbidity

[14]

Baltic Sea

2008
10

Envisat/MERIS

Chlorophyll-a

[15]

Baltic Sea

2016

Sentinel3A/OLCI

Chlorophyll-a, total suspended solids, colored dissolved organic matter

[16]

North Sea

1994
2017

Various/Modis, MSI, OLI

Seagrass (Zostera)

[17]

South China Sea

1979
2014

Various/CZCS/ SeWiFS, MODIS

Chlorophyll-a

[18]

Different oceanic waters

1997
2013

Various/MODIS, MERIS, SeaWiFS

Color (Forel-Ule)

[19]

a

TM: Thematic Mapper/ERS2/SARdata: Synthetic Aperture Radar data/MODIS: Moderate Resolution Imaging Spectroradiometer/MERIS: Medium Resolution Imaging Spectrometer/OLCI: Ocean Land Colour Instrument /MSI: Multispectral Instrument/ OLI: Operational Land Imager/CZCS: Coastal Zone Color Scanner/SeaWiFS: Sea viewing Wide Field of view Sensor.

9.3 Other airborne applications 9.3.1 Aircrafts Considering the strong development of satellite remote sensing, aircrafts were rather few used except for military imaging applications. However, a first study reported by Grenier at al. [20] described a project for the study of water quality of Saint-Laurent stream (Quebec), downstream Montreal, by using data from multispectral optical sensors (MEIS II, PMI) boarded in aircrafts flying at 12,000 m height and giving a 4 m ground resolution. Simultaneous water samples were taken with aircraft passages in October 1989, for the laboratory measurement: turbidity, color, dissolved organic carbon (DOC), chlorophyll, and TSS. After calibration, the regression analysis gave determination coefficients values between 0.6 and 0.9 depending on the sensor and spectral bands used.

UV-Visible Spectrophotometry of Waters and Soils

9.3 Other airborne applications

303

In the early 2000s, a review by Ritchie et al. [21] cited some remote sensing applications by spectral sensors on aircraft to study sediment patterns from reflectance measured between 700 and 800 nm and distribution of chlorophyll in Chesapeake Bay from reflectance around 550 and 700 nm [22]. The Ocean Data Acquisition System (ODAS) instrument used was composed of three radiometers with 15 nm bands and the aircraft flew at around 500 m giving a spatial resolution of 5 m.

9.3.2 Drones In his review, McDonald [23] reported that drones or unmanned aerial vehicles (UAV) have applications for urban stormwater management. More than 10 works were cited for applications on rivers, reservoirs, or lakes water quality. All UAV were equipped with camera or multispectral instruments and have shown good prediction of surface water quality parameters such as turbidity, suspended solids, and chlorophyll. This was possible from the exploitation signal of red, green, and blue (RGB) regions of visible spectrum for camera or from the 400
500, 660, or 780 nm wavelengths for spectral sensors. A recent work, Kupssinsku¨ et al. [24] described a methods for chlorophyll-a and suspended solids prediction through remote sensing and machine learning. Water samples must be collected for modeling with the remote signal from Sentinel-2 satellite equipped with the MSI or a camera/sensor boarded on a drone. Spectral bands from visible to infrared are available for the purpose from MSI or RGB signal from camera drone. A comparison of both systems showed a better resolution for drone and a higher temporal frequency. More recently, an innovative research reported the assessment of microbial water quality in an irrigation pond from drone-based imaging [25]. Several cameras with different set of lens were mounted on a drone flying at 400-m height. The resulting images were exploited through regression tree approach, taking into account the characteristics of water samples. This study has shown that UAV imagery provided the same level of accuracy in estimating concentrations of Escherichia coli across the study pond, compared to in situ measurements of physicochemical and biological water quality parameters. Besides classical optical sensors (camera, spectral sensor) boarded on drones and measuring reflectance or absorbance of surface water, some fluorescence applications can be reported such as the one of Duan et al. [26]. High-quality fluorescence spectra of the superficial aquatic layer featuring spectral signatures from oils’ DOM, algae, etc. were acquired by a compact and lightweight laser-induced fluorosensor (LIF), boarded an aerial drone. The system provided a good alternative to conventional pulsed-laser fluorosensors and continuous wavelength (CW)-laser

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9. Remote sensing and high-frequency monitoring

Scheimpflug systems and exhibited certain advantages in terms of performance and simplicity. Even if it could only be used at nighttime, several experiments were carried out on oil spills in a river showing a strong fluorescence signal at 500 nm. Using sufficiently high CW powers and combining with a suitable modulation scheme for background subtraction, daylight operation should be feasible, considerably extending the present capability. This drone-based remote LIF technique was shown to be a relevant detection technique, especially in the studies of limited areas.

9.4 Surface applications 9.4.1 Boats and buoys Even if the solution seems to be evident, there are few experiments for water quality monitoring from boats. Levin et al. [27] studied the inherent optical properties in the Baltic Sea from reflectance measurements at 550 nm during 14 cruises performed in the southern Baltic in 1999
2005 onboard the research vessel “Oceania.” The results showed that data from the Baltic Sea were close to the ones acquired in other places (Pacific ocean, Arabian sea). Besides experiments from research vessels, some tentative works were carried out with unmanned remote boats. A first work was carried out for the study of the dilution treated wastewaters in receiving medium [28]. A radio-controlled 40 mini boat was designed for sampling and measurement of physicochemical parameters (temperature, dissolved oxygen, and conductivity). After sampling, the measurement of nitrate, total organic carbon (estimated), and other specific compounds such as phenols is carried out on the bank with a portable UV multichannel sensor. It was used for the study of urban and industrial treated wastewater discharges near Lyon (France). This device was built up after a first prototype designed for run-of-the river sampling and measurement in a 12 km evacuation pipe of treated wastewater in Chambery (France) [29]. A simple floating raft of 3v diameter was equipped with an autosampler and a measurement part, including a multiparameter probe, for sampling and measurement at fixed interval. A nautical drone was designed for the monitoring of estuarine and coastal waters in the context of Water Framework Directive by Daniel et al. [30]. It was able to go up to 500 m off the coastline in less than 5 minutes; take measurements of temperature, salinity, and turbidity; and collect water samples for biological laboratory analysis. An Autonomous Surface Vehicle for Water Quality Monitoring was designed by Dunbabin et al. [31] in Australia. A 160 solar powered

UV-Visible Spectrophotometry of Waters and Soils

9.4 Surface applications

305

catamaran was deployed on Lake Wivenhoe with an array of 50 floating sensor network nodes for communication and event detection. Several sensors were integrated: multiparameter probe (measuring temperature, conductivity, chlorophyll, turbidity, dissolved oxygen, and incident radiation); wind sensor; and profiling sonar. Another monitoring boat was proposed by Hitz et al. [32] with the specificity of having a submersible multiparameter probe for measurement from surface to 500 depth. It was used for the study of cyanobacteria blooms in lake Zurich (Switzerland). Buoys are sometimes used instead of boats or surface vessel. Chaffin et al. [33] studied the accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America). With the aim of the detection of cyanobacterial blooms from water quality probe measurements, water sampling and measurement were carried out close to two buoys equipped with a multiparameter probe, including fluorescence detection. Significant correlations occurred between buoy data and water sample data for cyanobacterial biomass, total algae, and turbidity. However, probes clogging and interferences may occur leading to the need of regular cleaning and calibration. Moreover, the measurement of nitrate concentration could complete the procedure of early warning for potentially toxic blooms. Several autonomous buoys can be used for the monitoring of large water bodies [34]. The study reported the implementation of a small fleet of 50 buoys deployed on a lake or reservoir for monitoring objective. Each buoy is a 1-ft-diameter spherical system with a sensory probe, an external motor and propeller, GPS, and remote communication system. The environmental sensors are limited to a multiparameter probe for basic physicochemical parameters, but optical sensing could be added if any. More recently, Zhou et al. [35] studied flushes of large amounts of organic material into lakes in the event of rainstorm in China. A multiparameter probe with a fluorimetric sensor was fixed on two buoys for the measurement of fluorescent DOM (FDOM). Field samples were collected during storm events in order to complete buoys’ information. The results suggest that rainstorm events enhanced the export to the lake of colored, hydrophobic, and aromatic DOM. The application of in situ fluorescence sensors provides an early warning of DOC surge incidents caused by rainstorm events and may be useful in advising drinking water treatment plant managers of changes in raw water DOM quality and treatability. Finally, Claustre et al. [36] recently described the BiogeochemicalArgo (BGC-Argo) network of profiling floats carrying sensors that enable observation of as many as six essential biogeochemical and biooptical variables: oxygen, nitrate, pH, chlorophyll-a, suspended

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particles, and downwelling irradiance, using UV
visible detection for some parameters. Able to move vertically up to 2000-m depth and horizontally, before sending the acquired data from the surface through the Iridium satellite, floats can bring to the community of oceanographic researchers, essential data for important issues such as the ocean acidification or ocean carbon uptake. The extension of this network has the potential to become a pivotal observation system that links satellite and on-site (ship-based, for example) observations in the near future.

9.4.2 High-frequency grab sampling In their study, Hansen and Singh [37] used high-frequency sensor data to reveal across-scale nitrate dynamics in response to hydrology and biogeochemistry in Intensively Managed Agricultural Basins. The analysis was conducted on 5-year USGS (United States Geological Survey) sensor data from eight riverine locations within Iowa, United States, including nitrate, discharge, and water temperature, acquired every 15 minutes between 2012 and 2017. The results show that nitrate (NO3) was strongly correlated with discharge for all sites and correlation increased significantly with drainage area and with crop coverage across watersheds. However, the highest NO3 concentrations reported often occurred at intermediate-sized discharge events, with high-peak NO3 likely to occur in response to projected regional changes in climate. In a recent work, Qin et al. [38] used high-frequency monitoring to reveal how hydrochemistry and dissolved carbon respond to rainstorms at a karstic critical zone, Southwestern China. Around 50 grab samples were taken daily for the analysis of DOC and major ions, and water temperature (T), dissolved oxygen (DO), electrical conductivity (EC), pH, and discharge were continuously measured at high temporal resolution (15-minute interval) using a multiparameter probe and a flowmeter. The results showed that the hydrochemical behavior and dissolved carbon dynamics are highly sensitive to hydrological variations in the karstic critical zone influenced by monsoon, which has high chemical transformation rates and experiences strong anthropogenic impact. High-frequency monitoring is more often applied for the study of nitrate dynamics but other experiments can be cited. In a recent work, Rosset et al. [39] used high-frequency monitoring fluorescence during 3 years for the study of drivers of seasonal and event-scale DOC dynamics at the outlet of two mountainous peatlands. For that, a fluorescence sensor gave every 30 minutes a FDOM signal which was correlated by DOC measurement from grab samples. The DOC concentration time series can be decomposed into a seasonal baseline interrupted by many short, intense peaks of higher concentrations. At the seasonal scale,

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DOC concentration baseline variations are mainly explained by peat water temperature, which controls integrative DOC production processes within the peatland. Finally, peatlands should not be considered as uniform landscapes. Distinct peatland units within the same peatland complex contribute differently to the DOC transfer processes to inland waters. In situ fluorescence measurement was also recently studied [40] combined with rainfall radar data, to provide new insights into natural organic matter transport dynamics in an urban river. Recent application of high-frequency grab sampling was for monitoring of nitrate isotopes (15 and 18) for pollution sources tracking in two watersheds in China [41]. This approach appeared to be more relevant than the traditional monitoring protocol based on measurements conducted during few rainfall events.

9.4.3 Handheld devices An integrated water quality monitoring device placed in a backpack was designed for the quick measurement of several parameters thanks to a multiparameter probe, and an innovative UV sensor. On the station chosen, the water to be analyzed is pumped into the device using a sampling tube integrating a pump immersed in the watercourse. The multiparameter probe allows the direct measurement of the electrical conductivity, temperature, pH, oxidation
reduction potential, turbidity, and oxygen content of the water sampled. The water is then analyzed using a double optical path UV
visible spectrophotometer adapted to different concentration levels. Nitrate and DOC concentrations can be easily determined [42]. After results displayed on smartphone screen, within 1 minute, the water is discharged. This system was validated during a research project [43] that was conducted for 1 year (September 2018
December 2019) for the identification of nitrate emission and abatement zones at a fine spatial resolution, by measuring water quality every 50 to 100 m in the watersheds of the Bay of Douarnenez and the Lieue de Gre`ve (Brittany, France). The portable measurement was validated from water samples and lab analysis. The adjustment was excellent with a determination coefficient of 0.99 and 0.95 for nitrate and Dissolved Organic Carbon (DOC), respectively (for more than 200 samples). Furthermore, the qualitative analysis of evolution of spectra along the river continuum, including all tributaries, drains, ditches, plant rejects, and other point sources observed in the field is a powerful tool to interpret hydrology and anthropic impacts in watersheds, complementary to all well-known parameters measured. The designed procedure makes it possible to increase the number of subwatersheds that can be

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monitored in the same day, and to increase the resolution of the measurement networks from one point per several tens or hundreds of km2 to one point per subwatershed of a few km2. High spatial resolution measurement allows the localization of highly charged groundwater resurgences and the identification of potential sites for the development of buffer zones. In all contexts, measurement at high spatial resolution allows a better understanding of the functioning of a watershed and the nature of the flows in space, and thus time, that structure the watershed. It also allows the definition of strategies for the implementation of agri-environmental measures within green algae control plans and the nature of a temporal monitoring network. Besides UV
visible spectrophotometry, handheld fluorometer was also proposed by Chen et al. [44]. A field, lightweight laser fluorometer based on the method of laser-induced fluorescence was developed for water quality monitoring. The hyperspectral LIF technique has the potential to simultaneously detect multiple water quality parameters of interest, which is particularly advantageous in optically complex waters. Under the laser excitation at 405 nm, the peak near 685 nm in the emission spectra corresponds to chlorophyll-a, the peak nearby 470 nm corresponds to Raman scattering (also linked to TSM response), and the peak near 508 nm corresponds to CDOM constituent. Simultaneous estimates of chlorophyll-a, CDOM, and TSM measured by the laser fluorometer were observed to agree well with those measured by laboratory methods after sampling, (R2 . 0.85) in Hangzhou Bay water (China). This procedure allowed successful high-resolution and high-frequency monitoring in a complex estuarine system.

9.4.4 On-site systems In a recent study, Luna Juncal et al. [45] proposed a mobile monitoring station integrated to a trailer, for the real-time measurement of nitrate and physicochemical water quality parameters every 15 minutes. Several experiments were successfully conducted in Australia showing that contrary to traditional high-frequency monitoring systems (not easy to move) [46], the mobile system developed can be deployed at different target locations on demand. Werner et al. [47] used a UV sensor to acquire UV absorption values from a headwater stream in central Germany, every 15 minutes for more than 1 year. After the DOC calibration by Partial Least Squares Regression (PLSR) from UV signal of 28 grab samples, SUVA254 and S275
295, slopes of UV spectrum between 275 and 295 nm, were calculated. The results provide a mechanistic explanation of the seasonally changing characteristics of DOC
discharge relationships and therefore

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can be used to infer the spatiotemporal dynamics of DOC origin in riparian zones from the DOC dynamics of headwater streams. Nitrate or DOC are not the only parameters studied with highfrequency monitoring. Solids fluxes dynamics were also followed by using turbidimetry correlation [48] for TSS assessment, and conductimetry as a proxy for total dissolved solids (TDS). An experiment over a period of 12 months with 15 minutes frequency measurement was conducted in a mixed restored prairie and agricultural catchment in Nebraska. TDS fluxes were about three times higher than TSS fluxes from the catchment as a whole. However, the TSS fluxes were higher in the agricultural section of the catchment than from the restored prairie. Mistick and Johnson [49] recently studied high-frequency analysis of DOC storm by UV sensor correlation, the responses in headwater streams of contrasting forest harvest history. Two headwater streams were selected for continuous monitoring of discharge and DOC, one in a clear-cut and other in relatively mature forest. Hourly data of DOC, from UV sensor, and stream discharges and rainfall intensity, from a close meteorological station, were acquired during the winter of 2018. The DOC response to storms was larger and faster at the clear-cut site and elevated storm responses may be due to changes in flow paths related to forest harvesting. However, low antecedent flow and greater storm intensity lead to significantly elevated DOC storm response. A review by Korshin et al. [50] examines the potential of spectroscopic methods (absorbance and fluorescence) for assessing the efficiency of water treatment. The variations of absorbance and fluorescence values along a treatment are linked to the reduction of dissolved organic pollution and also correlated with the formation of by-products. The review concludes that the current level of the development and implementation of spectroscopic methods for online/realtime water quality monitoring is far from its real potential. A recent application by Sahraei et al. [51] described a mobile trailer for high-frequency measurements of stable isotopes and other water quality parameters (20 minutes interval) from groundwater, stream water, and precipitation. UV sensor was used for nitrate and surrogate parameters and the results explain the response of runoff components and the impact on antecedent wetness periods. Moreover, several applications based on high-frequency monitoring of DOC and nitrate were recently proposed for understanding biogeochemical and hydrological processes and nutrients dynamics in watersheds [52
55]. As a conclusion of this section, Blaen et al. [56] recalled the importance of optical techniques for in situ nutrient monitoring (nitrate and DOM) and reviewed several studies carried out between 2011 and 2015 using optical systems (colorimetric, UV, and fluorescence probes) for

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half a month to 4 years. Considering that unnecessarily high resolution of sampling can result in excessive memory and power consumption, and certain monitoring techniques can increase maintenance requirements and waste generation They proposed an adaptive monitoring scheme for reducing monitoring frequency during low biogeochemical activity or variability (during low flow period, for example) and increasing the measurement rate for biogeochemically active period (e.g., high flow events). This procedure can be managed from data variations monitoring of physical linked parameters (rainfall, water level, etc.) for a remote adjustment of water monitoring frequency. Among the different techniques proposed for high-frequency monitoring of nitrate and DOM in freshwater, UV sensors are the most frequently used. For example, the results of field experiences using UV/ Vis sensors for high-resolution monitoring of nitrate in groundwater concluded recently that high-resolution UV/Vis monitoring should greatly contribute to a better understanding of groundwater processes in the future [57]. Another study [58] on the use of a UV
Vis submersible spectrophotometric probe confirmed that this technique facilitates rapid and robust measurement of the DOC content under remote field conditions. It also showed that qualitative information about DOM composition are possible through the ratios of absorbance values at specific wavelengths. For longer monitoring periods, the performance of UV sensors can be limited by turbidity [57] or fouling [59], and a specific attention (local calibration, maintenance) is thus required. A recent experimentation, using a homemade UV system with a dual pathlength [42] for high-frequency monitoring of river water during a rain period in Brittany (France), integrated into a field monitoring station with multiprobe (physicochemical parameters), flowmeter and sampler, confirmed the relevance of high-frequency monitoring [60]. In order to study the impact of rainy events on DOC evolution of water, the monitoring station was installed at the mouth of the Fremur River (Brittany, France). During 9 days, including two heavy rainfalls, UV spectra were acquired every 15 minutes and the second-derivative spectra were calculated (see Chapter 3) from spectra for DOC estimation at 295 nm [42]. Fig. 9.2 and Fig. 9.3 show, respectively, the set of secondderivative spectra and the evolution of river discharge and of the value of the second derivative at 295 nm. Finally, high-frequency monitoring of water quality can strongly improve the understanding of the complex spatial patterns and temporal dynamics of biogeochemical cycling in systems sensitive to environmental change [56,61]. Considering that high-frequency monitoring of nutrients is subject to research and pilot studies up to now, a transition from research to implementation in operational practice is needed [62].

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FIGURE 9.2 Set of second-derivative spectra acquired during 9 days.

FIGURE 9.3 Evolution of discharge measured at the mouth of the Fremur river (Brittany) (solid line), during two rainy events, and corresponding second derivative absorbance value at 295 nm (dotted line).

This transition requires efficient and cost-effective monitoring programs designed from clear monitoring objectives.

9.5 Underwater applications Autonomous underwater vehicles (AUV) were developed in the last century for scientific (marine geosciences) and military purposes [63]. From the 2000s the developments of AUV have rapidly emerged as vital

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tools for marine geoscientists, especially those involved in seafloor mapping and monitoring [64]. In a recent study, de Lima et al. [65] described the outcomes, field experiences, and feedback gathered from the use of underwater drones equipped with sensors and video cameras in various pilot applications in The Netherlands, in collaboration with local water managers. In the past decades, the first experiments were the exploration of deep layers of the ocean. Numerous works dealing with reef monitoring using video cameras and underwater sampling were also possible. Then, underwater drones were equipped with water quality monitoring instruments such as camera, multiparameter probes, and algae optical sensors for turbidity, chlorophyll-a, and phycocyanin measurements. Data can be acquired every 10 seconds due to the frequency limitation of the dissolved oxygen sensor. The insights gained by using underwater drones as a mobile monitoring system showed that, in many cases, these tools are a valuable complement to other methods, as they provide a unique combination of three-dimensional data and underwater images collection. In their paper, Pena-Pereira et al. [66] described a sensor system boarded into an AUV for real-time acquisition of water quality parameters. The AUV called CWolf from the Fraunhofer Institute was a 70 long, 10 diameter, and 135 kg weight submersible boarding a multisensors set for the in situ measurement of dissolved oxygen, conductivity, temperature, and nitrate by UV spectrophotometry. It was used for the study of the impact of fish farms on seawater quality thanks to selected dive profiles and monitoring campaigns.

9.6 Wireless sensor networks Before completing this chapter, let us consider wireless sensor network (WSN) which represents a promising technology for water quality monitoring and management. WSN began to be used for water quality monitoring in the early 2000s but have gained increasing attention in recent years as the devices and communication techniques have improved [67]. Initially, conceived around the measurement of basic physicochemical water quality parameters (pH, temperature, dissolved oxygen, conductivity, oxydo-reduction potential, and turbidity) [68], the concept of WSN moved toward more sophisticated systems such as the one recently described by Martı´nez et al. [69] integrating an online ionic chromatographic analyzer with a UV detector for nitrate and nitrite measurement. A promising project was recently proposed by Chen and Han [70] in the frame of a pilot project around the smart city infrastructure Bristol is open (United Kingdom). The sensing part is a multiparameter probe

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for the measurement of pH, temperature, dissolved oxygen, conductivity, oxydo-reduction, and turbidity, with TSS estimated from the turbidity value, TDS estimated from conductivity value, and salinity calculated from conductivity and temperature. The system is completed by a video camera to capture video images of the water surface. Several systems were deployed on harbor
wharf. The water quality sonde was immerged near water surface (approximately 50 cm deep) and the camera firmly placed on ground. A data transmission module utilized the common harbor Wi-Fi network. The great interest of this system is the possible exploitation of water surface image for the detection of oil spill or algae blooms. Once the sky reflectance is treated, images captured by satellites could be also considered for a better water quality monitoring. A long-term observation was recently reported by Ye and Kameyama [71] with the monitoring of 15 water quality parameters in Japan from 1982 to 2016. Basic physicochemical and pollution parameters, E. coli and some organics (chloroform, formaldehyde, nonylphenol, and anionic surfactants) were measured after sampling in the whole Japan (at 6484 sites). Several parameters require a spectrophotometric detection, often after a specific reaction. After data cleaning, it was shown that wastewater management has been effective throughout Japan, but water degradation was registered at some sites which will be the targets for future management and monitoring. The seasonal variability of stream water quality response to storm events captured was studied by Fovet et al. [72]. A 4-year dataset covering 177 storm events and combining high-frequency records of stream flow, turbidity, nitrate and DOC concentrations, and piezometric levels was used to characterize the storm responses in a headwater agricultural catchment (Brittany, France). Among field sensors, a UV
visible portable probe was deployed at the outlet of the watershed. Given the storm patterns around storm events, the role of groundwater fluctuations as a major control factor was underlined. This study demonstrated that high-frequency records of water quality are important for explaining the variability of catchment storm responses

9.7 Remote sensing techniques appraisal Even if airborne remote sensing techniques are promising and give always numerous applications in different domains, including water quality monitoring, there exist some limitations, which are as follows: 1. The occurrence of satellite observation with the presence of an adapted sensor must be compatible with the objectives of experiment (revisit time, spatial resolution),

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2. The presence of clouds during the satellite passage does not allow a relevant data acquisition and data acquired shall not be considered in this case, 3. The time to result is depending on the availability of working images, data pretreatment (e.g., atmospheric and geographical corrections), and on the existence of estimation model requiring a strong calibration from in situ sampling and analysis, 4. The empirical models used for the estimation of one parameter in one water body are site specific and cannot be generalized, 5. Due to the coarse spectral and spatial resolution of the satellite dataset, as well as methodological limitations, only spectral band ratio methods, empirical methods, and two biooptical inversion methods were employed, 6. Considering the total costs of construction of satellites/aircraft and sensors, launching, operation, and image processing, airborne remote sensing can be much expensive than other ground techniques if it is especially dedicated to water quality monitoring. For other remote sensing techniques applied for water quality monitoring, the following limitations and perspectives can be noticed (Table 9.2). Several advantages can be drawn from these “ground” techniques among which are the precision and rapidity of measurement and the relative low-cost deployment once the sensors/system acquired. On the other hand, the current level of the development and implementation of spectroscopic methods for online/real-time water quality monitoring is far from its real potential [50]. However, the effort to be made in order to get the same level of information that the one given by airborne remote sensing techniques must TABLE 9.2 Nonsatellite remote sensing techniques. Monitoring technique

Water body

Limitations

Boats

Ocean, large streams

Costs of construction and operation

Buoys, floats

Coastal, rivers, lakes

Few systems available, autonomy

On-site, online

Rivers, wastewater, drinking water

Logistic, cost of systems

Handheld, portable

Small streams, ponds, lakes

Few optical sensors, field operation costs

Underwater

Ocean, coastal

Cost, heavy deployment

Wireless networks

Coastal, rivers, lakes

Few experiences, communication network, data management, data security

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be great to cover the same area of observation. They are better tools for local or high-frequency monitoring, depending on research and operational questions.

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C H A P T E R

10 Drinking water quality assessment and management Nicolas Beauchamp1, Ianis Delpla2, Caetano Dorea3, Christian Bouchard2, Marie-Florence Thomas4, Olivier Thomas4 and Manuel Rodriguez2 1

Department of Civil and Water Engineering, Laval University, Que´bec, Canada, 2Graduate School of Land Management and Regional Planning (ESAD), Laval University, Que´bec, Canada, 3Department of Civil Engineering, University of Victoria, Victoria, Canada, 4EHESP School of Public Health, Rennes, France

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10.1 Introduction Water is required to comply with basic human needs (hydration, sanitation, and hygiene), urban and institutional needs, industrial applications, agriculture, energy production, or fire control. After its use, water is considered as a waste and discharged in water bodies (rivers, lakes, sea) or reused, generally after treatment. As drinking water is produced from water resources, its quality must be checked from source to the tap. Given the risk of waterborne disease and outbreaks among population, drinking water guidelines and regulations focus principally on microbiological and chemical contaminants, but surrogate parameters, such as the ones derived from UV spectra, can also be used for water quality control and management. The main applications of UV spectra are the monitoring of water quality from sources to tap, notably the characterization and monitoring of the natural organic matter (NOM) and its removal by various treatment processes, the monitoring of nitrate concentrations, and the characterization and the prediction of concentrations of disinfection by-products (DBPs), in routine process and as an early warning system (EWS). UV transmittance at certain wavelengths is also monitored where UV disinfection is used, to ensure that a sufficient intensity of UV radiation reaches the targeted pathogenic microorganisms. Finally, UV spectrophotometry can also be applied to bottled waters.

10.2 From source to tap water The quality of source waters can be influenced by land use, geology, and by the climatic conditions or events (floods, droughts, forest fires, etc.). For surface water, rainfalls are often responsible for raw water quality degradation, requiring a modification of the treatment operating conditions to remove turbidity and organic matter in excess in most of the cases [1]. The application of UV visible spectrophotometry for water quality monitoring is largely presented in the Chapters 8, 9, 11, and 12. Even if its sensitivity and selectivity are limited for the analysis of dissolved compounds, UV visible measurement is simple to use and several UV indexes can be proposed as surrogate parameters. This can explain the fact that UV visible sensors have been developed in the past two decades for online water quality monitoring. With the aims of water treatment assistance and early warning, UV visible sensors were recognized as a good alternative for total organic carbon (TOC) estimation (see Chapter 5), considering the minimal maintenance requirement

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in comparison to laboratory or online TOC instruments. This point was underlined in an EPA review of available online water quality monitoring sensor technologies [2]. In another review on advances in online drinking water quality monitoring systems [3], the advantages and limitations of available commercial sensors (among which UV visible sensors) were reported and the main conclusion was that there was no universal system for water quality monitoring and contaminant detection. Besides the use of UV visible spectrophotometry for the measurement/estimation of basic parameters for drinking water quality monitoring, a different approach can be envisaged, based on the characterization of NOM in relation to drinking water treatment [4]. In this context, UV visible methods, TOC measurement, and fluorescence were considered as general parameters for the characterization of NOM. From these considerations, standards and guidelines for drinking water quality being principally related to specific compounds, and concentrations or values being generally low, the use of UV visible spectrophotometry for routine monitoring seems limited to the quantification of high absorbing compounds. Thus the main parameters to be measured by UV spectrophotometry in drinking water are nitrate, and surrogate UV parameters can be also proposed for dissolved organic matter (DOM) or dissolved organic carbon (DOC) or TOC. In the following sections, the use of UV visible spectra for monitoring source water, especially NOM content, and nitrate concentration will be discussed and illustrated. The use of UV visible spectra in two critical water treatment processes, mainly to optimize coagulation and to monitor DBPs generated by chlorine, will also be discussed.

10.2.1 Source water monitoring UV spectra monitoring could prove useful for monitoring surface water sources used for drinking water supply. Examples are presented in Fig. 10.1 comparing spectra acquired during a dry period (baseline conditions) and a wet period in two different rivers. Clear differences are observed in raw UV spectra (Fig. 10.1, left) with an increase in absorbance that reflects the increase in turbidity and organic matter in raw water with the rain. After a rain period, the UV spectra shape is conserved, although the absorbance decreased following the suspended sediments and colloids concentrations in water. This phenomenon could be observed in surface waters in very different places subjected to different climates, pedology and geology, in Brittany (France) in the River Le Meu or in Que´bec in the River Chaudie`re. These two rivers have a comparable agricultural and residential land use.

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FIGURE 10.1 Comparison between baseline and rainfall conditions in a typical river in Que´bec (QC) and France (Brittany-BR). Left: Raw UV spectra (cell 35 mm for QC spectra, 100 mm for BR spectra). Right: Second-derivative UV spectra (cell 100 mm, step 20).

The absorption of UV light is mainly associated with the presence of chromophoric DOM [5]. Consequently, DOC has been correlated with UV visible absorbance at 254, 350, and 440 nm [5]. Specifically, the absorbance in the spectral region between 250 and 280 nm was linked to the presence of aromatic molecules [6,7]. The use of UV transformation tools such as second derivatives allows to reveal the position of the shoulder around 250 280 nm that is linked to the presence of aromatic compounds. The shapes of second-derivative spectra are also clearly marked according to the different sampling periods (dry/rain), with some positive peaks increasing or appearing with rainfalls at 310, 335, and 370 nm in Fig. 10.1. Using a similar UV transformation technique, the simultaneous measurement of both nitrate and DOC has been proposed [8]. For nitrate, the second-derivative absorbance at 226 nm (SDA 226) was proposed [8]. The method was tested on more than 500 samples from small rivers and drinking water sources of two agricultural watersheds located in Brittany, France, taken during dry and wet periods. For DOC concentration, the determination of the second-derivative absorbance at 295 nm (SDA 295) was proposed after nitrate correction. Organic matter absorbing slightly in the 270 330 nm window, a long optical pathlength (e.g., 100 mm) must be selected in order to increase the sensitivity of the measurement for low DOC values. The comparison between the proposed method and the standardized procedures for nitrate and DOC measurement gave a good adjustment with standard methods for both parameters, for ranges of 2 100 mg.L21 for NO3 and 1 30 mg.L21 for DOC [8]. Other UV transformation methods could prove useful to better characterize the source water DOM and help manage the drinking water treatment process. Specific UV absorbance (SUVA) and UV ratios have been suggested as the indicators of aromaticity, coagulability, and reactivity of the organic matter. The SUVA at 254 nm is defined as the ratio

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between UV254 nm and the DOC concentration and correlates to the aromatic content of humic samples [9]. While it has been less used in the literature, the ratio of absorbance 250 and 365 nm (E2/E3) could allow to estimate the aromaticity of freshwaters [9]. The E2/E3 ratio value is high when aromaticity and molecular size of aquatic humic solutes are low [10]. High ratio values could indicate important proportions of relatively small-molecular-size humic substances [11]. These different indices could prove useful to optimize coagulation process and achieve performance measurement.

10.2.2 Coagulation optimization and performance assessment In drinking water treatment, coagulation of particles and NOM should be properly achieved for subsequent flocculation and removal in downstream processes such as settling and filtration. This process involves the addition of a coagulant, typically a metal salt such as aluminum sulfate (alum) or ferric sulfate (the most widely employed coagulants). It has been shown that, between NOM and particles, NOM often exert the highest coagulant demand [12 14]. Because UV vis spectrophotometry can be used to evaluate the amount of both particles and NOM in water (see Chapters 5 and 7), it provides useful information for the optimization of the coagulation process. Indeed, by measuring the amount of NOM and particles present in the water to coagulate, usually lake or river water, UV vis spectrophotometry allows us to predict and adjust the coagulant dose in a feedforward manner. Monitoring the same parameters in the treated water (settled or filtered) provides a feedback on the performance of the processes in terms of NOM and particle removals. An example of UV visible spectra of a surface water (Lake Jaune, Beauport, Canada) and of the same water after coagulation flocculation and filtration is shown in Fig. 10.2. To evaluate the NOM content of the water before the coagulant dose adjustment, the single wavelength absorbance at 254 nm of lab-filtered water sample (UV254) could be typically used. Values of UV254 for surface waters typically vary from 0.040 to 0.400 cm21 (unit equivalent to a. u. as 1 cm pathlength cells are commonly used for raw waters) but can reach higher values in water with very high NOM content. Settled and filtered waters generally exhibit UV254 values between 0.020 and 0.080 cm21, depending on the NOM ease of coagulation, the initial amount of NOM content in the raw water, and the degree of optimization of the coagulation process. The maximum UV254 removal achievable by coagulation has been shown to be well correlated with SUVA of the raw water in an asymptotic manner. This is illustrated in Fig. 10.3 combining the results from [13,14] and [15]. It should be noted that

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FIGURE 10.2 Raw and filtered waters UV visible spectra (Lake Jaune, Beauport, Canada) (cell 10 mm for raw waters and 100 mm for filtered waters).

FIGURE 10.3 Maximum UV254 removal by coagulation flocculation settling filtration according to raw water SUVA. SUVA, specific UV absorbance.

these studies, however, differ in their definition of the optimal coagulant dose and coagulation pH, as discussed in a later paragraph. Various studies have tried to find a relationship between the optimal coagulant dose and raw water UV254, to use as a feedforward coagulation strategy. Based on their analysis of five surface waters with different physicochemical characteristics, Pernitsky and Edzwald [14] reported optimal doses of alum between 198 and 286 mg cm L21. Edzwald and Kaminski [13], in a study on two surface waters, reported a very similar range for one water (205 to 267 mg cm L21), but a slightly lower one for the other surface water (157 to 174 mg cm L21). This difference was attributed to the higher SUVA of the second water (4.3 compared to 3.0 L mg-m21) because it is known that NOM with high SUVA is more amenable to coagulation. Beauchamp et al. [15] also provide guidelines for optimal coagulation dose based on UV254. Their study of eight different waters with SUVA ranging from 1.73 to 4.98 L mg-m21

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yielded very similar results for all waters, with an optimal alum dose of 180 6 25 mg cm L21. This is illustrated in Fig. 10.4, where even the data from [13] and [14] fall within that range, except for one data point (labeled “outlier”) from [14]. The difference between the optimal alum doses around the average of 180 mg cm L21 was attributed to differences in SUVA and turbidity of the raw waters. One of the issues with this approach is to define what the “optimal coagulation dose” actually is. Generally, it refers to a coagulant dose for which further addition of coagulant does not provide a significant improvement in the settled of filtered water quality, but it can also be a simple target for a given parameter. For example, in their full-scale experiment, Edzwald and Kaminski [13] adjusted the alum dose to obtain a target UV254 value for filtered water between 0.030 and 0.035 cm21 and deducted their optimal alum/UV254 ratio from these results. This approach evaluates the performance of the process by a feedback mechanism (is the target UV254 reached?), and deducted from these results a feedforward mechanism for coagulant dose control (e.g., to reach the UV254 target, the coagulant dose should range between 205 and 267 mg cm L21). This approach applied in a full-scale plant did not prove successful for Beauchamp et al. [15]. The variability of the raw water source made it challenging to consistently reach the UV254 target when the raw water UV254 was high ( . 0.220 cm21). In this study, a percentage of removal of UV254 proved to be a better parameter to provide a feedback on the performance of the physicochemical treatment process, because the SUVA of the raw water, and hence the maximum UV254 removal (see Fig. 10.3), was much more stable than the UV254 of the raw water. Finally, the recommended alum/UV254 ratios from Beauchamp et al. [15] come from a different definition of optimal dose that is based on jar-test results. In these jar-tests, the goal was

FIGURE 10.4

Optimal alum dose based on UV254 of the raw water.

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specifically to coagulate and remove the NOM responsible for the formation of DBPs upon chlorination. Hence, the optimal coagulation dose was defined as the one when an increase in coagulant dose did not provide a significant reduction (0.5 μg DBP/mg alum) in DBP concentration obtained in a subsequent chlorination experiment of the filtered waters. In other words, the alum/UV254 ratios represent a point of diminishing return in terms of DBP removal. Another issue with the ideas of optimal dose and optimal UV254 removal is that these two metrics do not consider a very important parameter in the coagulation of NOM, namely, the coagulation pH, that is, the pH of the water after the addition and initial hydrolysis of the metal salt coagulant. It has been shown that the coagulation of NOM is enhanced under acidic conditions, at pH values between 5.5 and 6.5, depending on the temperature of the water and the coagulant used. In Figs. 10.3 and 10.4, the coagulation pH used was not always the same, and it was not necessarily in the optimal range for all waters. For example, in waters with high alkalinity, where it is challenging to reach such pHs, the actual optimal dose might be higher than suggested in Fig. 10.4, and it might explain the uncertainty on the optimal UV254 removal for a given SUVA in Fig. 10.3. The nuance between the definition of the optimal dose, the variability of the raw water source quality, and the other important coagulation conditions, notably coagulation pH and the use of alternative polymeric coagulants, might explain the variability observed in actual optimal alum/UV254 ratios. Nonetheless, these recommendations are a good starting point when one wants to optimize its coagulation dosage strategy. UV visible spectrophotometry proves a very valuable tool to adjust coagulant doses and to assess the performance of the coagulation process. Applications of online UV vis spectrophotometry probes for optimizing coagulant doses are even commercially available nowadays.

10.3 Estimation of concentrations of disinfection by-products Chlorination is one major final treatment step in the production of drinking water in order to ensure its disinfection (primary or secondary), that is, the respect of standards and guidelines regarding the microbiological risk in treated water and to guarantee the protection of consumer’s health through the distribution system. If chlorine and its derivatives such as hypochlorite have an effect on microbiological water quality, they also react with many dissolved compounds, including the residual organic matter. The products of the reactions between NOM and disinfectants are known as DBPs. DBPs have been analyzed for decades in drinking water and some of themes are now regulated in

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various countries. As some DBPs may have a potential impact on human health, the monitoring of these substances is important. Globally, DBPs are produced from the reaction between residual NOM existing at low concentration after water treatment (usually less than 2 mg.L21 of DOC) and chlorine. When chlorine is used as a disinfectant, various groups of DBPs can be formed, including trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs), haloacetamides, haloketones (HKs), halomethanes, nitrosamines, etc. The toxicity of more than 500 DBPs has been characterized and some DBPs are potentially toxic [16]. A review of DBPs toxicity reported that 68 DBPs were genotoxic among 85 studied [17]. A recent work concluded that the great majority of 50 DBPs tested cause genotoxic effects indirectly [18]. Exposure to DBPs have also been associated with potential reproductive outcomes (stillbirth and growth retardation) [17,19]. In this context, the formation of DBPs and their relation to NOM have been largely studied, and UV spectrophotometry has been regularly proposed as a surrogate parameter for DBP formation. One of the first works showing a strong correlation for river and lake water between the absorbance value at 254 nm (A254) and the DOM expressed as nonpurgeable TOC and the THM precursors was proposed more than 30 years ago [20]. Other correlations were obtained for the same type of water between the difference of absorbance at 272 nm during chlorination (ΔA272) and the concentration of total halogenated compounds formed (TOX) [21,22], with a variable correlation for THMs. The choice of the wavelength 272 nm follows that a ΔA peak is observed at or close to this wavelength [23], hence, providing a more precise mean of estimating DBP concentrations, with smaller relative errors. An example of a differential spectrum is shown in Fig. 10.5. Roccaro and Vagliasindi [24] showed that DBP concentrations are best correlated to ΔA at 272 nm than at 254 nm. This question, included in a

FIGURE 10.5 Typical spectra (left panel) and differential spectra (right panel) obtained upon chlorination of pretreated (coagulated flocculated filtered) water (from Lake Jaune, Beauport, Canada) (cell 100 mm).

