Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering: Concepts, Techniques, and Practice 0128095822, 9780128095829

Table of contents :
Front-Mat_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Sci
Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering
Copyrig_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Scien
Copyright
Dedicati_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Scie
Dedication
Contribut_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Sci
Contributors
About-the-edi_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied
About the editors
Prefac_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Scienc
Preface
Chapter-1---Sustainable-poll_2021_Pollution-Assessment-for-Sustainable-Pract
1 . Sustainable pollution assessment practices
1.1 Introduction
1.2 Sustainable development concept
1.2.1 Social sustainability
1.2.2 Environmental sustainability
1.2.3 Economic sustainability
1.2.4 Land sustainability
1.3 Sustainable development and the ambient environment
1.4 Land environment
1.5 Global environmental problems and restoration initiatives
1.5.1 Global warming and climate change
1.5.2 Chemicals in the environment
1.5.2.1 Persistent organic pollutants
1.5.2.2 Metals
1.5.2.3 Health care waste
1.5.2.4 Electronic waste
1.5.2.5 Mitigation measures
1.5.3 Pollution of marines and rivers
1.5.3.1 Oil spill
1.5.3.2 Plastic debris in marine environment
1.5.3.3 Freshwater bodies
1.5.3.4 Conservation and sustainable use of oceans, seas, and marine resources
1.5.4 Extinction of species and biodiversity
1.5.4.1 Marine ecosystem
1.5.4.2 Animals
1.5.4.3 Forests
1.5.4.4 Mitigation measures
1.5.5 Environmental pollution in developing countries
1.5.5.1 Industries and population
1.5.5.2 Air pollution
1.5.5.3 Water pollution and management
1.6 Interconnection of environmental problems
1.7 Geoenvironmental engineering aspects
1.8 General pollution assessment framework
1.9 Summary and concluding remarks
References
Further reading
Chapter-2---Risk-analys_2021_Pollution-Assessment-for-Sustainable-Practices-
2 . Risk analysis and management
2.1 Introduction
2.2 Decision trees
2.3 Optimum decision criteria
2.3.1 Maximum expected monetary value criterion
2.3.2 Minimax criterion
2.4 Expected value of perfect information
2.5 Statistical measures in decision-making analyses
2.5.1 Decision analysis of limited spill
2.5.2 Decision analysis of catastrophic spill
2.5.3 Worth of additional statistical measures to the MEMV
2.6 Extended environmental cost
2.6.1 Limited leakage (remediation cost less than US$2 million)
2.6.2 Catastrophic leakage (remediation cost exceeds US$2 million)
2.6.3 The lost value of groundwater and modified decision analysis
2.7 Utility theory
2.7.1 Utility concept
2.7.2 Exponential utility model
2.7.3 Partial involvement in projects
2.7.4 The use of the exponential utility in spillage and leakage problems
2.7.5 Application
2.7.6 Bayesian decision theory
2.8 Risk assessment
2.9 Basic elements of human health risk assessment
2.9.1 Hazard identification
2.9.2 Exposure assessment
2.9.3 Toxicity assessment
2.9.3.1 Introduction
2.9.3.2 Sources of toxicity information
2.9.3.2.1 Epidemiological studies
2.9.3.2.2 Animal studies
2.9.3.2.3 Supporting studies
2.9.3.3 Toxicological parameters
2.9.3.3.1 Noncarcinogenic effects
2.9.3.3.2 Carcinogenic effects
2.9.3.3.3 Weight-of-evidence classification
2.9.3.3.4 Slope factor calculation
2.9.4 Exposure route considerations
2.10 Risk characterization
2.10.1 Calculation of carcinogenic risks
2.10.2 Calculation of noncarcinogenic hazards
2.11 Risk management
2.11.1 Elements of a risk management program
2.11.1.1 Hazards identification program
2.11.1.2 Consequence analysis
2.11.1.3 Risk mitigation
2.11.2 Quantified risk assessment
2.12 Role of regulatory agencies
2.13 Regulatory approaches
2.13.1 Risk-based mitigation criteria
2.13.2 Numerically based mitigation criteria
2.14 Mitigation technologies for polluted soils
2.14.1 Natural attenuation
2.14.2 Containment
2.14.3 Removal and treatment
2.14.4 In situ treatment
2.14.5 Selection of mitigation options
2.15 Summary and concluding remarks
References
Further reading
Chapter-3---Environmental-app_2021_Pollution-Assessment-for-Sustainable-Prac
3 . Environmental applications of remote sensing
3.1 Environmental problems and remote sensing
3.2 Concepts and foundations of remote sensing
3.2.1 Spectral bands for imaging
3.2.2 Spectral signature and atmospheric windows
3.2.3 Imaging quality and information content
3.3 Remote sensing instruments and platforms
3.3.1 Imaging systems
3.3.1.1 Optical imaging systems
3.3.1.2 Thermal imaging systems
3.3.1.3 Radar imaging systems
3.3.2 Nonimaging systems
3.3.2.1 Satellite altimeters
3.4 Ocean surface circulation and marine debris application
3.4.1 Ocean surface circulation
3.4.2 Remote sensing of marine debris
3.5 Unmanned aerial systems
3.5.1 Why now? Why is adaptation so slow?
3.5.2 UAV components
3.5.3 Environmental applications of UAS
3.5.4 State of the art for UASs
3.6 Future directions and Earth observation in Europe
3.6.1 Copernicus
3.6.2 Earth Explorers
3.6.3 Meteorology
3.7 Summary and remarks
Acknowledgments
References
Chapter-4---Geographic-information-sys_2021_Pollution-Assessment-for-Sustain
4 . Geographic information system: spatial data structures, models, and case studies
4.1 Introduction
4.2 General information organization and data structure
4.2.1 Data and information
4.3 Geographic data and geographic information
4.3.1 Information organization
4.3.2 Data perspective
4.4 Information organization of graphical data
4.4.1 Levels of data abstraction
4.4.2 Relationship perspective of information organization
4.4.3 Spatial relationships
4.5 The operating system perspective of information organization
4.5.1 The application architecture perspective of information organization
4.6 Fundamental concepts of data
4.6.1 Spatial versus nonspatial data
4.6.2 Databases for spatial data
4.6.3 Data models and modeling
4.7 Case studies
4.7.1 Case 1: application of geographic information system–based spatial analyses in soil chemistry, Colorado, United States
4.7.2 Case 2: land use classification in Al-Qassim region, Saudi Arabia
4.7.3 Case study 3: delineation of copper mineralization ones at Wadi Ham, northern Oman Mountains, using multispectral Landsat 8 ...
4.7.3.1 Site characteristics
4.7.3.2 Image processing of Landsat 8 data
4.7.3.3 Spectral characteristics analysis
4.7.3.4 Mineralization: delineation and mapping
4.8 Summary and concluding remarks
References
Further reading
Chapter-5---Geophysi_2021_Pollution-Assessment-for-Sustainable-Practices-in-
5 . Geophysical methods
5.1 Introduction
5.2 Electrical resistivity methods
5.2.1 Electrical resistivity theory
5.2.2 Electrical properties
5.2.3 Field procedures
5.2.4 Electrode configurations
5.2.5 Interpretation methods
5.3 Electromagnetic methods
5.3.1 Basic theory
5.4 Electromagnetic techniques
5.4.1 Frequency domain methods
5.4.2 Time domain methods
5.4.3 Natural source methods
5.4.4 Interpretation methods
5.5 Seismic methods
5.5.1 Basic theory
5.5.2 Seismic energy amplitude loss
5.5.3 Seismic sources and receivers
5.5.4 Seismic surveys
5.5.5 Seismic refraction
5.5.6 Seismic reflection
5.5.7 Surface waves
5.6 Ground-penetrating radar
5.6.1 Basic theory
5.6.2 Field procedures and data processing
5.6.3 Interpretation
5.7 Gravity and magnetic methods
5.7.1 Gravity theory
5.7.2 Gravity field procedures
5.7.3 Gravity data processing
5.7.4 Magnetic theory
5.7.5 Earth's magnetic field
5.7.6 Magnetic field procedures
5.7.7 Magnetic data processing
5.7.8 Material properties
5.7.9 Gravity and magnetic interpretation techniques
5.7.10 Data presentation
5.7.11 Magnetic anomaly shapes
5.7.12 Regional and residual gravity anomalies
5.7.13 Data enhancement
5.7.14 Modeling
5.8 Summary and concluding remarks
References
Further reading
Chapter-6---Site-in_2021_Pollution-Assessment-for-Sustainable-Practices-in-A
6 . Site investigation
6.1 Introduction
6.2 Site investigation approach
6.3 Phase I investigations
6.3.1 Collecting information
6.3.1.1 Sources of information on site history
6.3.1.2 Geologic and hydrogeologic information
6.3.1.3 Hydrologic information
6.3.2 Field reconnaissance
6.3.3 Development of a conceptual model
6.3.4 Establishing the work plan
6.4 Phase II investigations
6.5 Geophysical techniques
6.6 Hydrogeological investigations
6.6.1 Drilling methods
6.6.1.1 Hollow-stem auger
6.6.1.2 Solid-stem auger
6.6.1.3 Cable-tool drilling
6.6.1.4 Air-rotary drilling
6.6.1.5 Air-percussion rotary or down-hole hammer
6.6.1.6 Reverse circulation drilling
6.6.1.7 Hydraulic rotary
6.6.2 Sampling methods
6.6.2.1 Drill cutting samples
6.6.2.2 Core samples
6.6.3 Well installation techniques
6.6.3.1 Drive point wells
6.6.3.2 Individual wells
6.6.4 Monitoring well design components
6.6.4.1 Diameter
6.6.4.2 Casing and screen material
6.6.4.3 Sealing materials
6.6.4.4 Screen length and depth of placement
6.6.4.5 Location and number
6.6.5 Well decontamination procedures
6.7 Hydrogeochemical investigation
6.7.1 Subsurface environment
6.7.1.1 pH and alkalinity
6.7.1.2 Redox potential
6.7.1.3 Salinity and dissolved constituents
6.7.1.4 Soil matrix
6.7.1.5 Temperature and pressure
6.7.1.6 Microbial activity
6.7.2 Sampling considerations
6.7.2.1 Sampling location
6.7.2.2 Sampling frequency
6.7.2.3 Sample type and size
6.7.2.4 Vadose zone sampling
6.7.2.5 Groundwater sampling
6.8 Geochemical data collection
6.8.1 Sources of errors
6.8.1.1 Field errors
6.8.1.2 Analytical errors
6.8.1.3 Indirect measurement
6.8.1.4 Data handling
6.8.2 Sampling methods and types
6.9 Geochemical data analysis
6.10 Case study I: landfill site investigation: Phase 1: assessment of the geoengineering conditions
6.10.1 Introduction
6.10.2 Geotechnical investigation
6.10.3 Geomechanical analysis
6.10.3.1 Settlement analysis based on relative density measurements
6.10.3.2 Settlement analysis based on plate bearing test results
6.11 Conclusion
6.12 Case study I: landfill site investigation: Phase 2: assessment of the geoenvironmental conditions
6.12.1 Introduction
6.12.2 Monitored boreholes
6.12.3 Results and discussion
6.12.3.1 Gas analysis
6.12.3.2 Water analysis
6.12.3.2.1 Groundwater from installed wells
6.12.3.2.2 House water tanks
6.12.3.2.3 House wells
6.12.3.2.4 Possible migration pathway
6.12.4 Conclusion
6.13 Case study II: assessment of land salinization spread in arid lands
6.13.1 Spectral response of salt-affected soils
6.13.2 The reflectance spectra of gypsum and halite
6.13.3 Remote sensing data and techniques
6.13.4 Temporal variations of land-cover and landscape features
6.13.5 Remote detection of secondary salinity
6.13.6 Hyperspectroscopy
6.14 Summary and concluding remarks
References
Further reading
Chapter-7---Subsurface-p_2021_Pollution-Assessment-for-Sustainable-Practices
7 . Subsurface pollutant transport
7.1 Introduction
7.2 Modeling process
7.3 Transport mechanisms in soil
7.3.1 Advection
7.3.2 Diffusion
7.3.2.1 Effects of soil properties on Ds
7.3.3 Dispersion
7.3.4 Sorption
7.4 Transport equation
7.5 Solute transport models
7.5.1 Conservative tracer
7.5.2 Reactive chemical species
7.5.3 Spill of pollutants
7.5.4 Pollutant plume
7.6 Mass transfer limitations during pollutant transport
7.6.1 Single-rate mass transfer approach
7.6.2 Multirate mass transfer approach
7.7 Experimental determination of adsorption characteristics
7.7.1 Batch method
7.7.2 Circulation-through-column method
7.7.3 Column method
7.7.3.1 Moment analysis
7.7.3.2 Curve fitting
7.8 Modeling of pollutant transport using second postulate of irreversible thermodynamics
7.8.1 Aqueous phase liquid (APL) transport
7.8.2 Nonaqueous phase liquid transport
7.8.2.1 Saturated condition
7.8.2.2 Unsaturated conditions
7.9 Advanced modeling: the stochastic approach
7.10 Summary and concluding remarks
References
Further reading
Chapter-8---Indoor-air-quality--po_2021_Pollution-Assessment-for-Sustainable
8 . Indoor air quality: pollutants, health effects, and regulations
8.1 Introduction
8.2 Indoor air quality
8.3 Sources and characteristics of major IAPS
8.3.1 Volatile organic compounds
8.3.2 Formaldehyde
8.3.3 Particulate matter
8.3.4 Nitrogen dioxide
8.3.5 Carbon dioxide
8.3.6 Carbon monoxide
8.3.7 Ozone
8.3.8 Radon
8.3.9 Airborne biological pollutants
8.3.9.1 Bacteria and fungi
8.3.9.2 House dust mites
8.4 Other related studies on the health effects of IAPs
8.5 Sampling and measurements of IAPs
8.5.1 Data collection and regulations
8.5.2 Criteria for sampling locations and duration
8.5.2.1 Spatially average measurements
8.5.2.2 Sampling for spatial average indoor concentration
8.5.3 Methods of sampling
8.5.3.1 Active and passive air sampling
8.5.3.2 Whole-air sampling
8.6 Influence of outdoor air pollution on IAQ
8.7 Measures to minimize entry of outdoor polluted air indoors
8.8 IAQ guidelines and building regulations
8.9 Sick building syndrome, green buildings, and wellbeing
8.9.1 Sick building syndrome
8.9.2 Green buildings and wellbeing
8.10 Summary and conclusions
References
Further reading
Chapter-9---Outdoor-air-pollutants--sour_2021_Pollution-Assessment-for-Susta
9 . Outdoor air pollutants: sources, characteristics, and impact on human health and the environment
9.1 Introduction
9.2 Sources of outdoor air pollutants
9.2.1 Natural sources
9.2.2 Man-made sources
9.2.3 Concentration of air pollutants in the outdoor
9.3 Categories of air pollutants
9.3.1 Criteria pollutants
9.3.2 Air toxics and other air pollutants
9.3.3 Stratospheric ozone
9.4 Anthropogenic emissions inventory by sector
9.5 Air pollutant main indicators
9.5.1 Particulate matter
9.5.1.1 Composition and emission
9.5.1.2 Human health effects
9.5.1.3 Environmental effects
9.5.2 Ozone
9.5.2.1 Formation
9.5.2.2 Human health effects
9.5.2.3 Environmental effects
9.5.3 Nitrogen dioxide
9.5.3.1 Sources
9.5.3.2 Human health effects
9.5.3.3 Environmental effects
9.5.4 Carbon monoxide
9.5.4.1 Sources
9.5.4.2 Human health effects
9.5.4.3 Environmental effects
9.5.5 Sulfur dioxide (SO2)
9.5.5.1 Sources
9.5.5.2 Human health effects
9.5.5.3 Environmental effects
9.6 Air toxics
9.6.1 Nonvolatile metals
9.6.1.1 Sources
9.6.1.2 Human health effects
9.6.1.3 Environmental effects
9.6.2 Acid aerosols
9.6.3 Volatile metals
9.6.4 Fluoride
9.6.5 Polycyclic aromatic hydrocarbons
9.6.6 Biological pollutants
9.6.7 Bushfire smoke
9.6.8 Dust storm
9.6.9 Blast fumes
9.6.10 Mine dust
9.6.11 Coal burning
9.7 Monitoring and measurement
9.8 Monitoring of air pollutants in the United Arab Emirates
9.9 Global environmental impact of climate change
9.9.1 Causes of climate change
9.9.2 Economic impact of climate change
9.9.3 Environmental impacts of climate change
9.9.4 Control of global temperature rise
9.10 Summary and concluding remarks
References
Further reading
Chapter-10---Modeling-air-pol_2021_Pollution-Assessment-for-Sustainable-Prac
10 . Modeling air pollution by atmospheric desert
10.1 Introduction
10.2 Atmospheric chemistry–climate model
10.3 Atmospheric dust chemistry
10.4 Sensitivity of dust removal to chemical aging
10.5 Climate forcing of aeolian dust
10.6 Public health impacts of aeolian dust
10.7 Summary and concluding remarks
References
Chapter-11---Tropospheric-air-p_2021_Pollution-Assessment-for-Sustainable-Pr
11 . Tropospheric air pollution—aviation industry's case
11.1 Introduction
11.2 Aviation and greenhouse gas emissions
11.2.1 Aviation and carbon dioxide
11.2.2 Aviation emission inventories
11.2.3 Aviation and environmental impact
11.3 European Union Emissions Trading System
11.4 Aviation CO2 management
11.4.1 Aviation CO2 emissions calculation
11.4.2 Data planning and reporting
11.4.3 Annual greenhouse gas index
11.4.4 Aviation's climate impact
11.5 Carbon cycle and climate system
11.5.1 The slow carbon cycle
11.5.1.1 Chemical weathering
11.5.1.2 Heat and pressure
11.5.1.3 Animal and plant organic matter
11.5.1.4 Natural processes
11.5.1.5 Marine environment
11.5.2 The fast carbon cycle
11.5.3 Effects of changing the carbon cycle
11.6 Monitoring techniques
11.6.1 Monitoring types
11.6.2 Infrared absorption characteristics of gases
11.6.3 Commercial gas sensors
11.7 Greenhouse gas remote sensing instruments
11.7.1 Satellite instruments
11.7.1.1 Atmospheric Infrared Sounder
11.7.1.2 Orbiting Carbon Observatory
11.7.1.3 CO2 sounder lidar
11.7.2 Airborne instruments
11.7.2.1 Airborne laser isotope spectrometer
11.7.2.2 Aircraft laser infrared absorption spectrometer
11.7.2.3 Atmospheric vertical observations of CO2 in earth's troposphere
11.7.2.4 CO2 laser absorption spectrometer
11.7.2.5 Differential absorption carbon monoxide measurement
11.7.2.6 Nondispersed infrared airborne CO2 detector
11.7.2.7 Tropospheric ozone and tracers sensor
11.7.2.8 Atmospheric remote sensing instrument
11.8 Summary and concluding remarks
References
Further reading
Chapter-12---Health-econom_2021_Pollution-Assessment-for-Sustainable-Practic
12 . Health economics of air pollution
12.1 Introduction
12.2 Definition of air pollutants
12.3 Causes of air pollution
12.3.1 Effects of air pollution on health: epidemiological indication
12.3.2 Monitoring of air pollution: air-quality index
12.3.3 Policy in preventing air pollution
12.4 Effects of air pollution on health: the economic evidence
12.4.1 Health and life: the valuation
12.4.2 Value of a statistical life: the ordinary method for calculating mortality cost
12.4.3 VSL for each country and intracommunity and international equity
12.4.4 Severity and persistence of air pollution
12.5 Impacts of policy: an empirical approach
12.5.1 Practice and contemplation: economic evaluation
12.5.2 Sectoral technical evidence and its limits
12.5.3 Costs and effects of air quality: the assessment
12.5.4 “Price+expenditure+environment”: the rational structure
12.5.5 “Pricing, expenditure, and environment”: the proof of productivity
12.5.6 “Pricing, expenditure, and environment”: the chronological framework
12.6 Summary and concluding remarks
References
Further reading
Chapter-13---A-decision-support-system-for-rankin_2021_Pollution-Assessment-
13 . A decision support system for ranking desalination processes in the Arabian Gulf Countries based on hydrodynamic modeling e ...
13.1 Introduction
13.2 Impact of climate change and coastal effluents on seawater salinity and temperature
13.2.1 Seawater salinity and temperature
13.2.2 Seawater quality impacts on desalination
13.2.3 Climate variability
13.2.4 Long-term response simulation to climate change and coastal effluents
13.2.4.1 Mathematical modeling
13.2.4.2 Long-term observations
13.2.4.3 Statistical analysis
13.2.4.4 Far-field hydrodynamics modeling
13.2.4.5 Far-field and particle tracking
13.2.4.6 Coupling near- and far-field hydrodynamics
13.3 Data use
13.3.1 Area description
13.3.2 Baseline hydrology
13.3.3 Water resources
13.4 Hydrodynamic modeling
13.4.1 Model description
13.4.2 Model setup and calibration
13.4.2.1 Domain and grid resolution
13.4.2.2 Initial and boundary conditions
13.4.2.3 Model simulation design
13.4.2.4 Heat flux and evaporation
13.4.2.5 River input
13.4.2.6 Physical parameters
13.4.2.7 Numerical parameters
13.4.3 Model validation
13.4.3.1 Tide
13.4.3.2 Currents
13.4.3.3 Salinity and temperature
13.4.3.4 Evaporation
13.5 Environmental impacts due to climate change and costal effluents
13.5.1 Input data preparation for model simulation
13.5.2 Future scenarios
13.5.2.1 Salinity
13.5.2.2 Temperature
13.6 Impact of seawater salinity and temperature on performance of desalination processes
13.6.1 Decision support matrix
13.6.1.1 Thermal response to seawater salinity and temperature changes
13.6.1.2 Reverse osmosis response to seawater salinity and temperature changes
13.6.2 Decision support matrix approach
13.6.2.1 Salinity–decision support matrix
13.6.2.2 Temperature–decision support matrix
13.6.3 Evaluating long-term impact of salinity and seawater temperature changes on desalination performance
13.6.3.1 Least negatively impacted ranking
13.6.3.2 Projected results for Al Quwain, United Arab Emirates
13.6.4 Projected results in other gulf desalination plants
13.7 Summary and concluding remarks
References
Further reading
Chapter-14---Recent-analytical-methods_2021_Pollution-Assessment-for-Sustain
14 . Recent analytical methods for risk assessment of emerging contaminants in ecosystems
14.1 Introduction
14.1.1 What are emerging contaminants?
14.1.2 Human impact on the environment
14.1.3 Major sources of emerging contaminants
14.2 Emerging contaminants in the environment
14.2.1 Classes of emerging contaminants
14.2.2 Concentrations of emerging contaminants in the ecosystem
14.2.2.1 Pharmaceuticals and personal care products
14.2.2.2 Disinfection by-products
14.2.2.3 Perfluorinated compounds
14.2.2.4 Polybrominated diphenyl ethers
14.2.2.5 Benzotriazoles and dioxane
14.3 Emerging contaminants and regulatory considerations
14.4 Sample collection techniques for emerging contaminants
14.4.1 Considerations in selecting sampling matrices
14.4.2 Sampling techniques
14.4.2.1 Water sampling
14.4.2.2 Sediment sampling
14.4.2.3 Biota sampling
14.4.2.4 Air sampling
14.5 Sample preparation, extraction, and cleanup
14.5.1 Advances in sample preparation
14.5.2 Extraction methods for environmental matrices
14.5.2.1 Extraction from water samples
14.5.2.2 Extraction from sediment/soil samples
14.5.2.3 Extraction from biota samples
14.5.2.4 Extraction from air samples
14.5.3 Cleanup methods
14.6 Instrumental analytical methods
14.6.1 Analytical considerations
14.6.2 Overview of common analytical methods
14.6.2.1 Liquid chromatography methods
14.6.2.2 Gas chromatography methods
14.6.2.3 Nuclear magnetic resonance spectroscopy methods
14.6.3 Latest analytical methods
14.6.3.1 Disinfection by-products
14.6.3.2 Pharmaceuticals and personal care products
14.6.3.3 Benzotriazoles and dioxane
14.6.3.4 Polybrominated diphenyl ethers
14.6.3.5 Polyfluorinated compounds
14.7 Summary and concluding remarks
Acknowledgments
References
Further reading
Chapter-15---Water-quality-at-Jebe_2021_Pollution-Assessment-for-Sustainable
15 . Water quality at Jebel Ali Harbor, Dubai, United Arab Emirates
15.1 Introduction
15.2 Site description
15.3 Review of previous studies of harbor water
15.4 Study approach
15.5 Previous records
15.6 Sample collection and analysis
15.6.1 Sampling locations
15.6.2 Selection of test parameters
15.7 Discharged treated wastewater
15.7.1 Treatment processes employed before discharge
15.7.1.1 Wastewater treatment at EPCL
15.7.1.2 Wastewater treatment at Gulf Food Industries
15.7.1.3 Wastewater treatment at Gulf Denim
15.7.1.4 Wastewater treatment at Emirates Can
15.7.1.5 Sewage treatment plants
15.7.2 Characteristics of discharged treated wastewater
15.7.2.1 General characteristics
15.7.2.2 Fluoride and cyanide
15.7.2.3 Organic matter
15.7.2.4 Nutrients
15.7.2.5 Metallic impurities
15.7.2.6 Trace organic compounds (organic pollutants)
15.7.2.7 Coliform bacteria
15.7.3 Discharges from other sources
15.7.3.1 Discharged cooling water
15.7.3.2 Stormwater
15.7.3.3 Other possible discharges
15.7.4 Impact of discharge sources on harbor water
15.8 Harbor water quality
15.8.1 General characteristics of harbor water
15.8.1.1 Temperature
15.8.1.2 pH
15.8.1.3 Dissolved and suspended solids
15.8.1.4 Anions
15.8.1.5 Dissolved oxygen
15.8.1.6 Organic matter
15.8.1.7 Nutrients
15.8.1.8 Metallic impurities
15.8.1.9 Trace organic compounds
15.8.1.10 Biological characteristics
15.8.2 Variations in parameters with depth
15.8.3 Harbor water quality status
15.9 Summary and concluding remarks
15.10 Recommendations
Acknowledgment
References
Chapter-16---Sediment-quality-at-Je_2021_Pollution-Assessment-for-Sustainabl
16 . Sediment quality at Jebel Ali Harbor, Dubai, United Arab Emirates
16.1 Introduction
16.2 Previous records
16.3 Methodologies
16.3.1 Sampling locations
16.3.1.1 Selection of test parameters
16.4 Results and discussion
16.4.1 Sediment properties
16.4.2 General characteristics of harbor sediments
16.4.3 Organic matter
16.4.4 Metallic impurities
16.4.5 Trace organic compounds
16.5 Harbor sediment quality assessment
16.6 Conclusion
16.7 Recommendations
Acknowledgment
References
Chapter-17---Inland-desalination--tec_2021_Pollution-Assessment-for-Sustaina
17 . Inland desalination: techniques, brine management, and environmental concerns
17.1 Introduction
17.2 Desalination capacity
17.3 Conventional desalination techniques
17.3.1 RO technique
17.3.2 ED technique
17.3.3 MSF technique
17.3.4 MED technique
17.4 Emerging desalination technologies
17.4.1 Technologies based on novel membranes
17.4.2 Vapor compression distillation
17.4.3 Semibatch RO
17.4.4 Forward osmosis
17.4.5 Reverse electrodialysis
17.4.6 Membrane distillation
17.4.7 Humidification–dehumidification
17.4.8 Adsorption desalination
17.4.9 Pervaporation
17.4.10 Microbial desalination cells
17.4.11 Ion concentration polarization
17.4.12 Capacitive deionization
17.4.13 Clathrate hydrates
17.4.14 Supercritical water desalination
17.4.15 Hybrid systems
17.5 Brine characteristics
17.6 Brine management
17.6.1 Evaporation ponds and energy recovery
17.6.2 Deep well injection
17.6.3 Freeze
17.6.4 Discharge to sewage network
17.6.5 Reuse
17.6.6 Zero liquid discharge
17.6.7 Salt recovery
17.7 Environmental issues
17.7.1 Brine disposal
17.7.2 GHG emissions
17.7.3 Noise
17.8 Environmental assessment
17.8.1 Environmental impact assessment
17.8.2 Environmental lifecycle assessment
17.9 Summary and concluding remarks
References
Further reading
Chapter-18---Pollution-asse_2021_Pollution-Assessment-for-Sustainable-Practi
18 . Pollution assessment of nanomaterials
18.1 Introduction
18.2 Nanomaterials and nanoparticles
18.2.1 Categories
18.2.2 Classes
18.2.2.1 Metal oxides
18.2.2.2 Carbon products
18.2.2.3 Metals
18.2.2.4 Zero-valent metals
18.2.2.5 Quantum dots
18.2.2.6 Nanoclays
18.2.2.7 Polymers
18.2.2.8 Emulsions
18.3 Physicochemical properties
18.3.1 Crystallinity
18.3.2 Composition
18.3.3 Particle size
18.3.4 Aspect ratio
18.3.5 Surface area
18.3.6 Reactivity
18.3.7 Surface charge
18.3.8 Zero point of charge
18.3.9 Solubility
18.3.10 Degradation/persistence
18.3.11 Biodegradation
18.4 The life cycle of ENMs
18.5 The transport of ENMs
18.5.1 Transport in the atmospheric environment
18.5.2 Transport in the hydrosphere environment
18.5.3 Transport in the biosphere (soil) environment
18.5.4 Transport in plants
18.6 The fate of ENMs in environmental ecosystems
18.6.1 The fate of ENMs in the atmosphere environment
18.6.2 The fate of ENMs in the hydrosphere environment
18.6.3 The fate of ENMs in the biosphere environment
18.6.4 The fate of ENMs in the human body
18.6.5 The fate of ENMs in animals
18.6.6 The fate of ENMs in plants
18.7 Bioavailability and toxicity
18.7.1 Bioavailability
18.7.2 Toxicity
18.8 Regulations and standards
18.8.1 The United States
18.8.2 Canada
18.8.3 Japan
18.8.4 The Netherlands
18.8.5 Switzerland
18.8.6 Denmark
18.8.7 Germany
18.9 Risk assessment methods and future directions
18.10 Summary and concluding remarks
References
Chapter-19---Noise-pollution-and-it_2021_Pollution-Assessment-for-Sustainabl
19 . Noise pollution and its impact on human health and the environment
19.1 Introduction
19.2 Noise fundamentals
19.2.1 Differences in sound levels and decibels
19.2.2 Equivalent continuous sound levels
19.2.3 Sound pressure
19.2.4 A-Weighting scale
19.3 Overview of noise pollution problem
19.4 Policy and standards
19.4.1 World Health Organization
19.4.2 United States
19.4.3 European Commission
19.4.4 India
19.5 Noise exposure sources
19.5.1 Aircraft noise exposure
19.5.2 Road traffic and railway noise exposure
19.5.3 In-vehicle noise exposure
19.5.4 Worksite noise exposure
19.5.5 Construction site noise exposure
19.5.6 Occupational and household noise exposure
19.6 Noise pollution impact
19.6.1 Human health impact
19.6.1.1 Hearing loss
19.6.1.2 Tinnitus
19.6.1.3 Sleeping disorders
19.6.1.4 Annoyance and stress
19.6.1.5 Cardiovascular effects
19.6.1.6 Cognitive impairment in children
19.6.2 Health impact on animals
19.6.2.1 Impact on animals’ communication
19.6.2.2 Animal vocal adjustment to noise pollution
19.6.2.3 Stressor impact on animals
19.6.2.4 Impact on acoustic diversity
19.7 Identification methods for regional noise-affected habitats
19.7.1 Modeling results in unprotected land environment
19.7.2 Modeling results in protected land environment
19.7.3 Modeling results in marine environment
19.8 Noise control measures and sustainability
19.8.1 Sustainable building design
19.8.2 Noise mapping
19.8.3 Control measures
19.8.3.1 Use of barriers and berms along roadside
19.8.3.2 Use of acoustic building materials
19.8.3.3 Roadway vehicle noise source control
19.8.3.4 Road surface and pavement material control
19.8.3.5 Public awareness and education
19.8.3.6 Legislation
19.9 Environmental noise pollution management
19.9.1 Noise management categories
19.9.2 Health-related outcomes of remedial measures
19.10 Summary and concluding remarks
References
Further reading
Chapter-20---Assessment-of-radiat_2021_Pollution-Assessment-for-Sustainable-
20 . Assessment of radiation pollution from nuclear power plants
20.1 Introduction
20.2 Radioactive decay
20.3 Environmental radiation
20.4 Sources and types of radwaste
20.4.1 Low-level radioactive waste
20.4.2 Intermediate-level radioactive waste
20.4.3 High-level radioactive waste
20.4.4 Wastes from decommissioning nuclear plants
20.4.5 Legacy wastes
20.5 Geologic disposal of high-level radioactive waste
20.5.1 Outer space
20.5.2 Subduction zones
20.5.3 Ice caps
20.5.4 Geologic isolation on land
20.5.5 Reservoir rock types for geologic isolation
20.5.5.1 Shale
20.5.5.2 Salt vaults
20.5.5.3 Volcanic tuffs
20.5.5.4 Crystalline rock cavities
20.6 Future challenges
20.7 Environmental effects of nuclear power
20.7.1 Radioactive waste
20.7.2 Thermal discharge
20.7.3 Gaseous releases
20.7.4 Milling, mining, and enrichment issues
20.7.5 Accidents, terrorism, and cost issues
20.8 Nuclear regulations
20.8.1 International atomic energy agency
20.8.2 The nuclear energy agency
20.9 Nuclear power plant accidents and incidents
20.10 Emission of radioactive materials
20.11 How dangerous is nuclear radiation?
20.12 Effects on human health
20.13 Case study I: Chernobyl, Ukraine
20.13.1 The chernobyl plant and site
20.13.2 The 1986 chernobyl accident
20.13.3 Immediate impact
20.13.4 Environmental and health impacts
20.13.5 Progressive closure of the plant
20.13.6 Chernobyl today
20.13.7 Lessons learned
20.14 Case study II: Fukushima, Japan
20.14.1 The nuclear accident
20.14.2 Fukushima Daiichi reactors
20.14.3 Radioactive release and contamination
20.14.4 Public health and return of evacuees
20.14.5 Recovery and on-site remediation
20.14.6 Current status
20.15 Nuclear safety
20.16 Summary and concluding remarks
References
Chapter-21---Artificial-intelligence-and-_2021_Pollution-Assessment-for-Sust
21 . Artificial intelligence and data analytics for geosciences and remote sensing: theory and application
21.1 Introduction
21.2 Machine learning applications
21.2.1 Mineral mining
21.2.2 Environmental monitoring
21.2.3 Mineral exploration
21.3 Satellite images and Landsat hyperspectral data processing
21.3.1 Machine learning
21.3.2 Decision tree
21.3.3 Multiple-criteria decision analysis method PROAFTN
21.3.4 Hybrid classification model
21.4 Decision tree
21.4.1 Algorithm
21.4.2 Implementation in R
21.4.3 Model tree
21.5 PROAFTN method
21.5.1 Initialization
21.5.2 Fuzzy indifference relation
21.5.3 Membership evaluation
21.5.4 Categorization
21.5.5 PROAFTN learning
21.5.6 Determination of PROAFTN intervals
21.5.7 Classification model
21.5.8 Hybrid DT and PROAFTN
21.5.9 Classification model development
21.6 Case study I: hybrid DT and PROAFTN method utilization for soil classification from Landsat satellite images
21.6.1 Data description
21.6.2 Results
21.6.3 Summary
21.7 Case study II: java-based analytical method for mineral exploration at Flin Flon, Saskatchewan, Canada
21.7.1 Site description
21.7.2 Java systematic feature extraction tool and its structure
21.7.3 Data analysis
21.8 Summary and concluding remarks
References
Chapter-22---Lifecycle-ass_2021_Pollution-Assessment-for-Sustainable-Practic
22 . Lifecycle assessment of aquaponics
22.1 Introduction
22.2 Aquaponic systems
22.2.1 Mechanism of aquaponics cycle
22.2.2 Main components of aquaponics
22.2.3 Types of aquaponic systems
22.2.3.1 Aquaponic system inputs and outputs
22.2.4 Aquaponic system water management
22.2.5 Types of products of aquaponic systems
22.2.6 Coupled versus decoupled systems
22.3 Assessment of aquaponic systems
22.3.1 Sustainability in aquaponics
22.3.2 Types of assessment
22.3.2.1 Environmental sustainability
22.3.2.2 Economic sustainability
22.3.2.3 Social sustainability
22.3.2.4 Overall sustainability assessment
22.4 Challenges and recommendations
22.5 Concluding remarks
Acknowledgments
References
Index_2021_Pollution-Assessment-for-Sustainable-Practices-in-Applied-Science
Index
A
B
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D
E
F
G
H
I
J
K
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M
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O
P
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T
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Citation preview

Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering Edited by Abdel-Mohsen O. Mohamed Evan K. Paleologos Fares M. Howari

Butterworth-Heinemann is an imprint of Elsevier The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-809582-9 For information on all Butterworth-Heinemann publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Deans Acquisitions Editor: Carrie Bolger Editorial Project Manager: Rachel Pomery Production Project Manager: Poulouse Joseph Cover Designer: Mark Rogers

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I dedicate this work to my divine wife, who has provided me with endless support over the last 37 years, and to our two beloved daughters. Abdel-Mohsen O. Mohamed To my wife Cleo, for all the wonderful years; to Shlomo, for the continuous inspiration; and to the extraordinary group of friends I made at the Department of Hydrology in Tucson. Evan K. Paleologos This work is wholeheartedly dedicated to Suha, Natalie, Yousef, and Adam who shared their words of advice and encouragement. Thank you for your support! Fares M. Howari

Contributors Mohamed Abdelkader King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Feras Al-Obeidat College of Technological Innovations, Zayed University, Abu Dhabi, United Arab Emirates Saed Al Awadi Environment, Health and Safety Department, Ports, Customs and Free Zone Corporation, Dubai, United Arab Emirates Durra M. AlBlooshi College of Natural and Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates Ayub Ali City of Gold Coast, Surfers Paradise, Queensland, Australia Marina Astitha University of Connecticut, CT, Storrs, United States Neil Banerjee Faculty of Science, Western University, London, Canada Mahmoud Bataineh Department of Chemistry, Trent University, Peterborough, ON, Canada R. Alberto Bernabeo College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates Angeliki Boura Ministry of Foreign Affairs, Athens, Greece Vasileios Dimitropoulos Business Planning & Reporting, European Investment Bank, Luxembourg Abubaker A. Elhakeem Dubai Municipality, Dubai, United Arab Emirates Walid A. Elshorbagy College of Engineering University of Arizona, Tucson, AZ, United States Christine M.J. Gallampois Department of Chemistry, Umea˚ University, Umea˚, Sweden Habes Ghrefat College of Science, King Saud University, Reyadh, Kingdom of Saudi Arabia Waleed Hamza Biology Department, UAE University, Al Ain, United Arab Emirates

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Contributors

Fares M. Howari College of Natural and Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates Hassan D. Imran Department of Civil and Environmental Engineering, UAE University, Al Ain, United Arab Emirates Jibran Iqbal College of Natural and Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates Lina A. Kamareddine Department of Civil and Environmental Engineering, UAE University, Al Ain, United Arab Emirates Vlassis A. Karydis Max Planck Institute for Chemistry, Mainz, Germany Qasim Khan Civil and Environmental Engineering Department, UAE University, Al Ain, United Arab Emirates Niaz Khan Environment, Health and Safety Department, Ports, Customs and Free Zone Corporation, Dubai, United Arab Emirates Klaus Klingmu¨ller Max Planck Institute for Chemistry, Mainz, Germany Jos Lelieveld Max Planck Institute for Chemistry, Mainz, Germany; Cyprus Institute, Nicosia, Cyprus Munjed A. Maraqa Department of Civil and Environmental Engineering, UAE University, Al Ain, United Arab Emirates Farhi Marir College of Technological Innovations, Zayed University, Abu Dhabi, United Arab Emirates Constantine Mavrocordatos European Space Agency - Earth Observation Projects, Department ESA/ESTEC, Noordwijk, The Netherlands Nikolai A. Maximenko International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, HI, United States Stelios P. Mertikas Laboratory of Geodesy & Geomatics Engineering, School of Mineral Resources Engineering, Technical University of Crete, Chania, Crete, Greece Kevin Mickus Department of Geography, Geology and Planning, Missouri State University, Springfield, MO, United States

Contributors

Abdel-Mohsen O. Mohamed Uberbinder, Inc., Seattle, WA, United States; EX Scientific Consultants, Abu Dhabi, United Arab Emirates Evan K. Paleologos College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates Katrina E. Paleologos College of Biological Sciences, University of Minnesota, Twin Cities, MN, United States Panagiotis Partsinevelos Sense Lab Research, School of Mineral Resources Engineering, Technical University of Cret, Chania, Crete, Greece Madduri V. Rao Quality and Laboratory Development, Precision Scientific Laboratories, Dubai, United Arab Emirates Vale´ria G.S. Rodrigues Sa˜o Carlos Engineering School, University of Sa˜o Paulo, Sa˜o Carlos, Brazil Aristeidis Samitas College of Business, Zayed University, Abu Dhabi, United Arab Emirates Emma L. Schymanski Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-surAlzette, Luxembourg Mohamed Y.E. Selim College of Engineering, UAE University, Al-Ain, Abu Dhabi, United Arab Emirates Costas Siriopoulos College of Business, Zayed University, Abu Dhabi, United Arab Emirates

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About the editors Abdel-Mohsen O. Mohamed earned his PhD from McGill University, Canada, where he was later employed as Associate Director of the Geotechnical Research Centre and lecturer at the Department of Civil Engineering and Applied Mechanics. He has held many senior positions in the United Arab Emirates (UAE), including Associate Provost and Chief Academic Officer at Zayed University, Dean of Research and Graduate Studies at Abu Dhabi University, and Research Director at the UAE University. He has 13 patents in areas of sustainable use of elemental sulfur and alkaline solid wastes, production of sulfur cement and concrete, carbon sequestration and utilization for the treatment of solid wastes, and stabilization of sand dunes. He has authored and coauthored 8 books, edited another 12 books, and published over 280 papers in refereed journals and international conference proceedings. He has been the recipient of several university and nationwide research accolades. He is the General Managing Director of EX Scientific Consultants, Abu Dhabi, UAE, Senior Advisor, Uberbinder, Inc., Seattle, Washington, USA, and an editorial board advisor for a number of scientific journals. SCOPUS ID: 7402739291; https://orcid.org/0000-0002-0971-6940; Research gate profile: https://www.researchgate.net/profile/Abdel_Mohsen_Mohamed/scores. Evan K. Paleologos is Professor and Chair of Civil Engineering at Abu Dhabi University, United Arab Emirates (UAE). He received his PhD from the University of Arizona, Tucson, AZ, USA. His expertise is in the flow of water and the transport of contaminants in porous media. Prior to the UAE he worked for the US high-level nuclear waste management program at Yucca Mountain, Nevada, later becoming tenured Associate Professor at the University of South Carolina, Columbia, SC, USA, where he was also Director of the Graduate Program in Environmental Geosciences and faculty of the Honors College. He subsequently moved to Greece at the Technical University of Crete and while there he also served as Science Advisor to the Greek Minister of the Environment and Deputy Chairman of the Board of EYDAP, the city of Athens Water Supply and Sewerage Company. He is coauthor of four books on environmental risk analysis and geoenvironmental engineering and of over 100 refereed papers. He is the recipient of several awards in the United States, Greece, and the UAE and is currently the associate editor of two international scientific journals. ORCID: https://orcid.org/0000-0002-3582-2288; Research gate profile: https://www.researchgate.net/profile/Evan_Paleologos. Fares M. Howari is Professor of Environmental Sciences and Dean of College of Natural and Health Sciences at Zayed University. He served as Professor and Chair of the Department of Applied Sciences and Mathematics at Abu Dhabi University as well as a director of Abu Dhabi University Center of Excellence of Environment, Health and Safety. He is a water resources and environmental scientist with research development and administration expertise. He served as a professor of environmental sciences and coordinator of the Environmental Sciences Program at the University of Texas, PB. He also joined the Center for International Energy and Environmental Policy as program coordinator,

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About the editors

and as an environmental scientist at the Bureau of Economic Geology at the University of Texas at Austin. He has valuable experience in management, strategic planning and administration, and creative leadership. ORCID: https://orcid.org/0000-0002-8655-5810; Research gate profile: https://www.researchgate.net/profile/Fares_Howari2.

Preface It has been over 30 years since the landmark Brundtland Report introduced sustainability as the obligation of a present generation to deliver to the next a state of affairs in the environment that would allow this to prosper and enjoy a quality of life not hampered by present-day activities aiming at short-term benefits. Fundamental to striving for sustainable development is the advancement and application of environmentally responsible waste and pollution assessment methods, remediation technologies, and management systems. Assessment of pollution in a given medium (air, water, and soil) may be conducted under a wide variety of conditions and at varying scales and levels of sophistication. As a result, assessments can produce a wide range of outcomes. Problem conditions may range from simple settings with almost self-evident identification of one or two key hazards, to highly complex urban and industrial settings with diverse human activities, where the source, location, and inception of pollution can be difficult to identify. This, in many cases, is coupled with complexities in the geological, hydrogeological, geochemical, and biological site conditions, the presence and need for protection of valuable ecosystems, the potential for far-field and/or long-term health implications, and numerous other considerations that render vulnerability assessments very demanding. This broad range of complexity, uncertainty, and needs means that assessments of pollution require a range of sophisticated tools and techniques, from field surveys, say, of major sources of groundwater pollution to detailed surveys of chemical or microbiological pollutant loads, to modeling of, for example, the leaching potential of pesticides used in a catchment. This increased complexity of environmental problems has necessitated the creation of multidisciplinary groups from all fields of science, engineering, public health, economics, and social science, to work together to assess and provide answers to the multifaceted aspects and implications of environmental problems. This book has attempted to bring together teams of experts from different fields to provide the latest information on the methods and techniques through which pollution is assessed on land, air, and water. Thus the book is divided into the following parts: Part 1: Pollution Assessment in the Geosphere, in which we discuss sustainable pollution assessment practices; risk analysis and management; environmental remote sensing; geographic information systems; geophysical methods; site investigation; and subsurface pollutant transport; Part 2: Pollution Assessment in the Atmosphere, where we explore indoor and outdoor assessment of air pollutants; modeling of air pollution by atmospheric desert dust; troposphere air pollution; and health economics; Part 3: Pollution Assessment in the Hydrosphere, where we review the case study of the Arabian Gulf used to describe a decision support system for ranking desalination processes in the Arabian Gulf countries based on hydrodynamic modeling evaluation of future changes in feed water properties; analytical methods for risk assessment of emerging contaminants in ecosystems; water and sediment quality issues and techniques; brine management; and environmental concerns related to inland desalination; and Part 4: Emerging Issues in Environmental Pollution Assessment, where we look at emerging issues with expose´s of nanomaterial pollution; noise pollution and its impacts on human health and the environment; radiation pollution from nuclear power plants; the use of artificial intelligence and data analytics for geosciences and remote sensing applications; and finally lifecycle assessment of aquaponics.

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Part 1: Pollution Assessment in the Geosphere commences with Chapter 1 and concludes with Chapter 7. Chapter 1: Sustainable Pollution Assessment Practices, authored by Mohamed, A.M.O. and Paleologos, E.K., focuses on the broad assessment of pollution in the atmosphere, hydrosphere, geosphere, and biosphere within the context of sustainable development. The global environmental problems and the actions toward restoration of the environment are discussed. An overview of the impact from the exploitation and overutilization of natural systems, the deterioration of the environment through the disposal of ever-increasing solid and liquid waste volumes, the pollution of air and water, and the elimination of species, flora, and fauna are summarized with case studies from around the world. Some best-case studies that improve welfare, while not at the cost of environmental degradation or the well-being of current or future generations, are highlighted. The chapter also discusses evidence of global environmental impacts such as: global climate change; pollution of air, land, and water due to accidents during the transportation of oil or other products by ship; plastic debris in rivers and oceans, and effluent discharge into freshwater bodies; growing quantities of waste as a result of chemical product utilization in all human activities, from agriculture to medicine, to energy and industrial and manufacturing processes, to everyday products; and the decreasing species of wildlife. References and excerpts from important national and international conventions and legislation regarding these problems are also provided. Chapter 2: Risk Analysis and Management, authored by Paleologos, E.K. and Mohamed, A.M.O., presents an introduction to elements of risk analysis and risk management. Decision trees, payoff tables, and criteria to reach optimum decisions are presented, as well as some statistical measures that provide insights into decision-making. Furthermore, an exposition of topics of environmental economics is used to illustrate that, similar to lifecycle analysis, what is considered as the cost of environmental degradation in costebenefit analyses can influence decisions and lead to less or more environmentally friendly solutions. Subsequently, elements of utility theory are introduced to incorporate the attitude to risk of individuals and corporations into risk analyses. This part, which deals with decision-making analyses, concludes with an elementary exposition of Bayesian decision theory illustrated through a problem of air emissions from waste to energy facilities. The second part of this chapter discusses human health risk assessment for environmental hazards by detailing elements of hazard identification, exposure assessment, toxicity assessment, and risk characterization. Various models of exposure assessment and risk characterization are presented. The steps of risk management programs are described, as well as the role of regulatory agencies and their approaches to risk. Finally, the chapter concludes with a presentation of mitigation measures for pollution in soils that apart from technical and regulatory issues touches upon the efficiency of technological solutions, the economic cost to remediate, the degradation in the function of environmental systems, and the impact to the health of the population. Chapter 3: Environmental Applications of Remote Sensing, authored by Mertikas, S.P., Partsinevelos, P., Mavrocordatos, C., and Maximemko, N., provides a brief exposition of remote sensing applications in geosciences and engineering disciplines. Remote sensing, the science and technology through which characteristics and properties of targets on Earth can be determined from a distance, has provided systematic, dedicated, and repetitive observations of the planet’s surface, which include the atmosphere, water, land, living species, vegetation, pollution, and climate, from global to local scales. Satellite observations have contributed to the spectacular improvement of the accuracy of

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weather forecasts over the last few decades. Remote sensing has provided the means for detecting and quantifying the rates of pollution by mapping and monitoring sources of pollution and the degree of remediation for their management. It bears the means to respond and facilitate environmental management and supports making sound and evidence-based decisions in relation to Earth’s resources at a global scale and across different continents, nations, and domains. Remote sensing currently supplies essential Earth observations to protect irreplaceable resources and provides support for sustainable economic growth, disaster resilience, management of energy and mineral resources, food and water security, and sustainability. It provides powerful tools for understanding the past and present conditions of Earth systems and components, as well as the interplay between them. Thus remote sensing could assist us to solve environmental problems, address and mitigate risks, and deliver skillful predictions of the future behaviors of Earth systems (natural disasters, state of oceans, atmosphere, land, vegetation, food, public health, etc.). And by translating these Earth observations into decision-making, remote sensing could help mobilize actions to mitigate these effects to the benefit of the human race. A companion chapter to this is Chapter 21 on artificial intelligence and big data, as remote sensing and autonomous airborne systems rely on algorithms for, among others, navigational purposes and data interpretation. Chapter 4: Geographic Information System: Spatial Data Structures, Models, and Case Studies, authored by Howari, F.M. and Ghrefat, H. provides a basic overview and summary of basic concepts of a Geographic Information System (GIS). Initially, the general concepts related to information organization and data structure are briefly described and related to different GIS representations of real-world geographical data. Different perspectives of information organization are discussed, which include various types of GIS spatial relationships together with the underlying information organization structure. Case studies are presented to illustrate the above and to show GIS providing valuable insight into: screening and cleaning geochemical data to reveal patterns between chemical elements and their distribution in the soil; studying land cover and vegetation changes in parts of Saudi Arabia using Landsat TM and ETMþ data for the period 1990e2013; and detecting and mapping copper mineralization zones through the use of multispectral Landsat 8 (Operational Land Imager) data. Chapter 5: Geophysical Methods, authored by Mickus, K., reviews the use of geophysical methods in the assessment of subsurface pollution within soil, groundwater, sediment, and bedrock. To use any geophysical technique, one is required to understand a few basic steps, which include the theoretical basis and the data collection and interpretation methods. These processes, which are unique to each of the geophysical methods, are discussed and examples of the basic interpretation methods are demonstrated. Specifically, while geophysical methods do not identify the type of pollutant or determine the type of rock, soil, or sediment in the subsurface, they can define the physical properties (e.g., density, electrical resistivity, magnetic susceptibility, seismic velocity) of the subsurface environment, which can be subsequently interpreted to identify a pollutant plume and infer the nature of the soil, sediment, or bedrock. A wide variety of geophysical methods used in pollution studies, such as electrical resistivity, electromagnetics, ground penetrating radar, seismology, gravity, and magnetics, are discussed to demonstrate that depending on a problem’s specific conditions, one method may prove to be more appropriate than others. For a wide class of subsurface pollution problems, electrical resistivity, electromagnetics, and ground penetrating radar techniques appear to have an advantage in detecting contaminants within groundwater as they flow through soils, sediments, and

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bedrock, while the other methods are useful in determining additional subsurface features (e.g., depth to bedrock, location of cavities) that can be used in estimating the extent and risk of pollution. Chapter 6: Site Investigation, authored by Mohamed, A.M.O., Howari, F.M., Ghrefat, H. and Paleologos, E.K., explains the various phases in site investigation. Phase 1 investigations are preliminary in nature and are designed to furnish a comprehensive overview of available site information. Phase 2 investigations consist of site characterization and groundwater monitoring wells. Direct methods, such as boreholes, piezometers, and geotechnical analyses of soil samples, are discussed, while indirect methods that include aerial photography, ground penetrating radar, and earth conductivity and resistivity geophysical studies, are detailed. Issues in hydrogeochemical investigations and two case studies are presented, the first related to geoenvironmental investigations at a waste dump site, and the second involving land salinization assessment in arid lands. Field investigations at polluted sites present unique challenges in that they involve, in many cases, heterogeneous, anisotropic environments contaminated by a complex diversity of pollutants. Optimization of the monitoring systems employed involves elements such as network density, spacing, depth, and frequency of sampling, where conflicting priorities may exist in terms of installation and sampling cost, probability of detection, and remediation cost. Thus the highly variable nature of subsurface conditions makes it impossible to define general investigation strategies that would be appropriate in all cases, but careful consideration of site-specific conditions determines cost-effective field campaigns. No proper site remediation plan can be implemented unless the findings of the site and modeling investigations are used in concert with close field supervision during the remediation process. The concept of phased site investigation provides the basis for this approach to avoid remedial cost overruns. Finally, the chapter concludes with a case study that demonstrates that the integration of data at different scales, such as field and remote sensing data, can be used successfully, as in this case, for mapping saline soils. Chapter 7: Subsurface Pollutant Transport, authored by Mohamed, A.M.O., Paleologos, E.K., and Maraqa, M.A., is concerned with the exposition of pollutant transport models by presenting initially the basic physical mechanisms by which miscible (soluble) and immiscible (nonsoluble) pollutants are transported in the subsurface environment. The physical and chemical interaction mechanisms that govern the transport of organic and inorganic pollutants in the subsurface environment are examined. Single-rate and multirate mass transfer processes for accurate evaluation of pollutant transport, fate, and remedial measures are discussed. For both saturated and unsaturated subsurface materials, the pollutant transport modeling of soluble and nonsoluble pollutants using the second postulate of irreversible thermodynamics is presented. Laboratory experimental methods used to determine sorption characteristics are examined. A variety of analytical models for transport equations are used to elaborate on the level of complexity of the problems under investigation. Over the last 50 years, the heterogeneity of the subsurface environment, which has been exhibited through several orders of variability in the parameters’ and variables’ measured values, has given rise to a stochastic description of subsurface pollutant transport. In this, the parameters and variables that enter the governing transport equations are treated not as deterministic, but as stochastic variables described by their mean, variance, and covariance function, and other high-order statistical moments. Some basic issues in the stochastic modeling of pollutant transport in soils are highlighted.

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Part 2: Pollution Assessment in the Atmosphere commences with Chapter 8 and concludes with Chapter 12. Chapter 8: Indoor Air Quality: Pollutants, Health Effects, and Regulations, authored by Paleologos, K.E., Selim, M.Y.E., and Mohamed, A.M.O., summarizes some important information that relates to the characteristics, sources, and health effects of some of the most significant “indoor air pollutants,” such as volatile organic compounds, formaldehyde, particulate matter, nitrogen dioxide, carbon dioxide, carbon monoxide, radon, ozone, and airborne biological pollutants. Many health studies are presented and discussed in terms of their significance to the general population and to specific segments of it. Subsequently, the chapter presents methods for indoor air sampling, the recommended locations and frequency of sampling, together with the regulatory limits for specific indoor air pollutants, and the principles of active, passive, and whole-air sampling methods. The relation between outdoor and indoor air pollution is discussed and measures to reduce the entry of contaminated exterior air into an indoor environment are explained. Finally, specific building regulations of “indoor air quality” from a number of countries are presented, with the chapter concluding with the important topic of design practices, which moves away from the creation of “sick building syndrome” conditions to the concept of “green building,” and by extending the discussion from the health aspects of indoor air quality to a broader perspective of experiencing a “feeling of well-being” inside buildings. Chapter 9: Outdoor Air Pollutants: Sources, Characteristics, and Impact on Human Health and the Environment, authored by Mohamed, A.M.O., Maraqa, M.A., Howari, F.M., and Paleologos, E.K., presents the sources and characteristics of outdoor air pollutants along with the health and environmental effects of these pollutants. Emphasis is given to monitoring air pollutants by exploring the type of monitoring programs, sampling methods, emission standards, quality assurance aspects, and methods of data analysis and display, and by presenting a case study of air monitoring in a specific country. The chapter further explores the case of climate change by looking into its causes and economic and environmental impacts. Chapter 10: Modeling Air Pollution by Atmospheric Desert Dust, authored by Lelieveld, J., Abdelkader, M., Astitha, M., Karydis, V.A., and Klingmu¨ller, K., discusses modeling aspects of air pollution by atmospheric desert dust. Modeling challenges include the incorporation and parametrization of small-scale meteorological conditions, such as the mobilization of dust into the atmosphere, and small-scale soil properties and saltation bombardment into the much larger grid size of models. The dust source strength depended on the grid resolution of the models, which required tuning of the parameters in emission submodels. The global ECHAM/MESSy Atmospheric Chemistry (EMAC) atmospheric chemistryeclimate model was applied, which included an advanced emission submodel that interactively accounted for meteorological conditions and high-resolution topography data derived from satellite observations. The latter appeared to be critical in representing dust emissions in the Middle East. To account for mineral cation chemistry on the particle surface area, induced by the uptake of anthropogenic pollutants, the dust scheme of the EMAC model incorporated composition data collected over the major deserts of the world. Chapter 11: Troposphere Air PollutiondAviation Industry’s Case, authored by Bernabeo, R.A., Paleologos, E.K., and Mohamed, A.M.O., discusses the impact of CO2 emissions from a specific industry, that of aviation, together with the monitoring of CO2 by airlines, and forward-looking schemes by the aviation industry, such as the EU’s Emission Trading Scheme. A technical discussion

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is made of how to calculate direct emissions of CO2 by aircraft and how to represent the contribution of the range of other gases, which include nitrogen oxides, contrails, and water vapor on radiative forcing. The discussion covers calculation issues relating to CO2 emissions for individual flights and national ones, as well as global inventories. It also reviews the carbon dioxide sources, both natural and anthropogenic, and the carbon cycle to provide the background for discussing the CO2 emissions from the aviation industry. It further provides some basic background on traditional nondispersive infrared absorption techniques as well as modern spectroscopic instruments such as direct absorption spectroscopy, cavity ring-down spectroscopy, cavity-enhanced off-axis-integrated cavity output spectroscopy, and Fourier transform infrared spectroscopy that are used to quantify CO2 emissions by aviation, concluding by discussing instruments mounted on satellites for monitoring greenhouse gases. Chapter 12: Health Economics of Air Pollution, authored by Siriopoulos, C., Samitas, A., Dimitropoulos, V., Boura, A., and AlBlooshi, D.M., reports on the economic cost of public health impacts of ambient and household air pollution, with reference to the countries of the WHO European Region. It also presents a framework that can provide practical guidance on where and how to strengthen policy responses to problems related to air pollution’s health impacts. Present-day economics uses a standard method for assessing the cost of mortality at the level of society: the “value of statistical life,” as derived from aggregating individuals’ willingness to pay to secure a marginal reduction in the risk of premature death. This permits researchers and policymakers to assess the comparative magnitude of the value that societies attach to a given health impact, and of proposals to mitigate it, using money as a common metric. In contrast, a standard and commonly agreed method by which to measure the cost of morbidity is not yet available. Recent practice and available evidence provide a rationale for using an additional 10% of the overall cost of mortality as a best estimate for the additional cost of morbidity. It is estimated that in 44 WHO European Member States the societal costs are equivalent to more than 1% of the respective gross domestic product and in only four of the 48 Member States considered in the analysis do these societal costs amount to less than 1%. Part 3: Pollution Assessment in the Hydrosphere includes Chapters 13e17. Chapter 13: A Decision Support System for Ranking Desalination Processes in the Arabian Gulf Countries Based on Hydrodynamic Modeling Evaluation of Future Changes in Feed Water Properties, authored by Elhakeem, A., Mohamed, A.M.O., and Elshorbagy, W., discusses the development of a three-dimensional hydrodynamic model of the Arabian Gulf (AG) using Delft3D-Flow in a fully prognostic baroclinic mode. Water salinity and temperature values were obtained from observations spanning over 73 years until 1996 for the AG, the Strait of Hormuz, and the Gulf of Oman and were used to produce seasonal evaporation and surface density spatial distribution maps for the AG to compare them with available information. The long-term impacts of climate change and coastal effluents on seawater salinity and temperature of the AG were evaluated for two scenarios of the IPCC-AR4 that account for surface warming and emissions of short-lived greenhouse gases and aerosols. Using the current capacity and production rates of coastal desalination, power, and refinery plants, two projection scenarios until the year 2080 with 30-year intervals were developed and indicated an overall increase of the salinity and temperature in the AG and a decrease of precipitation. In addition, long-term hydrodynamic simulation results for the AG response to 10 projection scenarios were

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used to evaluate the performance of desalination processes in terms of chemical and electrical operational costs in 2020, 2050, and 2080, considering the combined impact of climate change and coastal effluent. The performance of four desalination processes (multistage flash, multiple-effect distillation, hybrid, and reverse osmosis) were evaluated using seawater salinity and temperature results at coastal desalination plants. The changes in seawater salinity and temperature were applied in a formulated decision support matrix (salinity-DSM and temperature-DSM) to produce an adjusted desalination operational cost. The resulting adjusted costs were used to develop future desalination technology rankings based on the least negatively impacted desalination process in terms of chemical and electrical operational costs. A total of 32 plants in Kuwait, Saudi Arabia, Qatar, Bahrain, United Arab Emirates, and Iran were evaluated to advise on the most appropriate planning approaches at each location until 2080. The main finding of this study indicated that the impact of future ambient conditions in the Gulf on the operational cost of the considered desalination technologies is most likely to be significant under the considered factors, and considering the proposed approach the multieffect distillation technology is expected to be the most economical, robust, and least affected by changes in seawater temperature and salinity. Chapter 14: Recent Analytical Methods for Risk Assessment of Emerging Contaminants in Ecosystems, authored by Bataineh, M., Gallampois, C.M.J., and Schymanski, E.L., describes recent developments in monitoring and toxicological studies involving emerging contaminants (ECs), which allow regulatory bodies to develop standards to protect human populations and ecosystems. ECs can be found in ecosystems because of their incomplete removal during wastewater treatments and other processes. The analysis of ECs remains challenging because it involves analysis of chemicals with widely varying properties in a broad spectrum of environmental matrices and at very low concentration levels, which require sensitive and selective analytical techniques. The focus in this chapter is on the occurrence and level of detection of five classes of ECs: pharmaceuticals and personal care products, disinfection by-products, perfluorinated compounds, polybrominated diphenyl ethers, and benzotriazoles and dioxane. Sample preparation is an area where new improvements have occurred, aiming at simplification, lower costs, and “greener” procedures. Some novel sampling techniques for the detection of ECs in different environmental matrices are presented, such as: (1) water grab samples from inland and/or offshore; (2) large volume-solid-phase extraction for water samples, (3) passive samplers (POCIS, Chemcatchers, SR, SPMD), (4) sediment grab samples (Van Veen and/or gravity free fall corer), (5) biota grab samples with different trophic levels (sediment microorganisms, mussels, fish, and mammals), and (6) air passive samplers (inland and/or offshore). Chapter 15: Water Quality at Jebel Ali Harbor, Dubai, UAE, authored by Maraqa, M.A., Ali, A., Rao, M.V., Hamza, W., Imran, H.D., and Al Awadi, S., presents a study of the influence of discharged treated wastewater on harbor water quality in the Jebel Ali Free Zone area of Dubai. An extensive sampling protocol was implemented to identify target pollutants, which covered all discharge locations, several harbor stations, and a reference point in the Arabian Gulf. Water temperature, pH, and total dissolved solids were found to be lower, while total suspended solids and dissolved oxygen were generally higher than minimum objective limits. Ammonia exhibited an increase at deeper depths and the inner and outer basin, while sulfide at several locations was lower than the harbor water objective limit, and phosphorus exceeded the objective limit at the east and west corner of the inner basin. Fe, Zn, and Al were higher than the values at the reference location. Total petroleum hydrocarbon levels exceeded the harbor objective limit by orders of magnitude and as to the organic matter indicators, biochemical oxygen demand and total organic carbon, these were higher near the bottom and

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highest in the inner and outer basins. The phytoplankton communities found in the study area matched those known along the coastal area of the Arabian Gulf, although the algae-rich harbor water might be the result of organic discharge and bacterial action. On the other hand, phytoplankton diversity and density drop over time may have resulted from seasonal variations, or the dredging of the harbor bottom’s sediments. The presence of high coliform bacteria in marine water, as was the case of the inner basin, was a strong indication of discharge from untreated domestic sources. Chapter 16: Sediment Quality at Jebel Ali Harbor, Dubai, UAE, authored by Maraqa, M.A., Ali, A., Rao, M.V., Hamza, W., Imran, H.D., and Khan, N., expands the previous chapter’s study by presenting the results of sediment analysis at the same harbor to: (1) establish a sediment quality baseline for the ecosystem of the study area, and (2) propose targets for sediment quality that will sustain long-term aquatic ecosystem health. Experimental results showed that sediments at the harbor were characterized by a fine texture containing clay and silt, which was different than what was found at a reference location in the Arabian Gulf. Sulfide, phosphorus, and total petroleum hydrocarbon (TPH) levels were higher in inner and outer basin sediments than in the harbor’s main channel. Many metals were at higher levels in the harbor sediments than in those at the reference location. Levels of chromium, copper, and zinc associated with sediments are of environmental concern with all these metals’ harbor levels exhibiting serious concerns to aquatic life. Lead was found to potentially represent a biologically adverse effect at parts of the harbor, while TPH and polynuclear aromatic hydrocarbon levels were higher than at the reference station and represented a biological risk in parts of the harbor. Chapter 17: Inland Desalination: Techniques, Brine Management, and Environmental Concerns, authored by Khan, Q., Maraqa, M.A., and Mohamed, A.M.O., reviews the available desalination techniques, brine management, and environmental impact of land-based desalination plants. The main conventional desalination technologies of multistage flash, multiple-effect distillation, and reverse osmosis are detailed and while dominating the market, there exist some emerging technologies with the potential for full-scale inland desalination. Regardless of the technology used, several environmental concerns associated with inland desalination exist. The reject brine from desalination plants can alter the physical and chemical properties of soils and may also find its way to groundwater, degrading it and making it inappropriate for drinking or irrigation. While there have been some efforts directed for better management of the brine, more work needs to be done from a research and practical point of view. Heavy energy consumption by desalination plants is responsible for greenhouse gas generation, making improvement and optimization of existing processes, or shifting to new technologies that employ renewable energy resources in the desalination process, a necessary condition for the continuation of desalination as a vital source of fresh water in desert regions. Finally, the last part of this book, Part 4: Emerging Issues in Environmental Pollution Assessment, starts with Chapter 18 and concludes with Chapter 22. Chapter 18: Pollution Assessment of Nanomaterials, authored by Mohamed, A.M.O., Rodrigues, V.G.S., and Paleologos, K.E., summarizes some of the recent scientific information on emerging nanomaterials and highlights the potential and the threat that they pose to human health and the environment. Widespread application of nanotechnology in a variety of industrial applications has spearheaded important developments, but has also created new waste materials of unknown behavior in the environment, which could potentially impact ecosystems and human health. Engineered nanomaterials (ENMs)

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enter the environment via different exposure routes, which include solid and liquid waste from domestic sources and industrial activities, accidental spillages, and atmospheric emissions. All these exposure pathways allow ENMs to disperse through the environment. ENM fate and transport in the environment are largely dependent on material properties such as surface chemistry, particle size, and biological and abiotic processes in environmental media. Depending on these properties, ENMs may stay in suspension as individual particles and aggregate, dissolve, or react with other materials. To date, little is known about the behavior of ENMs in the environment and the dominant physical and chemical factors that affect their movement and their toxicological effects once they enter a living organism. They also pose, because of their size and properties, unique challenges in establishing appropriate biomarkers and techniques and instruments to detect and monitor them, etc. Therefore health, safety, and environmental management of nanotechnology are important aspects to mitigate risks, improve the benefits, and transform opportunities into technological development in, among others, medical applications, flexible and communication devices, portable energy, food conservation, agriculture productivity, and pest control. Chapter 19: Noise Pollution and Its Impact on Human Health and the Environment, authored by Mohamed, A.M.O., Paleologos, E.K., and Howari, F.M., addresses the sources, health impacts, risk assessment, and mitigation measures of noise pollution. Noise pollution is recognized as a major problem for the quality of life in urban areas, especially since its increase as a result of car numbers and an expansion to almost 24-h activities in cities. Economic and population growth has increased the tendency toward noise generation. A major challenge is the quantification of the noise effects on the population. Noise is considered a growing health threat, and if left unchecked it could result to hazardous conditions. The WHO estimates that 10% of the world population is exposed to sound pressure levels that could potentially cause noise-induced hearing loss. In the United States, it is estimated that about 10 million people have already suffered irreversible hearing damage from noise, and 30e50 million people are exposed daily to hazardous noise levels. Epidemiological studies have found that cardiovascular diseases are consistently correlated with exposure to environmental noise. Detailed studies to evaluate the extent of potential health effects of noise exposure are needed that would assist national and local governments to understand the health impacts of environmental noise and help them develop policy and management strategies and action plans for noise control. Chapter 20: Assessment of Radiation Pollution from Nuclear Power Plants, authored by Iqbal, J., Howari, F.M., Mohamed, A.M.O., and Paleologos, E.K., reviews the challenges that nuclear energy must overcome to remain part of a sustainable energy mix. Different types of radioactive waste pollution from nuclear power plants, environmental and health effects, and nuclear regulations in different countries are discussed. Nuclear power plants raise issues of catastrophic failure, which has met with anxiety from the public, and of an extremely high cost that is not restricted to operation, but expands also to nuclear waste disposal and the need to find and construct appropriate repository sites capable of providing isolation from human populations for thousands of years. Associated with both nuclear plants and repository sites are safety and security measures and costs that extend well into the future. Eventually, given that no absolute guarantee can ever be made about the safety of nuclear power plants or waste repositories, especially for time horizons beyond any meaningful prediction, the use of nuclear energy boils down to a decision by societies of what level of risk is acceptable to them. Chapter 21: Artificial Intelligence and Data Analytics for Geosciences and Remote Sensing: Theory and Application, authored by Al-Obeidat, F., Marir, F., Howari, F.M., Mohamed, A.M.O., and

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Banerjee, N., presents two case studies to address the limitation of conventional statistics in dealing with hyperspectral data of satellite and airborne images. The first case study presents the development of an artificial intelligence (AI) and data analytics algorithm capable of classifying hyperspectral data to support remote sensing, and Geographic Information System researchers to understand and predict changes in natural Earth processes. The new classification algorithm is based on a fuzzy approach combining decision tree classifiers with fuzzy multiple-criteria decision analysis classifiers. The second case study presents the development of an AI tool that extracts features from the hyperspectral data to transform a 2D satellite and airborne image into a pseudo-3D image to enhance edge contrast and produce multidirectional sun-shaded images and their edges. Such 3D images are very useful in supporting the discovery and mining of valuable minerals or other geological materials from ores, lodes, veins, seams, and reef or placer deposits, and overall to improve the efficiency and effectiveness of mineral exploration. Chapter 22: Lifecycle Assessment of Aquaponics, authored by Kamareddine, L.A. and Maraqa, M.A., provides a critical review regarding the environmental, economic, and social impacts of aquaponics, along with evaluating the numerous tools to assess aquaponics, and discussing the challenges that face these systems. The most common method used to assess sustainability is by conducting a lifecycle assessment in which all inputs and outputs are collected, and the cost of each item is provided. Previous studies showed that different geographic locations and climates highly affect the design and operating costs, as well as the environmental and economic sustainability, of aquaponic systems. The authors highlight the need for additional studies under different climate conditions and geographical locations to compare aquaponics with aquaculture, hydroponics, and conventional agriculture. Environmental problems have entered not only the scientific, but also the daily discourse since the second half of the 20th century. The extent of the problems is such that perhaps the word “pollution” indicates the effects and potential impact of environmental issues. The meaning of the word itself has become so broad as to be capable of being attached and referred to as: air pollution, river pollution, groundwater pollution, marine water pollution, soil pollution, solid waste pollution, liquid waste pollution, noise pollution, and light pollution, among others. Assessing the impact of all these diverse pollution events in every potential medium, air, water, and soil, is a tall task that cannot be met in a comprehensive way by any single book. The editors of this work are aware of these limitations, but hope that this book, which was developed by a select group of scientists and researchers, will provide a view of environmental problems that is not limited to a particular scientific field, and will constitute a valuable demonstration of the multidisciplinary and interdisciplinary nature and need for pollution assessments, which is not realized so broadly, outside the academic community. A.M.O. Mohamed E.K. Paleologos F.M. Howari

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Sustainable pollution assessment practices

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Abdel-Mohsen O. Mohamed1, 2, Evan K. Paleologos3 1

Uberbinder, Inc., Seattle, WA, United States; EX Scientific Consultants, Abu Dhabi, United Arab Emirates; College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates

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1.1 Introduction Over the past few decades, many international organizations have increased attention to the problems of excessive natural resource use and reduction, waste generation and buildup, and the impact of pollution on human health and the environment. Because the industrial sectors are major sources of generation of these problems, they are faced with increased rigorous environmental laws and regulations by various environmental regulatory agencies, and considerable pressure and lawsuits from public and private groups. Therefore, industries are continually challenged throughout the phases of a project life cycle. Increasingly, municipalities and other urban centers’ responsible bodies and agencies are looking into the problem of abandoned industrial buildings’ spaces, which then become a source of environmental and social degradation (De Sousa, 2002). Environmental considerations are advocated in the Code of Ethics of Professional Societies, as in the American Institute of Architects’ 2012 Code of Ethics and Professional Conduct (AIA, 2012), in which a separate article (Canon VI), entitled Obligations to the Environment, details the professional obligations for protecting the environment by mandating that: • • •

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Members should promote sustainable design and development principles in their professional activities. E.S. 6.1 Sustainable Design: In performing design work, Members should be environmentally responsible and advocate sustainable building and site design. E.S. 6.2 Sustainable Development (SusDev): In performing professional services, Members should advocate the design, construction, and operation of sustainable buildings and communities. E.S. 6.3 Sustainable Practices: Members should use sustainable practices within their firms and professional organizations, and they should encourage their clients to do the same.

Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering. https://doi.org/10.1016/B978-0-12-809582-9.00001-3 Copyright © 2021 Elsevier Inc. All rights reserved.

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To achieve sustainability in engineering projects, we need to look past the usual focus on economics, duration, quality, and performance to include the following goals (Mohamed and Paleologos, 2018): a. b. c. d. e.

Natural environment protection, conservation, or rehabilitation, Waste minimization and recycling, and safe storage and disposal of waste, Energy conservation and promotion of renewable resource technologies and practices, Natural resources conservation and reuse, and Environmental impact assessments, which, apart from protection of the water, air, and soil, may include assessments of the effects on the landscape and the community and the use of environmentally friendly, new industrial or commercial designs and processes, and even aesthetic and noise considerations.

Notably, in many countries, the preceding goals are merged within the engineering approaches and methods at every phase of project implementation. However, stakeholders such as owners, planners, designers, vendors, suppliers, constructors, users, and operators are faced with a challenging number of tasks for achieving SusDev: (a) limited resources related to effectiveness and efficiency of project delivery; (b) a lack of tools, resources, mechanisms, and incentives to help continue with projects; (c) a lack of awareness, potential impact, and implications of newly adopted environmental standards, policies, and regulations; (d) a lack of fully appreciating potential opportunities and benefits introduced by adopting SusDev solution to its projects; and (e) a lack of developing reliable quantitative measures and or indicators to be able to evaluate the actual benefits and associated costs. In achieving SusDev, industries would take an aggressive role to implement short- and long-term solutions for the use of recyclable materials and available limited resources. All entities involved in the process would benefit from implementing SusDev to projects, as briefly described subsequently (Mohamed and Paleologos, 2018): a. Owners: They are the direct beneficiary of the economic benefits of implementing innovative strategies to finance and manage resources in adopting SusDev agenda. As an example, clear incentives are provided in the Estidama (the Arabic word for sustainability) regulations set forth by the Abu Dhabi Urban Planning Council of the United Arab Emirates (UAE). b. Designers and constructors: Their activities for sustainable design and construction could meaningfully enhance the quality of the environment and contribute positively to the overall outcomes of projects. c. Vendors and suppliers: They would have robust incentives to supply recyclable materials, energyefficient materials, products, and systems, and overall SusDev technologies. This chapter discusses the SusDev concept, the environmental ecosystems, and some of the major global environmental problems we are currently facing, their interrelations and their impact on the practice of geo-environmental engineers, as well as the development of a general framework for assessing pollutants from the SusDev viewpoint.

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1.2 Sustainable development concept The SusDev concept suggests a new intelligent approach that resolves our universal determination to improve human health, well-being, and the environment within the existing ecosystem’s boundaries. This would require innovative solutions to improve human health and the environment without imposing additional degradation on the environment or affecting current human well-being or future generations. The first official use of the term SusDev appeared in the 1987 landmark report Our Common Future (UN, 1987) by the World Commission on Environment and Development (WCED), which defined SusDev as Development that meets the needs of the present without compromising the ability of future generations to meet their own needs. This report is also known as the Brundtland report, after Norwegian Prime Minister Dr. Gro Harlem Brundtland, who chaired the 1983 United Nations World Commission on Environment and Development and commissioned the report (Fig. 1.1). SusDev consists of economic development, social equity, and environmental protection. How to implement and attain a balance among these three parts remains unclear (Drexhage and Murphy, 2010). The Brundtland report provided the momentum for the 1992 Rio Earth Summit and the Rio Declaration and Development, which contained 27 principles of SusDev.

FIGURE 1.1 Dr. Gro Harlem Brundtland, honored during the International Water/World Future Energy/EcoWaste Summit and Exhibition at Abu Dhabi, Jan. 18e21, 2016. Photo by E.K. Paleologos.

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Principle 3 reaffirmed that The right to development must be fulfilled so as to equitably meet developmental and environmental needs of present and future generations. Principle 4 asserted environmental protection as an essential part of SusDev: In order to achieve sustainable development, environmental protection shall constitute an integral part of the development process and cannot be considered in isolation from it.

Agenda 21 was also the outcome of the Rio Declaration” and its 40 Chapters on the means of implementing social, economic, and environmental components of sustainability. After Rio, several international conferences were held, but as stated in the 2002 United Nations Economic and Social Council report (2002), progress toward reaching the goals set at Rio has been slower than anticipated. Discussion on SusDev has been energized by the emphasis on climate change and the interrelation between these issues. Although the precise meaning of SusDev remains unclear beyond a generation’s obligation not to impact on the future of the next generations, most explanations of the term sustainable refer to (National Science and Technology Council, 1994) the viability of natural resources and ecosystems over time, and to the improvement of the living conditions of the world’s poorest and of economic development that is inclusive of all countries and people. SusDev can be defined as the measure of the level of integration and balance between social, environmental and economic realities and constraints that are changing with time. Because SusDev is a lively approach, it requires flexibility and readiness to modify solution measures with respect to anticipated environmental changes, human desires, and progress in technology. Therefore, one would accept that today’s actions that contribute to SusDevs may be thought damaging tomorrow if the setting has changed. As reported by Mohamed and Paleologos (2018), over time, we have to ensure SusDevs by preserving a dynamic balance between the rapidly increasing human population and: (a) the demands, (b) the ability of the physical environment to retain the wastes generated, as a result of human activities, (c) the changing opportunities due to new knowledge development and technological advances, and (d) the values, ambitions and organizations that channel human behavior. Similarly, Pirages (1994) stated, visions of a sustainable world must naturally change in response to shifts in any part of this dynamic relationship. The basic elements of SusDev, as reported by Mohamed and Antia (1998), are: education, determining environmental limits, efficient use of natural resources, integrated environmental management systems, new technologies and technology transfer, perception and attitude changes, population stabilization, refining market economy, social and cultural changes, and waste reduction and pollution prevention. As reported by Mohamed and Antia (1998), fundamentally, SusDev aims for the satisfaction of human needs, the maintenance of ecological integrity, the achievement of equity and social justice and the provision of social self-determination. Therefore, the real challenge is to find means and measures to put SusDev into practice. In the following sections, we discussed issues related to the main pillars of SusDev: social, environmental, and economic sustainability.

1.2.1 Social sustainability Social sustainability (SocSus) is defined as a measure of the human’s welfare. SocSus is not a concern regarding simple existence, but a wish to flourish and have the best lifestyle for which could dream. The socioculturally most prominent issue that influences sustainability is intergenerational equity, which means that we just use the natural resources we need and leave the rest to future generations.

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Therefore, we must endeavor to increase the standards of living of people who lack shelter, clean water, and adequate food to survive. Additional elements for consideration are population growth, human health, cultural needs, and a clean environment in which to live, which have a general direct impact on human well-being and should not be ignored in favor of economic prosperity in the short term. To promote SocSus, we need to (1) encourage community participation; (2) emphasize measuring the full accounting cost of the life cycle of products cradle-to-grave, including associated social costs; (3) promote improvements in social organization systems and community well-being over measurable economic benefits; (4) use natural and recyclable materials in a way that increases impartiality and fairness and reduces societal disturbances; and (5) transfer funds, know-how, and technology to those who need them.

1.2.2 Environmental sustainability The natural environment is defined as the physical context within which we live” As discussed earlier, SusDev requires us to know and work within the boundaries of the environmental ecosystems, which have limited resources. For example, (1) trees and wildlife are renewable resources, if we permit undamaged habitat conditions for wildlife and plants to redevelop; and (2) because minerals are renewed at a slow rate, their use reduces current reserves. Therefore, we are requested to develop a viable plan for the optimal use of both renewable and nonrenewable resources. In addition, because of our increased human activities, we need to minimize the environmental impact on global ecosystems such as air, water, and soil. To help natural ecosystems cope with some imposed impacts, external changes must be small enough to allow healthy recovery. Protection of endangered species, wetlands, and biodiversity are basic elements of maintaining a healthy ecosystem. To preserve natural resources, Goodland and Daly (1995) drew a set of input and output rules: (1) The input rules are classified into renewable and non-renewables. For renewables, the harvest rates shall be maintained within the regenerative capacity of the natural system, whereas for nonrenewables, the depletion rates should be equal to the rate at which renewable substitutes are developed by human invention and investment. In addition, part of the profits shall be put in a reserve fund to support research and development in areas of innovative solutions for SusDev alternatives (El Sarafy, 1991). (2) The output rule that relates to waste emissions being to air, water, and soil from a project should be within the assimilative capacity of the local environments (air, water bodies, surface and subsurface soils) to absorb and retain, without exceeding its assimilative capacity (Mohamed and Antia, 1998).

1.2.3 Economic sustainability Economic sustainability (EcoSus) does not simply refer to gross national product, exchange rates, inflation, and profit, but it relates to production, distribution, and consumption of goods and services (Mohamed and Antia, 1998). The movement and transfer of goods and services have a noteworthy effect on environmental ecosystems because those ecosystems are the source of unmanufactured or unused materials and the stockrooms for rejected goods. Short financial gains have been the motivation behind much of the un-SusDev that has happened. Organizations will adopt SusDev concepts if it is demonstrated that sustainable solutions are not costly. To evaluate SusDev, we must consider the economic losses due to reduction and degradation of

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the environmental ecosystem used and develop a plan to mitigate both short-term and long-term environmental impacts. Traditional decision-making analyses account for only the costs and benefits of resource exploitation and use without considering the inherent cost of the environmental degradation of air, water, or soil or the cost of discarding an item after the end of its useful life, which includes that of the remediation a society has to incur, which limits its financial resources (UN (United Nations) et al., 2003). Once a shift has occurred in economic cost-benefit analyses that would account for environmental costs, SusDev would be recognized to be of more economical benefits than current solutions and designs (Paleologos, 2008). In view of Principle 4 of the Rio Declaration and Development, environmental protection is an essential part of the SusDev process. This is unlike out-of-date models that first emphasize the financial aspects of the project and then develop remedial measures to the resultant environmental problems. Such a process is shown in Fig. 1.2 (Mohamed and Antia, 1998), in which “(1) the ambient physical environment, ecosystem, and natural resources represent the natural system (NatSys), and (2) the features of production of goods and services represent the economic system (EcoSys).” As a result, the typical use of the NatSys by the EcoSys would lead to a substantial reduction in natural resources and an upsurge in environmental problems and associated costs, such as air pollution, surface water and groundwater pollution, marine and ecosystems pollution, the generation of solid and liquid waste, and above all, greenhouse gas and global warming. The resultant impacts on the NatSys would vary as a result of the geographical location; the existing environmental status involves the natural environment and the current economy. In addition, as noted by Goodland and Daly (1995), EcoSus involves the consumption of interest rather than capital and is defined as the amount one consumes during a period and still be as well off at the end of the period. Notably, the preceding description focused only on man-made capital; however, it could be extended to include the natural capital. Therefore, by integrating the economy with the environment, the EcoSus could be redefined (Mohamed and Antia, 1998) as the maintenance of capital in general, both man-made and natural.