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general kinetic study of DBPs formation, was studied by Korshin [23], who confirmed the strong correlation of ΔA272 and DBPs formed during the advancement of the chlorination. The effects of chlorine dose, reaction time, and temperature on the formation of DBPs during chlorination water were examined by Roccaro et al. [25]. The kinetics of chlorine consumption and DBPs release were influenced by temperature, and correlations between chlorine consumption, THMs, HANs, or other DBP species with ΔA272 nm were always strong. An improvement of the use of ΔA272 was proposed by considering the ΔA272 variation during 2 hours at a pH value of 7 [24]. This new parameter gave strong correlations with DBPs, regardless of the chlorination conditions (chlorine dose and water temperature) and NOM properties (raw, treated, and fractionated samples). A recent literature review [26] examined the relationships between DBP concentrations and differential absorbance, and the various parameters that impact those relationships. The chlorine dose was shown to have no impact on DBP ΔA relationships, much like the water temperature during chlorination. The pH and the bromide concentration of the water to be chlorinated, however, did change the regression parameters of the DBP ΔA relationships. Beauchamp et al. [26] also pointed out that linear regressions between DBP concentrations and ΔA applied widely. The DBP chloral hydrate is a noticeable exception: its relationship to ΔA has been shown to be highly curvilinear, well-suited for an exponential regression [23]. However, although linear relationships between DBP concentrations and ΔA are widely applicable, the regression parameters often changed from one water to another depending on water quality. Few studies examined the role of individual moieties of organic molecules to explain the differential absorbance spectra resulting from chlorination. Korshin et al. [22] first studied the chlorination of resorcinol, an aromatic compound very reactive with chlorine. In Ref. [27], a correlation between ΔA272 and NOM was shown, and the role of aromatic compounds dissolved in water was discussed. Aromatic compounds are most often associated with absorbance in the spectral region of 250 to 280 nm, and their chlorination has hence been considered responsible for the observed changes in absorbance upon chlorination of natural and filtered waters. Nonetheless, it has been known for a while that DBPs can be formed upon chlorination of various types of organic moieties [28]. An experimental work based on NOM fractionation showed that the nature of NOM influenced the above correlations [29]. For surface waters characterized by a SUVA lower than 2 L mg21 m21, with low-molecular-weight fractions (,2000 Da) and low aromaticity, the formation of THMs and HAAs after chlorination was

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not well correlated with SUVA nor with ΔA272. The results suggested that low- or non-UV-absorbing NOM play important roles in the formation of DBPs in waters with low SUVA. Beauchamp et al. [30] studied the changes in UV spectra upon chlorination of various organic compounds with different moieties (phenols, amino acids, carboxylic acids, and ketones). The study identified phenolic and diketone moieties as the ones responsible for the initial decrease of absorbance around the wavelength 270 nm. However, the ongoing chlorine reaction with phenol produced more important changes in the 230 250 nm region once the initial decrease centered around 270 nm was complete (after 30 minutes of contact time in the experiment, although shorter contact times were not studied). This triggered interest in using multiple regression with multiple wavelengths to predict DBP concentrations more precisely. Finally, while the amino acid moieties themselves did not contribute significantly to changes in UV spectra, they were considered significant contributors to the yield of HANs in natural waters [30]. Always based on UV spectrophotometry, a study showed that fractions obtained after high-pressure size-exclusion chromatography (HPSEC) of water before and after coagulation can be characterized by the absorbance slope index calculated from the absorbance values at 220, 230, 254, and 272 nm [31]. Depending on the fractions and the nature of NOM, this parameter is correlated with SUVA values of water before fractionation, and with Trihalomethane formation potential (THMFP). This approach (HP-SEC-UV) was coupled with DOC measurement and THMFP analyses in order to study the effect of TiO2 photocatalytic oxidation on NOM properties of two Australian surface waters [32]. The UV absorbance at wavelengths greater than 250 nm and the DOC content of fractions decreased significantly with treatment, following NOM composition change with molecular weight profiles corresponding to compounds absorbing weakly around 250 260 nm. The photocatalytic oxidation yields DBP with low aromaticity and low molecular weight, such as low-molecular acids and neutral compounds, which did not seem to contribute significantly to the formation of THMs. A recent method combining HP-SEC, UV absorbance, and fluorescence measurement was proposed by Li et al. [33]. Applied to the samples of 16 drinking water sources (China), DOM was characterized by A254 and A280 nm, and protein-like and humic-like substances were revealed by fluorescence (excitation at 280 nm). The developed system was a simple fluorescence sensor using only one UV280 nm LED as light source. Chlorination tests were conducted before and after coagulation and anion exchange, and the absorbance values, A254 and A280, showed similar correlations with the yields of DBPs, while the humiclike fluorescence obtained from LED sensors correlated well with both

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THMs and HAAs. Anion exchange exhibited more reduction of DBPs yields (with removal of humic substances) and UV absorbance and fluorescence signals than enhanced coagulation (retaining large molecular weight biopolymers). Another method was also proposed for the characterization of chlorination of DOM and DBP formation with two alternative parameters [34]: the differential logarithm of DOM absorbance at 350 nm (ΔLnA350) and the change of the spectral slope in the range of wavelengths 325 375 nm (ΔSlope325 375). These parameters were used to quantify individual DBPs species formed and chlorine consumption and ΔLnA350 was more reliable than ΔA272 nm, for 10 water sources. This method was applied to examine the effects of chlorination time on the kinetics of chlorine consumption and release of several DBPs groups. In this way, Roccaro et al. [35] showed that the spectral slopes of the logtransformed spectra are strongly correlated with SUVA but more simple to measure (no DOC measurement to do) and more relevant. For example, in the range of 280 350 nm spectra slopes were correlated with the yields of THMs and HAAs. These results strongly support the notion that multiwavelength monitoring of the absorbance spectra of drinking water and its logarithmic transformation constitutes a promising approach for water quality monitoring in different conditions. In a quest to improve the universality of DBP ΔA relationships, Beauchamp et al. [36] suggested the use of absorbance values of multiple wavelengths in a multilinear regression model. In this study, the best predictors for DBP concentrations were obtained from all wavelengths of the UV visible spectra and differential spectra between 200 and 600 nm. Interestingly, the highest correlations between DBPs (THMs and HAAs) and ΔA were obtained using two or three predictors: ΔA250 and ΔA425 for DCAA; A250, ΔA250, and ΔA425 for TCAA; and A270, ΔA270, and ΔA425 for THMs. It is hypothesized that the absorbance before chlorination (A270 and A250) is an important predictor of DBP concentrations because it somehow represents the amount of DBP precursors in the water. On the contrary, the differential absorbance predictors likely represent the evolution of the chlorination reaction with the various organic moieties involved in the formation of each individual DBP. Noticeably, this study pointed to the use of a wavelength in the visible region of the UV visible spectrum. A more recent study [37] involving the chlorination of both model compounds and natural waters also concluded that “long wavelengths” ( . 300 nm) are expected to be well correlated with DBP concentrations. All of these studies [34,36,37] point to the use of wavelengths at the beginning of the visible region (B380 nm) that are usually correlated with the yellowish color of water. Color of water (the conventional physical parameter) had already been

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identified as an indicator of the presence of DBP precursor [38]. Clearly, the incorporation of long wavelengths in DBP ΔA relationships should be explored further if this tool is to be used and applied more widely in the foreseeable future. Finally, the impacts of present and future climatic changes on surface water sources and on treated waters must be considered by assessing its effect on the potential of DBP formation. A recent study has shown that the probability of THM exceeding standard values in tap water may be increased by the end of the century [39]. Using a representative set of drinking water distribution systems supplied by surface waters in southern Que´bec (n 5 108), it was shown that a shift from 21.2% to 28.4% of probability of exceeding the THM standard value (using the high-emissions scenario RCP 8.5 (Representative Concentration Pathway) of the Intergovernmental Panel on Climate Change (IPCC) could be expected for the horizon 2080 compared to the period of 2006 09 [39]. In a similar perspective, heavy rainfalls could have a considerable impact on DBP formation as it could influence NOM levels in source water for long term [40 42]. A study led on a large water treatment plant (supplying Que´bec city, Canada) showed that the raw and treated water quality is degraded during and following spring rainfalls regarding TOC, SUVA, and THMs [42]. Moreover, an increase in DBPs reactivity with contact time was also noted in the experiments of simulated distribution systems, leading to a twofold increase in THMs and HAAs in waters representing the extremity of the city’s distribution system [41]. In this challenging context of climate changes on drinking water supply, UV spectrophotometry could constitute an interesting monitoring tool to help improve treatment operation and resilience to climatic conditions or events and safeguard the quality of water supplied to the citizens [40,43].

10.4 Early warning systems In order to ensure a good quality of drinking water in any points of distribution, the first criterion is that quality standards or guidelines must be fulfilled. However, another criterion to be considered for a full safety of the distribution network is the rapid detection of potential contamination of water, or change in water quality, before or after treatment. During an intentional or accidental chemical contamination of water resource, many situations may be considered according to the location of the observed event (e.g., withdrawal point or water tower), knowledge and nature of the substance, and the contamination area depending on the quantity and properties of the chemical. The risk management of contaminated water supply requires the knowledge of

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precise information on the presence of the potential pollutants and its quantity if possible, in order to ensure its detection and the efficiency of treatment systems to be implemented [44]. Therefore an accurate realtime detection of chemical (or biological) contaminants is required for planning and implementing mitigation measures to protect the water supplies [45]. Early warning systems (EWS) or contamination warning systems (CWS) could be used to detect anomalous changes in water quality due to contamination event(s). Once a water quality anomaly is detected, the water utility operator is alerted, and further actions (e.g., sampling and analysis) could be undertaken to identify and quantify the contaminant [2]. An accidental microbial contamination (outbreak) cannot be detected directly by UV spectrophotometry, but a chemical modification of water composition may be revealed by EWS, including UV sensing. Inversely, the use of biomonitoring elements for the detection of chemical/toxic contamination is possible. In their recent work, Hassan et al. [46] proposed a real-time monitoring method of water quality of stream water using sulfur-oxidizing bacteria (SOB) as bioindicator. In this study, an online toxicity monitoring system based on SOB was developed and operated for more than 6 months’ real-time assessment of water quality of natural stream water. In the presence of diluted swine wastewater or toxic chemicals (hexavalent chromium of 2 mg L21 or nitrite of 30 mg L21), SOB activity was inhibited by 80% to 90% within 1 hour. After the toxic shock load, the replacement of sulfur particles in reactors allows the reset of the EWS. Disturbances for a water system could be related to different unusual events that lead to water contamination, such as accidental discharges or spills, and natural or intentional pollution events. Some water quality proxies could be used as alarm parameters to quickly detect suspicious water quality changes, including parameters linked to UV spectra. These parameters should then show a good sensitivity, a low probability of false alarms, and a versatile response to different contaminant sources [47]. On the other hand, the application of near-infrared (NIR) spectroscopy to detect chemicals in highly diluted aqueous solution [48] remains a challenging task due to the dominant absorption of water molecules in the infrared region. The results obtained with NIR seem to suggest that there is a potential, at least for certain types of compounds, to detect changes in specific regions of the water spectrum. In particular, results from principal component analysis (PCA) seem to suggest that the presence of compounds which cannot form hydrogen bonds through hydroxyl groups can be more readily detected. EWS (or CWS) can be implemented either online or on-site. In the first case, EWS is used for the detection of incidents either before the treatment plant or after in the distribution network. In the other case,

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10.4 Early warning systems

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EWS should be field portable to be used on-site in the case of a suspicion of water contamination or poisoning, for the confirmation or information about the presence of a contaminant or toxic substance in water resource or in the drinking water production system. Online EWS was recently proposed to detect water quality anomalies such as contaminant intrusion in distribution systems. Multiparametric systems, such as a package of eight sensors developed by Hach Homeland Security Technologies (temperature, pH, turbidity, conductivity, oxidation reduction potential, UV254, nitrate, and phosphate), were successfully used for the detection of glyphosate [49] or cadmium [50] contaminations. This approach was also applied to the classification of contaminants such as herbicides (atrazine and glyphosate); heavy metals (cadmium nitrate and nickel nitrate), or inorganic salts (sodium fluoride and sodium nitrate) [51]. An experimental study was carried out from a multivariate statistical analysis of UV information between 230 and 400 nm, based on a PCA and the exploitation of the chi-square distribution in the principal component subspace [52]. This method was successfully tested for a contamination of drinking water by phenol. Another application of such sensors was proposed for the discrimination of water sources and blends in a complex drinking water distribution network (Barcelona, Spain) [53]. UV information is also exploited thanks to a PCA model and this method is able to contribute to the early detection of accidental events. In this case, simple UV parameters, such as the absorbance at 254 nm (A254) or the second derivative of absorbance at 310 nm (A310), can be useful for the early detection of water quality changes [54]. Furthermore, EWS or CWS would be capable of monitoring the safety of a given water supply, by analyzing, interpreting, and communicating that data to the appropriate people so that decisions could be made to protect the public’s health [55]. In this context, a field-portable CWS was designed for the regional health and security authorities (ARS Bretagne and SDIS 35, France). With the aim to be used on-site by nonexperimented operators, the software of the system asks for the different tests to be made in the case of accidental contamination (real or suspected). The heart of the system is a communicating UV spectrophotometer associated with classical sensors, capable of sending via cell phone or Internet, all relevant information (e.g., UV spectra, physicochemical parameters, referenced pictures and coordinates, etc.) to experts for a quick remote interpretation. This procedure can facilitate the confirmation of the contamination by the analytical laboratory [56]. This CWS has been successfully applied for different situations: • An accidental chemical substance(s) spill characterized by the presence of a truck, tank, or container near a water resource and the

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presence of a flow to the resource. In this case, the first diagnostic of the spilled substances’ nature and volume as well as the flow time and the characteristics of the receiving environment (the river flow as an example) allow the estimation of the contamination levels and the expected propagation area. In these conditions, a sampling on a relevant point (defined with or without remote exchange with an expert) and the selective research of the substance(s) with the help of the CWS gives a first diagnosis for a rapid decision. Further sampling downstream the suspected discharge allows following the pollution cloud, if any. • An intentional contamination suspected and characterized by a water tower breaking or by the presence of a suspected container near a water resource, without visible flow. In this case, the CWS allows researching the contamination nature and content (screening). Such a situation was encountered with the reporting of a suspected container close to a water tower. After verification, the UV spectrum of the residual liquid in the container showed a strong peak of absorbance at 245 nm, like a sample taken at the outlet of the water tower (showing a slight peak at the same wavelength). The decision of stopping the water supply was immediately taken, and the presence of diazinon suspected was remotely confirmed by experts and further analysis in the laboratory. • Water quality degradation characterized by physical or biological signs without visible contamination source. In this case, the use of the all components of CWS (multiparameters probe, UV and fluorescence spectrophotometer, and colorimetric kit tests) allows to establish the first diagnosis of the presence and nature of the contaminant(s) and thus to find a probable pollution source. For example, an unusual UV spectra shape will allow to detect an organic pollution or a high conductivity will be associated to a mineral pollution. This approach could be improved by the implementation of multiple solid phase extraction step (MSPE)/UV systems for a better on-site identification of micropollutants [57]. Finally, online or field-portable EWS or CWS must better demonstrate clear operational benefits (such as better water quality, decreased operating costs, or reduced customer complaints) [3]. The argument of water security is not sufficient, given the maintenance, technical expertise and cost required, and the number of false alarms often associated with them. Thus these systems should systematically be tested/verified by a third party with verification schemes matching utility practices [58]. Recently, Yaroshenko et al. [59] reviewed some ways for real-time water quality monitoring with chemical sensors. If there are still many

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10.5 Bottled drinking waters

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obstacles for having one sensing approach that would satisfy different situations, the most successful systems based on chemical sensing or its combination with other methods rely on specificity of a coating material that is capable of accurate detection of certain water pollutants, with molecularly imprinted polymers [60], providing an increased flexibility for the designing of those systems. In their work Soheilifar et al. [61] tested an application of molecularly imprinted polymer in solid-phase microextraction coupled with HPLC-UV for the analysis of dibutyl phthalate in bottled water.

10.5 Bottled drinking waters There exist a lot of bottled drinking waters with a majority of mineral and spring waters. Most often, the chemical composition of the water is displayed on the bottle label. The knowledge of water quality is interesting for the consumer, but this information is unfortunately missing on the label of bottled waters produced in countries where the regulation does not apply, as for example, in the United States, where the chemical composition of water is replaced by nutrition facts (fat, cholesterol, vitamins, etc.). Besides their chemical composition, bottled drinking waters are quite safe with respect to microbiological standards. However, as Leveˆque and Burns state [62], there should be campaigns focused on reducing bottled water use, and on increasing recycling rates in areas where the water quality is known to be adequate. In contrast, using bottled water may sound reasonable in areas where water infrastructures are lacking and drinking water quality may pose a threat to human health.

10.5.1 Spring water Spring water is collected directly from a natural underground spring and bottled at source. Its mineralization is specific and constant and some physical treatment can be made on-site such as aeration, filtration, or, for sparkling water, regasification with its own gas (generally carbon dioxide). The chemical composition of a spring water must be in compliance with drinking water quality standards. Fig. 10.6 shows UV spectra of some still bottled spring water from Canada and France (Eska, Saint Georges, and Zilia). The different UV spectra have the same shape due to the very good quality of water. A spring water contains a negligible concentration of organic matter (no absorbance above 250 nm) and is free of microorganisms, but the nitrate concentration is variable from one water to another. Table 10.1 presents the concentration of major

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10. Drinking water quality assessment and management

FIGURE 10.6 UV spectra of source bottled waters.

TABLE 10.1

Composition of source bottled waters (Fig. 10.6).

Concentration (mgL21) 21

Ca

Eska

Saint Georges

Zilia

25

8

11

5

5.1

3

19

15

1

1.1

1.3

82

52

67.7

8

6

5

27

15

21

Mg

1

Na 1

K

HCO32 SO4

22

2

Cl

NO32

2.2

TDS

85

140

104

mineral compounds obtained from the respective labels corresponding to spring waters with a low mineralization.

10.5.2 Mineral water As spring water, a natural mineral water originates underground, but its chemical composition has specific properties recognized by the medical authorities (e.g., Acade´mie Nationale de Me´decine for French mineral waters) for having specific health benefits. It is often coupled with the existence of a thermal station. Given its geochemical environment, the mineralization of a mineral water is very stable and sometimes high, explaining that its composition may derogate to drinking

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339

10.5 Bottled drinking waters

water quality standards for some minerals (e.g., sulfate, chloride, sodium, etc.). Contrary to spring water, no treatment is accepted for mineral water. Fig. 10.7 shows UV spectra of some still bottled mineral water from France (Evian, Hepar, Vittel, and Volvic). Table 10.2 presents the concentration of major mineral compounds obtained from the respective labels. The selected still waters represent a large sample of different mineral composition, from a carbonato-calcic and magnesian water (Evian) to sulfato-carbonato-calcic ones (Hepar and Vittel), with a low mineralized water (Volvic). As for spring waters, the different UV spectra have the same general shape due to the very good quality of water. The absorbance is null above 250 nm (no organic matter), but the nitrate concentration is variable from one water to another. Mineral water is also free of microorganisms.

FIGURE 10.7 UV spectra of still mineral bottled waters.

TABLE 10.2

Composition of still mineral bottled waters (Fig. 10.7).

Concentration (mgL2) Ca

21 21

Mg

1

Na 1

Evian

Hepar

Vittel

Volvic

78

555

202

9.9

24

110

36

6.1

5

14

3,8

9.4

K

1

HCO32

357

403

402

65.3

SO422

10

1479

306

6.9

2

5.7

Cl

4.5

8.4

NO32

3.8

6.3

TDS

309

2580

841

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10. Drinking water quality assessment and management

FIGURE 10.8 Example of UV spectrum of a still bottled water from a public water source.

10.5.3 Other bottled waters A third type of bottled water producing and selling in some regions, such as in North America, is the packaging of drinking water distributed from public water source (a municipal network). In this case, the treatment is consequent with a demineralization step and a possible remineralization before bottling. Except for demineralized water, the composition of this type of water is varying with the quality of resource and the efficiency of the treatment. Fig. 10.8 shows an example of UV spectrum of such a bottled drinking water. Its mineralization being very low, its UV spectrum is also very low and flat.

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[47] G. Langergraber, J.V.D. Broeke, W. Lettl, A. Weingartner, Real-time detection of possible harmful events using UV/vis spectrometry, Spectro. Europe; Revue Litteraire Mensuelle 18 (4) (2006) 19 22. [48] F. Be´en, S. Beernink, Y.J.A. Weesepoel, Feasibility of Using Near-Infrared Measurements to Detect Changes in Water Quality, KWR Watercycle Research Institute, 2020. Available from: https://edepot.wur.nl/521204. [49] H. Che, S. Liu, K. Smith, Performance evaluation for a contamination detection method using multiple water quality sensors in an Early Warning System, Water 7 (4) (2015) 1422 1436. Available from: https://doi.org/10.3390/w7041422. [50] S. Liu, H. Che, K. Smith, C. Chen, A method of detecting contamination events using multiple conventional water quality sensors, Environmental Monitoring and Assessment 187 (1) (2015) 1 11. Available from: https://doi.org/10.1007/s10661-0144189-4. [51] S. Liu, H. Che, K. Smith, T. Chang, A real time method of contaminant classification using conventional water quality sensors, Journal of Environmental Management 154 (2015) 13 21. Available from: https://doi.org/10.1016/j.jenvman.2015. 02.023. [52] D. Hou, J. Zhang, Z. Yang, S. Liu, P. Huang, G. Zhang, Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution, Optics Express 23 (13) (2015) 17487 17510. Available from: https://doi.org/10.1364/oe.23.017487. [53] R. Lo´pez-Rolda´n, S. Platikanov, J. Martı´n-Alonso, R. Tauler, S. Gonza´lez, J.L. Cortina, Integration of UV-visible spectral and physicochemical data in chemometrics analysis for improved discrimination of water sources and blends for application to the complex drinking water distribution network of Barcelona, Journal of Cleaner Production 112 (2016) 4789 4798. Available from: https://doi-org/ 10.1016/j.jclepro.2015.06.074. [54] A.J. Byrne, T. Brisset, C.W.K. Chow, J. Lucas, G.V. Korshin, Development of online surrogate parameters using UV-VIS spectroscopy for water treatment plant optimisation, Water: Journal of the Australian Water Association 41 (2) (2014) 94 100. [55] D. Kroll, Monitoring for terrorist-related contamination, in: S. Ahuja (Ed.), Handbook of Water Purity and Quality, Elsevier, Amsterdam, New York, 2009, pp. 343 377. Available from: https://doi.org/10.1016/B978-0-12-374192-9.00015-7. [56] B. May, Potable water contamination emergency: the analytical challenge, in: K.C. Thompson, U. Borchers (Eds.), Water Contamination Emergency Monitoring, Understanding and Acting, RSC Publishing, Cambridge, 2011, pp. 110 116. Available from: https://doi.org/10.1039/9781849733199-00110. [57] M. Brogat, E. Baures, A. Sellier, F. Mercier, M. Doloy, O. Thomas, et al., Automatic and predictive fractionation of organic micropollutants in contaminated water, Environmental Chemistry (2016). Available from: https://doi.org/10.1071/en15135. [58] J. Raich, Review of sensors to monitor water quality. European Reference Network for Critical Infrastructure Protection (ERNCIP) project, European Commission, Joint Research Center, Report EUR 26325 EN, 2013. [59] I. Yaroshenko, D. Kirsanov, M. Marjanovic, P.A. Lieberzeit, O. Korostynska, A. Mason, et al., Real-time water quality monitoring with chemical sensors, Sensors 20 (12) (2020) 3432. Available from: https://doi.org/10.3390/s20123432. [60] J.W. Lowdon, H. Dilie¨n, P. Singla, M. Peeters, T.J. Cleij, B. van Grinsven, et al., MIPs for commercial application in low-cost sensors and assays an overview of the current status quo, Sensors and Actuators B: Chemical (2020) 128973. Available from: https://doi.org/10.1016/j.snb.2020.128973.

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[61] S. Soheilifar, M.J. Rajabi-Moghaddam, G. Karimi, A. Mohammadinejad, V.S. Motamedshariaty, S.A. Mohajeri, Application of molecularly imprinted polymer in solid-phase microextraction coupled with HPLC-UV for analysis of dibutyl phthalate in bottled water and soft drink samples, Journal of Liquid Chromatography & Related Technologies 41 (9) (2018) 552 560. Available from: https://doi.org/ 10.1080/10826076.2018.1488138. [62] J.G. Leveˆque, R.C. Burns, Drinking water in West Virginia (USA): tap water or bottled water what is the right choice for college students, Journal of Water and Health 16 (5) (2018) 827 838. Available from: https://doi.org/10.2166/wh.2018.129.

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C H A P T E R

11 Urban wastewater Olivier Thomas and Marie-Florence Thomas EHESP School of Public Health, Rennes, France

O U T L I N E 11.1 Introduction

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11.2 Sewers 11.2.1 Fresh domestic effluent 11.2.2 Variation of quality according to time 11.2.3 Evolution along the sewer 11.2.4 Effect of rain 11.2.5 Nondomestic load in a urban wastewater network 11.2.6 Synthesis and other applications

348 348 351 353 354 357 359

11.3 Treatment processes 360 11.3.1 Primary settling assistance 360 11.3.2 Physicochemical treatment assistance 363 11.3.3 Biological processes 366 11.3.4 Complementary technique: membrane filtration and activated carbon 370 11.4 Applications 11.4.1 Fixed biomass treatment plant 11.4.2 Extensive process 11.4.3 Ozone treatment for treated effluent

372 372 374 378

11.5 Classification of wastewater 11.5.1 Typology of urban wastewater from UV spectra shape 11.5.2 Automatic classification of water and wastewater

378 378 380

References

382

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00005-8

347

© 2022 Elsevier B.V. All rights reserved.

348

11. Urban wastewater

11.1 Introduction Urban wastewater is most likely the best experimental field for the application of UV visible spectrophotometry. The first reason is that organic pollution is composed of unsaturated compounds, which allows suggesting simple relations between the characteristics of UV spectrum and the value of aggregate parameters such as chemical oxygen demand (COD), biological oxygen demand (BOD5), etc. (see Chapter 4). As a consequence, many researches have been conducted on this topic for 50 years [1 3] and several authors have proposed the measurement of the absorbance at 254 nm. Even if this wavelength seems to correspond to a shoulder in the spectrum, the reason for the choice of this wavelength was more practical than scientific (254 nm being the main radiation below 300 nm of low-pressure mercury lamp), because of possible interferences from industrial discharges. But even if multiwavelength methods are nowadays more adapted for spectra exploitation and for the estimation of some water quality parameters, the main interest of using UV spectrophotometry for urban wastewater quality monitoring is the significance of UV spectrum shape, the evolution of which gives useful information. First, this chapter presents some studies concerning the evolution of raw wastewater quality in sewer and, second, the effect of some treatment processes. Two examples of treatment plants are then presented, before a synthesis leading to the proposal of wastewater classification.

11.2 Sewers 11.2.1 Fresh domestic effluent Raw urban wastewater is composed of fresh domestic effluents, the quantity and quality of which vary according to time and space (Fig. 11.1). Two other factors must be considered in order to explain some specific variations: rainfall for unitary sewers and industrial discharges for all types of sewers. The evolution of a wastewater UV spectrum is always the same from the source to the discharge after treatment. The shape is always decreasing except for short wavelengths where nitrate formation can lead to higher absorption. Another observation to be made is that UV spectra of domestic or urban wastewater have practically always the same shape, whatever the environment or the country is. A fresh domestic wastewater is composed of soluble, colloidal, and suspended constituents produced by the physiological and domestic activity of human beings. Urine, feces, as well as detergents, particulate

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11.2 Sewers

FIGURE 11.1

349

Evolution of the UV spectrum from the source to the discharge.

matter, grease, and several other common pollutants are daily rejected in wastewater. Among all these compounds, urine is probably one of the most important components with respect to UV absorption and organic (and nitrogen) pollution concentration. Detergents also strongly absorb (particularly the benzenic forms) and can explain the peak often encountered at 225 nm (see Chapter 4). Fig. 11.2 shows the UV spectra of human urine and an anionic surfactant, both of which being considered as the main factors explaining the shape of the UV spectrum of fresh domestic wastewater. As the simpler evolution factor of wastewater is certainly due to the presence and behavior of particles, it is interesting to study the coarse distribution of their size. A simple experiment can be carried out on a wastewater sample [4]. The UV spectrum of the raw sample is compared to the spectra of filtrates obtained after 1, 0.45, 0.1, and 0.01 µm filtration steps (Fig. 11.3). The effect of the different fractions constituting the colloidal and solid phases can be related to spectra variation, the absorbance of which decreases with the filtration step level.

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11. Urban wastewater

FIGURE 11.2 UV spectra of urine (dilution 400), DBS 10 mg L21 (dodecylbenzenesulfonate) and fresh domestic wastewater (dilution 2).

FIGURE 11.3 UV spectra of urban wastewater before and after several filtration steps.

Fig. 11.4 shows that the different spectra corresponding to the various cutting sizes can either be featureless or present a specific shape. In the latter case, the adsorption of some organic compounds such as benzenic surfactants may be responsible for the shoulder at 225 nm on the difference spectrum corresponding to smaller colloids (between 0.01 and 0.1 µm). Another observation already made in Chapter 7 is that the average slope of spectra increases as the particles size decreases.

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FIGURE 11.4 Differential spectra of urban wastewater before and after several filtration steps from (Fig. 11.3).

11.2.2 Variation of quality according to time Municipal wastewater is linked to the water supply consumption of inhabitants and of municipal and security services (watering, washing). Wastewater flow varies according to the season of the year, weather conditions, day of the week, and time of the day. Under dry weather conditions, daily wastewater flow shows a diurnal pattern close to the water demand. Wastewater flow fluctuations depend on the sewers’ storage volume and on the time required for the wastewater to reach the treatment plant. Industrial discharges tend to reduce the peak flows. In small communities, two daily peaks are generally observed, while only one is noted in larger cities. In the latter case, the length and complexity of the sewer network tend to smooth the daily flow variation [5]. The variations of pollution fluxes or load (BOD5, quantity for instance) depend on flow variations. Thus the amount of pollution as well as the quality of effluents may vary according to the network size and the importance of industrial discharges collected, on the one hand, and the type of sewer, separated or combined (i.e., also collecting rain water), on the other hand. Fig. 11.5 presents the evolution of some UV spectra of raw sewage sampled hourly during 24 hours at the entrance of a wastewater treatment plant (WWTP) (after the grit chamber) [4]. In this example concerning a medium urban area of about 10,000 inhabitants, the sewer system is mainly of separated type. The corresponding values of the main parameters are also reported in Fig. 11.5, so that the spectra correspond to the filtered samples and to suspended solids.

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11. Urban wastewater

FIGURE 11.5 Wastewater quality variation with time.

The study of these UV spectra enables the survey of the evolution of both the amount of pollution (by the estimation of parameters) and the quality of the effluents. The relation between concentration and flow is clearly shown in this example. Indeed, flowrates and UV spectra present the same variation. During the night, the sample presents a spectrum characterized by rather low absorbance values with a monotonous shape without any shoulder. This can be easily related to a low domestic activity. On the contrary, daytime spectra present a more important pattern, with the shoulder associated with the presence of surfactants. A correlation between the values of flowrates and aggregate parameters plus some specific compounds (detergents) could easily be established. This example shows that the knowledge of time variation in wastewater quality is very important for sewer and treatment plant management.

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11.2.3 Evolution along the sewer The variation of wastewater quality is not only related to the human activities, but also to some physicochemical factors occurring along the sewer. Fresh organic matter composition can vary from the source (houses, for example) to the treatment plant because of physicochemical phenomena (solids settling, phase transfer, oxidation, etc.). Fig. 11.6 shows two sets of spectra of urban wastewater sampled at the beginning of a sewer network (upstream point) and at the inlet of the treatment plant [4]. Sampling has been made at the beginning of the afternoon, taking into account the transfer time from one sampling station to another. Some differences have to be noticed between spectra. First, the spectrum of wastewater sampled upstream of the network is more important than the one sampled at the inlet of the WWTP. The corresponding values of the main parameters explain this observation with 1055 and 818 mg L21 for COD and 467 and 315 mg L21 for BOD5, respectively, from upstream to the treatment plant. This variation in concentrations can be due to several reasons such as the input of cooling, watering or even clear parasite water into sewer, or the partial biodegradation of organic matter. The comparison of differential spectra between the raw and the filtered sample is interesting (Fig. 11.6). These spectra are related to the characteristics of suspended solids of samples. The total suspended solids (TSS) concentrations are 502 and 669 mg L21, respectively, from upstream of the sewer to the treatment plant, meanwhile the corresponding spectrum seem to be divided twice. The observed increase can be explained, on the one hand, by solids input (by incoming water) or formation (related to the biodegradation) and, on the other hand, by the aggregation of colloids in suspended solids [6]. This is confirmed by the fact that particles of larger size absorb less in the UV region than smaller ones (see Chapter 7).

FIGURE 11.6 Wastewater evolution along a sewer. UV spectra (dilution 5) of raw and filtered samples and spectra corresponding to TSS. TSS, total suspended solids.

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11. Urban wastewater

11.2.4 Effect of rain The first striking feature of urban storm runoffs is their great variability, with quite different pollution values (loads and concentrations) from site to site. This variability is closely linked to the nature of storm events. Indeed, each rainfall is different from the other and induces specific phenomena in combined sewage [7]. Nevertheless, average values still stand out, which enable a comparison with wastewater in dry weather conditions (Table 11.1) [8]. This comparison shows a dilution effect of dissolved pollution (pointed out by Nitrogen Total Kjeldahl (NTK) variation) but an increase of suspended solids. Another conclusion is a change in the nature of the suspended pollution, mostly organic in dry periods and more mineral during rain. This phenomenon is mainly explained by the suspended matter influx, leading a concentration to maximum that is five times more important than for wastewater. Moreover, the organic load seems to be less biodegradable, as pointed out by a higher COD/BOD5 ratio. Fig. 11.7 presents the evolution of urban sewage during a storm event [9]. The dry weather sample was taken after the grit chamber and the wet weather sample at the overflow of the main interceptor leading to the treatment plant. The sewer system in this town is combined in the center and separated in the outskirts. The first wet weather sample (sample 2) is highly concentrated since it shows absorbance values twice as high as the ones of dry weather (sample 1). After this first load, the following sample (sample 3) is diluted, showing the lowest absorbance values of the spectra set. Then, sample concentration slowly increases to become slightly higher than during dry weather (sample 1) at the end of the rain (sample 6). Normalized spectra are compared in Fig. 11.8, showing that the shape of urban storm runoff spectra is rather diffuse at the beginning of the rain. During the storm event, the slope between 200 and 240 nm TABLE 11.1

Average concentration for combined sewage and wastewater [8].

Parameter 21

TSS (mg L ) Volatile fraction of TSS (%) 21

COD (mgO2 L ) 21

BOD5 (mgO2 L ) COD/BOD5 ratio 21

Nitrogen Total Kjeldhal (mgN L )

Urban storm runoff

Wastewater

176 2500

150 500

40 65

70 80

42 900

300 1000

15 301

100 400

3.4 6.0

2

21.0 28.5

30 100

TSS, total suspended solids; BOD5, biological oxygen demand; COD, chemical oxygen demand.

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11.2 Sewers

355

FIGURE 11.7 Evolution of wastewater UV spectra during a storm event (all samples were diluted twice and thus absorbance values multiplied twice).

FIGURE 11.8 Evolution of wastewater normalized UV spectra during a storm event.

gradually becomes steeper while the absorbance above 250 nm decreases relatively. Spectra shape thus tends to progressively look like the one of the dry weather spectra as the rain gets closer to the end. This phenomenon takes place around an isosbestic point located at approximately 234 nm, showing that sewage can be compared to a mixture of two major components [10]. The steep slope in the first part of the dry weather spectra is an indication that the soluble matter is preponderant during dry weather conditions, while the high absorbance values above 250 nm suggests that suspended solids predominate during rain. Sample analysis (Table 11.2) confirms the information brought by UV spectra observation showing that the first wet weather sample (sample 2) is heavily loaded in terms of COD. The following is diluted (sample 3) with a lower COD and conductivity, but with a higher TSS

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TABLE 11.2 Evolution of nonspecific parameters during a wet weather period and comparison with wastewater in dry weather conditions [4].

Weather 21

Conductivity

mS cm

21

1

2

3

4

5

6

Dry

Rainy

Rainy

Rainy

Rainy

Rainy

0.78

0.48

0.38

0.66

0.65

0.86

COD

mgO2 L

430

2400

310

560

590

510

Raw

%

29

90

90

75

61

50

Settleable

%

31

7

4

19

25

33

Supracolloidal soluble

%

40

3

6

6

14

17

21

TSS

mg L

207

1489

437

319

375

248

Raw

%

54

87

85

79

77

56

%

46

13

15

21

23

44

88

63

66

79

81

86

53

25

15

25

35

38

Settleable a

Supracolloidal TVS DOC

% 21

mg L

a

Total volatile solids (fraction of TSS of organic nature). COD, chemical oxygen demand; TSS, total suspended solids.

FIGURE 11.9

UV spectra and difference spectra of sample 2 of Fig. 11.7 (diluted

twice).

concentration than during dry weather (sample 1). After the first part of the rain (samples 4, 5, 6), COD values are slightly higher than those corresponding to dry weather. Fig. 11.9 shows the UV spectra of colloidal and particulate fractions of a sample collected during the first storm period (sample 2).

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UV spectra of TSS and raw wastewater compared to UV spectra of the sample during the dry period (see Fig. 11.4) are featureless because of the great proportion of settleable particles (90% of TSS) that diffuse light by diffraction. Chemical response is very weak : for example, the shoulder at 225 nm that indicates the presence of detergents does not appear clearly (see Chapter 5). Another experiment was carried out with a submersible UV/VIS spectrophotometer implemented in the pretreatment unit of a large-scale WWTP (350,000 person-equivalent) to monitor the rapid changes in TSS and COD occurring during rain events [11]. Calibration was proven to be difficult for fast composition-varying streams, but the device was able to monitor qualitatively sudden quality changes, in spite of the noise affecting the signal. The use of spectra in the visible and near-mid-ultraviolet range was also proposed [12] with principal component analysis (PCA) and partial least squares (PLS) processing for the monitoring of TSS in municipal WWTPs. An experiment was conducted along three WWTP process lines with different primary and secondary treatment units. The clustering observed in PCA score plots was mainly attributed to the suspended solids’ fraction present and highlighted differences in solids’ quality between plants and along the treatment lines. Thus local PLS calibration models to estimate TSS gave satisfactory results for each plant.

11.2.5 Nondomestic load in a urban wastewater network Actually, an urban wastewater network does not collect only domestic wastewater. It is composed of numerous subnetworks (or branches) collecting wastewaters of a defined area. In order to have a better understanding on the resulting quality of wastewater downstream a urban network, a study was carried out in different branches of the wastewater network of an urban area of 150,000 inhabitants (Sherbrooke, South Quebec) [13]. Four different subnetworks were chosen and sampled (industrial, commercial, hospital, and university). Three monitoring sites were identified by area plus another one downstream the subnetwork. Flowrates were measured during 3 days (between Monday and Thursday) of dry weather in September 2006 and wastewater samples were hourly taken for quality measurement (pH, temperature, conductivity, TOC (total organic carbon), COD, and TSS) and UV spectra acquisition. The main results are displayed in Fig. 11.10 and the following observations could be drawn: • For the hospital area (700 beds and 5000 employees), the flowrate followed the expected evolution (low during night and daily maximum with activities) and the spectra shape accompanied the

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FIGURE 11.10

11. Urban wastewater

Flowrate values and UV spectra of urban wastewater network of

Sherbrooke [13]

flowrate with maximum absorbance values for the maximum of activities. The shoulder around 225 nm due to surfactants is always present except in the heart of the night, without washing activity. The pollution parameters were in average not high with 272 µS cm21

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359

for conductivity, 34 mg L21 for TOC, 171 mg L21 for COD, and 51 mg L21 for TSS. • For the commercial area (200 stores), the observations are close to the ones of the hospital area, even for pollution parameters. • For the university area, the flowrate evolution did not show the same pattern as the two previous ones but presented a strong variability probably due to industrial parasite water collected in the subnetwork (the university area being located on a hill). The pollution parameters were slightly higher than the two previous area for conductivity (629 µS cm21) and COD (308 mg L21). • For the industrial area, including 150 companies of different activities with more than 5000 jobs, the flowrate evolution was rhythmed by pumping periods approximately every 5 hours, given the geographical localization of the industrial area preventing a gravitary sewage network. Between lift pumping, the background flowrate is three times lower than for flow peaks with 1000 m3 d21. UV spectra intensity followed the flowrate evolution with a low signal for two samples taken during the background period. However, the sample taken during the first pumping time presented relatively higher absorbance values between 200 and 240 nm than for the other samples, showing the presence of nitrate in wastewater. The pollution parameters were higher than those of other areas with 81 mg L21 for TOC, 406 mg L21 for COD and 98 mg L21 for TSS.

11.2.6 Synthesis and other applications Fig. 11.11 presents a synthesis of the main phenomena involved in wastewater quality variation along a sewer network, emphasizing on solid transfers during dry and wet weather. This presentation shows the great interest of using UV spectrophotometry in the understanding of phenomena. Another application not presented in this chapter is the diagnosis of wastewater quality in sewer for the detection of barred industrial discharge, for example. In the case of suspicious junction, it is easy to plan an experiment with sampling for parameters measurement and UV spectra acquisition. The checking of particular wastewater quality will be explained in the next chapter. Other use of UV spectrophotometry for urban wastewater characterization can be equally envisaged. For example, the UV study of gel chromatography fractions leads to the revelation of various groups of compounds that can be classified by their molecular weight. This will be presented for the characterization of humic-like substances in urban compost in Chapter 13.

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11. Urban wastewater

FIGURE 11.11 Organic matter of urban wastewater: evolution along sewer [4].

11.3 Treatment processes The application of UV spectrophotometry for the study of treatment processes is dependent on the user’s needs. From primary treatments to the global efficiency estimation of the treatment plant, several applications are presented in this section.