FIGURE 1.2 Interface between main driving systems and resultant environment impacts. From Mohamed, A.M.O., Paleologos, E.K., 2018. Foundations of Geo-Environmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques, Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688; Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, Amsterdam, The Netherlands, pp. 707.

1.2 Sustainable development concept

9

The word “environment” currently means integration of the environmental system with the economic system in which current economic principles apply. Researchers are adopting environmental values that are external to the classic economic system and developing innovative methods to account for monetary values on intangible non-market (and non-marketable) components of the environment.

1.2.4 Land sustainability As demonstrated in Fig. 1.3 (Mohamed and Paleologos, 2018; Mohamed and Antia, 1998; Serageldin, 1993), land sustainability could be achieved by integrating the (1) ecological system, which that accounts for ecosystem integrity, carrying capacity, biodiversity, and associated global issues; (2) economical system, which accounts for growth, equity, and efficiency; and (3) social system, that considers variables such as employment, participation, social mobility, cultural identity, and institutional development. In the ecological system objectives, scientists emphasize the “preservation of the integrity of the ecological system that are critical to the overall stability of the ecosystem, and quantify its impact in units of physical, chemical and biological entities” (Mohamed and Antia, 1998). For the economic system objectives, “economists pursue to maximize human welfare within the existing capital and available technologies and utilize measurement standards of units such as money or perceived value.” (Mohamed and Antia, 1998). For the social system objectives, “sociologists stress that the key factors in SusDev are human capital with a range of requirements and desires and utilize units such as wellbeing and social empowerment” (Mohamed and Antia, 1998). The optimum SusDev solution for land development should be located within “the intersection of the three spheres of ecologically viable, economically feasible, and socially desirable,” as shown in Fig. 1.4 (Campbell and Heck, 1997). SusDev would occur only when “the management goals and actions are concurrently ecologically viable, economically feasible, and socially acceptable” (Mohamed and Antia, 1998). This, in turn, indicates the reliability of the environmental solution and

FIGURE 1.3 Components of ecological, economic, and social objectives that contribute to sustainable development. From Mohamed, A.M.O., Paleologos, E.K., 2018. Foundations of Geo-Environmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques, Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688; Serageldin, L., 1993. Making development sustainable, Finance Dev. 30, pp. 6e10.

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FIGURE 1.4 Sustainable solution for the development of sustainable land. From Mohamed, A.M.O., Paleologos, E.K., 2018. Foundations of Geo-Environmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques, Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688; Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, Amsterdam, The Netherlands, pp. 707.

the acceptance of the political system. Lack of accomplishment of one or more of the three areas would contribute to environmental unbalance and subsequent failure in achieving SusDev. It is usually difficult to attain in practice balance among these three spheres before reaching a point at which damage has become obvious and is either irreversible or can be reversed at an extremely high cost. For example, Lake Koroneia in the province of Macedonia, Greece, reported by Mohamed and Paleologos (2018), has almost disappeared because of a lack of balance in the ecological, economical, and social dimensions of activities in the region. The lake was connected to an underlying aquifer that combined with precipitation to provide water to the lake. Intense well drilling for irrigation of the farm-rich surrounding area and a lack of understanding of the hydrogeologic connection of the lake to the aquifer led to a drop in the aquifer level by tens of meters below the bottom of the lake. Subsequent studies found that precipitation on its own could not provide the needed quantity of water to sustain the lake, but by then the 45 sq. km. lake had almost disappeared. Failure to attain a hydrologic balance, together with a poor understanding and lack of social acceptance to limit groundwater withdrawal rates, has led the region to a stage where agricultural activities are no longer economically feasible.

1.3 Sustainable development and the ambient environment To evaluate the capacity of an ambient environment, one must evaluate its ability to self-regenerate. To do so, we need to investigate sustainable yield, defined as the rate at which nature, with the aid of human management practices, can replace what has been consumed. This concept was developed for harvesting forests and fisheries. This is an example of a dynamic system equilibrium, because natural factors (i.e., climate and ecological productivity) are time rateedependent. In addition, Muschett (1997) highlighted that when the harvest rate exceeds the renewal rate, a new state of equilibrium, with a lower resource base, is reached. One must understand the limits to which nature will permit renewal processes to take place without affecting both the resource base and the ecological system. In a similar manner, environmental scientists and engineers have come to realize the important role the ambient environment (physical, chemical, and biological) has on absorbing and adsorbing, diluting, diffusing,

1.3 Sustainable development and the ambient environment

11

and transforming pollutants within the environmental systems. This assimilative capacity limits the amount of acceptable contamination levels set by various regulatory bodies without causing harmful effects to the human health and the environment (Mohamed and Antia, 1998). The principle of sustainable yield is important when one estimates or predicts the transport of pollutants from a pollutant source such as a land disposal site or a regional source such as water bodies or air, or global warming and climate change (Muschett, 1997). The complexities and interactions of natural processes increase as the geographic scale increases. Therefore, to achieve SusDev, one would consider the environmental system’s assimilative capacity to be able to determine the maximum carrying capacity of the ecosystems, the industrial activities, and the resultant waste discharge to the air, water, and soil systems (Mohamed and Antia, 1998). Fig. 1.5 displays a general framework for an environmental quality management system reported by Mohamed and Paleologos (2018), Mohamed and Antia (1998), and Muschett (1997). The process involves a number of iterative steps to examine alternative strategies, alternative designs for pollutant discharge, the results of numerical and analytical models to calculate pollutant concentrations, and the assimilative capacity of the environmental system under investigation. Hence, one would identify one or more viable environmental strategies for implementation.

START

Measure ambient environmental quality

NO

Is allowable limit exceeded? YES Inventory sources of pollutants Determine ambient assimilative capacity Determine environmental management strategies Model ambient concentration with strategies

YES

Is allowable limit exceeded? NO Implement environmental management strategies

FIGURE 1.5 General framework for pollutant management. From Mohamed, A.M.O., Paleologos, E.K., 2018. Foundations of Geo-Environmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques, Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688; Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, Amsterdam, The Netherlands, pp. 707.

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To determine whether a water body is acceptable for the use of human and biological species, one needs to calculate (Mohamed and Antia, 1998): “(a) the amount of pollutants discharged into the water body and the resultant assimilative capacity, (b) the concentrations of individual pollutants in the water and in the tissues of aquatic organisms, and (c) the toxicity, the environmental risks, and overall associated hazard index.” The environmental system poses two constraints, water flow and bioaccumulations, in relation to water quality. The former is time-dependent; i.e., it varies from one season to another and differs according to recurrent extractions for human use. Hence, the system’s assimilative capacity is limited. The latter is related to a bioaccumulation increase in contaminants in continually higher levels in the food chain, which decreases the concentration of contaminants in the water. The preceding conditions coupled with a high population density and industrial activities would make SusDev a difficult task to achieve.

1.4 Land environment When considering global environmental issues, land is defined in broader terms than its customary colloquial use as (Mohamed and Antia, 1998): 1. As per the United Nations (UN) Food and Agriculture Organization, A Framework for Land Evaluation (FAO, 1976): Land comprises the physical environment, including climate, relief, soils, hydrology and vegetation, to the extent that these influence potentials for land use. It includes the results of past and present human activity, e.g. reclamation from the sea, vegetation clearance, and also adverse results, e.g. soil salinization. Purely economic and social characteristics, however, are not included in the concept of land; these form part of the economic and social context.

2. As per the UN General Assembly Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa (UNCCD, Sep. 12, 2017): Land means the terrestrial bio-productive system that comprises soil, vegetation, other biota, and the ecological and hydrological processes that operate within the system.

As reported by Mohamed and Paleologos (2018), these definitions are consistent with others reported in the literature: a. a natural land unit that is distinctive from an administrative land unit, which can be of any size (individual ownership, municipality, province, state, etc.), and which normally encompasses several natural units or parts of them. b. land system units and landscape-ecological units that are building blocks of a watershed or a geographic unit. The repeated reference to land and land resources in the UN desertification documents may be taken to mean land and its individual land components. c. the components of a natural land unit that can be termed as land resources, including physical, environmental, infrastructural, social and economic components, in as much as they are attributes

1.4 Land environment

13

of the land unit. Aquifers, surface and near-surface freshwater bodies are considered within the land resources component. d. The linkages between water and land that are so intimate at the management level, i.e., the water elements cannot be excluded from the land. In this comprehensive approach, a natural unit of land environment has both vertical and horizontal aspects. The vertical aspect is measured from the atmosphere down to the hydrosphere, which consists of water forms such as oceans, lakes, streams, snowpack, glaciers, polar ice caps, and groundwater. The horizontal aspect includes (1) the geosphere, also termed the lithosphere, which is represented by a sequence of soil, terrain, minerals, organic matter, pore fluid, and porewater; and (2) the biosphere, which includes the land use elements, i.e., living organisms, and the environment. Therefore, the entire unit of land environment could be termed the geomicrobiosphere, which requires full interaction among the four ecosystems (i.e., atmosphere, hydrosphere, geosphere, and biosphere) that constitute the land environment (Mohamed and Paleologos, 2018), as shown in Fig. 1.6. Environmental ecosystems terminologies (atmosphere, biosphere, geosphere, and hydrosphere) are thus given: • •

As per Linsley (1987) and Parker and Corbitt (1993), the term atmosphere refers to the envelope of gasses surrounding the Earth and is further subdivided into regions based on altitude. As per Manahan (1991), the term biosphere refers to living organisms (flora and fauna) and their environments on the surface of the Earth.

FIGURE 1.6 Interactions of environmental ecosystems (atmosphere, biosphere, geosphere, and hydrosphere). Adopted from Mohamed, A.M.O., Paleologos, E.K., 2018. Foundations of Geo-Environmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques, Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688.

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Chapter 1 Sustainable pollution assessment practices

As per Parker and Corbitt (1993), the term geosphere refers to the complex and variable mixture of minerals, organic matter, pore fluid, and air that make up the soil. Finally, as per Friedman (1987) and Parker and Corbitt (1993), the term hydrosphere refers to water in various forms such as oceans, lakes, streams, snowpack, glaciers, polar ice caps, and water under the ground (groundwater).

In addition, according to Mohamed and Antia (1998) and Anderson and Hillwalker (2012), the contaminant transport or transfer between environmental ecosystems has a major impact on its bioavailability for adsorption and desorption, mass transfer, and uptake by microorganisms. The rate of pollutant transfer is important and may affect the suitability of land in which to live. The physicochemical reactions, biological composition and interactions, and mineralogical composition would control the portion of bioavailable components that interact within the environmental ecosystems (Mohamed and Antia, 1998). Pathways of contaminants to humans are inhalation, ingestion, and/or skin contact. In approaching land development and use, the problems of waste management, pollution, and environmental conservation, which are persistent, must be considered from local, regional and global environmental points of view. Our experience in past decades has shown that local interventions in most cases transcend local effects to create regional or even global impacts. For example, (1) deforestation of the Amazon creates new space for human activities but also has an impact on the regional and global climate and on species diversity, (2) gaseous emissions in one country may create acid rain problems in another (Johnson and Gordon, 1987), and (3) river contamination or excess water extraction affects water quantity and quality in downstream countries.

1.5 Global environmental problems and restoration initiatives 1.5.1 Global warming and climate change Clearly, the gravest challenge that humanity faces in the 21st century is warming of the atmosphere, which threatens to trigger several large-scale, uncontrolled changes in the planet’s oceans, weather patterns, and biosphere. This came forcefully to the world stage with the recent 21st UN Conference on Climate Change held in Paris, France, where a universal climate agreement was reached on Dec. 12, 2015 (COP21/CMP11, 2015). The agreement adopted by 195 countries calls for national contributions toward the reduction of greenhouse gas (GHG) emissions, which countries will not be able to roll back after 2020, with the target to achieve carbon neutrality by the middle of the century (see FCCC, 2015 for the text of the Paris Agreement). Despite worldwide efforts thus far, the overarching target, which showed global warming of 2.7e3.0
C, is to keep the rise in temperature below 2
C by the end of the century. Important consequences of this agreement include tangible actions toward green economies. Examples include (Mohamed and Paleologos, 2018) (1) Japan’s decision to attain by 2030 a 22%e24% share by renewable sources in its power generation, (2) the European Union’s (EU’s) target of 27% renewable energy in its total energy mix, (3) significant forestation actions in countries, such as Cambodia, the Democratic Republic of Congo, or of halting deforestation by 2030 in Mexico and Brazil, and (4) relocation of ports (i.e., Maldives) in areas less vulnerable to strong winds and rising sea levels, etc.

1.5 Global environmental problems and restoration initiatives

15

Atmospheric gas constituents are 78.09% nitrogen (N2), 20.95% oxygen (O2), and 0.93% argon (Ar) by volume, respectively. In addition, “0.03% by volume of minor constituents of trace gases, such as carbon dioxide (CO2), methane (CH4), hydrogen (H2), helium (He), Kr, and neon (Ne), have important contributions to radiative and biological processes in the environment” (Mohamed and Antia, 1998). Also, the atmosphere contains suspended and liquid particles with variable water vapor (H2O) concentrations. Consequently, owing to changes in atmospheric constituents, there is a tendency for the temperature to rise close to the earth’s surface, resulting in GHG, or global warming. Table 1.1 (Blasing, 2014) compares GHG concentrations in the troposphere with their levels before the beginning of the industrial revolution (set at about 1750), as collected and analyzed by the US Department of Energy’s Carbon Dioxide Information Analysis Center. Chlorofluorocarbons (CFCs) shown in the table are not found in nature and were produced in the 1930s for refrigeration, aerosols, foam manufacturing, and air-conditioning. Because there no removal processes or sinks exist for CFCs in the lowest part of the atmosphere, they are transported into the stratosphere and release free chlorine atoms. They are broken down by UV radiation, thus causing depletion of the atmosphere’s ozone layer. CFCs also contribute to global warming because some CFC molecules are thousands of times more efficient in trapping heat than CO2. CFCs have gradually been phased out since 1987; however, they are expected to persist in the atmosphere well into the 21st century. Hydrochlorofluorocarbons (HCFCs) are also man-made and were produced in the 1980s as an alternative to CFCs. HCFCs are broken down in the lowest part of the atmosphere, and hence pose a much smaller threat to the ozone layer than CFCs. However, HCFCs are also potent GHGs. The rise of seawater levels as a result of recorded temperature increases is attributed to multiple factors such as (1) fluid expansion, (2) precipitation and evaporation, (3) ice melting, and (4) an increase in the strength and harshness of life-threatening events, such as drought, floods, hurricanes,

Table 1.1 Greenhouse gas concentrations (Blasing, 2014). Gas

Pre-1750 tropospheric concentration

Recent tropospheric concentration

CO2 (parts per million) CH4 (ppb) N2O (ppb) O3 (ppb)

280 722 270 237

395.4 1762e1893 324e326 337

Concentrations below in parts per trillion CFC-11 (CCl3F) CFC-12 (CCl2F2) CF-113 (CCl2CClF2) HCFC-22 (CHClF2) HCFC-141b (CH3CCl2F) HCFC-142b (CH3CClF2) ppb, parts per billion.

Zero Zero Zero Zero Zero Zero

234e236 527 74e79 210e231 21e24 21e23

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wildfires, and shifts in flora and fauna patterns. All of these factors have serious influences on human health and the environment. Therefore, there is an urgent need for global action, such as those agreed upon in the 21st UN Conference on Climate Change, to reverse the human effects of global warming and climate change. As reported by Mohamed and Paleologos (2018), “evidence of the global warming and climate change is manifested in melting of the glaciers across the whole planet; examples are: (a) the Muir Glacier in Alaska, as reported by K. Chang, NY Times, Science, The Big Melt Accelerates, May 19, 2014; (b) the Yakutat Glacier in Alaska that withdrawn 5 km; (c) the Italian Alps, where melting of snow and ice has exposed the bodies of WWI soldiers (The Telegraph, Melting glaciers in northern Italy reveal corpses of WW1 soldiers, Jan 17, 2014); (d) the declining of the Kilimanjaro glacier, which shrunk from an area of 7.5 square miles in 1912 to less than 1.5 square miles today. It is expected to vanish completely by 2030; and (e) other glaciers such as the Quelccaya Ice Cap in Peru, and the Puncak Jaya Glacier in Indonesia.” It is further predicted that “melting of the glaciers could cause a sea level rise of 0.3e0.6 m (1e2 feet).” In addition, “the melting of Greenland and Antarctica would also contribute to a rise of the sea level.” Because the Greenland contains 10% of the world’s ice it would contribute to raise of the sea level by 7 m (23 feet). As reported by Morlighem et al. (2014), the “decrease in ice sheet height takes place at a rate of 600 to 20 mm/year at the periphery of the glaciers.” However, for carved ice-covered valleys below sea level, it has severe implications because the ice sheet is susceptible to warming from ocean water. In addition, one expects that “the real risk to sea water level rise would come from the Antarctic Ice Sheet.” This sheet is defined as one of the two polar caps on Earth containing 61% of the fresh water. If the ice sheet is melted, it would increase the level of water in the world’s oceans by 70 m. The West Antarctic’s ice sheet is unstable and attached to a bed below sea level, with warm ocean currents that contribute to the weakening of its attachment to the seabed. Furthermore, in a news conference (NY Times, May 12, 2014), the National Aeronautics and Space Administration announced, “Today we present observational evidence that a large sector of the West Antarctic ice sheet has gone into irreversible retreat. It has passed the point of no return.” West Antarctic can create a 3-m (10-feet) sea level rise in the oceans (Mohamed and Paleologos, 2018). In the Arctic, the rise in temperatures doubles compared with that of the average global temperature rise. Ice melting contributes to variations in the albedo of these areas; hence, (1) energy reflection is reduced in regions where white ice or snow-covered surfaces disappear, and (2) land and sea have the potential to warm up faster with an accelerated warming-up cycle (Mohamed and Paleologos, 2018). With warming of the Arctic, it is expected that enormous amounts of CO2 and CH4, which are stored within the permafrost, would be released into the atmosphere. Therefore, concentration of GHGs would increase dramatically and accelerate global climate change (NRC, 2015). Hope and Schaefer (2015) estimated that “the mean net present cost, of such an event, would be at about USD 43 trillion, and with an increase of 13%, the total projected cost shall reach USD 300 trillion.” Johansen (2006) highlighted other environmental indicators, such as “(a) extreme hydrologic conditions with precipitation events discharging in a few hours water equivalent to the sum of many months, thus leading to floods, (b) extreme droughts in arid and semiarid regions, and (c) changes in the flora and fauna due to warmer temperatures.” The preceding factors contributed to an acknowledgment of the global warming problem and the establishment of the Intergovernmental Panel on Climate Change (IPCC), the UN Environment

1.5 Global environmental problems and restoration initiatives

17

Programme (UNEP), and World Health Organization (WHO). The IPCC Climate Change Assessment Study (2014) conducted a global warming study and reported the following findings: 1. Geosphere: In the geosphere, over the last 30 years, each successive decade has been warmer than the previous ones. In the northern hemisphere, during the period from 1880 to 2012, the average land and ocean surface temperatures experienced a warming of 0.85 (0.65e1.06)
C. Furthermore, recorded rate of surface temperature warming over the last 14 years (1998e2012) was 0.05C per each 10 years, whereas the rate over the past 61 years (1951e2012) was 0.12
C per each 10 years. Therefore, the warming rate in the period from 1998 to 2012 is smaller in comparison with that from 1951 to 2012. In view of precipitation, there was an observed increase since 1901. 2. Hydrosphere: It was reported that 90% of stored energy in the climate system during the period from 1971 to 2010 is attributed to ocean warming. In “the top 75 m of the ocean, the water warmed by 0.11 (0.09e 0.13)
C per each decade during the period from 1971 to 2010. Furthermore, the upper layer from 75 to 700 m has shown considerable warmings during the same period. Ocean surface water salinity is another indication of global warming and water cycle across the ocean environment. Areas of high salinity indicate excessive evaporation, whereas those low in salinity indicate an increase in precipitation. Owing to an increase in CO2 in the environment, the ocean’s water become acidic with recorded increase of 26%. During the period from 2002 to 2011, in the Greenland and Antarctic, ice sheets have been losing mass at an accelerated rate. Worldwide glaciers have continued to decrease. In the Artic sea, the ice extent has been decreasing at a rate from 3.5% to 4.1% per each decade over the period from 1979 to 2012. In addition, the overall average global mean sea level has increased by 0.19 m during the period from 1901 to 2010. The detected influences of climate change on the natural system are clear. For example, the changing rate of precipitation and/or melting snow and ice would alter hydrological systems and affect the quality and quantity of water resources. Biological species living in soils, freshwater bodies, and marine environment have altered activities such as migration forms and geographic distribution owing to overall climate change. Johansen (2006) reported some examples of these observed changes: (1) in the United States, maple syrup is declining in New England, and in the past 80 years, half of products have been lost and the main season has shifted from Mar .to Feb; (2) in Canada, owing to heat and dryness, butterflies migrated to the North in the Canadian prairies; (3) in the United Kingdom, the growing season increased by 1 month since the 1900s in central England; and (4) in Britain, it was observed that (i) during Oct. 2001, the temperature was the warmest in the past 300 years, (ii) butterflies migrated from North America, and (iii) birds migrated from the Mediterranean. In relation to crop yields, the harmful effect of climate change on crop yield and harvesting are clear across the globe. In addition, climate change is expected (Mohamed and Paleologos, 2018) to (1) induce heat stress; (2) increase storms, floods, precipitation, landslides, drought, air pollution, water scarcity, and sea water level rises; and (3) decrease water and food availability, and agricultural incomes. These environmental stresses are amplified in areas that lack essential infrastructure and services. To reduce the effects on climate change, the world community has taken positive steps, as reflected by Goal 13 of the 2030 UN General Assembly’s global sustainable development goals (SDGs), which

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Chapter 1 Sustainable pollution assessment practices

call for urgent action to combat climate change and its impacts (https://sustainabledevelopment.un. org/post2015). In brief, the associated targets of Goal 13 are to: (1) integrate climate change measures within policies, strategies, and planning; (2) develop risk management strategies to combat climate-related hazards and natural disasters, (3) provide training, workshops, and awareness campaigns to enhance human and institutional capacity on issues such as early warning, adaptation, impact reduction, and overall mitigation measures; (4) mobilize and implement the Green Climate Fund initiative proposed by developed countries to support the UN Framework Convention on Climate Change; and (5) promote mechanisms for building human capacity in areas of planning and management.

1.5.2 Chemicals in the environment Various chemical products are manufactured and have been employed in industrial activities such as agriculture, medical services, medicine, oil and gas, energy, and manufacturing processes, and in everyday use by humans. During the product life cycle, chemicals are released into the environmental ecosystems of air, water, and soil (UNEP, 2005). There is a tremendous increase in chemical production across the world, as reported by the Organization for Economic Co-Operation and Development (OECD). It is anticipated that by 2020, one-third of global chemical production will by produced and used by non-OECD countries. Such an amount is about 85% higher than that produced in 1995. Management methods and technologies of raw chemicals and their associated waste disposal practices have been well-established in developed countries. However, most developing countries have yet to develop a sound system because they still rely on landfill codisposal, dump on bare soil, or burn in open air. Therefore, in developing countries, one would expect an increase in environmental problems and human healtheinduced risks (UNEP, 2005). In the following section, we will briefly discuss some impacts of those chemicals.

1.5.2.1 Persistent organic pollutants Persistent organic pollutants (POPs) are long-lived and bioaccumulate in the food chain. They are transported by air and water currents for long distances and are concentrated in low-temperature regions at high elevations. POPs are produced by the chemical industry and are used in the agricultural industry as pesticides. They pose great health risks to animals, marine life, and the human population in the form of neural disorders, endocrine disturbance, and carcinogenic effects (Diamanti-Kandarakis et al., 2009). Because of the inappropriate use of agrichemicals, the WHO reported that about 355,000 people die annually from unintentional poisoning (WHO, 2015). In 2001, the Stockholm Convention on POPs, listed in Table 1.2, was held with the aim of (Gorman and Tynan, 2003) (1) eliminating the manufacture and use of certain POPs, (2) restricting transport across countries, (3) developing strategies to identify stored stockpiles, and (4) implementing management strategies to identify POPs sources and mitigating their discharge into the environment.

1.5.2.2 Metals The mining and smelting of metal ores have increased the frequency and occurrence of potentially toxic elements (Pb, As, Cd, Zn, and Cu) at the ground surface (Rodrı´gez et al., 2009). The reported literature highlights the impact of Pb and Cd on human health and the environment. An example is (1) in Brazil, where two areas, one in Santo Amaro (Bahia) and another in the Ribeira Valley (Parana´), showed high Pb concentrations in children’s blood (Rodrı´gez et al., 2009).

1.5 Global environmental problems and restoration initiatives

19

Table 1.2 Some international conventions for the control of chemicals and their associated waste disposal methods (UNEP, 2005). Convention name Johannesburg Plan of Implementation (WSSD, 2002)

Cited paragraph(s)/ article(s)

Brief description as stated in corresponding convention (articles or paragraphs)

Paragraph 23 (Prioritized during regional consultations in Europe and the Asia and Pacific region)

Renew the commitment, as advanced in Agenda 21, to sound management of chemicals throughout their life cycle and of hazardous wastes for sustainable development as well as for the protection of human health and the environment, inter alia, aiming to achieve, by 2020. . chemicals are used and produced in ways that lead to the minimization of significant adverse effects on human health and the environment, using transparent science-based risk assessment procedures and science-based risk management procedures . . support developing countries in strengthening their capacity for the sound management of chemicals and hazardous wastes by providing technical and financial assistance. Prevent and minimize waste and maximize reuse, recycling and use of environmentally friendly alternative materials, with the participation of government authorities and all stakeholders, in order to minimize adverse effects on the environment and improve resource efficiency, with financial, technical and other assistance for developing countries. Promote reduction of the risks posed by heavy metals that are harmful to human health and the environment, including through a review of relevant studies, such as the United Nations Environment Program global assessment of mercury and its compounds. . to protect human health and the environment from persistent organic pollutants.

Paragraph 22 (Prioritized during regional consultations in the Asia and Pacific region)

Paragraph 23g

Stockholm Convention on Persistent Organic Pollutants (Stockholm Convention, 2001) Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade (Rotterdam Convention, 2001)

Article 1

Article 1

. to promote shared responsibility and cooperative efforts among Parties in the international trade of certain hazardous chemicals in order to protect human health and the environment from potential harm and to contribute to their environmentally sound use, by facilitating information exchange about their characteristics, by providing for a national decision-making process on their import and export and by disseminating these decisions to Parties. Continued

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Table 1.2 Some international conventions for the control of chemicals and their associated waste disposal methods (UNEP, 2005).dcont’d Cited paragraph(s)/ article(s)

Brief description as stated in corresponding convention (articles or paragraphs)

Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (UN, 1989) International Convention for the Prevention of Pollution from Ships 1973), as modified by the Protocol of 1978 (MARPOL, 1973/78)

Preamble

. to protect, by strict control, human health and the environment against the adverse effects which may result from the generation and management of hazardous waste and other wastes.

Article 17

International Convention on the Prevention of Marine Pollution by the Dumping of Wastes and Other Matter (London Convention, 1972)

Article 2

International Convention on the Prevention of Marine Pollution by the Dumping of Wastes and Other Matter (London Convention, 1972) Agenda 21 (UNCED, 1992b)

Article 12

The Parties to the Convention accept the obligation to promote, in consultation with other international bodies and with assistance from [United Nations Environment Programme] UNEP and coordination with the Executive Director of UNEP, the necessary support for Parties who may require technical assistance in the following areas: (a) training scientific and technical staff, (b) obtaining equipment and monitoring installations where needed, (c) ease the adoption of additional measures and conditions [ .. ], and (d) encourage research; preferably within the concerned countries, in order to promote realization of the aims and objectives of this convention. Contracting Parties shall individually and collectively protect and preserve the marine environment from all sources of pollution and take effective measures, according to their scientific, technical and economic capabilities, to prevent, reduce and where practicable eliminate pollution caused by dumping or incineration at sea of wastes or other matter. The Contracting Parties pledge themselves to promote, within the competent specialized agencies and other international bodies, measures to protect the marine environment against pollution caused by hydrocarbons, including oil and their wastes.

Convention name

Chapter 22, Paragraph 3

The main objectives are: (a) ensure that radioactive wastes are safely managed, transported, stored and disposed of, and (b) protect human health and the environment.

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Table 1.2 Some international conventions for the control of chemicals and their associated waste disposal methods (UNEP, 2005).dcont’d Convention name Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management (IAEA 1997)

Cited paragraph(s)/ article(s)

Brief description as stated in corresponding convention (articles or paragraphs)

Article 1

The objectives of this Convention are to: (a) achieve and maintain a high level of safety worldwide in spent fuel and radioactive waste management [ .], (b) ensure that during all stages of spent fuel and radioactive waste management there are effective defenses against potential hazards [ .. ], and (c) prevent accidents with radiological consequences and to mitigate their consequences should they occur during any stage of spent fuel or radioactive waste management.

Moreover, related epidemiological surveys have shown (2) high blood Pb levels in children’s who were born after the closure of lead smelter (de Andrade Lima and Bernardez, 2011), “Pb discharged from aging water pipes, as in the city of Flint, Michigan, USA, may leach into the water system resulting in elevated levels of lead in the bloodstream or deaths (Nov. 13 2015 class action suit against the Governor, the State of Michigan, and the City of Flint officials: http://flintwaterstudy.org/2015/11/ class-action-lawsuit/), and (3) Cr, which is widely used in chrome plating, stainless steel, and daily products, could become a cancer-causing agent under certain environmental conditions. Notably, Cr could find its way into the human body via inhalation, as reported by the US Department of Health and Human Services (2008), and by oral ingestion from contaminated water, as reported by Linos et al. (2011).

1.5.2.3 Health care waste The implementation of strict regulations and uniform practices of the disposal of health care waste are challenging for most countries because of (1) the high cost of burning; (2) the variety of sources of origins of health care waste, such as hospitals, small clinics, and individual medical practices; and (3) the difficulty of imposing strict collection and management practices. In addition, pharmaceuticals and toxic components in personal care products (PPCPs) are continually dumped into water bodies in the form of sewage sludge. Although, most PPCPs are not toxic at their small dosage, their continuous discharge could result in bioaccumulation within microorganisms and cause irrecoverable damage and ecological succession (Daughton and Ternes, 1999).

1.5.2.4 Electronic waste Electronic waste (e-waste) generated from components of electrical and electronic equipment is an emerging problem. In 2014, it was reported (Mohamed and Paleologos, 2018) that about 42 million megatons (Mt) were produced and distributed: (1) 1 Mt from lamps, (2) 6.3 Mt from computer screens, (3) 3 Mt from cellphones, pocket calculators, PCs, and printers; (4) 12.8 Mt from vacuum cleaners, toasters, microwave ovens, and cameras; (5) 11.8 Mt from washing and dishwashing machines and dryers; and (6) 7 Mt from cooling and freezing equipment.

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Chapter 1 Sustainable pollution assessment practices

The existence of a variety heavy metals, PCBs and ozone-depleting substances poses severe human health risks such as impaired mental development, lung, liver, and kidney damage (Mohamed and Paleologos, 2018) and major environmental catastrophes, such as that reported for lead poisoning in Guiyu, China (Balde´ et al., 2015). A limited number of countries have inventory for e-waste or policies and management systems. Most countries ship e-waste to developing countries for materials and component extraction for reuse: for example, current agreements between the United States, China, and Taiwan.

1.5.2.5 Mitigation measures The UNEP, OECD, and other international organizations have developed frameworks and systems for (1) evaluating the current situation in each country and reporting on chemical use, (2) assessing the safety of chemicals and cooperating on data collection related to environmental safety, and (3) exchanging information related to innovative control and mitigation measures for large-scale accidents. Table 1.2 (UNEP, 2005) lists some international conventions that legalize chemicals substances and their waste disposal management. In addition, Goal 12 of the 2030 UN General Assembly’s global SDGs calls to ensure sustainable consumption and production patterns (https://sustainabledevelopment.un.org/post2015). Its related targets are: a. Implementation of the agreed-upon framework on sustainable consumption and production of materials, goods, and services; b. By 2020, it is expected to (1) achieve environmentally sound management systems throughout the life cycle of produced chemicals and waste, and (2) achieve a significant reduction in discharged pollutants to air, water, and soil to minimize adverse impacts on human health and the environment; c. By 2030, it is expected to (1) achieve full sustainable development (SD) management practices and efficient use of natural resources; (2) reduce the per capita global food waste by half; (3) substantially reduce waste generation via adoption of 3Rs, i.e., reduction, recycle and reuse; (4) encourage organizations to integrate SD concepts into the reporting cycle; (5) promote SD in public procurement practices; and (6) ensure that all people across the world have pertinent information to implement SD and promote lifestyles in harmony with nature.

1.5.3 Pollution of marines and rivers 1.5.3.1 Oil spill Pollutants find their ways into the seas through various transport routes such as rivers, chemical accidents, oil spills from ships and shoreline facilities, and unlawful dumping by commercial or recreational marine vessels (ITOPF, 2015; NOAA, n.d.). The National Research Council (NRC) of the US National Academies (NRC, 2003) estimated that “annually 1.3 million Mt of oil enters the world’s waters and 55% of which is due to anthropogenic sources such as petroleum extraction at about 5%, transportation accounts for 22%, and the remaining is due to the consumption of petroleum by cars, boats, and urban runoff.” However, “natural seepage from geologic formations below the seafloor contributes to about 600,000 Mt of the oil released, annually” (Mohamed and Paleologos, 2018).

1.5 Global environmental problems and restoration initiatives

23

FIGURE 1.7 Density of oil from 7000 oil spills (1999e2002 in the Mediterranean Sea, from synthetic aperture radar images of European Commission project FP6 MIDIV: Mapping Illicit Discharges from Vessels. Adopted from IFAW (International Fund for Animal Welfare). 2005. “Chronic Oil Pollution in Europe,” A Status Report. Royal Netherlands Institute for Sea Research, pp. 85.

In Europe, many small and large oil spills have resulted in frequent long-lasting oil pollution of coastal waters. Thus, chemical releases from transportation facilities, recreational marine containers, and spills from coastal facilities affect human health and the environment, as witnessed by the reported positive correlations between shipwrecked seabird deaths and shipping practices in various areas. The associated environmental impact on the transport of shipping vessels was recognized by the International Convention for the Prevention of Pollution From Ships (MARPOL 73/78), shown in Table 1.2, and oil discharges were panned in protected areas defined within Europe under MARPOL. However, continuous discharge of oil during operations continues to occur because of a lack of strict regulations and enforcement mechanisms. Such actions in the short term may not be identified because they would not have the same impact as large accidental oil spills. However, they would have long-lasting effects on environmental ecosystems. For example, the Mediterranean and Baltic Seas were designated protected areas because they are significant marine areas for migrating birds. However, aerial surveys, shown in Fig. 1.7, indicate that “the seas have large amounts of oil, irrespective of the overall global decline of oil released into the sea from six million Mt in the 1970s to the current annual 1.3 million Mt” (NRC, 2003; IFAW, 2005). Mohamed and Paleologos (2018) reported that “with the leadership of the UNEP, conservation programs for world regional seas (11 regions) have been adopted by about 130 countries and 11 UN, and regional organizations with the aim of reduction of pollution loads and conservation of regional seas ecosystems.”

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Chapter 1 Sustainable pollution assessment practices

1.5.3.2 Plastic debris in marine environment An emerging pollution problem related to the presence of plastic debris in the oceans and rivers has risen into the public domain. Floating debris and microparticles beneath the surface of the water are trapped into the oceans’ currents. They are projected to cover an area as small as the UAE to that about twice the size of Canada. Photodegradation of microparticles and plastics renders it small enough so that they could be swallowed by microorganisms and hence enter the food chain. Mohamed and Paleologos (2018) reported that “about 250,000 tonnes of these plastic clutters the oceans, pollutes the sea floor and beaches, and consumed by birds, fish, and other aquatic organisms.” This issue was not taken seriously by marine scientists because of the abundance of marine life. However, Woodall et al. (2014) reported “the presence of extensive debris on the seafloor of the Mediterranean Sea, the North Atlantic Ocean, and the South West of the Indian Ocean.”

1.5.3.3 Freshwater bodies Pollution of freshwater bodies is a critical issue, and there is a need for international cooperation to preserve and reestablish the water quality in the international rivers. The quality of freshwater in the Rhine, which originates from Switzerland and flows through France, Germany, and the Netherlands exiting at the North Sea, deteriorated significantly (from 1850 to 1980) owing to industrial, agricultural, and municipal waste discharge. Associated problems include (1) high salinity in the drinking water of downstream countries such as the Netherlands because of dumped waste from France and Germany’s potash mines; (2) agrochemical pollution, such as the 1986 Sandoz accident, in which, as the result of a fire, toxic chemicals were released into the river; (3) heavy discharge loads from municipal wastewater treatment plants; (4) heavy use of the river by shipping; and (5) the intense presence of industrial processes along the river, which include chemical, coal, steel, food, textiles, automobile manufacturing, and refineries. As a result of the Sandoz accident, the Dutch Government adopted the 1987 Rhine Action Program, which calls for (1) the reduction of Cd, Hg, Pb, and dioxins in the river water by 50%-70% by 1995; (2) tightening of industrial plants’ safety regulations; and (3) restoration of the riverside environment and drinking water safeguards. Subsequent regulations, such as Rhine 2020 and the 2000 EU Water Framework Directive have been put in place to address nitrogen, heavy metal, and pesticide concentrations that did not meet the targets set by the Rhine Action of 1987. Another example is the case of Danube River, which passes through 10 countries. The river is contaminated by raw sewages, fertilizers and pesticides, hydrocarbons, pharmaceuticals, and microplastics, as well as chemicals generated from refining, pulp and paper, and mining and metallurgical industries. Environmental agencies reported the illegal dumping of highly toxic waste over the years. As reported by Mohamed and Paleologos (2018), in 1984, 364 drums of toxic wastes were discharged into the river originating in Italy and moved away to the coastal waters of Turkey. Also, in Oct. 2010, 184 million gallons of high alkaline red mud was discharged into the river owing to the collapse of a tailing dam, which was designed to hold arsenic and mercury liquid waste generated from aluminum-processing plant in Hungary. As a protection measure for the Danube River, in 1991, the Danube River Delta was selected as a Wetland of International Importance under the Ramsar Convention (RAMSAR, 2015) and assigned UN Educational, Scientific and Cultural Organization Man and Biosphere Status. In addition, in 1994, the Basin countries signed the Danube River Protection Convention, which constitutes the legal framework for trans-boundary management of the river (Murphy, 1997).

1.5 Global environmental problems and restoration initiatives

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Finally, the Colorado River in the United States originates from the Rocky Mountains and flows through seven states before entering northern Mexico. The river flow is greatly diminished because of (1) constant water withdrawals to irrigate California’s Imperial Valley and provide community water to Phoenix and Tucson; (2) electricity through the Hoover Dam and Lake Mead, Las Vegas, Nevada; and (3) community water supply to Los Angeles and San Diego cities. Mohamed and Paleologos (2018) reported that the river’s natural salinity has increased from 50 parts per million (ppm) to over 2000 ppm in certain locations owing to reduced flow and salt leaching from irrigation practices. This, in turn, mandated the installation of a desalination plant near the boundary with Mexico. In addition, pesticide pollution is a serious problem near the Imperial Valley, California, and in the lower part of the river, which contributed to a decrease in fish habitats. According to the US National Park Service (1946), the river basin has been reduced to desert, and its indigenous species of fish and birds faces a challenging future (UNEP, 2004).

1.5.3.4 Conservation and sustainable use of oceans, seas, and marine resources In 2015, the UN General Assembly approved 17 new SDGs and 169 their associated targets with expectations for full implementation by 2030 (https://sustainabledevelopment.un.org/post2015). Among them, Goal 14 calls for the conservation and sustainable use of the oceans, seas, and marine resources with the following targets: 1. By 2020, all members are expected to (1) manage and protect marine and coastal ecosystems; (2) effectively regulate harvesting, end overfishing and illegal fishing activities, and implement science-based management plans; (3) conserve a minimum of 10% of coastal and marine areas; (4) eliminate subsides for fisheries; and (5) adopt the roles and regulations of the World Trade Organization for negotiating subsides for fisheries. 2. By 2025, all members are expected to prevent and reduce all kinds of marine pollution. 3. By 2030, all members are expected to increase the economic benefits by adopting the sustainable use and management of marine resources and contribute to building research capacity and knowledge transfer mechanisms.

1.5.4 Extinction of species and biodiversity The biosphere has been characterized as a biotic holocaust. Because of increased human populations and related activities, the Earth entered the sixth mass extinction episode (Avise et al., 2008). Species extermination is attributed to “pollution increase, landscape transformation, habitat elimination, overexploitation of natural resources, and introduction of unknown species into areas of sensitive habitats” (Mohamed and Paleologos, 2018). The National Research Council (NRC, 2011) and Wilson (1992) reported that “the rates of species’ extinction [will] range from 10% to 37% by 2050.”

1.5.4.1 Marine ecosystem Table 1.3 presents excerpts from the 2008 book In the Light of Evolution, Volume II: Biodiversity and Extinction by the NRC, National Academies of the USA, and displays evidence of the decline of various marine species (Avise et al., 2008; Mohamed and Paleologos, 2018). Avise et al. (2008) attributed the extinction of marine ecosystems to “habitat destruction, pollution increase, nutrient runoff, dredging activities, global warming, acidification, and overfishing.”