11.3.1 Primary settling assistance The purpose of primary sedimentation is to remove the larger suspended particles named settleable solids. About 50% 70% of TSS and 30% 40% of the BOD5 can be eliminated during this operation, which necessitates a residence time in a clarifier of about 2 hours. Fig. 11.12 shows the settling effect on UV spectra and spectra related to settleable matter and suspended matter (after filtration at 1.2 µm) are presented in Fig. 11.13. During settling, the decrease in absorbance on the whole spectrum, especially between 230 and 350 nm where TSS are responsible for a diffuse absorbance, indicates the removal of settleable solids. About 54% of the suspended solids and 29% of the COD are removed by settling (Fig. 11.12). Actually, settleable solids are also constituted of a part of colloids that can be trapped with larger solids during the settling operation. Their corresponding spectrum represents about 35% of the absorbance due to suspended matter (Fig. 11.13).

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11.3 Treatment processes

FIGURE 11.12

361

Spectra of raw and 2-h settled sewage.

FIGURE 11.13 Spectra of the settleable and suspended matter from raw sewage.

In order to help the user in the design of a settling tank (to build or to check), a simple experiment can be proposed (Fig. 11.14). A wastewater sample is introduced into a 2-m-high glass column of 100-L capacity, and aliquots are carefully withdrawn for different heights and times. UV spectra are acquired for a quick estimation of the TSS (after a previous calibration if needed). The aim of the experiment is to study the value of the settling yield in function of time or height (Fig. 11.15). The settling test is carried out over 3 hours. The UV spectra evolution shows that the signal decrease with time is generally quick at the beginning of the experiment (until 30 45 minutes) and slows down

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11. Urban wastewater

FIGURE 11.14 Settling test of urban wastewater and corresponding UV spectra.

afterward. These phenomena are typical of hindered settling. This tendency is available for all heights studied (Fig. 11.14). At the end, all spectra are quickly similar due to the thickening of the settled solids bed. After the test, the settling efficiency can be studied, from the curves of Iso-TSS removal yield (Fig. 11.15). Then, the efficiency evolution with time can be represented for a given height. From this curve, it is easy to determine the settling time and hence the climbing velocity to apply, in order to obtain a given efficiency.

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363

FIGURE 11.15 Settling study. Iso-TSS removal yield curves and evolution of settling efficiency with time (for H 5 2 m). TSS, total suspended solids.

11.3.2 Physicochemical treatment assistance The purpose of chemical treatment based on coagulation flocculation is to facilitate the elimination of suspended solids and colloids, organic as well as mineral, from wastewater. The destabilization of colloids by the addition of coagulants (such as ferric chloride) leads to the agglomeration of particulate matter for a better settling process. The flocculation step can be improved by the addition of organic or mineral polymers (called flocculants). The separation of the chemical sludge produced by coagulation flocculation from treated water is realized by settling. Such a treatment leads to a yield of 80% 90% of TSS and 70% 80% of the COD and BOD5. Apart from phosphorus, soluble matter is not removed. 11.3.2.1 Jar test Many variables affect the mechanisms of coagulation flocculation. The selection and optimum dosage of coagulants and flocculants, the determination of optimum pH, and the optimization of operating conditions (mixing energy and time of rapid and slow mixing) are effectively determined by using a laboratory test called Jar test. It is a valuable tool for the optimization of existing plant operations, and also for a new design or plant expansion. This test can be carried out with the help of UV spectrophotometry. Fig. 11.16 presents the evolution of spectra with the coagulant concentration. The optimum dosage of coagulant corresponds to the lowest residual turbidity or the lowest TSS content in the supernatant. However, the use of turbidity is not always appropriate (see Chapters 5 & 7), and TSS measurement is time-consuming and therefore too slow to be used for process control. The UV spectrophotometric study enables the control of the effect of ferric chloride dosage. A decrease in absorbance is observed

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FIGURE 11.16 Influence of the amount of FeCl3 on the UV spectra during a Jar test.

all along the spectrum, especially between 230 and 350 nm where colloids and TSS are responsible for a diffuse absorbance. The decrease of the shoulder located at around 225 nm, characteristic of anionic surfactants [14], shows that the chemical process enables the removal of a part of detergents. Other dissolved matters that can be noticed mostly between 200 and 240 nm, inducing an absorbance with a steep slope, are not removed. UV spectrum of urban effluent from a chemical treatment plant has thus a structureless shape like the one of raw wastewater. The absorbance values globally decrease as the wavelength increases. However, the effect of the coagulation flocculation process can be noted. Indeed, the absorbance value above 250 nm is very low, showing that the concentration of suspended solids and colloids, the optical effect of which being more sensible in this wavelength range (see Chapter 7), is also very low. 11.3.2.2 Problem of sample aging A physicochemical process based on coagulation flocculation produces treated wastewater mainly composed of soluble and colloidal matter. A part of supracolloids responsible for TSS is always present, and their remaining concentration depends on the coagulant dose. The stability of the treated wastewater is not well known, and it is not rare to see this type of sample aging with an increase in TSS concentration with time, after the coagulation flocculation process. For example, Fig. 11.17 presents the evolution of the TSS concentration of a sample stored at 4 C. The measured concentrations, as well as

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365

FIGURE 11.17 Evolution of TSS concentration versus time of a wastewater treated by coagulation flocculation (sample stored at 4 C). TSS, total suspended solids.

FIGURE 11.18

UV spectra (fresh and aged) and spectra of SS and colloids.

the UV estimation based on the use of the coefficient contribution of TSS reference spectra (see Chapter 5), increase with time up to 25% 50% of the initial value. Fig. 11.18 shows the normalized spectra and the spectral contribution of TSS and colloids. This experiment can be completed by the study of the evolution with time of contribution coefficients of the different fractions involved in the phenomenon: suspended solids, colloids, and surfactants (Fig. 11.19). The evolution of UV spectra during aging shows a small decrease of absorbance values for the short wavelengths and a corresponding increase in the higher ones. This is due to both an increase in suspended solids and a decrease in colloids, clearly shown in Fig. 11.17, resulting in a transfer from the colloidal fraction to the supracolloidal one (leading to a TSS increase). The finest colloids flocculate and produce suspended solids, perhaps aggregated on existing particles. In the case of a

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366

FIGURE 11.19

11. Urban wastewater

Evolution of TSS and colloid coefficients versus time. TSS, total sus-

pended solids.

physicochemical treated wastewater, the remaining suspension treated by coagulation flocculation is not stable, and the agglomeration process is not completed. In order to have a better understanding of the phenomenon, the evolution factors are studied. The process of agglomeration/dispersion can be influenced by the presence of a residual coagulant, but the study of residual iron has shown a weak decrease with time and cannot explain the sample evolution. The evolution of surfactant contribution coefficient shows, on the contrary, a decrease in their quantity, particularly in the first part of the sample aging, whatever the coagulant dosage (Fig. 11.19). Surfactants have thus a role of dispersion stabilizing almost any suspension. Moreover, they can adsorb themselves on solids [14], thus destabilizing the suspension. So, with the surfactants concentration decreasing in solution, the colloids can flocculate or adsorb themselves on suspended solids, explaining both the decrease of colloids and surfactants in the sample.

11.3.3 Biological processes Biological processes are widely used for wastewater treatment. Since an important part of organic matter of wastewater is biodegradable, the presence of microorganisms in a process will degrade this main pollution form. The concerned fraction is estimated by the BOD5 measurement (see Chapter 5), the principle of which is to reproduce the phenomenon of biodegradation and to quantify the corresponding oxygen consumption (demand). In contrast to the physicochemical process, the aim of biological processes is to remove the soluble biodegradable

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367

part of organic matter. This is carried out generally in the presence of oxygen in an aeration basin and leads to the (partial) mineralization of matter and to sludge production. This last point is very important because soluble products are converted into solids (activated sludge) easily removed by settling in a clarifier (and recirculated). The interest in UV spectrophotometry for the study of biodegradation has already been demonstrated in Chapter 5 with the study of samples during BOD measurement evolution. The same evolution is more or less observed for biological WWTPs. Fig. 11.20 presented the spectra of inlet and outlet of a large treatment plant characterized by a rather short mean residence time in the aeration basin (few hours). This process, working with a high-load F/M ratio (food on microorganisms), does not lead to the complete biodegradation of organic matter as it is shown by the outlet spectrum shape. The efficiency is quantified by removal yields calculated from different aggregate parameters (Table 11.3). A second small biological WWTP characterized by a longer mean residence time in the aeration basin, up to 1 day or more, has been studied (Fig. 11.21). In this case, corresponding to a low-load biological process, the mineralization process is completed and N compounds are oxidized into nitrate, giving a high absorbance at shorter wavelengths as already shown before. The resulting spectra thus show a great difference. A last example (Fig. 11.22) concerns a more efficient WWTP type integrating a denitrification step in the biological process. This is possible by using, for example, an anoxic zone before the aeration step and

FIGURE 11.20 Spectra of inlet and outlet of biological WWTP (high load ratio). WWTP, wastewater treatment plant.

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11. Urban wastewater

TABLE 11.3 (WWTPs).

Removal yield for the studied biological wastewater treatment plants WWTP1

WWTP2

WWTP3

High load

Low load

Denitrification

695/242

417/85

837/22

65

80

97

312/96

179/24

343/ , 5

69

86

. 98

44/29

38/7

73/8

34

81

89

TSS inlet/outlet

204/82

146/36

501/10

Yield%

60

75

98

58

30

2

Type COD inlet/outlet

a

Yield% BOD inlet/outlet

a

Yield% TOC inlet/outlet Yield% a

a

NGL outlet a

a

21

mg L . BOD, biological oxygen demand; COD, chemical oxygen demand; TSS, total suspended solids; TOC, total organic carbon; NGL, nitrogen global.

FIGURE 11.21

Spectra of inlet and outlet of biological WWTP (low loading rate) outlet (diluted 2). WWTP, wastewater treatment plant.

by recirculating the mixed liquor (wastewater and biological sludge) from the outlet of the aeration basin. In this case, the nitrate formed can be assimilated as a source of oxygen by specialized microorganisms and can be reduced into gaseous form.

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369

FIGURE 11.22 Spectra of inlet and outlet of biological WWTP (denitrification) inlet (dilution 2). WWTP, wastewater treatment plant.

Fig. 11.22 shows the effect of a denitrification plant, reducing the discharged nitrate concentration. The removal yields of these last two examples are generally better than for the first treatment plant (Table 11.3). A first general comment is that the characteristics of treated wastewater obviously depend on the process chosen in the treatment plant, but no one can totally remove the organic pollution. The efficiency, calculated in Table 11.3 for the whole treatment plant (integrating the efficiency of primary settling for TSS and related parameters), generally increases with the mean residence time of wastewater. The best results are obtained for a WWTP with a denitrification process, with more than 95% of yield on the different parameters. For the high-organic-load process, the mean efficiency is about 66%, except for the TOC (34%). This is due to the fact that the biodegradation of organic compounds is partial, as only one-third of the organic carbon has been mineralized. Considering the differential spectrum of effluents corrected by the contribution of nitrate, the resulting shape can be explained by the presence of a few particles, residual organic compounds and small carboxylic acids as organic matter is not totally mineralized (Fig. 11.23). These compounds contribute to the remaining organic carbon. Another study proposed the quantitative monitoring of an activated sludge reactor using online UV visible and near-infrared (NIR) spectroscopy [15]. In this work, UV visible and NIR spectroscopy were used to monitor an activated sludge reactor using in situ immersion probes connected to the respective analyzers by optical fibers. Calibration models based on PLS regression were developed for COD, nitrate

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FIGURE 11.23 Spectrum of outlet 3 without nitrate (see Fig. 11.21) and spectra of some carboxylic acids (acetic, propionic, and butyric acids).

(N-NO32), and TSS. The results obtained demonstrated that both techniques were suitable for consideration as alternative methods for monitoring and controlling wastewater treatment processes.

11.3.4 Complementary technique: membrane filtration and activated carbon Some complementary techniques can be used for wastewater treatment, such as membrane processes, for example. Urban wastewater treatment by membrane (e.g., microfiltration) can be envisaged up to a virtual disinfection, but the industrial development of these processes is still limited by the rather low value of the permeate fluxes and by membrane fouling that is not always reversible. In order to have a better understanding of membrane fouling, UV spectrophotometry can be useful. Fig. 11.24 presents the UV spectra evolution of a secondary effluent, from a biological treatment plant, before and after microfiltration. The decrease of the flux is due to membrane fouling. The stabilized flux is obtained after about 60 minutes of filtration. The spectra of raw sewage and its filtrate show that some material is retained by the membrane, leading to membrane fouling. It seems, however, that the quantity of matter retained decreases as filtration time increases. Indeed, after 1 hour of filtration, the quantity of matter retained by the membrane is lower than the one retained after 15 minutes of filtration. This can be explained by the fact that particles retained by filtration are not only colloidal particles, but also soluble matter that adsorb on the membrane. After 1 hour of filtration, the membrane is so clogged that soluble matter cannot adsorb anymore and, therefore, are no longer retained [16].

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371

FIGURE 11.24 Microfiltration of a biological treatment plant outlet. Permeate flux versus time, spectra of the effluent before and after filtration, and spectra of the matter retained by the membrane depending on the filtration time.

In a study on the investigation of membrane fouling used for wastewater treatment in a membrane bioreactor (MBR), UV visible spectrophotometry was used for the comparison between the mixed liquor present in the MBR and the foulant deposited on the membrane surface [17], after treatment with NaOH and filtration. The spectra acquired between 190 and 400 nm were exploited after normalization by the DOC content. The spectrum of the mixed liquor was structureless, while spectra of the foulant after 3, 5, 7, 9, and 30 days of operation show different peaks linked to phenolic and protein-like compounds. The intensity and the width of the peaks increased with time indicated that large-molecular substances appeared. This was confirmed by the particle size distribution, reflecting a wide range of biopolymer. In two recent works on micropollutant removal in advanced wastewater treatment with powdered activated carbon (PAC) [18,19], it was shown that UV absorbance measurements at 254 nm can be proposed as a reliable surrogate parameter to monitor and control the removal of organic micropollutants. Correlations between the tested organic micropolluant removal and corresponding UVA254 reduction were determined in lab-scale adsorption batch tests. The behavior of 12 [18] and 14 [19] pharmaceuticals with concentrations from 0.1 to 100 µg L21 was

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studied, and UVA254 measurements were used to evaluate adapted PAC dosing strategies. In addition, fluorimetric measurements [19] were used for the determination of PAC doses and contact times. However, further investigation must be carried out for other micropollutants of different nature and for the adjustment of PAC dosing in different conditions of effluent quality.

11.4 Applications Applications concerning the study of two WWTPs are presented. As the previous examples come from treatment plants with classical physicochemical or biological processes (activated sludge), the two following examples have been chosen for their particular design. The first is a coupled biological process integrating both a trickling filter and a biofilter. This treatment plant type is rather rare, but its efficiency is quite good. The second application is an extensive process, comprising several lagoons (aerated or not). This design is very interesting for economical reasons, particularly in sunny (and dry) areas. These examples are very different; on the one hand, because of the process types, one intensive (short residence time) and the other extensive (several days of treatment) and, on the other hand, by the management mode of the degrading biomass.

11.4.1 Fixed biomass treatment plant This WWTP in a medium-size urban area (about 200,000 inhabitants) was located in Nimes, in the South of France. It included two biological processes with fixed biomass, with a clarifier in between. The first step is a trickling filter using plastic material and the second one is a biological filtration on immersed bed (with pouzzolane). Several instantaneous samples have been taken along the treatment, at the inlet, after pretreatment (screening), after the first biological step, after the clarifier, and after the biofilter corresponding to the outlet of the plant. The acquisition of UV spectra was quickly made, and the results are presented in Fig. 11.25. The raw wastewater has a regular shape, and the other spectra give some information on the characteristics of wastewater along the treatment, such as the degradation of organic matter and the nitrate formation with the biofiltration step. The spectra interpretation must take into account that the sampling mode (grab samples) is not relevant to the aim of the experiment (integrated sampling should have been preferable). From these data, the study of differential spectra (Fig. 11.25) gave another type of information.

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11.4 Applications

FIGURE 11.25

373

UV spectra evolution during biological treatment (dilution 5).

More precisely, the spectrum evolution shows that the effect of screening on wastewater is equivalent to a primary settling. Coarse particles, the size of which is above 100 µm, are removed, and the resulting UV spectrum presented a diffuse shape due to the presence of supracolloids and colloids not removed by physical treatment. An important part of organic matter was removed in the trickling filter, and a slight nitrate formation could be deduced from a decrease of absorbance after 240 nm and a small increase of absorbance between 200 and 210 nm with a Gaussian characteristic shape of nitrate. The shoulder at 225 nm disappeared, showing that a great part of anionic surfactants was removed. The nitrification and organic matter removal was completed in the biofilter. Residual absorbance after 240 nm is close to zero, showing a very good efficiency of the process on organic matter degradation, and the Gaussian shape below 240 nm indicates a good nitrification. The formation of nitrate was obvious and shown either from raw spectra shape or from the differential spectrum. The difference between the raw wastewater UV spectrum and the treated wastewater was negative, demonstrating nitrate formation (Table 11.4). The measurement of aggregate parameters confirmed these observations (Table 11.4). During the first biological step, 67% of COD and TSS were removed with 55% of BOD5. However, most of the surfactants were degraded. The final biological step improved the global efficiency by more than 90% for TSS, BOD5, and COD, but the DOC removal was equal to 67%, showing that residual organic by-products still remained

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374 TABLE 11.4

11. Urban wastewater

Parameters evolution during treatment. Raw

Screening

Trickling bed

Biofilter

pH

7.8

7.7

7.3

7.1

TSS (mg L21)

220

75

60.5

15

BOD5 (mgO2 L21)a

355

256

160

, 20

COD (mgO2 L21)

577

416

192

48

DOC (mg L21)

57

57

31

19

IC (mg L21)

70

70

74

31

Surfactants (mg L21)a

11

10

,5

,5

21 a NO2 3 (mg L )

0

0

10

68

a

Estimated by UV. COD, chemical oxygen demand; TSS, total suspended solids, TOC, total organic carbon; IC, inorganic carbon.

FIGURE 11.26 UV spectra differences (from spectra of Fig. 11.25).

(mainly carboxylic acids). The decrease of hydrogenocarbonates was due to nitrification bacteria (autotrophs) (Fig. 11.26).

11.4.2 Extensive process The treatment plant of Meze (more than 20,000 inhabitants) is located in the South of France, near Montpellier. It is made up of 11 lagoons or ponds of different volumes and functions. After two deep lagoons, the first four ponds are aerated, followed by three no aerated (natural)

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11.4 Applications

FIGURE 11.27

375

Extensive treatment plant of Meze, and sampling points.

ponds and by two final lagoons for polishing. The residence time of wastewater was about 2 months. The surface area of the lagoons varied from 15,000 to 39,000 m2, and the average depth was 1 m. An experiment was carried out in the summer of 1999. Eleven sampling points corresponding to the main steps of the treatment plant were chosen (Fig. 11.27). Fig. 11.28 shows the evolution of UV visible spectra of wastewater along the whole treatment process. The choice of considering the visible region for this application was related to the phenomena involved in this type of extensive treatment, with the presence of algae and chlorophyll-A in some lagoons. In addition to the biodegradation process occurring either in suspended biomass or in sediments, the design of basins of weak depth allowed working with direct or indirect sun radiation effects. In contrast to the other processes, the evolution of spectra showed an increase of absorbance for the whole spectrum, but limited to the first steps. This was due to the existence of suspended biomass, particularly in the aerated basins. Thus the effect of biodegradation was clearly shown in the first step with the removal of most of surfactants (both partially degraded and adsorbed in the biomass floc). Then, the spectra shape decreased up to the final lagoon but showed a residual diffuse absorbance for all steps. The study of UV absorbance evolution, as well as the one of DOC concentration (Fig. 11.29),

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11. Urban wastewater

FIGURE 11.28

Evolution of UV visible spectra of wastewater during extensive treatment steps (dilution 5). Up, spectra between 200 and 800 nm; down, spectra between 200 and 350 nm.

FIGURE 11.29

DOC (dissolved organic carbon) evolution with the extensive treatment

efficiency.

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11.4 Applications

377

confirmed these observations and showed that anthropogenic organic matter was still present up to the entry of final lagoons. The evolution of TSS spectra in the second part of the treatment showed (Fig. 11.30) that the TSS nature changed from a classical composition (already studied previously) with the presence of organic compounds adsorbed in particles, to another one characterized by a more structureless UV spectrum shape with an increase for the shorter wavelengths. Considering that the last part of the treatment is characterized by the presence of microphytes (plankton), confirmed by the green visual observation of the sample, and obviously by the visible spectra of samples, it is interesting to focus on the study of the evolution of treated wastewater quality at the end of the treatment. The study of the visible spectra of samples of the three last lagoons shows a specific absorbance related to the presence of chlorophyll-a, with a peak at around 680 nm. This peak, shifted to 664 nm in an acetone extract, is used for the determination of chlorophyll [20]. Notice that the use of the second derivative is interesting (Fig. 11.31). The presence of microphytes can be explained both by the weak depth of basins allowing the penetration of solar radiation and by the concentration of nutrients in treated wastewater (nitrate and phosphates) rapidly assimilated. In these conditions, photosynthesis is possible, leading to a correlative consumption of the residual organic matter used by the biocenose. The resulting global DOC removal is more than 95%, and this efficiency must be compared to the one of intensive processes, such as the previous example (67%).

FIGURE 11.30

Spectra of TSS retained in the last lagoons. TSS, total suspended solids.

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11. Urban wastewater

FIGURE 11.31 Visible spectra (and second derivative) of nondiluted samples for the final lagoons (pathlength 50 mm).

11.4.3 Ozone treatment for treated effluent A recent study reported the application of online UV absorption measurements for ozone process control in secondary effluent [21]. Ozone treatment is one of the existing processes for the elimination of trace organic compounds or the disinfection of treated effluent before its discharge in receiving water body. In this experiment, two online UV254nm sensors were used to follow the relative reduction of absorbance values before and after ozone treatment, taking into account interferences from residual ozone and turbidity mainly. This method was used for the ozone process control and estimation of trace organic compound elimination.

11.5 Classification of wastewater All the previous examples show that there exists determinism between the shape of UV spectra and the evolution of wastewater quality with treatment. It is thus possible to try to propose a wastewater classification based on the UV characteristics. Two approaches can be proposed. The first is to find the most frequent spectrum types encountered and to give useful information to the user and the second approach is to give a more automatic method based on the semideterministic method of spectra exploitation.

11.5.1 Typology of urban wastewater from UV spectra shape Starting from the experience and the previous examples, four groups of urban wastewater can be proposed (Fig. 11.32). One group concerns the raw wastewater often characterized by the presence of shoulders at

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11.5 Classification of wastewater

FIGURE 11.32

379

Main wastewater groups (concentration in mg L21).

around 225 and 260 270 nm. The general spectrum is important, regularly decreasing, and a dilution of the sample is often needed in order to prevent absorbance saturation, particularly for short wavelengths. The shoulders can be explained by the presence of surfactants (of benzenic type) and of other anthropic organic compounds, often containing

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aromatic structures. The residual absorbance in the 320 350 nm regions is due to suspended solids. The three other groups are related to treated wastewaters. The second type is wastewater treated by physicochemical process. The resulting spectrum shape is decreasing, with no specific structure. The spectrum remains important as the suspended solids and supracolloidal fraction are only removed. The shoulder specific to the presence of surfactants is lowered as they are partially adsorbed in solids. The third type is related to classical biological treatment. Depending on the mean residence time and on organic loading, the spectrum retains its initial shape, but its decrease is more or less important. The biodegradation process seems to affect all compounds of the readily degradable fraction. The last group concerns wastewater that contains nitrate. In this case, the organic matrix is becoming very simple with the presence of residual carboxylic acids. The nitrate concentration can vary and reach more than 50 mg L21 but can be reduced to a few mg L21 if a denitrification process exists.

11.5.2 Automatic classification of water and wastewater This automatic classification is based on the value of the coefficient contribution of the reference spectra used in the semideterministic method (see Chapter 3). The reference spectra are related to suspended solids, colloids, dissolved organic compounds, surfactants, and nitrate. For the determination of the coefficient values, an important experiment was carried out [22]. The deconvolution procedure was applied, on the one hand, to several raw or treated samples taken at the inlet and outlet of several hundreds of WWTPs of different types and, on the other hand, to a lot of surface water samples polluted by treated wastewater discharges. Table 11.5 shows the coefficients of the reference spectra for some of these samples (the coefficient values correspond to samples without dilution). The interpretation of the coefficients can lead to a rapid characterization of the effluent type (inlet or outlet) and of the treatment plant type (chemical, biological, etc.). Raw wastewater is characterized by high values of the first three coefficients (corresponding to suspended, colloidal, and dissolved matter). The sum of coefficients is also very high for raw wastewater, as well as for the outlet of a chemical treatment plant. The latter is characterized not only by a high concentration of colloidal and dissolved matter (due to the process) but also by a very low contribution of suspended matter. For a treated sample from a biological treatment plant, the sum of the coefficients (except the fourth, corresponding to nitrate) is much lower than for the former samples. Indeed, the coefficients generally decrease as the quality improves, except for the contribution of

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11.5 Classification of wastewater

TABLE 11.5 Coefficient values from the deconvolution method for various types of wastewater and surface water. Coefficient values Type Raw wastewater

a1

a2

a3

a4

a5

(a1 1 a2 1 a3 1 a4 1 a5)

4.60

6.00

4.20

0

0.03

15.10

Chemical treatment

0.05

8.44

3.36

0

0.07

11.92

Biological treatment

1.64

0.50

0.92

0.19

0.05

3.11

Nitrification plant

0.50

0.38

0.54

2.88

0.02

1.44

Denitrification plant

0.18

0.25

0.58

0.47

0.01

1.02

Surface water (receiving medium)

0.28

0.80

0.24

1.18

0.04

1.36

Surface water (without anthropogenic pollution)

0.02

0.03

0.02

0.06

0.00

0.07

Treated wastewater

nitrate, the maximum of which is obviously related to the nitrification process. For surface water, all coefficients are close to zero. From the coefficient values, a generalization can be proposed for the classification of wastewater (Fig. 11.33). This classification is based both on the value of the sum of the coefficients, excluding the fourth one corresponding to nitrate, and on the magnitude of the first (TSS) and fourth (nitrate) coefficients. This method can be applied in order to show the presence of anthropogenic organic compounds in natural water or to check the efficiency of a chemical or biological treatment. Finally, another study proposed the classification of wastewater quality using supervised pattern recognition techniques, also based on multiwavelength UV spectra deconvolution [23]. Thanks to an in situ immersion probes to monitor the basic physicochemical parameters followed by online UV spectrum acquisition (200 700 nm) and deconvolution procedure [22], the collected data were then treated with a series of supervised pattern recognition techniques. A PCA (principal component analysis) followed by a discriminant analysis was used for the classification of wastewater effluent according to their origin in three major categories: municipal, industrial, and hospital. This method can be applied for the detection of illegal or accidental discharges in a sewer network for example.

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FIGURE 11.33 Method for the classification of water and wastewater based on the values of reference UV spectra contribution.

References [1] N. Ogura, T. Hanya, Ultraviolet absorption of the sea water, in relation to organic and inorganic matters, International Journal of Oceanology and Limnology 1 (1967) 91 102. [2] M. Mrkva, Automatic u.v.-control system for relative evaluation of organic water pollution, Water Research 9 (1975) 587 589. Available from: https://doi.org/10.1016/ 0043-1354(75)90086-X. [3] M.K.V. Briggs R., Recent advances in water quality monitoring, Water Treatment Examination 17 (1968) 107 120. [4] S. Vaillant, Matie`re organique dans les eaux use´es urbaines, caracte´risation et e´volution, PhD Thesis University of Pau et Pays de l’Adour, 2000. [5] S. Qasim, Wastewater Treatment Plants Planning, Design and Operation, Technomic Publishing Co, Lancaster, Basel, 1994. [6] F. Valiron, J.P. Tabuchi, Maıˆtrise de la pollution urbaine par temps de pluie: e´tat de l’art, Tec&Doc Lavoisier (1992). [7] S. Michelbach, Origin, resuspension and settling characteristics of solids transported in combined sewage, Water Science and Technology. 31 (1995) 69 76. Available from: https://doi.org/10.1016/0273-1223(95)00324-G. [8] A. Saget, G. Chebbo, M. Desbordes, Urban discharges during wet weather: what volumes have to be treated? Water Science and Technology 32 (1995) 225 232. Available from: https://doi.org/10.1016/0273-1223(95)00559-6. [9] S. Vaillant, M.-F. Pouet, O. Thomas, Methodology for the characterisation of heterogeneous fractions in wastewater, Talanta 50 (1999) 729 736. Available from: https:// doi.org/10.1016/S0039-9140(99)00200-3. [10] S. Gallot, O. Thomas, Fast and easy interpretation of a set of absorption spectra: theory and qualitative applications for UV examination of waters and wastewaters, Fresenius’ Journal of Analytical Chemistry 346 (1993) 976 983. Available from: https://doi.org/10.1007/BF00322762.

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References

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[11] A. Maribas, M.D.C.L. Da Silva, N. Laurent, B. Loison, P. Battaglia, M.N. Pons, Monitoring of rain events with a submersible UV/VIS spectrophotometer, Water Science and Technology 57 (2008) 1587 1593. Available from: https://doi.org/ 10.2166/wst.2008.144. [12] N.D. Lourenc¸o, F. Paixa˜o, H.M. Pinheiro, A. Sousa, Use of spectra in the visible and near-mid-ultraviolet range with principal component analysis and partial least squares processing for monitoring of suspended solids in municipal wastewater treatment plants, Applied Spectroscopy 64 (2010) 1061 1067. Available from: https://doi.org/10.1366/000370210792434332. [13] E. Baures, E. Helias, G. Junqua, O. Thomas, Fast characterization of non domestic load in urban wastewater networks by UV spectrophotometry, Journal of Environmental Monitoring 9 (2007) 959 965. Available from: https://doi.org/ 10.1039/B704061J. [14] L. Djellal, E. Theraulaz, O. Thomas, Study of LAS behaviour in sewage using advanced UV spectrophotometry, Tenside, Surfactants, Detergents 34 (1997) 316 320. Available from: https://doi.org/10.1515/tsd-1997-340506. [15] M.C. Sarraguc¸a, A. Paulo, M.M. Alves, A.M.A. Dias, J.A. Lopes, E.C. Ferreira, Quantitative monitoring of an activated sludge reactor using on-line UV-visible and near-infrared spectroscopy, Analytical and Bioanalytical Chemistry 395 (2009) 1159 1166. Available from: https://doi.org/10.1007/s00216-009-3042-z. [16] M.F. Pouet, A. Grasmick, Microfiltration of urban wastewater : the roles of the different organic fractions in fouling the membrane, Proceeding of Euromembrane’95 Conference, Bath, Edited by W. R. Bowen, R. W Field and J.A Howell 1 (1995) 482 487. [17] R. Aryal, S. Vigneswaran, J. Kandasamy, Application of ultraviolet (UV) spectrophotometry in the assessment of membrane bioreactor performance for monitoring water and wastewater treatment, Applied Spectroscopy 65 (2011) 227 232. Available from: https://doi.org/10.1366/10-05848. [18] J. Altmann, L. Massa, A. Sperlich, R. Gnirss, M. Jekel, UV254 absorbance as real-time monitoring and control parameter for micropollutant removal in advanced wastewater treatment with powdered activated carbon, Water Research 94 (2016) 240 245. Available from: https://doi.org/10.1016/j.watres.2016.03.001. [19] A.D. Ziska, M. Park, T. Anumol, S.A. Snyder, Predicting trace organic compound attenuation with spectroscopic parameters in powdered activated carbon processes, Chemosphere 156 (2016) 163 171. Available from: https://doi.org/10.1016/j. chemosphere.2016.04.073. [20] L.L. Shipman, T.M. Cotton, J.R. Norris, J.J. Katz, An Analysis of the Visible Absorption Spectrum of Chlorophyll a Monomer, Dimer, and Oligomers in Solution, Journal of the American Chemical Society 98 (1976) 8222 8230. Available from: https://doi.org/10.1021/ja00441a056. [21] M. Stapf, U. Miehe, M. Jekel, Application of online UV absorption measurements for ozone process control in secondary effluent with variable nitrite concentration, Water Research 104 (2016) 111 118. Available from: https://doi.org/10.1016/j.watres.2016. 08.010. [22] O. Thomas, F. Theraulaz, C. Agnel, S. Suryani, Advanced UV examination of wastewater, Environmental Technology 17 (1996) 251 261. Available from: https://doi. org/10.1080/09593331708616383. [23] C.M. Tsoumanis, D.L. Giokas, A.G. Vlessidis, Monitoring and classification of wastewater quality using supervised pattern recognition techniques and deterministic resolution of molecular absorption spectra based on multiwavelength UV spectra deconvolution, Talanta 82 (2010) 575 581. Available from: https://doi.org/10.1016/j. talanta.2010.05.009.

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C H A P T E R

12 Industrial wastewater Olivier Thomas and Marie-Florence Thomas EHESP School of Public Health, Rennes, France

O U T L I N E 12.1 Introduction

386

12.2 Wastewater characteristics 12.2.1 Generalities 12.2.2 Influence of industry nature 12.2.3 Variability of industrial wastewater quality 12.2.4 Quantitative estimation

386 386 386 389 391

12.3 Treatment processes 12.3.1 Physicochemical processes 12.3.2 Biological processes 12.3.3 Hyphenated processes

395 395 397 399

12.4 Waste management 12.4.1 Sampling assistance 12.4.2 Treatability tests assistance 12.4.3 Spills detection 12.4.4 Shock-loading management 12.4.5 External waste management

400 401 401 405 407 410

12.5 Environmental impact 12.5.1 Discharge 12.5.2 Groundwater survey

412 412 413

References

414

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00013-7

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© 2022 Elsevier B.V. All rights reserved.

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12. Industrial wastewater

12.1 Introduction The UV spectrophotometric study of industrial wastewater can be considered as a challenge because of the potential presence of nonabsorbing compounds, on the one hand, and of the huge variety of effluents released by industrial units, on the other hand, which are very different in their activities and products from one industry to another. However, as industrial effluents are of a complex nature, some absorbing substances, explaining that the huge majority of industrial raw wastewater samples present a rather wellstructured UV spectrum, often accompany nonabsorbing compounds. In a first part, the UV spectra of raw effluents of some industrial activities are examined. Then, some practical qualitative and quantitative applications, mainly concerning petrochemistry, are proposed. These applications will show the interest of procedures developed for process assistance or treatment control. Concerning this last point, all real examples presented in this section are mainly taken from French and US industries, collecting their effluents through a sewer network inside industrial plants and including physical pretreatment tanks (settlers, flotators, etc.), before generally a unique treatment plant. In some countries, the regulation may oblige the industries to treat their wastewater from all units before mixing, leading to an increase in the number of possible applications of UV spectrophotometry for wastewater quality control.

12.2 Wastewater characteristics 12.2.1 Generalities Raw industrial effluents can be classified according to the dominant nature of pollution, organic or mineral (Table 12.1), and may be characterized by a high concentration of organic (and/or mineral) compounds, due either to a few major pollutants (e.g., chemical industry effluents) or to a huge number of molecules, the concentration of which is very low (pulp and paper or food industry). Raw industrial wastewater is generally produced continuously during the industrial process but can include some other liquid wastes such as washing residues and sometimes process water, in the case of an incident. For these reasons, all industrial effluents may vary in quality and, obviously, in the corresponding pollution load [1,2].

12.2.2 Influence of industry nature One way of studying the influence of industry nature on the shape of UV spectra is to consider the organic fraction. First, food industry

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12.2 Wastewater characteristics

TABLE 12.1

Types of industrial wastewater and treatment.

Type

Characteristics

Industry (example)

Treatment

Organic

High organic pollution, easily biodegradable

Food industry

Biological simple

Organic

High organic pollution, not easily biodegradable

Refinery Petrochemistry

Biological adapted

Organic

High organic pollution, nonbiodegradable

Organic synthesis

Physicochemical Biological adapted

Mineral

Low organic pollutionToxicsHigh suspended solids

Steel industryElectroplating industryExtractive industry

Physicochemical

Miscellaneous Organic pollution (with major pollutants), high salinity

Chemistry

PhysicochemicalBiological adapted

Miscellaneous Organic pollution (mixture), salinity

Pulp and paper industry

PhysicochemicalBiological adapted

wastewater is generally characterized by a high content of organic biodegradable pollution coming from not only the process of natural matrices but also of suspended solids easily retained by a physical treatment. The corresponding UV spectra present a featureless variation with no evident peaks, but with a shoulder around 275 nm (Fig. 12.1). The general shape is conserved from one food industry to another, but this observation must be validated for other types of food industries (spectra of food dye or drinks units will probably be different). Another kind of industrial activity processing natural products is the pulp and paper industry, the wastewater quality of which is characterized by a rather high concentration of suspended solids (e.g., residual cellulose fibers) and of organic, non easily biodegradable load, depending on the process chosen for pulp production (chemical or semichemical). The corresponding UV spectra are generally featureless, similar to food industries with a slight shoulder around 280 nm, probably because of phenolic compounds, and a noticeable residual absorbance after 300 nm due to suspended solids (Fig. 12.1). Contrary to the two first classes of industrial wastewater, the chemical industry, and particularly the organic synthesis units, gives the most structured UV spectra for wastewater (Fig. 12.1) as far as the molecules

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FIGURE 12.1 Example of UV spectra of food, pulp and paper and chemical industries.

synthetized are complex, as, for example, for pharmaceutical units. Taking into account the huge variety of molecules synthesized nowadays, it is impossible to show some characteristic examples of UV spectra of wastewater. Therefore each industry has to be considered as unique with its own potential applications. Actually, some chemical activities as refineries are not so variable from the point of view of wastewater quality because of the large quantities of primary matter and products (Fig. 12.2). Near units, the UV spectrum shape often shows peaks or marked shoulders due to the presence of additives used in the process, acting as revelatory of the organic pollution load [3]. Without these compounds and considering that saturated organic compounds do not absorb, it would be difficult to use the UV signal for wastewater quality control. However, a non parametric measurement based on a comparison of UV absorption spectra can be used for the characterization of wastewater quality variability [4]. Obviously, as far as wastewater from industrial units is mixed along sewers, the general shape becomes featureless at the inlet of the treatment plant, as it will be shown afterward. For the petrochemical industry, the characteristics and evolution of UV spectrum shape observed for refineries are conserved, however, with betterdefined peaks. These last are explained by the presence of additives, intermediates, and other chemicals (Fig. 12.2).

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12.2 Wastewater characteristics

FIGURE 12.2

Example of spectra of refineries and petrochemical industries.

TABLE 12.2 Coarse classification of industrial wastewater based on UV spectrophotometry. Spectrum characteristics

Industry

Observations

Structured (peaks)

Refinery Petrochemistry Chemistry (organic synthesis

Major pollutant(s)

Shapeless

Food Pulp and paper

Suspended solids

According to some characteristics of UV spectra (peaks, shoulders, or residual absorbance at higher wavelengths), an attempt of typology of raw industrial wastewater can be proposed (Table 12.2). This coarse classification is mainly related to the presence of major organic pollutants in the sample. The other parameters for organic pollution characterization (total organic carbon, TOC; chemical oxygen demand, COD; and biological oxygen demand, BOD5), for physicochemical measurement (suspended solids conductivity, pH, etc.), or for specific compound determination can obviously complete the classification. Another classification method also based on the use of UV spectra analysis was proposed for the effluent management of a fuel park wastewater treatment plant [5]. The exploitation of UV spectra with principal component analysis and cluster analysis led to the identification of two different groups of effluents and of some process chemicals used for fuel production.

12.2.3 Variability of industrial wastewater quality This point is very important mainly in view of the design of a measurement strategy for industrial wastewater quality control [3]. Among the analytical techniques available for the study of the complexity and qualitative variability of the medium, UV spectrophotometry is well

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adapted to quality variation control of industrial wastewater, as for urban wastewater (see Chapter 11). An example is given in Fig. 12.3 showing the spectra of different effluents, sampled from the outlet of different units of a refinery. The first observation is that all spectra are structured with at least one peak in the 250
300 nm region due to the presence of cyclic compounds such as aromatic compounds such as phenols. The second one is that some fluxes present a peak around 230 nm, related to the presence of sulfur compounds (see Chapter 6). A more precise study was carried out from the samples of another refinery site for a better understanding of effluent quality variation with time. Fig. 12.4 displays the two sets of spectra corresponding to the inlet of the wastewater treatment plant and to the outlet of the desalting unit. For each site, the two sets of spectra cross together after normalization of UV absorption values between 200 and 350 nm showing a hidden isosbestic point (HIP) (see Chapter 3) [7]. This point is more or less marked, magnified in Fig. 12.4, and, considering the experimental error related to the absorbance measurement, the isosbestic point (IP) must actually rather be considered as an isosbestic surface. The presence of HIP gives useful information, contrary to the corresponding raw spectra (before normalization) which do not present a direct IP. An interesting application of the use of HIP is the estimation of the variability V of an effluent according to the following relation [4]. The significance of HIP is discussed in Chapter 3 (quality conservation).   Npi V5 12 3 100 Nt where Nt is the total number of spectra, and Npi, the number of spectra crossing at the HIP (or direct IP if any).

FIGURE 12.3 UV spectra of process waters in a refinery (pH 5 11, pathlength: 10 mm, dilution: 10) [6].

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FIGURE 12.4 Use of HIP for the variability study [example of refinery wastewater (A) inlet of treatment plant, (B) outlet of desalting unit] [4]. A, normalized spectra; B, zooms. HIP, hidden isosbestic point.

Taking into account this approach, the two effluents present some differences (Fig. 12.4). Sample 1 (inlet of wastewater treatment plant) is characterized by an HIP at 276 nm, with a variability of 13%; meanwhile, sample 2 has an HIP at 250 nm with a variability of 67%. These results can be explained by the “buffer” effect of the fluxes mixing at the inlet of the treatment plant. This can be generalized for all industrial plants, the effluent variability decreasing from the process units to the wastewater treatment plant, particularly if the industrial site is large. This qualitative variability parameter (V) is particularly interesting in the survey of a network (incident detection, connection error, etc.) and for the optimization of the wastewater treatment plant design, complementary to quantitative parameters.

12.2.4 Quantitative estimation One of the most important applications of UV spectrophotometry, particularly for industrial wastewater quality control, is the rapid estimation of the concentration of some substances or some parameter

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12. Industrial wastewater

values, among which are aggregate organic parameters and N or P compounds (Table 12.3). In the case of industrial wastewater, several other compounds can also be involved, such as sulfide, hexavalent chromium, or other specific organic molecules. This last group of compounds is considered hereafter with regard to their economic and/or environmental importance. For example, Fig. 12.5 presents several specific compounds encountered in refinery and petrochemical wastewater. Thus phenol, EPA (ethylpropylacrolein), TBC (tertiobutylcatechol), NMP (N-methylpyrolidone), and nitrite can be detected in effluents or process water [23]. Moreover, the estimation of complementary aggregate parameters, such as total oxygen demand (TOD), is possible from the estimation of one of the previous organic compounds [3] (Fig. 12.6). The deconvolution method (already described, see Chapter 3) has been applied to the survey of treated wastewater of several different industries (chemical, petrochemical, and electroplating). In some cases, when the UV response of the matrix is very important, the working

TABLE 12.3 wastewater.

Quantitative applications of UV spectrophotometry for industrial

Parameters

Applications

Principle

References

COD, TOC, BOD5, TSS

Urban, petrochemical, distillery, pulp and paper effluents

Deconvolution PLS

[2,8
10]

TSS, colloids

Coagulation
flocculation process assistance, pulp and paper, and agrofood effluents

Deconvolution

[11
14]

Nitrates

Urban, petrochemical, paper, and distillery effluents

Deconvolution

[15,36]

Organic and ammonium nitrogen and phosphorus

Urban, petrochemical, paper, and distillery effluents

UV/UV system, deconvolution

[17]

Sulfide and mercaptans

Petrochemical effluents

Deconvolution

[8,18,19]

Hexavalent chromium

Electroplating effluents

Deconvolution

[20]

Surfactants

Urban effluents

Deconvolution

[21]

Phenols and photodegraded compounds

Chemical effluents

UV/UV system, deconvolution

[22]

BOD, biological oxygen demand; COD, chemical oxygen demand; TOC, total organic carbon; TSS, total suspended solids; PLS, partial least square.

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FIGURE 12.5

393

UV spectra of specific compounds in refineries.

FIGURE 12.6 Comparison between measured and estimated values of TOD in petrochemical wastewater (the estimation is made from the ethylpropylacrolein determination; see Fig. 12.5) [3]. TOD, total oxygen demand.

wavelength range must be modified in order to allow the parameter calculation. This is the case for the survey of electroplating wastewater, where nitrate concentration is very high and chromium (VI) concentration rather low (Fig. 12.7). Table 12.4 shows the correlation between the results obtained with standard methods and with UV spectrophotometry. The number of samples used for comparison is greater than 50 for all parameters. Except for the TSS (total suspended solids) estimation, the determination coefficient values are close or greater than 0.9. These quantitative results, quite good for a rapid and direct estimation method, are explained by the fact that the calculation is validated only if the quadratic error is lower than an accepted one (e.g., 1%). If not, for about

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FIGURE 12.7 UV spectra of electroplating effluents: (Sample 1) with 6.6 g L21 of nitrate and (Sample 2) with 5.4 g L21 of nitrate and 450 μg L21 of chromium (VI). TABLE 12.4 Correlation between UV spectrophotometry results and industrial water quality parameters. Parameters

Industry

TOC

Petrochemical

BOD

Sea food

COD

Petrochemical

COD

Pulp and paper

TSS

Petrochemical

NO2 3

Chemical

Cr IV

Electroplating

Phenols

Petrochemical 2

Sulfide (HS )

Petrochemical

Rangea 21

5
60 mgC L

21

1
30 mgO2 L

21

5
150 mgO2 L

21

120
300 mgO2 L 21

10
100 mg L 21

1
50 mg L

21

10
300 μg L

21

5
100 mg L

21

0.5
15 mg L

R2

References

0.91

[4]

0.96

[23]

0.89

[6]

0.95

[10,36]

0.77

[4]

0.99

[10]

0.96

[20]

0.90

[22]

0.95

[18]

a

For a pathlength of 10 mm, without dilution. BOD, biological oxygen demand; COD, chemical oxygen demand; TOC, total organic carbon.

10% of the samples, the result is not displayed but the information is interesting and can be exploited earlier. In some cases, the UV absorption signal can be noisy and prevent the interest using UV
visible spectrophotometry for quantitative analysis. In order to improve the spectrum quality, some smoothing methods can be proposed among which the wavelet decomposition [24]. In this study, the wavelet method gave the best result with a better sensitivity and determination coefficient (0.99) at 486 nm for a copper solution. This method was successfully improved for the analysis of several trace metal ions in

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395

mixture (copper, cobalt, and nickel) at mg L21 levels in hydrometallurgy wastewater, with the use of ratio spectra derivative [25].

12.3 Treatment processes As for the treatment of urban wastewater, two main treatment types are used, either based on physicochemical or biological principles.

12.3.1 Physicochemical processes Physicochemical treatments include separation techniques and processes involving a chemical reaction. As decantation and filtration have already been studied before for urban wastewater or for natural water, only complementary processes, sometimes largely used for industrial wastewater, are presented in this section. A more simple treatment is the pH correction, very often used for regulation compliance (the pH value of the treated effluent must roughly be between 5.5 and 8.5). A pH modification can be used for metallic compound precipitation usually as hydroxide forms, in alkaline conditions, or for humic substances removal, in acidic conditions. The effect of this last treatment can be shown for landfill leachates treatment in Chapter 13. Coagulation
flocculation is very often proposed for the removal of colloidal fraction and fine particles. The UV study of the coagulant concentration effect (e.g., FeCl3) has already been presented for drinking water production and urban wastewater treatment (Chapters 10 and 11). But a study showed the interest of UV
vis spectrophotometry for the monitoring of oil sands tailings quality during coagulation
flocculation by polymer [26]. More precisely, the optimal dose of polymer used for dewatering was determined from the absorbance measurement at 190 nm. Another treatment, currently used for industrial wastewater, is the adsorption on active carbon for organic pollutants removal. UV spectrophotometry can be proposed for the study of the effect of granular active carbon (GAC) on the adsorption of organic compound of a chemical effluent (Fig. 12.8). This example shows that the molecule, characterized by an absorption peak at 238 nm (not identified), is well adsorbed; meanwhile, the one absorbing at 260
270 nm is not retained. The corresponding removal rates of TOC are, respectively, 27%, 36%, and 51% for the three GAC concentrations (5, 10, and 20 g.L21). UV spectrophotometry can thus be used for process control and for the quality monitoring of the treated effluent.

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FIGURE 12.8 Spectra of chemical wastewater during GAC adsorption tests (TOC values are, respectively, 1373, 1001, 877, and 670 mg L21). GAC, granular active carbon; TOC, total organic carbon.

FIGURE 12.9 Comparison of AOP and biological process for the treatment of colored textile wastewater. AOP, advanced oxidation processes.

Before considering the interest of UV spectrophotometry for the control of biological processes, a last physical treatment type can be presented. Advanced oxidation processes (AOPs) are more and more used, because of their destruction power, preventing the pollution transfer (as it is the case for the other processes). AOP schemes include ozonization, photooxidation, and photocatalysis processes, these last being based on the effect of UV radiation. Fig. 12.9 presents the effect of some AOPs on the treatment of colored wastewater from the textile industry. Two tests have been carried out, the first one with UV radiation alone (photodegradation) and the second

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one with UV and hydrogen peroxide (0.1 mol H2O2. g21COD) [27]. The results are compared to those from the existing biological treatment plant. The gain for the AOP solutions is clearly demonstrated, particularly in the UV peroxide process. The discoloration is very rapid, and the corresponding TOC removal rate is about 90% for 15 minutes of treatment. Finally, a last study proposed a rapid characterization of agro-industrial effluents for environmental fate by UV
visible and infrared (IR) spectroscopy, from fractions obtained by centrifugation [28]. A centrifugation process was applied at various speeds between 3000 and 15,000 rpm and carried out separately on two different livestock effluents (dairy farm and pig anaerobic digestate), in order to obtain supernatants and precipitates, which were studied separately. By means of UV
vis spectroscopy, it was shown that there is an evident change between the spectra of the centrifuged samples and those of raw effluents and, that at a higher centrifugation rate, there is a continuous decrease in the absorbance values while wavelengths increase. This fact indicates that in the supernatants, the most hydrophilic components remain, with lower molecular weight and with a less degree of conjugation.

12.3.2 Biological processes Some examples of industrial biological treatment monitoring by UV spectrophotometry are presented. Despite the high pollution load of some industrial wastewater, such as for chemical and petrochemical industry, for example, and the presence of non easily biodegradable organic compounds (in some cases, BOD5 cannot be measured), biological processes can be adapted to treat this type of pollution. In this case, the biomass is “naturally” acclimated (microorganisms of biological reactors), but nutrients (N and P compounds) have to be added if needed. The first example (Fig. 12.10) shows the evolution of UV spectra of raw and treated wastewater for one refinery and one petrochemical site, with two different biological treatment plants, one with fixed biomass, and the other with activated sludge. In both cases, the treatment efficiency is good, with a TOC removal of around 90% (either measured or UV-estimated) and the presence of nitrate in the treated effluent. Another example is related to the biological treatment of pulp and paper wastewater (Fig. 12.11). The shape of raw wastewater is less structured than for chemical or petrochemical industry as previously seen, and the effect of biological treatment is characterized by the presence of a residual absorption on the spectrum of the treated wastewater,

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FIGURE 12.10 Treatment plants efficiency for refinery (site 1) and petrochemical (site 2) wastewater.

FIGURE 12.11 Effect of biological treatment on UV spectra of pulp and paper wastewaters (ww 1 and 2).

probably due to the remaining fine cellulose particles. Notice that, in contrast with the last example, no nitrate seem to be produced. The TOC removal rate is less (85%), and the raw effluent contains few N compounds (particularly organic N). In another study [29], the effectiveness of a slaughterhouse wastewater treatment by activated sludge was monitored at lab scale, by UV
visible spectrophotometry and fluorescence spectroscopy. Correlations between COD and the presence of porphyrins, linked to residual blood and estimated by the absorbance value at 416 nm, was satisfactory (R2 . 0.92) even if depending on effluent nature. These experiments were carried out to estimate the hydraulic retention time necessary to remove the biodegradable COD.

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12.3.3 Hyphenated processes Generally, industrial treatment plants integrate hyphenated processes with pretreatment steps more important than for urban wastewater treatment. In this case, UV spectra allow showing the effectiveness of the different treatment processes. A first example is shown on Fig. 12.12 for a treatment plant of a refinery (different from the one in Fig. 12.10), including a separator of hydrocarbons (API tank) and a sand filtration unit before a trickling filter. The raw wastewater composition is characterized by the presence of phenolic compounds with an absorption peak around 265 nm. The effect of the two pretreatment steps is evident on the particulate fraction (including the effect of emulsified hydrocarbons), but does not affect the dissolved matrix. This last is removed (at least the phenolic compounds) by the biological step, the consequence of which is a removal of almost 90% of the TOC. At the end of the treatment, cooling water (pumped from the sea) is mixed with treated wastewater, explaining the nitrate dilution and the presence of chloride in the discharge. Fig. 12.13 presents the raw and treated spectra of a colored textile wastewater already studied in Fig. 12.9. The treatment plant includes a physicochemical step and a biological process (activated sludge). The UV
visible spectra show that the first step leads to the removal of compounds responsible of at least 50% of wastewater color. The effect of the biological step completes the TOC removal up to 80%, without degrading aromatic amines, responsible for the 270 nm shoulder. This well-known problem is easily shown with the use of UV
visible spectrophotometry. Since the relation between the UV spectrum shape and organic content (qualitative and quantitative) is evident in the previous industrial cases, a

FIGURE 12.12

Spectra of refinery wastewater during treatment (dilution twice).

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FIGURE 12.13 Spectra of raw and treated textile wastewater.

TABLE 12.5 Comparison of total organic carbon (TOC) removal yields in percentage (calculated or UV-estimated) for different refinery treatment plants (site E has been studied several times). Origin

TOC removal

Estimated TOC removal (deconvolution method)

Estimated TOC removal (A265)

Estimated (area under spectrum)

Site A

76.3

65.3

55.0

54.3

Site B

91.2

84.8

73.7

41.8

Site C

88.5

87.8

78.1

85.7

Site D

78.9

75.6

77.1

43.6

Site E (a)

87.2

78.1

37.2

65.0

Site E (b)

83.9

81.5

57.1

71.6

Site E (c)

89.3

87.5

73.5

80.7

Site E (d)

87.8

79.9

62.8

49.8

comparison concerning two ways of calculation of the efficiency of the organic pollution removal has been studied [3]. In Table 12.5, the results of TOC removal ratio and some UV yield estimations show that the TOC removal estimated from the UV deconvolution method is quite satisfactory.

12.4 Waste management The first interest of UV spectrophotometry is to give some qualitative information such as for natural water and urban wastewater.

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From a more practical point of view, the study of UV spectra of industrial wastewater can be envisaged for several other purposes such as sampling assistance, treatability study, wastewater quality control (incidents detection, waste characterization, etc.), treatment control (efficiency, troubleshooting, etc.), and environmental impact study [4].

12.4.1 Sampling assistance The first application of wastewater management can be the checking of sample quality. Fig. 12.14 shows the spectra of different samples of the same effluent (taken from the same location) of a petrochemical plant, one from a composite 24-hour sampling procedure (with cold temperature conservation) and two from grab sampling, taken at 30-second intervals. The spectra are very different, even for the two grab samples (differences due to suspended solids) and particularly between grab and composite samples. Two explanations can be advanced: a qualitative variation with time and the qualitative evolution of the composite sample during its storage. This example confirms that UV spectrophotometry can be used for the quality control of sample evolution [30,31] and for the study of the phenomenon of wastewater sample aging [32].

12.4.2 Treatability tests assistance A treatability study is often needed for designing an industrial treatment plant. As biological processes are often preferred (because of their

FIGURE 12.14 Comparison of UV spectra of composite and grab samples of a petrochemical effluent.

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FIGURE 12.15 Evolution of UV spectra during biodegradation and photodegradation tests of an industrial effluent.

efficiency and simplicity to run), biodegradation tests are carried out. A sample of the industrial wastewater to be tested is introduced into a flask in the presence of microorganisms, generally coming from urban biological treatment plants. The evolution of BOD5 and COD is monitored during at least 1 day. Fig. 12.15 presents the evolution, with time, of the UV spectra of the biodegradation test of a petrochemical effluent, showing the relative quick degradation of the main organic pollutant absorbing around 235 nm (within a few hours). Using the UV/UV photodegradation system used for the determination of some minerals (total nitrogen and phosphorous), as presented in Chapter 6, a photodegradation test can be proposed in order to reduce the time of the biodegradation treatability study. The evolution of UV spectra with the photodegradation time can be compared to the one of the biodegradation tests (Fig. 12.15). The evolution of spectra is very close, but much more rapid, as the same result can be obtained within few minutes. Studying the evolution of spectra more accurately, some slight differences can be seen, showing two ways of degradation leading to the degradation of the organic matrix. A comparison of the two tests applied on different fluxes of petrochemical wastewater was made [33], showing a quite good adjustment between the results (Fig. 12.16). This method, very interesting, might be carefully validated for other types of industrial wastewater, before being used extensively. Another approach can be proposed in order to try to avoid the experimental step of degradation. Based on the relation between the light absorption of a substance and its chemical properties (see Chapter 2), the exploitation of the Shape Factor (presented in Chapter 3) is chosen for the purpose [34]. The Shape Factor is defined from the ratio between the second derivative and the absorbance value of a given peak, multiplied by the width at the half height of the peak (Chapter 3). As this parameter does not

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403

FIGURE 12.16 Comparison between biodegradation and photodegradation tests for petrochemical effluents [27].

take into account the position of the peak, the relative Shape Factor (RSF or SF*) is defined as follows: SFT 5 2

DðλÞ 3H3λ AðλÞ

where D(λ) is the value of the second derivative measured at the wavelength λ, A(λ) is the absorbance value measured at the same wavelength λ, and H, the width at the half height of the peak (of wavelength λ). Before explaining the proposed procedure, several photodegradation tests have been carried out on different samples of industrial wastewater from chemical industries. Fig. 12.17 shows the evolution of UV spectra for the different tests. The three groups of spectra (well, few, and unstructured) are represented. The evolution is different, depending on the initial spectrum shape. Two effluents of the first group (well-structured spectrum) present a direct IP, at least at the beginning of the test. This corresponds to the degradation of a major pollutant in the first minutes of the photodegradation experiment. The two others are characterized by a variation very low and even by no evolution. From these results, and particularly for the first minutes, it is possible to calculate a pseudo half reaction time (t1/2) [34]. The results are presented in Table 12.6. The presence of the wavelength of the peak in the relation tends to balance the SF* value by the energy associated with the peak (hC/λ).

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FIGURE 12.17 UV spectra evolution during photodegradation tests (respective dilutions of 100, 5, 5, and 50 for samples A, B, C, and D) [33]. TABLE 12.6 values.

Characteristics of the industrial wastewater samples (Fig. 12.17) and SF

Effluent

Cond (mS cm21)

pH (upH)

COD (g L21)

TOCi (g L21)

t1/2 (min)

SF (102)

SF*

λ (nm)

A

0.15

9.6

550

86.7

3.2

13

29

221

8

24

287

5.5

15

254

11

36

350

0.04

0.1

229

0.2

0.5

277

0

0

—

B

C

D

0.1

5.4

185

7.5

7.9

13.1

20

1

6

5.3

0.5

1.6

0.4

13.3

. 30

COD, chemical oxygen demand; TOC, total organic carbon.

Indeed, it can be roughly demonstrated that a given molecule is more easily degraded when its spectrum presents one or several peaks in the higher wavelengths (in the visible region). The results presented in Table 12.6 show that an effluent characterized by a well-structured

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spectrum is more rapidly degraded than one with a featureless spectrum. This observation is at least right for the initial pollutants but has to be confirmed for the by-products of the degradation. This approach shows that the prediction estimation of wastewater treatability seems to be possible from the direct study of UV spectra [34,35].

12.4.3 Spills detection TOC monitoring is often used for industrial wastewater quality and sometimes completed by the TOD (total oxygen demand). Fig. 12.18 presents the TOD variation with time (during almost 2 years) monitored at the inlet of the wastewater treatment plant of a petrochemical site [3]. Some peaks of concentration have been registered, and the chromatographic analysis of the associated samples has permitted to often determine the major pollutant responsible of the high value of TOD. However, considering that TOD measurement is sensible to interferences such as ammonia, TOC (by definition more adapted to the quantification of organic pollution) has been preferred. Thus a modified monitoring phase has been carried out in the same site. For each TOC measurement (every 3 hours), a UV spectrum has been acquired. Considering that the maximum admitted value for the protection of the biological reactor of the plant is 500 mg L21 of TOC, three peaks of pollution have been detected (Fig. 12.19). The study of the corresponding UV spectra (for TOC peaks) and a comparison with the one of regular wastewaters shows that these three incidents are related to three different accidental discharges in sewers.

FIGURE 12.18

Evolution of TOD and identification of major pollutants [phenol, nitrite, ethylpropylacrolein (epa) and tertiobutylcathecol (tbc)]. TOD, total oxygen demand.

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FIGURE 12.19

TOC and UV monitoring of petrochemical wastewater (the absorbance values are for a 10-mm optical pathlength quartz cell). TOC, total organic carbon.

FIGURE 12.20 Spill detection and related pollutants (the absorbance values are calculated from the dilution factor).

Moreover, the study of UV spectra shows that incidents 1 and 3 are characterized by the presence of a few concentrated pollutants. The UV spectra of effluents and those of main pollutants already mentioned before are presented in Fig. 12.20. Starting from this information, and after a more complete study, a UV monitor was installed close to the main source of the potential accidental discharge. An automatic procedure (UV diagnostic (UVDIAG)) for incident diagnosis and pollutant identification (if possible) was designed and included in the monitoring procedure (see algorithm in Fig. 12.21).

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FIGURE 12.21

407

General procedure for incident diagnosis (UV diagnosis (UVDIAG)) [36].

12.4.4 Shock-loading management It is well known that a biological treatment plant is sensitive to pollution shock loads, especially if the biomass is not adapted [34]. This is the reason why storage tanks are often placed before the treatment plant for the regulation of organic loads. In this case, the choice of the location of monitoring tools is important in order to detect, as soon as possible, the abnormal variability of the effluent before its eventual storage. For this purpose, on line monitoring can be carried out for any interesting site on the sewer network before reaching the treatment plant. Furthermore, the outlet monitoring is needed both for regulatory compliance and posttreatment if needed. Inlet and outlet monitoring allows controlling the impact of the shock load on the treatment plant efficiency and, if necessary, diverting the outlet effluent into a supplementary storage tank for a further treatment (Fig. 12.22). The monitoring can be carried out using

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12. Industrial wastewater

FIGURE 12.22 General procedure for shock load management (A, detection and storage of the suspect effluent, a; B, treatment of effluent with a dilution factor, d).

FIGURE 12.23 Proposed procedure synopsis for shock load management [Si and So are the concentration (e.g., TOC) at the inlet and outlet of the plant, respectively, and γr the efficiency yield of the process]. TOC, total organic carbon.

pollution parameter (e.g., TOC) or UV spectrophotometry. Based on the respective values of effluent quality at the inlet and outlet of the treatment plant, a procedure can be applied for shock load management (Fig. 12.23). This procedure includes a treatability diagnosis (photodegradation test) of the effluent if its quality is abnormal. Thus different cases can be observed according to the UV spectra shapes between inlet and outlet, leading to different removal yields. Fig. 12.24 shows, the four main cases encountered in chemical wastewater treatment plants. Table 12.7 gives the corresponding organic loads and removal yields.

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12.4 Waste management

FIGURE 12.24

Fate of incidents after biological treatment (example of chemical wastewater).

TABLE 12.7 Chemical oxygen demand (COD) values and removal yields (Si and So see Fig. 12.23). Observations about UV spectra

Inlet COD (mgO2 L21)

Outlet COD (mgO2 L21)

γr (%)

Si reg/So reg

1100

180

84

Si reg/So irreg

2080

1056

49

Si irreg/So reg

3230

1755

45

Si irreg/So irreg

1900

1080

43

The four main cases shown in Fig. 12.24 are the following: • Case 1: Characterizing the absence of a pollution shock load, it presents a significant absorbance decrease corresponding to high organic load abatement: UV spectra (Si and So) are regular. • Case 2: It shows similar UV spectra, even if the yield is medium. It can be explained by the presence of nonabsorbing compounds that are partially degraded. The limits of the method can be overcome by the use of complementary analysis (IR spectrophotometry, TOC measurement, etc.) in order to detect the compounds responsible.

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• Case 3: It presents the situation where the accidental effluent has been managed, thanks to the photodegradability test. The characteristic shape of the inlet UV spectrum is due to the presence of unknown absorbing compounds. Fig. 12.25 presents the evolution of the inlet UV spectrum according to irradiation time: after 12 minutes, it can be noticed that the UV spectrum is quite close to a regular spectrum. The medium yield can be imputed to the low activity of biomass not yet adapted to new compounds. • Case 4: It describes the situation where UV spectra (Si and So) are irregular and have, moreover, a similar structured shape. The characteristic shape is still present in So, showing that the major pollutants are partially degraded. The Case 5 (not shown) can be characterized by irregular UV spectra of different shapes related to the potential presence of heterogeneous materials, for example, coming from the biomass death (in the case of a biological treatment plant). In order to prevent and better manage, the unexpected situations (Cases 2, 4, and 5), UV spectra of accidental effluents can be stored in a data bank that can be systematically consulted for the admission of effluents in the wastewater treatment plant as it is the case for the management of external wastes.

12.4.5 External waste management A last application concerning the use of UV spectrophotometry for waste management is the quality control of external wastes, brought on a given centralized treatment plant. Wastes from septic tanks, industrial

FIGURE 12.25 Photodegradation test of inlet effluent (Case 3): evolution of UV spectra according to irradiation time (pathlength: 2 mm, no dilution).

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liquid wastes (high loaded wastewater or process water), or washing effluents (e.g., from tanks) can be collected in small amounts by trucks and brought to a centralized treatment plant. These wastes of various origins (Table 12.8) are characterized by a huge qualitative and quantitative variability. However, all these wastes present more or less structured UV spectra, as shown in Fig. 12.26. In order to control the quality of wastes brought to the treatment plant, a simple procedure can be used for the reception of trucks and the acceptance of liquid wastes. Based on the acquisition of the UV spectrum of the waste to be treated, a comparison with a reference spectrum shape (selected from a spectral library, characteristics of the different types of wastes) was proposed (Fig. 12.27). In function of the comparison result (by using one of the tools presented in Chapter 3), the waste was accepted or rejected. TABLE 12.8

Main pollutants associated to external wastes.

Industry or water type

Main pollutants

Perfumeries

Organic matter, solvents (alcohol, ketones, etc.)

Fine chemistry

Various chemical products

Electroplating

Surfactants, citric acid, various minerals, etc.

Leachates

Organic matter, surfactants, etc.

Water with hydrocarbons

Hydrocarbons, alcohols

Trucks and tanks washing water

Various chemical products

Chemical process water

Specific chemical products

FIGURE 12.26

Spectra of biologically treated external wastes (variable dilution).

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FIGURE 12.27 Procedure for external waste acceptance.

With respect to procedures based on the measurement of classical parameters (COD, TOC, etc.), this one leads to a more relevant quality control for external waste management, with a time gain leading to the increase of truck acceptance.

12.5 Environmental impact 12.5.1 Discharge UV spectrophotometry can also be used, such as for urban wastewater (see Chapter 11), for the study of the environmental impact of treated wastewater. Fig. 12.28 displays the different spectra acquired for a dispersion study of treated industrial (petrochemistry) wastewater discharge in seawater. The set of spectra shows an IP due to the mixing of wastewater with seawater, the spectrum of which presents a sharp absorbance at 205 nm due to chloride (notice that the difference between the spectra of nitrate and chloride solutions is chemometrically easy to exploit). After 100 m, the impact of the discharge is difficult to see.

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FIGURE 12.28 UV spectra of industrially treated wastewater discharge and receiving medium (seawater).

FIGURE 12.29

Space variation of UV spectra of groundwater of a petrochemical site.

12.5.2 Groundwater survey A last application concerns the groundwater quality survey of an industrial site. Specific industries, such as, for example, refineries, petrochemical, or chemical sites, are obliged to control the groundwater quality. For the purpose, several boreholes (wells) are used, from which groundwater is regularly sampled (e.g., every month) for analysis. The use of UV spectrophotometry can give interesting qualitative (and quantitative) information, as it can be seen in Figs. 12.29 and 12.30 [3]. In Fig. 12.29 are displayed the spectra of groundwater sampled from five wells of an industrial site (petrochemistry). The examination of the

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12. Industrial wastewater

FIGURE 12.30 Time variation of UV spectra of groundwater of a petrochemical site (well 1, Fig. 12.29).

spectra shape shows that one area (well 1) is highly contaminated with a major pollutant (not identified), presenting two peaks of absorption around 220 and 250 nm. Furthermore, wells 2 and 3 present a residual diffuse absorbance (above 300 nm), probably related to emulsified hydrocarbons. The evolution with time of the groundwater quality of well 1 (Fig. 12.30) shows that the detected pollution has appeared in February and was still present (but diluted) in April.

References [1] D.L. Ford, J.M. Eller, E.F. Gloyna, Analytical parameters of petrochemical and refinery wastewater, Journal of the Water Pollution Control Federation 43 (1971) 1712
1723. [2] O. Thomas, Metrologie des eaux residuaires. Lavoisier, Tech et Doc., Paris, Liege, 1995 33
34. [3] H. El Khorassani, Caracte´risation d’effluents industriels par spectrophotome´trie UV applique´e a` l’industrie petrochimique, PhD Thesis, University of Aix-Marseille I, 1998. [4] O. Thomas, E. Baure`s, M.F. Pouet, UV spectrophotometry as a non-parametric measurement of water and wastewater quality variability, Water Quality Research Journal of Canada 40 (2005) 51
58. Available from: https://doi.org/10.2166/wqrj.2005.004. [5] N.D. Lourenc¸o, C.L. Chaves, J.M. Novais, J.C. Menezes, H.M. Pinheiro, D. Diniz, UV spectra analysis for water quality monitoring in a fuel park wastewater treatment plant, Chemosphere 65 (2006) 786
791. Available from: https://doi.org/10.1016/j. chemosphere.2006.03.041. [6] F. Pouly, La spectrophotometrie UV. Une approche analytique alternative aux besoins du raffinage, PhD Thesis, University of Aix-Marseille I, 2001. [7] M.F. Poue¨t, E. Baure`s, S. Vaillant, O. Thomas, Hidden isosbestic point(s) in ultraviolet spectra, Applied Spectroscopy 58 (2004) 486. Available from: https://doi.org/10.1366/ 000370204773580365.

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References

415

[8] F. Pouly, E. Touraud, O. Thomas, C. Langellier, C. Busatto, Monitoring wastewater with UV spectrophotometry-multiparameter analyzers allow online wastewater management, Hydrocarbon Processing 80 (2001) 76
79. [9] O. Thomas, F. The´raulaz, V. Cerda`, D. Constant, P. Quevauviller, Wastewater quality monitoring, TrAC Trends in Analytical Chemistry 16 (1997) 419
424. [10] G. Langergraber, N. Fleischmann, F. Hofsta¨dter, A. Weingartner, Monitoring of a paper mill wastewater treatment plant using UV/VIS spectroscopy, Water Science and Technology (2004) 9
14. [11] S. Vaillant, M.F. Pouet, O. Thomas, Methodology for the characterisation of heterogeneous fractions in wastewater, Talanta 50 (1999) 729
736. Available from: https:// doi.org/10.1016/S0039-9140(99)00200-3. [12] S. Bayle, N. Aze´ma, C. Berho, M.-F. Pouet, J.-M. Lopez-Cuesta, O. Thomas, Study of heterogeneous suspensions: a new quantitative approach coupling laser granulometry and UV
vis spectrophotometry, Colloids and Surfaces A: Physicochemical and Engineering Aspects 262 (2005) 242
250. Available from: https://doi.org/10.1016/j. colsurfa.2005.04.040. [13] C. Behro, Caracte´risation de fractions he´te´roge`nes par me´thodes optiques, PhD Thesis, University of Pau et pays de l’Adour, 2003. [14] S. Vaillant, La matie`re organique des eaux re´siduaires : caracte´risation et e´volution, PhD Thesis, University of Pau et Pays de l’Adour, 2000. [15] B. Roig, C. Gonzalez, O. Thomas, Measurement of dissolved total nitrogen in wastewater by UV photooxidation with peroxodisulphate, Analytica Chimica Acta 389 (1999) 267
274. Available from: https://doi.org/10.1016/S0003-2670(99)00212-3. [16] G. Langergraber, N. Fleischmann, F. Hofsta¨dter, A multivariate calibration procedure four UV/Visible spectrometric quantification of organic matter and nitrate in wastewater, Autmonet (2022) 9
14. [17] B. Roig, C. Gonzalez, O. Thomas, Simple UV/UV-visible method for nitrogen and phosphorus measurement in wastewater, Talanta 50 (1999) 751
758. Available from: https://doi.org/10.1016/S0039-9140(99)00203-9. [18] F. Pouly, E. Touraud, J.F. Buisson, O. Thomas, An alternative method for the measurement of mineral sulphide in wastewater, Talanta 50 (1999) 737
742. Available from: https://doi.org/10.1016/S0039-9140(99)00201-5. [19] B. Roig, E. Chalmin, E. Touraud, O. Thomas, Spectroscopic study of dissolved organic sulfur (DOS): a case study of mercaptans, Talanta 56 (2002) 585
590. Available from: https://doi.org/10.1016/S0039-9140(01)00580-X. [20] H. El Khorassani, G. Besson, O. Thomas, Direct UV visible determination of chromium (VI) in industrial wastewaters, Quimica Analitica 16 (1997) 239
242. [21] L. Djellal, E. Theraulaz, O. Thomas, Study of LAS behaviour in sewage using advanced UV spectrophotometry, Tenside, Surfactants, Detergents 34 (1997) 316
320. Available from: https://doi.org/10.1515/tsd-1997-340506. [22] B. Roig, C. Gonzalez, O. Thomas, Monitoring of phenol photodegradation by ultraviolet spectroscopy, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 59 (2003) 303
307. Available from: https://doi.org/10.1016/S1386-1425(02)00172-5. [23] P. Chevakidagarn, BOD5 estimation by using UV absorption and COD for rapid industrial effluent monitoring, Environmental Monitoring and Assessment 131 (2007) 445
450. Available from: https://doi.org/10.1007/s10661-006-9490-4. [24] F. Zhou, H. Zhu, C. Li, A pretreatment method based on wavelet transform for quantitative analysis of UV
vis spectroscopy, Optik 182 (2019) 786
792. Available from: https://doi.org/10.1016/j.ijleo.2019.01.115. [25] F. Zhou, C. Li, H. Zhu, Y. Li, Simultaneous determination of trace metal ions in industrial wastewater based on UV
vis spectrometry, Optik 176 (2019) 512
517. Available from: https://doi.org/10.1016/j.ijleo.2018.09.075.

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¨ rmeci, P.H. Simms, Determination of the optimum polymer dose for [26] A.M. Salam, B. O dewatering of oil sands tailings using UV-vis spectrophotometry, Journal of Petroleum Science and Engineering 147 (2016) 68
76. Available from: https://doi. org/10.1016/j.petrol.2016.05.004. [27] Y. Coque, Proposition d’outils d’optimisation de proce´de´s d’oxydation avance´e par UV:H2O2., PhD Thesis, University of Pau et Pays de l’Adour, 2002. [28] G.A. Iocoli, O.I. Pieroni, M.A. Go´mez, M.B. Alvarez, J.A. Galantini, Rapid characterisation of agro-industrial effluents for environmental fate by UV
visible and infrared spectroscopy from fractions obtained by centrifugation, International Journal of Environmental Analytical Chemistry 97 (2017) 756
767. Available from: https://doi. org/10.1080/03067319.2017.1354993. [29] J.N. Louvet, B. Homeky, M. Casellas, M.N. Pons, C. Dagot, Monitoring of slaughterhouse wastewater biodegradation in a SBR using fluorescence and UV-Visible absorbance, Chemosphere 91 (2013) 648
655. Available from: https://doi.org/10.1016/j. chemosphere.2013.01.011. [30] O. Thomas, F. The´raulaz, Analytical assistance for water sampling, TrAC Trends in Analytical Chemistry 13 (1994) 344
348. [31] L. Djellal, E. Theraulaz, Study of LAS behaviour in sewage using advanced UV spectrophotometry. Tenside, Surfactants, Detergents, 1997. [32] E. Baure`s, C. Berho, M.F. Pouet, O. Thomas, In situ UV monitoring of wastewater: a response to sample aging, Water Science and Technology: A Journal of the International Association on Water Pollution Research 49 (2004) 47
52, doi:10.2166/ wst.2004.0015. Available from: https://doi.org/10.2166/wst.2004.0015. [33] L. Castillo, H. El Khorassani, P. Trebuchon, O. Thomas, UV treatability test for chemical and petrochemical wastewater, Water Science and Technology 39 (1999) 17
23. Available from: https://doi.org/10.1016/S0273-1223(99)00249-8. [34] C. Muret-Marty, Caracte´risation de la DCO dure d’effluents industriels en vue de leur traitabilite´, PhD Thesis, INSA Lyon, 2001. [35] C. Muret, M. Pouet, E. Touraud, O. Thomas, From UV spectra to degradability of industrial wastewater/definition anduse of a ‘shape factor’, Water Science & Technology 42 (2000) 47
52, doi:10.2166/wst.2000.0494. Available from: https://doi. org/10.2166/wst.2000.0494. [36] H. El Khorassani, P. Trebuchon, H. Bitar, O. Thomas, Minimisation strategy of petrochemical wastewater organic load, Water Science & Technology 42 (2000) 15
22. Available from: https://doi.org/10.2166/wst.2000.0489.

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C H A P T E R

13 Polluted soils, composts, and leachates Olivier Thomas1 and Guillaume Junqua2 1

EHESP School of Public Health, Rennes, France, 2HSM, Univ. Montpellier, IMT Mines Ales, IRD, CNRS, Ales, France

O U T L I N E 13.1 Introduction

417

13.2 Polluted soils 13.2.1 Characterization of polluted soils 13.2.2 Treatment of polluted soils

418 419 422

13.3 Composts 13.3.1 Characterization of solid wastes 13.3.2 Composting of solid wastes

426 426 427

13.4 Landfill leachates 13.4.1 Characterization of leachate 13.4.2 Leachate treatment 13.4.3 Coagulation flocculation with FeCl3

430 430 433 433

References

437

13.1 Introduction Leachates are produced by percolation of rain water through a solid matrix, such as solid wastes from urban or industrial landfills, or polluted soils, for example (Fig. 13.1). They are also associated to the

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00009-5

417

© 2022 Elsevier B.V. All rights reserved.

418

13. Polluted soils, composts, and leachates

FIGURE 13.1 Transfer mechanisms from solid to liquid phase.

degradation of organic matter present in domestic wastes and its liquefaction under anaerobic conditions. Leachates are highly polluted solutions (or suspensions) characterized by a high salinity and organic content. Consequently, they are rather complex to study or analyze. The analysis of such complex samples as leachates and aqueous or organic extracts of solid matrices is mostly oriented toward mineral constituents with inductively coupled plasma-optical emission spectroscopy, for example, and/or organic fraction with chromatography and spectroscopic techniques after sample preparation. In this context, UV visible spectrophotometry can be useful, as for wastewater, for a fast characterization of this type of sample or its treatment monitoring. Depending on the nature of organic components, leachates and aqueous extracts can limit the interest of the approach. In this case, an extraction step of the solid matrix with an organic solvent can be necessary in order to have more specific information and less interference. In this chapter, some applications dealing with landfill leachates, contaminated soils, and solid waste composts are presented.

13.2 Polluted soils Land contamination can result from a variety of intended, accidental, or naturally occurring activities and events [1]. Contaminated lands include (1) sites contaminated by improper handling or disposal of toxic and hazardous materials and wastes, (2) sites where toxic materials may have been deposited as a result of natural disasters or acts of terror, and

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(3) sites where improper handling or accidents resulted in release of toxic or hazardous materials that are not wastes. More often, polluted soils are often associated to ancient raw industrial landfills. They are characterized by high concentrations of specific organic (or mineral) compounds related to the processes that were used on the site. Pollutants present are varied, generally toxic and persistent, since they can remain into the soil for a long period after the end of the industrial activity. Among the main priority compounds, with respect to environmental and health impacts, polycyclic aromatic hydrocarbons (PAHs) and petroleum hydrocarbons are often encountered in ancient coking plants, gas works, gas stations, or refinery sites. Since such pollutants have to be considered as potential pollutants for groundwater, it is interesting to show how UV visible spectrophotometry can be applied for their study. Considering their physicochemical properties and the matrix complexity, an extraction step is necessary before spectrum acquisition.

13.2.1 Characterization of polluted soils The extraction step can be carried out either with water or preferably with an organic solvent, hydrocarbons being very few soluble. Before extraction, the soil sample is pretreated (drying, grinding, and sieving) according to the first steps of the procedure proposed by Lenz et al. [2]. In the following example, the solid/liquid ratio used for the extraction is 1 g soil/10 mL solvent and a filtration step on glass fiber filter was carried out before spectra acquisition. 13.2.1.1 Polycyclic aromatic hydrocarbons Two PAHs-contaminated soils of different origins were studied. Soil A (sandy soil) came from an ancient coking plant, and soil B (clay soil) from a rather recent creosote production site. Spectra were acquired after mechanical agitation with deionized water for 1 and 24 hours (Fig. 13.2). Direct examination of aqueous sample spectra leads to the following observations: • Water-soluble matter and UV absorbance increased with extraction time, but the shape of UV spectra was globally not modified for each studied soil. • On the opposite, UV spectra shapes were quite different according to the nature and age of pollution. Soil A showed concave spectra with an absorbance that decreased rapidly beyond 210 nm, showing that the degradation process was advanced. Nevertheless, a small shoulder was observed around 250 nm. • UV spectra of soil B presented a marked shoulder at 225 nm and a significant absorbance up to 350 nm. Small shoulders were also

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FIGURE 13.2 UV spectra (filtered) of PAHs-contaminated soils aqueous leachates (dilution 10). PAHs, polycyclic aromatic hydrocarbons.

observed around 250 nm and 270 280 nm. These particularities are characteristic of the presence of industrial pollutants such as phenolic compounds, which can be found in PAHs-contaminated soils and compounds belonging to the PAHs family. Moreover, the residual pollution was more important for soil B. UV visible spectrophotometry enables giving indications about the pollution maturation level in contaminated soils. Stabilized leachates from old contaminated soils were characterized by a monotonous decreasing spectrum (soil A), while younger ones showed a more specific spectrum where additional compounds were responsible for peaks and shoulders (soil B). In order to avoid huge interferences from the natural organic matrix of the soil (humic-like substances), an extraction by sonication using acetonitrile was tested on the two contaminated soils. After 1 hour of sonication, UV spectra of the supernatant were acquired (Fig. 13.3). From a qualitative point of view, the direct examination of UV spectra showed approximately the same shape for the two soils, with several peaks due to the presence of specific compounds such as PAHs. Moreover, because of the dilution factor, it could be assumed that acetonitrile extraction was more efficient than water extraction, especially for nonpolar compounds such as PAHs. As expected, the two specific peaks located at 254 and 288 nm could be observed on the two UV spectra. From a quantitative point of view, the global PAHs concentration can be estimated with the use of PAHs index previously defined in Chapter 4. The value of the ratio A254nm/A288nm gives information on the importance of PAHs levels in the contaminated soils (Table 13.1).