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Table 1.3 Percent decline in various marine environments. Estuaries and coastal areas

· Whales · Seabirds/shorebirds · Sea turtles · Oysters

Location

Loss (%)

Global Global Global Global

59e85 57e61 87 91

Scotian shelf North Sea North Sea Global

96 97 99 90

North Carolina Northwest Atlantic Gulf of Mexico

87e99 60e89 45e99

Caribbean Indo-West Pacific Florida Caribbean Global

80e93 46 89 90 61

Shelf & pelagic fisheries

· Atlantic cod 4e16 kg · Fish 16e66 kg · Fish · Large predatory fish

Coastal and pelagic sharks sharks · All · All sharks · All sharks

Coral reefs

· Live coral cover coral cover · Live sponges · Commercial · Reef fish density · Corals’

Adopted from Avise, J.C., Hubbell, S.P., F.J. A. (eds.), National Research Council, 2008. In The Light of Evolution: Volume II: Biodiversity and Extinction. National Academies Press, pp. 432, and Mohamed, A.M.O., Paleologos, E.K. 2018. Foundations of GeoEnvironmental Engineering e Basic Soil Properties of Relevance to Different Remedial Techniques. Elsevier, New York, NY, ISBN: 978-0-12-804830-6, pp. 688.

1.5.4.2 Animals Most amphibians (frogs, salamanders, and caecilians) are found in the tropics and have been affected by environmental devastation from the use of fertilizers and pesticides, global warming, flooding, and transferrable diseases that are common in warm regions. For mammalian biodiversity, it is expected that most impacts would come from habitat loss and disintegration, infectious disease, contamination increase, and global warming. As reported by the International Union for Conservation of Nature (IUCN, 2007), since AD 1500, 74 mammal species have become extinct, 1094 species are currently threatened, and 2652 species have no cause for concern.

1.5.4.3 Forests Mohamed and Paleologos (2018) reported that the Amazon contains about 40% of the world’s tropical forest, and because of deforestation, warmer temperatures, and variable rainfall, it is expected that the extinction of more than 20% of the current tree species will occur.

1.5 Global environmental problems and restoration initiatives

27

1.5.4.4 Mitigation measures When prior UN resolutions are reviewed, it seems that the recommendation of the UN Declaration on the Human Environment in 1972, which stated “Man has special responsibility to safeguard and wisely manage the heritage of wildlife and its habitat which are now gravely imperiled by a combination of adverse factors. Nature conservation including wildlife must therefore receive importance in planning for economic development” has not been observed by Member States. Furthermore, with the increase in the human population, increased pressure on agricultural and habitation spaces, and increased amounts of used chemical fertilizers and generated waste, the overall impacts of climate change on ecosystems and biological diversity will increase. To cope with this serious situation, UN Agenda 21 was developed to improve the conservation of biological diversity and the sustainable use of biological resources, and to support the Convention on Biological Diversity (http://www.unep.org/Documents.Multilingual/Default. asp? DocumentID¼52&ArticleID¼63). In view of “the conservation of biological diversity and the sustainable use of biological resources,” the UN Agenda 21 calls on Member States to:“(a) develop National strategies, (b) integrate Convention strategies into National development strategies, (c) develop policies and procedures for the fair and equitable sharing of benefits derived from research and development, (d) carry out country-related studies with particular reference to socio-economic aspects, (e) produce annual reports on biodiversity based upon National assessments, (f) foster the traditional methods and the knowledge of indigenous people and their communities, (g) implement mechanisms for the sustainable use of biotechnology and its safe transfer, (h) promote broader international and regional cooperation in advancing scientific and economic understanding of the importance of biodiversity and its functions in ecosystems, and (i) develop measures to implement the rights of countries of origin of genetic resources or countries providing genetic resources.”

1.5.5 Environmental pollution in developing countries 1.5.5.1 Industries and population Developing countries have supported and developed various industries as a measure to enhance the economic prosperity of their populations. During the first phase of economic development, light industries were supported. However, with time, other industries such as petrochemical, textile, pharmaceutical, and metal were supported. For example, according to The Times of India, Business (Jun. 3, 2014), “in textiles, China and India were the two top world exporters in 2013, the former with $274, and the later with $40 billion exports, followed by Italy, Germany, and Bangladesh with $36, $35, and $28 billion exports, respectively.” According to the European Chemical Industry Council (CEFIC, 2014), “in the chemical industry, China dominated worldwide sales in 2014 with $1047.3 billion, equaling the combined sales of the next top six countries” (Fig. 1.8). In addition, according to the World Steel Association (2015) “the major steel-producing countries in 2014 were China with 822.7, Japan with 110.7, USA with 88.2, India with 86.5, South Korea and Russia with 71.5, and Germany with 42.9 million tonnes of crude steel production.” Furthermore, “the top pharmaceutical markets in 2013 were USA with $339.7 billion, Japan with $94 billion, China with $86.8 billion, Germany with $45.8 billion, France with $37.2 billion, and Brazil with $30.7 billion.” Moreover, Abpi (2013) reported that “the growth from 2011 to 2013 was dominated by China’s 22% increase, Brazil’s by 18%, and Japan’s 16% increase, whereas the other three countries had an increase between 4% to 6%.” With the increase in heavy industries, pollution loads increased and

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Chapter 1 Sustainable pollution assessment practices

Chemical Sales ($billion) Chemical Sales ($billion) 1,047.3

465.7

In

di

a

Ta

iw

an

56.1

ia

e

62.9

ss

nc

70.2

Ru

a Fr

72.2

il

a re Ko . S

78.4

az

132.1

Br

149.7

n Ge rm an y

A

pa

US

Ja

Ch

in

a

151.6

FIGURE 1.8 Chemical industry’s top-selling countries (CEFIC, 2014).

discharged into air, water, and soil. In addition, with the high population growth rate and deforestation of tropical forests, people tend to migrate to urban cities, increasing urban population and pollution loads in various environmental ecosystems.

1.5.5.2 Air pollution Fig. 1.9 shows data for CO2 emissions from selected countries with an annual production of more than 100 million metric tons (data from IEA, 2008). Those countries are in South Asia, Eastern Europe, the Middle East, and North and South America. The figure shows the high levels of CO2, which has an effect on the quality of air in those countries. Further discussion about air pollution assessment and impacts on human health and well-being can be found in this book in Chapter 8 for an assessment of indoor air pollution (authored by Paleologos et al., 2020), Chapter 9 for an assessment of outdoor air pollution (authored by Mohamed et al., 2020c), Chapter 10 for an assessment and modeling of desert dust (authored by Lelieveld et al., 2020), Chapter 11 for an assessment of the impact of air pollution from the aviation industry (authored by Bernabeo et al., 2020), and Chapter 12 for an assessment of the economic impact of air pollutants (authored by Syriopoulos et al., 2020). Air pollution in China is a major problem with severe impacts on the health of humans and biological species. Data from China’s Ministry of Environment Protection and published by C. Sam, C. Luo, and W. Feng in South China Morning Post (http://multimedia.scmp.com/china-air-pollution-in2014/) on Feb. 22, 2014 are presented in Fig. 1.10. The figure highlights that in most northern cities, the intensity of air pollution ranged from moderate to heavy, particularly during the winter season when energy use was at its peak values. Verstraeten et al. (2015) reported that there is a strong impact of air pollution in one country on other regions in the world, especially when it comes to ozone pollution and global warming. For example, the effect of air pollution on the ozone layer has been recorded in the western part of the United States, regardless of noteworthy efforts to reduce ozoneforming chemicals in that region. This explains why air pollutants are being exported to the western United States from China and other parts of the world such as India.

1.5 Global environmental problems and restoration initiatives

29

FIGURE 1.9 CO2 emission from selected countries with an annual production of more than 100 million metric tons.

With respect to SO2 concentrations in ambient air, data reported from the monitoring networks of UNEP and the WHO, which are located in most cities across the world, indicated that SO2 concentrations in cities of developing countries are similar to or more than those recorded in industrialized countries. A similar conclusion was reported for suspended particulate matter (PM10) in the air. Mohamed and Antia (1998) reported that in Mexico City, the level of air pollution in the ambient air is very high, to the degree that the quantity of PM10 that a person inhales per day corresponds to the smoking of 40 cigarettes. With the introduction of policies and regulations across the world, there are clear indications that an improvement in the air quality has taken place.

1.5.5.3 Water pollution and management Many investigators have reported that the problem of water quality is severe owing to inadequate municipal and industrial treatment of wastewater, the discharge of untreated industrial waste, and pesticides from agriculture activities into rivers and groundwater. As examples, in 1989, the UNEP Monitoring Framework reported various pollutant concentrations in rivers, such as (1) dieldrin concentration at 3.0 mg/L in the Rufiji river, Tanzania, (2) dichlorodiphenyltrichloroethane concentration at 0.3 mg/L in Ganca Janchito River, Colombia, (3) dieldrin concentrations at 30.6 mg/L in the Gombak River, Malaysia, and (d) polychlorinated biphenyl concentrations at 0.4-6.9 mg/L at all monitoring

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Chapter 1 Sustainable pollution assessment practices

FIGURE 1.10 Air pollution records on Feb. 22, 2014 across Chinese cities. Air pollution in the Central Mid and East cities is characterized as “Moderate” and “Heavy” in general; however, few cities towards the North East are classified as “Serious.” The remaining cites are classified as “Good.” Data are from China’s Ministry of Environment Protection and published by C. Sam, C. Luo, and W. Feng in South China Morning Post (http://multimedia. scmp.com/china-air-pollution-in-2014/).

points in Indonesia. Because of the increase in population and intensive industrial activities, one would expect an increase in these reported concentrations. To cope with such problems, there are relentless attempts by various decision-making bodies to tackle the problems of water and sanitation in developing countries. Mohamed and Paleologos (2018) reported on the number of initiatives created by international organizations: a. The UN MDGs set a target for 2015 in which Member States had to reach to at least half of the proportion of people without access to safe drinking water and basic sanitation (Sachs, 2005). b. In 2013, the WHO established a consortium of nearly 100 worldwide organizations, identified as a “Household Water Treatment and Safe Storage Network” to “promote innovative solutions, entrepreneurship, and enhance research and development collaborations among various countries.”

1.6 Interconnection of environmental problems

31

c. The WHO declared 2005e15 the Decade of Water, with the objective of “establishing the framework to eventually provide full access to water supply and sanitation for all people.” Based on data reported in the 2004 WHO/UN Children’s Fund report, it was clear that the amount of people with access to improved quality water and hygiene increased in various countries (Mohamed and Paleologos, 2018). However, additional commitments and framework developments are needed to meet the UN MDGs, because access remains limited in some countries. Furthermore, as reported by Montgomery and Elimelech (2007), in developing countries, issues related to water quality, hygiene, and health remain at the forefront. The authors concluded that “(a) there is an urgent need for more water and hygiene services since they impact the human health and the environment, (b) studies related to water sustainability and hygiene services should take into account the effect of the environmental settings, culture diversity, and economics on the implementation and long-term maintenance of water treatment systems, (c) developing countries should try to find solutions to overcome the lack of investments and political will, and (d) water authority, health authority, and education sector should work together to develop and implement related and common projects.” For municipal solid waste management, notwithstanding substantial improvements in implementing the 3Rs principles and other treatment processes in most industrialized countries, the amount of waste that needs to be disposed of keeps on increasing. For example, Mohamed and Paleologos (2018) reported that in the United States in 2012, after recycling, the amount of waste that needed to be disposed of was almost double the total waste generated in 1960, when recycling was still in its early stages. To further highlight this problem, the following examples from the United States and EU were highlighted by Mohamed and Paleologos (2018): (1) in the United States, recycling and composting practices, which accounted for less than 10% until 1980, increased to 34.5% in 2012. However, per capita municipal solid waste generation in the United States has increased almost linearly from 1.22 kg/person per day in 1960 to a peak of 2.15 kg in 2000. Since then, it appeared to have been stabilized to about 2 kg/person per day. (2) In the EU, recycling and composting practices increased from 17% in 1995 to 42% in 2013. In the EU’s 27 Member States, average per capita municipal solid waste generation stood at 1.3 kg/person per day in 1995 and appears to have been stabilized to a little less than 1.4 kg/person per day during 1999e2009. In the preceding sections, we discussed examples from major industrialized countries because, as reported by Mohamed and Paleologos (2018), “developing countries tend to emulate practices and lifestyle standards of developed ones as evidenced by the correlation between waste generation and income.” To that end, the unlimited increase in municipal solid waste and the absence of viable technologies are worrisome, because of the latest publications of the World Bank indicating that global generated waste will almost double that currently produced by 2025. In such a situation, as reported by Paleologos et al. (2015) the per capita rate is expected to increase from the current 1.2 kg/person per day to 1.42 kg/person per day.

1.6 Interconnection of environmental problems The deterioration of the environment and the highly critical issue of global warming have come to dominate the global agenda, as stated by Mohamed and Paleologos (2018): “global environmental problems are caused by excessive stresses set by the quantitative expansion and qualitative changes of

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human activities. With the industrial revolution as a turning point, the striking expansion of human activities and the population increase have posed significant pressures on the global environment.” Also, many investigators attributed the effects of anthropogenic activities on the global environment to (1) increased emissions of CO2, which contribute to global warming and acid rain; (b) increases in deforestation and desertification, and decreases in biological biodiversity, which induce stress on the land environment; (3) use of CFC-related products, which are responsible for depleting the ozone layer; and (4) increased chemical discharges and accidents, which contaminate freshwater bodies, marine environments, soil, and groundwater. The interrelation of the global environmental problems could be demonstrated in the following examples: (1) it is known that CFC substances deplete the ozone layer. However, they are also instrumental in increasing the atmospheric temperature that contributes to global warming. (2) With climate change, flora and fauna change, the hydrologic cycle experiences extreme conditions, and desertification increases, with an impact on global warming. (3) Deforestation of tropical forests reduces wild species and affects global warming. (4) Marine pollution deters the absorption of CO2 by marine water bodies and affects aquatic life, increasing global warming. The preceding sections discussed a number of processes that affect the global environment. However, we currently lack a basic understanding of the manner by which the impact will occur, as stated by Mohamed and Paleologos (2018): “Equally important, most of the processes are characterized by a non-linear behavior and we lack the scientific understanding to predict what alterations in one would entail for another process. This means that we do not know the tipping point, which when reached changes can become unpredictable and the magnitude and impact of events may not be of the same order of what was experienced in the past.” Ecosystems have a delicate balance, and it becomes practically intolerable to restore such a balance if changes occur to them. On a global scale, the abolition of major species, far-reaching alteration of flora and fauna, and drastic changes in the surrounding landscape from that with which we are familiar could happen when a tipping point has passed. The following two cases that highlight such effects were reported by Mohamed and Paleologos (2018): “(1) at about 18,000 years ago, the glacial maximum global sea levels were about 110e120 m below current levels, meaning that the Arabian Gulf, which has a current maximum depth of 80 m, was dry and the sea boundary was at the Straits of Hormuz. (2) Owing to ice cap melting, the global sea level rises, resulting in flooding the Arabian Gulf and increasing the current sea level by about 2e3 m. Such an event has been reported in the literature and termed the Big Flood, which is common to all the civilizations of the Arabian Gulf region, indicating the extent and impact of global events.” The preceding sections highlight the interconnectivity of the environmental problem and the urgent need to arrive at innovative solutions to save our beloved environment.

1.7 Geoenvironmental engineering aspects Creative measures to alleviate the degradation of environmental ecosystems need to be intricate because of the interconnecting nature of the subsystems of the atmosphere, hydrosphere, geosphere, and biosphere, which constitute the land environment, as discussed earlier. Therefore, international global collaborative efforts should be reached to develop viable solutions.

1.7 Geoenvironmental engineering aspects

33

Existing mitigation measures for the complicated environmental issues are handled by geoenvironmental engineers and scientists, which include engineering professionals in geotechnical, environmental, agricultural, and chemical areas; and scientists in geology, geochemistry, microbiology, biotechnology, hydrology, oceanography, materials, and soils areas. These specialists use information to solve thought-provoking environmental problems. Therefore, in this context, one may identify geoenvironmental engineering as a blend of geotechnical and environmental engineering with associated scientific fields that would apply earth science principles to solve land environmental problems (Mohamed and Antia, 1998). As a result, the geoenvironmental engineer must be mindful of the developments in other related areas described earlier, as well as of the manner by which these advances in related disciplines may affect the development and implementation of sound engineering solutions and designs to current and future environmental issues. The complexity of the environmental problems could be elaborated by using as an example the problem of pollutant transport and transfer within the land environment, which has a direct impact on bioavailability and bioaccumulation potentials within the environmental ecosystems. As a pollutant transports into various mobile phases such as air, water, or soil, it diffuses and disperses quickly as a result of fluid movement within the mobile phase. Mechanisms such as boundary mass transfer, diffusion, adsorption, desorption, absorption, and dispersion are generally involved in movement processes within the mobile phases. All of these mechanisms are important to pollutant transport phenomena within the interacting environmental ecosystems. Readers who are interested in pollutant transport are advised to read Chapter 7 of this book on modeling and analysis on pollutant transport in soils (authored by Mohamed et al., 2020b). In addition, humans, plants, and biological species that constitute the biosphere exist within the other three environmental ecosystems: the atmosphere, geosphere, and hydrosphere. Fig. 1.11 shows the direct and indirect pathways of pollutant transfer through the environmental ecosystems, and finally to humans. For example, as a result of disposal accidents, pollutants transport to soil, water, or air, and hence will reach humans through air inhalation and drinking water or food intake. In another way, plants and animals could uptake pollutants as a result of agricultural practices

l, osa Disp ents d i c Ac

SOIL WATER AIR

Air,

Direct contact

POLLUTANT

Wat er

HUMAN Nutrients

Up

tak

e

BIOSPHERE Plants & Animals

es, cin edi cs M d, eti Foo Cosm

FIGURE 1.11 Pathways of pollutants within environmental ecosystems, and finally to human. Adopted from Mohamed, A.M.O., Antia, H.E. 1998. Geoenvironmental Engineering. Elsevier, Amsterdam, The Netherlands, pp. 707.

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Chapter 1 Sustainable pollution assessment practices

and affect humans’ through food intake, medicine, and the daily use of cosmetics. Various pathways for the entry of pollutants within the various environmental ecosystems of the atmosphere, hydrosphere, and geosphere are shown in Fig. 1.12. Pollutants could be adsorbed and absorbed in soils (geosphere), and depending on the degree of saturation, some components could be volatilized into the air (atmosphere). Similarly, pollutants are dissolved in water (hydrosphere), and depending on their nature, some components could be volatilized into the air. We currently lack a complete understanding of the various mechanisms involved in pollutant interactions from the physical, chemical, and biological viewpoints. In addition, the impact of external environmental loads on the physical, chemical, and biological systems is unclear. To seek an understanding of such mechanisms, scientists must collaborate to develop knowledge-based systems that accommodate all related disciplines and help policy-makers to arrive at valuable and informative decisions.

1.8 General pollution assessment framework Pollution assessment regarding air, water, or soil is generally performed with an understanding of the various initial and boundary conditions, and the physical settings and scales of the problem under investigation (Foster and Hirata, 1988). The physical conditions may be simple settings with welldefined initial and boundary conditions of both the physical setting and the pollutants in question. On the other hand, they could be complex from the viewpoint of the group of interacting pollutants with a high level of toxicity, the interacting human environment, and unclear sources of pollutants. One could add to such scenarios the geological and hydrological settings of the site in question.

FIGURE 1.12 Pathways for entrance of pollutants into environmental ecosystems. Adopted from Mohamed, A.M.O., Antia, H.E. 1998. Geoenvironmental Engineering. Elsevier, Amsterdam, The Netherlands, pp. 707.

1.8 General pollution assessment framework

35

Therefore, any pollution assessment should be composed of (1) initial investigative studies of possible causes of surface and groundwater contamination; (2) full mineralogical, physical, and chemical analyses of the geological setting; (3) complete chemical and microbiological analyses of the pollutants in question; (4) comprehensive modeling of the pollutant transport in both the surface and subsurface soils; and (5) a thorough characterization of the toxicity, risks, and potential hazards. To perform this task properly, we must employ a group of talented people with backgrounds in earth, geophysical, and biological sciences, and geotechnical, geoenvironmental, hydrogeological, and environmental engineering. Readers who are interested in site assessments are advised to read Chapter 6 of this book on site investigation (authored by Mohamed et al., 2020a). In addition, decision-makers and the community should be well-versed in “. the limitations of existing technologies .” (US National Academies, 1994); hence, decisions should focus on up-front protection of the environment rather than relying on the effectiveness of potential mitigation measures or remediation actions after pollution has occurred. This point was made clear in the report by the Committee on Ground Water Cleanup Alternatives of the US National Academies (1994) Alternatives for Ground Water Cleanup, in which of 77 sites reviewed, only eight had reached cleanup goals. In the same report, it was unequivocally stated that “. although [this report] highlights many success stories, the committee wishes to emphasize that these successes are rare .” The committee concluded that “with the exception of a combination of factors of very simple geology, fully dissolved in the water chemicals, and limited quantities and duration of contamination sources (with only 14 out the 77 sites falling in this category) where full cleanup was technologically feasible, for the remaining sites cleanup to health-based standards was impossible, the only feasible solution being that of containment.” Before moving ahead with a pollution assessment, one should determine the boundaries to be able to protect other areas around the site under investigation. Moreover, because of the site complexity, separation of the acting variables is needed, which may require independent field investigations to assess the extent of pollution. Finally, because of the high cost of related site investigations, an optimization process is needed. Fig. 1.13 shows the controlling variables that determine the potential pollution potential (PPP) that would occur in a specific medium. These variables are described as (1) the boundary conditions being confined or unconfined conditions; (2) medium attenuation capacity in view of chemical, biological, biodegradation, diffusion, sorption, and dilution processes, (3) expected pollutant travel time being in decades, years, months, weeks, or days; (4) characteristics of the medium itself; (5) medium type being air, water, or soil; and (6) medium vulnerability that depends on the intrinsic physical characteristics. These variables, with the potential pollutant loadings (PPL) (low to high), will determine the severity of the PPP at a specific site. For example, if one investigates the PPL and the medium vulnerability (MV), one could have wide-ranging possibilities from low (L) to high (H). Therefore, the upper right corner in Fig. 1.13, in which there is a combination of H for both PPL and MV, results in an evaluation characterized as extreme PPP. If the PPP shown in Fig. 1.13 is calculated in terms of probability, it might become a recognized classification technique of environmental risk associated with sources of contaminations (i.e., of the potential that the medium might be polluted at concentrations above respective standards set by various environmental regulatory agencies). Given the virtual impossibility to be able to quantify and assess fully the physical, chemical, and biological processes and their interactions that operate in a pollution case, policy-makers should look for cautionary actions and measures that prioritize the protection of human health and the environment. As stated by Lerche and Paleologos (2001), this calls

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Chapter 1 Sustainable pollution assessment practices

Ex

tre

me

Hi gh

Po Mo llu d tio era t n Po e te nt ial Lo w Ne gli gib le Low

High Medium Vulnerability Medium Type (Air, water, soil) Medium Characteristics

Pollutant Travel Time (decades, years, months, weeks, days) Medium Attenuation Capacity (chemical, biological, biodegradation, diffusion, sorption, dilution, etc.) Confined

Boundary Conditions Unconfined

FIGURE 1.13 Proposed risk-based pollution evaluation. Modified from Foster, S.S.D. Hirata, R. 1988. Groundwater Pollution Risk Assessment: A Methodology Using Available Data. WHO/ PAHO-CEPIS Technical manual, Lima, Peru.

for “a transition from a framework where full technical quantification was expected to one of riskbased decision-making, i.e., decisions taken under uncertainty, where different scenarios with associated probabilities, consequences, and costs are taken into account.” This point was also emphasized in The Rio Declaration on Environment and Development (Principle 15), where, when there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation. During site assessment, a number of questions and associated assessment components need to be determined, as outlined in Table 1.4. These kinds of information should be determined during the survey and inventory phases of the site investigation. More information about site investigation can be found in Foster et al. (2002), Zaporozec (2002), and in Chapter 6 in this book on site investigation.

1.9 Summary and concluding remarks

37

Table 1.4 Assessment of pollutant loading components. No.

Queries

Assessment components

1

Which pollutants?

2

How mobile?

3

How is it released?

4

How much?

5

What concentration?

6

How long?

7

Type of medium

Assess human activity and possible associated pollutants: (a) type of activity, (b) distribution category (point, multipoint, diffuse, etc.), and (c) pollutant characteristics. Assess pollutant transport and attenuation in specified medium: (a) scope of pollutant elimination or degradation, and (b) scope of pollutant retardation. Assess mode of pollutant disposition in specified medium: (a) basic medium characterizations (physical, chemical, biological, and mineralogical in the case of soil. Estimate quantity of pollutant released from human activity Estimate spatial and temporal concentration of pollutants in specified medium Assess duration of application of pollutant load in specified medium: (a) probability of pollutant release, and (b) pollutant application rate. Classification of medium as homogeneous or heterogeneous.

1.9 Summary and concluding remarks Sustainable development, the balance of social and economic activities within the context and limits imposed locally, regionally, and globally by the natural environment ecosystem, has proven no longer to be a theoretical concept, but a need that must find the practical means to be implemented if the future of humankind is not to be threatened. In this task, scientists and engineers need provide the knowledge for potential impacts, find ways to address those impacts, and provide the required tools to policymakers to contribute to the development of social consensus and direct appropriate economic activities. In this chapter, we discussed the global outlook for environmental problems, as well as an assessment of pollution in the atmosphere, hydrosphere, geosphere, and biosphere from the viewpoint of sustainable development, and suggested remedial action and measures for restoring the environment. In addition, worldwide case studies in relation to the extensive use of natural systems, the extinction of flora and fauna, and the degradation and deterioration of the environment owing to the uncontrolled generation of solid and liquid waste, air, and water pollutants, were discussed. Solutions and remedial measures that positively enhance human well-being and do not degrade the environment further were highlighted.

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The chapter also discussed global climate change, pollution of the air, water, and soil, plastic debris in rivers and oceans, the decrease in wildlife species, and the generation of large quantities of waste owing to chemical industries in various sectors such as agriculture, food processing, medicine, oil and gas, energy, manufacturing processes, and everyday products. National and international conventions and legislation regarding these problems were also highlighted. A proper assessment of pollution risks that include investigative studies to evaluate contaminant sources, a comprehensive evaluation of chemical and biological components, and modeling of contaminant transport in air, soil, and water suggest that experienced professionals from the earth, geophysical, and biological sciences, and hydrogeology, geology, geoenvironmental, and environmental engineering are needed to tackle such problems. This chapter also argues that traditional scientific and engineering disciplines need to evolve by encompassing scientific knowledge from related disciplines to be address the complexity of the problems we are currently facing.

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Paleologos, K.E., Selim, M.Y.E., Mohamed, A.M.O., 2020. Indoor Air Quality: Pollutants, Health Effects, and Regulations. Chapter 8. In: Mohamed, A.M.O., Paleologos, E., Howari, F. (Eds.), Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering: Concepts, techniques, and Practice, 1st edition. Elsevier. ISBN-13: 978-0128095829; ISBN-10: 0128095822. Parker, S.P., Corbitt, R.A. (Eds.), 1993. Encyclopedia of Environmental Science and Engineering. McGraw-Hill Inc., New York. Pirages, D., 1994. Sustainability as an evolving process. Futures 26 (2), 197e205. RAMSAR. http://www.ramsar.org/. (Accessed August 22, 2015) Rodrı´guez, L., Ruiz, E., Alonso-Azca´rate, J., Rinco´n, J., 2009. Heavy metal distribution and chemical speciation in tailings and soils around A Pb-Zn mine in Spain. J. Environ. Manag. 90, 1106e1116. Rotterdam Convention, 2001. http://www.pic.int/. Sachs, J.D., 2005. Investing in Development: A Practical Plan to Achieve the Millennium Development Goals. UN Development Programme, New York, NY. Serageldin, L., 1993. Making development sustainable. Finance Dev. 30, 6e10. Stockholm Convention, 2001. http://chm.pops.int/. Syriopoulos, K., Samitas, A., Dimitropoulos, V., Boura, A., AlBlooshi, D.M., 2020. Health Economics of Air Pollution. Chapter 12. In: Mohamed, A.M.O., Paleologos, E., Howari, F. (Eds.), Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering: Concepts, techniques, and Practice, 1st edition. Elsevier. ISBN-13: 978-0128095829; ISBN-10: 0128095822. UN (United Nations), European Commission, IMF (international monetary fund), OECD (organization for economic cooperation and development), World Bank, 2003. In: Handbook of National Accounting - Integrated Environmental and Economic Accounting. Studies in Methods” Series F, No.61, Rev.1. UN, New York, p. 572. UN Agenda 21 on Biological Diversity. http://www.unep.org/Documents. Multilingual/Default.asp? DocumentID¼ 52& ArticleID¼63. UN United Nations World Commission on Environment and Development, 1987. Our Common Future. Oxford University Press. UN, 1989. Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal, 22 March 1989, the Conference of Plenipotentiaries in Basel, Switzerland. http://www.basel.int/ TheConvention/Overview/tabid/1271/Default.aspx. UNCCD, 2017. Secretariat of the United Nations Convention to Combat Desertification. Global land Outlook, 1st edition, p. 320. https://knowledge.unccd.int/sites/default/files/2018-06/GLO%20English_Full_Report_rev1.pdf. UNEP (United Nations Environment Programme), 1989. Environmental Data Report. United Nations Environmenta1 Program, New York. UNEP (United Nations Environment Programme), 2005. GEO5 Global Environment Outlook: Environment for the Future We Want. Chemicals and Waste, pp. 168e192 (Chapter 6). UNEP (United Nations Environment Programme, Arias, E., Albar, M., Becerra, M., Boone, A., Chia, D., Gao, J., Mun˜oz, C., Parra, I., Reza, M., Saı´nz, J., Vargas, A., 2004. “Gulf of California/Colorado River Basin,” GIWA Regional Assessment 27. University of Kalmar, Kalmar, Sweden, p. 96. United Nations Economic and Social Council, 2002. Implementing agenda 21: report of the secretary-general. In: Commission on Sustainable Development acting as the preparatory committee for the World Summit on Sustainable Development, 28 Januarye8 February 2002. US Department of Health and Human Services, September 2008. Draft toxicological profile for chromium. In: US Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry, p. 610. US National Academies, 1994. “Alternatives for Ground Water Cleanup,” Report by the Committee on Ground Water Cleanup Alternatives, Water Science and Technology Board, Board on Radioactive Waste Management, Commission on Geosciences, Environment, and Resources.

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Verstraeten, W.W., Neu, J.L., Williams, J.E., Bowman, K.W., Worden, J.R., Boersma, F., August 10, 2015. Rapid increases in troposphoric ozone production and export from China. Nat. Geosci. 8, 690e695. https://doi.org/ 10.1038/ngeo2493. WHO (World Health Organization), 2015. The Health and Environment Linkages Initiative (HELI): Toxic Hazards. Accessed 24 August 2015. http://www.who.int/heli/risks/toxics/chemicals/en/. WHO/UNICEF, 2004. Meeting the MDG Drinking Water and Sanitation Target: A Mid-term Assessment of Progress. WHO, Geneva. Wilson, E.O., 1992. The Diversity of Life. Belknap Press of Harvard University Press, Cambridge, MA. Woodall, L.C., Sanchez-Vidal, A., Canals, M., Paterson, G.L.J., Coppock, R., Sleight, V., Calafat, A., Rogers, A.D., Narayanaswamy, B.E., Thomson, R.C., December 17 , 2014. The deep sea is a major sink for microplastic debris. Royal Society Open Science 1. https://doi.org/10.1098/rsos.140317, 140317. World Commission on Environment and Development (WCED), 1987. Report of the World Commission on Environment and Development: Our Common Future, Transmitted to the General Assembly as an Annex to Document A/42/427. Development and International Co-operation: Environment. WorldSteel Association, 2015. World steel in figures 2015, report, p. 17. https://www.worldsteel.org/dms/ internetDocumentList/bookshop/2015/World-Steel-in-Figures-2015/document/World%20Steel%20in% 20Figures%202015.pdf. WSSD, 2002. World Summit on Sustainable Development (WSSD). Johannesburg Summit, Johannesburg, South Africa, 26 August - 4 September 2002. https://sustainabledevelopment.un.org/milesstones/wssd. Zaporozec, A. (Ed.), 2002. Groundwater Contamination Inventory: A Methodological Guide. IHPVI Series on Groundwater No 2. UNESCO, Paris.

Further reading Lenton, R., Wright, A.M., Lewis, K., 2005. “Health, Dignity, and Development: What Will it Take?” UN Millennium Project Task Force on Water and Sanitation. Earthscan, London. UN (United Nations, 2012. In: Report of the United Nations Conference on Sustainable Development. Rio de Janeiro, Brazil, 20e22 June 2012. A/CONF.216/16, p. 86. http://www.un.org/ga/search/view_doc.asp? symbol¼A/CONF.216/16&Lang¼E. UN, 2015. Transforming Our World: The 2030 Agenda for Sustainable Development,” A/RES/70/1. https:// sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable% 20Development%20web.pdf. UNDP (United Nations Development Programme), 2005. Human Development Report. Oxford University Press, Oxford. UNESCO, 2011. From Green Economies to Green Societies: UNESCO’s Commitment to Sustainable Development, p. 79. France. http://unesdoc.unesco.org/images/0021/002133/213311e.pdf. UN Agenda 21, Report of the United Nations Conference on Environment and Development (Rio de Janeiro, 3e14 June 1992), Chapter 12, Managing Fragile Ecosystems: Combating Desertification and Drought, http:// www.unccd.int/en/about-the-convention/history/Agenda-21-Chapter-12/Pages/default.aspx.

CHAPTER

Risk analysis and management

2

Evan K. Paleologos1, Abdel-Mohsen O. Mohamed2, 3 1

College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates; 2Uberbinder, Inc., Seattle, WA, United States; 3EX Scientific Consultants, Abu Dhabi, United Arab Emirates

2.1 Introduction In many practical applications an engineer/scientist is faced with a situation where knowledge of the processes involved, their interaction and relative significance of each, the impact of natural or engineered system components, and the sensitivity of the processes to several factors is incomplete, and thus understanding remains at a rudimentary and qualitative level. For example, constructed wetlands that mimic natural marshes are used as an inexpensive, environmentally friendly, and aesthetically appealing way to treat domestic wastewater. Removal of nitrogen, phosphorus, heavy metals, and pathogens, as well as decomposition of organic matter, is accomplished through several physical, chemical, and biological processes. These processes interact with each other and depend on external conditions, such as temperature, atmospheric aeration, etc., wastewater quantity and quality indicator parameters and their fluctuations, type of flow, and design characteristics, which include, among others, type of vegetation, length and width of the constructed wetland, bed compartmentalization, and composition of the substrate material. All these factors compose a highly complex system, segments of which we understand at least qualitatively, but which as a whole eludes our capabilities to describe in a quantitative way through a model, and hence to predict its behavior, except empirically. In other cases, uncertainty enters our decisions to design solutions that can prevent or mitigate environmental problems because of the limited data that are available. This may arise due to budget constraints in field characterization, or may have been collected at different time periods, may contain equipment or human errors, or even represent information at a measurement scale that does not correspond to the modeling scale. Data may also exhibit significant variability, as in the case of groundwater flow or contaminant transport characterization, where parameters may exhibit quite a lot of variations within small areas, or for precipitation data spatial and temporal fluctuations are expected, thus necessitating the use of nonlinear dynamics or fractal models to describe them. Actually, for a complex system with limited data availability to elucidate its functions, it is recommended to rely on simple models that capture the essential aspects of its behavior, or on empirical ones, rather than attempting to construct sophisticated, highly mathematical models, which by definition will have to include many assumptions to make the description of all the processes and their interrelations mathematically tractable. The latter presents the danger that the superstructure of the Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering. https://doi.org/10.1016/B978-0-12-809582-9.00002-5 Copyright © 2021 Elsevier Inc. All rights reserved.

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heavy modeling apparatus may overshadow whatever information is contained in the data and together with the easy production of graphs it may lead to unreasonable interpretations of results that are mere numerical artifacts. All these aspects of scientific and engineering decision-making, that is the incomplete knowledge of processes and their interaction, the complexity of a total system’s behavior, and the variability, scaling issues, errors, and limited sampling of data, are usually lumped together under the common title of uncertainty. Examples of the uncertainty that enters engineering projects include construction of a dam, when the size of future flood events and the geology of a site are not known; deciding between alternative foundation designs, loads, and materials for bridge construction, when the supporting capacity of the soil is not fully known, and the traffic capacity may have changed over the years; and selecting the location and construction material of a landfill or an artificial pond, when the hydrogeologic conditions may not be fully known. Characterization and quantification of the uncertainty in a project, or in any aspect of our lives where we must decide, is the venue of probability theory, and analysis of the behavior of data that have been collected falls under the theory of statistics. The unknown parameters of a problem, or in other words all the possible ways that natural processes may operate, beyond our comprehension, now, or evolve in the future, are described mathematically by the term state of nature. Thus the first component and the initial task of an analysis that will determine our actions or decisions in a project is to enumerate all possible states of nature of a problem and attempt to assign probabilities of occurrence to each of these states. It is important here to recognize that for most projects there exists information beyond the measurement values, which is not quantifiable and termed qualitative or soft information. Examples of soft information include the experience of an engineering team from other similar projects, transmission of oral information by colleagues familiar with the broad area where the project is planned, knowledge of the general geology of the site, etc., all of which in practice are incorporated during the decision-making process. Traditional statistical analysis does not utilize this soft information and is concerned with reaching conclusions only through the analysis of the samples’ characteristics. Additionally, it does not account for the economic cost of making decisions and of selecting an action, which can be incurred by overestimation or underestimation of the parameters that are analyzed using sampling statistics. For example, pharmaceutical companies run, among others, clinical studies to assess the effectiveness of a drug and they conduct target group studies to assess the market appeal of their product. Underestimation of the effectiveness would result in the appearance of the product as less effective than it is, whereas underestimation of the market appeal would lead to shortages in the market. In contrast, overestimation of the former may be construed as misleading the public, and in the case of the latter it may result in overproduction. In subsequent parts of this chapter we will show that there exists a probabilistic mechanism through which soft information can be incorporated in the decision-making analysis. Almost all engineering problems do not have a single solution, but there usually exist multiple options to address an issue of different technical characteristics, effectiveness, time life, maintenance, safety, health and environmental impact, monitoring and legal requirements, and costs. The various options that are available, depending also on the state of nature that we design for, constitute the second component of a decision-making problem and are designated as actions or decisions. Decisions have consequences, which depend both on the action taken and the state of nature that came to be, and these in some cases can far exceed expected or projected costs. For example, one of the

2.1 Introduction

45

largest catastrophes related to dam failure, cited by UNESCO as one of five cautionary tales caused by the failure of engineers and geologists, occurred in 1963 at the Vajont Dam, Italy. Intense days of rain and slope instability led to massive material landslide into the reservoir, resulting in water overtopping the dam, and then moving down the valley, flooding downstream villages, and causing about 2000 deaths. Another example of an unanticipated consequence because of engineering decision together with an extreme state of nature was the 2007 collapse of the eight-lane, steel truss I-35W Mississippi River Bridge in Minneapolis, Minnesota, USA, where the collapse was caused according to the US National Transportation Safety Board (NTSB) by a design flaw in its too-thin gusset plates and the weight placed on the bridge during the evening rush hour when the bridge failed. More routine, but, however, not factored in to the usually expected costs of operation of projects are the spills of chemicals during their transport, the leaks from underground storage tanks of gas stations or from the bottom liners of landfills, the air pollution incidents from factories, the soil and water pollution from agricultural and animal industries, and the land subsidence and salt water intrusion as a result of overpumping, which has necessitated expenditure of millions of dollars for water diversion, levee construction, and artificial recharge pond projects, etc. (Duckstein et al., 2007). Thus the elements of a decision-making theory that utilizes the statistical analysis of data together with soft information to reach decisions that have consequences measured (for simplicity at this stage) in monetary terms are as follows (Berger, 1985): (1) basic elements of decision theory, (2) state of nature, (3) decisions or actions, and (4) loss function. The fact that statistical estimation of an uncertain parameter, q, representing the state of nature has economic repercussions is addressed through the loss function, Lf, which depends on the particular action taken, a, and on q, that is Lf is a function Lf (q, a). The term loss is used in the statistical terminology, whereas in economic decision theories it is designated as gain or utility. The random variable, which during sampling is utilized to estimate an unknown population parameter, q, will be designated here with X, and a specific value, which the variable can take during an experiment, will be indicated by x. For example, to estimate the market appeal, measured by the willingness to buy q, of a mobile phone with a new feature, a study sampling 1000 people may be conducted. The true value of q for the whole population is not known a priori before the product appears in the market and has spent some time there for people to respond to its perceived competitive advantage, and hence companies try to estimate this value based on a limited sample of the general population. The random variable (rv) that represents in this sample group the willingness to buy is denoted by X (in this case a Bernoulli rv) and can take values, which for each sample group member, are: x ¼ 1 (willingness to buy) or x ¼ 0 (nonwillingness to buy). Obviously, the rv X is characterized by a probability density function, which because it depends on the unknown parameter q will be symbolized by f (x, q). Risk analysis consists of defining all the potential events (states of nature) E that can cause a project to fail, the probability P(E) that an event is assessed to have for occurring and the consequences, which depend on the event and action taken to address them. The analysis of risk is followed by risk management, which attempts to mitigate the risks, either by taking measures to reduce the likelihood of an event from taking place, or actions implemented to moderate the consequences (Van Gelder et al., 2004). This chapter outlines the basic necessary tools that are required in the analysis of risk in various engineering problems, discusses some elements of statistical decision theory, and in particular Bayesian decision theory, and subsequently expands on topics related to risk management.

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2.2 Decision trees Decision trees are perhaps the easiest way to visualize and solve sequential decision problems and they have an advantage when the complexity of the pair (decision state of nature) increases (Raiffa, 1997; Lerche and Paleologos, 2001). It is also an excellent way to structure and focus one’s thinking on the various risks that exist for a project and what actions to take to address them. Constructing a decision tree starts from the left and when a decision needs to be made, a decision node represented by a square is placed. All the possible actions or decisions form the branches of the tree as in the case of Fig. 2.1, where two decisions are shown, a1 and a2. Continuing the decision tree to the right, a chance node is placed after each decision, represented by a circle, to indicate that aspects of the current situation or future events are unknown and unpredictable. All possible states of nature should be listed (shown as three possible states, s1, s2, s3, in Fig. 2.1) and are associated with a fixed value of probability of occurrence for each state. This is an important point that in some respects limits the analysis done through decision trees, i.e., that a fixed value of probability of occurrence for each potential event is used, which means that either sampling is not conducted and subjective probability values are assigned without the use of data, or if sampling is performed then the full information from the sampling probability distribution function is not utilized, only the expected value. This point will be explored in a later section and the difference in analysis between the use of decision trees and Bayesian decision theory will be detailed. Finally, at the end of each path the consequence of decision ai when event sj appears is entered, usually measured in monetary terms. Decision trees can expand vertically, if many decisions are under consideration, or horizontally, if the sequence of a particular decision-event pair (ai, sj) necessitates subsequent decisions, after the appearance of an event sj. The branches of a decision tree do not have to extend equally.