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13.2 Polluted soils

FIGURE 13.3 UV spectra of PAHs-contaminated soils acetonitrile extracts. PAHs, polycyclic aromatic hydrocarbons.

TABLE 13.1 Data related to polycyclic aromatic hydrocarbons (PAHs)contaminated soils (A and B). Sample

PAHs UV (g kg21)

A254nm/A288nm

Soil A

4.3

1.61

Soil B

6.7

2.87

For field application, the sonication step for acetonitrile extraction can be replaced by 10 minutes of manual extraction with hand. This procedure has been compared to sonication and led to similar results [5]. 13.2.1.2 Petroleum hydrocarbons Fig. 13.4 presents spectra of water extract (dilution 10) and acetonitrile extract (dilution 250) of a soil contaminated by petroleum hydrocarbons. As for PAHs extraction, the use of an organic solvent is more efficient. The corresponding spectrum was more structured, showing two specific peaks, located at 228 and 256 nm. Most unsaturated hydrocarbons, especially alkylated derivatives of benzene, absorb in this region. Nonaromatic hydrocarbons absorb mainly in vacuum UV. The UV visible spectrum of aqueous extract was quite monotonous. A marked shoulder can be observed around 210 nm and beyond, the absorbance decreases quickly until 250 nm and then rather smoothly up to 350 nm. The specific and strong absorbance at 210 nm can be imputed

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FIGURE 13.4 UV spectra of aqueous and acetonitrile extracts of soil contaminated by petroleum hydrocarbons (dilution 10 for aqueous extract and 250 for acetonitrile one).

to the degradation products of petroleum hydrocarbons, especially carboxylic acids.

13.2.2 Treatment of polluted soils 13.2.2.1 Polycyclic aromatic hydrocarbons The monitoring of a biological treatment was carried at laboratory scale for two PAHs-contaminated soils, soil 1 mainly polluted by heavy PAHs, and soil 2 mainly contaminated by light PAHs. The evolution of UV spectra, according to treatment time, is shown in Fig. 13.5, for the two experiments. Table 13.2 presents the evolution of, the global PAH concentration [measured by high performance liquid chromatography (HPLC) and estimated by UV] on the one hand, and, the ratio, A254nm/A288nm on the other hand. No significant difference exists between HPLC measurement and UV estimation. The yield of decontamination is roughly of 75% for soil 1 after 110 days of treatment. For soil 2, it aims for only 20% after 130 days of treatment. These results are in agreement with those of the literature. Heavy PAHs are less sensitive to microbiological degradation during biological treatment, which takes place into the soil under natural conditions [6]. The ratio A254nm/A288nm decreases regularly with treatment time from 2.9 to 1.7 for soil 1 mainly polluted by light PAHs but remains constant and equal to 1.3 for soil 2. This ratio, the value of which is linked to the proportion of light PAHs in the contaminated soil, reflects the degradation degree of PAH during biological treatment (Fig. 13.6). It may be

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13.2 Polluted soils

FIGURE 13.5 UV monitoring of biological treatment of PAHs-contaminated soils. Soil 1, sampling at 0, 35, 50, 110, and 220 days, and soil 2, sampling at 0, 40, 80, 100, and 130 days (by decreasing order of absorbance values at 200 nm). PAHs, polycyclic aromatic hydrocarbons.

TABLE 13.2 Data related to monitoring of polycyclic aromatic hydrocarbons (PAHs)-contaminated soil (1 and 2) treatment. Sample

PAHs HPLC (g kg21)

PAHs UV (g kg21)

A254nm/A288nm

Soil 1/0 day

8.2

6.7

2.89

Soil 1/35 days

4.2

4.9

2.29

Soil 1/50 days

3.7

3.9

2.12

Soil 1/110 days

2.2

2.2

1.76

Soil 1/220 days

2.0

2.2

1.67

Soil 2/0 day

2.9

2.3

1.32

Soil 2/40 days

2.9

2.0

1.33

Soil 2/80 days

2.8

1.9

1.35

Soil 2/100 days

2.8

1.9

1.35

Soil 2/130 days

2.3

1.8

1.35

HPLC, high performance liquid chromatography.

considered as a soil evolution index and allows estimating the potential biological treatability of a PAHs-contaminated soil. In conclusion, PAHs and soil evolution indexes are simple and rapid tools for the characterization of PAHs-contaminated soils in terms of level and repartition of pollution and for the prediction of their potential biotreatability. From an environmental point of view, they permit pointing out sensitive zones and defining priorities in terms of decontamination. For on-site analysis, a handheld kit, using a field UV

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FIGURE 13.6 Variation of soil evolution index with treatment time during biological treatment (soil 1 with light PAHs, soil 2 with heavy PAHs). PAHs, polycyclic aromatic hydrocarbons.

spectrophotometer, has been developed and gives the two index values directly [5]. 13.2.2.2 Petroleum hydrocarbons Fig. 13.7 presents UV visible spectra of acetonitrile extracts of a petroleum hydrocarbon-contaminated soil during a biological treatment on-site (biopiles). The treatment time increases from sample 1 to sample 4 (different biopiles) and, as far as the UV spectrum is less structured, the composting process is more advanced. In the same time, the pollution level decreased as the absorbance values all over the UV range, and particularly at 228 and 256 nm, decreased. Hydrocarbon concentration measured by infrared (IR) spectrophotometry, after a carbon tetrachloride extraction step [6] can be estimated by the absorbance value at 256 nm (A comparison between IR and UV measurements for 15 different polluted soils is shown in Fig. 13.8). The absorbance ratio A228nm/A256nm can reflect, during a biological treatment, the composting degree of soils contaminated by petroleum hydrocarbons, as for PAHs pollution. Quantitative data are collected in Table 13.3. As it can be observed, the composting process starts (A228nm/ A256nm . 1.9) at the beginning of the biological treatment on-site (sample 1). During the next steps (samples 2 and 3), the composting is efficient (1.9 , A228nm/A256nm , 1.5). A value of A228nm/A256nm lower than 1.5 for sample 4 indicates that the final composting phase is reached, that is, that decontaminated soils can be potentially reused. In conclusion, simple UV indexes presented in this chapter enable a rapid diagnosis of hydrocarbonated pollution in terms of location,

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13.2 Polluted soils

FIGURE 13.7

UV monitoring of a petroleum hydrocarbon soil biological treatment.

FIGURE 13.8 Correlation between IR and UV measurements of petroleum hydrocarbons. IR, infrared.

TABLE 13.3

Monitoring of the composting of petroleum hydrocarbon-polluted soils.

Sample

HC IR (%)

HC UV est. (%)

A228nm/A256nm

1

13

15

2.0

2

12

13.5

1.9

3

9.5

7

1.7

4

3

4

1.5

HC, hydrocarbon; IR, infrared.

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distribution, and aging. They can also be useful in the monitoring of natural attenuation of polluted soils.

13.3 Composts Solid waste treatment is nowadays very important because their quantity increases regularly and their storage in landfills tends to be forbidden. Incineration is more and more used in urban area, but composting, often associated with biological sludges from wastewater treatment plant or with green wastes, can be an economical eco-friendly solution. As for other processes, the quality control of the mixture, the compost, has to be carefully checked before and during the treatment, particularly because the destination of the final mature compost (in fact, mineralized) may be for an agricultural use.

13.3.1 Characterization of solid wastes Many tests and indexes are regularly proposed in order to evaluate the characteristic of composts. The first way is based on the study of the structural evolution of the organic matrix with the characterization of functional or structural groups. Fluorescence spectroscopy, IR spectroscopy, or (NMR) nuclear magnetic resonance RMN measurement have been developed to follow humic-like substances in soil, and to study urban compost [7]. The second approach consists of the study of the evolution of both humic and fulvic fractions. This way seems to be more operational. The evolution of the humic nature of the organic matter during composting or after mixture with soil can be estimated by one of the following methods: C/N ratio [8], humification index [11], or biodegradability index [10]. This approach is also based on UV visible properties of aqueous or organic extracts of composts or soils. Several methods are available from UV visible spectra exploitation (Table 13.4), often coupling with fluorimetry. Relationships between the specific absorbance at 254 nm [specific UV absorbance (SUVA)] and dissolved organic matter or water extractable organic matter were established. Some other UV indexes like ratio of absorbance A205nm/ A250nm, or A250nm/A250nm or even A465nm/A665nm have also been proposed for the characterization of the evolution of humic-like substances [12,13]. Other works used the UV spectral deconvolution method for the assessment of the humification level or the maturity of the compost. This will be explained in the following section.

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13.3 Composts

TABLE 13.4 Some works on compost characterization using UV visible spectrophotometry. UV vis wavelengths

Topic

Method

Samples

References

Maturity assessment

UV

UVSDa, 200 350

Mix of green waste and sewage sludge

[9]

Humification

UV vis, Fluo

A205/A250, A465/A665

Domestic waste

[12]

Water extractable organic matter

UV vis, Fluo, FTIRa

SUVA254, A250/A365 Slope 350 400

Domestic waste

[13]

Humification

UV, NIRa

UVSD

Green waste, sewage sludge and pine bark

[16]

Spectral parameters

UV vis

300, 465, 665, 850 nm

Humic products

[14]

Humification parameters

UV vis

280, 472, 664 nm

Olive mill wastes

[15]

DOM

UV vis, gel permation chromatography, FTIR, fluo

Integral A226 A400nm

Biosolid

[16]

DOM

UV vis, Fluo, FTIR

250 300, SUVA254, SUVA280, 465,

Manure composting

[17]

Humification, stability

UV vis, X-ray diff., IR

Textile, green and paper wastes

[18]

665nm

260 280 nm, 460 480 nm, 600 670 nm

a

UV spectral deconvolution, Fourier transform-IR, near-IR.

13.3.2 Composting of solid wastes The aim of solid waste treatment is both to reduce their size and to stabilize their organic content from a chemical point of view. In order to obtain qualitative and quantitative information about humification of anthropogenic organic matter during the treatment process or waste valorization, a simple analytical method has been developed [19]. The use of UV spectrophotometry was chosen in order to evaluate the potentiality of this technique for the proposal of an index of maturity of

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FIGURE 13.9

13. Polluted soils, composts, and leachates

UV spectra of evolution according to composting time (aqueous

extracts).

urban waste compost. Fig. 13.9 shows the aqueous leachates of composts, with an increasing spectrum according to composting time. This last seems to contain more soluble and absorbing compounds than the fresh compost, with an enlargement of a shoulder at around 260 270 nm. As the UV spectrum shape is relatively featureless, a complementary procedure is used for a fine characterization of organic matter extracted by a basic medium (K4P2O7). This method, already used in a previous work [9], was based on the separation of the extract by low-pressure gel chromatography with a study of the fractions by UV spectrophotometry. The selected gel (Sephadex G75) allows the separation of macromolecules of apparent molecular weight (MW) between 3000 and 70,000 Da. During the elution process, the molecules with higher MW are eluted first, the smaller ones being retained in the gel according to their MW (Fig. 13.10). The experiment has been carried out on leachates corresponding to the beginning and end of the compost process of the previous compost (156 days of composting). The examination of chromatograms and the corresponding UV spectra shapes show the organic matter evolution during composting. The chromatogram corresponding to the fresh compost is characterized by three peaks of absorbance (followed at 270 nm), the first being more intense. The UV spectra of the related fractions (15, 39, and 52) are different, but without specific feature. For the matured compost, the chromatogram is simpler with only two peaks, the second peak corresponding to the high values of absorbance at 270 nm. The related fractions (17 and 42) seem to correspond to the two first fractions of the fresh compost but their UV spectra are different, showing a clear shoulder at 260 270 nm. Assuming that the two first fractions of each

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13.3 Composts

429

FIGURE 13.10 Chromatograms (and UV spectra of fractions) of extracts of fresh compost (FC) and treated compost (TC).

leachate correspond roughly to the same groups of compounds, the evolution during composting tends to lower their MW with degradation process. More precisely, compounds with apparent MW . 70,000 Da (fraction 15 of fresh compost) are degraded into smaller molecules, as it can be seen with the strong decrease of the first peak intensity of the matured compost. At the same time, compounds formed during the composting process seem to be characterized not only by lower MW than the initial ones, but also by the presence of aromatic structures explaining the shoulder of the corresponding UV spectrum (fraction 42 of the final compost). For example, phenolic compounds generally considered to be the building blocks of humic substances, were identified [9,19] with other functional groups (carboxylic and hydroxylic).

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Thus, during the composting process, mineralization is a competitive reaction with respect to humification, characterized by a high aromaticity level. The latter observation demonstrates the increase of complexity of organic matter with increasing maturity level. Further exploitation should be made by one of the proposed methods (Table 14.4).

13.4 Landfill leachates Landfill leachates are generally considered to be highly polluted liquids, containing various organic compounds refractory to biodegradation. In fact, their composition varies, on the one hand, with the nature of disposed solid wastes, and, on the other hand, with the landfill storage duration and the treatment process, if any. Organic matter of landfill leachates can be characterized globally by fluorescence spectroscopy, for example [3], or by the determination of various parameters [20]. Specific organic compounds can be detected by FTIR [2] or by chromatographic techniques [21]. Global parameters such as chemical oxygen demand (COD), biological oxygen demand, dissolved organic carbon (DOC), nitrogen forms, COD and optical methods, and particularly UV spectrophotometry, are also used [4]. The following examples deal with the characterization of leachates, with respect to their origin, and present some treatment experiments.

13.4.1 Characterization of leachate 13.4.1.1 Direct examination of UV spectra The applicability of conventional and nonconventional parameters for municipal landfill characterization was recently proposed by Baettker et al. [22]. The authors concluded that the combined used of distinct parameters (among which those obtained with UV visible spectrophotometry) allows specific details such as the type of the organic matter and leachate decomposition stage. In order to demonstrate the interest of UV visible spectra, leachates from four municipal solid waste landfills, Augsburg and Munich (Germany), Grospierres and Saint-Bre`s (France), were studied (Fig. 13.11). All these samples are diluted 20 times, except Saint Bre`s (40 times). The direct examination of leachate spectra shows, for all samples, a decreasing monotonous shape between 200 and 400 nm. Nevertheless, some individual particularities can be noticed. Augsburg and Munich leachates were characterized by a rather low absorbance ratio between the beginning and the middle of the spectrum

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13.4 Landfill leachates

FIGURE 13.11 UV spectra of urban landfill leachates (diluted 20, except Saint-Bre`s, diluted 40).

TABLE 13.5 Aggregate parameters of the studied landfill leachates (Fig. 13.11). Leachate

21

COD (mgO2 L )

DOC (mgC L )

Conductivity (mS cm21) [Cl2 (mg L21)]

21

Augsburg

550

230

4.8 [430]

Grospierres

930

160

3.1 [540]

Munich

690

150

9.0 [2550]

1560

670

6.4 [1100]

Saint-Bre`s

COD, chemical oxygen demand; DOC, dissolved organic carbon.

(300 nm). At the same dilution, the spectrum of Grospierres leachate showed a higher absorbance on the studied range, suggesting a higher organic load. The same hypothesis can be formulated for Saint-Bre`s leachate, which was more diluted. A shoulder, around 270 280 nm, is more important for the Grospierres and Saint-Bre`s samples. These observations are confirmed by the values of other parameters such as COD and DOC (Table 13.5). The spectrum of Munich leachate is more concave with no marked accident, and its absorbance decreases rapidly between 200 and 230 nm, due to a more important salinity, brought by chloride ion. This spectrum also gives an indication of the composition of the leachate. Indeed, simple organic molecules resulting from the initial degradation process (e.g., carboxylic acids) absorb at the beginning of the UV wavelength range and a few humic-like substances are present (low shoulder at 270 nm).

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13.4.1.2 pH effect The studied leachates have a pH close to 7.5 8.5. After sample acidification by the addition of sulfuric acid until pH 2, Munich and Augsburg leachates show the same shape and are globally not affected by the pH change, proving the low concentration of humic-like substances (Fig. 13.12). Moreover, these leachates are characterized by an important level of inorganic carbon 78% and 71% of total carbon, respectively (Table 13.6). As a small difference exists particularly at the shortest wavelengths, it can be assumed that inorganic carbon is eliminated by acidification and that the decrease in absorbance can be due to the disappearance of hydrogenocarbonate ions, which slightly absorbs in this wavelength window. On the contrary, Grospierres and Saint-Bre`s leachates are very sensitive to pH (Fig. 13.13). After acidification, the shape of their UV spectra is the same, but the absorbance decreases all along the wavelength range. The organic load is lowered, as shown by COD values before and after acidification (Table 13.7). In the same way, the shoulder

FIGURE 13.12 pH effect on Augsburg and Munich leachates (diluted 5). TABLE 13.6 Carbon concentration [organic, dissolved organic carbon (DOC), and inorganic (IC)] for Augsburg and Munich leachates. Leachate

pH

DOC (mgC L21)

IC (mgC L21)

Augsburg

7.7

160

560

2.0

160

,1

7.5

140

350

2.0

140

,1

Munich

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13.4 Landfill leachates

FIGURE 13.13

pH effect on Grospierres and Saint-Bre`s leachates (diluted 5).

pH effect on the organic load of Grospierres and Saint-Bre`s leachates.

TABLE 13.7

Grospierres

Saint-Bre`s

930

1560

COD (mgO2 L ) after acidification

520

1040

COD reduction (%)

44

33

21

COD (mgO2 L ) before acidification 21

COD, chemical oxygen demand.

around 270 280 nm is less important after treatment. This phenomenon is supported by the observation of a precipitate after acidification, probably linked to the presence of humic-like substances.

13.4.2 Leachate treatment UV spectrophotometry also provides information about the evolution of leachates during classical treatments. Some treatment trials were carried out on the previous landfill leachates. Coagulation flocculation tests with FeCl3 (500 mg L21) and UV photooxidation coupled or not with a chemical oxidizing agent (H2O2, 0.1 mole L21) were tested. UV spectra before and after treatment are presented.

13.4.3 Coagulation flocculation with FeCl3 Coagulation flocculation with FeCl3 only enables the elimination of a rather small part of the organic load. This part is nevertheless more

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13. Polluted soils, composts, and leachates

important in acidic conditions as shown for Grospierres leachate in Fig. 13.14. COD data before and after treatment are given in Table 13.8 for Grospierres leachate. The results show that a pH decrease to 5, in the presence of FeCl3, leads to the removal of about 30% of the COD, probably explained by the precipitation of humic-like substances. 13.4.3.1 Photooxidation The efficiency of photooxidation treatment on leachates (at pH 2) was studied with UV spectrophotometry. Different attempts were made in the presence of an oxidizing agent, of variable concentration (Table 13.9). Figures 13.15 and 13.16 present, respectively, the results for Augsburg and Saint-Bre`s leachates, for an irradiation time of 15 minutes with an oxidant concentration (H2O2) of 0.1 mole L21. A real efficiency is observed both from the evolution of (TOC) total organic carbon (or DOC) and UV spectra.

FIGURE

13.14

(FeCl3 5 500 mg L21).

Effect

of

coagulation flocculation

on

Grospierres

TABLE 13.8 Chemical oxygen demand (COD) data for Grospierres leachate treatment (coagulation flocculation). pH 5

pH 8

930

930

COD (mgO2 L ) after treatment

660

800

COD reduction (%)

29

14

21

COD (mgO2 L ) before treatment 21

UV-Visible Spectrophotometry of Waters and Soils

leachate

435

13.4 Landfill leachates

TABLE 13.9

Effect of H2O2 concentration on TOC reduction.

H2O2 concentration (mole L21)

Total organic carbon (TOC) reduction (%) Grospierres leachate

0.01

10

0.025

20

75

0.05

62

87

0.1

86

91

FIGURE 13.15

TOC reduction (%) Augsburg leachate

UV spectra of Augsburg leachate before and after photooxidation (dilu-

tion 5).

The specific shoulder around 270 280 nm disappears after treatment for Saint-Bre`s leachate, which is a sign of organic load degradation. For the two studied leachates, significant absorbance values are obtained at the beginning of the UV range (200 240 nm), where degradation products, especially organic acids or nitrate/nitrite ions show specific absorption. Finally, the influence of the irradiation time is reported in Fig. 13.17 for Grospierres leachate (pH 5). The decrease in absorbance is more important when the irradiation time increases, as expected. This phenomenon is correlated to the DOC reduction (DOCf/DOCi), which was measured during the photooxidation tests (Table 13.10). In conclusion, the direct examination of UV spectra reveals some differences according to the organic load and to the management of urban solid waste landfill. For instance, Fig. 13.11 shows quite different UV spectra for Grospierres and Munich, which have nearly the

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13. Polluted soils, composts, and leachates

FIGURE 13.16 UV spectra of Saint-Bre`s leachate before and after photooxidation (dilution 40).

FIGURE 13.17

Evolution UV spectra of Grospierres leachate during the photooxida-

tion treatment.

same DOC value. This can be explained by the variability of the nature of soluble organic matter that can be affected by the origin of the wastes (rural vs urban). On the other hand, higher organic loads could be expected for urban landfills (Augsburg and Munich). UV spectra do not confirm this hypothesis, and one may suppose that these leachates have been pretreated (e.g., by coagulation flocculation). Indeed, no precipitate was observed either by acidification at pH 2 or by addition of FeCl3. Further investigation should be made to confirm the interest of this approach.

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References

TABLE 13.10 Dissolved organic carbon (DOC) reduction according to irradiation time (Grospierres leachate). Irradiation times

DOCf/DOCi

0

1

81

0.75

250

0.46

567

0.29

1635

0.10

References [1] USEPA, Contaminated land. ,https://www.epa.gov/report-environment/contaminated-land., 2018 (accessed 11.02.21). [2] S. Lenz, K. Bo¨hm, R. Ottner, M. Huber-Humer, Determination of leachate compounds relevant for landfill aftercare using FT-IR spectroscopy, Waste Management 55 (2016) 321 329. Available from: https://doi.org/10.1016/j.wasman.2016.02.034. [3] A. Baker, M. Curry, Fluorescence of leachates from three contrasting landfills, Water Research 38 (2004) 2605 2613. Available from: https://doi.org/10.1016/j.watres. 2004.02.027. ¨ man, C. Junestedt, Chemical characterization of landfill leachates - 400 para[4] C.B. O meters and compounds, Waste Management 28 (2008) 1876 1891. Available from: https://doi.org/10.1016/j.wasman.2007.06.018. [5] E. Touraud, O. Cloarec, M. Croˆne, O. Thomas, Kit de diagnostic rapide de la contamination des sols par les HAP, De´chets - Revue Francophone d’e´cologie Industrielle, N 25 (2002). Available from: https://doi.org/10.4267/dechets-sciences-techniques. 2349. [6] M. Crone, Diagnostic de sols contamine´s par des hydrocarbures aromatiques polycycliques (HAP) a` l’aide de la spectrophotometrie UV, PhD Thesis, Institut National des Sciences Appliquees (INSA), Lyon, France, 2000. [7] M. Mohinuzzaman, J. Yuan, X. Yang, N. Senesi, S.L. Li, R.M. Ellam, et al., Insights into solubility of soil humic substances and their fluorescence characterisation in three characteristic soils, Science of the Total Environment 720 (2020) 137395. Available from: https://doi.org/10.1016/j.scitotenv.2020.137395. [8] R. Guo, G. Li, T. Jiang, F. Schuchardt, T. Chen, Y. Zhao, et al., Effect of aeration rate, C/N ratio and moisture content on the stability and maturity of compost, Bioresource Technology 112 (2012) 171 178. Available from: https://doi.org/ 10.1016/j.biortech.2012.02.099. [9] M. Domeizel, a Khalil, P. Prudent, UV spectroscopy: a tool for monitoring humification and for proposing an index of the maturity of compost, Bioresource Technology 94 (2004) 177 184. Available from: https://doi.org/10.1016/j.biortech.2003.11.026. [10] E. Iglesias Jime´nez, V. Perez Garcia, Evaluation of city refuse compost maturity: a review, Biological Wastes 27 (1989) 115 142. Available from: https://doi.org/ 10.1016/0269-7483(89)90039-6. [11] M. De Nobili, F. Petrussi, Humification index (HI) as evaluation of the stabilization degree during composting, Journal of Fermentation Technology 66 (1988) 577 583. Available from: https://doi.org/10.1016/0385-6380(88)90091-X.

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[12] F.E. Mora´n Vieyra, V.I. Palazzi, M.I. Sanchez de Pinto, C.D. Borsarelli, Combined UV-Vis absorbance and fluorescence properties of extracted humic substances-like for characterization of composting evolution of domestic solid wastes, Geoderma 151 (2009) 61 67. Available from: https://doi.org/10.1016/j.geoderma.2009.03.006. [13] X. He, B. Xi, Z. Wei, X. Guo, M. Li, D. An, et al., Spectroscopic characterization of water extractable organic matter during composting of municipal solid waste, Chemosphere 82 (2011) 541 548. Available from: https://doi.org/10.1016/j.chemosphere.2010.10.057. [14] E. Filcheva, M. Hristova, P. Nikolova, T. Popova, K. Chakalov, V. Savov, Quantitative and qualitative characterisation of humic products with spectral parameters, Journal of Soils and Sediments 18 (2018) 2863 2867. Available from: https:// doi.org/10.1007/s11368-018-2021-4. [15] F. Sellami, S. Hachicha, M. Chtourou, K. Medhioub, E. Ammar, Maturity assessment of composted olive mill wastes using UV spectra and humification parameters, Bioresource Technology 99 (2008) 6900 6907. Available from: https://doi.org/ 10.1016/j.biortech.2008.01.055. [16] R. Albrecht, J. Le Petit, G. Terrom, C. Pe´rissol, Comparison between UV spectroscopy and Nirs to assess humification process during sewage sludge and green wastes cocomposting, Bioresource Technology 102 (2011) 4495 4500. Available from: https:// doi.org/10.1016/j.biortech.2010.12.053. [17] Y. Yang, W. Du, Z. Cui, T. Zhao, X. Wang, J. Lv, Spectroscopic characteristics of dissolved organic matter during pig manure composting with bean dregs and biochar amendments, Microchemical Journal 158 (2020) 105226. Available from: https://doi. org/10.1016/j.microc.2020.105226. [18] S. Biyada, M. Merzouki, K. Elkarrach, M. Benlemlih, Spectroscopic characterization of organic matter transformation during composting of textile solid waste using UV visible spectroscopy, infrared spectroscopy and X-ray diffraction (XRD), Microchemical Journal 159 (2020) 105314. Available from: https://doi.org/10.1016/j. microc.2020.105314. [19] P. Prudent, M. Domeizel, C. Massiani, O. Thomas, Gel chromatography separation and UV spectroscopic characterization of humic-like substances in urban composts, Science of the Total Environment 172 (1995) 229 235. [20] B.P. Naveen, D.M. Mahapatra, T.G. Sitharam, P.V. Sivapullaiah, T.V. Ramachandra, Physico-chemical and biological characterization of urban municipal landfill leachate, Environmental Pollution 220 (2017) 1 12. Available from: https://doi.org/10.1016/j. envpol.2016.09.002. [21] M.S. Beldean-Galea, J. Vial, D. Thie´baut, M.V. Coman, Analysis of multiclass organic pollutant in municipal landfill leachate by dispersive liquid-liquid microextraction and comprehensive two-dimensional gas chromatography coupled with mass spectrometry, Environmental Science and Pollution Research 27 (2020) 9535 9546. Available from: https://doi.org/10.1007/s11356-019-07064-z. [22] E.C. Baettker, C. Kozak, H.G. Knapik, M.M. Aisse, Applicability of conventional and nonconventional parameters for municipal landfill leachate characterization, Chemosphere 251 (2020) 126414. Available from: https://doi.org/10.1016/j.chemosphere.2020.126414.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

14 Agricultural and natural soils, wetlands, and sediments Olivier Thomas and Marie-Florence Thomas EHESP School of Public Health, Rennes, France

O U T L I N E 14.1 Introduction

439

14.2 Agricultural soils

440

14.3 Natural soils

444

14.4 Wetlands

445

14.5 Sediments

448

Ackowledgments

451

References

451

14.1 Introduction Soil quality is one of the three components of environmental quality, besides water and air quality. Contrary to water and air quality defined mainly by their degree of pollution, soil quality is not limited to the degree of soil pollution but is commonly defined much more broadly as “the capacity to function within ecosystem and land-use boundaries to sustain biological productivity, maintain environmental quality, and promote plant and animal health” [1]. This definition reflects the complexity and site specificity of the

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00008-3

439

© 2022 Elsevier B.V. All rights reserved.

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14. Agricultural and natural soils, wetlands, and sediments

belowground part of terrestrial ecosystems as well as the many linkages between soil functions and soil-based ecosystem services. Indeed, soil quality is more complex than the quality of air and water, not only because soil constitutes a heterogeneous medium with solid, liquid, and gaseous phases, but also because soils can be used for a larger variety of purposes. This multifunctionality of soils is also addressed when soil quality is defined from an environmental perspective as “the capacity of the soil to promote the growth of plants, protect watersheds by regulating the infiltration and partitioning of precipitation, and prevent water and air pollution by buffering potential pollutants such as agricultural chemicals, organic wastes, and industrial chemicals” [2]. In some cases the pollution impact of soils is so important that the basic use of soils mentioned earlier can be impossible. This was the topic of Chapter 13, with the study of polluted soils, solid waste compost, and leachates. In the present chapter, some applications dealing with the quality assessment of agricultural (crop, grassland) and natural (forest, peatland) soils will be considered. It will extend to wetlands (peatland being one type of wetland) and sediments.

14.2 Agricultural soils Among several dozen definitions since the 1800s [3] for agricultural soils, a simple one is “a dynamic natural body on the surface of the Earth in which plants grow, composed of mineral and organic materials and living forms” [4]. However, more than their composition, soil structure is of great importance. As for water quality monitoring, soil quality assessment can be carried out remotely or locally (field sensing or sampling) (Fig. 14.1). Bottinelli et al. [5] underlined the influence of soil macrofauna (such as earthworms) on soil structure. Soil structure regulates a large number of ecological functions, including control of water infiltration, percolation and retention, gas exchanges, soil organic matter and mineral nutrient dynamics, soil microbial biomass, and diversity and activity and the susceptibility of soil to erosion. For the study of soil structure, the visual soil evaluation (VSE) is of great interest [6]. As stated by Ball et al. [7], VSE methods are particularly valuable for detecting compaction and can reveal changes in aeration and water saturation, including those related to weather extremes. Applications for topsoil (visual evaluation of soil structure or VESS) and subsoil (sub-VESS) are available. Visual assessments also have the potential to assess the risk of surface water runoff and nutrient loss [7]. From visual assessment to the study of optical properties of soils by reflectance, the link

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14.2 Agricultural soils

FIGURE 14.1

441

Soil quality assessment (credit O. Thomas).

is obvious with the aim of getting specific information on soil composition. The implementation of soil spectroscopy could find valuable application in the soil information system and would represent a great progress in the field of soil analysis [8]. This is in this way that Stenberg et al. proposed to think about a global soil spectral library [9] which will be managed by Viscarra Rossel in order to characterize the world’s soil [10]. Soil data from 92 countries representing 7 continents were acquired during the 2010s. The global vis NIR library comprised reflectance spectra between 400 and 2500 nm of more than 12,000 sites. It describes soil quality variation providing an integrative measure of the soil, which can be used for both qualitative and quantitative soil analyses [10], after pretreatment of data (using wavelets) and chemometrics analysis (e.g., machine learning). With the same method, the reflectance spectroscopy between 350 and 2500 nm, Mousavi et al. proposed recently [11] a simple way for the rapid estimation of soil plasticity (Atterberg limits). After signal smoothing with the Stivisky Golay method, three types of spectra (ratio index, normalized difference index, and difference index) were exploited with PLS (partial least squares) algorithm to extract wavebands’ combination and calibrations between Atterberg limits and reflectance spectra. Actually, before the acquisition of the global soil spectral library, Viscarra Rossel et al. used in situ measurements of color, mineral composition, and clay content of soil by near-IR (NIR) spectroscopy [12] and proposed a method based

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TABLE 14.1 Some works on soil characterization using UV visible spectrophotometry. Topic

Spectroscopy

Method

Samples

References

NO3, DOC

UV (210 300 nm)

XAD-8, PLS

Forest floor

[17]

FA

UV, fluo

UVSDa

Wetland

[19]

OM

UV, Fluo, NMRa

SUVA

Agricultural soil

[20]

WHEOM, HS

UV vis, Fluo

SUVA254, A250/ A365

Soils after fire

[21]

Nitrate, WEOC

UV

UVSD

Agricultural soil

[18]

WEOM

UV vis, Fluo

SUVA254, SUVA 280, A400/A600

Agricultural soil

[22]

WEOM, DOC,

UV vis, fluo

SUVA, PARAFAC

Agricultural soil

[23]

Organic amendment (WEOM, HLS)

UV vis, Fluo

SUVA254, A250/ A365

Dune soil

[24]

Organic amendment (SOM, WHEOM)

UV, Fluo

SUVA254, A250/ A365

Calcareous soil

[25]

Atrazine adsorption

UV, MEKCa

UVSD

Agricultural soil

[26]

DOM

Fluo

Fluo indices

Agricultural soil

[27]

Nitrate

IESa, UV

A205, A300

Different soils

[28]

DOM, DOC

Fluo

PARAFAC

Agri soils of China

[29]

DOM

Fluo

PARAFAC

Different soils of China

[30]

a

UV spectral deconvolution, nuclear magnetic resonance, micellar electrokinetic chromatography, and ion electrode specific. DOM, dissolved organic matter; FA, fulvic acid; HLS, humic-like substances; HS, humic substance; SOM, soil organic matter; SUVA, specific UV absorbance; WEOC, water extractable organic carbon; WEOM, water extractable organic matter.

on UV Vis NIR diffuse reflectance spectroscopy to determine the composition of mineral organic mixes in soil [13]. Besides the direct measurement of optical properties of soil (reflectance spectra), some methods based on soil extracts examination were proposed in the past two decades. Fuentes et al. showed the usefulness of

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14.2 Agricultural soils

443

UV visible and fluorescence spectroscopies to study the chemical nature of humic substances (HS) from soils and composts [14]. Their results showed that the humic properties of the different organic systems (considered in the experiments on alkaline extracts of HS) and organic composts (wineries, olive wastes, manure) were well described using three complementary indexes derived from the UV visible: the EET/EBz ratio (A253 nm/A220 nm), A280 nm, and from fluorescence spectra: the Milori index [15]. The characterization of natural soils and sediments with UV spectrophotometry, applied after leaching tests, is possible [16]. UV spectrophotometry can also be used to estimate nitrates, dissolved organic carbon (DOC), and DOC fractions after XAD fractionation in forest-floor leachates [17] or in agricultural soil water extracts [18]. The exploitation of spectra of aqueous and alkaline extracts allows determining oxidation and humification indexes [16], even if the corresponding UV spectra present different featureless and smooth shapes. Moreover, as some organic compounds of soils and sediments are not soluble in water, a complementary organic extraction step was carried out. The choice of acetonitrile, as for PAHs (polycyclic aromatic hydrocarbons) characterization (see Chapter 4), was made because of the necessity of using a non-UVabsorbing solvent. Table 14.1 presents some works on soil characterization based on the use of UV visible spectrophotometry and fluorescence. Finally, UV spectra of different humus are presented in Fig. 14.2 [31]. The direct examination of the UV spectra of acetonitrile extracts of different types of humus shows a rather strong absorption at the beginning of the spectrum, which decreases rapidly over 220 nm. A clear shoulder at 274 nm can be noticed for the leafy humus. This shoulder is not so

FIGURE 14.2

UV visible spectra of different types of humus (acetonitrile extracts,

dilution 5) [31].

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14. Agricultural and natural soils, wetlands, and sediments

marked in the case of pine humus, for which a little shoulder appears at around 230 nm. Because its UV spectrum is not structured, it can be supposed that the degree of maturation is more advanced for the pine humus. The spectrum of peat presents some differences, corresponding to other natural conditions. A rather large shoulder between 250 and 350 nm suggests the presence of condensed organic matter, and the specific absorption at 210 nm (presence of nitrates) indicates that the mineralization process is going on. These observations lead to the assumption that a typology of humus that takes into account the nature of vegetation and natural conditions, and based on UV visible spectra, could be proposed.

14.3 Natural soils In Table 14.1, some applications do not address specifically agricultural soils. Simonsson et al. [17] sampled forest-floor leachates for nitrate measurement, DOC fractionation, and dissolved organic matter (DOM) characterization (Fig. 14.3). UV spectra acquired had a shape identical to water samples containing nitrate or DOC (see Chapter 8) and their exploitation was carried out with PLS models. Vergnoux et al. [21] studied 30 different forest soils after fire events (from very recent to old). Water extractable organic matter (WEOM) and fractionation on XAD-8 and XAD-4 resins were carried out before spectroscopic analysis (UV absorption and fluorescence measurements).

FIGURE 14.3 UV visible spectra of three filtered leachates containing respectively 42 mg L21 DOC and 0.0 mg L21 NO3-N; no DOC and 2.8 mg L21 NO3-N; 29 mg L21 DOC and 3.9 mg L21 NO3-N. Source: From M. Simonsson, K. Kaiser, R. Danielsson, F. Andreux, J. Ranger, Estimating nitrate, dissolved organic carbon and DOC fractions in forest floor leachates using ultraviolet absorbance spectra and multivariate analysis. Geoderma, 124 (2005) 157 168. https://doi.org/10.1016/j.geoderma.2004.04.010.

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14.4 Wetlands

445

After a fire event, HS and WEOM can be affected but can be recovered after some time, depending on fire intensity and duration. Besides forest soils, peatlands have a huge potential in global climate change mitigation strategies [32]. Soil carbon sequestration by peatlands being comparable to all agricultural land, their restoration should be an important mitigation measure. Peacock [33] used UV visible absorbance spectroscopy as a proxy for the quantity and quality of peatland DOC. A multiwavelengths approach [A254 nm, A263 nm, A230 nm, specific UV absorbance (SUVA254)] was proposed for DOC characterization. Cook et al. [34] quantified tropical peatland DOC using UV visible absorbance. A two-wavelength model description (A270 nm and A700 nm) for DOC prediction was used following the method proposed by Tipping et al. [35]. Another experiment using remote sensing was carried out by Torabi Haghighi et al. [36] in order to analyze peatland changes. From Landsat images, the normalization difference vegetation index was determined before and after peat extraction. Finally, Beyer et al. [37] recently proposed multisensory data to derive peatland vegetation communities using a fixed wing unmanned aerial vehicle (UAV). From data acquired sequentially from three sensors (RGB, multispectral, and thermal), all classes of vegetation were correctly predicted giving to UAV a strong advantage compared to satellite observation (one UAV flying under the cloud cover).

14.4 Wetlands The study of optical properties of wetlands is less developed than for other water bodies. One of the first studies was more than 20 years ago with the work of Bubier et al. [38] on spectral reflectance measurements of boreal wetlands and forest mosses from high-resolution data acquired in the laboratory after sampling. Mainly centered around the optical properties of sphagnum mosses and lichens, this study concluded on the interest of remote detection systems for further research. Ten years after, Becker et al. [39] proposed a classification-based assessment of the optimal spectral and spatial resolutions for the coastal wetland imagery of Great Lakes. Sardana et al. [40] used optical methods to study the DOM and photoreactivity in a wastewater-constructed wetland. Field measurements of SUVA254 nm, spectral slope ratios, and fluorescence signals were determined and used for the purpose. Recently, Doughty and Cavanagh [41] used an UAV for mapping coastal wetland biomass. A DJI Matrice quadcopter equipped with a multispectral camera capturing five spectral bands (blue, green, red, red

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14. Agricultural and natural soils, wetlands, and sediments

edge, and NIR) gave reflectance spectra from which vegetation indices were obtained for spatial and temporal analysis of biomass. Amani et al. [42] propose the use of multisource optical satellite imagery (Rapideye, Sentinel 2, ASTER, and Landsat8) for the spectral analysis of wetlands. With the aim of the classification of wetlands, the exploitation of some bands (NIR, red edge, and red) was discriminant. Besides remote measurements, field experiments can be carried out. Several sediments and water samples were taken on the site of the Vernier Marsh located in an ancient meander of the Seine River, near Rouen (Northwest France). The main water body of the Vernier Marsh is the Grand Mare (sampling stations 4 and 5), which is a peatland of 47 ha. The Vernier Marsh is the largest remaining wetland in the region (around 4500 ha with 1600 ha of peaty zone). The Grand Mare being in the lowest part of the marsh, all water bodies converge in it. Five sites were chosen for the sampling of water and sediments (Fig. 14.4). Station 1 was at the arrival of Le Ruel River in north of the Grand Mare, point 2 was in the Petite Mare, point 3 in La Crevasse River, and points 4 and 5 in South Grand Mare. These sampling stations were chosen in relation to their organic carbon concentration. Fig. 14.5 shows the UV spectra of water from the sampling points of Fig. 14.4. It is clear that stations 1, 2, and 3 of the annexes are characterized by a high concentration of organic matter. A shoulder is observed at 270 nm, characteristic of aromatic structures such as humic-like substances (HLS), or precursors of HLS, potentially present in peatland. The dark color of the samples can also be explained by this type of compounds (humified organic matter). In Grand Mare samples, the organic matter concentration is lower, and UV spectra are also less structured. This can be explained both by a dilution effect (inlet from the Seine

FIGURE 14.4 Sampling points of Grand Mare and annexes (Vernier Marsh, France).

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14.4 Wetlands

447

FIGURE 14.5 UV visible spectra of water samples from Fig 13.3, dilutes five times (left) and after normalization (right).