FIGURE 2.1 Decision tree.

2.2 Decision trees

47

For example, by considering the bottom liner of a landfill, decisions may be between a type II composite liner system, composed of clay, geotextiles, and geomembranes at a cost of about $9 million for a 100-acre landfill, or a type III double liner system, consisting of two composite systems at a cost of about $14 million for the same area (Illinois EPA, 2003; ITRC, 2003). Both systems meet the US Federal Regulations with the type III, which provides additional protection, having been mandated in a few states such as Connecticut, Massachusetts, New York, and Kentucky, and in some special cases in other states too. The states of nature that may be considered in a decision tree may be leakage or no leakage from a landfill’s cell with the corresponding probabilities. Here, for simplicity reasons, we are defining only two states of nature, where, in reality, one may wish to differentiate between various levels of leakage, say with an upper limit to the expected cost of remediation, and assign different probabilities to each leakage level. In the case though of the branch with the event leakage, one may wish to consider the decision of what type of clean-up technology to use. Thus at the end of this event a decision node may be placed with decisions between two technological options: pump-and-treat or permeable reactive barrier (PRB) (Mohamed and Antia, 1998). Each of these two new branches will be followed by a chance node, where the various states of nature to be considered reflect the chance of encountering various hydrogeologic formations at the site, which would affect the effectiveness of each system (Paleologos, 2008). Pump-and-treat has been seen to work well primarily in homogeneous single aquifers (state of nature s11) but are very inefficient in layered aquifers or highly heterogeneous ones (states of nature s12 or s13) (US National Academy of Sciences, 1994, 1999). Finally, the costs are clearly a function of the initial decision on the type of liner, the occurrence of a leak or not, the remediation technology selected, and the hydrogeologic environment encountered. A segment of the decision tree that describes this situation is presented in Fig. 2.2. Decision trees can also be useful to investigate the benefits of purchasing insurance, and up to what coverage and premium to cover for the eventuality of pollution from a municipal or hazardous waste facility, or to evaluate the impact of regulation changes in the future, which will make, say, air emission standards at incineration waste plants more stringent (Lempp et al., 2002; Paleologos et al., 2018).

FIGURE 2.2 Decision tree for choice of landfill bottom liner. PRB, Permeable reactive barrier.

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Chapter 2 Risk analysis and management

In the latter case, decisions might have been made at some point in the past related to the type of filters, which under the regulations applicable when the decision was made were deemed to be adequate in meeting the emission limits of a country (US EPA, 2002; EUR-Lex, 2007, 2010; U.S. Code 42 USC x 7413, 2012; Edwards, 2014; Paleologos et al., 2018). The states of nature related to potential air pollution violations have been assessed and the consequences analyzed, based on the past record of the type of filters in the industry and existing regulations. However, since operation of waste incineration plants runs for decades, the decision tree may extend to include a decision node that may investigate the question of filter upgrade under the possibility of a regulatory change.

2.3 Optimum decision criteria 2.3.1 Maximum expected monetary value criterion Let us consider a situation that is common to many commuters, that is in the daily driving routine there exist two alternative routes to go to work. One that is shorter and takes 10 min and the other, which is 40% longer, and takes 14 min to reach work under normal traffic conditions. However, the first, exactly because it is shorter, has much more traffic and 30% of the time during the rush hour the trip can take up to 30 min. The longer route is lighter in traffic and even during rush hour the worst that can happen is to be delayed by 3 extra min (Table 2.1). Let us create a so-called payoff table for this situation instead of a decision tree. The expected value of decision 1 to take route 1 is simply: E½D1 ¼ 10  0:7 þ 30  0:3 ¼ 16 min

(2.1)

The expected value of decision 2 is given by: E½D2 ¼ 14  0:7 þ 17  0:3 ¼ 14:9 min

(2.2)

The criterion used more often in decision-making analyses (Benjamin and Cornell, 1970; Berger, 1985) is the one that selects as the optimum decision the one that, on average, returns the highest benefit (or equivalently produces the smallest loss). This is called the maximum expected monetary value (MEMV) criterion. In this case MEMV would pick D2: route 2 as the optimum decision for our travel plans, since on average it returns the lowest commute time. One may argue that this is an impractical or even an artificial way to make the best decision since in real life we are not seeking to optimize on average the return from a multitude of projects, but we want to make the best decision for a specific, single project. However, MEMV mimics a way that our brain makes logical connections and reaches decisions. Many commuters prefer to take a longer route, but Table 2.1 Payoff table: travel time.

2.3 Optimum decision criteria

49

which is safer to avoid potential long delays during rush hour, accidents because of heavy traffic, etc. These people automatically drive on a longer route every day, without checking the traffic conditions of a day, because their cumulative experience, after many trials on both routes, taught them that they prefer a comfortable, few minutes longer commute than a shorter one, which may present unpleasant surprises or requires them to be continuously alert during heavy traffic. Our brain in this case operates by selecting as the best option the one that on average guarantees the least travel time under all conditions. This is also the first allusion here of what will be discussed in more detail later, that the theory of probability is nothing more than a mathematical extension of the theory of logic, or as Laplace stated it, probability theory is simply common sense reduced to calculation (Laplace, 1812).

2.3.2 Minimax criterion Let us consider now another payoff table of a project that contains monetary returns. Table 2.2 includes a situation where there exists the possibility of loss from a project. This can be the case of resource exploration and exploitation, where a potential location may present the likelihood of high profits (such as site 2 under scenario 1 shown in Table 2.2), but for which there exist also the possibility of high losses if the actual quantity or quality of reserves does not correspond to the ones estimated (such as the same site, but under another scenario, scenario 2, shown in Table 2.2). Using the MEMV criterion returns an expected profit of $5.4 million for the first site and $14.2 million for the second site, and hence MEMV would select site 2 as the best option for exploitation. To apply the minimax criterion, the procedure to be followed is to construct the regret table by subtracting from each number in a column the largest number in that column. The regret table that corresponds to Table 2.2 is shown in Table 2.3. Table 2.2 Payoff table: resource exploitation.

Table 2.3 Resource exploitation.

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The procedure implemented in Table 2.3 is based on the idea that, if an initially unknown event were somehow to be revealed to us (if we had 20/20 hindsight), we could look back at our decisions, and either feel content (have no regret) about a decision, or might regret it, because if the other decision was picked we would have done so much better. Thus if S1 turned out to be the true state of nature, we would have no regret if we made decision D2, since this was the best that we could do under S1, but if we made decision D1, we would obtain a profit of $5 million, but we could have obtained $20 million, an opportunity differential of $15 million that we lost. Similarly, for S2, D1 was the best that we could do under this state of nature, and therefore we feel satisfied with our decision, whereas regarding D2, not only did we lose $9 million, but we could have earned $7 million instead, a differential of $16 million of opportunity loss. Hence, the maximum regret that one can have if S1 came to be realized is e$15 million, and correspondingly for S2 the maximum regret is e$16 million. The minimax criterion then selects as an optimum decision the one that returns the minimum of these maximum regrets, i.e., minimax would choose exploitation of site 1 because this has the smallest regret of e$15 million. Clearly, minimax is a very conservative criterion, where the basis of the decision is to avoid the highest possible loss, or in other terms, making decisions by trying to stay away from the consequences of worst-case scenarios. It is also true that this criterion is part of our brain’s logical apparatus, which when faced with the possibility of dangerous situations, seeks to avoid worst-case possible scenarios as a survival mechanism. This criterion is used by established, large corporations to eliminate for consideration projects, which will put at risk their assets, expose them to potentially class-action lawsuits or unstable economic environments, etc., for the sake of realizing a very large profit. Minimax strategies constitute the basis for game theory, developed by von Neumann and Morgenstern (2004). It is obvious that since the starting points of the logical basis of the MEMV and the minimax criteria are completely different, and the scope of their use is also different, the recommended action may differ if one were to use the former or the latter criterion. Thus in the example used in this section, the MEMV’s recommended action for resource exploitation would be the second site, whereas the minimax’s suggested action would be the first site. Finally, it is apparent that the decision criteria developed mathematically to select an action under uncertainty are no more than logical rules, already employed in everyday decisions.

2.4 Expected value of perfect information Returning to the payoff Table 2.2 of resource exploitation, a company may wish to know what should be the most that it should pay for data collection during exploration to receive more information, which would help reduce the uncertainty related to the site’s potential to mine a resource and assist the company in making a better decision. What is clear from Table 2.2 is that if the state of nature S1 were to occur the best decision would be D2 with a payoff of $20 million. This state has an 80% chance of materializing. Correspondingly, if the state of nature that were to occur were S2 then the best decision would be D1, returning a payoff of $7 million. Thus if one could obtain perfect information from exploratory studies, then, on average, the maximum worth of this decision problem that contains two potential sites for investment would have been: EVwPI ¼ 0:8  20 þ 0:2  7 ¼ $17:4 million

(2.3)

2.5 Statistical measures in decision-making analyses

51

Here, EVwPI stands for expected value with perfect information (Duckstein et al., 1978; Duckstein, 1986). Another way to look at this is that if perfect information were available that would allow us to reach in each case the best decision, then for situations that will appear to favor by four to one the characteristics of the second site relative to the first site, the average rate of return in the long run would be $17.4 million. The expected value for the second site with the incomplete information, which was obtained from the exploratory studies, was found to be: EVwoPI ¼ E½D2 ¼ 0:8  20 þ 0:2  ð9Þ ¼ $14:2 million

(2.4)

Here, EVwoPI stands for expected value without perfect information. It should be remembered that D2 was the action that was recommended, according to the MEMV criterion, in the previous section, i.e., the second site was the preferred location for exploitation. Hence, this site is the one that is of interest to assess whether further expenditure in field studies would result in uncertainty reduction and improve decision-making. The expected value of perfect information (EVPI) is defined then as the absolute value of: EVPI ¼ jEVwPI  EvwoPIj ¼ j17:4  14:2j ¼ $3:2 million

(2.5)

In this case the EVPI provides the maximum amount that a company should be willing to spend, on average, for exploratory studies to improve its decision-making.

2.5 Statistical measures in decision-making analyses The maximum expected monetary value (MEMV) has been used extensively in engineering, such as in flood risk and dam failure (Goldman, 1997; Thompson et al., 1997), aquifer remediation studies (James et al., 1996), etc. Other rules that have been proposed include, among others, the concepts of utility function (Lindgren, 1971; Krzysztofowicz, 1986; Raiffa, 1997) and stochastic dominance (Keeney and Raiffa, 1993; Clemen, 1996), both of which incorporate a decision maker’s attitude towards risk. In this section we expand the application of the MEMV criterion with the use of three additional statistical measures that shed light on the variability of returns, while maximizing expected outcomes (Lerche and MacKay, 1999; Paleologos and Lerche, 1999). The situation in Fig. 2.3 depicts a single branch of a much larger decision tree that involves decisions on transportation, burial, operation, and monitoring of hazardous waste facilities (Papapetridis and Paleologos, 2011a, 2012). The branch shown involves action a1, which includes only transportation and cost of burial of hazardous waste at a facility. Other actions, such as a2, may involve construction, operation, or monitoring of the facility. The interest here is to investigate the effect of a low probability, catastrophic event on the decision to transport hazardous waste. The standards for transportation of hazardous waste are found for the USA in EPA’s Resource Conservation and Recovery Act (RCRA), Part 263 (EPA, 2019) and involve Environmental Protection Agency (EPA) identification numbers, tracking of hazardous waste shipments, labeling and packaging requirements, etc. Additional requirements exist for the export or import of hazardous waste and the transboundary movement of hazardous waste. A discussion of these together with information on the Canadian and European Union’s regulations is provided in Mohamed and Paleologos (2017).

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FIGURE 2.3 Branch of a decision tree without and with a catastrophic spill scenario.

Action a1 considers a bid, B, for transportation of the waste from the locations of origin to the facility. The decision involves a cost C11, if no accident and spill occur during transportation, and a higher cost C21, if there is a limited spill en route, which would require clean-up, recollection of the material spilled and of any contaminated soil at the location of the accident, and transportation of all of them to the facility. In the case where a large, catastrophic spill were to occur, the cost of the remedial measures taken, of clean-up, recollection, and transportation of the extra contaminated material becomes much larger at C31. Equivalently, the cost of burial of the waste, the tip fee at the waste disposal facility that a transporter has to pay, will escalate from C13 to C23 to account for the additional quantities of a limited spill and to a much larger cost C33 in the case of a catastrophic spill. Data related to the frequency of accidents by trucks on highways can be obtained by the Department of Transportation and can be used to estimate the probabilities of q1: no spill, and q2: limited spill, taken here to be 90% and 10%, respectively. In the case of the catastrophic spill scenario the probability of limited spill has been modified by 1% to allow for the inclusion of the q3: catastrophic spill in the analysis. The following statistical measures are included now in decision making: the expected value, E, of each branch, the variance, s2, which measures the deviation from the mean, and the volatility:

n ¼
s=E
(2.6)

2.5 Statistical measures in decision-making analyses

53

Volatility is a measure of the relative importance of the deviation of data with respect to their mean behavior. A second measure is the probability of obtaining a value greater than a fixed number V, given for the Gaussian distribution as: 1 PðX  VÞ ¼ 1  pffiffiffiffiffiffi 2p

Zb N

 2 u exp  du 2

(2.7)

Here, the standardized variable inside the integral is: u ¼ (X e E)/s and the upper limit is: b ¼ (V e E)/s. If a value greater than zero (V ¼ 0) is sought, then Eq. (2.7) becomes: 1 1 PðX  0Þ ¼  pffiffiffiffiffiffi 2 2p

Zb

 2 u exp  du 2

(2.8)

0

The upper limit is given now as: b ¼ eE/s ¼ (n)1. A high volatility (n[1) makes jbj  1 and then Eq. (2.8), through Taylor series expansion, becomes: Pð0Þ z

1 1  pffiffiffiffiffiffi b 2 2p

(2.9a)

For a low volatility n  1 the upper limit jbj[1 and then asymptotic expansion of the integral in Eq. (2.8) yield (Paleologos and Lerche, 1999):  2 1 1 b 1 Pð0Þ z ð1 þ sgnðEÞÞ þ pffiffiffiffiffiffi b exp  (2.9b) 2 2 2p These expressions can be utilized as: 1. If all the parameters of the decision tree are given, then E and s (and hence b) of a branch can be calculated, and then one can use Eq. (2.9a) or Eq. (2.9b) to evaluate whether the bid B will exceed or at least be equal to all the costs. 2. If a certain minimum probability is required that the bid will exceed or at least be equal to all the costs, say P(0) ¼ 90%, then b and E and s can be back-calculated and the range of the parameters of the branch that will yield the required probability P(0) can be assessed. The analysis of the two scenarios, of the limited and catastrophic spills, is presented next.

2.5.1 Decision analysis of limited spill Let us designate the probability of no spill by ps, the probability of limited spill by pf, and that of a catastrophic spill by pk. The expected value of action a1 is given by: Ea1 ¼ B  C21  C23 þ ps ðC21  C11 Þ þ ps ðC23  C13 Þ

(2.10)

A minimum bid, which would not bring any profit, if all transportation took place with no spill accident would be Bmin ¼ C11 þ C13, and equivalently B ¼ C21 þ C23, if a limited spill occurred. On the average a no-loss situation would occur if expression (2.10) were greater or equal to zero.

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Taking, for example, C11 ¼ 0.2, C13 ¼ 0.5 (in millions of dollars), respectively, and considering that both quantities would double in the event of a spill yields Bmin ¼ 0.7, and using this value in Eq. (2.10) a1 ¼ 0:07, s gives Emin min ¼ 0:21, n ¼ 3, and P(0) ¼ 37%. If one were to offer bids that were multiples a1 þ ðk 1ÞB of the minimum bid, B ¼ k  Bmin, then the expected value would become Ek ¼ Emin min and k  2 2 a1 2 sk ¼ ps kBmin  C11  C13  Emin  ðk  1ÞBmin þ / ¼ smin : This indicates that increasing the bid would indeed improve the average return, but the uncertainty about this return would remain the same (although the volatility would decrease). Equivalently, if the probability of a profit-bearing bid, say P(0), was set at 75%, then from tables of the standardized normal distribution (Benjamin and Cornell, 1970) the value of the upper limit jbj ¼ .675 is obtained and various bids can be tried to assess the mean and standard deviation that would satisfy this value.

2.5.2 Decision analysis of catastrophic spill The expected value of action a1 if a one-in-a-thousand probability of a catastrophic spill is included is given by: EKa1 ¼ Ea1  pk ½ðC31 þ C33 Þ  ðC21 þ C23 Þ

(2.11)

where Ea1 in Eq. (2.11) is the same quantity as that given in Eq. (2.10) and it was taken that ps þ pf þ pk ¼ 1: It is clear from Eq. (2.11) that the expected value of a situation that considers a catastrophic event will always be smaller of the expected value which does not include the impact of an extreme event, no matter how small the probability of the extreme event is set. The quantities C31 and C33 are always larger than their corresponding C21 and C23, and hence the second term on the right-hand side of Eq. (2.11) is strictly positive and since it is subtracted from Ea1, an expected value EKa1 < Ea1 will always be returned. However, despite the mathematical validity of this inequality, the probability of extreme events is usually several orders of magnitude smaller than the difference in cost between catastrophic and limited spills, hence the second term on the right-hand side of Eq. (2.11) is practically very small, and hence EKa1 z Ea1 : Thus the MEMV does not clearly differentiate the average return between a limited and a catastrophic pollution event. The minimum bid under optimum conditions is the same as in the previous case, but now a much higher bid, B ¼ C31 þ C33, than in the case of limited spill must be deliberated if a catastrophic spill is included. The average, in the long run, no-loss bid would be: B  C31 þ C33  ps ðC31 C11 Þ  ps ðC33 C13 Þ  pf ðC31 C21 Þ  pf ðC33 C23 Þ. Keeping the cost of transportation and burial for no spill and limited spill the same as previously, the highest expense in the case of a catastrophic spill would be borne for remediation at the site of accident, while the quantity to be buried would not be dissimilar to that of a limited spill, i.e., we set C31 ¼ 100 and C33 ¼ 1 (in millions of dollars). a1 With Bmin ¼ 0.7, the same as before, Eq. (2.11) yields EK;min ¼ 0:17 (a negative value more than double that of the limited spill), sk;min ¼ 3:17 (an order of magnitude uncertainty increase), and n ¼ 18:6 (a six times increase in volatility). Similar to the limited spill case, bids that are multiples of Bmin improve the average return, but do not alter the uncertainty. Thus consideration of a catastrophic scenario in decision-making analyses should not be done lightly, merely as an exercise in what-if scenarios; however, after careful thought of its plausibility because its inclusion can alter drastically the perspective on a project, this may lead either to

2.6 Extended environmental cost

55

overconservative solutions or elimination of the project from consideration. In the case that an extreme environmental disaster in a project may occur, a corporation should seek to place a ceiling on its financial exposure through a consortium, insurance, or limited liability agreements.

2.5.3 Worth of additional statistical measures to the MEMV Some general conclusions can be drawn from the foregoing analysis. The addition to the criterion of MEMV of three simple statistical measures, variance, volatility, and probability of exceeding a fixed value of the return of a project, sheds significant light on decisions. The probability of exceedance can be used to mandate a priori a fixed probability of monetary return that would be acceptable, and from which the range of parameters of a project that are worthwhile or not to bid for can be assessed. If in a project there exists a chance that it can fail, and because a minimum bid is considered the amount just meeting the cost of the project under optimum conditions, multiples of this bid will of course increase the MEMV correspondingly, but no matter how large these bids are they will not improve the uncertainty about the return of the project. Inclusion of an extreme scenario will always yield worse than expected returns than if such a case were not considered. However, the full effect of a catastrophic situation is not revealed in the expected return through the MEMV since this does not differ significantly from that of a limited spill case but in the substantial increase in uncertainty. This may lead to overdesign and additional measures for systems backup, monitoring, etc., and is perhaps the reason why in many projects catastrophic scenarios are not considered. To compound the situation, the probability of extreme events cannot be assessed through the frequentist theory of probability, i.e., of the notion that a specific event appearing m times out of a total of N trials has a probability m/N, and, if instead experts’ opinions were utilized it has been seen that the human brain has difficulty in assessing the likelihood of extreme events (Kahneman and Tversky, 1979, 2000; Kahneman, 2012).

2.6 Extended environmental cost We return now to the problem of selecting the landfill bottom liner discussed briefly in Section 2.2. The decision between a Type II and a Type III bottom liner (which with its double liner system provides an extra level of protection) will be contrasted. The cost difference between the two options is about $5 million (Illinois EPA, 2003; ITRC, 2003). The historical average value of probability of limited contamination leaks that do not exceed 2 million US dollars in remediation costs in existing landfills is considered here to be about q ¼ 0.3 (probability of landfill failure in Wyoming was assessed to be 0.23 (Wyoming, 2004), whereas other sources place it higher, and US EPA has stated that all landfills will eventually leak (USEPA, 1988; Christenson and Cozzarelli, 2003)). The analysis in this section will be kept at the simplest possible level, without consideration of the attitude of an organization to risk, with a simple loss function, and without utilizing more advanced probabilistic mathematical tools (Paleologos, 2008, 2009).

2.6.1 Limited leakage (remediation cost less than US$2 million) For a limited leak a private operator of a landfill does not need to analyze the options, since the cost difference between the bottom liners and the remediation that may be required does not warrant

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selection of the Type III bottom liner. We will however proceed with a simple probabilistic coste benefit analysis to evaluate the level of insurance, which can be considered by a private operator to protect against a leak (Paleologos, 2008). Since for a new landfill its probability that it would leak is not known a priori it will be designated by the unknown parameter q. Based on historical data, a histogram that describes this parameter can be constructed, which can be modeled by a probability distribution and its moments, which can then be used for a more detailed mathematical analysis (namely, a Bayesian decision analysis). The leakage or no-leakage from a landfill can be described by a Bernoulli random variable:  1 SuccessðPollutionÞ Probability q (2.12) X¼ 0 Failure Probability 1  q The moments of Bernoulli are: E[q] ¼ q and the variance Var(q) ¼ q (1 e q). The action space, for a private operator that has decided that it is not worth to exceed the regulatory requirements for the bottom liner by investing in the Type III is given by (Paleologos, 2008): • Decision a0: The organization does not purchase insurance for leakage

(2.13)

• Decision a1: The organization purchases insurance for leakage Let us designate with B the cost to remediate a leakage that does not to exceed $2 million and with C the insurance cost, which would cover entirely any remediation cost up to $2 million (hence for simplicity the deductible is zero) (Paleologos, 2008). A simple loss function Lf (q, a) is given in Fig. 2.4 (Paleologos, 2008). The expected loss for actions a0 and a1 is given, respectively, by: E½Lf ðq; a0Þ ¼ B q; and E½Lðq; a1Þ ¼ C

(2.14)

The value for which selection of an action becomes indifferent is at probability q ¼ C/B. Eq. (2.14) contains the unknown probability q and further mathematical treatment can be done, as will be shown

FIGURE 2.4 Decision tree for insurance consideration in a leakage scenario.

2.6 Extended environmental cost

57

in subsequent sections, through Bayesian decision theory. For the time being, if we use for simplicity the fixed average value from data of existing landfills of probability q ¼ 0.3 and B ¼ $2 million this returns a value of C ¼ $600,000. Hence, a private operator can select either to face, with probability 30%, a remediation cost of up to $2 million, or spend up to $600,000 for insurance, which will cover him/her up to $2 million of clean-up costs (Paleologos, 2008).

2.6.2 Catastrophic leakage (remediation cost exceeds US$2 million) A private operator of a landfill is primarily concerned about the possibility of a pollution event where remediation will far exceed the historically expected, limited level of pollution. The question therefore that is posed here is: if one is unwilling to opt for the more expensivedand not mandated, at least in most States in USA and in EuropedType III liner, what is the level of pollution that may convince one to alter his/her decision? (Paleologos, 2008). Eq. (2.12) is modified now, and in the place of no pollution we consider as zero-base pollution any event with remediation cost below $2 million, which a landfill operator has budgeted for and is prepared to absorb (Paleologos, 2008). In the same expression the case of pollution is represented now by a catastrophic event, where the remediation cost, B, above the ‘zero-base’ clean-up cost is significant. C is again the insurance cost to be assessed, the amount that is worthwhile to spend to protect from the likelihood of a catastrophic leak (Paleologos, 2009). The action space is phrased now in more general terms (Paleologos, 2008): • Decision a0: The operator selects a minimum  mandated type of bottom liner and does not take measures against a catastrophic event.

(2.15)

• Decision a1: The operator takes measures for a catastrophic pollution event The loss function with the appropriate definitions of actions and events remains the same as in Fig. 2.4 (Paleologos, 2009). The probability of catastrophic failure is now much smaller than q ¼ 0.3, which was inferred from limited pollution incidents (Paleologos, 2009) and used in the previous case and is q ¼ 5%. Such a case can occur, as is shown in Fig. 2.5, when the monitoring network of wells fails to detect the subsurface contamination plume that originated from a landfill cell, and this can travel, extend laterally and vertically, and contaminate a large area (Papapetridis and Paleologos, 2011b; Paleologos et al., 2014). If a catastrophic pollution event’s clean-up cost reaches B ¼ $20 million (more than any amount needed for limited remediation) then the insurance cost C that a landfill operator should be willing to pay (Paleologos, 2009) to protect for a catastrophic spill is $1 million, based on Eq. (2.14). This amount for clean-up cost is at the lower end of the range of remediation cost for older open dumps (between $20,000 and $30,000) in the United States, which had operated with no controls, accepting also industrial or chemical waste. It is much lower than the clean-up cost that has been incurred at Superfund sites, past industrial and manufacturing sites, or at air force bases (Hocking et al., 2006a). For example, the construction, operation and maintenance, and monitoring and reporting of a pumpand-treat system (Hocking et al., 2006b) at a former manufacturing site at Oakley, California, had exceeded $34 million before it was replaced by a more efficient PRB system.

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300

250

200

150

100 0

50

100

150

200

250

300

FIGURE 2.5 Numerical simulation of a contamination plume in heterogeneous soils past a monitoring well network.

Hence, a private operator of a municipal landfill will benefit by expending 1 million extra for insurance, beyond the construction cost of the basic bottom liner, to protect from the possibility of a catastrophic event (Paleologos, 2008). However, even for this extreme case this amount does not reach the additional cost of a Type III liner and no profit-based organization would select this option, except if it is obliged to do so by regulations (Paleologos, 2008).

2.6.3 The lost value of groundwater and modified decision analysis Decision analysis has been done until now by equating in monetary terms the cost of the damage caused by pollution to the cost of remediating an affected site. This is the usual practice in risk analyses, which, however, compensates for only part of the damage, since it does not account for the decline in the operation and function of an ecosystem as a result of pollution. Examples of the latter include, among others, reduction in the number of species as a result of deforestation, or from the disposal of brine and chemicals by desalination plants; soil deterioration because of salt buildup from the use of fertilizers or by recycled water for irrigation; and decline in the quality of water and air from marine, river, lake, and groundwater pollution, or from automobile or industrial emissions, respectively. It is well understood in everyday transactions that for several commodities, not necessarily those that are rare or expensive, but for which there exists some special interest, repair of damages does not return the full value of the good that was affected (Paleologos, 2008). Examples of such situations involve cars, antique furniture, paintings, etc., where it is clearly mentioned in ads if they have been in an accident, or if they have been restored, in both cases with their value being significantly reduced, even if the repair or restoration was completely successful. On the other hand, approaches, such as the hedonic adjustment, which is used since the 1980s to calculate the inflation rate in USA, and which allows the substitution of one good by another in tracking price inflation, does not appear to be reasonable for water. The unique function of water as a life-sustaining liquid does not allow its value to

2.6 Extended environmental cost

59

be assessed through a proxy liquid. The question therefore is what the full value of water is, and hence what the cost of pollution is that must be entered in decision analyses assessments (Paleologos, 2008). The groundwater, even if perfectly cleaned up, starts, in most cases, as high-quality drinking water and is returned, after remediation from a pollution event, as irrigation water (Paleologos, 2008). For example, graywater, even if treated with the most advanced technologies, can be used only for restricted applications. The European Commission in a June 2018 briefing announced that it would introduce legislation addressing water reuse in agricultural irrigation by proposing four classes of reclaimed water quality (EU Legislation in Progress Briefing, 2018; Paleologos et al., 2019). Even if secondary, tertiary, and advanced treatment are applied, the top water class in European Union would be allowed solely for food crop irrigation. In addition, several European countries do not allow recycled water to be used inside buildings for toilet flushing for the fear of cross-contamination of the public water supply network. For example, in France, where greywater is understood in its broad context (i.e., excluding only the wastewater from toilets and urinals), collection, treatment, and reuse of greywater is not approved for domestic purposes. ANSES (French Agency for Food, Environmental and Occupational Health and Safety) conducted a four-year study and concluded that installation of non-potable water systems inside dwellings can pose cross-contamination risks to the public water system. ANSES recommended that regulations for greywater reuse are considered only for specific locations subject to repeated water shortages and for uses, after treatment, that are limited to toilet flushing; watering of green areas, exempting vegetable gardens and agricultural irrigation; and outdoor surface washing, excluding high-pressure washing (Paleologos et al., 2019). Hence, the point is that polluted, and subsequently remediated, groundwater is affected by at least a decline in value from drinking to irrigation quality water (Paleologos, 2008): Lost Value of Groundwater ¼ Value of Drinking Water  Value of Irrigated Water

(2.16)

The estimated volume of the polluted groundwater can be utilized to assess the lost value as the difference in price between drinking and irrigated water. In every country or state, public water utilities have details for water charging for irrigation (UNC, 2018) that can range all the way from $0.2 in British Columbia, Canada, to $1440 in the Netherlands per 1000 m3 of water (Cornish et al., 2004). For example, in California, which utilizes tiered and water-saving incentive approaches to determine drinking water rates, this is charged at about $0.55 per m3, whereas the price of irrigation water is on average $0.05 per m3 for relatively low withdrawals (Cornish et al., 2004). Using these rates and the size of a real plume in the United States that affected 6,813,000 m3 of groundwater (Boggs et al., 1992) the lost value of groundwater in this case would be (Paleologos, 2008): Lost Value of Groundwater ¼ ð0:55  0:05Þ  6; 813; 000 z 3:4 million dollars

(2.17)

Returning to the simple calculation of Section 2.5.1, even in the case of limited pollution, if this additional cost were to be included the cost of insurance to be considered would be raised to $1.6 million from the amount of $600,000 that was previously calculated. However, this calculation does not capture the value of groundwater lost through a pollution event. The reason is that the absolute prices for drinking and irrigation water that are set in each country do not reflect open market conditions. They are for most countries, to a large or small extent, subsidized prices because of a historical evolution that has always viewed water as a free public good, and hence do not reflect the loss incurred to the value of the water. Instead of the absolute price one can use the ratio M of the subsidized prices of drinking and irrigated water to measure the relative value of water used in a society at a specific time period. Thus

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for the prices quoted for California, drinking water has, on average, an 11 times higher value than irrigated water. Thus a second stipulation is (Paleologos, 2008): The ratio M of drinking to irrigation water prices reflects the relative value of water uses (2.18) The third logical argument has to do with the fact that the cost of repairs for damage can reach at a maximum the value of the good that can be returned. Insurance companies would rather pay a lump sum for the estimated value of a car than repair it, if repairs to fix it are higher than the market value of the car before an accident. This can be stated as (Paleologos, 2008): If the cost to remediate is B then the value of the cleaned water must be at least B

(2.19)

Cleaned water is used at best for irrigation needs, hence the value of the cleaned or irrigationquality water is B, which means that the value of the original drinking-quality water that was polluted was MB, and hence the cost of pollution is (Paleologos, 2008): Total Cost of Pollution ¼ Remediation Cost þ Lost Value of Groundwater ¼ B þ ðM  1ÞB ¼ MB

(2.20)

The decision-making analysis can now be repeated using this modified cost of pollution. For a limited pollution event, with all parameters of Section 2.6.1 kept the same, i.e., probability of failure q ¼ 0.3, and remediation cost of B ¼ 2 million, and with California’s approximate drinking to irrigation water ratio of M ¼ 11, the total cost of pollution (Paleologos, 2008) is given by Eq. (2.20) as $22 million. This returns from Eq. (2.14) an amount equal to $6.6 million as the breakeven point between the choice of additional measures and the likelihood of facing $2.2 million in damages with a probability of 30%. Proper accounting of the lost value has now made the cost of the Type III liner the cheapest option and has reversed the decision. Obviously, for catastrophic pollution events, such as that analyzed in Section 2.6.2, not only is the use of advanced liners justified, but also the cost of extensive monitoring systems and other measures to prevent and detect pollution. Thus definition of the cost of pollution that can extend beyond the limited scope of remediation, but which tries to recapture the true value of water that is affected, can change drastically the conclusions of a costebenefit probabilistic analysis. It must be kept in mind also that the lost value discussed here considered only the utility of a water resource regarding its application for drinking or irrigation purposes. The true cost of polluting water may be much broader, such as the pollution of river and coastal waters by hog farms, and the subsequent damage to the fishing and tourist industries that occurred after hurricane flooding in North Carolina. This state and Iowa are the two states where the largest concentration of hog farms takes place in the United States. About 5 billion kg of wet animal waste are generated in North Carolina every year, which are stored in lagoons to decompose. When storms and hurricanes hit the state, the lagoons can overflow, with the animal waste entering tributaries of rivers and ending up in the coastal environment (Pierre-Louis, 2018). In addition to environmental damage, recent studies have found that irrespective of income, communities near hog-concentrated farms have the lowest life expectancy in the state and high infant mortality rates (Kravchenko et al., 2018). As a final note to this section it is useful to remember that in environmental assessments, Albert Einstein’s quote: Not everything that can be counted counts, and not everything that counts can be counted, applies. In other words, because commodities are measurable in money, this does not make money the universal measure of the worth of everything, nor does it make the analysis more objective.

2.7 Utility theory

61

2.7 Utility theory 2.7.1 Utility concept Until now we have avoided further complications with the concept of consequences by equating them directly to money. However, even money itself, or the worth of a monetary amount, varies from one individual to another, and depends on the economic status of an individual and his/her attitude toward the risk of gaining or losing money. The value of $10,000 is not the same for a regular salaried professional and a multimillion-dollar investor. On the other hand, there exist quotes in every culture, such as: frugality includes all other virtues (Cicero), and fortune sides with him who dares (Virgil), which are clearly opposite, one advocating a cautious and the other a risk-taking approach to life and to economic decisions. When making decisions, people exhibit preferences to some outcomes (prospects) over others, or they are neutral between them. The mathematical structure of the theory on preferences (Lindgren, 1971; Von Neuman and Morgenstern, 2004) is based on a number of simple axioms: Axiom 1: For two prospects P and Q it holds that one is preferred over the other, or they are equally attractive: P > Q

or

P < Q

PwQ

(2.21a)

then P  R

(2.21b)

or

Axiom 2: Transmissivity property: If P  Q and Q  R;

This axiom guarantees the logical consistency of choices made in a sequence of decisions. To describe the next two axioms the concept of a random prospect is introduced: the random prospect [P, Q]p is a mixture of prospects P and Q, where one has the chance of encountering prospect P with a probability p or prospect Q with probability (1 e p). Axiom 3: Substitution principle: For a fixed order between two prospects, if they are included in a random mixture, where they have the same chance of occurring p, and each mixture contains the same prospect P, then the order of the mixtures is the same as the order of the original two prospects. If, for example, P1 > P2 , then for any probability p and any prospect P one has: ½P1 ; Pp > ½P2 ; Pp

(2.21c)

Axiom 4: If three mixtures are ordered as P1 > P2 > P3 , then there exist mixtures [P1, P3]p and [P1, P3]q such that: P1 > ½P1 ; P3 p > P2 > ½P1 ; P3 q > P3

(2.21d)

This implies that one can find a mixture of the most desirable and least desirable prospect, where the probability p of P1 is high enough to justify selection of the mixture over an intermediate prospect P2, and conversely that there exists a mixture [P1, P3], where P1 has such a low probability q of occurring that P2 is preferable. For example, if P1 ¼ $100,000, P2 ¼ $10,000, and P3 ¼ e$5000, there certainly exist situations where a high chance of winning P1 against the risk of losing P3 is more desirable than P2. These axioms imply several corollaries, from which the utility function is defined. If P > Q, then one can find a mixture that satisfies: P > ½P; Qq > Q

for 0 < q < 1

(2.22a)

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Chapter 2 Risk analysis and management

1

U(x)

0 x

FIGURE 2.6 General shape of the utility function.

or, in other terms: P ¼ ½P; Qp¼1 > ½P; Qq > Q ¼ ½P; Qp¼0

for 0 < q < 1

(2.22b)

for 0 < q; r < 1

(2.22c)

and for a probability r > q: P ¼ ½P; Qp¼1 > ½P; Qr > ½P; Qq > Q ¼ ½P; Qp¼0

This allows the definition of the utility function as the value of the subscript in Eq. (2.22c), which falls between 0 and 1, and which identifies the position of each prospect in the order of all prospects that lie between a very desirable prospect P of utility u(P) ¼ 1, and a least desirable prospect Q of utility u(Q) ¼ 0. A general shape of a utility function for money is shown in Fig. 2.6, which indicates that at very low amounts, below a threshold, the utility does not depart from the value of zero, and at the other end, above a certain level, incremental amounts of money do not increase the desirability of situations, i.e., the utility function becomes flat. Usually, we are not interested in the overall shape of the utility function for all amounts of money, but we are concerned about operating within a certain range, with the lowest amount set as the point 0 in the x-axis. In that case, locally, the utility function may be concave down, as on the left of Fig. 2.7, or concave up, as on the right of Fig. 2.7. The shape of the utility function on the left of Fig. 2.7

FIGURE 2.7 Utilities for risk-averse (left) and risk-seeking (right) behaviors.

2.7 Utility theory

63

FIGURE 2.8 Utility function based on prospect theory (Kahneman and Tversky, 1979).

indicates that the magnitude of an incremental change in money is smaller than that in the utility function, i.e., an increase in the amount of money does not make an equivalent change in the desirability of the outcome. This is denoted as a risk-averse behavior. The opposite is presented on the right of Fig. 2.7. A change in the amount of money increases the desirability of the outcome, which characterizes a risk-seeking behavior. Kahneman and Tversky (2000), Nobel Laureate in Economic Sciences-, and Tversky (1979, 2000) presented a modification of the utility theory, called prospect theory, by exhibiting through several case studies that some people’s choices were not consistent with the basic tenets of the utility theory. Among others, they proposed a value function (a modification of the utility function), which is generally concave for gains, convex for losses, and steeper for losses than for gains (Fig. 2.8). In their value function, probabilities were replaced by weights, which were lower than the corresponding probabilities, except in the range of low probabilities.

2.7.2 Exponential utility model Let us consider that one faces a situation (fair lottery) L, where one of two actions can be taken: the first can lead to outcome A, and the second can result in outcome B, both having equal probabilities P ¼ .5. The value that will be returned if one is involved in many of these situations is E ¼ (A þ B)/2. An amount CE that one would be willing to accept in exchange for the rights to this lottery is called the certainty equivalent of L (or risk-adjusted value in oil-industry terminology (Lerche and MacKay, 1999)). This individual is called risk averse if and only if CE < E, risk seeking if CE > E, and risk neutral if CE ¼ E, and this choice is consistent for all similar decision situations (Krzysztofowicz, 1986; Raiffa, 1997).

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A utility function, U, is a means of standardizing in the same units the outcomes of a decision that are not measurable in monetary terms, and it incorporates a person’s preference for specific outcomes and his/her attitude toward risk. This attitude to risk is modeled for a situation by the shape of the utility function. Thus U is concave in the range of currency amounts where a risk-averse behavior is expected; convex where a risk-seeking behavior would be displayed; and linear where a risk-neutral attitude would prevail. Exponential utility function models, described by Eq. (2.23), have been employed in several decision-making situations (Krzysztofowicz, 1986; Keeney and Raiffa, 1993; Lerche and Paleologos, 2000; Paleologos and Lerche, 2000) to represent risk-averse behaviors: UðxÞ ¼ 1  ex=RT

(2.23)

The exponential utility function described in Eq. (2.23) has the shape shown on the left of Fig. 2.7. As the dollar amount x becomes large, U(x) tends to 1; U(0) equals zero; and for negative x, that is losses, the utility function becomes negative. The parameter RT in Eq. (2.23) is called risk tolerance and it controls how risk averse a utility function is: large values of RT make U(x) flatter suggesting a more risk-tolerant behavior, whereas small values make the utility function more concave, representing a more risk-averse attitude.

2.7.3 Partial involvement in projects The decision tree of Fig. 2.9 depicts a situation of transport and storage of chemical or radioactive waste, where B represents the contract to be determined considering the potential for spill during

FIGURE 2.9 Decision tree for transportation and storage. Adapted from Paleologos, E.K., Lerche, I., 2000. Working interest optimization in the transport and burial of hazardous wastes. J. Environ. Geosci. 7 (2), 106e114.