FIGURE 14.6 Spectra of sample extracts (from Fig. 14.4).

River) and by citing a lesser input of fresh organic matter. Indeed, the stations 1, 2, and 4 are characterized by an important vegetation (mainly trees such as birch) present on the banks, while Grand Mare is surrounded with reed bed. The set of normalized spectra presents an isosbestic point, illustrating the evolution of organic matter from simple molecules, giving a steeper slope at the beginning of the spectrum (samples 1 and 2), to more complex molecules, smoothing the UV spectrum especially after 250 nm. The presence of simple molecules can be explained by the first step of organic matter decomposition of fresh vegetation, further degraded and transformed in HS. A procedure of acetonitrile extraction was carried out on sediments. Fig. 14.6 presents the UV spectra of the extracts. The shape of UV spectra is structured and characterized by the presence of a first sharp peak at 275 nm and a second one at 410 nm. One can also note a first shoulder at 265 nm, and a second one at 315 nm.

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14. Agricultural and natural soils, wetlands, and sediments

The differences between samples of annexes (1, 2, and 3) and samples of the Grand Mare (4 and 5) are the crests of the peaks. An evolution or humification index could be the ratio of the absorbance measured at these two wavelengths. The study of the C/N ratio evolution shows a gradient of mineralization from the annexes to the Grand Mare.

14.5 Sediments A recent study by Xiao at al. [43] reported an explanation of long-term changes in lake color based on optical properties of lake sediments. The studied lake color (Lake Lundebyvannet, Norway) is becoming browner over the last decades with industrial pollution (acid rain) but this evolution was present for a long time. A long-term study (1800 to 2015) of DOM from sediments using UV visible and fluorescence spectroscopy revealed that the lake browning is a result of multiple drivers varying in strength over time. Water color was assessed from A410 nm and spectral slopes (S275 295 and S350 400) were calculated from Ln-transformed absorption coefficients as the proxies of DOM molecular weight and aromaticity. Fluorescence data (PARAFAC) confirmed the evolution of HLS classes. If terrestrial DOM dominated sediment DOM during the whole period, different drivers were involved in the explanation of lake browning such as afforestation, changes in land use, variation of S deposition, increased temperature, and precipitation. These findings could be generalized from studies on the lake sediments of other countries. A more simple experiment was conducted by Junqua [16] with acetonitrile extracts of two sediments sampled in a lake: surface (immersed or not) and deeper (10 15 cm). The color of the samples was brown

FIGURE 14.7 UV visible spectra of acetonitrile extracts of surface and deep sediments (no dilution for surface sediments, dilution 1/3 for deep sediments).

UV-Visible Spectrophotometry of Waters and Soils

14.5 Sediments

FIGURE 14.8

449

UV visible spectra of interstitial waters before and after filtration

(no dilution).

and black, respectively. A difference can be observed in the visible range of the spectra (Fig. 14.7) with a clear absorbance peak at 664 nm, specific of chlorophyll-a for surface sediment. The decrease of absorbance in the visible range goes with an increase of absorbance, and specific pattern of the spectrum in the UV region (200 300 nm), where degradation products may absorb, possibly explained by a more advanced decomposition process of organic matter in deeper samples (sed. 1 and 2). Fig. 14.8 shows UV visible spectra of interstitial water (raw and filtered) for sediments 2 and 3. Their shape is monotonous. For raw samples, the residual absorbance is rather important over 250 nm due to suspended matter. UV visible spectra of filtered samples show the absorption of organic matter characterized by a slight shoulder around 280 nm. A complementary experiment was carried out with fresh leave extracts with acetonitrile in order to confirm the presence of chlorophyll in the organic extracts of surface sediments (extract 2). A comparison was made with an extraction with acetone (extract 1), according to standard methods [44]. UV visible spectra seem very close (Fig. 14.9) except a difference observed between 400 and 420 nm, where acetone still absorbs slightly. The absorption in the visible region observed previously (664 nm) is due to the pigment. On the other hand, quantitative estimation of DOM from sediments by using UV visible spectrophotometry was proposed by Khan et al. [45]. The absorbance value at 272 nm, after water extraction and filtration, was correlated to TOC (total organic carbon) with a determination coefficient of 0.98. Another study for the quantification of HS in permafrost sediments, after a three-step extraction, was carried out by

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14. Agricultural and natural soils, wetlands, and sediments

FIGURE 14.9 Extraction of chlorophyll from fresh leaves with acetone (1) and acetonitrile (2).

Shirshkova et al. from absorbance values at 337 nm [46]. The results shown that there was close linear correlation (R2 5 0.92 0.99) between the optical parameter and the content of organic carbon in the humic fractions. In recent years, some spectroscopic studies were applied to sediment characterization. Derrien et al. [47] carried out a comparative study of the chemical fractions and origins of HS in a watershed from spectroscopic and molecular characterization of soils and sediments. After the distinction of humic acid fractions from fulvic acid (FA) by the HS characteristics of SUVA using UV visible spectrophotometry, fluorescence excitation emission matrix-parallel factor analysis (EEM-PARAFAC) and high-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR Ms) were used. The results provided an in-depth insight into the chemical and structural heterogeneity of bulk HS, which could be even beyond the differences observed along the two HS origins (terrestrial vs endogenous/microbial). In another work, Fox et al. [48] proposed an adapted extraction and analytical approach for the characterization of natural organic matter (NOM) in low-carbon sediments. A NOM extraction and purification scheme was developed using sequential extraction with ultra-purewater (MQ) and sodium pyrophosphate at pH 10 (PP), combined with purification by dialysis and solid-phase extraction (SPE) in order to isolate different fractions of sediment-associated NOM. The FTIR and UV visible (SUVA) spectroscopic analysis of extracts showed that the water-soluble fraction (MQ-SPE) had a higher fraction of aliphatic and carboxylic groups, while the PP-extractable NOM (PP-SPE and PP . 1 kD) had higher fractions of unsaturated groups and higher residual metals. The electrospray ionization Fourier transform ion cyclotron

UV-Visible Spectrophotometry of Waters and Soils

References

451

resonance spectrometry (ESI-FT-ICR-Ms) data confirmed these observations. Finally, Gontijo et al. [49] proposed recently a multiproxy approach involving ultrahigh-resolution Ms and self-organizing maps to investigate the origin and quality of sedimentary organic matter across a subtropical reservoir. After alkaline extraction of humic and FAs, absorbance spectra (240 600 nm) and fluorescence data were recorded before elemental (C, N) and isotopic analysis (δ13C, δ15N), nuclear magnetic resonance (13C NMR), and FT-ICR-Ms. The results showed that samples from the upstream part of the reservoir were older and more decomposed. Differences between upstream and dam areas indicated that agriculture lands were related to microbially derived HS. Changes in HS composition revealed that internal reservoir processes may have influenced HS quality across the reservoir.

Ackowledgments The authors wish to thank Guillaume Junqua for his contribution to the first- and secondedition chapters [O. Thomas, G. Junqua, MF. Thomas Leachates and organic extracts from solids, in: UV-visible spectrophotometry of water and wastewater, O. Thomas, C. Burgess (Eds.), Elsevier, Amsterdam (2007, 2017)].

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in Agronomy 132 (2015) 139 159. Available from: https://doi.org/10.1016/bs. agron.2015.02.002. [9] B. Stenberg, R.A. Viscarra Rossel, A.M. Mouazen, J. Wetterlind, Visible and near infrared spectroscopy in soil science, Advances in Agronomy 107 (2010) 163 215. Available from: https://doi.org/10.1016/S0065-2113(10)07005-7. [10] H.H.S. Franco, R.M.L. Guimara˜es, C.A. Tormena, M.R. Cherubin, H.S. Favilla, Global applications of the visual evaluation of soil structure method: a systematic review and meta-analysis, Soil and Tillage Research 190 (2019) 61 69. Available from: https://doi.org/10.1016/j.still.2019.01.002. [11] F. Mousavi, E. Abdi, A. Ghalandarzadeh, H.A. Bahrami, B. Majnounian, N. Ziadi, Diffuse reflectance spectroscopy for rapid estimation of soil Atterberg limits, Geoderma 361 (2020). Available from: https://doi.org/10.1016/j.geoderma.2019.114083. [12] R.A. Viscarra Rossel, S.R. Cattle, A. Ortega, Y. Fouad, In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy, Geoderma 150 (2009) 253 266. Available from: https://doi.org/10.1016/j.geoderma.2009.01.025. [13] R.A. Viscarra Rossel, R.N. McGlynn, A.B. McBratney, Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy, Geoderma 137 (2006) 70 82. Available from: https://doi.org/10.1016/j.geoderma.2006.07.004. [14] M. Fuentes, G. Gonza´lez-Gaitano, J.M. Garcı´a-Mina, The usefulness of UV-visible and fluorescence spectroscopies to study the chemical nature of humic substances from soils and composts, Organic Geochemistry 37 (2006) 1949 1959. Available from: https://doi.org/10.1016/j.orggeochem.2006.07.024. [15] D.M.B.P. Milori, H.V.A. Galeti, L. Martin-Neto, J. Dieckow, M. Gonza´lez-Pe´rez, C. Bayer, et al., Organic matter study of whole soil samples using laser-induced fluorescence spectroscopy, Soil Science Society of America Journal 70 (2006) 57 63. Available from: https://doi.org/10.2136/sssaj2004.0270. [16] G. Junqua, E. Touraud, O. Thomas, Caracte´risation de la matie`re organique naturelle et de son e´volution dans les sols et les se´diments a` l’aide du couplage US/UV, De´chets Sciences et Techniques 34 (2004) 14 18. [17] M. Simonsson, K. Kaiser, R. Danielsson, F. Andreux, J. Ranger, Estimating nitrate, dissolved organic carbon and DOC fractions in forest floor leachates using ultraviolet absorbance spectra and multivariate analysis, Geoderma 124 (2005) 157 168. Available from: https://doi.org/10.1016/j.geoderma.2004.04.010. [18] M. Hassouna, F. Theraulaz, C. Massiani, Direct estimation of nitrate, total and fractionated water extractable organic carbon (WEOC) in an agricultural soil using direct UV absorbance deconvolution, Talanta 71 (2007) 861 867. Available from: https:// doi.org/10.1016/j.talanta.2006.05.067. [19] H. Yu, Y. Song, R. Liu, B. Xi, E. Du, S. Xiao, Variation of dissolved fulvic acid from wetland measured by UV spectrum deconvolution and fluorescence excitationemission matrix spectrum with self-organizing map, Journal of Soils and Sediments 14 (2014) 1088 1097. Available from: https://doi.org/10.1007/s11368-014-0869-5. [20] M.L. Ferna´ndez-Romero, J.M. Clark, C.D. Collins, L. Parras-Alca´ntara, B. LozanoGarcı´a, Evaluation of optical techniques for characterising soil organic matter quality in agricultural soils, Soil and Tillage Research 155 (2016) 450 460. Available from: https://doi.org/10.1016/j.still.2015.05.004. [21] A. Vergnoux, R. Di Rocco, M. Domeizel, M. Guiliano, P. Doumenq, F. The´raulaz, Effects of forest fires on water extractable organic matter and humic substances from Mediterranean soils: UV-vis and fluorescence spectroscopy approaches, Geoderma 160 (2011) 434 443. Available from: https://doi.org/10.1016/j. geoderma.2010.10.014. [22] Z. He, M. Zhang, A. Zhao, O.M. Olanya, R.P. Larkin, C.W. Honeycutt, Quantity and nature of water-extractable organic matter from sandy loam soils with potato

UV-Visible Spectrophotometry of Waters and Soils

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[24]

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[27]

[28]

[29]

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[31]

[32]

[33]

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cropping management, Agricultural & Environmental Letters 1 (2016) 160023. Available from: https://doi.org/10.2134/ael2016.06.0023. P. Sharma, Y. Laor, M. Raviv, S. Medina, I. Saadi, A. Krasnovsky, et al., Compositional characteristics of organic matter and its water-extractable components across a profile of organically managed soil, Geoderma 286 (2017) 73 82. Available from: https://doi.org/10.1016/j.geoderma.2016.10.014. S. Daouk, M. Hassouna, A. Gueye-Girardet, S. Niang, H.R. Pfeifer, UV/Vis Characterization and fate of organic amendment fractions in a dune soil in Dakar, Senegal, Pedosphere 25 (2015) 372 385. Available from: https://doi.org/10.1016/ S1002-0160(15)30005-9. O.R. Mouloubou, P. Prudent, S. Mounier, J.L. Boudenne, M.G. Abaker, F. The´raulaz, An adapted sequential chemical fractionation coupled with UV and fluorescence spectroscopy for calcareous soil organic matter study after compost amendment, Microchemical Journal 124 (2016) 139 148. Available from: https://doi.org/10.1016/ j.microc.2015.08.007. D.L.D. Lima, C.P. Silva, G.L. Erny, V.I. Esteves, Comparison between MEKC and UV spectral deconvolution to follow sorption experiment in soil, Talanta 81 (2010) 1489 1493. Available from: https://doi.org/10.1016/j.talanta.2010.02.057. N.Y. Musadji, L. Leme´e, L. Caner, G. Porel, P. Poinot, C. Geffroy-Rodier, Spectral characteristics of soil dissolved organic matter: long-term effects of exogenous organic matter on soil organic matter and spatial-temporal changes, Chemosphere 240 (2020). Available from: https://doi.org/10.1016/j.chemosphere.2019.124808. R. Shaw, A.P. Williams, A. Miller, D.L. Jones, Assessing the potential for ion selective electrodes and dual wavelength UV spectroscopy as a rapid on-farm measurement of soil nitrate concentration, Agriculture (Switzerland) 3 (2013) 327 341. Available from: https://doi.org/10.3390/agriculture3030327. J. Gao, C. Liang, G. Shen, J. Lv, H. Wu, Spectral characteristics of dissolved organic matter in various agricultural soils throughout China, Chemosphere 176 (2017) 108 116. Available from: https://doi.org/10.1016/j.chemosphere.2017.02.104. X.Q. Qin, B. Yao, L. Jin, X.Z. Zheng, J. Ma, M.F. Benedetti, et al., Characterizing soil dissolved organic matter in typical soils from China using fluorescence EEM PARAFAC and UV visible absorption, Aquatic Geochemistry 26 (2020) 71 88. Available from: https://doi.org/10.1007/s10498-019-09366-7. G. Junqua, Caracte´risation rapide de la matie`re organique des sols et de se´diments par spectrophotome´trie UV-visible; essai de typologie et estimation des parame`tres, C, N, P. Ph.D. thesis. University of Pau et Pays de l’Adour, France, 2002. J. Leifeld, L. Menichetti, The underappreciated potential of peatlands in global climate change mitigation strategies /704/47/4113 /704/106/47 article, Nature Communications 9 (2018). Available from: https://doi.org/10.1038/s41467-018-03406-6. M. Peacock, C.D. Evans, N. Fenner, C. Freeman, R. Gough, T.G. Jones, et al., UVvisible absorbance spectroscopy as a proxy for peatland dissolved organic carbon (DOC) quantity and quality: Considerations on wavelength and absorbance degradation, Environmental Sciences: Processes and Impacts 16 (2014) 1445 1461. Available from: https://doi.org/10.1039/c4em00108g. S. Cook, M. Peacock, C.D. Evans, S.E. Page, M.J. Whelan, V. Gauci, et al., Quantifying tropical peatland dissolved organic carbon (DOC) using UV-visible spectroscopy, Water Research 115 (2017) 229 235. Available from: https://doi.org/10.1016/j. watres.2017.02.059. E. Tipping, H.T. Corbishley, J.F. Koprivnjak, D.J. Lapworth, M.P. Miller, C.D. Vincent, et al., Quantification of natural DOM from UV absorption at two wavelengths, Environmental Chemistry 6 (2009) 472 476. Available from: https://doi. org/10.1071/EN09090.

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14. Agricultural and natural soils, wetlands, and sediments

[36] B.K.A. Torabi Haghighi, M.W. Menberu, H. Darabi, J. Akanegbu, Use of remote sensing to analyse peatland changes after drainage for peat extraction, Land Degradation & Development 29 (2018) (2018) 3479 3488. [37] F. Beyer, G. Jurasinski, J. Couwenberg, G. Grenzdo¨rffer, Multisensor data to derive peatland vegetation communities using a fixed-wing unmanned aerial vehicle, International Journal of Remote Sensing 40 (24) (2019) 9103 9125. Available from: https://doi.org/10.1080/01431161.2019.1580825. [38] J.L. Bubier, B.N. Rock, P.M. Crill, Spectral reflectance measurements of boreal wetland and forest mosses, Journal of Geophysical Research Atmospheres 102 (1997) 29483 29494. Available from: https://doi.org/10.1029/97jd02316. [39] B.L. Becker, D.P. Lusch, J. Qi, A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery, Remote Sensing of Environment 108 (2007) 111 120. Available from: https://doi.org/10.1016/j. rse.2006.11.005. [40] A. Sardana, B. Cottrell, D. Soulsby, T.N. Aziz, Dissolved organic matter processing and photoreactivity in a wastewater treatment constructed wetland, Science of the Total Environment 648 (2019) 923 934. Available from: https://doi.org/10.1016/j. scitotenv.2018.08.138. [41] C.L. Doughty, K.C. Cavanaugh, Mapping coastal wetland biomass from high resolution unmanned aerial vehicle (UAV) imagery, Remote Sensing 11 (2019). Available from: https://doi.org/10.3390/rs11050540. [42] M. Amani, B. Salehi, S. Mahdavi, B. Brisco, Spectral analysis of wetlands using multisource optical satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing 144 (2018) 119 136. Available from: https://doi.org/10.1016/j.isprsjprs.2018.07.005. [43] Y. Xiao, T. Rohrlack, G. Riise, Unraveling long-term changes in lake color based on optical properties of lake sediment, Science of the Total Environment 699 (2020) 134388. Available from: https://doi.org/10.1016/j.scitotenv.2019.134388. [44] A.E.E.W. Rice, R.B. Baird, Standard methods for the examination of water and wastewater (APHA, AWWA), 2017. [45] S. Khan, W. Yaoguo, Z. Xiaoyan, L. Jingtao, S. Jichao, H. Sihai, Relationship for the concentration of dissolved organic matter from corn straw with absorbance by using UV visible spectrophotometer, International Journal of Environmental Pollution and Remediation 2 (2014) 10 15. Available from: https://doi.org/10.11159/ ijepr.2014.004. [46] L.T. Shirshova, D.A. Gilichinsky, N.V. Ostroumova, A.M. Yermolayev, Application of spectrophotometry to quantification of humic substances in permafrost sediments, Kriosfera Zemli XIX (4) (2015) 96 102. [47] M. Derrien, Y.K. Lee, J.E. Park, P. Li, M. Chen, S.H. Lee, et al., Spectroscopic and molecular characterization of humic substances (HS) from soils and sediments in a watershed: comparative study of HS chemical fractions and the origins, Environmental Science and Pollution Research 24 (2017) 16933 16945. Available from: https://doi.org/10.1007/s11356-017-9225-9. [48] P.M. Fox, P.S. Nico, M.M. Tfaily, K. Heckman, J.A. Davis, Characterization of natural organic matter in low-carbon sediments: Extraction and analytical approaches, Organic Geochemistry 114 (2017) 12 22. Available from: https://doi.org/10.1016/j. orggeochem.2017.08.009. [49] E.S.J. Gontijo, P. Herzsprung, O.J. Lechtenfeld, C. de Castro Bueno, J.A.C. Barth, A.H. Rosa, et al., Multi-proxy approach involving ultrahigh resolution mass spectrometry and self-organising maps to investigate the origin and quality of sedimentary organic matter across a subtropical reservoir, Organic Geochemistry 151 (2021). Available from: https://doi.org/10.1016/j.orggeochem.2020.104165.

UV-Visible Spectrophotometry of Waters and Soils

C H A P T E R

15 UV spectra library Olivier Thomas1 and Marine Brogat2 1

EHESP School of Public Health, Rennes, France, 2School of Public Health, Ecole des Hautes Etudes en Sante´ Publique (EHESP), Rennes, France

O U T L I N E 15.1 Introduction

455

15.2 Spectra acquisition

456

15.3 Spectra of compounds 15.3.1 Acids and salts 15.3.2 Aldehydes and ketones 15.3.3 Amines and related compounds 15.3.4 Benzene and related compounds 15.3.5 Pesticides 15.3.6 Pharmaceuticals 15.3.7 Phenol and related compounds 15.3.8 Phthalates 15.3.9 Polycyclic aromatic hydrocarbons 15.3.10 Surfactants 15.3.11 Solvents 15.3.12 Inorganic compounds

459 459 467 475 490 497 520 535 557 562 578 583 587

Acknowledgments

605

References

605

15.1 Introduction In contrast to the other fields of spectroscopy, there exist a few data related to UV
visible spectrophotometry. One important reference

UV-Visible Spectrophotometry of Waters and Soils DOI: https://doi.org/10.1016/B978-0-323-90994-5.00002-2

455

© 2022 Elsevier B.V. All rights reserved.

456

15. UV spectra library

library has been published in 1992 [1], including a lot of spectra of organic compounds with the evolution of molar absorptivity with wavelength. This library is general and not dedicated to water and wastewater applications. An important electronic database of about 10,500 spectra/data sheets (ASCII-format) as well as about 3500 graphical representations and additional photochemistry information from published papers for about 2000 substances is available online [2]. The database is subdivided into 28 substance groups and is regularly upgraded since 1998. This electronic database is accessible online and is operated as an open-access database. It aims to gather, from scientific publications, UV
visible spectroscopic data (mainly the evolution of molar absorptivity with wavelength), the traceability of which is not guaranteed. Moreover, this database is more dedicated to atmospheric applications. Some other electronic databases can be proposed by instrument manufacturers. For example, one of the largest existing databases [3] gives access to nearly 31,000 UV
vis spectra of organic molecules but need specific software. The “science-softCon UV/Vis1 Photochemistry Database” (http://www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV
Vis
NIR (extreme ultraviolet to near infrared spectral region) spectral data and other photochemical information assembled from published peer-reviewed paper database UV/Vis 1 photochemistry [4]. The library presented in this chapter is coming from several studies led by the research groups of the main author. It includes the spectra of 146 organic and mineral compounds of 12 substance groups, and the great majority of spectra (115) were acquired following the traceability procedure explained hereafter. For the 31 new spectra, the acquisition was slightly different regarding the aim of their use [5]. In contrast to the previous libraries, the spectra are presented for a pathlength cell of 10 mm as the variation of absorbance values with wavelengths, and not the molar absorptivity coefficients. A table groups all useful information, including the concentration of the sample solution. This choice of presentation allows users to rapidly show the applicability of UV
visible spectrophotometry for their own applications.

15.2 Spectra acquisition The acquisition of UV
visible spectra was carried out through different steps, for 115 compounds: • sample selection • compound (name, n-CAS, M, solubility) • source and purity (supplier, purity) • other parameters (concentration, pH, temperature, solvent)

UV-Visible Spectrophotometry of Waters and Soils

15.2 Spectra acquisition

457

• sample preparation • standard solution prepared from two replicates (three if possible) • weighed mass of substance: for each about 500 mg if water soluble • balance (accuracy 0.1 mg) • glassware (cleaning and drying using best available practices) • checking pipette accuracy before transfer (if any) • checking water quality for solution with acquisition of spectra against air • checking solvent quality with acquisition of spectra against air and water (Abs , 0.1 at 200 nm if not another wavelength window) • checking buffer quality with acquisition of spectra against air and water (Abs , 0.1 at 200 nm if not another wavelength window) • checking if pH conditions are changed by addition of sulfuric acid or sodium hydroxide suppression at the beginning of spectra (for phenolic compounds) • temperature (20 C 6 1 C in a regulated room with temperature registration, no equilibrium time required) • spectrum measurement • conditions (spectrophotometer single beam, Anthe´lie Secomam) • spectral bandwidth (fixed 2 nm) • wavelength range (200
900 nm) • scan speed (1800 nm min21) • optical pathlength (10 mm) • cell quality (QS Quartz Suprasil) • lamp change (350 nm) • data pitch (0.5 nm) • temperature (20 C 6 1 C in a regulated room with temperature registration, no equilibrium time required) More precisely, the general traceability procedure for spectra production is presented in Table 15.1. For the 31 new spectra (among 14 pesticides and 15 pharmaceutical and personal-care products) another procedure was used for the spectra measurement. As the acquisition of UV spectra of selected compounds must feed a local database for the development of an MSPE (multiple solid phase extraction)/UV method [5], the spectrophotometer used was the final detector of the built-in SIA (sequential injection analysis)-MSPE/ UV prototype [6]. The UV lamp was a deuterium Toblelight Lamp DTL 6/50 with a circulation QS Quartz Suprasil cell of 20 mm pathlength and a volume of 40 µL, connected by optical fiber to a CCD (charge coupled device) microspectrophotometer BWTEK BRC112E-U. The wavelength range was 200
400 nm and the bandwidth of 1 nm. The acquisition of

UV-Visible Spectrophotometry of Waters and Soils

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15. UV spectra library

TABLE 15.1 Traceability procedure for spectra production (115 compounds) (WL, wavelength; N, test fails). Step

Nature

Tests

Action

1

Spectrophotometer check by manufacturer

2

Pharmacopeia check

Full procedure

If N back 1

3

Check with standards (Hellma)

WL 6 0.5 nm Abs 6 0.01

If N back 2

4

Spectra acquisition







4a

On air (without cell)




Spectrum storage

4ba

On solvent (water or buffer in proportion or organic solvent)




Spectrum storage

4cb

On sample (two replicates)




Spectrum storage

4d

On solvent




Spectrum storage

4e

Test results spectra 4b/4d

diff Abs , 0.005 A?

If N back 3

4f

Validation on spectra 4c normaliszd

diff Abs , 0.005 A?

If N no reporting

5

Reporting







5a

Storage of spectra as ASCII files




Air and solvents Samples normalized

5b

Plotting spectra (GraphPad Prism 2.01), including molecular structure and characteristics

WL 200
450 nm Abs 0
2.5 A

Sample (1 sp. or 3 if pH effect)

c

a

Can be repeated in case of pH effect on solutions. Can be repeated if no solvent change (maximum five samples). Corrected for the mean of solvent contribution and normalized to an exact concentration based on the exact sample weights. b c

spectra was carried out according to the main points of Step 4 mentioned in Table 15.1. After a blank measurement on solvent (ultrahigh-quality water, UHQ water), two measurements are realized. The circuit is then cleaned thanks to 10 times volume of UHQ water, before another measurement. As all compound solutions were coming from mother solutions prepared in an accredited laboratory (LERES, EHESP, France), no significant differences were observed between the two measurements of a same compound solution. Finally, the pathlength being 20 mm, the absorbance values of the reported spectra were expressed for a 10 mm pathlength as the other spectra of the library.

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3 Spectra of compounds 15.3.1 Acids and salts 15.3.1.1 Acetic acid

General Name

Acetic acid

CAS No.

64-19-7

Formula

C2H4O2

Molecular weight

60.05 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

3058.6 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RPE-ACS, 99.9%

(Fig. 15.1)

FIGURE 15.1

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

202.0

2.277

UV-Visible Spectrophotometry of Waters and Soils

459

460

15. UV spectra library

15.3.1.2 Butyric acid

General Name

Butanoic acid

CAS No.

107-92-6

Formula

C4H8O2

Molecular weight

88.11 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

6755.8 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

P.A. 99%

(Fig. 15.2)

FIGURE 15.2

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

206.3

2.003

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.1.3 Ethylenediaminetetraacetic acid

General Name

Ethylenediaminetetraacetic acid disodium salt, dihydrate

CAS No.

6381-92-6

Formula

C10H14N2Na2O8, 2H2O

Molecular weight

372.24 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

26.6 mgL21

pH

5.3

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RPE-ACS 100%

(Fig. 15.3)

FIGURE 15.3

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

461

462

15. UV spectra library

15.3.1.4 Formic acid

General Name

Formic acid

CAS No.

64-18-6

Formula

CH2O2

Molecular weight

46.03 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

24231.9 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

P.A. 99%

(Fig. 15.4)

FIGURE 15.4

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

225.2

2.490

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.1.5 Oxalic acid

General Name

Oxalic acid dihydrate

CAS No.

877-24-7

Formula

C2H2O4, 2H2O

Molecular weight

126.07 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

62.7 mgL21

pH

3.3

Pathlength

10 mm

Reference product

RDH

Purity

Normadose

(Fig. 15.5)

FIGURE 15.5

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

463

464

15. UV spectra library

15.3.1.6 Propionic acid

General Name

Propanoic acid

CAS No.

79-09-4

Formula

C3H6O2

Molecular weight

74.08 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

5647.0 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity

P.A. 99%

(Fig. 15.6)

FIGURE 15.6

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205.0

2.221

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.1.7 Sodium salicylate

General Name

Sodium salicylate

CAS No.

578-36-9

Formula

C7H5O3Na

Molecular weight

160.11 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

21.8 mgL21

pH

6.1

Pathlength

10 mm

Reference product

PROLABO

Purity

Rectapur

(Fig. 15.7)

FIGURE 15.7

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

203.7

2.282

2

231.2

0.630

3

298.7

0.336

UV-Visible Spectrophotometry of Waters and Soils

465

466

15. UV spectra library

15.3.1.8 Potassium sodium tartrate

General Name

Potassium sodium tartrate tetrahydrate

CAS No.

6381-59-5

Formula

C4H4NaO6K, 4H2O

Molecular weight

282.22 g Mol-1

Spectra acquisition Solvent

H2O

Concentration

20.2 mgL21

pH

5.9

Pathlength

10 mm

Reference product

ACROS

Purity

P.A.

(Fig. 15.8)

FIGURE 15.8

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.2 Aldehydes and ketones 15.3.2.1 Acetaldehyde General Name

Acetaldehyde

CAS No.

75-07-0

Formula

C2H4O

Molecular weight

44.05 g Mol-1

Solubility

Miscible in H2O, EtOH, etc.

Refractive index

1.331

Spectra acquisition Solvent

H2O

Concentration

14.824 mgL21

pH

3.6

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RPE P.A

(Fig. 15.9)

FIGURE 15.9 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

277.1

1.972

UV-Visible Spectrophotometry of Waters and Soils

467

468

15. UV spectra library

15.3.2.2 Acetone General Name

Acetone

CAS No.

67-64-1

Formula

C3H6O

Molecular weight

58.08 g Mol-1

Solubility

Miscible in H2O, EtOH, etc.

Refractive index

1.359

Spectra acquisition Solvent

H2O

Concentration

1.565 mgL21

pH

6.2

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

P. spectro (RS)

(Fig. 15.10)

FIGURE 15.10 Peak n

Wavelength (nm)

Absorbance (a.u.)

1

266.1

1.195

Remark: Also registered as solvent; see Section 15.3.11.

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.2.3 Benzaldehyde General Name

Benzaldehyde

CAS No.

100-52-7

Formula

C7H6O

Molecular weight

106.13 g Mol-1

Solubility (H2O)

4 gL21 (20 C)

Refractive index

1.546

Spectra acquisition Solvent

H2O

Concentration

3.3 mgL21

pH

5.2

Pathlength

10 mm

Reference product

FLUKA

Purity

99% (GC)

(Fig. 15.11)

FIGURE 15.11 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

251.2

1.094

2

288.7

0.120

UV-Visible Spectrophotometry of Waters and Soils

469

470

15. UV spectra library

15.3.2.4 2-Butanone

General Name

2-Butanone

CAS No.

78-93-3

Formula

C4H8O

Molecular weight

72.11 g Mol-1

Solubility

Miscible in H2O, EtOH, etc.

Refractive index

1.379

Spectra acquisition Solvent

H2O

Concentration

1.037 gL21

pH

5.15

Pathlength

10 mm

Reference product

PROLABO

Purity

P.A.

(Fig. 15.12)

FIGURE 15.12

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

268.1

1.703

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.2.5 Butyraldehyde

General Name

Butyraldehyde

CAS No.

123-72-8

Formula

C4H8O

Molecular weight

72.11 g Mol-1

Solubility

EtOH

Refractive index

1.379

Spectra acquisition Solvent

EtOH (10 mL)/H2O (40 mL)

Concentration

5.192 gL21

pH

4.54

Pathlength

10 mm

Reference product

RDH

Purity

99.5% (GC)

(Fig. 15.13)

FIGURE 15.13

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

228.9

1.047

2

282.9

0.362

UV-Visible Spectrophotometry of Waters and Soils

471

472

15. UV spectra library

15.3.2.6 Diisobutylketone

General Name

2,6-dimethyl-4-heptanone

CAS No.

108-83-8

Formula

C9H18O

Molecular weight

142.24 g Mol-1

Solubility

EtOH

Refractive index

1.412

Spectra acquisition Solvent

EtOH (10 mL)/H2O (40 mL)

Concentration

366.8 mgL21

pH

5.3

Pathlength

10 mm

Reference product

PROLABO

Purity

P.A.

(Fig. 15.14)

FIGURE 15.14

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

248.2

0.394

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.2.7 Formaldehyde

General Name

Formaldehyde

CAS No.

50-00-0

Formula

CH2O

Molecular weight

30.03 g Mol-1

Solubility

EtOH

Refractive index

1.412

Spectra acquisition Solvent

Pure reagent

Concentration

400 gL21

pH




Pathlength

10 mm

Reference product

CARLO ERBA

Purity

40% 1 10% MeOH

(Fig. 15.15)

FIGURE 15.15

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

473

474

15. UV spectra library

15.3.2.8 Isobutyl methyl ketone

General Name

4-Methyl-2-pentanone

CAS No.

108
10-1

Formula

C6H12O

Molecular weight

100.16 g Mol-1

Solubility (H2O)

19 gL21 (20 C)

Refractive index

1.396

Spectra acquisition Solvent

H2O

Concentration

5.051 gL21

pH

6.3

Pathlength

10 mm

Reference product

LABOSI (Fisher)

Purity

Analypur

(Fig. 15.16)

FIGURE 15.16

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

273.2

1.846

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3 Amines and related compounds 15.3.3.1 Aniline General Name

Aniline

CAS No.

62-53-3

Formula

C6H7N

Molecular weight

93.13 gMol-1

Solubility (H2O)

34 gL21 (20 C)

Refractive index

1.58

Spectra acquisition Solvent

H2O

Concentration

20.1 mgL21

pH

6.3

Pathlength

10 mm

Reference product

ALLTECH

Purity

99%

(Fig. 15.17)

FIGURE 15.17 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

230.3

1.232

2

279.5

0.215

UV-Visible Spectrophotometry of Waters and Soils

475

476

15. UV spectra library

15.3.3.2 p-Anisidine General Name

4-Methoxybenzenamine

CAS No.

104-94-9

Formula

C7H9NO

Molecular weight

123.15 g Mol-1

Solubility (H2O)

1 gL21 (20 C)

Refractive index

1.55

Spectra acquisition Solvent

EtOH (10 mL)/H2O (40 mL)

Concentration

12.5 mgL21

pH

6.8

Pathlength

10 mm

Reference product

Chem. Service

Purity

98.5%

(Fig. 15.18)

FIGURE 15.18 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

232.3

0.770

2

295.7

0.184

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.3 2-Chloroaniline General Name

2-Chloroaniline

CAS No.

95-51-2

Formula

C6H6CIN

Molecular weight

127.57 gMol-1

Solubility

EtOH

Refractive index

1.59

Spectra acquisition Solvent

EtOH/H2O

Concentration

10.0 mgL21

pH

5.3

Pathlength

10 mm

Reference product

ACROS

Purity

98%

(Fig. 15.19)

FIGURE 15.19 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

204.7

2.607

2

232.0

0.602

3

284.6

0.169

UV-Visible Spectrophotometry of Waters and Soils

477

478

15. UV spectra library

15.3.3.4 4-Chloroaniline

General Name

4-Chloroaniline

CAS No.

106-47-8

Formula

C6H6ClN

Molecular weight

127.57 gMol-1

Solubility

EtOH

Refractive index

1.55

Spectra acquisition Solvent

EtOH/H2O

Concentration

10.2 mgL21

pH

6.4

Pathlength

10 mm

Reference product

MERCK

Purity

.99%

(Fig. 15.20)

FIGURE 15.20 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

238.9

0.911

2

290.2

0.121

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.5 2-Chloro-4-methylaniline General Name

2-Chloro-4-methylaniline

CAS No.

615-65-6

Formula

C7H8ClN

Molecular weight

141.60 gMol-1

Solubility

EtOH

Refractive index

1.39

Spectra acquisition Solvent

EtOH/H2O

Concentration

10.7 mgL21

pH

5.6

Pathlength

10 mm

Reference product

ACROS

Purity

98%

(Fig. 15.21)

FIGURE 15.21 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

204.7

2.607

2

232.0

0.602

3

284.6

0.169

UV-Visible Spectrophotometry of Waters and Soils

479

480

15. UV spectra library

15.3.3.6 3,4-Dichloroaniline

General Name

3,4-Dichloroaniline

CAS No.

95-76-1

Formula

C6H5Cl2N

Molecular weight

162 gMol-1

Solubility

EtOH

Spectra acquisition Solvent

EtOH/H2O

Concentration

10.9 mgL21

pH

5.8

Pathlength

10 mm

Reference product

ACROS

Purity

98%

(Fig. 15.22)

FIGURE 15.22 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

208.5

2.100

2

242.4

0.677

3

295.9

0.115

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.7 Diethylamine

General Name

Diethylamine

CAS No.

109-89-7

Formula

C4H11N

Molecular weight

73.14 gMol-1

Solubility

H2O

Refractive index

1.38

Spectra acquisition Solvent

H2O

Concentration

21.3 gL21

pH

6.3

Pathlength

10 mm

Reference product

PROLABO

Purity

Rectapur

(Fig. 15.23)

FIGURE 15.23

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

481

482

15. UV spectra library

15.3.3.8 Diethanolamine

General Name

Diethanolamine

CAS No.

111-42-2

Formula

C4H11NO2

Molecular weight

105.14 gMol-1

Solubility

H2 O

Refractive index

1.47

Spectra acquisition Solvent

H2O

Concentration

19.9 mgL21

pH

7.4

Pathlength

10 mm

Reference product

LABOSI (Fisher)

Purity

Pure 99%

(Fig. 15.24)

FIGURE 15.24

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.9 Glutamic acid

General Name

L-Glutamic acid

CAS No.

56-86-0

Formula

C5H9NO4

Molecular weight

147.13 gMol-1

Solubility (H2O)

86 gL21 (20 C)

Refractive index

1.54

Spectra acquisition Solvent

H2O 1 H2SO4 96% (3.4% v/v)

Concentration

20.0 mgL21

pH

2.5

Pathlength

10 mm

Reference product

MERCK

Purity

P.A.

(Fig. 15.25)

FIGURE 15.25

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

483

484

15. UV spectra library

15.3.3.10 Glycine

General Name

Aminoacetic acid

CAS No. of glycine

56-40-6

Formula

C2H5NO2

Molecular weight

75.07 gMol-1

Solubility (H2O)

250 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 gL21

pH

6.85

Pathlength

10 mm

Reference product

RDH

Purity

P.A.

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










(Fig. 15.26)

FIGURE 15.26

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.11 4-Nitroaniline

General Name

4-Nitroaniline

CAS No.

100-01-61

Formula

C6H6N2O2

Molecular weight

138.13 gMol-1

Solubility (H2O)

0.8 gL21 (20 C)

Refractive index




Spectra acquisition Solvent

EtOH/H2O

Concentration

10.0 mgL21

pH

5.6

Pathlength

10 mm

Reference product

RDH

Purity

99.5%

(Fig. 15.27)

FIGURE 15.27 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

227.6

0.952

2

379.7

2.017

UV-Visible Spectrophotometry of Waters and Soils

485

486

15. UV spectra library

15.3.3.12 m-Toluidine General Name

3-Methylaniline

CAS No.

108-44-1

Formula

C7H9N

Molecular weight

107.16 gMol-1

Solubility

EtOH

Refractive index

1.57

Spectra acquisition Solvent

EtOH/H2O

Concentration

10.4 mgL21

pH

6.3

Pathlength

10 mm

Reference product

MERCK

Purity

.99%

(Fig. 15.28)

FIGURE 15.28 Peak n

Wavelength (nm)

Absorbance (a.u.)

1

203.9

2.688

2

232.9

0.728

3

281.4

0.140

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.13 p-Toluidine General Name

4-Methylaniline

CAS No.

106-49-0

Formula

C7H9N

Molecular weight

107.16 gMol-1

Solubility (H2O)

7.4 gL21 (20 C)

Refractive index

1.55

Spectra acquisition Solvent

H2O

Concentration

9.9 mgL21

pH

6.55

Pathlength

10 mm

Reference product

MERCK

Purity

.99%

(Fig. 15.29)

FIGURE 15.29 Peak n

Wavelength (nm)

Absorbance (a.u.)

1

232.9

0.765

2

285.2

0.139

UV-Visible Spectrophotometry of Waters and Soils

487

488

15. UV spectra library

15.3.3.14 Tyrosine

General Name

L-Tyrosine

CAS No.

60-18-4

Formula

C9H11NO3

Molecular weight

181.19 gMol-1

Solubility (H2O)

0.45 gL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

50.1 mgL21

pH

6.0

Pathlength

10 mm

Reference product

LABOSI (Fisher)

Purity

Analypur

(Fig. 15.30)

FIGURE 15.30

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

224.3

1.686

2

274.8

0.300

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.3.15 4,40 -Diaminodiphenylmethane

General Name

Bis(4-aminophenyl)methane MDA

CAS No.

101-77-9

Formula

C13H14N2

Molecular weight

198.27 gMol-1

Solubility (H2O)

125 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

pH

6.2

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

97.5%

(Fig. 15.31)

FIGURE 15.31

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

241

0.715

2

286

0.109

UV-Visible Spectrophotometry of Waters and Soils

489

490

15. UV spectra library

15.3.4 Benzene and related compounds 15.3.4.1 Benzene General Name

Benzene

CAS No.