2.7 Utility theory

65

transportation and/or leakage during storage or burial of the waste. The cost escalates depending on whether spill occurred during transportation, in which case the total waste to be stored and its corresponding cost has increased C3 > C1, or whether there is leakage at the storage site, in which case C2 > C1 and C4 > C3 (Paleologos and Lerche, 2000; Lerche and Paleologos, 2001). The expected value, E, of the decision tree is given by: E ¼ B  ps ðp1 C1 þ p2 C2 Þ  pf ðp3 C3 þ p4 C4 Þ;

(2.24)

and the variance is: s2 ¼ E2  E2 ¼ ps p1 ð1  ps p1 ÞC12 þ ps p2 ð1  ps p2 ÞC22 þ pf p3 ð1  pf p3 ÞC32 þ pf p4 ð1  pf p4 ÞC42

 2 p2s p1 p2 C1 C2 þ p2f p3 p4 C3 C4 þ ps pf ðp1 C1 þ p2 C2 Þðp3 C3 þ p4 C4 Þ ; (2.25) where E2 is the second statistical moment. The volatility, n, is defined as: v ¼ s=E

(2.26)

Volatility provides a measure of the relative importance of the fluctuations about the mean value, with n > 1 indicating considerable uncertainty. In some cases, though, as in the case of catastrophic events investigated earlier, a low-probability but high-impact event can lead to serious financial damage, even to bankruptcy, even though the expected value of a project may be high and its volatility low (Lerche and Paleologos, 2001). In such a case it makes sense to assume a smaller fraction of a project, taking, on the one hand, a reduction in potential gains, but avoiding, on the other hand, catastrophic losses. Thus with a working interest fraction, W, the expected value of the project in Fig. 2.9 becomes: EW ¼ ps ½ p1 WðB  C1 Þ þ p2 WðB  C2 Þ þ pf ½P3 WðB  C3 Þ þ p4 WðB  C4 Þ ¼ W½B  ps ðp1 C1 þ p2 C2 Þ  pf ðp3 C3 þ p4 C4 Þ ¼ WE:

(2.27)

2.7.4 The use of the exponential utility in spillage and leakage problems Using the exponential utility model in Eq. (2.23) the monetary consequences of each branch in Fig. 2.9, with a working fraction W in the project, are translated into utility units as follows: U ½WðB  Ci Þ ¼ 1  exp½ WðB  Ci Þ = RT;

i ¼ 1; 2; 3; 4

(2.28)

The expected utility value of the decision tree now, using the utilities in Eq. (2.28), becomes:    WðB  C1 Þ WðB  C2 Þ EU ¼ 1  ps p1 exp  þ p2 exp  RT RT    (2.29) WðB  C3 Þ WðB  C4 Þ  pf p3 exp  þ p4 exp  RT RT

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Chapter 2 Risk analysis and management

Thus the expected utility, according to the definition, equals the certainty equivalent CE measured in utility units (Clemen, 1996; Raiffa, 1997), that is:  EU ¼ UðCEÞ ¼ 1  exp CE=RT (2.30) Substituting Eq. (2.29) into Eq. (2.30) yields the certainty equivalent amount, the value of a project after having adjusted for the risks of spillage and leakage and the risk attitude of a corporation:     WðB  C1 Þ WðB  C2 Þ þ p2 exp  CE ¼  RT ln ps p1 exp  RT RT    (2.31) WðB  C3 Þ WðB  C4 Þ þ pf p3 exp  þ p4 exp  RT RT This can be easily simplified to:        WC1 WC2 WC3 WC4 CE ¼ WB  RT ln ps p1 exp þ p2 exp þ pf p3 exp þ p4 exp RT RT RT RT (2.32) If the risk tolerance RT is high relative to the costs (RT / N), then CE becomes (Paleologos and Lerche, 2000): CE / W½B  ps ðp1 C1 þ p2 C2 Þ  pf ðp3 C3 þ p4 C4 Þ ¼ WE

(2.33)

The certainty equivalent as RT / N tends to the expected value of partial involvement in a project given in Eq. (2.27). If, on the other hand, RT / 0, then (Paleologos and Lerche, 2000): CE/WðB  C4 Þ

(2.34)

This means that when an entity has very low tolerance to risk, the project will only be considered if the contract price B exceeds the highest anticipated costs C4. Eq. (2.34) sheds light on the high cost of the measures taken during the transportation and burial of radioactive waste since societies have extremely low tolerance to the occurrence of extreme events in these cases. When W ¼ 0, Eq. (2.32) gives CE ¼ 0. When W ¼ 1, i.e., an entity takes over the whole project, CE  0 only when: B  RTlnfps ½ p1 expðC1 = RTÞ þ p2 expðC2 = RTÞ þ pf expðC3 = RTÞ þ p4 expðC4 = RTÞg ¼ G1 ðRTÞ (2.35) When the value of a project, considering the worth of money under risk that an entity places, is greater than or equal to zero, CEmax  0, it makes sense to be part of a project at any fractional working interest that returns CE  0. If CEmax  0, then there is no range of working interest that will return a positive risk-adjusted value to a corporation. Depending on the value of RT, the costs, and the probabilities there is a working interest Wmax that maximizes CEmax. The certainty equivalent reaches a maximum value when vCE=vW ¼ 0, which happens for a value of W that satisfies:

2.7 Utility theory

ðB  C1 Þps p1 expðWC1 = RTÞ þ ðB  C2 Þps p2 expðWC2 = RTÞ þ ðB  C3 Þpf p3 expðWC3 = RTÞ þ ðB  C4 Þpf p4 expðWC4 = RTÞ ¼ 0

67

(2.36)

If B > C4  Ci (for i ¼ 1, 2, 3), then Eq. (2.36) has no solution for W. This means that CE is a monotonically increasing (or decreasing) function of W in 0  W  1. Since CE ¼ 0 when W ¼ 0, and CE  0 when inequality (Eq. 2.35) holds, it follows that when B  G1(RT) one should take 100% working interest in the project, otherwise when B  G1(RT), then CE  0 everywhere, and one should not become involved in the project. The more common situation is when the contract price B is less than C4 (and sometimes lower than C3 and/or C2). B is never less than C1, because then no involvement in a project would be considered. In the situation where C4 > B > C1, C2, C3, dividing Eq. (2.36) by exp(C3W/RT), separating the term containing C4 from the rest, and taking the logarithm of the resulting expression results in:   h RT Wmax ¼ ln ðpf p4 ðC4  BÞÞ1 fðB  C1 Þps p1 exp C4  C3 i ð Wmax ðC3  C1 Þ = RTÞ þ ðB  C2 Þps p2 expð Wmax ðC3  C2 Þ = RTÞ þ ðB  C3 Þpf p3 g (2.37) Eq. (2.37) is usually solved numerically for the parameters of a project to obtain a curve of Wmax versus RT. Then, for a value of RT, by inserting the corresponding Wmax into Eq. (2.32) the maximum certainty equivalent CEmax can be obtained. If CEmax  0, then, by both increasing and decreasing W away from Wmax, one can obtain the two values of W at which CE in Eq. (2.32) crosses zero. These two values then provide the range of W where CE will exceed zero, i.e., the project is profitable for all other parameters held fixed.

2.7.5 Application We provide now a numerical illustration of the previous analysis to determine a working interest in a project, which employs a parabolic utility function, instead of the exponential model presented previously. The mathematical calculations for this model follow the same procedure as presented for the exponential model and are omitted for ease of presentation. Details of the calculations can be found in Lerche and Paleologos (2001). The situation considers estimates of spillage during transportation at about 1% so that, referring to the initial decision tree, pf ¼ 0.01 and ps ¼ 0.99. The probabilities of leakage during the burial phase of the project are assumed to be unaffected by what has occurred during transportation of the waste, i.e., p1 ¼ p3 and p2 ¼ p4. For this illustration the leakage probability was taken to be 10% of the spillage probability, p2 ¼ p4 ¼ 0.001 (and then p1 ¼ p3 ¼ 0.999). In the event of leakage at the burial site, but no spill during transportation, remediation costs, which may include excavation of leaking containers, repackaging, and reburial of material as well as addressing pollution problems at the site, were estimated to be 10 times higher than normal operating costs, i.e., C2 ¼ 10C1. If there is spillage, then not only must the spilled material be collected and repackaged but so too must any contaminated material. The cost of such an event was set at twice the basic no spill/no leakage estimated cost, i.e., C3 ¼ 2C1. The highest costs occur in the spill/leakage scenario when collection of the contaminated material, disinterment, repackaging, reburial, and remediation costs have to be borne. These costs are set at 20 times those of the basic scenario, i.e., C4 ¼ 20C1.

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The decision-maker’s concern is to estimate whether a corporation should be involved, and at what level, in this project depending on the contract price offered, G, and for given, but variable, amounts of risk tolerance RT. Thus one first estimates the statistical moments E, s2, and n2, which depend on costs and probabilities of each scenario and the contract price G. For the numerical values given previously, setting m ¼ G/C1, and following the procedure of Lerche and Paleologos (2001), Fig. 2.10A and B can be generated. Fig. 2.10A plots Wmax versus (RT/C1) for various values of m, and Fig. 2.10B provides curves of the maximum CE for increasing risk tolerance (both standardized by the basic cost C1) as a function of m. In Fig. 2.10B the parameter m represents the minimum ratio of contract G to basic estimated costs C1 for a project to be even considered by a corporation. Several factors are apparent from these graphs. First, as the tolerance to risk increases (RT/C1 increasing) the maximum working interest a corporation can take increases; the higher the contract price, G, relative to the costs of the basic no spill/no leakage scenario, C1, the faster the optimal working interest reaches 100% for a fixed corporate risk tolerance (Fig. 2.10A). In Fig. 2.10B the maximum CE increases linearly with RT/C1 until Wmax ¼ 1 (which was found after mathematical manipulations (Lerche and Paleologos (2001) to lead to the condition (RT/C1) (m e m) ¼ 0.093, as shown in Fig. 2.10B), and then CEmax/C1 becomes independent of RT/C1 and is directly proportional to m. The critical curve at which this crossover occurs is shown as a dashed line on Fig. 2.10B.

(A) 1

W max

0.8

m=1.020

0.6

m=1.022

0.4

m=1.025

0.2

m=1.040

0 0

50

100

RT/C1

(B) 0.012

CE max /C1

m=1.040

0.008

(RT/C1)(m-m*)=0.093 m=1.030

0.004

m=1.025 m=1.022

0 0

25

RT/C 1

FIGURE 2.10 (A) Wmax versus (RT/C1). (B) CEmax/C1 versus (RT/C1).

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2.7.6 Bayesian decision theory Bayesian decision theory is based on the work by Bayes (1764) and places probability theory, in contrast to the frequentist approach, as an extension of inductive logic (Jeffreys, 1948; Polya, 1954; Cox, 1961). Exposition of Bayesian decision theory lies outside the scope of this chapter and an interested reader can consult the work by Jaynes (1957, 1968, 1978, 1983, 1984, 1990, 1993, 2003), Berger (1985), Loredo (1990), and Gregory and Loredo (1999). The Bayesian approach has proven to be extremely fruitful in various scientific disciplines and has been applied, among others, in public health and genetic studies and clinical trials (Etzioni and Kadane, 1995; Kadane, 1996; Christakos and Hristopoulos, 1998; Christakos, 2000, 2002; Christakos et al., 2005; Sorensen and Gianola, 2006; US Food and Drug Administration, 2010), earthquake analyses (Ching and Glaser, 2003; Stavrakakis and Drakopoulos, 2005), hydrologic and soil studies (Davis et al., 1972, 1976, 1979; Ganoulis, 1994; Gelder, 1996; Woodbury and Rubin, 2000; Or et al., 2001; Neuman, 2003; Van Gelder et al., 2004; Ye et al., 2004), structural and traffic design (Van Noortwijk et al., 2004; Song et al., 2006), signal processing (Ruanaidh and Fitzgerald, 1996), astrophysics (Loredo, 1990; Gregory and Loredo, 1999), and air emission violations (Paleologos et al., 2018). The application of Bayesian Decision Theory is outlined now in the case of a waste incineration facility that was established some time ago under a certain regulatory framework that is due to be amended and become more stringent. The facility must assess whether it can meet the new emission standards with its existing technology, or whether it needs to update some of its filters and/or its processes to guarantee compliance. At the simplest level the facility is facing two decisions (Paleologos et al., 2018): A0 : Continue business as usual and take no new measures A1 : Install new emission systems and establish new processes

(2.38)

Of course, decision A0 exposes the facility to a penalty, which is designated by B for each day of violation. Let us define Y as the days of violation in a monitoring period of N days and by C the daily investment cost of a new emission system that is considered by the facility (Paleologos et al., 2018). The loss function (Berger, 1985) for the compliance of the facility to new emission standards can be given as follows (Paleologos et al., 2018):  BY if A ¼ A0 LðA; YÞ ¼ (2.39) CN if A ¼ A1 Deviation from the emission standards for each day j ( j ¼ 1, 2, ., N) during the period N is described by the Bernoulli variable X (Paleologos et al., 2018):  1 if there is violation (2.40) X¼ 0 if there is compliance where the probability not to comply with the emission standards any given day is P(X ¼ 1) ¼ q, and P(X ¼ 0) ¼ 1 e q. The total number of days Y that violation of the emission standards takes place in a period of N days is given by (Paleologos et al., 2018):

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Y¼

N X

XðjÞ;

(2.41)

j¼1

and the random variable Y ¼ {y successes (violations) in N days} is described by a binomial probability distribution B(N, q) (Paleologos et al., 2018):   N y q ð1  qÞNy where y ¼ 0; 1; 2; .; N (2.42a) fY ðyÞ ¼ y with: expected value < Y > ¼ Nq

(2.42b)

and variance VarðYÞ ¼ Nqð1  qÞ

(2.42c)

The binomial distribution assumes that Y is the sum of independent random Bernoulli variables. In the absence of perfect control systems, one would like air emission violations to be random, of low probability of occurrence and environmental impact, and independent of each other. Thus, potential correlation of violations (for example, non-compliance during specific periods of operation or shifts) can be viewed as providing valuable information to investigate what distinct activities, which diverge from regular operations, took place during those periods, for which facility controls proved not to be efficient, and hence guide us to discontinue them (Paleologos et al., 2018). The Bayesian Decision Theory defines the goal or risk function G(A, q) as the expected value of the loss function with respect to the random variable Y (Berger, 1985; Paleologos et al., 2018): GðA0 ; qÞ ¼ EY ½LðA0 ; YÞ ¼ EY ½BY ¼ B EY ½Y ¼ BNq

(2.43a)

GðA1 ; qÞ ¼ EY ½LðA1 ; YÞ ¼ EY ½CN ¼ CN

(2.43b)

The two expressions indicate what would be the average loss depending on the decision (or action) undertaken. A standard cost-benefit analysis would then seek to find the decision that minimizes the goal function (Lerche and Paleologos, 2001). Fig. 2.11 indicates that decision A0 would be the optimal if the probability to violate emission standards any given day is below the ratio C/B, otherwise decision A1 is preferable. If for example a scrubber with precipitation for waste throughput of 150,000 t yr1 at a cost of about $4.8mi was considered, then C for an amortization period of 15 years and 6% interest rate would be $1,320 daily. For a U.S. EPA penalty of $37,500 for each day of violation applied, the breakeven point is found at q ¼ 3.5%. In contrast, if for the same system the European Commission’s daily penalty of V9,009 (about $10,811) is considered, the ratio of C/B ¼ 1,320/10,811 ¼ 12.2%, i.e., violation of the emission standards in 12.2% of the cases needs to be realized before a decision to update the flue gas cleaning system is reached in EU (Paleologos et al., 2018). The Bayesian analysis further proceeds to estimate the probability density function (pdf) of the unknown probability of non-compliance q, the so-called prior pdf. This can be done by using historical enforcement and compliance data, such as those provided by U.S. EPA (USEPA, 2017), or in the absence of those by utilizing qualitative information about the occurrence of air pollution violations. Thus, expert opinion that non-compliance would occur, for example, on the average, once quarterly can be used to construct the subjective prior pdf of q, p(q) (Paleologos et al., 2018). Finally, the Bayes risk R(A), the expected value of the goal function with respect to q, is defined as (Berger, 1985; Paleologos et al., 2018):

2.8 Risk assessment

71

FIGURE 2.11 Regions of optimal goal function. p

RðAÞ ¼ E ½GðA; qÞ ¼

Z GðA; qÞpðqÞdq

(2.44)

The criterion for the best decision based on the Bayes risk depends on the ratio R(A0)/R(A1). When this ratio is greater than one then the optimal decision is A1; when the ratio is less than one the optimal decision is A0 (Paleologos et al., 2018).

2.8 Risk assessment In the previous sections we provided some elements of the statistical decision theory that is used in projects that are characterized by uncertainty to reach optimal decisions. Risk assessment is the process of determining the nature and magnitude of adverse effects posed by a given hazard (Mohamed and Antia, 1998). Hazardous situations to which the risk assessment process may be advantageously applied include contaminant releases into the environment, hazardous waste generation and disposal, emission of air pollutants, and polluted sites. These hazards, and many others, may pose significant risks to human health and the environment. The objective of risk assessment is to produce the data necessary to make the best possible decision concerning a potentially hazardous situation. Such information will typically permit an evaluation of the acceptability of the hazard, as well as highlight and provide valuable insight into those features and exposure pathways (EPs) with the greatest risk potential, and upon which the remedial effort should be focused. In general, risk assessment offers an estimate of the likelihood of occurrence of adverse effects from exposure of humans and ecological receptors to chemical, biological or other hazardous agents present in the environment (Mohamed and Antia, 1998). It typically requires input from practitioners in the various fields and disciplines related to the hazardous situation under investigation, notably

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engineering, chemistry, biology, ecology, and statistics. A major challenge of the risk assessment process is that the calculated risk estimates are often based on incomplete information and, hence, are characterized by uncertainty. Risk assessment is the first of a two-phased approach to handling a hazardous situation. If the baseline risk assessment, which is conducted under the assumption of no-action to control or mitigate the pollution or reduce the risk, yields an unacceptable level of risk, a program of action to reduce the risk (e.g., cleanup, regulation, education) is embarked upon in a risk management phase. In effect, the risk assessment phase provides a numerical estimate of the probability of injury or harm from a hazardous situation while the risk management phase combines the risk assessment [the scientific input] with the directives of [the enabling] regulatory legislation, together with socio-economic, technical, political, and other considerations, to reach a decision as to whether or how much to control future exposure of suspected toxic agents [substances] (Mohamed and Antia, 1998). Thus risk management can be construed as the selection of an acceptable level of a hazardous substance. Risk assessment is motivated by consideration for human health and/or the ecology. A human health risk assessment is concerned principally with the health risks posed to potentially exposed human populations, whereas ecological risk assessment focuses on the adverse effects on the environment or ecosystem (Mohamed and Antia, 1998). Although both types of assessment involve different processes, they do share a common philosophy and some data requirements. Thus a carefully planned and coordinated data collection phase could provide information necessary to assess both risk types. The remainder of the chapter will concentrate on the human health risk assessment process.

2.9 Basic elements of human health risk assessment A human health risk assessment characterizes the adverse health effects due to human exposure to hazardous substances (Mohamed and Antia, 1998). Estimates of the extent to which humans have been, or could be, exposed to the hazard and the toxicity of the substance are utilized in the risk assessment process to evaluate the present or future human health risks. Human health risk assessment can be performed for various environmental hazards, such as chemical contaminants, ultraviolet radiation, and electromagnetic fields. One of the more common applications is site risk assessment, which is the determination of the risks to human health from exposure to hazardous substances at a contaminated site. Although specific cases of risk assessment may differ considerably in scope, they do share a common philosophy and methodology (Mohamed and Antia, 1998). In general, a human health risk assessment consists of the following basic steps (Mohamed and Antia, 1998): 1. Hazard Identification (HI): This involves the identification of the nature and extent of pollution, and a determination whether exposure to the pollutants can potentially increase the incidence of an adverse health effect; 2. Exposure Assessment (EA): Identifies the pathways or routes through which the pollutants of concern might reach humans or ecological receptors, and quantifies the frequency, magnitude and duration of such exposures; 3. Toxicity Assessment (TA): Evaluates the potential adverse effects of each pollutant of concern, and characterizes the relationship between the amount of exposure (dose) and the magnitude of the adverse health effect (response) in humans;

2.9 Basic elements of human health risk assessment

73

4. Risk Characterization (RC): Characterizes the nature and magnitude of the risk posed by integrating the findings of the exposure and toxicity assessments; also evaluates the attendant uncertainty, so that proper judgement can be made regarding the acceptability, or otherwise, of the predicted risk estimates. These elements of the human risk assessment process are outlined in Fig. 2.12 and discussed in more detail in the subsequent sections of this chapter. Additional and complementary information can be found elsewhere in the literature (e.g., Asante-Duah, 1996; Mohamed and Antia, 1998).

2.9.1 Hazard identification The hazard identification (HI) step of the health risk assessment process entails the identification of the pollutants present at a site, and a determination whether exposure to the pollutants would produce adverse health effects in humans. A review is conducted of all available site environmental monitoring data for identifying potential chemicals or pollutants of concern upon which the assessment will focus (Mohamed and Antia, 1998). The selection of chemicals with potential to impair human health, for consideration in the risk assessment process, may be guided by such factors as mobility, persistence, bioaccumulation, concentration and toxicity, and fate and transport properties.

FIGURE 2.12 Components of human health risk assessment. Redrawn from Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, IBSN:0-444-89847-6, 707p.

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Historical information reliably associating certain chemicals with the site is quite pertinent. Generally, such chemicals should be selected for inclusion in the risk assessment process, even if other considerations, such as noted earlier, suggest otherwise. In addition to the identification and characterization of potentially hazardous pollutants, a determination of their extent in their primary sources and in each of the environmental media (e.g., air, soil, sediments, surface and groundwater) is an essential part of the process (Mohamed and Antia, 1998). Also, of interest are the characteristics of (1) the sources of the pollutants, and (2) the environmental setting, notably its possible impact on the fate, transport, and persistence of the pollutants. Details regarding data collection and evaluation, i.e., the gathering and analyzing of relevant site data and identification of chemicals with potential adverse health effects on exposed populations, can be found in the literature (Mohamed and Antia, 1998). If a pollutant is judged to be potentially harmful to humans, it is further investigated in the exposure and toxicity phases of the risk assessment process.

2.9.2 Exposure assessment Exposure assessment (EA) may be defined as the contact of a chemical or physical agent (i.e., contaminant) with humans or ecological receptors. EA is the (qualitative or quantitative) determination of the magnitude, frequency and duration of exposure, the nature and size of population potentially at risk and the pathways by which the pollutants may reach the risk population (Mohamed and Antia, 1998). A major aspect of the EA process involves the identification of the exposure scenarios, and will typically include a determination of (Mohamed and Antia, 1998): 1. environmental transport media (e.g., soil, sediment, groundwater, surface water, air), 2. pollutant sources, fate and transport, including intermedia transfers (e.g., release of pollutants from biosphere to hydrosphere, production of fugitive dust during excavation), 3. exposure points (e.g., on-site, off-site, sediments, groundwater, surface water), 4. exposed pathways (e.g., dermal absorption, ingestion, inhalation), 5. exposed population (e.g., sensitive sub-populations such as children, the elderly and pregnant women; population size), 6. exposed durations (e.g., acute or short term, chronic or long term). The exposure assessment begins after relevant data on the site pollutants have been gathered and evaluated, and those pollutants of potential concern, which will be the focus of the RA, have been selected. The EA process consists of three major steps: (a) characterization of exposure setting (CES), (b) identification of exposure pathways (IEP); and (c) quantification of exposure (QE) (Mohamed and Antia, 1998). Step 1: Characterization of exposure setting (CES): This step involves the identification of: (a) the physical characteristics of the site, and (b) the population potentially at risk, and subgroups with increased risk potential (Mohamed and Antia, 1998). Important physical characteristics of the site include: (a) climate (e.g., temperature, precipitation), (b) meteorology (e.g., wind speed and direction), (c) geology (e.g., location and characteristics of underlying strata), (d) vegetation (e.g., non-vegetated, forested, grassy), (e) soil type (e.g., sandy, organic, acid, basic), (f) groundwater hydrology (e.g.,

2.9 Basic elements of human health risk assessment

75

depth, direction and type of flow), and (g) surface water location and description (e.g., type, flow rates, salinity) (Mohamed and Antia, 1998). Common sources of this type of information include county soil surveys, wetland maps, aerial photographs, and study reports (e.g., preliminary assessment (PA), site investigation (SI), and remedial investigation (RI)) (Mohamed and Antia, 1998). lt may also be useful to consult technical experts, such as hydrogeologists, geochemists, ecologists, etc. The populations potentially at risk may be characterized by factors that influence exposure, notably, proximity to pollution source, behavior and activity patterns, and by the presence of sensitive sub-populations (Mohamed and Antia, 1998). In general, the following populations will be potentially at risk (Mohamed and Antia, 1998): 1. people residing on or near the affected site and therefore have the greatest risk potential, 2. distant and local consumers of water supply, sea food (e.g., fish), or agricultural products (e.g., fruits and vegetables) originating from the contaminated site region, 3. people whose behavior patterns or activities put them at increased risk, e.g., children due to increased likelihood of soil contamination on playgrounds, construction, and other outdoor workers, individuals exposed to chemicals during occupational activities, heavy consumers of home-grown vegetables and locally caught fish, and 4. people with increased sensitivity to pollutant exposures (e.g., infants and children, pregnant and nursing women, the elderly, and those with chronic illnesses). Step 2: Exposure pathway identification (EPI): EPs relate to the routes that a pollutant follows, or would follow, to reach the population at risk. EPs are identified and characterized by integrating available information on the sources, locations, mechanism(s), and type of pollutant releases, and the locations and activity patterns of inhabitants. EPs involve: (1) nature and physicochemical properties of released pollutants, (2) type of the environmental ecosystem for pollutant interaction, retention, transport, or migration, (3) exposure point, i.e., location of potential contact between pollutants and humans, and (4) exposure route, i.e., pollutant uptake mechanisms by humans (e.g., ingestion, inhalation, dermal contact). An EPI process embodies the following steps (Mohamed and Antia, 1998): (1) identification of pollutant sources and the receiving environmental ecosystem, (2) assessment of pollutant retention and transfer within the atmosphere, biosphere, and hydrosphere, (3) identification of exposure points and exposure routes, and (4) identification of complete EPs. 1. Identification of contaminant sources and the receiving environmental ecosystem: The sources of pollutants at a site, the release mechanism(s), and the receiving environmental ecosystem may be determined from site investigation reports, monitoring data, and information on source locations. Some information may be obtained deductively. For example, the presence of polluted soil near a tank would identify the tanks as a likely source of the pollution, leakage, or rupture as the release mechanism, and the ground as the receiving medium. A listing of potential pollutant sources, nature of the release, and the hosting environmental ecosystem is shown in Table 2.4. 2. Assessment of pollutant retention and transport: The fate and transport of pollutants are evaluated for predicting future exposures and identifying likely sources of currently polluted ecosystems. This step is essentially qualitative and is aimed at identifying current and/or future receiving media of the site pollutants. It is to be noted that a pollutant, after release into the environment,

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Table 2.4 Common pollutant release sources (Mohamed and Antia, 1998). Receiving medium

Release mechanism

Release source

Air

· Volatilization

· Surface wastes; lagoons, ponds, pits, spills · Contaminated surface water · Contaminated surface soil · Contaminated wetlands · Leaking drums · Contaminated surface soil · Waste piles · Contaminated surface soil · Lagoon overflow · Spills, leaking containers · Contaminated groundwater · Surface or buried wastes · Contaminated soil or buried wastes · Surface · Contaminated surface soil · Lagoon overflow · Spills, leaking containers · Contaminated surface soil · Waste piles · Contaminated surface soil wastes; lagoons, ponds, · Surface pits, spills · Contaminated groundwater · Surface or buried wastes · Contaminated soil · Contaminated soil, surface water · Sediment, groundwater or air

Surface water

Groundwater Soil

· Fugitive dust generation · Surface runoff overland · Episodic flow · Groundwater seepage · Leaching · Leaching · Surface runoff overland · Episodic flow dust · Fugitive generation/ deposition

Sediment

Biota

· Tracking · Surface runoff · Groundwater seepage · Leaching · Uptake (direct

contact, ingestion, inhalation)

2.9 Basic elements of human health risk assessment

77

may be: (a) transported (e.g., by convection in water or on suspended sediment, or through the atmosphere), (b) transformed biologically (e.g., biodegradation) or chemically (e.g., photolysis, hydrolysis, oxidation, reduction) or physically (e.g., precipitation, volatilization), and (c) retained or accumulated (e.g., at the release source, in the receiving media) (Mohamed and Antia, 1998). The physical, chemical, and environmental fate parameters of potentially hazardous chemicals may be used to predict their fate at a given site. Some commonly used fate parameters include organic carbon partition coefficient (Koc), distribution coefficient (Kd), octanol/water partition coefficient (Kow), solubility, Henry’s law constant, vapor pressure, diffusivity, bioconcentration factor (BCF), and half-life (Mohamed and Paleologos, 2017; Mohamed and Antia, 1998). Site-specific characteristics that may influence a chemical’s fate and transport include soil moisture content, organic carbon content, and carbon exchange capacity (Mohamed and Antia, 1998). The water table location is also an important characteristic in as much as a high water table increases the potential of releasing pollutants from soils into the groundwater. Using chemical, site-specific, and monitoring data, contaminant fate and transport within and between each medium may be evaluated. Thus contaminated and/or potentially contaminated areas are identified. 3. Identification of exposure points and exposure routes: Exposure points, where the identified pollutants may potentially encounter humans, are determined by considering the location of inhabitants and their activities nearby the polluted or potentially polluted areas. Polluted ecosystem and sources are obvious potential on-site exposure points, particularly if the site has current or future use. Potential off-site exposure points are generally downgradient or downwind of the contaminated site. Exposure routes, through which the identified pollutants can enter the human body, depend on the exposure ecosystem (e.g., groundwater, surface water, sediment, soil/ dust, air, and food) and the activity patterns at the exposure points. The major EPs are ingestion, inhalation, and dermal contact (US National Academy Press, 2003). Ingestion is the route involving eating and/or drinking. Pollutants present in food or drinks are passed straight to the digestive system, then to target areas or organs in the body where they could cause adverse health effects. The solubility of ingested pollutants is an important factor that influences the pollutant’s transport and metabolism in the human body. Lipophilic (i.e., fat-soluble) chemicals, such as benzene or DDT, are rapidly absorbed into the body on ingestion. Hydrophilic (i.e., watersoluble) chemicals may be absorbed throughout the body since the chemistry of human metabolism is water-based. Consumption of fish polluted with methyl alcohol, a chemical known to cause damage to the central nervous system and even death, is a classic example of this exposure route (Mohamed and Antia, 1998).

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Inhalation involves absorption of airborne chemicals during breathing. On inhaling, pollutants pass through the lungs to the blood stream, and from there to the brain and the rest of the body. An important factor that influences the severity of the resulting adverse health effect is the solubility of the pollutant in the blood. A common example of this route of exposure is the inhalation of carbon dioxide from, for example, smoking, automobile exhaust, and industrial processes. Carbon monoxide has a much stronger binding affinity for hemoglobin than does oxygen. Consequently, it can irreversibly displace oxygen from hemoglobin (the iron-based organic compound by means of which oxygen is transported through the blood), and curtail the amount of oxygen in the blood, leading to oxygen starvation of cells. The inhalation of particulate matter (PM), possibly carrying hazardous pollutants, is very much dependent on the particle size (PM10, PM2.5). The smaller-sized particles are more likely to penetrate deeper and, hence, potentially cause more harm. Dermal or skin absorption involves contact of the skin with pollutants, and is enhanced by breaks (e.g., cuts, scratches, abrasions) in the skin, increased concentrations of pollutants, and decreased particle size (Mohamed and Antia, 1998). The fat or oil solubility of the pollutant is also an important absorption enhancement factor. Oil- or solvent-based chemicals, being fat or oil soluble, are readily absorbed by the lipophilic layers of the skin. It is for this reason that gasoline, for example, would remain on and be detectable on the skin for a long period, even after repeated soap and water washing. Notably, the existence of an exposure point is not necessarily indicative of the presence of an exposure route. For example, if contaminated soil (the exposure point) is touched while wearing gloves, the potential exposure route (dermal contact) is blocked. 4. Identification of complete exposure pathways: An EP is considered complete if the following elements are present: (1) pollutant sources, (2) mechanism of pollutant release into the environmental ecosystems, (3) exposure point where contact can occur, and (4) exposure route by which contact can occur (Mohamed and Antia, 1998). However, an EP will be considered incomplete if any of the elements above are absent. Contact with polluted media while in protective clothing is an example of an incomplete (dermal contact) EP. Another example involves a situation in which a pollutant is released into the air in an unpopulated area. Complete pathways are identified by integrating information on pollutant sources, release mechanisms, fate and transport, and exposure points and routes (Mohamed and Antia, 1998). Human monitoring data, if available, may be used to support conclusions on the completeness of an EP, particularly if the data indicates chemical accumulation or related effects. However, data lacking confirmatory evidence, i.e., negative data, do not imply that a pathway is incomplete. Step 3: Quantification of exposure (QE): A determination of the magnitude, frequency, and duration of human contact to a pollutant may be made based on the identified complete pathways and information on the populations potentially at risk. Generally, this is accomplished in two stages: (a) estimation of exposure concentration, and (b) calculation of chemical intakes (Mohamed and Antia, 1998). 1. Estimation of exposure concentration: Exposure concentrations may be estimated by using monitoring data alone or in combination with environmental fate and transport models. Use of monitoring data alone is adequate in cases where there is direct contact with a chemical in a polluted medium, or where monitoring is/was conducted directly at an exposure point, such as a drinking water well (Mohamed and Antia, 1998). Monitoring data yield good estimates of current exposure concentrations. Use of monitoring data alone may not be adequate in a number of

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instances, namely (1) exposure points are remote from the pollutant sources, and there are mechanisms for the release and transport of pollutants to the exposure points (e.g., air dispersion, surface water transport) (Mohamed and Antia, 1998), (2) in the absence of temporal distribution of data, with which long-term future exposure concentrations may be predicted, and (3) no reading is registered because the concentrations of the pollutants are below the monitoring equipment’s detection limit. In these instances, monitoring results have been utilized in association with pollutant transport models to predict exposure concentrations. A variety of models for estimating exposure concentrations in various environmental ecosystems (e.g., groundwater, surface water, air, soil, sediment) are available and well documented in the literature (Mohamed and Antia, 1998; US EPA, 1988). A useful discussion of important elements of exposure concentration determination in various environmental ecosystems. 2. Calculation of chemical intakes: Pollutant ingestion is defined as the amount a chemical in contact with the receptor’s exchange boundary (e.g., skin, lungs), which is available for absorption (Mohamed and Antia, 1998). It is different from the absorbed dose, which is the amount of chemical absorbed into the blood stream. Pollutant intake, hence, dose, is clearly dependent on the exposure point concentrations in the relevant media. The dose may be obtained from the intake by application of an absorption factorda function of physiological parameters such the gastrointestinal absorption rates. If the absorption is unknown or cannot be reliably estimated, a 100% absorption can be assumed, resulting in the equality of the dose and the chemical intake. This is obviously a conservative estimate of the actual dose. It is also often assumed, for the sake of simplicity, that the potential receptor remains at the same location and is exposed to the same ambient concentration. This assumption yields a conservative estimate since, in general, some time will be spent away from the exposure point(s), and lower or near-zero chemical exposures will be endured. The pollutant intakes are expressed by Eq. (2.45): I¼C 

CR  EFD 1  BW AT

(2.45)

where I is the intake, the amount of chemical at the exchange boundary (mg/kg body weight-day); C is the concentration of pollutant, the average concentration contacted over the exposure period (mg/litre of water); CR is the contact rate, the amount of contaminated medium contacted per unit time or event (litres/day); EFD is the exposure frequency and duration, which describes how long and how often exposure occurs and is often calculated using two terms (EF and ED); EF is the exposure frequency (days/year); ED is the exposure duration (years); BW is the body weight, the average body weight over the exposure period (kg); and AT is the average time over which exposure is averaged (days). Three categories of variables can be identified in the chemical intake (Eq. 2.45): (1) chemicalrelated, i.e., exposure concentration (C), (2) population-related, i.e., contact rate (CR), exposure frequency (EF), duration (ED), and body weight (BW), and (3) assessment process-based, i.e., averaging time (AI) (Mohamed and Antia, 1998). Specialization of the general intake equation (Eq. 2.45) to the various exposure media and exposure routes can be found in Asante-Duah (1996).

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2.9.3 Toxicity assessment 2.9.3.1 Introduction Toxicity assessment (TA) evaluates the potential for selected contaminants to cause adverse health effects in exposed individuals. TA generally involves two steps: (a) hazard assessment and (b) doseeresponse assessment. Hazard assessment (HA) aims to determine whether exposure to contaminants can cause an increase in the incidence of an adverse health effect (e.g., cancer, birth defects, mental retardation). The nature and strength of the evidence of causation are also characterized in the HA step. The doseeresponse assessment (DRA) evaluates (quantitatively) the toxicity information and characterizes the relationship between the dose of pollutant administered or received and the incidence of adverse health effects in the exposed population (Mohamed and Antia, 1998). From this quantitative doseeresponse relationship, the potency of the pollutant is estimated. Toxicity values, which can be used to estimate the incidence or potential for adverse health effects in terms of human exposure to the pollutant are derived. Toxicity values (e.g., reference doses and slope factors) are used in the risk characterization step to estimate, at varying human exposure levels, the likelihood of occurrence of adverse effects (Mohamed and Antia, 1998). A schematic representation of typical doseeresponse curves is given in Fig. 2.13. In general, response increases with dose. The smaller the dose required to cause a given effect, the more potent (toxic) is the substance. The doseeresponse concept, upon which the toxicity assessments are based, assumes that noncarcinogenic effects have a threshold dose below which no observable adverse effect occurs, while carcinogenic effects are non-threshold substances, i.e., produce a response at even the smallest dose, as illustrated in Fig. 2.13. TAs usually employ one of two approaches depending on whether carcinogenic or noncarcinogenic effects are involved.

2.9.3.2 Sources of toxicity information

Response

Toxicological information, for use in assessing the potential for a substance to cause adverse health effects (carcinogenic and non-carcinogenic) in humans, are obtained primarily from epidemiological

Nonthreshold (carcinogenic)

Threshold (carcinogenic)

Dose (Exposure)

FIGURE 2.13 A schematic representation of typical doseeresponse relationships.

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(human) studies and experimental animal studies. Secondary or supporting sources of such information include in vitro tests and comparison of structureeactivity relationships.

2.9.3.2.1 Epidemiological studies Epidemiological data indicating a strong clear-cut relationship between a pollutant and an adverse health effect are the most convincing evidence of the risk potential of a pollutant. Such positive data are, however, not often available due to a variety of factors. For example, since humans are normally exposed to toxic pollutants inadvertently or by accident, data may not have been generated for the specific pollutant of concern. Where data are available, some important characteristics of the exposure (e.g., concentration) may be lacking or poorly defined. Also, the exposed population tend to be quite limited (in size) and rather varied (e.g., by age, sex, activity patterns, genetics, and other factors that may influence susceptibility). Thus available human exposureeresponse data may not be enough for a quantitative assessment. In these situations, the limited data are better used in a supporting role to support conclusions drawn from animal studies. Clearly, if adequate high-quality data are available, they should be given priority over data obtained from animal or any other studies, since our primary interest is with humans. In this instance, animal studies data may play a supporting role.

2.9.3.2.2 Animal studies Because of the inherent difficulty in obtaining adequate data on human doseeresponse to all pollutants of interest, toxicity information gathered in animal studies are commonly used to infer the potential of a pollutant to cause an adverse effect in humans. It is assumed that humans and animals are similar in their response to pollutants, i.e., if a pollutant causes an adverse effect in an animal so will it in humans (Mohamed and Antia, 1998). However, extrapolating animal data to humans is complicated by a variety of factors that differ among species and may potentially affect the response to the pollutant of interest. These factors include sex, strains, body size, lifespan, genetic homogeneity, metabolism, and excretion patterns. In general, the likelihood that a pollutant will have an adverse effect in humans is considered to increase as similar results are observed across sexes, strains, and routes of exposure in animal studies (Mohamed and Antia, 1998). Commonly used animals are rat, mouse, rabbit, guinea pig, dog and monkey. Animal species that are most like humans in terms of physiology, metabolism, and pharmacokinetics are normally given priority. A common approach for making interspecies comparisons is to use standardized scaling factors, such as mg/kg body weight, ppm in the diet or water, mg/m2 body surface area per day, and mg/kg body weight per lifetime (Mohamed and Antia, 1998).

2.9.3.2.3 Supporting studies There are several other studies that may be used to assess a chemical’s potential health effect on humans. These studies, which are used primarily to support conclusions drawn from epidemiological and animal studies, include (a) metabolic and other pharmacokinetic studies, (b) cell cultures or microorganism studies, and (c) structureeactivity studies. (Mohamed and Antia, 1998). Metabolic studies seek evidence of a pollutant’s toxicity potential by comparing the metabolism of a pollutant which exhibits toxic effects in animals with the corresponding metabolism in humans (Mohamed and Antia, 1998). Cell cultures or microorganism studies investigate a chemical’s potential for biological activity, hence potential for and possible mechanisms of carcinogenicity, by testing for point mutations, numerical and structural chromosome aberrations, DNA damage/repair, and cell transformation (Mohamed and Antia, 1998). Structureeactivity studies estimate the toxicologic activity of a chemical from known toxicologic activities of structurally related chemicals.

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2.9.3.3 Toxicological parameters Human health risk assessment usually evaluates the toxic effect of pollutants differently depending on whether the pollutant is a carcinogen (cancer causing) or a noncarcinogen.

2.9.3.3.1 Noncarcinogenic effects Noncarcinogenic pollutants lead to toxic endpoints known as noncancer or systemic toxicity, i.e., which do not include cancer or gene mutations. It should be noted that carcinogens also commonly exhibit noncancer effects, i.e., systemic toxicity. In general, Pollutants with noncancer toxicity have a different effect on different organs of the body and, hence, are often referred to as systemic toxicants. Factors that determine which organs are most impacted include dose, route of exposure, oil or fat solubility, and the chemical’s effect on enzyme activity. Common systemic toxicants and their target organs include: 1. hepatotoxic chemicals, which mostly affect the liver (e.g., carbon tetrachloride, tetrachloroethane), 2. nephrotoxic chemicals, which affect the kidneys (e.g., halogenated hydrocarbons), 3. hematopoietic toxins, which affect the blood or blood cells (e.g., aromatic compounds such as benzene, phenols, aniline, nitrobenzene and toluidine), 4. neurotoxic substances, which affect the nerve system (e.g., methyl alcohol, carbon monoxide, heavy metals and organometallics), 5. anesthetics or narcotic chemicals, which affect consciousness (e.g., acetylene hydrocarbons, olefins, aliphatic ketones, aliphatic alcohols, esthers, paraffin hydrocarbons, ethyl and isopropyl ether). Noncarcinogenic effects are often characterized by a threshold dose below which no effect occurs. The concept is like that of a drug which yields no beneficial effect if administered in too small a dose. The dose at which no effects are observed in humans or experimental animals is known as the no observed effect level (NOEL). In situations where data that definitively identifies a NOEL are not available, a safe threshold level may be determined based on the lowest observed effect level (LOEL). NOEL is the exposure level at which no effect at all has been observed; usually, effects, albeit of no toxicological importance, are observed (Mohamed and Antia, 1998). The no observed adverse effect level (NOAEL) is used to characterize situations involving doses that do not produce an adverse effect. It is the highest dose at which no significant adverse effect is observed; where NOAEL has not been specifically identified, the lowest observed adverse effect level may be used instead (Mohamed and Antia, 1998). The reference dose (RfD) is defined as the maximum amount of a toxic substance that an individual can absorb with no adverse health effects, and is used as an estimate of the non-carcinogenic pollutant which produces no appreciable risk of adverse effects in the general human population, including any sensitive subgroups (Mohamed and Antia, 1998). A commonly employed unit of measure is mg/kg body weight/day. Chronic RfD relates to long-term exposures and is defined as an estimate of the daily exposure level that will not produce any appreciable risk of deleterious effects during the lifetimes of the exposed populations, including sensitive subpopulations (Mohamed and Antia, 1998). Subchronic RfD is used for estimating non-carcinogenic effects associated with short term exposures, spanning approximately between 2 weeks to 7 years. Developmental RfD is used to estimate potential adverse effects on a

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developing organism because of a single exposure event. Additional information relating to the estimation of the various RfDs can be found.