71-43-2

Formula

C6H6

Molecular weight

78.11 gMol-1

Solubility (H2O)

700 mgL21 (20 C)

Refractive index

1.50

Spectra acquisition Solvent

CH3CN/H2O (5% v/v)

Concentration

587.3 mgL21

pH

5.8

Pathlength

10 mm

Reference product

RDH

Purity

P.A.

(Fig. 15.32)

FIGURE 15.32 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

204.9

1.891

2

244.1

0.323

3

249.9

0.457

4

255.6

0.527

5

260.9

0.383

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.4.2 Chlorobenzene

General Name

Phenyl chloride

CAS No.

108
90-7

Formula

C6H5Cl

Molecular weight

112.56 gMol-1

Solubility (H2O)

500 mgL21 (20 C)

Refractive index

1.52

Spectra acquisition Solvent

CH3CN/H2O (5% v/v)

Concentration

56.9 mgL21

pH

3.6

Pathlength

10 mm

Reference product

JANSSEN CHIM.

Purity

99.5%

(Fig. 15.33)

FIGURE 15.33 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

218.4

2.384

2

261.0

0.071

UV-Visible Spectrophotometry of Waters and Soils

491

492

15. UV spectra library

15.3.4.3 Ethylbenzene

General Name

Ethylbenzene

CAS No.

100-41-4

Formula

C8H10

Molecular weight

106.17 gMol-1

Solubility

EtOH

Refractive index

1.49

Spectra acquisition Solvent

CH3CN/H2O (5% v/v)

Concentration

48.7 mgL21

pH

5.9

Pathlength

10 mm

Reference product

JANSSEN CHIM.

Purity

99.5%

(Fig. 15.34)

FIGURE 15.34 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

209.0

1.540

2

261.5

0.050

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.4.4 Toluene General Name

Methyl benzene

CAS No.

108
88-3

Formula

C7H8

Molecular weight

92.14 gMol-1

Solubility

EtOH

Refractive index

1.49

Spectra acquisition Solvent

CH3CN/H2O (5% v/v)

Concentration

41.5 mgL21

pH

5.7

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

99.5%

(Fig. 15.35)

FIGURE 15.35

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

210.4

1.950

2

268.1

0.062

UV-Visible Spectrophotometry of Waters and Soils

493

494

15. UV spectra library

15.3.4.5 m-Xylene

General Name

1,3-Dimethyl benzene

CAS No.

108-38-3

Formula

C8H10

Molecular weight

106.17 gMol-1

Solubility (H2O)

200 mgL21 (20 C)

Refractive index

1.49

Spectra acquisition Solvent

MeOH/H2O (0.5% v/v)

Concentration

42.3 mgL21

pH

5.9

Pathlength

10 mm

Reference product

ACROS ORGANICS

Purity

Spectrograde

(Fig. 15.36)

FIGURE 15.36

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

212.9

1.437

2

271.4

0.057

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.4.6 o-Xylene

General Name

1,2-Dimethyl benzene

CAS No.

95-47-6

Formula

C8H10

Molecular weight

106.17 gMol-1

Solubility (H2O)

200 mgL21 (20 C)

Refractive index

1.50

Spectra acquisition Solvent

MeOH/H2O (0.5% v/v)

Concentration

45.24 mgL21

pH

5.9

Pathlength

10 mm

Reference product

ACROS ORGANICS

Purity

99%

(Fig. 15.37)

FIGURE 15.37 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

211.3

1.427

2

264.9

0.058

UV-Visible Spectrophotometry of Waters and Soils

495

496

15. UV spectra library

15.3.4.7 p-Xylene

General Name

1,4-Dimethyl benzene

CAS No.

106-42-3

Formula

C8H10

Molecular weight

106.17 gMol-1

Solubility

EtOH

Refractive index

1.49

Spectra acquisition Solvent

CH3CN/H2O (0.5% v/v)

Concentration

45.24 mgL21

pH

5.9

Pathlength

10 mm

Reference product

SUPELCO

Purity

Etalon

(Fig. 15.38)

FIGURE 15.38 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

214.6

1.110

2

267.8

0.079

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5 Pesticides 15.3.5.1 2-4-Dichlorophenoxy acetic acid (2-4-D) General Name

2-4-Dichlorophenoxy acetic acid

CAS No.

94-75-7

Formula

C8H6Cl2O3

Molecular weight

221.04 gMol-1

Solubility (H2O)

0.6 gL21at 20 C

Spectra acquisition Solvent

MetOH/H2O (0.3% v/v)

Concentration

21.1 mgL21

Pathlength

10 mm

Reference product

ALLTECH

Purity

99%

Remark

Spectra at pH 5 4

(Fig. 15.39)

FIGURE 15.39 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

203.9

2.902

2

229.8

0.825

3

282.5

0.185

UV-Visible Spectrophotometry of Waters and Soils

497

498

15. UV spectra library

15.3.5.2 Alachlor

General Name

Alachlor

CAS No.

15972-60-8

Formula

C14H20CINO2

Molecular weight

269.8 gMol-1

Solubility (H2O)

183 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.2%

(Fig. 15.40)

FIGURE 15.40

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.3 Atrazine

General Name

Atrazine

CAS No.

1912-24-9

Formula

C8H14CIN5

Molecular weight

215.68 gMol-1

Solubility (H2O)

70 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (98%)

(Fig. 15.41)

FIGURE 15.41

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

224.2

1.682

2

264.7

0.156

UV-Visible Spectrophotometry of Waters and Soils

499

500

15. UV spectra library

15.3.5.4 Carbaryl

General Name

Carbaryl

CAS No.

63-25-2

Formula

C12H11NO2

Molecular weight

201.2 gMol-1

Solubility (H2O)

120 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

8.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.8%

(Fig. 15.42)

FIGURE 15.42

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

220

2.785

2

279

0.196

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.5 Chlorpyrifos

General Name

Chlorpyrifos

CAS No.

2921-88-2

Formula

C9H11CI3NO3PS

Molecular weight

350.59 gMol-1

Solubility (H2O)

1.4 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

1.0 mgL21

Pathlength

40 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.43)

FIGURE 15.43

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

228

0.067

2

288

0.035

UV-Visible Spectrophotometry of Waters and Soils

501

502

15. UV spectra library

15.3.5.6 Chlortoluron

General Name

Chlortoluron

CAS No.

15545-48-9

Formula

C10H13CIN2O

Molecular weight

212.7 gMol-1

Solubility (H2O)

70 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.6%

(Fig. 15.44)

FIGURE 15.44

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

210

2.80

2

241

1.345

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.7 Diazinon

General Name

Diazinon

CAS No.

333
41-5

Formula

C12H21N2O3PS

Molecular weight

304.35 gMol-1

Solubility (H2O)

40 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

98.3%

(Fig. 15.45)

FIGURE 15.45

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

247

0.269

UV-Visible Spectrophotometry of Waters and Soils

503

504

15. UV spectra library

15.3.5.8 Dichlorprop

General Name

Dichlorprop

CAS No.

120-36-5

Formula

C9H8CI2O3

Molecular weight

235.06 gMol-1

Solubility (H2O)

350 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.4%

(Fig. 15.46)

FIGURE 15.46

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

229

0.624

2

284

0.181

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.9 Dimethoate

General Name

Dimethoate

CAS No.

60-51-5

Formula

C5H12NO3PS2

Molecular weight

229.26 gMol-1

Solubility (H2O)

23.3 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.6%

(Fig. 15.47)

FIGURE 15.47

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

505

506

15. UV spectra library

15.3.5.10 Dinoterb

General Name

2-(1,1-dimethyl-ethyl)-4,6-dinitrophenol

CAS No.

1420
07-1

Formula

C10H12N2O5

Molecular weight

240.21 gMol-1

Solubility (H2O)

4.5 mgL21 (20 C, pH 5 5)

Spectra acquisition Solvent

MetOH/H2O (2.6% v/v)

Concentration

10 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (99%)

(Fig. 15.48)

FIGURE 15.48 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

217.6

0.313

2

269.5

0.271

3

374.5

0.278

4

408.7

0.225

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.11 Diquat

General Name

Diquat

CAS No.

85-00-7

Formula

C12H12N2

Molecular weight

362.06 gMol-1

Solubility (H2O)

700 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.8%

(Fig. 15.49)

FIGURE 15.49

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

309

1.01

UV-Visible Spectrophotometry of Waters and Soils

507

508

15. UV spectra library

15.3.5.12 Diuron

General Name

Diuron

CAS No.

330-54-1

Formula

C9H10CI2N20

Molecular weight

233.09 gMol-1

Solubility (H2O)

42 mgL21 (25 C)

Spectra acquisition Solvent

CH3CN 1 MetOH (1 1 1) in H2O (0.2% v/v)

Concentration

22.9 mgL21

Pathlength

10 mm

Reference product

SIGMA

Purity

98% min.

(Fig. 15.50)

FIGURE 15.50

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

214.0

2.566

2

249.7

1.657

3

280.6

0.107

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.13 Hexazinone

General Name

Hexazinone

CAS No.

51235-04-2

Formula

C12H20N4O2

Molecular weight

252.31 gMol-1

Solubility (H2O)

33 gL21 (20 C)

Spectra acquisition Solvent

MetOH/H2O (0.4% v/v)

Concentration

16.8 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

Etalon

(Fig. 15.51)

FIGURE 15.51

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

247.9

2.045

UV-Visible Spectrophotometry of Waters and Soils

509

510

15. UV spectra library

15.3.5.14 Isoproturon

General Name

Isoproturon

CAS No.

1912-24-9

Formula

C12H18N2O

Molecular weight

206.28 gMol-1

Solubility (H2O)

59 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.52)

FIGURE 15.52

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

202

2.274

2

239

1.285

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.15 Linuron

General Name

Linuron

CAS No.

330-55-2

Formula

C9H10CI2N2O2

Molecular weight

249.1 gMol-1

Solubility (H2O)

64 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.53)

FIGURE 15.53

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

210

2.128

2

246

1.264

3

283

0.082

UV-Visible Spectrophotometry of Waters and Soils

511

512

15. UV spectra library

15.3.5.16 Malathion

General Name

Malathion

CAS No.

121-75-5

Formula

C10H19O6PS2

Molecular weight

330.36 gMol-1

Solubility (H2O)

145 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

100.0 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (98%)

(Fig. 15.54)

FIGURE 15.54

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.17 Metazachlor

General Name

Metazachlor

CAS No.

67129-08-2

Formula

C14H16CIN3O

Molecular weight

277.75 gMol-1

Solubility (H2O)

450 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.7%

(Fig. 15.55)

FIGURE 15.55

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

513

514

15. UV spectra library

15.3.5.18 Metolachlor

General Name

Metolachlor

CAS No.

51218-45-2

Formula

C15H22CINO2

Molecular weight

283.8 gMol-1

Solubility (H2O)

530 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

97.6%

(Fig. 15.56)

FIGURE 15.56

Peak n degree

Wavelength (nm)

Absorbance (a.u.)










UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.19 Paraquat

General Name

Paraquat dichloride

CAS No.

1910-42-5

Formula

C12H14Cl2N2

Molecular weight

257.16 gMol-1

Solubility (H2O)

.100 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

31.1 mgL21

Pathlength

10 mm

Reference product

SIGMA

Purity

Etalon

(Fig. 15.57)

FIGURE 15.57

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

259.1

2.048

UV-Visible Spectrophotometry of Waters and Soils

515

516

15. UV spectra library

15.3.5.20 Parathion

General Name

Parathion

CAS No.

56-38-2

Formula

C10H14NO5PS

Molecular weight

291.26 gMol-1

Solubility (H2O)

24 mgL21 (24 C)

Refractive index

1.53

Spectra acquisition Solvent

MetOH/H2O (0.3% v/v)

Concentration

18.6 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (98%)

(Fig. 15.58)

FIGURE 15.58

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

276.9

0.487

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.21 Simazine

General Name

Simazine

CAS No.

122-34-9

Formula

C7H12CIN5

Molecular weight

201.66 gMol-1

Solubility (H2O)

5 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

4.9 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (99%)

(Fig. 15.59)

FIGURE 15.59

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

223.9

0.894

2

264.8

0.085

UV-Visible Spectrophotometry of Waters and Soils

517

518

15. UV spectra library

15.3.5.22 Terbuthylazine

General Name

Terbuthylazine

CAS No.

5915-41-3

Formula

C9H16CIN5

Molecular weight

229.71 gMol-1

Solubility (H2O)

130 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

98.8%

(Fig. 15.60)

FIGURE 15.60

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

224

1.46

2

261

0.115

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.5.23 Terbutryn

General Name

Terbutryn

CAS No.

886-50-0

Formula

C10H19N5S

Molecular weight

241.36 gMol-1

Solubility (H2O)

25 mgL21 (20 C)

Spectra acquisition Solvent

MetOH/H2O (1% v/v)

Concentration

11.71 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal (98%)

(Fig. 15.61)

FIGURE 15.61

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

225.7

1.537

UV-Visible Spectrophotometry of Waters and Soils

519

520

15. UV spectra library

15.3.6 Pharmaceuticals 15.3.6.1 1,7 Ethinylestradiol

General Name

1,7 Ethinylestradiol

CAS No.

57-63-6

Formula

C20H24O2

Molecular weight

296.40 gMol-1

Solubility (H2O)

11 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

5.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.3%

(Fig. 15.62)

FIGURE 15.62

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

278

0.125

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.2 Acetaminohen

General Name

Acetaminophen

CAS No.

103-90-2

Formula

C8H9NO2

Molecular weight

151.2 gMol-1

Solubility (H2O)

14000 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

98.0%

(Fig. 15.63)

FIGURE 15.63

Peak degree

Wavelength (nm)

Absorbance (a.u.)

1

243

0.815

UV-Visible Spectrophotometry of Waters and Soils

521

522

15. UV spectra library

15.3.6.3 Atenolol

General Name

Atenolol

CAS No.

29122-68-7

Formula

C14H22N2O3

Molecular weight

266.34 gMol-1

Solubility (H2O)

300 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

.98%

(Fig. 15.64)

FIGURE 15.64

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

224

0.526

2

274

0.088

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.4 Caffeine

General Name

Caffeine

CAS No.

58-08-2

Formula

C8H10N4O2

Molecular weight

194.19 gMol-1

Solubility (H2O)

22,000 mgL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (BioXtra)

Purity

.99%

(Fig. 15.65)

FIGURE 15.65

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205

2.479

2

273

0.912

UV-Visible Spectrophotometry of Waters and Soils

523

524

15. UV spectra library

15.3.6.5 Carbamazepine

General Name

Carbamazepine

CAS No.

298-46-4

Formula

C15H12N2O

Molecular weight

236.27 gMol-1

Solubility (H2O)

112 mgL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

.98%

(Fig. 15.66)

FIGURE 15.66

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

211

1.23

2

285

0.505

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.6 Ciprofloxacine

General Name

Ciprofloxacine

CAS No.

85721-33-1

Formula

C17H18FN3O3

Molecular weight

331.34 gMol-1

Solubility (H2O)

67 mgL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.6%

(Fig. 15.67)

FIGURE 15.67 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

207

0.48

2

277

1.20

3

315

0.38

4

329

0.34

UV-Visible Spectrophotometry of Waters and Soils

525

526

15. UV spectra library

15.3.6.7 Clofibric acid

General Name

Clofibric acid

CAS No.

882-09-7

Formula

C10H11O3Cl

Molecular weight

214.5 gMol-1

Solubility (H2O)

580 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Dr. Ehrenstorfer

Purity

98%

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

227

0.835

2

279

0.082

(Fig. 15.68)

FIGURE 15.68

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.8 Diatrozoate

General Name

Diatrizoate

CAS No.

737-31-5

Formula

C11H8O4N2I3,Na

Molecular weight

635.9 gMol-1

Solubility (H2O)

.5000 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Dr. Ehrenstorfer

Purity

98%

(Fig. 15.69)

FIGURE 15.69

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

238

0.807

UV-Visible Spectrophotometry of Waters and Soils

527

528

15. UV spectra library

15.3.6.9 Diclofenac

General Name

Diclofenac

CAS No.

15307-79-6

Formula

C14H10CI2NNaO2

Molecular weight

318.13 gMol-1

Solubility (H2O)

2.4 mgL21 (25 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

5.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99%

(Fig. 15.70)

FIGURE 15.70

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

276

0.168

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.10 Erythromycine

General Name

Erythromycine

CAS No.

59319-72-1

Formula

C37H67NO13

Molecular weight

733.93 gMol-1

Solubility (H2O)

1.4 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

1.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

95.5%

(Fig. 15.71)

FIGURE 15.71

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

299

0.009

UV-Visible Spectrophotometry of Waters and Soils

529

530

15. UV spectra library

15.3.6.11 Ibuprofen

General Name

Ibuprofen

CAS No.

15687-27-1

Formula

C13H1802

Molecular weight

206.28 gMol-1

Solubility (H2O)

21 mgL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99,9%

(Fig. 15.72)

FIGURE 15.72

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

221

0.810

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.12 Methylparaben

General Name

Methylparaben

CAS No.

99-76-3

Formula

C8H8O3

Molecular weight

152.15 gMol-1

Solubility (H2O)

2000 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.73)

FIGURE 15.73

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

255

0.92

UV-Visible Spectrophotometry of Waters and Soils

531

532

15. UV spectra library

15.3.6.13 Sulfamethoxazole

General Name

Sulfamethoxazole

CAS No.

723-46-6

Formula

C10H11N3O3S

Molecular weight

253.8 gMol-1

Solubility (H2O)

610 mgL21 (27 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.74)

FIGURE 15.74

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

265

1.221

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.6.14 Trimethoprim

General Name

Trimethoprim

CAS No.

738
70-5

Formula

C14H18N4O3

Molecular weight

290.32 gMol-1

Solubility (H2O)

400 mgL21 (25 C)

Spectra acquisition Solvent

H2O

Concentration

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.5%

(Fig. 15.75)

FIGURE 15.75

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

203

1.67

2

273

0.18

UV-Visible Spectrophotometry of Waters and Soils

533

534

15. UV spectra library

15.3.6.15 Warfarin

General Name

Warfarin

CAS No.

81-81-2

Formula

C19H16O4

Molecular weight

308.3 gMol-1

Solubility (H2O)

40 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.9%

(Fig. 15.76)

FIGURE 15.76

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

204

2.468

2

284

0.631

2

305

0.711

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7 Phenol and related compounds 15.3.7.1 Phenol General Name

Phenol

CAS No.

108-95-2

Formula

C6H6O

Molecular weight

94.11 gMol-1

Solubility (H2O)

90 mgL21 (20 C)

pKa

10.0 (25 C)

Refractive index

1.54

Spectra acquisition Solvent

H2O

Concentration

9.6 mgL21 (pH 5 1.7, pH 5 12.1)

Pathlength

10 mm

Reference product

FLUKA

Purity

.99.5%

(Fig. 15.77)

FIGURE 15.77 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

213.0

1.285

2

270.3

0.331

3

234.5

2.014

4

285.6

0.550

UV-Visible Spectrophotometry of Waters and Soils

535

536

15. UV spectra library

15.3.7.2 4-Chloro-3-methylphenol General Name

4-Chloro-3-methylphenol

CAS No.

59-50-7

Formula

C7H7ClO

Molecular weight

142.58 gMol-1

Solubility (H2O)

3.84 mgL21 (20 C)

pKa

9.3 (25 C)

Spectra acquisition Solvent

H2O

Concentrations

14.8 mgL21 (pH 5 1.4) and 14.9 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 98%

(Fig. 15.78)

FIGURE 15.78 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

226.7

0.745

2

279.4

0.163

3

243.7

1.121

4

297.2

0.270

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.3 2-Chlorophenol General Name

2-Chlorophenol

CAS No.

95-57-8

Formula

C6H5ClO

Molecular weight

128.57 gMol-1

Solubility

EtOH

pKa

8.6 (25 C)

Refractive index

1.55

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

19.2 mgL21 (pH 5 1.4) and 21.4 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

RDH

Purity

GC 98%

(Fig. 15.79)

FIGURE 15.79 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

213.9

0.918

2

274.0

0.208

3

237.1

0.942

4

292.4

0.434

UV-Visible Spectrophotometry of Waters and Soils

537

538

15. UV spectra library

15.3.7.4 3-Chlorophenol General Name

3-Chlorophenol

CAS No.

108-43-0

Formula

C6H5ClO

Molecular weight

128.57 gMol-1

Solubility

EtOH

pKa

9.1 (25 C)

Refractive index

1.55

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

18.6 mgL21 (pH 5 1.4) and 18.7 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

RDH

Purity

GC 98%

(Fig. 15.80)

FIGURE 15.80 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

218.1

0.795

2

274.1

0.204

3

239.8

1.033

4

290.4

0.357

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.5 4-Chlorophenol General Name

4-Chlorophenol

CAS No.

106-48-9

Formula

C6H5ClO

Molecular weight

128.57 gMol-1

Solubility (H2O)

27 gL21 (20 C)

pKa

9.4 (25 C)

Refractive index

1.55

Spectra acquisition Solvent

H2O

Concentrations

20.0 mgL21 (pH 5 1.5) and 19.5 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

FLUKA

Purity

GC . 99%

(Fig. 15.81)

FIGURE 15.81 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

226.0

1.337

2

279.3

0.236

3

244.8

1.739

4

298.0

0.378

UV-Visible Spectrophotometry of Waters and Soils

539

540

15. UV spectra library

15.3.7.6 m-Cresol General Name

3-Methylphenol

CAS No.

108-39-4

Formula

C7H8O

Molecular weight

108.14 gMol-1

Solubility (H2O)

25 gL21 (20 C)

pKa

10.1 (25 C)

Refractive index

1.54

Spectra acquisition Solvent

H2O

Concentrations

20.1 mgL21 (pH 5 1.4) and 20.0 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 98%

(Fig. 15.82)

FIGURE 15.82 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

214.8

1.189

2

272.1

0.280

3

237.5

1.568

4

287.5

0.498

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.7 o-Cresol General Name

2-Methylphenol

CAS No.

95-48-7

Formula

C7H8O

Molecular weight

108.14 gMol-1

Solubility (H2O)

25 gL21 (20 C)

pKa

10.3 (25 C)

Refractive index

1.54

Spectra acquisition Solvent

H2O

Concentrations

19.7 mgL21 (pH 5 1.4) and 19.9 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 99%

(Fig. 15.83)

FIGURE 15.83 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

216.2

1.260

2

271.3

0.298

3

236.6

1.622

4

287.4

0.591

UV-Visible Spectrophotometry of Waters and Soils

541

542

15. UV spectra library

15.3.7.8 p-Cresol General Name

4-Methylphenol

CAS No.

106-44-5

Formula

C7H8O

Molecular weight

108.14 gMol-1

Solubility (H2O)

25 gL21 (20 C)

pKa

10.3 (25 C)

Refractive index

1.54

Spectra acquisition Solvent

H2O

Concentrations

22.1 mgL21 (pH 5 1.4) and 22.0 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 98%

(Fig. 15.84)

FIGURE 15.84 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

221.9

1.053

2

276.9

0.312

3

236.7

1.539

4

294.4

0.465

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.9 4,5-Dichlorocatechol General Name

4,5-Dichloro-1,2-benzenediol

CAS No.

3428-2-8

Formula

C6H4Cl2O2

Molecular weight

179.0 gMol-1

Solubility

H2O

Spectra acquisition Solvent

H2O

Concentrations

19.2 mgL21 (pH 5 1.6) and 19.9 mgL21 (pH 5 11.9)

Pathlength

10 mm

Reference product

Helix Biotech Corp.

Purity

99%

(Fig. 15.85)

FIGURE 15.85

Peak n

Wavelength (nm)

Absorbance (a.u.)

1

289.9

0.314

2

315.1

1.333

UV-Visible Spectrophotometry of Waters and Soils

543

544

15. UV spectra library

15.3.7.10 2,3-Dichlorophenol General Name

2,3-Dichlorophenol

CAS No.

576-24-9

Formula

C6H4Cl2O

Molecular weight

163.0 gMol-1

Solubility

EtOH

pKa

7.4 (25 C)

Refractive index

1.54

Spectra acquisition Solvent

EtOH/H2O (2% v/v)

Concentrations

20.3 mgL21 (pH 5 1.4) and 21.0 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 98%

(Fig. 15.86)

FIGURE 15.86 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

276.7

0.223

2

241.1

0.855

3

297.1

0.494

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.11 2,4-Dichlorophenol

General Name

2,4-Dichlorophenol

CAS No.

120-83-2

Formula

C6H4Cl2O

Molecular weight

163.0 gMol-1

Solubility (H2O)

25 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentrations

7.8 mgL21 (pH 5 1.6) and 7.9 mgL21 (pH 5 12.0)

Pathlength

10 mm

Reference product

RDH

Purity

Pestanal

(Fig. 15.87)

FIGURE 15.87 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

221.2

0.478

2

283.2

0.133

3

245.9

0.587

4

306.1

0.220

UV-Visible Spectrophotometry of Waters and Soils

545

546

15. UV spectra library

15.3.7.12 2,5-Dimethylphenol General Name

2,5-Xylenol

CAS No.

95-87-4

Formula

C8H10O

Molecular weight

122.1 gMol-1

Solubility

EtOH

pKa

10.4 (25 C)

Spectra acquisition Solvent

EtOH/H2O (2% v/v)

Concentrations

22.5 mgL21 (pH 5 1.5) and 23.2 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

MERCK

Purity

GC 98%

(Fig. 15.88)

FIGURE 15.88 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

218.0

0.932

2

274.3

0.225

3

239.7

0.959

4

290.3

0.433

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.13 4,6-Dinitro-2-methylphenol General Name

4,6-Dinitro-o-cresol

CAS No.

534-52-1

Formula

C7H6N2O5

Molecular weight

198.1 gMol-1

Solubility

EtOH

Spectra acquisition Solvent

EtOH/H2O (2% v/v)

Concentrations

17.5 mgL21 (pH 5 1.4) and 19.0 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

SUPELCO

Purity

Pure . 98%

(Fig. 15.89)

FIGURE 15.89 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

214.8

1.133

2

271.5

1.429

3

220.9

1.015

4

265.7

0.671

5

371.1

1.495

UV-Visible Spectrophotometry of Waters and Soils

547

548

15. UV spectra library

15.3.7.14 2-Nitrophenol General Name

2-Nitrophenol

CAS No.

88-75-5

Formula

C6H5NO3

Molecular weight

139.11 gMol-1

Solubility

EtOH

pKa

7.2

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

15.7 mgL21 (pH 5 1.4) and 18.2 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

RDH

Purity

P.A.

(Fig. 15.90)

FIGURE 15.90 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

212.6

1.822

2

278.1

0.943

3

348.5

0.465

4

227.6

2.182

5

281.3

0.609

6

414.0

0.674

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.15 3-Nitrophenol General Name

3-Nitrophenol

CAS No.

554-84-7

Formula

C6H5NO3

Molecular weight

139.11 gMol-1

Solubility (H2O)

30 gL21 (40 C)

pKa

8.3

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

17.2 mgL21 (pH 5 1.4) and 20.7 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

RDH

Purity

Indicator

(Fig. 15.91)

FIGURE 15.91 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

211.6

1.800

2

228.8

1.094

3

273.9

0.857

4

320.2

0.289

5

227.0

2.051

6

253.4

1.516

7

290.3

0.615

8

387.4

0.218

UV-Visible Spectrophotometry of Waters and Soils

549

550

15. UV spectra library

15.3.7.16 4-Nitrophenol General Name

4-Nitrophenol

CAS No.

100-02-7

Formula

C6H5NO3

Molecular weight

139.11 gMol-1

Solubility

H2O

pKa

7.15

Spectra acquisition Solvent

EtOH/H2O (0.25% v/v)

Concentrations

22.2 mgL21 (pH 5 1.7) and 23.1 mgL21 (pH 5 12.1)

Pathlength

10 mm

Reference product

SUPELCO

Purity

P.A.

(Fig. 15.92)

FIGURE 15.92 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

226.2

0.966

2

317.6

1.094

3

227.1

0.854

4

397.0

2.475

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.17 Pentachlorophenol General Name

Pentachlorophenol

CAS No.

87-86-5

Formula

C6HCl5O

Molecular weight

266.34 gMol-1

Solubility (H2O)

20 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (0.25% v/v)

Concentrations

15.0 mgL21 (pH 5 1.4) and 14.5 mgL21 (pH 5 12.0)

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RPE (99%)

(Fig. 15.93)

FIGURE 15.93 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

230.1

0.539

2

301.8

0.126

3

221.4

2.555

4

250.5

0.593

5

319.8

0.302

UV-Visible Spectrophotometry of Waters and Soils

551

552

15. UV spectra library

15.3.7.18 Pyrocatechol General Name

1,2-Benzenediol

CAS No.

120-80-9

Formula

C6H6O2

Molecular weight

110.11 gMol-1

Solubility (H2O)

435 mgL21 (20 C)

pKa

9.3

Refractive index

1.60

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

17.6 mgL21 (pH 5 1.4) and 19.0 mgL21 (pH 5 12.0)

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RPE (99%)

(Fig. 15.94)

FIGURE 15.94 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

216.2

1.148

2

275.4

0.433

3

280.0

1.087

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.19 2-Tert-butyl-4-methylphenol General Name

2-Tert-butyl-4-methylphenol

CAS No.

2409-55-4

Formula

C11H16O

Molecular weight

164.25 gMol-1

Solubility

EtOH

Refractive index

1.49

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

17.6 mgL21 (pH 5 1.6) and 21.0 mgL21 (pH 5 12.0)

Pathlength

10 mm

Reference product

ALDRICH

Purity

99%

(Fig. 15.95)

FIGURE 15.95 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

217.1

0.559

2

277.6

0.197

3

241.9

0.624

4

296.6

0.280

UV-Visible Spectrophotometry of Waters and Soils

553

554

15. UV spectra library

15.3.7.20 2,4,6-Trichlorophenol General Name

2,4,6-Trichlorophenol

CAS No.

88-06-2

Formula

C6H3Cl3O

Molecular weight

197.45 gMol-1

Solubility

EtOH

pKa

6.0

Refractive index

1.49

Spectra acquisition Solvent

EtOH/H2O (0.25% v/v)

Concentrations

18.5 mgL21 (pH 5 1.4) and 21.8 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

SUPELCO

Purity

Pure

(Fig. 15.96)

FIGURE 15.96 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205.4

2.310

2

285.9

0.164

3

217.9

1.782

4

246.3

0.670

5

315.0

0.371

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.7.21 2,4,6-Trimethylphenol General Name

2,4,6-Trimethylphenol

CAS No.

527-60-6

Formula

C9H12O

Molecular weight

136.19 gMol-1

Solubility

EtOH

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentrations

20.3 mgL21 (pH 5 1.5) and 21.0 mgL21 (pH 5 12.2)

Pathlength

10 mm

Reference product

SUPELCO

Purity

Pure

(Fig. 15.97)

FIGURE 15.97 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205.6

2.819

2

277.1

0.235

3

216.1

2.238

4

241.1

1.108

5

295.2

0.504

UV-Visible Spectrophotometry of Waters and Soils

555

556

15. UV spectra library

15.3.7.22 Bisphenol A General Name

Bisphenol A

CAS No.

80-05-7

Formula

C15H16O2

Molecular weight

228.29 gMol-1

Solubility

120 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentrations

10.0 mgL21

Pathlength

10 mm

Reference product

Sigma-Aldrich

Purity

.99%

(Fig. 15.98)

FIGURE 15.98

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

224.5

0.665

2

276

0.153

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.8 Phthalates 15.3.8.1 Butyl benzyl phthalate General Name

Butyl benzyl phthalate

CAS No.

85-68-7

Formula

C19H20O4

Molecular weight

312.36 gMol-1

Solubility (H2O)

2.7 mgL21 (20 C)

Spectra acquisition Solvent

MetOH/H2O (20% v/v)

Concentration

27.1 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

98.6%

(Fig. 15.99)

FIGURE 15.99

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

228.9

0.362

2

276.1

0.064

UV-Visible Spectrophotometry of Waters and Soils

557

558

15. UV spectra library

15.3.8.2 Di-butyl phthalate General Name

Di-butyl phthalate

CAS No.

84-74-2

Formula

C16H22O4

Molecular weight

278.35 gMol-1

Solubility (H2O)

11.2 mgL21 (20 C)

Refractive index

1.49

Spectra acquisition Solvent

MetOH/H2O (0.2% v/v)

Concentration

10.2 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity

P.A.

(Fig. 15.100)

FIGURE 15.100

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

229.6

0.147

2

275.0

0.026

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.8.3 Di-ethyl phthalate General Name

Di-ethyl phthalate

CAS No.

84-66-2

Formula

C12H14O4

Molecular weight

222.24 gMol-1

Solubility (H2O)

1.080 gL21 (25 C)

Refractive index

1.50

Spectra acquisition Solvent

MetOH/H2O (0.2% v/v)

Concentration

10.2 mgL21

Pathlength

10 mm

Reference product

SIGMA

Purity

99%

(Fig. 15.101)

FIGURE 15.101

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

229.7

0.435

2

275.7

0.076

UV-Visible Spectrophotometry of Waters and Soils

559

560

15. UV spectra library

15.3.8.4 Potassium hydrogen phthalate General Name

Potassium hydrogen phthalate

CAS No.

877-24-7

Formula

C8H5KO4

Molecular weight

204.22 gMol-1

Spectra acquisition Solvent

H2O

Concentration

18.4 mgL21

Pathlength

10 mm

Reference product

UCB

Purity

P.A.

(Fig. 15.102)

FIGURE 15.102

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

229.2

0.711

2

281.2

0.140

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.8.5 Di(2-ethylhexyl) phthalate General Name

Di(2-ethylhexyl) phthalate, Bis(2-ethylhexyl) phthalate

CAS No.

117-81-7

Formula

C24H38O4

Molecular weight

390.6 gMol-1

Spectra acquisition Solvent

EtOH/H2O (1% v/v)

Concentration

20.0 mg L21

Pathlength

10 mm

Reference product

Sigma-Aldrich (Fluka)

Purity

99.7%

(Fig. 15.103)

FIGURE 15.103

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

204

1.169

2

230

0.629

3

277

0.173

UV-Visible Spectrophotometry of Waters and Soils

561

562

15. UV spectra library

15.3.9 Polycyclic aromatic hydrocarbons 15.3.9.1 Acenaphthene General Name

Acenaphthene

CAS No.

83-32-9

Formula

C12H10

Molecular weight

152.19 gMol-1

Solubility (H2O)

3.9 mgL21 (20 C)

Refractive index

1.60

Spectra acquisition Solvent

EtOH/H2O (0.5% v/v)

Concentration

3.1 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

99%

(Fig. 15.104)

FIGURE 15.104 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

225.7

0.663

2

288.4

0.054

3

320.1

0.014

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.2 Acenaphthylene General Name

Acenaphthylene

CAS No.

208-96-8

Formula

C12H8

Molecular weight

152.19 gMol-1

Solubility (H2O)

16.3 mgL21 (20 C)

Spectra acquisition Solvent

EtOH/H2O (0.2% v/v)

Concentration

5.2 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

98%

(Fig. 15.105)

FIGURE 15.105

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

228.7

1.494

2

264.8

0.079

3

321.7

0.328

UV-Visible Spectrophotometry of Waters and Soils

563

564

15. UV spectra library

15.3.9.3 Anthracene General Name

Anthracene

CAS No.

120-12-7

Formula

C14H10

Molecular weight

178.23 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

EtOH/CH3CN (0.5% v/v)

Concentration

3.9 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

99%

(Fig. 15.106)

FIGURE 15.106 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

221.4

0.279

2

252.2

2.722

3

324.1

0.072

4

338.1

0.130

5

356.8

0.179

6

376.0

0.159

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.4 Benzo(a)anthracene General Name

Benzo(a)anthracene

CAS No.

56-55-3

Formula

C18H12

Molecular weight

228.29 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

3.9 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.107)

FIGURE 15.107 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205.2

0.627

2

223.6

0.918

3

257.5

0.884

4

268.3

0.995

5

276.7

1.716

6

285.7

2.044

7

327.0

0.160

8

339.3

0.177

9

357.6

0.122

UV-Visible Spectrophotometry of Waters and Soils

565

566

15. UV spectra library

15.3.9.5 Benzo(a)pyrene General Name

Benzo(a)pyrene

CAS No.

50-32-8

Formula

C20H12

Molecular weight

252.31 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

4.8 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.108)

FIGURE 15.108 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

203.1

0.732

2

222.2

0.518

3

226.8

0.526

4

256.8

0.865

5

265.4

1.011

6

283.0

0.887

7

295.0

1.105

8

346.2

0.256

9

364.7

0.492

10

383.1

0.534

11

402.0

0.061

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.6 Benzo(b)fluoranthene General Name

Benzo(b)fluoranthene

CAS No.

205-99-2

Formula

C20H12

Molecular weight

252.31 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

4.1 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.109)

FIGURE 15.109 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

206.0

0.690

2

223.7

0.839

3

257.4

0.913

4

276.1

0.565

5

289.6

0.579

6

300.3

0.682

7

347.9

0.249

8

367.2

0.152

UV-Visible Spectrophotometry of Waters and Soils

567

568

15. UV spectra library

15.3.9.7 Benzo(g,h,i)perylene General Name

Benzo(g,h,i)perylene

CAS No.

191-24-2

Formula

C22H12

Molecular weight

276.33 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

5.8 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.110)

FIGURE 15.110 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

209.2

1.277

2

223.1

0.947

3

276.1

0.491

4

287.0

0.761

5

298.9

0.941

6

328.7

0.117

7

343.4

0.182

8

362.2

0.380

9

381.4

0.427

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.8 Benzo(k)fluoranthene General Name

Benzo(k)fluoranthene

CAS No.

207-08-9

Formula

C20H12

Molecular weight

252.31 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

5.6 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.111)

FIGURE 15.111 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

217.0

0.720

2

237.3

1.116

3

268.3

0.395

4

294.7

0.841

5

306.9

1.073

6

359.9

0.118

7

378.4

0.223

8

398.6

0.238

UV-Visible Spectrophotometry of Waters and Soils

569

570

15. UV spectra library

15.3.9.9 Chrysene General Name

Chrysene

CAS No.

218-01-9

Formula

C18H12

Molecular weight

228.29 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

5.5 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.112)

FIGURE 15.112 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

222.5

0.706

2

259.5

1.565

3

267.7

2.361

4

280.5

0.276

5

293.6

0.258

6

306.6

0.277

7

319.7

0.272

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.10 Dibenz(a,h)anthracene General Name

Dibenzo(a,h)anthracene

CAS No.

53-70-3

Formula

C22H14

Molecular weight

278.35 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

MetOH/CH2Cl2 (1/1) in CH3CN (1% v/v)

Concentration

4.9 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.113)

FIGURE 15.113 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

222.8

0.939

2

286.0

1.548

3

295.8

2.050

4

319.8

0.321

5

332.1

0.269

6

347.0

0.222

UV-Visible Spectrophotometry of Waters and Soils

571

572

15. UV spectra library

15.3.9.11 Fluoranthene General Name

Fluoranthene

CAS No.

206-44-0

Formula

C16H10

Molecular weight

202.25 gMol-1

Solubility (H2O)

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

5.1 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

98%

(Fig. 15.114)

FIGURE 15.114 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

202.8

1.860

2

213.4

2.151

3

235.2

2.459

4

276.1

1.102

5

285.1

1.589

6

322.6

0.337

7

340.5

0.446

8

358.1

0.451

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.12 Fluorene General Name

Fluorene

CAS No.

86-73-7

Formula

C13H10

Molecular weight

166.22 gMol-1

Solubility (H2O)

CH3CN

Refractive index

1.31

Spectra acquisition Solvent

EtOH/CH3CN (1% v/v)

Concentration

6.7 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

98%

(Fig. 15.115)

FIGURE 15.115

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

208.5

1.755

2

263.3

0.794

3

287.7

0.248

4

299.4

0.331

UV-Visible Spectrophotometry of Waters and Soils

573

574

15. UV spectra library

15.3.9.13 Indeno(1,2,3-cd)pyrene General Name

Indeno(1,2,3-cd)pyrene

CAS No.

193-39-5

Formula

C22H12

Molecular weight

267.33 gMol-1

Solubility

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

5.7 mgL21

Pathlength

10 mm

Reference product

SUPELCO

Purity

99%

(Fig. 15.116)

FIGURE 15.116 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

213.0

0.743

2

251.2

1.203

3

275.5

0.425

4

302.1

0.579

5

314.9

0.432

6

359.5

0.269

7

376.9

0.230

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.14 Naphthalene General Name

Naphthalene

CAS No.

91-20-3

Formula

C10H8

Molecular weight

128.17 gMol-1

Solubility

CH3CN

Refractive index

1.59

Spectra acquisition Solvent

CH3CN

Concentration

5.8 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

99%

(Fig. 15.117)

FIGURE 15.117 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

219.6

1.846

2

267.6

0.144

3

275.5

0.157

4

283.0

0.107

UV-Visible Spectrophotometry of Waters and Soils

575

576

15. UV spectra library

15.3.9.15 Phenanthrene General Name

Phenanthrene

CAS No.

85-01-8

Formula

C14H10

Molecular weight

178.29 gMol-1

Solubility

CH3CN

Refractive index

1.59

Spectra acquisition Solvent

CH3CN

Concentration

5.2 mgL21

Pathlength

10 mm

Reference product

MERCK

Purity

.97%

(Fig. 15.118)

FIGURE 15.118 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

213.8

1.147

2

252.1

2.092

3

273.9

0.448

4

291.9

0.403

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.9.16 Pyrene General Name

Pyrene

CAS No.