2.9.3.3.2 Carcinogenic effects Carcinogenic effects, unlike noncarcinogenic effects, are often based on the nonthreshold model, which assumes that even one molecule of a cancer-causing agent can lead to the disease. This is the so-called one-hit model. The toxicity value most often used to quantify carcinogenic effects is the slope factor and the associated weight-of-evidence classification (Mohamed and Antia, 1998).

2.9.3.3.3 Weight-of-evidence classification The pollutant is assigned a class that reflects the strength of the evidence pointing to a carcinogenic effect in humans. Based on (1) human studies, and (2) animal studies, the evidence is considered enough, limited, inadequate, no data, or evidence of no effect. Depending on the potential of the pollutant to have carcinogenic effects in humans and/or experimental animals, a weight-of-evidence is assigned according to the classification system depicted in Table 2.5.

2.9.3.3.4 Slope factor calculation The slope factor (SF) may be defined in several ways. It is: 1. a measure of the (potential) carcinogenic effect of a chemical, 2. an upper-bound estimate of the probability of developing cancer from a unit intake of a potential carcinogen over a lifetime, 3. the risk of cancer (i.e., proportion of exposed population that develop cancer) per unit dose, 4. a toxicity value that quantitatively defines the relationship between dose and response, and is usually, but not always, regarded as the 95% confidence limit of the slope of the doseeresponse curve, with units of (mg/kg-day). 5. Toxicity values are expressed in terms of risk per unit concentration. These measures, known as unit risks, may be defined for inhalation as well as for oral exposure routes, and are related to the slope factor as follows: Unit risk for airðinhalationÞ ¼ slope factor  inhalation rate=70 kg

(2.46)

Unit risk for waterðoralÞ ¼ slope factor  consumption rate=70 kg

(2.47)

Table 2.5 Weight-of-evidence classification system for carcinogenicity. Group

Description

A B1 or B2

Human carcinogen Probable human carcinogen B1 indicates that limited human data are available B2 indicates enough evidence in animals and inadequate or no evidence in humans Possible human carcinogen Not classifiable as to human carcinogenicity Evidence of noncarcinogenicity for humans

C D E

· · ·

Adapted from Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, IBSN:0-444-89847-6, 707p.

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Typical values commonly used are inhalation rate ¼ 20 m3 day1, and consumption rate ¼ 2 litres day1. Unit risk for air and drinking water are usually expressed in mg/m3, and mg/L, respectively.

2.9.4 Exposure route considerations Toxicity values (e.g., RfD, SF) are, in general, dependent on the exposure route (oral or inhalation). In the absence of RfDs or SFs for the dermal route of exposure, it may, in some instances, be appropriate to use oral RfD and oral SF to estimate non-carcinogenic and carcinogenic effects, respectively, in addition to dermal exposures. A precondition is that the adverse effect does not occur directly at the point of application; skin carcinogens, for example, should not be evaluated using the oral slope factor. Notably, direct use of oral SF or oral RfD for a dermal exposure route is not correct for differences in absorption and metabolism between the dermal and oral routes. Thus absorption factors, typically 10% and 1% for organic and inorganic chemicals, respectively, are usually applied. Depending on the specific chemical involved, the oral SF or RfD for dermal exposure may over- or underestimate the risk of adverse health effect, thus increasing the uncertainty of the calculated risks. However, this is not expected to significantly underestimate the total risk, considering the other routes of exposure embodied in the risk assessment process. Oral SF or RfD may also be used to characterize inhalation exposure routes where inhalation SF or RfD is not available. Similarly, inhalation SF or RfD may be used as a surrogate for both ingestion and dermal exposures in cases where oral SF or RfD is lacking (Mohamed and Antia, 1998).

2.10 Risk characterization Risk characterization (RC) integrates exposure and toxicity assessments to provide qualitative and quantitative estimates of risk to exposed or potentially exposed populations. The risks to potentially exposed populations are characterized by non-carcinogenic hazard quotients and hazard indices and/or carcinogenic risks. The RC provides risk information to the risk manager for integration with other nontechnical factors (e.g., economics, regulation, technical feasibility) to arrive at an appropriate course of action for mitigating the estimated risk (Mohamed and Antia, 1998). The RC process involves a number of steps: (1) to gather and organize available exposure and toxicity information; (2) to verify the consistency and validity of the key assumptions common to both the exposure and toxicity outputs for each pollutant and exposure pathway of concern. The goal of the verification phase is to ensure that the exposure estimates are in concert with the assumptions used in developing the toxicity values; and (3) to calculate risk and hazard quotients for carcinogenic and noncarcinogenic effects for each pathway analyzed (Mohamed and Antia, 1998).

2.10.1 Calculation of carcinogenic risks The risk of carcinogenic effects is defined as the incremental probability of an individual developing cancer over a lifetime from exposure to a cancer-causing agent (carcinogen). Carcinogenic risks are calculated by multiplying the route-specific cancer slope factor by the estimated intakes, to obtain the

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85

excess or incremental individual lifetime cancer risk (Mohamed and Antia, 1998). Typically, carcinogenic effects are calculated using the linear low-dose and one-hit models (Asante-Duah, 1996). Linear low-dose model: CR ¼ CDI  SF

(2.48)

CR ¼ I  expðCDI  SFÞ

(2.49)

One-hit model:

where CR is the probability of an individual developing cancer (nondimensional); CDI is the chronic daily intake for long-term exposure (i.e., averaged over a 70-year lifetime) (mg/kg-day); and SF is the slope factor ([mg/kg-day]1). In general, the linear-dose model is valid only at low risk levels (i.e., less than 0.01); however, where chemical intakes are high, and potential risks are estimated to be greater than 0.01, the one-hit model is considered more appropriate (Mohamed and Antia, 1998). In case of a mixture of pollutants, the cumulative health risks are usually based on the assumption of additivity of the carcinogenic effects, and the aggregate cancer risk for all pollutants and relevant exposure routes/pathways may be estimated using the following equations (Mohamed and Antia, 1998). For the linear low-dose model at low risk levels: Total cancer risk ¼

p X n X ½CDIij  SFij 

(2.50)

j¼1 i¼1

For the one-hit model used at high carcinogenic risk levels: Total cancer risk ¼

p X n X

½1  expð CDIij  SFij Þ

(2.51)

j¼1 i¼1

where CDIij is the chronic daily intake for the ith contaminant and jth pathway; SFij is the slope factor for the ith contaminant and jth pathway/exposure route; n is the total number of carcinogens; and p is the total number of pathways or exposure routes. Notably, incremental risks ranging between 104 and 107 are normally considered acceptable. However, since populations may be exposed to additional pollutants, cancer risks well below the 106 should be targeted, to allow for a reasonable margin of safety (Mohamed and Antia, 1998).

2.10.2 Calculation of noncarcinogenic hazards Noncarcinogenic effects are defined by the hazard quotient (HQ) and/or the hazard index (HI). HQ is defined as the ratio of a single chemical exposure level, over a specified period, to the reference dose for the chemical obtained from a similar exposure duration: HQ ¼

E RfD

(2.52)

where HQ is the hazard quotient; E is the chemical exposure; and RfD is the reference dose (mg/kgday).

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The aggregate noncancer risk due to multiple pollutants and exposure routes/pathways may be estimated by the total hazard index expressed by Eq. (2.53) subject to the following assumptions: (a) a receptor will experience the same reasonable maximum exposure from each of the multiple pathways, and (b) similar toxicological endpoints are involved: " # p X p X n n X X Eij Total Hazard Index ¼ ½HQij (2.53) ¼ RfDij j¼1 i¼1 j¼1 i¼1 where Eij is the exposure level (or intake) for the ith contaminant and jth pathway; RfDij is the acceptable intake level (or reference dose) for the ith contaminant and jth pathway/exposure route; n is the total number of chemicals presenting noncarcinogenic effects; and p is the total number of pathways or exposure routes. The benchmark HI is unity (1). The likelihood of an adverse effect increases as HI increases. Below the benchmark value of one, adverse health effects are deemed unlikely; above one, adverse health effects are considered very likely to occur. Since humans may be exposed to contaminants from sources unrelated to those accounted for in the risk assessment process, it is recommended that HI well below 1 be considered the safe level, thus extending the margin of safety. The HI is usually calculated separately for chronic (long-term) and subchronic (short-term) exposure periods, by a simple modification of Eq. (2.53). The total chronic HI and the total subchronic HI are obtained by replacing the exposure level (Eij) by the chronic daily intake (CDIij) and the subchronic daily intake (SDIij), respectively. Notably, the hazard quotient risk calculation tool for persistent organic pollutants (POPs) provides interactive tools that enable officials to use risk-based approaches to prioritize and manage POP and other hazardous substance polluted sites (http://www.popstoolkit.com/tools/HHRA/NonCarcinogen.aspx). The tool accounts for the dosages of accidental soil ingestion, water and food ingestion, inhalation of polluted particles, and dermal contact, and it has been used to calculate the Hazard Quotient (HQ) for threshold pollutants (Mohamed and Paleologos, 2018).

2.11 Risk management Chapter 1 of this book, entitled Sustainable Pollution Assessment Practices, the authors discuss the concept of sustainable development and its relation to environmental protection. Principle # 4 of the 1992 UN Conference on Environment and Development (UNCED) characterized sustainable development as: In order to achieve sustainable development, environmental protection shall constitute an integral part of the development process and cannot be considered in isolation from it. Therefore in developing risk management strategies we should concentrate on environmental protection. This can only be achieved when the developed risk management strategies are ecologically viable, economically feasible, and socially desirable. In doing so, the solution will be environmentally sound and politically acceptable.

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In many aspects the foundations of risk management strategies are the same as those underlying the existing spectrum of regulations and standards for pollutants in the environment, such as drinking and bathing water standards, ambient air and water quality criteria, air emissions from incinerators, discharges to surface waters from wastewater treatment plants and land spreading of sewage sludge (Mohamed and Antia, 1998). Establishing risk management strategies for polluted sites is a complex exercise due to factors, such as: (a) the heterogeneous nature of the soil, (b) difficulties in characterizing the occurrence and predicting the transport and fate of pollutants in soils, (c) the unknown and mixed variety of substances in polluted soils, (d) the multiple pathways by which pollutants may reach humans and other receptors, and (e) the uncertainty and highly variable exposure conditions (Mohamed and Antia, 1998). In the following sections, the basic elements of a sound risk management strategy are discussed.

2.11.1 Elements of a risk management program An effective risk management program should provide responsible administrative control, logical application of the best possible technical analyses, and the involvement of the public. A successful risk management plan must be credible, organized, thorough, i.e., address the concerns of the public, relevant, doable, and economical, and based on existing technology with flexibility to adapt to later advances. In addition, a thorough program identifies potential scenarios for accidental releases, potential consequences of such releases, and the actions planned to alleviate a problem when it occurs (Paleologos and Lerche, 2002). The key elements of the risk management program are shown in Fig. 2.14 and discussed in the following sections.

2.11.1.1 Hazards identification program The basic objectives of this phase are to define the system and to identify in broad terms the potential hazards. The review process during this stage should provide a summary of information that indicates potential human health effects and the level at which exposures present risk to human inhabitants. The basic steps in the hazard identification program are: (1) identification of the potential hazard(s). Is it a toxic release, an explosion, a fire, etc.?; (2) identification of the subcomponents of the facility, i.e., soil, groundwater, surface water, air, and receptors, which give rise to the hazard(s); and (3) identification of the boundary of the study. Will it include detailed study of risks from physical and environmental factors, earthquakes, etc.? For example, at waste disposal sites, a preliminary analysis determines whether waste materials present at the site can be transported off-site. The analysis identifies the available pathways, determines whether any human or wildlife receptors can be reached by the waste, and identifies a set of transport and exposure scenarios. Each scenario encompasses a complete EP. The potential consequence of each scenario is the transmission of risk to receptors in the environment. A common hazard ranking scheme is: (a) class I hazards: negligible effects; (b) class II hazards: marginal effects; (c) class III hazards: critical effects; and (d) class IV hazards: catastrophic effects.

2.11.1.2 Consequence analysis The preliminary hazards analysis represents a first attempt to identify the gross facility and the set of events that can lead to hazards while the facility is still in a preliminary investigation stage. This phase

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PROBLEM IDENTIFICATION (1) Define areas of concern. (2) Identify pollutant sources.

HAZARD CHARACTERIZATION (1) Characterize pollutant sources. (2) Map pollutant boundaries.

EXPOSURE ASSESSMENT (1) Identify potential pathways. (2) Identify potential receptors. (3) Identify exposure pathways. (4) Develop a conceptual model. (5) Determine the extent of pollutant migration.

CONSEQUENCE ASSESSMENT (1) Assess pollutant toxicity. (2) Evaluate human health risks. (3) Evaluate socio-economic impacts. lmplement monitoring programs

No

Significant risks indicated?

Yes RISK MITIGATION (1) Develop risk profile for site. (2) Develop strategy for risk mitigation. lmplement monitoring programs

No

Is remediation required?

Yes RISK MITIGATION PLAN (1) Develop remedial action options. (2) Identify regulatory criteria. (3) Dertermine cleanup goals. (4) Select appropriate remedial option.

IMPLEMENTATION OF SELECTED OPTION (1) Evaluate option performance. (2) Evaluate socio-economic impacts.

FIGURE 2.14 Key elements of the risk management program for polluted sites (Mohamed and Antia, 1998).

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89

begins with the task of identifying potential pathways in which a release might occur. The study of hazards identification indicates the pathways that initiate the risks, i.e., soil, groundwater, surface water, air, and receptors. Hence, the risk study begins by following the potential course of events. The first question that needs to be posed is What is the possibility of transporting hazardous waste constituents with groundwater? Groundwater is the pathway with the greatest long-term human health consequences because aquifers supplying drinking water can be contaminated by chemical wastes for an indefinite time following a release. Wastes not present in the liquid phase can be dissolved and leached by the action of surface or groundwater and transported off-site. Two dominant factors control the extent and rate at which infiltrating water or groundwater can affect the transport of mobile waste components: (1) the depth of the bottom of the waste material relative to the height of the upper most layer of groundwater, and (2) the hydraulic conductivity of the waste material and adjoining soil. Surface water can furnish a rapid and direct transport route for PM and soluble chemical species. For the surface water transport to be complete, waste materials must be within the influence of runoff, and the surface elevation and slope must be able to support the movement of the runoff at a velocity capable of carrying the wastes in suspension. Once moved by surface water, the pollutants can be deposited atop soils, yielding a source for a direct contact pathway. Waste components can be transported through the air if exposed to and affected by air currents. The waste may be transported either in the vapor phase, if their vapor pressure favors their volatilization, or adhered to soil particles, if the soil is in a friable form. Wastes are also transported in the air when fugitive dusts are created. Heavy machinery, automobiles, or even foot traffic in an area of exposed waste material can generate dust upon which waste can be adsorbed. If captured by the wind, these dusts can be transported off-site. Another method of airborne transport of waste is the movement of gases generated by the digestion of landfill materials. Methane is commonly produced in landfills, and as it migrates to the surface, it can carry volatile waste components through the soil and into the atmosphere. To examine the use of the land and other resources proximate to the waste site, it is necessary to evaluate the possibility of transporting waste materials by various receptors (Mohamed and Antia, 1998). The basic steps in the consequence analysis phase are (Mohamed and Antia, 1998): (1) calculate the amount of toxic material. Conceptually, this part of the analysis, which represents an assessment of the exposure/release sequence, is a key input into the consequence analysis. All the other inputs such as type of waste, site location, population density, and prevailing weather patterns are site specific; (2) follow the trajectory of the lethal toxins in the environment, i.e., exposure assessment (Mohamed and Paleologos, 2018), and (3) assess the health effects, i.e., doseeresponse analysis.

2.11.1.3 Risk mitigation Specific means used to control potential releases or their consequences to the environment must be identified. Fig. 2.15 shows the possible remedial action techniques for controlling potential release of contaminants at a contaminated waste site. Also, to mitigate the release of toxic elements to the air, provision should be given for the use of: (1) scrubbing systems to neutralize or remove hazardous components, (2) flare systems to destroy hazardous compounds, and (3) secondary containment devices to temporarily hold the hazardous material until it is further processed before release. After the potential consequence analysis has been considered, a decision can be made regarding the acceptable level of risk.

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FIGURE 2.15 Possible remedial action techniques for a contaminated site. Redrawn from Mohamed, A.M.O., Antia, H.E., 1998. Geoenvironmental Engineering. Elsevier, IBSN:0-444-89847-6, 707p.

2.11.2 Quantified risk assessment Quantitative risk assessment estimates the degree of adverse effect due to release of specific wastes from a site. It is a tool to calculate a numerical value for existing and future health risks associated with exposures by means of identified complete pathways. To accomplish this end, the steps in a quantitative risk assessment are: (1) specify the identity and rate of release of contaminants from the source, (2) quantify the rate of transport, (3) quantify the rates of exposure and uptake by receptors, and (4) quantify the degree of toxicological effects. Data are developed on pollutant residues in the various transport mechanisms within the environmental ecosystems. Assumptions and estimates based on the availability of data in relation to the transport, fate, and toxic effects of the compounds at hand are also applied. A typical scenario developed to describe exposure from an aquifer utilized as drinking water would include leaching of wastes in contact with groundwater, transport of those leached materials with the movement of groundwater toward domestic wells, and human exposure by ingestion. Sampling of groundwater from a receptor’s drinking water source is a must to quantify pollutant type and level of concentrations, and a profile of the receptor’s water use is needed. The exposure and uptake data are correlated with known toxicological effects of similar dosages. Finally, the quantitative assessment furnishes numerical values for the leaching, transport rates, and duration over which the residues are expected to remain in the groundwater.

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2.12 Role of regulatory agencies Regulatory criteria that influence risk management include national, state or provincial, and/or local statutes. Pertinent regulation may include those specific to risk management of polluted sites as well as those of a more general or environmental nature. In general, regulations can be categorized as locationspecific, pollutant-specific, or action/technology-specific. Location-specific regulations include those related to the site where the pollution and remediation occur. Pollutant-specific regulations include those relating to type, quantity, and/or character of the waste. Action-technology-specific regulations include those pertaining to the means and methods of risk management. An important aspect of regulation categories relates to when in the risk management process each category of regulation can reasonably be considered. While location-specific regulation can be brought to bear on the remedial considerations relatively early (when site identification has been made), consideration of pollutant-specific regulations may await site characterization information. Likewise, action-technology-specific regulations can only be considered after the concepts, alternatives, and technologies of the risk management implementation are formulated (Mohamed and Antia, 1998). The power exercised by regulatory agencies comes together with the responsibility for accountability of their decisions to all interested parties. This includes different levels of the government, the courts, citizens and special interest groups, and to the industry they regulate Paleologos et al. (2020). This 360-degree perspective of the accountability of regulatory agencies was detailed in a special report of the UK House of Lords (2004) (Paleologos et al., 2020) and is depicted in Fig. 2.16. However,

FIGURE 2.16 Regulatory agency’s 360-degree accountability.

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government interventions have been seen to limit the independence of regulatory agencies, and this more so in countries with a short history of institutional controls on the executive power (Ҫetin et al., 2013; Paleologos et al., 2020).

2.13 Regulatory approaches One of the first issues faced by agencies responsible for assessing the suitability of soils or other conditions at polluted sites is whether numerical criteria should be established. Numerical criteria, which are adopted, for example, by Canada, France, Germany, the Netherlands, Norway, and the United Kingdom, offer several advantagesdthey are relatively easy to use and administer, they facilitate communication between interested parties, and they reduce confusion. However, numerical criteria that are intended for broad application are insensitive to site-specific conditions and often imply a level of understanding or confidence in the underlying science that may or may not exist. These limitations have contributed to decisions made by some regulatory agencies to refrain from setting numerical criteria. As an alternative, these agencies have chosen to establish procedures that intend to determine site-specific objectives only. The procedures typically involve risk assessment in which the health risks that site users, visitors, or neighbors can experience are estimated. Examples of agencies that have selected this approach include US EPA, California Department of Health Services, and the New York Department of Environmental Conservation. While the latter approach can consider site-specific factors, it also imposes burdens upon all parties to apply the procedures correctly and defend the results. The methodology and equations are the subject of considerable debate and their use requires that issues such as inherent uncertainties in interpreting toxicological information and assigning a definition to acceptable risk levels should be addressed.

2.13.1 Risk-based mitigation criteria The US EPA approach for remediation involves a site-specific risk assessment conducted according to procedures which are comprehensively presented in the Superfund Public Health Evaluation Manual (Mohamed and Antia, 1998). The steps involved in remediation goal setting are: (1) selection of indicator compounds for a given site; (2) estimation of both short-term concentration (STC) and longterm concentration (LTC) of the indicators in media at points of maximum human exposures, and comparison with applicable or relevant and appropriate standards (e.g., drinking water standards); (3) estimation of human sub-chronic and chronic daily intakes (SDI and CDI). SDI is the projected human intake averaged over a short time and it is defined as the peak short-term concentration multiplied by the human intake factor times body weight factor. CDI is the projected human intake averaged over 70 years and it is defined as the peak long-term concentration multiplied by the human intake factor times the body weight factor; and (4) the hazard indices for sub-chronic and chronic exposures (HIS and HIC) are then computed. HIS is defined as the ratio between SDI and AIS, where AIS is the acceptable daily intake for sub-chronic daily exposure which does not cause adverse effects during short term exposure. HIC is defined as the ratio between CDI and AIC, where AIC is the acceptable daily intake for chronic exposure which does not cause adverse effects during long term exposure (Mohamed and Antia, 1998).

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The target levels for remediation are determined differently for indicator compounds with standards versus those without. If standards exist, that sets the upper limit on target levels. For those without standards, the compounds are divided into two groups: (a) chemicals with non-carcinogenic toxic effects, and (b) potential carcinogens. For non-carcinogens, the daily intake for each compound must be maintained equal to or less than the acceptable daily intake. In addition, for multiple substances and routes, the overall hazard index must be maintained less than or equal to one. For carcinogens, remediation is intended to maintain the cancer risk in the range of 104 to 107 with 106 established as a target point. The target concentration is that concentration that will produce a chronic daily intake associated with this range of risks (Mohamed and Antia, 1998).

2.13.2 Numerically based mitigation criteria Over the past 40 years, the factors considered in establishing soil remediation criteria have changed both in terms of the number considered and in the relative importance assigned to individual factors. While some of the earlier efforts considered only one or two factors (such as background concentrations and analytical detection limit), some of the current initiatives by various agencies consider several factors and utilize techniques such as environmental fate modelling and health risk assessment (Mohamed and Antia, 1998). In establishing numerically based criteria for remediation, the following factors are generally considered: background or ambient concentrations of pollutants, environmental mobility of pollutants, relationship between soil and water quality, health of terrestrial plants and animals, human health, aesthetics, limits of analytical capability, and land use (Mohamed and Antia, 1998). 1. Background Conditions: Typical background concentrations that naturally exist is one of the most frequently used factors in setting criteria. Background concentrations are generally assumed to represent environmentally sound and acceptable conditions and establish the ultimate conditions that remedial actions can achieve (Mohamed and Antia, 1998). Information about background concentrations provides little guidance for compounds that are solely anthropogenic since the background concentration is zero. Another limitation of this factor is defining what background means and whether it is necessary to differentiate between background concentrations in various types of areas such as urban and rural areas. The relationship of soil to crustal rock can be obtained from data shown in Table 2.6, which lists average mass concentration of 50 chemical elements in soils and surficial rocks. The 10 most abundant elements in soils are O > Si > Al > Fe > C > Ca > K > Na > Mg > Ti, whereas in crustal rocks they are O > Si > Al > Fe > Ca > Mg ¼ Na > K > Ti > P. The elements in the list from carbon to calcium, except for F, Al, and P, are quantified as macro-elements. The remaining 40 elements, therefore, are micro-elements (Mohamed and Antia, 1998). The soil enrichment factor (SEF), defined as the ratio of soil to crustal rock concentrations, is shown in Table 2.6. The SEF is a quantitative measure of relative enrichment (or depletion) of a chemical element in soil as compared with rock. Given the variability of both soil and rock composition, depletion or enrichment can be classified as: (1) SEF less than 0.5 would indicate significant depletion, (2) SEF greater than 0.5 and less than 2.0 would indicate no significant depletion or enrichment, (3) SEF greater than 2.0 and less than 10.0 would indicate some enrichment, and (4) SEF greater than 10.0 would indicate strong enrichment. For most of the microelements, there is a close

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Table 2.6 Mean elemental content (mg/kg) of soil and crustal rocks, and the soil enrichment factor (SEF) (Mohamed and Antia, 1998). Element

Soil

Crust

SEF

Li Be B C N O F Na Mg Al Si P S Cl K Ca Se Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Rb Sr y Zr Nb Mo Ag Cd

2.4Eþ1 9.2E1 3.3Eþ1 2.5Eþ4 2.0Eþ3 4.9Eþ5 9.5Eþ2 1.2Eþ4 9.0Eþ3 7.2Eþ4 3.1Eþ5 4.3Eþ2 1.6Eþ3 1.0Eþ2 1.5Eþ4 2.4Eþ4 8.9E0 2.9Eþ3 8.0Eþ1 5.4Eþ1 5.5Eþ2 2.6Eþ4 9.1E0 1.9Eþ1 2.5Eþ1 6.0Eþ1 1.7Eþ1 1.2E0 7.2E0 3.9E1 8.5E1 6.7Eþ1 2.4Eþ2 2.5Eþ1 2.3Eþ2 1.1Eþ1 9.7E1 5.0E2 3.5E1

2.0Eþ1 2.6E0 1.0Eþ1 4.8Eþ2 2.5Eþ1 4.7Eþ5 4.3Eþ2 2.3Eþ4 2.3Eþ4 8.2Eþ4 2.8Eþ5 1.0Eþ3 2.6Eþ2 1.3Eþ2 2.1Eþ4 4.1Eþ4 1.6Eþ1 5.6Eþ3 1.6Eþ2 1.0Eþ2 9.5Eþ2 4.1Eþ4 2.0Eþ1 8.0Eþ1 5.0Eþ1 7.5Eþ1 1.8Eþ1 1.8E0 1.5E0 5.0E2 3.7E1 9.0Eþ1 3.7Eþ2 3.0Eþ1 1.9Eþ2 2.0Eþ1 1.5E0 7.0E2 1.1E1

I.2E0 3.5E1 3.3E0 5.2Eþ1 8.0Eþ1 I.0E0 2.2E0 5.2E1 3.9E1 8.8E1 1.1E0 4.3E1 6.2E1 7.7E1 7.1E1 5.9E1 5.6E1 5.2E1 5.0E1 5.4E1 5.8E1 6.3E1 4.6E1 2.4E1 5.0E1 8.0E1 9.4E1 6.7E1 4.8E0 7.8E0 2.3E0 7.4E1 6.5E1 8.3E1 1.2E0 5.5E1 6.5E1 7.1E1 3.2E0

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Table 2.6 Mean elemental content (mg/kg) of soil and crustal rocks, and the soil enrichment factor (SEF) (Mohamed and Antia, 1998).dcont’d Element

Soil

Crust

SEF

Sn Sb I Cs Ba La Hg Pb Nd Th U

1.3E0 6.6E1 1.2E0 4.0E0 5.8Eþ2 3.7Eþ1 9.0E2 1.9Eþ1 4.6Eþ1 9.4E0 2.7E0

2.2E0 2.0E1 1.4E1 3.0E0 5.0Eþ2 3.2Eþ1 5.0E2 1.4Eþ1 3.8Eþ1 1.2Eþ1 2.4E0

5.9E1 3.3E0 8.6E0 1.3E0 1.2E0 1.2E0 1.8E0 1.4E0 1.2E0 7.8E1 1.1E0

correspondence between soil and crustal rock concentrations. Hence, it would be safe to say that by taking SEF ¼ 1, the background concentration of various elements will correspond to that of the bedrock shown in Table 2.6. 2. Environmental Mobility: This refers to the ability and/or ways with which a substance can move in the environment. Relatively mobile substances include those that are relatively soluble in water or volatile. Mobility is also influenced by environmental conditions, such as soil properties and the characteristics of the groundwater regime. Mobile substances are likely to move off-site and/or encounter various types of receptors. Mobility is not explicitly considered in most remediation criteria. Factors such as soil pH (Alberta Criteria, Canada), soil characteristics (Ontario Criteria, Canada) and soil organic matter (The Netherlands Criteria) are considered. As environmental fate modelling techniques come into wider use, it is probable that environmental mobility will increase in importance. Environmental fate modelling techniques can also be expected to become an integral part of efforts to develop site specific remediation criteria (Mohamed and Antia, 1998). 3. Relationship Between Soil and Water Quality: This is an obvious one and several agencies that have issued remediation criteria for soil have also issued complementary criteria for groundwater. In many cases the groundwater criteria are derived from drinking water guidelines and assume that the groundwater is used directly as drinking water (British Columbia, Quebec, and the Canadian Council of Ministers of the Environment, CCME). Environmental partitioning of pollutants between soil and groundwater has been used to set the reference values in The Netherlands (Mohamed and Antia, 1998). 4. Health of Terrestrial Plants and Animals: Information concerning the health of terrestrial plants has been used by several agencies in setting remediation criteria to avoid phytotoxic or other adverse effects on grazing animals. Regarding concentrations of substances in soil which are capable of adversely affecting vegetation, the scientific literature provides enough information for selected pollutants. Most of the available data pertains to agricultural crops and the pollutants of

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5.

6.

7.

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concern typically include boron, copper, nickel, and zinc. The data available about adverse effects on grazing animals are a result of conditions observed in agricultural animals. For example, disorders associated with excessive amounts of molybdenum, selenium, and copper in diets or soils have been observed in cattle and sheep. There is very little information of this type for organic compounds (Mohamed and Antia, 1998). Human Health: This is usually in the form of assessment of health risks, which have been increasingly used over the past few years, in order to develop remediation criteria. In some of the recently developed methodologies, human health considerations are the primary factor in setting remediation criteria. This philosophy is often predicated on the assumption that criteria that are sufficiently protective of human health will be sufficiently protective of the environment. For some pollutants this is known or suspected not to be the case. Examples include zinc and some esters which can cause phytotoxic effects (environmental degradation) before being of concern to human health. An approach based only on human health may be capable of establishing soil criteria for areas with potential to produce odors or taint locally grown produce. Risk assessment requires numerous assumptions to be made regarding the people being exposed, the pathways of exposure, the relationship between dose and response, and the environments in which exposure can occur. For carcinogens, it is assumed that any dose poses some level of risk and, therefore, there are the additional prerequisites of defining acceptable risk. Each of these aspects is laden with uncertainties and in some instances, there is considerable debate as to proper procedures (Mohamed and Antia, 1998). Aesthetics: Pollutants in the environment can be sources of odors, staining of soil, discoloration, film or foams on water, and impart disagreeable tastes to water, plants, and the animals that live in such environments. Many odors or tastes can be detected at concentrations lower than those needed to cause other types of adverse effects. Criteria based on aesthetic considerations are needed to avoid such effects. While those pollutants most likely to cause aesthetic concerns are well known, the concentration in the soil at which those effects occur are not well documented. Consequently, this factor is not often considered when establishing remediation criteria (Mohamed and Antia, 1998). Analytical Capabilities: Analytical detection limits have been used by several agencies in establishing remediation criteria. For some anthropogenic substances, it has been assumed that any measurable concentration is unacceptable for relatively sensitive land uses, such as residential or agricultural, and that a maximum acceptable concentration for less sensitive land use could be defined as a multiple (a factor of 10 or 100) of the detection limit. The use of analytical capabilities in setting criteria likely will diminish as other factors rise in importance. Weaknesses of the analytical capabilities are: (a) detection of a substance does not automatically mean that an adverse effect will occur, (b) analytical detection limits have steadily improved over the past several decades, thus providing a constantly changing target, (c) the detection limit achieved on soil samples is a function of interfering compounds or conditions that may be present in samples, and (d) analytical detection limits are sensitive to the procedures followed during sample collection, transportation to the laboratory, handling, and transportation (Mohamed and Antia, 1998). Land Use: This is a frequently used factor in establishing remediation criteria. The types of land use most often addressed are residential/parkland (RIP), agricultural (AG), and commercial/ industrial (C/I). Virtually all agencies that differentiate according to land use advocate the use of

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lower criteria for AG  RIP < C/I. Establishing a limit for each land use is arbitrary; for example, C/I criteria are higher than AG and RIP by a factor of 1e20 depending on the agency. The principle of multi-functionality advocated by The Netherlands is another way of considering various land uses in establishing remediation criteria. This principle is defined as preserving the properties of a soil which are of importance for its various possible functions (uses) such as growing crops, being a source of drinking water, and providing a suitable habitat for plants and animals. Conversely, the United Kingdom Department of the Environment has rejected the concept of multi-functionality on the basis that the cost of bringing every polluted site back to a state suitable for every conceivable use would be disproportionate to the benefits (Mohamed and Antia, 1998). In summary, the criteria-based approach involves direct adoption or adaptation of the existing environmental quality criteria (EQCs) in consideration of site-specific circumstances. The EQCs are intended to be conservative for protecting human health and the environment. The EQCs may be adopted (modified) to account for site-specific environmental or socioeconomic conditions. For example, at locations where the background level of a pollutant is higher than the national criterion for that pollutant, it may be appropriate to modify the criterion for that specific location to ensure that remediation objectives are not set at levels below ambient concentrations. When remediation criteria adopted or adopted for site-specific use are exceeded, the need for remedial action is indicated. Remediation is complete when pollutant levels have been reduced below the levels of the remediation objectives established to protect and sustain the intended current or future use of soil or water at the site in question. The elements that need to be considered in the development and implementation of soil and groundwater quality criteria are: (1) site classification scheme to screen polluted sites and rate them according to their apparent hazard, i.e., low hazard, high hazard, and catastrophic; (2) properties and characteristics of chemical, transport and fate of pollutants; (3) range of reasonable land use and exposure scenarios; (4) generic risk assessment and risk management protocol; and (5) comprehensive, multidimensional listings of acceptable soil and groundwater quality criteria for range of site characteristics and exposure scenarios (Mohamed and Antia, 1998).

2.14 Mitigation technologies for polluted soils Mitigation technologies for polluted soils can be grouped, as illustrated in Table 2.7, into: (1) natural attenuation, (2) containment, (3) waste subtraction for succeeding on-site or off-site treatment, (4) insitu treatment, and (5) land disposal (US National Academy of Sciences Press, 1994, 1999).

2.14.1 Natural attenuation Natural attenuation requires that the natural processes existing in the subsurface continue to provide adequate environmental protection. For example, due to an existence of a thick clay layer beneath a disposal site, dissolved pollutants may be attenuated before an unacceptable risk to the environment occurs. Another example is the use of naturally occurring wetlands for pollutant attenuation (Mohamed and Antia, 1998). For this technique to be implemented, subsurface monitoring is required to ensure that the natural attenuation processes continue to deliver the expected protection to human health and the environment.

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Table 2.7 Mitigation technologies for polluted soils. Mitigation type Natural attenuation Containment

Removal and treatment

In situ treatment

Available technologies

· Clay deposits · Wetlands (covering systems; slurry walls; · Physical sheet piling) · Chemical (organic based; inorganic based) · Thermal (vitrification) · Product recovery · Groundwater extraction · Soil vapor extraction · Soil flushing (surfactant based; solvent based) · Electrochemical · Biological

2.14.2 Containment In general, this technique involves creating barriers or immobilizing pollutants for preventing unacceptable pollutant migration. Techniques such as physical, chemical, and thermal are used Mohamed and Antia (1998). 1. Physical: Covering systems, pumping wells, steel casing, and slurry walls of cement and bentonite are typical physical barriers for vapor and groundwater migration. These systems are discussed in the literature by many authors, see, for example, Mohamed and Antia (1998). 2. Chemical: This technology attempts to encapsulate the pollutants directly, hence reducing the potential leachability. In microencapsulation technology, a liquid monomer is mixed with the polluted soil and then a catalyst is injected to polymerize the monomer and encase the polluted soil. In inorganic-based encapsulation processes, cement is used to solidify the polluted soil (Mohamed and Antia, 1998). Critical issues such as (1) compatibility of the waste material with the solidifying agents, (2) effect of the environmental conditions (e.g., pH and oxidationreduction), (3) potential biological transformation, and (4) effect of freeze/thaw and wet/dry conditions are major concerns in evaluating technology performance. 3. Thermal: Thermal technique such as vitrification transforms the polluted soil into a solid mass like rock. This technology may create harmful gases; hence, air pollution monitoring is required during its application.

2.14.3 Removal and treatment For nonaqueous phase liquids (NAPLs), recovery of NAPLs, groundwater, soil vapor and polluted soils can be removed and treated on-site or off-site (Mohamed and Antia, 1998). 1. Product Recovery: NAPL products are recovered with the use of extraction wells and interceptor trenches. Subsurface monitoring is required to evaluate the removal efficiency of pollutants.

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2. Soil Vapor Extraction (SVE): SVE is used for recovering pollutants from the soil gas phase. The technique is applicable to organic pollutants with high vapor pressure. Monitoring wells are required to assess the technology. The extracted pollutants will be treated on-site or off-site. 3. Groundwater Extraction: Pump-and-treat and PRBs are usually used to recover dissolved pollutants in groundwater. Pump-and-treat is usually used as a containment technique if the pollutants are highly adsorbed by soils and the subsurface environment consists of a single and relative homogeneous aquifer. The extracted pollutants may be treated on-site or off-site. PRB, the deposition of zero-valency iron either in trenches or by boreholes in the soil pore space and in the direction of contaminant movement, has been seen to be a more efficient treatment method than pump-and-treat, especially in nonhomogeneous subsurface environments where pump-andtreat systems usually fail.

2.14.4 In situ treatment There are several in situ treatment techniques reported in the literature that the reader could utilize (see, for example, Mohamed and Antia, 1998; US National Academy of Sciences Press, 1994, 1999). 1. In Situ Soil Flushing: Chemical reagents such as: (1) surfactant-based techniques and (2) solventbased techniques are injected into the subsurface. Extraction wells located below the water table can be used to collect the polluted water for treatment. 2. Electrochemical Treatment: This is used to treat in situ polluted soils with low hydraulic conductivity. The technology relies on the use of a direct current and a set of anodes and cathodes to mobilize pollutants from the anode to the cathode. Extraction wells are used at the cathode to extract pollutants for treatment. 3. Biological Treatment: In situ bioremediation is usually used for organic polluted soils. Current technology enhances aerobic biodegradation by adding oxygen and nutrients into the subsurface. Biodegradation of some organic pollutants can create new toxic compounds. For example, anaerobic biodegradation of trichloroethylene can produce vinyl chloride, a more hazardous pollutant (Mohamed and Antia, 1998).

2.14.5 Selection of mitigation options The selection of an appropriate mitigation option depends on a careful assessment of both short- and long-term risks posed by the polluted site. The selection process ensures that the selected mitigation option aims at achieving the protection of human health and the environment, and full compliance with mitigation criteria specified by regulatory agencies. Mitigation alternatives considered for potentially polluted sites should be evaluated based on sustainable development concepts. The basic criteria in the sustainable development concept, as discussed in Chapter 1 of this book, are: 1. Environmental: This criterion is used to evaluate whether a mitigation option can eliminate the potential risk posed by the polluted site. 2. Social: This criterion is used to encourage public participation and account for the social costs associated with the mitigation option. 3. Technical: This criterion is used to evaluate the performance of a specific technology in achieving the requirements posed by the environmental criteria.

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4. Economic: This criterion is used to evaluate the total costs associated with a mitigation plan. 5. Land: This criterion is used to ensure land sustainability, which is achieved by integrating criteria 1e4.

2.15 Summary and concluding remarks This chapter presented an introduction to elements of risk analysis and assessment and risk management. Initially, the use of decision trees and payoff tables was discussed together with criteria to reach optimum decisions. The use of additional statistical measures to the expected value was shown to provide additional insight into decision-making. Furthermore, exposition to topics of environmental economics, such as the environmental cost of groundwater pollution, was detailed to demonstrate that viewing the cost of environmental degradation in a way that is limited to the cost of remedial actions only leads to less environmentally friendly solutions. The attitude to risk of individuals and corporations was incorporated into risk analyses using utility theory and examples were provided in hazardous waste transportation and burial problems. Finally, the section that dealt with decisionmaking analyses concluded with an elementary exposition of Bayesian decision theory illustrated through a problem of air emissions from waste to energy facilities. The second part of this chapter discussed human health risk assessment for environmental hazards by detailing its elements of hazard identification, exposure assessment, toxicity assessment, and risk characterization and presenting the various models of exposure assessment and risk characterization. The steps of risk management programs were described together with the role of regulatory agencies and their approaches to risk. Finally, the chapter concluded with the presentation of mitigation measures for pollution in soils.