129
00-0

Formula

C16H10

Molecular weight

202.25 gMol-1

Solubility

CH3CN

Spectra acquisition Solvent

CH3CN

Concentration

4.7 mgL21

Pathlength

10 mm

Reference product

MERCK

Purity

.97%

(Fig. 15.119)

FIGURE 15.119 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

232.1

1.278

2

239.8

2.065

3

254.0

0.341

4

263.1

0.706

5

272.6

1.165

6

306.6

0.343

7

319.2

0.821

8

333.0

1.229

UV-Visible Spectrophotometry of Waters and Soils

577

578

15. UV spectra library

15.3.10 Surfactants 15.3.10.1 Alkyl diphenyloxide disulfonate, disodium salt

General Name

Alkyl diphenyloxide disulfonate, disodium salt

CAS No.

28519-02-0/25167-32-2

Formula

C24H32O7S2.Na2

Molecular weight

520 gMol-1

Spectra acquisition Solvent

H2O

Concentration

50.0 mgL21

Pathlength

10 mm

Reference product

RHODIA (Rhodacal DSB)

Purity




(Fig. 15.120)

FIGURE 15.120

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

236.9

0.661

2

271.2

0.138

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.10.2 Dodecyl benzene sulfonate

General Name

Dodecyl benzene sulfonate

CAS No.

25155-30-0

Formula

C18H29NaO3S

Molecular weight

325.49 gMol-1

Spectra acquisition Solvent

H2O

Concentration

40.8 mgL21

Pathlength

10 mm

Reference product

FLUKA

Purity

Technic

(Fig. 15.121)

FIGURE 15.121

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

224.8

0.849

2

256.1

0.038

UV-Visible Spectrophotometry of Waters and Soils

579

580

15. UV spectra library

15.3.10.3 Nonyl phenol ethoxylate

General Name

Nonyl phenol ethoxylate

CAS No.

68412-53-3

Formula




Molecular weight




Spectra acquisition Solvent

H2O

Concentration

50.0 mgL21

Pathlength

10 mm

Reference product

RHODIA (Rhodafac RE-610)

Purity




(Fig. 15.122)

FIGURE 15.122

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

225.1

0.725

2

275.0

0.116

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.10.4 Octyl phenol ethoxylate

General Name

Octyl phenol ethoxylate

CAS No.

9002-96-1

Formula

C16H26O2

Molecular weight

250.4gMol-1

Spectra acquisition Solvent

H2O

Concentration

50.0 mgL21

Pathlength

10 mm

Reference product

RHODIA (Igepal CA-630)

Purity




(Fig. 15.123)

FIGURE 15.123

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

225.1

0.780

2

275.0

0.106

UV-Visible Spectrophotometry of Waters and Soils

581

582

15. UV spectra library

15.3.10.5 Sodium-N-methyl-N-oleoyl-taurate

General Name

Sodium-N-methyl-N-oleoyl-taurate

CAS No.

137-20-2

Formula

C21H40NNaO4S

Molecular weight

425.6 gMol-1

Spectra acquisition Solvent

H2O

Concentration

50.0 mgL21

Pathlength

10 mm

Reference product

RHODIA (Geropon T-77)

Purity




(Fig. 15.124)

FIGURE 15.124

Peak n

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.11 Solvents 15.3.11.1 Acetone General Name

Acetone

CAS No.

67-64-1

Formula

C3H6O

Molecular weight

58.08 gMol-1

Solubility (H2O)

Miscible

Refractive index

1.36

Spectra acquisition Solvent

H2O

Concentration

1.565 mgL21

pH

6.2

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

P. spectro (RS)

(Fig. 15.125)

FIGURE 15.125

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

266.1

1.195

UV-Visible Spectrophotometry of Waters and Soils

583

584

15. UV spectra library

15.3.11.2 Acetonitrile

General Name

Acetonitrile

CAS No.

75-05-8

Formula

C2H3N

Molecular weight

41.05 gMol-1

Solubility (H2O)

Miscible

Refractive index

1.36

Spectra acquisition Concentration

Pure

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RS-PLUS 99.9%

(Fig. 15.126)

FIGURE 15.126

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.11.3 Ethanol

General Name

Ethanol

CAS No.

64-17-5

Formula

C2H6O

Molecular weight

46.07 gMol-1

Solubility (H2O)

Miscible

Refractive index

1.36

Spectra acquisition Concentration

Pure

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RS 99.9%

(Fig. 15.127)

FIGURE 15.127

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

585

586

15. UV spectra library

15.3.11.4 Hexane

General Name

n-Hexane

CAS No.

110-54-3

Formula

C6H14

Molecular weight

86.18 gMol-1

Solubility (H2O)

9.5 mgL21 (20 C)

Refractive index

1.36

Spectra acquisition Solvent

H2O

Concentration

Pure

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

RS 98%

(Fig. 15.128)

FIGURE 15.128

Peak n

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12 Inorganic compounds 15.3.12.1 Ammonium chloride

General Name

Ammonium chloride

CAS No.

12125-02-9

Formula

NH4CI

Molecular weight

53.49 gMol-1

Solubility (H2O)

370 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

9554.0 mgL21

Pathlength

10 mm

Reference product

FLUKA

Purity

.99%

(Fig. 15.129)

FIGURE 15.129

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

587

588

15. UV spectra library

15.3.12.2 Hydrogen peroxide

General Name

Hydrogen peroxide

CAS No.

7722-84-1

Formula

H2O2

Molecular weight

34.01 gMol-1

Solubility (H2O)

Miscible (20 C)

Spectra acquisition Solvent

H2O

Concentration

342.0 mgL21

Pathlength

10 mm

Reference product

PROLABO (30% stabilized)

Purity

RP Normapur

(Fig. 15.130)

FIGURE 15.130

Peak n

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.3 Iodine

General Name

Iodine

CAS No.

7553-56-2

Formula

I2

Molecular weight

253.8 gMol-1

Solubility (H2O)

0.3 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

50.3 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity

Titrated solution 0.1 N

(Fig. 15.131)

FIGURE 15.131

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

226.0

1.673

2

286.5

0.164

3

348.4

0.108

UV-Visible Spectrophotometry of Waters and Soils

589

590

15. UV spectra library

15.3.12.4 Potassium cyanide

General Name

Potassium cyanide

CAS No.

151-50-8

Formula

KCN

Molecular weight

65.12 gMol-1

Solubility (H2O)

680 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

3648.0 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity




(Fig. 15.132)

FIGURE 15.132

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.5 Potassium dichromate

General Name

Potassium dichromate

CAS No.

7758-50-9

Formula

K2Cr2O7

Molecular weight

294.18 gMol-1

Solubility (H2O)

120 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

53.07 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity

Normapur

(Fig. 15.133)

FIGURE 15.133

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

258.3

0.730

2

349.0

0.560

UV-Visible Spectrophotometry of Waters and Soils

591

592

15. UV spectra library

15.3.12.6 Potassium iodate

General Name

Potassium iodate

CAS No.

7758-05-6

Formula

KIO3

Molecular weight

214.0 gMol-1

Solubility (H2O)

47 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

55.6 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

99.9%

(Fig. 15.134)

FIGURE 15.134

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.7 Potassium iodide

General Name

Potassium iodide

CAS No.

7681-11-0

Formula

KI

Molecular weight

166.01 gMol-1

Solubility (H2O)

1270 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

36.0 mgL21

Pathlength

10 mm

Reference product

MERCK

Purity

Suprapur

(Fig. 15.135)

FIGURE 15.135

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

227.0

2.210

UV-Visible Spectrophotometry of Waters and Soils

593

594

15. UV spectra library

15.3.12.8 Potassium metaperiodate

General Name

Potassium metaperiodate

CAS No.

7790-21-8

Formula

KIO4

Molecular weight

230.00 gMol-1

Solubility (H2O)

4.2 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

50.7 mgL21

Pathlength

10 mm

Reference product

LABOSI

Purity

Analypur

(Fig. 15.136)

FIGURE 15.136

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

223.7

2.109

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.9 Potassium permanganate General Name

Potassium permanganate

CAS No.

7722-64-7

Formula

KMnO4

Molecular weight

158.03 gMol-1

Solubility (H2O)

70 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

100.7 mgL21

Pathlength

10 mm

Reference product

UCB

Purity

Pure

(Fig. 15.137)

FIGURE 15.137 Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

310.8

1.102

2

506.1

1.149

3

524.4

1.531

4

544.8

1.476

5

564.5

0.837

UV-Visible Spectrophotometry of Waters and Soils

595

596

15. UV spectra library

15.3.12.10 Sodium chlorate

General Name

Sodium chlorate

CAS No.

7775-09-9

Formula

NaClO3

Molecular weight

106.44 gMol-1

Solubility (H2O)

1000 gL21 (10 C)

Spectra acquisition Solvent

H2O

Concentration

2034.3 mgL21

Pathlength

10 mm

Reference product

RDH

Purity

99.5%

(Fig. 15.138)

FIGURE 15.138

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.11 Sodium chromate

General Name

Sodium chromate tetrahydrate

CAS No.

10034-82-9

Formula

Na2CrO4, 4H2O

Molecular weight

161.97 gMol-1

Solubility (H2O)

873 gL21 (10 C)

Spectra acquisition Solvent

H2O

Concentration

56.4 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

99%

(Fig. 15.139)

FIGURE 15.139

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

274.1

1.530

2

372.3

1.975

UV-Visible Spectrophotometry of Waters and Soils

597

598

15. UV spectra library

15.3.12.12 Sodium cyanide

General Name

Sodium cyanide

CAS No.

143-33-9

Formula

NaCN

Molecular weight

49.01 gMol-1

Solubility (H2O)

480 mgL21 (10 C)

Spectra acquisition Solvent

H2O

Concentration

2061.3 mgL21

Pathlength

10 mm

Reference product

PROLABO

Purity




(Fig. 15.140)

FIGURE 15.140

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.13 Sodium hypochlorite

General Name

Sodium hypochlorite (in solution: “Eau de Javel”)

CAS No.

7681-52-9

Formula

NaOCl

Molecular weight

74.44 gMol-1

Solubility (H2O)

Very soluble

Spectra acquisition Solvent

H2O

Concentration

794.2 mgL21 (active chlorine)

Pathlength

10 mm

Reference product

JAVEL IDEAL

Purity

Commercial product

(Fig. 15.141)

FIGURE 15.141

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

291.4

1.780

UV-Visible Spectrophotometry of Waters and Soils

599

600

15. UV spectra library

15.3.12.14 Sodium nitrate (low concentration)

General Name

Sodium nitrate

CAS No.

7631-99-4

Formula

NaNO3

Molecular weight

84.99 gMol-1

Solubility (H2O)

880 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

20.7 mgL21

Pathlength

10 mm

Reference product

FLUKA

Purity

.99%

(Fig. 15.142)

FIGURE 15.142

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

205.6

2.102

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.15 Sodium nitrate (high concentration)

General Name

Sodium nitrate

CAS No.

7631-99-4

Formula

NaNO3

Molecular weight

84.99 gMol-1

Solubility (H2O)

880 mgL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

8864.0 mgL21

Pathlength

10 mm

Reference product

FLUKA

Purity

.99%

(Fig. 15.143)

FIGURE 15.143

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

301.6

0.837

UV-Visible Spectrophotometry of Waters and Soils

601

602

15. UV spectra library

15.3.12.16 Sodium nitrite (low concentration)

General Name

Sodium nitrite

CAS No.

7632-00-0

Formula

NaNO2

Molecular weight

69.00 gMol-1

Solubility (H2O)

820 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10.6 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

99%

(Fig. 15.144)

FIGURE 15.144

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

212.8

2.199

UV-Visible Spectrophotometry of Waters and Soils

15.3 Spectra of compounds

15.3.12.17 Sodium nitrite (high concentration)

General Name

Sodium nitrite

CAS No.

7632-00-0

Formula

NaNO2

Molecular weight

69.00 gMol-1

Solubility (H2O)

820 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

3007.0 mgL21

Pathlength

10 mm

Reference product

CARLO ERBA

Purity

99%

(Fig. 15.145)

FIGURE 15.145

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1

353.9

0.890

UV-Visible Spectrophotometry of Waters and Soils

603

604

15. UV spectra library

15.3.12.18 Sodium tetraborate decahydrate

General Name

Sodium tertraborate decahydrate (borax)

CAS No.

1303-96-4

Formula

Na2B4O7, 10H2O

Molecular weight

381.37 gMol-1

Solubility (H2O)

50 gL21 (20 C)

Spectra acquisition Solvent

H2O

Concentration

10368.1 mgL21

Pathlength

10 mm

Reference product

ALDRICH

Purity

ACS

(Fig. 15.146)

FIGURE 15.146

Peak n degree

Wavelength (nm)

Absorbance (a.u.)

1







UV-Visible Spectrophotometry of Waters and Soils

References

605

Acknowledgments The authors wish to thank Sylvie Spinelli and Catherine Gonzalez for their contribution to the first-edition chapter [S. Spinelli, C. Gonzalez, O. Thomas, UV spectra library, in: UVvisible spectrophotometry of water and wastewater, O. Thomas, C. Burgess (Eds.), Elsevier, Amsterdam (2007) pp. 267
356].

References [1] H.H. Perkampus, UV
Vis Atlas of Organic Compounds, 2nd (Ed.), VCH, Weinheim, 1992. [2] Science-softCon, UV/Vis 1 spectra data base (UV/Vis 1 photochemistry database). ,http://www.uv-spectra.de., 2016 (accessed 22.12.16). [3] Bio-Rad, UV-vis spectral databases. ,http://www.bio-rad.com/en-fr/product/uvvis-spectral-databases., 2016 (accessed 22.12.16). [4] A. Noelle, A.C. Vandaele, J. Martin-Torres, C. Yuan, B.N. Rajasekhar, A. Fahr, et al., UV/Vis 1 photochemistry database: Structure, content and applications, Journal of Quantitative Spectroscopy and Radiative Transfer 253 (2020) 107056. Available from: https://doi.org/10.1016/j.jqsrt.2020.107056. [5] M. Brogat, A. Cadiere, A. Sellier, O. Thomas, E. Baures, B. Roig, MSPE/UV for field detection of micropollutants in water, Microchemical Journal, Devoted to the Application of Microtechniques in All Branches of Science 108 (2013) 215
223. Available from: https://doi.org/10.1016/j.microc.2012.10.025. [6] M. Brogat, De´veloppement d’une me´thode d’extraction et de fractionnement sur multiples phases solides (MSP2E) de micropolluants organiques. PhD thesis, University of Rennes 1, 2014.

UV-Visible Spectrophotometry of Waters and Soils

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively. 1-propanthiol, 212
213 1,7 ethinylestradiol, 124t, 153t, 520 2 butanone, 35 2-4-dichlorophenoxy acetic acid (2-4 D), 497 2-butanone, 113t, 470 2-chloro 4 methylaniline, 479 2-chloroaniline, 116f, 477 2-chlorophenol, 128t, 129f, 129t, 132
133, 135t, 537 2-cresol, 128t, 129t 2-nitrophenol, 130f, 135t, 202t, 548 2-terbutyl 4-methylphenol, 128t, 129t 2-tert-butyl 4 methylphenol, 553 2,3-dichlorophenol, 128t, 544 2,4-D, 123t 2,4-dichlorophenol, 128t, 135t, 545 2,4,6-trichlorophenol, 128t, 554 2,4,6-trimethylphenol, 128t, 129t, 555 2,5-dimethylphenol, 128t, 129t, 546 3-aminophenol, 202t 3-chlorophenol, 128t, 129f, 129t, 538 3-cresol, 128t, 129t 3-nitrophenol, 130f, 549 3,4-dichloroaniline, 480 4-aminophenol, 202t 4-chloro 3-methylphenol, 128, 128t, 129t, 536 4-chloroaniline, 478 4-chlorophenol, 128, 128t, 129f, 129t, 135t, 539 4-cresol, 128t, 129t 4-nitrophenol, 130f, 135t, 202t, 550 4,40 -diaminodiphenylmethane, 116, 117f, 489 4,5-dichlorocatechol, 131f, 543 4,6-dinitro 2 methylphenol, 547 17-α-ethinylestradiol, 122

A Absorbance, 35t, 39
40 Absorptiometric method, 175t Absorption, 239f, 324

Absorption path length, 35t Absorptivity, 35t Accidental discharge, 288
291 Accuracy, 38
40 Acenaphthene, 138f, 140t, 562 Acenaphthylene, 137, 138f, 140t, 563 Acetaldehyde, 113, 113t, 114f, 156t, 467 Acetaminophen, 122
125, 124t, 125f, 521 Acetic acid, 459 Acetone, 35, 113t, 114f, 155, 156t, 377, 450f, 468, 583 Acetonitrile, 137, 138f, 143
144, 450f, 584 Acid blue, 100, 103, 104f Acid green, 100, 102f, 103, 103f, 104t Acid hydrolysis, 205
206 Active carbon, 395, 396f Adsorption, 269
270 Advanced oxidation process (AOP), 396, 396f Aggregate organic parameters, 162
163, 162t, 163f, 186, 186f, 187f Agricultural soils, 440
444 Aircrafts, 302
303 Alachlor, 123t, 498 Alcohol ethersulfates, 149 Alcohol sulfates, 149 Aldehyde, 112
113, 155, 467
474 Alizarin, 100, 101f, 102
103, 103f, 107
108, 108f, 109f Alkyl diphenyloxide disulfonate, 149, 150f, 578 American Society for Testing and Materials (ASTM), 40
41 Amines, 112, 114
117, 475
489 Ammonium, 99
100, 99f, 107, 119, 148, 203
205, 392t Ammonium chloride, 205, 587 Analgesics, 122
125, 125f Analytical process, 57f Aniline, 115, 475 Anisidine, 115
116, 116f Annexes, 446f

607

608

Index

Anthracene, 138f, 139, 139f, 140t, 141f, 564 Anthraquinone, 97 Anthraquinonic dyes, 97 Anthropogenic organic matter (AOM), 260
261, 261f Argentometric method, 214 Aromatic amines, 116 Aromatic compounds, 330
331 Atenolol, 124t, 125f, 522 Atomic absorption spectroscopy (AAS), 208 Atrazine, 119, 120f, 123t, 152, 153t, 499 Autonomous Surface Vehicle, 304
305 Autonomous underwater vehicles (AUV), 311
312 Averaging, 54
55 Azoic dyes, 97

B Baseline/rainfall conditions, 324f Bathochromic shift, 53, 98, 100, 108, 110, 115
118, 126
127, 128t, 130
132, 137, 141
142, 149 Beer
Lambert law, 34, 36, 40, 42f, 59
60, 74
75, 77
78 Benzaldehyde, 113t, 114f, 156t, 469 Benzene, 30, 31f, 118f, 119f, 139f, 141, 490
496 Benzenoid compounds, 32 Benzo[a]anthracene, 138f, 140t, 565 Benzo[a]pyrene, 137, 138f, 139f, 140t, 141f, 566 Benzo[b]fluoranthene, 138f, 140t, 141, 142f, 567 Benzo[g,h,i]perylene, 137, 138f, 139f, 140t, 568 Benzo[k]fluoranthene, 138f, 140t, 142, 142f, 143f, 569 Betamethasone-17-valerate, 48, 49f Biochemical oxygen demand (BOD), 3
4, 6 Biodegradable dissolved organic carbon (BDOC), 87
88, 175
176 Biodegradation process, 165
166 Biological oxygen demand (BOD), 62t, 162
165, 169, 172
174, 178
186, 290, 348, 351, 354, 360, 363, 367, 368t, 374t, 379f, 389, 392t, 394t, 401
402 Biological processes, 397
398 Bisphenol A, 556 Bisulfide, 210t Boats/buoys, 304
306

Bottled drinking waters, 337
340, 338f, 338t Bromide, 195 Bromothymol blue, 107
108, 109f BTEX, 112, 117 Butyl benzyl phthalate, 557 Butyraldehyde, 113t, 114f, 156t, 471 Butyric acid, 370f, 460

C Cadmium, 194 Caffeine, 122
125, 124t, 153t, 523 Calibration curve, 75 Canadian Council of Ministers of Environment Water Quality Index (CCME-WQI), 9
10, 176 Capillary electrophoresis, 196
197, 214 Carbamates, 119 Carbamazepine, 122
125, 124t, 125f, 153t, 524 Carbaryl, 122, 122f, 123t, 500 Carbohydrate, 96, 155
157, 234 Carbonyl, 96
97, 99, 113, 113t, 155, 156t Carbonyl sulfide (COS), 209 Chemical oxygen demand (COD), 3
4, 6, 62t, 162
165, 169, 174
175, 178
186, 253, 263t, 290, 348, 352f, 354
360, 354t, 356t, 363, 368t, 369
370, 373
374, 374t, 379f, 389, 392t, 394t, 398, 401
402, 404t, 409t, 412 Chemometric methods, 60
61 Chiro-optical phenomena, 30 Chloride, 214
215, 399, 412 Chlorination, 328
329 Chlorine, 213
217, 599 Chloroacetanilids, 119 Chlorobenzene, 119f, 491 Chlorpyrifos, 123t, 501 Chlortoluron, 120f, 123t, 502 Chromatograms, 429f Chromium, 194, 218
221, 392
393, 392t, 394f Chromophore, 30
32, 96, 235
236 Chromophoric dissolved organic carbon (CDOM), 8 Chrysene, 138f, 140f, 140t, 570 Ciprofloxacin, 122
125, 124f, 124t, 525 Cleaning procedures, 51 Clofibric acid, 122
125, 124t, 125f, 526 Coagulation, 392t Coagulation
flocculation, 363
366, 365f, 395

Index

Coagulation optimization, 325
328 Colloids, 67, 76
77, 86, 86f, 234
235, 240, 243
244, 247f, 250
254, 260, 265, 268
272, 291
292, 350, 356, 360, 363, 373, 380, 392t Color, 3
5, 26
27, 61
63, 74
75, 96
112, 235
238, 332
333, 448 Colored DOM (CDOM), 166
167 Colored scale, 61
63, 62t Colorimetric method, 194, 196
197, 211 Colorimetry, 205
206 Complexometric method, 221
226, 222t Composts, 426
430, 428f Contamination warning system (CWS), 333
336 Continuous flow analysis (CFA), 196
197 Copper, 110
112, 225t Corticosteroid, 48 Crystal violet, 105, 106f Cuvettes, 49
50 Cyclophosphamide, 124t

D Data integrity and security, 56
57 Data treatment, 54
56 Deconvolution method, 84, 184, 207, 240, 246, 250, 253
254, 380, 381t, 392t Deisopropylatrazine (DIA), 152 Derivative method, 81
83 Derivative spectra, 63
65, 68 Derivatives, 55
56 Desethylatrazine (DEA), 152 Detergents, 348
349 Di(2-ethylhexyl)phthalate (DEHP), 135, 137f, 561 Diatomic molecule, 29
30 Diatrizoate, 124t, 527 Diazinon, 122, 123t, 153t, 503 Dibenzo[a,h]anthracene, 138f, 140t, 571 Dibrom, 208t Di-butyl phthalate, 558 Dichlorprop, 123t, 504 Dichlorvos, 208t Diclofenac, 122
125, 124t, 125f, 153t, 528 Di-ethyl phthalate, 559 Diethylamine, 155, 156f, 481 Differential spectra, 68
69, 351f, 353, 372 Diffraction, 235, 239f, 240
243, 247
248, 250, 357 Diffusion, 235, 238
243, 239f, 246f, 250 Diisobutylketone, 113, 472 Dilution factor, 287

609

Dimethoate, 505 Dinoterb, 119, 122f, 123t, 506 Diode array spectrophotometers (DASs), 47 Diphenylcarbazide, 220, 220f Diquat, 123t, 507 Direct comparison of spectra, 61f, 69
70 Disinfection by-products (DBPs), 81, 322, 328
333 Disodium salt, 578 Dissolved organic carbon (DOC), 80
83, 178
186, 253, 262, 263t, 274
275, 277
279, 281, 292
293, 292f, 323
325, 331, 356t, 374t, 375
377, 376f, 430
431, 437t, 442
445 Dissolved organic matter (DOM), 80
81, 163
164, 166
169, 260
261, 261f, 263t, 323
325, 329, 331
332, 444
445, 448
450 Distillation, 196
197 Dithizone, 109
112, 111f, 112f, 225 Diuron, 120f, 123t, 508 Dixon test, 72
73 Dodecylbenzene sulfonate (DBS), 86f, 149
151, 150f, 151f, 350f, 579 Domestic wastewater, 348
349, 350f Drones, 303
304 Dye, 54, 97
107, 238

E Early warning system (EWS), 322, 333
337 Electromagnetic radiation (EMR), 26
27 Electromagnetic spectrum, 26
28, 27f, 28f Electronic databases, 456 Endocrine disruptors, 96 Environmental impact, 412
414 Eriochrome black T, 111f Erythromycin, 122
125, 124f, 124t, 529 Ethanol, 49f, 585 Ethylbenzene, 117, 118f, 492 Ethylenediaminetetraacetic acid, 461 Ethylpropylacrolein, 392 European Water Framework Directive, 9
10, 176 Exploitation methods, 88t External waste management, 410
412 Extinction value, 35t Extraction, 143
144, 150 Extractive industry, 387t Eyeonwater (EoW), 4, 4f

F Ferroin, 108, 110f

610 Filtration, 68, 108, 234
235, 241
243, 250
252, 268
269, 349, 350f, 351f, 360, 370
372, 395, 399 Flow injection analysis (FIA), 196
197 Fluoranthene (FLA), 138f, 140t, 142, 143f, 572 Fluorene, 140t, 142, 142f, 573 Food industry, 386
387, 387t, 389t Forel-Ule (FU) hue scale, 4 Forel-Ule Index (FUI), 5 Formaldehyde, 113, 113t, 114f, 155, 155f, 156t, 473 Formazin, 52
53 Formic acid, 462 Fourier transformation, 59
60, 68 Fractionation, 269f Freshwaters, 291
293, 291f Fulvic acid (FA), 450

G Generalized (multiple) standard addition method, 80 Glucose, 157f, 158f Glutamic acid, 155, 483 Glycine, 155, 157f, 484 Good spectroscopic practice, 36
37, 37f Granular active carbon (GAC), 395, 396f Gravimetric reference method, 268
269 Groundwater, 96, 281
285, 413
414

H Handheld devices, 307
308 Herbicides, 119
121 Hexane, 44, 45f, 46t, 586 Hexavalent chromium, 218
220, 219f Hexazinone, 119, 120f, 123t, 509 Hidden isosbestic point (HIP), 73
74, 291
292, 390 High-frequency grab sampling, 306
307 Holmium perchlorate, 55f Humic substances (HS), 237, 395, 442
443, 447, 450
451 Hydrocarbon, 148, 399, 411t, 413
414, 562
577 Hydrocolor, 4 Hydrogen peroxide, 396
397, 588 Hydrogen sulfide (H2S), 209, 210t Hydrolysis, 204
205 Hyperchromic effect, 102, 104, 108, 132
133, 141
143 Hyphenated processes, 399
400 Hypochlorite, 215
217, 216f

Index

Hypsochromic effect, 102 Hypsochromic shift, 53

I Ibuprofen, 122
125, 124t, 125f, 153t, 530 Idealized energy transitions, 29f Indeno[1,2,3-cd]pyrene, 138f, 140t, 142, 574 Industrial wastewater, 387t Infrared (IR) spectrophotometry, 59
60, 424 Inorganic nonmetallic constituents, 195
217 Insecticides, 119 Instrumental performance criteria, 37 Instrumental stray-light (ISL), 40 Integrated Water Monitoring Initiative, 13 Interferences, 37, 60
61, 65, 68, 75
87, 253, 348, 405 Investigative Monitoring, 12
13 Iodide, 195, 589 Iodometric method, 211 Ion chromatographic method, 196
197, 205
206, 213 Ionic strength, 54 Iron, 225t Isobutyl methyl ketone, 474 Isoproturon, 120f, 123t, 510 Isosbestic point (IP), 60, 61f, 72
73, 72f, 98, 102, 104, 110
112, 267, 268f, 275, 354
355, 390, 403, 412

J Jar test, 363
364

K Ketone, 113
114, 411t, 467
474

L Lakes, 260
261, 272, 274
281, 274f, 275f, 276f, 277t, 279f, 291
293 Landfill leachates, 430
436, 431t Laser diffraction, 246
247 Leachate treatment, 433 Leachates, 417
418 Light scattering, 52 Linear alkylbenzene sulfonates (LASs), 149 Linuron, 120f, 123t, 511 Luminescence, 30

M Malathion, 123t, 512 Maxwell’s electromagnetic theory, 52

Index

m-cresol, 540 Membrane bioreactor (MBR), 371 Membrane techniques, 196
197 Mercaptans, 392t Mercuric nitrate method, 214 Mercury, 194, 225t Mercury lamp, 155, 348 MERIS (Medium Resolution Imaging Spectrophotometer), 301 Metal, metallic compounds, 109, 234, 237f, 395 Metallic constituents, 217
226 Metazachlor, 123t, 513 Methyl isobutyl ketone, 113t Methyl red, 107, 108f Methylene blue active substances (MBAS), 150 Methylparaben, 122
125, 124t, 531 Metolachlor, 123t, 514 Mevinphos, 208t Micropollutant, 371
372 Mie diffusion, 250 Mielenz stray light method, 44f Mineral water, 260
261, 338
339, 339f, 339t, 340f Mineralization, 196
197 Minerals, 194, 194f Modern double-beam instruments, 47 MODIS (Moderate Resolution Imaging Spectroradiometer), 300 Molar absorptivity, 34, 35t Molecular spectra, 29 m-toluidine, 116f, 486 Multicomponent method, 78
80 Multiple linear regression (MLR), 60
61, 80, 84, 179 Multiple solid-phase extraction, 152 Municipal wastewater, 351 Mutlilinear regression, 78
80 m-xylene, 117, 118f, 494

N N (phosphonomethyl) glycine, 208t N wavelengths method, 77
78 N-acetyl-glucosamine, 202t Naphthalene (NAF), 138f, 139f, 140t, 575 National Water Quality Monitoring Council (NWQMC), 12 Natural organic matter (NOM), 260
261, 261f, 275, 281, 322
323, 325
328, 330
331, 333, 450
451 Natural soils, 444
445

611

Natural water, 62t, 69, 238t, 239t, 240, 244, 254, 260
285, 260f, 264f, 269f, 382f, 395, 400
401 Nautical drone, 304 Near-IR (NIR) spectroscopy, 369
370, 441
442 Nicotinic acid, 40, 42f Nitrate measurement, 198
200, 444 Nitrate, 61
63, 62t, 69, 73, 82
83, 86
87, 86f, 155, 195, 250, 252
253, 263t, 274
275, 277
279, 380, 392t, 399, 412 Nitrification, 373
374 Nitrite, 392, 405f Nitrite measurement, 200
201, 200f Nitrogen, 13, 41, 82
83, 103, 155, 195
205, 204t, 348
349, 354t, 392t, 402 N-methylpyrolidone, 392 Nonmetallic minerals, 194 Nonsatellite remote sensing techniques, 314t Nonyl phenol ethoxy phosphate, 150f Nonyl phenol ethoxylate, 580 Normalization, 60, 61f, 70
71, 71f, 248, 253, 274
275, 390

O

o-cresol, 541 Octoxynol-9, 149 Octyl phenol ethoxylate, 581 Octynol-9, 150f One spectrum transformation, 61
68 On-site systems, 308
311 Operational Monitoring, 12
13 Optimal spectrophotometric range, 44
49 Orange dyes, 97
99, 98f, 99f, 100f Organic carbon, 369, 389 Organic matter, 62t, 73, 353, 360f, 367, 369, 373, 375
377, 411t Organic matter evolution, 164, 165f Organic synthesis, 387t Organochlorine, 217 Orthophosphates, 205
208, 207f Oxalic acid, 463 Oxygen-demand methods, 162 o-xylene, 495 Ozone, 378

P

p-anisidine, 116f, 476 Parallel factor (PARAFAC), 167 Parallelism, 49
50 Paraquat, 119, 121f, 123t, 515

612

Index

Parathion, 122, 122f, 123t, 516 Partial least square (PLS) regression, 60
61, 84
85, 179
180, 357 Partial least squares (PLS) algorithm, 199 p-chlorophenol, 212
213 p-cresol, 542 Penanthrene (FEN), 138f, 139f Pentachlorophenol, 128, 128t, 129t, 135t, 551 Perchloric acid, 38
39 Performance assessment, 325
328 Peroxodisulfate, 155
157, 210t Pesticides, 112, 118
122, 123t, 457
458, 497
519 Petrochemistry, 387t, 389t, 412 Petroleum hydrocarbon-polluted soils, 425t Petroleum hydrocarbons, 394t, 419, 421
422, 422f, 424
426, 425f pH effect, 54, 432
433, 432f, 433f, 433t pH value, 72, 204
205, 279
281 Pharmaceuticals, 122
126, 520
534 Phenanthrene, 137, 139f, 140f, 140t, 576 Phenol, 65, 66f, 82
83, 113, 126
135, 127f, 387, 390, 392, 392t, 399, 405f, 406f, 457 Phenol index, 130
135 Phenol red, 104, 105f, 108 Phenylureas, 119 Phosphorus, 13, 205
209, 205f, 206f, 363, 392t Photodegradation, 201f Photon capture, 29f Photooxidation, 96, 155, 157f, 202
203, 204t, 205
206, 396, 434
436, 435f, 436f Phthalate, 113, 135, 557
561 Physical absorption, 235 Physical path length, 49
50 Physicochemical processes, 395
397 Physicochemical treatments, 395 Pollutants, 112, 348
349 Polluted soils, 96, 418
426 Pollution parameters, 267, 268f Polycyclic aromatic hydrocarbons (PAHs), 113, 136
146, 419
424, 420f, 421f, 421t, 423f, 423t, 424f, 562
577 Polynomial compensation, 83 Polyphenols, 130 Ponds, 260
261, 274
275, 274f Potassium chloride, 41 Potassium dichromate, 39, 41f, 164, 591 Potassium iodate, 592 Potassium iodide, 593 Potassium metaperiodate, 594 Potassium permanganate, 595

Potassium peroxodisulfate, 203 Potassium sodium tartrate, 466 Potentiometric method, 211
212, 214, 215f Principal component analysis (PCA), 60
61, 84
85, 179, 334
335, 357, 381 Principal component regression (PCR), 84
85 Prophenofos, 123t Propionic acid, 464 p-toluidine, 487 Pulp and paper, 386
387, 387t, 388f, 389t, 392t, 398f p-xylene, 496 Pyrene, 137, 138f, 139f, 140t, 577 Pyridylazo resorcinol (PAR), 109
110, 110f Pyrocatechol, 552

Q Quality of air/water, 439
440 Quantitative estimation, 391
395 Quantitative laws, 32
34

R Radiation, 28
32, 33f Rain influence, 268
270 Rainfalls, 322, 354
357 Raw water, 327f Rayleigh diffusion, 240 Reagent, 74
75, 96, 107
112 Redox titration method, 174 Reference materials, 38 Refinery, 387t, 389t, 390f, 391f, 392, 393f, 398f, 399, 399f, 400t Reflection, 30, 239f Remote sensing measurement, 299 Remote sensing techniques appraisal, 313
315 Reproducibility, 38
40 Resolution, 43
44 Rivers, 260
261, 285
288, 291
293

S Salinity, 13 Sample aging, 364
366, 401 Sample handing, 52 Sample presentation, 49
51 Sample storage, 52 Sampling assistance, 401 Sand-filtration test, 87
88 Satellites applications, 299
301 Savitzky
Golay method, 67
68 Scattering, 30, 32
34, 76
77, 240, 244
248

Index

Scene of monitoring, 298 Seawater, 260
261, 265, 287
288 Seawater quality monitoring, 302t Second derivative spectra, 293
294, 293f Second-derivative absorbance at 226 nm (SDA 226), 324 Second-derivative absorbance at 295 nm (SDA 295), 324 Sediments, 375, 448
451 Semideterministic approach, 181
183, 181f, 218
219 Semideterministic deconvolution method, 211
212 Sequential injection analysis (SIA), 196
197 Sewer, 251
252, 348
359, 386, 388 Shape factor, 60, 61f, 65
67, 402 Shock load, 407
408 Shock-loading management, 407
410 Signal smoothing, 67
68 Simazine, 120f, 123t, 517 Single/averaged standards, 79 Single-beam instruments, 47 Small tributaries quality, 270
272 Smoothing, 54
55 Sodium chlorate, 596 Sodium chromate, 597 Sodium cyanide, 598 Sodium hypochlorite, 599 Sodium nitrate, 600 Sodium nitrite, 602 Sodium-N-methyl-N-oleoyl taurate, 582 Sodium permanganate, 164 Sodium salicylate, 465 Sodium tertraborate decahydrate, 604 Soil quality assessment, 15
16, 441f Solid waste treatment, 426
430 Solid-phase extraction, 151
153 Solvatochromic shift, 53 Solvent polarity, 53 Solvent quality, 53 Source water monitoring, 323
325 Specific UV absorbance (SUVA), 166
169, 168f, 275
277, 324
328, 326f, 330
331, 333 Spectra slopes, 81 Spectral characteristics, 52
56 Spectral correction, 56 Spectral data presentation, 34
35 Spectral data quality, 36
49 Spectral normalization, 56 Spectral subtraction, 56

613

Spectrometer, 37 Spectrophotometry, 26, 30, 35t, 38
49, 46t, 196
197, 205
206 Spills, 405
406 Spring water, 337
339 Steel industry, 387t Stivisky
Golay method, 441
442 Stray-light, 40
43 Stripping water, 209 Sulfamethoxazole, 122
125, 124f, 124t, 153t, 532 Sulfate, 210t Sulfide, 209
211, 210t, 211f, 212f, 392t Sulfur, 110
112, 146
148, 209
213, 210t, 390 Sulfuric acid, 41f, 457 Sulfur-oxidizing bacteria (SOB), 334 Supracolloids, 234
235, 246
248, 251
252 Surface water, 96, 322, 380, 381t Surface water spectrum, 290f Surfactants, 86f, 113, 148
151, 246, 249
250, 290, 348
350, 352, 364
366, 373, 378
380, 392t, 411t, 578
582 Surveillance Monitoring, 12
13 Suspended solids, 61
63, 65, 68, 73
74, 86
87, 86f, 234, 240, 244, 246
255, 265, 269
271, 274
275, 291
292, 351, 353
355, 353f, 357, 360, 363
366, 363f, 365f, 366f, 377f, 378
380, 386
387, 401 Sustainable Development Goal (SDG), 13

T Tap water, 322
328 Temperature, 54 Terbuthylazine, 518 Terbutryn, 120f, 123t, 519 Tertiobutylcatechol, 392 Tetrathionate, 210t Textile, 238t, 239t, 396
397, 396f, 399, 400f Thiosulfate, 210t THM precursors, 329 Toluene, 118f Toluidine, 115
116, 116f Total dissolved solids (TDS), 309, 312
313 Total Kjeldahl nitrogen (TKN), 196
198, 201
203, 203f, 379f Total organic carbon (TOC), 6, 87, 163
164, 169, 178
186, 290, 322
323, 329, 368t, 389, 395
397, 396f, 399
400, 400t, 405
406, 406f Total organochlorine compounds (TOX), 217

614

Index

Total oxygen demand (TOD), 392, 393f, 404f, 405, 405f Total phosphorus, 207
208 Total suspended matter (TSM), 299
300 Total suspended solids (TSS), 62t, 234
235, 246f, 247
248, 250
251, 253
255, 300, 353, 353f, 354t, 356t, 357
361, 363
365, 365f, 366f, 369
370, 373
374, 377, 377f, 381, 392t, 393
394, 394t, 408f Traceability, 456
457, 458t Transfer mechanisms, 418f Transmission, 30 Transmittance, 35t Treatability, 60, 401
405 Treatability tests assistance, 401
405 Trends in water quality monitoring, 16
19 Triazins, 119 Trichlorophenol, 135t Trimethoprim, 122
125, 124f, 124t, 153t, 533 Tris (2-chloroethyl) phosphate, 208t Trivalent chromium, 221 Trophic state index (TSI), 5 Turbidimetry, 52
53, 244, 250
253 Two wavelengths method, 76
77, 80
81 Typology of wastewater, 378
380, 389 Tyrosine, 488

U Unmanned aerial vehicle (UAV), 303, 445
446 Urban landfill leachates, 431f Urban wastewater, 96, 150, 151f, 201f, 218, 220, 234, 235f, 238t, 239t, 248
249, 357
359, 358f, 395, 399
401, 412 Urea, 202t, 205 Uric acid, 63f UV ratios, 324
325 UV spectra, 198
200, 262
266, 264f, 267f, 270, 272, 273f, 274
277, 276f, 279, 279f, 281, 286
287, 286f, 287f, 290f UV spectra exploitation, 178
184, 178f

UV spectra handling, 61
74, 61f UV spectral deconvolution (UVSD) method, 117, 132
133, 199, 211
212 UV spectrophotometry, 176
177, 196
197, 348, 359
360, 363
364, 367, 370, 386, 391
397, 392t, 394t, 400
401, 407
408, 410
413 UV/UV method, 151, 197
198, 204, 204f, 208
209 UV
visible spectrophotometric method, 218, 224
225, 226f, 322
323, 326f, 419
420, 427t, 430
431, 442t, 443, 443f, 444f, 445, 447f, 448f, 449f

V Validation, 184
186, 185t, 204 Vandenbelt, Forsyth and Garrett (VFG) method, 48 Variability, 46t, 74, 354, 389
391, 407
408 Visible absorbance, 237
238 Visual soil evaluation (VSE), 440
441 Visualization, 61

W Warfarin, 122
125, 122f, 124t, 125f, 534 Waste management, 400
412 Wastewater analysis, 60
61 Wastewater treatment plants (WWTPs), 266
268, 266f, 288, 357 Water bodies, 262 Water extractable organic matter (WEOM), 444
445 Water Framework Directive, 304 Water pollution, 194 Water quality (WQ), 2
8, 3f, 266
268 Water quality indices, 176
177 Water quality monitoring (WQM), 8
15, 10f, 298f Water safety plans (WSPs), 14
15 Wavelength, 26
27, 38
39 Wetlands, 260
261, 272
274, 445
448 Wireless sensor networks (WSN), 312
313