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Hocking, G., Thurman, M.A., Givens, C.N., Lacko, C.M., 2006b. Installation of a full-scale permeable reactive barrier at a former manufacturing site in California. In: 5th International Conference on Remediation of Chlorinated and Recalcitrant Compounds, Monterey, Ca, May 22-25. Illinois EPA (Environmental Protection Agency), 2003. A study of the merits and effectiveness of alternate liner systems at illinois landfills. In: Research Paper Submitted in Fulfillment of House Resolution 715 of the State of Illinois 92nd General Assembly, 58 pp. Interstate Technology Regulatory Council (ITRC), 2003. In: Technical and Regulatory Guidance for Design, Installation, and Monitoring of Alternative Landfill Covers. ITRC Report, pp. 198. James, B.R., Gwo, J.-P., Toran, L., 1996. Risk-cost framework for aquifer remediation design. Water Resour. Plann. Manag. 122 (6), 414e420. ASCE. Jaynes, E.T., 1957. How Does the Brain Do Plausible Reasoning, vol. 1. Stanford University Microwave Laboratory Report 421, pp. 1e23 reprinted in Erickson and Smith (1988). Jaynes, E.T., 1968. Prior Probabilities. IEEE Trans. Systems Science and Cybernetics, SSC-4, pp. 227e241. Jaynes, E.T., 1978. Where do we stand on maximum entropy? In: Levine, R.D., Tribus, M. (Eds.), Maximum Entropy Formalism. MIT Press, Cambridge, MA, pp. 15e118. Jaynes, E.T., 1983. In: Rosenkrantz, R.D. (Ed.), Papers on Probability, Statistics and Statistical Physics, second paperbound ed. D. Reidel Publishing Co., Dordrecht, Holland. 1989 by Kluwer Academic Publishers. Jaynes, E.T., 1984. The Intuitive Inadequacy of Classical Statistics. Epistemologia, Special Issue on Probability, Statistics, and Inductive Logic, VII, pp. 43e73. Jaynes, E.T., 1990. Probability theory as logic,. In: Fougere, P. (Ed.), Proceedings of the Ninth Annual Workshop on Maximum Entropy and Bayesian Methods, Kluwer Publishers, Holland. Jaynes, E.T., 1993. A backward look to the future. In: Grandy Jr., W.T., Milonni, P.W. (Eds.), Physics and Probability. Cambridge University Press, UK, pp. 261e275. Jaynes, E.T., 2003. Probability Theory: The Logic of Science. Cambridge University Press, UK, p. 758. Jeffreys, H., 1948. Theory of Probability. International Series on Monograms in Physics, second ed. Clarendon Press, Oxford. 412 pp. (originally in 1939, later editions 1948, 1961, 1967, 1988). Kadane, J.B. (Ed.), 1996. Bayesian Methods and Ethics in a Clinical Trial Design. Wiley-International, p. 318. Kahneman, D., 2012. Thinking, Fast and Slow. Penguin Books, London, UK, p. 499. Kahneman, D., Tversky, A., 1979. Prospect theory: an analysis of decision under risk. Econometrica 47 (2), 263e291. Kahneman, D., Tversky, A. (Eds.), 2000. Choices, Values, and Frames. Cambridge University Press, UK, p. 860. Keeney, R.L., Raiffa, H., 1993. Decisions with Multiple Objectives. Preferences and Values Tradeoffs. Cambridge University Press, UK. Kravchenko, J., Rew, S.H., Akushevich, I., Agarwal, P., Lyerly, H.K., 2018. Mortality and health outcomes in North Carolina communities located in close proximity to hog concentrated animal feeding operations. N. C. Med. J. 79 (5), 278e288. https://doi.org/10.18043/ncm.79.5.278. Krzysztofowicz, R., 1986. Expected utility, benefit, and loss criteria for seasonal water supply planning. Water Resour. Res. 22 (3), 303e312. Laplace, P.S., 1812. Theorie Analytique des Probabilite´s. Courcier, Paris published also in 2018 by Forgotten Books in French, pp. 472. EU Legislation in Progress Briefing, 2018. Water Reuse: Setting Minimum Requirements. www.europarl.europa. eu/RegData/etudes/BRIE/2018/625171/EPRS_BRI(2018)625171_EN.pdf. Lempp, C., Lerche, I., Paleologos, E.K., 2002. Environmental concerns: catastrophic events and insurance. J. Energy Explor. Exploit. 20 (4), 309e324. https://doi.org/10.1260/014459802762231114. Lerche, I., MacKay, J.A., 1999. Economic Risk in Hydrocarbon Exploration. Academic Press, San Diego, CA. Lerche, I., Paleologos, E.K., 2000. Optimal involvement in multiple environmental projects under budgetary constraints. J. Stoch. Environ. Res. Risk Assess. 14 (6), 371e383.

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Raiffa, H., 1997. Decision Analysis: Introductory Lectures on Choices under Uncertainty. The McGraw-Hill Companies Inc., New York, NY, p. 309. Ruanaidh, J., Fitzgerald, W.J., 1996. Numerical Bayesian Methods Applied to Signal Processing. Springer Statistics and Computing, Springer, p. 244. Song, J.J., Ghosh, M., Miaou, S., Mallick, B., 2006. Bayesian multivariate spatial models for roadway traffic crash mapping. J. Multivariate Anal. 97 (1), 246e273. Sorensen, D., Gianola, D., 2006. Likelihood, Bayesian and MCMC Methods in Quantitative Genetics. Springer: Statistics for Biology and Health, p. 760. Stavrakakis, G.N., Drakopoulos, J., 2005. Bayesian probabilities of earthquake occurrences in Greece and surrounding areas. J. Pure Appl. Geophys. 144 (2), 307e319. The House of Lords UK, 2004. The Regulatory State: Ensuring its Accountability, Volume I Report. House of Lords, Select Committee on the Constitution, 6th Report of Session 2003-04, p. 98. Thompson, K.D., Stedinger, J.R., Heath, D.C., 1997. Evaluation and presentation of dam failure and flood risks. Water Resour. Plann. Manag 123 (4), 216e227. ASCE. UNC (University of North Carolina at Chapel Hill) School of Government, Environmental Finance Center, 2018. WI Water Rates Dashboard Rates as of May 15, 2018. https://efc.sog.unc.edu/resource/wisconsin-residentialwater-rates-dashboard. U.S. Code 42 USC x 7413, January 4, 2012. Title 42-The Public Health and Welfare Chapter 85-Air Pollution and Control Subchapter I-Programs and Activities Part A-Air Quality and Emission Limitations x 7413. Federal Enforcement, p. 9. US Food and Drug Administration, 2010. Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials. Guidance for Industry and FDA Staff, p. 50. US National Academy of Sciences, 1994. Alternatives for Ground Water Cleanup. Committee on Ground Water Cleanup Alternatives. National Research Council Report, Washington DC, p. 336. ISBN: 0-309-58624-0. US National Academy of Sciences, 1999. Groundwater and Soil Cleanup: Improving Management of Persistent Contaminants. Committee on Technologies for Cleanup of Subsurface Contaminants in the DOE Weapons Complex. National Research Council Report, Washington DC, p. 304. ISBN: 0-309-51961-6. US National Academy Sciences Press, 2003. Bioavailability of Contaminants in Soils and Sediments: Processes, Tools, and Applications. Committee on Bioavailability of Contaminants in Soils and Sediments. National Research Council. ISBN: 0-309-50578-X, 432pp. USEPA, 2017. Enforcement and Compliance History Online (ECHO): Facility Search-Enforcement and Compliance Data. Retrieved from: https://echo.epa.gov/facilities/facility-search. USEPA, August 30, 1988. (U.S. Environmental protection agency). Fed. Regist. 53 (168), 33345. USEPA (U.S. Environmental Protection Agency), 2019. Resource Conservation and Recovery Act (RCRA) Regulations, Title 40: Protection of Environment, Part 263-Standards Applicable to Transporters of Hazardous Waste. Available. https://www.epa.gov/rcra/resource-conservation-and-recovery-act-rcra-regulations#haz. USEPA (U.S. Environmental Protection Agency), January 2002. EPA Air Pollution Control Cost Manual, sixth ed., p. 752 EPA/452/B-02-001, Washington D.C. Van Gelder, P.H.A.J.M., Duckstein, L., Parent, E., 2004. A multicriteria approach to risk analysis, part I: framework. In: In Conference Probabilistic Safety Assessment and Management, Berlin, Germany. Van Noortwijk, J.M., Kalk, H.J., Chbab, E.H., 2004. Bayesian estimation of design loads. HERON (Netherlands) 49 (2), 189e205. Von Neumann, J., Morgenstern, O., 2004. Theory of Games and Economic Behavior, 60th Anniversary Edition. Princeton University Press, p. 739. (1st ed. 1943, 2nd ed. 1946, 3rd ed. 1953). Woodbury, A.D., Rubin, Y., 2000. A full-Bayesian approach to parameter inference from tracer travel time moments and investigation of scale effects at the Cape Cod experimental site. Water Resour. Res. 36 (1), 159e171. Wyoming Department of Environmental Quality (DEQ), March 9, 2004. Solid and Hazardous Waste Division. Remediation at Municipal Landfills, Draft Policy Paper, p. 4.

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Ye, M., Neuman, S.P., Meyer, P.D., 2004. Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff. Water Resour. Res. 40, W05113. Ҫetin, T., Sobaci, M.Z., Nergeleҫekenler, M., 2013. Independence and accountability of independent regulatory agencies: the case of Turkey. Eur. J. Law Econ. https://doi.org/10.1007/s10657-013-9432-x.

Further reading Hardin, G., 1968. The tragedy of the commons. Science 162, 1243e1248. Hardin, G., Baden, J. (Eds.), 1977. Managing the Commons. W. H. Freeman, San Francisco. Maggetti, M., Ingold, K., Varone, F., 2015. Having your cake and eating it, too: can regulatory agencies be both independent and accountable? Swiss Polit. Sci. Rev. 19 (1), 1e25. https://doi.org/10.1111/spsr.12015. McCay, B.J., Acheson, J.M. (Eds.), 1987. The Question of the Commons. University of Arizona Press, Tucson. OECD (Organisation for Economic Co-operation and Development), 2017. Creating a Culture of Independence: Practical Guidance against Undue Influence, the Governance of Regulators. OECD Publishing, Paris, France, p. 37. https://doi.org/10.1787/9789264274198-en. (Accessed 11 September 2018). Ostrom, E., 1990. Governing the Commons. Cambridge University Press, New York.

CHAPTER

Environmental applications of remote sensing

3

Stelios P. Mertikas1, Panagiotis Partsinevelos2, Constantine Mavrocordatos3, Nikolai A. Maximenko4 Laboratory of Geodesy & Geomatics Engineering, School of Mineral Resources Engineering, Technical University of Crete, Chania, Crete, Greece; 2Sense Lab Research, School of Mineral Resources Engineering, Technical University of Cret, Chania, Crete, Greece; 3European Space Agency - Earth Observation Projects, Department ESA/ESTEC, Noordwijk, The Netherlands; 4International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, HI, United States

1

3.1 Environmental problems and remote sensing Our world is facing consequences and risks primarily because of industrial progress, with deep and global ramifications for our planet’s future. The Earth’s environment has been profoundly transformed and changed leading at times to irrevocable consequences for nature’s cycles. Environmental vulnerabilities include air pollution, water and soil contamination, land degradation, retreat of glaciers, sea-level rise, deforestation, desertification, loss of biodiversity, soil depletion, ocean acidification, sewage pollution, eutrophication, and many more. And those vulnerabilities are further exacerbated by climate change (Petit at al., 1999; Yang et al., 2013), global warming, and by the great forces of nature (Steffen et al., 2004). Changes are fundamental in the functioning of the Earth system set out at the beginning of the industrial revolution. At present, systems and processes that regulate the stability and resilience of the Earth are under severe pressure. To assess potential risks and vulnerabilities and to tackle the drivers of environmental degradation but also confront and cope with the future of our planet, we have developed new observational and analytical tools to monitor the Earth and its changes. As our population soars and is expected to reach 9.7 billion people by 2050, new challenges stand in place for monitoring the environment and climate change with remote sensing. Sensors and detectors of electromagnetic radiation, carried for the most part by satellites, are imaging, monitoring, and mapping the Earth continuously. In that way, these remote sensors help us better understand the ecological and environmental interactions that underlie our Earth and are assisting us in planning and coordinating new ways to undertake remedial, curative, or preventive actions. This chapter introduces and describes the science of remote sensing, its concepts and its foundations. It examines several sensors and platforms used for monitoring the environment and its changes, and elaborates upon future directions of remote sensing. Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering. https://doi.org/10.1016/B978-0-12-809582-9.00003-7 Copyright © 2021 Elsevier Inc. All rights reserved.

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Remote sensing is defined as the science and technology by which characteristics and properties of targets on the Earth can be identified and determined from a distance and without making physical contact with them. This is achieved by detecting, recording, processing, and analyzing the reflected or emitted electromagnetic energydcollected by sensors carried by satellites, aircraft, and/or dronesd from Earth surface targets. Global observation with remote sensing allows the application of concepts, tools, methods, and conservation practices, which were previously applicable only at regional or local levels, to the planet as a whole as an integrated “Earth system.” Such global images allow phenomena happening at a global scale to be addressed properly not only for environmental planning, management, and treatment, but also for data harmonization and standardization to reach comprehensive and integrated comparisons and assessments. Reliable information on natural resources, soils, minerals, surface and ground waters, forests, marine resources, forest fire, marine contamination, and many more is thus collected by remote sensing in a timely, repeated, systematic, reliable, and cost-effective manner. The aim of this global monitoring is to describe the entire cause and effect of these interconnected and interacting environmental issues, to access their nature, extent, spatial distribution, and potential for environmental problems. Remote sensing monitoring of the environment thus helps (1) to examine the potential risks and vulnerabilities of the Earth system and measure and map its resilience to possible abrupt changes, (2) to clearly understand and justify the environmental change as well as the functioning and resilience of the Earth system, (3) to develop indicators and measures for monitoring progress in addressing and achieving environmental objectives, including biodiversity, biochemistry (e.g., terrestrial and marine biological carbon sinks), atmosphere, land system, soil, water, and climate changes, and (4) to work out ways to tackle the drivers of degradation of these linked, interacting, and international environmental, social, and economic transformations (Steffen et al., 2015), in particular across space, time, and organizational levels. In the 50 years since the first remote sensing satellitedthe Earth Resources Technology Satellitedlaunched by the United States in 1972, we have witnessed the rapid development of Earth observation platforms, instruments, and sensors, the continuous decrease of sensor electronics in size and cost, and have also seen further advances in processing speed, unlimited computer power on orbit and within the sensor itself, and data storage along with onboard recording capabilities (National Academies of Sciences, Engineering, and Medicine, 2018; Khorram et al., 2016; CEOS, 2019). These technological advances have boosted and expanded the applications of remote sensing to address diverse environmental issues and processes. Examples cover a wide range of all disciplines, including engineering, forestry, atmosphere, surface dynamics, geological hazards and disasters, public health, natural resources, ecosystem change, and many more. Remote sensing has provided new ways and technologies in understanding the Earth system, the climate, and its variations, by monitoring remotely the Earth’s surface and subsurface, the environmental processes and issues, the atmosphere, the land, and the oceans. It is also capable of observing locations that are hard to monitor, such as rainforests, polar regions, deep oceans, and high mountains. It also provides us with the tools to keep track of variations and complex interactions between environmental processes in time and space. In that way, we are able to isolate the natural variability from the driving mechanisms of the environmental change. Observations supported by remote sensing cover several priorities of environmental parameters, properties, and dynamics. These include global warming, sea and ice monitoring, sea-level change,

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solar radiation, aerosols, water vapor, precipitation, clouds, Earth mass change, surface biology and geology, greenhouse gases, ice elevation, sea surface topography, ocean surface winds and currents, ozone and trace gases, terrestrial ecosystem structure, surface topography, and vegetation. All in all, to monitor the environment and its physical, chemical, and biological components and processes with remote sensing requires (1) seamless and long-term observation of the Earth system, (2) proper archiving of remote sensing observations, along with their confidence and uncertainties, and finally (3) fast data availability and dissemination (Plummer et al., 2017).

3.2 Concepts and foundations of remote sensing Remote sensing consists of three parts: (1) the targets, which are the objects or phenomena of a region to be investigated, (2) the measurements made with remote sensing detectors of electromagnetic radiation carried either by satellite (Fig. 3.1), aircraft, or drones, and (3) the analysis and interpretation of remote-sensed data. The definition of remote sensing encompasses a wide range of subjects and may include human vision, X-ray equipment in medical applications, laser sensors used for particle monitoring in the atmosphere, and many more. Examples of measurements with remote sensing include conventional photography, radar imaging, aerial photography, gravity observations, and others. In practice, however, remote sensing investigations are restricted to imaging systems only, such as aerial photographs, satellite images, imaging spectrometers, scanners, and the like, and do not include systems with direct measurement of electromagnetic radiation, as carried out with gravimeters, magnetometers, or electricity detectors.

FIGURE 3.1 Example of remote sensing satellite orbits over the Earth. Satellites are placed commonly on sun-synchronous orbits so that they cross every place on the Earth at about 9:00e10:00.

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Targets of remote sensing could be large, such as the Earth, or the Moon, or other planets, or even small, such as the biological cells that are observed with microscopes in medicine and biology, or the microorganisms, metals, and crystals examined with electronic microscopes. Nonetheless, the primary aim of remote sensing here is the measurement and imaging of the Earth (land, ocean, and atmosphere), and the various processes and phenomena taking place on them, as well as their characteristics and properties. So, typical targets on the surface of the Earth are terrain, water, buildings, vegetation, ice, snow, etc. To be able to interpret and analyze the imaging process of remote sensing, it is good first to understand the properties and behavior of electromagnetic radiation as it is emitted, passes through the atmosphere, and is then reflected by surface targets (Fig. 3.2). As radiation reaches the Earth’s surface it may be reflected, absorbed, or retransmitted. The percentage of energy that corresponds to these processes depends upon the nature and structure of the target, the wavelength of the energy, and the incidence angle. Reflection is a predominant process taking place in remote sensing and commonly detected with sensors in the visible and near-infrared wavelengths (0.4e10 mm). In that case, activation and operation of sensors (photo cameras, for example) relies upon radiance generated from the Sun. Reflected electromagnetic energy could also be observed from artificially generated energy sources, such as radar of multispectral Sentinel-2 images (Fig. 3.2) or even from the flash of a photo camera.

3.2.1 Spectral bands for imaging The visible and near-infrared regions of the electromagnetic spectrum have been widely applied as leading spectral bands in remote sensing. This is because the visible and near-infrared bands exist to a

FIGURE 3.2 A simplified schematic for the principles of operation and components in a remote sensing system. The Sun is the radiating source of energy covering almost all spectral bands of the electromagnetic spectrum. Clouds and atmosphere may reflect but also absorb this solar radiation. Targets on the surface of the Earth may reflect, absorb, and retransmit the incoming radiation. Sensors on satellites and aircraft detect and record the reflected, emitted, or scattered radiation.

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larger extent, with respect to other bands, within the region of the highest and largest amount of radiation emitted by the Sun; in addition, sensor technology initiated its development using these spectral bands. Sensors of remote sensing detect energy arising from a target and record signal properties, intensity, and characteristics in various spectral bands. When these radiometric and spectral characteristics are evaluated, chemical and physical properties of a target could be identified and determined. Chemical composition and crystal structure of a surface target affect its reflectivity as a result of molecular and electrical interactions with the incoming radiation. Other properties of the surface, such as roughness, slope, orientation, and geometry of the set-up source-surface detector, also influence the transmitted radiation from a target. Hence, information regarding properties of a surface target is gathered by evaluating properties of the reflected electromagnetic signal as imprinted on a sensor after its interactions with matter, geometry, and composition of the target. Retransmitted thermal energy from the Earth’s surface originates from solar radiance after being absorbed and then retransmitted from the Earth on a larger wavelength. Evaluation of this type of emitted energy from the surface of the Earth allows us to investigate thermal properties of surface materials, vegetation type, material type, etc. Another type of emitted energy from the Earth is geothermal energy or the thermal energy coming from heating pipes, buildings, or fires. Such thermal energy is recorded by special remote sensing detectors, which also operate during the night as they do not rely on solar radiation. As can be seen, there are various regions of the electromagnetic spectrum. They cover a continuum extending over a wide range of energies and wavelengths, from wavelengths about 1011 m (ultralowfrequency waves) and 107 m (very long radio waves) to about 10
15 m (very short gamma rays). The electromagnetic spectrum has been divided, for convenience, into a number of specific spectral regions to suit the needs of a particular application or scientific field. The main spectral bands have been defined arbitrarily, because between certain spectral bands there are several subdivisions and the transition from one to another spectral band is not sudden but gradual. The reflective spectrum with wavelengths that are reflected or refracted with lenses and mirrors covers the region between 0.38 and 30 mm wavelengths and designates that segment of electromagnetic spectrum used by remote sensing (Table 3.1 and Fig. 3.3). An image considers every spectral recording and mapping irrespective of the wavelength or the sensor that created it. A photograph is an image that is being generated by electromagnetic radiation with wavelengths from 0.3 to 0.9 mm. The spectral regions used by remote sensing are in increasing order of wavelength: ultraviolet, visible, infrared, and microwaves (l ¼ 1 mme1 m). Visible light is this narrow spectral region, covering 0.40e0.70 mm wavelengths, which can be detected by the human eye. In visible light, applications of remote sensing include blue (l ¼ 0.450e0.495 mm), green (l ¼ 0.495e0.570 mm), and red (l ¼ 0.620e0.750 mm) spectral bands used mainly for panchromatic, multispectral, and hyperspectral imaging. The red and near-infrared spectrum are typically applied for vegetation imaging. Also, LIDAR, which stands for “light detection and ranging,” commonly uses green light (l ¼ 0.532 mm) for bathymetric and coastal mapping in the form of laser pulses to measure ranges from a satellite or aircraft to the Earth. LIDAR transmits a vertical laser pulse of near-infrared frequency toward the Earth’s surface, which reflects from targets and returns to the sensor. The time difference between transmission and reception of pulse defines the range and thus the ground terrain

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Table 3.1 The electromagnetic spectrum used in remote sensing. Spectral region

Abbreviation

Wavelength range

Observations, remarks

Ultraviolet

UV

0.30e0.38 mm

Visible Near infrared Shortwave infrared Midwave infrared

VIS NIR SWIR MWIR

0.40e0.70 mm 0.70e1.30 mm 1.30e3.00 mm 3.00e6.00 mm

Scattered by atmosphere Glass absorbs it when l < 0.36 mm. Reflected sunlight Photographs

Thermal Very-long wavelength infrared Far infrared Submillimeter wave

TIR VLWIR

6 e14 mm 14 e30 mm

FIR SMMW

30 e1000 mm (1 mm) 0.1e1.00 mm

Microwaves

MW

1 mme1.00 m

Radio frequency

RF

1 mme10,000 km

Mixture of solar reflected and thermally emitted radiation 100% atmospheric transmission Absorbed by lenses Emissive detection Thermal emission Astronomy, atmospheric research Object temperature, density, and distribution of atmosphere Do not depend on solar radiation Independent of aerosols, clouds

can be retrieved. Airplanes and helicopters with LIDAR equipment are also used for topographic and bathymetric mapping. Three-dimensional (3D) mapping of the terrain is carried out with near-infrared lasers, while LIDAR bathymetry is achieved with green light, which penetrates water to about 200 m to measure the seabed. Fig. 3.4 depicts the penetration depth of visible light (green [gray in printed version] and blue [dark gray in printed version]) for open ocean and coastal waters. The next spectral region is invisible infrared. This is divided into (1) the near infrared (0.70e1.30 mm), mostly contained in sunlight and photographs can be taken with it, (2) the shortwave infrared (1.30e3.00 mm), (3) the midwave infrared (3.00e6.00 mm), and (4) the thermal infrared (6.00e14.00 mm). The last spectral band is used for thermal mapping as this radiation is emitted by the surface of the Earth at average temperatures of about 27 C or by hot fires and lavas at about 300 C (Fig. 3.17). Finally, microwave radiation is used in atmospheric sounding, land, sea, and ice mapping but also for determining sea wind, wave height, and sea surface height with satellite altimeters. Microwave devices transmit but also receive this radiation and can operate over night in hazy, rainy, and cloudy conditions as they do not depend upon solar radiation.

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FIGURE 3.3 The electromagnetic spectrum, solar irradiance, atmospheric windows, and atmospheric absorptions.

3.2.2 Spectral signature and atmospheric windows Remote sensing examines to a great extent the radiation transmitted or reflected by objects on the surface of the Earth. So, surface objects could be investigated by analyzing the radiation reflected or transmitted by them. Using photo cameras, optical mechanical scanners, radar, imaging spectrometers,

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FIGURE 3.4 Penetration depth of visible light in open ocean and coastal areas. The wavelength of l ¼ 0.532 mm (green [gray in printed version]) and the spectral region of 420e460 mm exhibit capacities for remote sensing of the ocean bathymetry and mapping.

and other sensors we could investigate the spectral response of these surface objects in various wavelengths. Every object on Earth has its own specific distribution of reflected, transmitted, or absorbed radiation as a function of wavelength l. Examination of this spectral distribution allows us to discriminate shape, size, and physical and chemical properties for Earth’s objects or phenomena. The shape of the spectral response and its characteristics as a function of wavelength l is called the spectral signature of the object or phenomenon. Fig. 3.5 presents various such spectral signatures for some Earth surface materials. Not all incoming solar radiation in all wavelengths reaches the surface of the Earth. Gases, vapors, and particles in the atmosphere filter out radiation and only certain wavelengths are allowed to penetrate it. Part of this radiation is either absorbed or scattered in all directions. When radiation reaches the atmosphere of the Earth only certain wavelengths are selectively penetrating its mass.

FIGURE 3.5 The spectral signatures of basalt, sandstone, dry soil, and grass.

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These wavelengths for which penetration through the atmosphere is allowed are called atmospheric windows. Gases and various elements of the atmosphere, like carbon dioxide, ozone, and water vapor, obstruct and block propagation of electromagnetic radiation as a result of absorption and scattering. The location, range, and effectiveness of atmospheric windows is predetermined by the characteristics of absorption caused by gases in the atmosphere. Examples of atmospheric windows are shown in Figs. 3.3 and 3.6. Windows of the electromagnetic radiation in the atmosphere are important in remote sensing applications. In a way they specify those useful wavelengths that could be used for effectively creating and generating images in remote sensing. Electromagnetic energy, which is not allowed to penetrate the atmosphere itself, is not able to form images. Therefore corresponding wavelengths cannot be used for remote sensing. For example, water vapor in the atmosphere absorbs electromagnetic radiation in the region 5.4e7 mm entirely. Thus energy at these wavelengths cannot be valuable in remote sensing. In the infrared band, the most important atmospheric windows cover spectral regions from 3.5 to 4.1 mm and from 10.5 to 12.5 mm. The second spectral region from 10.5 to 12.5 mm is extremely important as it coincides with the region of maximum emitted radiation of the Earth in the thermal region. The most important atmospheric windows of the electromagnetic radiation are given in Table 3.2 and Fig. 3.3. In remote sensing, image is a function of two variables of the electromagnetic irradiance, f (x,y), where x, y denote coordinates and f is the value of a function related to irradiance of the image in the

FIGURE 3.6 Functions of spectral irradiance as emitted by the Sun and the definition of radiometric spectral resolution.

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Table 3.2 The main atmospheric windows of electromagnetic energy used in remote sensing. Spectral band

Wavelengths

Infrared and visible Near infrared Thermal infrared Microwaves

0.30e0.75 mm and 0.77e0.91 mm 1.55e1.75 mm and 2.05e2.40 mm 8.00e9.20 mm and 10.20e12.40 mm 7.70e11.50 mm and 2.00 mm

location (x,y). To process images properly with computers, it is necessary for the function f (x,y) to be digitized. A digital image is the image f (x,y), which has been transformed from an analog to a digital function with respect to both coordinates and to the value of the intensity f of the irradiance. Thus a digital image is considered as a matrix whose column and lines correspond to the coordinates in the image, while the value of each element in the matrix is the tone of gray (level of electromagnetic energy) at that point. The elements of such a digital image configuration are called picture elements or pixels. Fig. 3.7 shows an example of a brightness function with its corresponding gray values. In other words, a digital image is nothing but a new function DN(i,j) where the coordinates (x,y) have been converted into integers (i,j), and the irradiance f is now a function of integers DN (digital

FIGURE 3.7 An example of digitization of a brightness function.

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numbers) of the gray level at that point in the image. Fig. 3.7 shows an example of the quantization process and the corresponding quantized image. The process of digitization requires computations of quantities that are integer powers of two, that is, DN ¼ 2k, where DN denotes the number of gray levels (quantization) and k is the degree of quantization. For example, quantization at the level of 8 bits corresponds to 28 ¼ 256 levels of quantization, with DN (i,j) ¼ 0 corresponding to black (zero reflectance or no irradiance) and DN (i,j) ¼ 255 corresponding to white (maximum irradiance). For a digitization of 7 bits quantization corresponds to 27 ¼ 128 levels of gray values, and so on.

3.2.3 Imaging quality and information content Two property drivers are important in representing quality and information content of an image: resolution and sampling frequency. Resolution is the ability to discern and discriminate objects in an image clearly. It could be the spectral, radiometric, spatial, and temporal resolution of an image. Sampling frequency is the speed of data recording as gathered by an image sensor. This is also broken down into spectral, radiometric, spatial, and temporal sampling. Let us consider an example of the solar energy distributed as a function of wavelength before it reaches the top of the atmosphere. Such an exoatmospheric solar irradiance is depicted in Fig. 3.6. Then, electromagnetic energy from the Sun penetrates the atmosphere and in the sequel interacts with targets on the Earth’s surface. Consider that the amount of radiation scattered, emitted, or reflected is recorded by a detector. Fig. 3.8 shows, as an example, spectral zones as recorded at certain locations in the spectral irradiance diagram. The width Dl ¼ l2 e l1, where l1 is the lower and l2 is the upper cutoff wavelengths, the location (i.e., A, B, C, D, E, F), and the number (six in the example of Fig. 3.6) of recorded spectral bands represent the spectral resolution of a remote sensing system. Thus spectral resolution represents the capability of a remote sensing system to distinguish individual targets, such as vegetation, mineral types, contaminated water areas, pollution regions, etc. The application of many spectral bands in a remote sensing system increases greatly the instrument capability for detecting and differentiating various surface targets. Also, the spectral resolution of spectral bands A, B, and C is better than the spectral resolution of D as the width of their wavelength sampling is narrower that D. Spectral sampling also refers to the way in which the curve of spectral irradiance is recorded by a remote sensing system. As shown in Fig. 3.6, samples have been taken at discrete intervals, A, B, C, ., F, to capture the curve of reflected/emitted radiance. It is obvious that if the spectral resolution is finer (narrower bandwidth), then the curve of spectral radiance is better reconstructed. Also, if fewer spectral zones are used for sampling, then less information is gathered with respect to the real picture of spectral reflectance. The spectral band or channel of a remote sensing detector is defined by its central wavelength at lc. The bandwidth of a channel is defined as the wavelength interval, Dl, between the upper l2 and lower l1 wavelength. The upper and lower cut-off wavelengths are defined at these wavelengths, which correspond to 50% of the maximum sensitivity of the remote sensor (Fig. 3.9). The difference between modern imaging spectrometers and older generation remote sensing detectors, such as the Multi-Spectral Scanner and Thematic Mapper of Landsat remote sensing systems, lies in their spectral sampling. The previous generation of sensors used selected samples for recording the spectral radiance curve. The current remote sensing systems make use of almost the entire width of the spectral recording. A spectrometer such as the Airborne Visible and Infrared Imaging Spectrometer

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FIGURE 3.8 The curves of spectral response have been recorded by Landsat-8 (USA) with eight spectral bands (top left, see also Irons et al., 2012), by Sentinel-2 (European Space Agency) with 13 spectral bands (top right), and by the Hyperspectral Imaging Satellite (HysIS, India) with 60 contiguous spectral bands in the range 0.4e0.95 mm and 265 contiguous spectral bands of Dl ¼ 10 nm bandwidth in the range 0.85e2.4 mm (lower diagram).

of the Jet Propulsion Lab, NASA, in the United States has many spectral bands to record in the region from 0.5 up to 24 mm and is capable of taking samples every 10 nm (nominal channel bandwidth), calibrated to within 1 nm. On the other hand, the old generation of Landsat satellites used three to seven spectral channels to record electromagnetic energy. Spatial resolution is the ability of a system to precisely digitize the coordinates (x,y) and refers to the capability of the system to distinguish objects on the surface of the Earth in their geometric dimension. As in spectral resolution, the geometric surface can be recorded with a specified resolution. An example of two recordings in different spatial resolutions is depicted in Fig. 3.10.

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FIGURE 3.9 Curve showing the sensitivity of a sensor to reflected or emitted energy.

Radiometric resolution is a measure of the capability of a sensor to pick out minute changes in irradiance between different targets on the surface of the Earth and thus we can clearly identify them. It corresponds to the number of discrete values implemented to quantify the output of a sensor between its lower (minimum) and upper (maximum) readings for radiance (Fig. 3.11). The level of radiometric resolution is expressed by the number of digital values (bits) used to record the maximum value of radiance when a sensor becomes saturated and thus the sensor cannot further record incoming energy. For example, if the number of quantization levels is 2, then the number of bits is k ¼ 1, and for 4, 16, 64, 128, 256, the required number of bits is, respectively, 2 bit (22 ¼ 4), 4 bit (24 ¼ 16), 6 bit (26 ¼ 64), 7 bit (27 ¼ 128),8 bit (28 ¼ 256), and so on. Current remote sensing instruments are designed with 11 bits or higher bit digitization. Finally, temporal resolution refers to how often the image can be acquired for a certain region on the surface of the Earth. For example, the Landsat satellite returns to the same location of the Earth to acquire images covering areas 185  185 km (Landsat-1, Landsat-2, and Landsat-3), every 18 days, or every 16 days (Landsat-4 and Landsat-5 with image dimensions 185  170 km and Landsat-7 and Landsat-8 with image dimensions 183  170). Another example is the French SPOT satellite, which has a 26-day repeat cycle. Although it seems that Landsat has a better temporal resolution, SPOT has the capability to repeat coverage every day if required. Higher temporal resolution is used to monitor rapid changes over forest fires, floods, etc.

3.3 Remote sensing instruments and platforms Over the last 40 years several types of sensors have been developed for remote sensing imaging. At present there are more than 1000 satellites that serve the discipline of remote sensing operated by almost 50 countries worldwide (Belward and Skoien, 2015; ESRE, 2017). Two different types of

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FIGURE 3.10 Example of different spatial resolutions. In image (A) none of the objects on the surface of the Earth covers an entire pixel size. Thus in this coarse spatial resolution, each pixel reflects electromagnetic energy arising from different targets within each pixel area. Image (B) has finer spatial resolution with small size of pixels. The lower images show the same picture at different spatial resolutions. The left one is with spatial resolution of 256X384 pixels and the other image is at higher resolution on the right (502X718 pixels).

sensors can be distinguished: passive and active (Fig. 3.12). Passive sensors are those that apply either reflected solar or self-emitted radiation from a surface target to operate. Active systems transmit their own energy, such as the radar systems, to illuminate or better radiate a target. Then, the energy is reflected by the target toward the remote sensing platform to finally create an image representing the backscattered radiation for that area on the ground. Various systems and operating modes are used for creating an image (Toth and Jozkow, 2016; WIGOS, 2013; Zhu et al., 2018). These are (1) fixed frame camera, (2) electroptical scanners, (3) “push broom” linear arrays, (4) “whiskbroom” linear arrays, (5) digital frame camera, and (6) hyperspectral arrays. Details of these systems are shown in Fig. 3.13.

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FIGURE 3.11 Examples of different radiometric resolutions. The left image is 8-bit radiometric resolution (256 discrete levels of digitization), the right image is 1-bit radiometric resolution (2 levels of digitization, black and white).

Since recent integration of passive and active systems has been implemented to generate images combining both active and passive systems, it is preferable to classify remote sensing systems into (1) imaging and (2) nonimaging systems. Three such types of imaging systems can be found in remote sensing: optical, thermal, and radar imaging (Fig. 3.14). We now examine these imaging systems separately.

3.3.1 Imaging systems 3.3.1.1 Optical imaging systems Three different systems are applied for optical imaging: panchromatic, multispectral, and hyperspectral systems (Joseph, 2015; Khorram et al., 2016, Mertikas, 1999a,b; National Academies of

FIGURE 3.12 Classification of remote sensing sensors based on active or passive operation mode.

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FIGURE 3.13 Various ways to creating an image: (A) fixed frame camera, (B) electroptical scanners, (C) “push broom” linear arrays, (D) “whiskbroom” linear arrays, (E) digital frame camera, and (F) hyperspectral arrays.

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FIGURE 3.14 Various categories of remote sensing instruments.

Science, Engineering and Medicine, 2018). In panchromatic imaging the used spectral bands are part of the visible and near-infrared spectrum. In multispectral imaging, several channels are used and the location of each band depends upon the application. Also, the width of each band is not necessarily the same for each band. Finally, in hyperspectral imaging, also called imaging spectrometers, hundreds of contiguous spectral bands are operated, each of which covers narrow widths in spectral bands of a few nanometers. These imaging spectrometers detect, measure, and map the spectral content of the incident electromagnetic energy (Table 3.3). This type of information plays a fundamental role in identifying the chemical composition of the surface object being sensed (Modello et al., 2008). In panchromatic imaging, commonly one spectral channel (wide visible) is selected to create a gray image with fine spatial resolution (pixels of several centimeters in geometric dimensions). In multispectral imaging, at least four spectral bands in blue, green, red, and near infrared are recorded, thus true color or false color images (such composite images are shown in Figs. 3.15 and 3.16) could be reconstructed, both in brightness and color. Each of these multispectral sensors is sensitive to a narrow spectral band. Hyperspectral imaging primarily consists of a set of images acquired at very narrow (about 10 nm) but contiguous spectral bands. The result is an integration of several composite and sharp images in different bands to be used for urban mapping and land-use classification, forest monitoring, soil detection through shaded canopies, mineral and oil exploration, bathymetry and shoreline mapping, aquatic vegetation detection, mapping of water quality, oil spill monitoring, agriculture mapping and monitoring, pollution and environmental monitoring, and many other applications. Figs. 3.17e3.20 show examples of images covering environmental subjects: the nuclear and tsunami accident in Fukushima, Japan, as taken by ASTER (Fig. 3.17); the signature imprinted on Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 (OLCI Instrument) images of the “Shamal” winds in Iraq and Iran (Fig. 3.18); the algal blooms in Qingdao, China, with Sentinel-2 satellite (Fig. 3.19); and air pollution in Milan, Italy, with Sentinel-5P (Fig. 3.20).

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Table 3.3 Various imaging systems covering the visible part of the near-infrared spectrum with the corresponding satellite platforms. Characteristics

Panchromatic

Multispectral

Hyperspectral

Satellites

Sentinel-2 (European) SPOT (France) Landsat, ETMþ (USA) IRS (India) QuickBird (commercial) GeoEye (commercial) WorldView (commercial) IKONOS (commercial), etc.

TRW Lewis AVIRIS (airborne, USA) EO-1 (USA) Mighty-Sat-II (USA) IMS-1 (India)

Spectral coverage

0.430e0.720 mm (visible)

Spectral bands Spatial resolution Applications

One, gray-scale image Submeter Mapping Earth observation, etc.

Sentinel-2 (European) Sentinel-3 (OLCI) (European) SPOT (France) Landsat, ETMþ (USA) ASTER (Japan) IRS (India) QuickBird (commercial) GeoEye (commercial) WorldView (commercial) IKONOS (commercial), etc. 0.430e0.720 mm (visible) 0.750e0.950 mm (infrared) Several bands Down to 1e5 m Vegetation monitoring Water depth Fire monitoring Military Environmental monitoring Agriculture, etc.

Remarks, comments

0.470e2.000 mm

32e256 bands Down to 2 m Mineral resource mapping Oil exploration Environmental monitoring Risk analysis Coastal, agriculture Disaster monitoring, etc.

Obstructed by cloud coverage Operated by solar radiation only Mostly sun-synchronous orbits to revisit locations early in the morning (9:00e10:00) Satellites at low orbits of 500e900 km with almost circular orbits Spectral bands at least blue, green, red, and near infrared

IRS, Infrared Sounder; OLCI, Ocean and Land Color Instrument.

3.3.1.2 Thermal imaging systems Thermal imaging operates in the spectral range between 9 and 14 mm. All objects with temperatures above absolute zero emit radiation that can be detected by thermal sensors. A thermal image is nothing else but a picture of thermal contrasts of the targets on the Earth’s surface as a variation of gray values. Usually, brighter tones of gray represent warmer objects, while darker tones depict cooler objects. Thermal imaging contributes to mapping and monitoring vegetation, forest fires, geology, soil and surface type determination, hydrothermal investigations, volcanic, mineral, and natural hazards monitoring, sea surface temperature, ocean color, and hydrology among other applications. The

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FIGURE 3.15 This image depicts the Province of Khuzestan, Iran, which experienced severe floods in the period MarcheApril 2019. Sentinel-2B images of band 2 (blue [gray in printed version], central wavelength: 492.4 nm), band 3 (green [light gray in printed version], central wavelength: 559.8 nm), and band 4 (red [dark gray in printed version], central wavelength: 664.6 nm) shown here have been combined to create a red/green/blue image (dark gray/light gray/gray image in printed version) of the area. Spatial resolution 10 m.

various imaging systems covering the thermal bands are shown in Table 3.4 and some examples of thermal imaging are depicted in Fig. 3.21.

FIGURE 3.16 A sample image of the fires in Australia in 2019. The left image is a pseudocolored image created by a combination of Sentinel-2 images using band 12 (l ¼ 2.190 mm, with 20 m resolution, blue gun [gray in printed version]), band 11 (l ¼ 1.610 mm, with 20 m resolution, green gun [dark gray in printed version]), and band 8A (l ¼ 0.865 mm, with 20 m resolution, red gun [light gray in printed version]). The right image is a composite of Sentinel-2 bands 2, 3, and 4 with resolution 10 m.

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FIGURE 3.17 Nuclear and tsunami accident. Both images are depicting the area before and after the earthquake and tsunami on March 11, 2011 at the Fukushima Daiichi Nuclear Power Plant, Japan. Images are RGB composites of ASTER satellite bands 1 (visible green/yellow, 0.52e0.60 mm), 2 (visible red, 0.63e0.69 mm), and 3 (near infrared, 0.76e0.86 mm). The left image shows the area before the tsunami date and was taken on February 24, 2011.

3.3.1.3 Radar imaging systems In the spectral band with wavelengths between 0.5 and 3 mm, sensors in remote sensing detect solar radiation that is reflected from the surface of the Earth. The reflected percentage of this radiation is a function of the wavelength of the radiation and depends on the properties of the target surface observed. In the case of colored vegetation, the structure of plant cells and their contained humidity

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FIGURE 3.18 Strong “Shamal” winds. The image on top is a Moderate Resolution Imaging Spectroradiometer capture on February 20, 2016 using NASA’s Aqua satellite shown in natural color image of dust streaming across eastern Iraq and western Iran. Dust storms are driven by northwest winds called “Shamal” and happen often because of long-term drought that has parched the wetlands. The lower right image are blue/green/red (dark gray/gray/ light gray in printed version) composites created by the Sentinel-3 satellite (Donlon et al., 2012) of the European Space Agency and its sensor Ocean and Land Color Instrument (OLCI) based on the optomechanical and imaging device. The OLCI instrument has a swath of 270 km width, capturing images with its push broom imaging spectrometer with five cameras at a resolution of 300 m.

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FIGURE 3.19 Algal bloom monitoring. The left top image is a Sentinel-2 RGB composite taken on July 1, 2019 over Qingdao, Shandong, China. It shows algal bloom pollution over this cove. The discoloration of the water results from the high density of pigmented cells. The right top image is over the same area captured again by Sentinel-2 on December 28, 2019.

count as the fundamental parameters in characterizing the spectral properties of the reflection. For water its turbidity and depth, while for ground the type of soil and its contained humidity prescribe the nature and type of reflection for the electromagnetic energy. In longer wavelengths different spectral characteristics prevail. Between 3 and 5 mm radiation, which can be sensed is dominated by the hot (about and above 300 Celsius) thermal bodies of the surface (e.g., fires), while in the band from 10 to 12 mm, the Earth surface acts as a transmitter of

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FIGURE 3.20 Air pollution. One of the first images from the Copernicus Sentinel-5P mission shows nitrogen dioxide over Europe on November 22, 2017. It shows high emissions over northern Italy and western Germany. Nitrogen dioxide pollutes the air mainly as a result of industrial fossil fuel combustion and road traffic. There are some gaps in the coverage here because Sentinel-5P cannot image through clouds (Credit: ESA). Sentinel-5P is able to map trace gases such as nitrogen dioxide, ozone, formaldehyde, sulfur dioxide, methane, carbon monoxide, and aerosolsdall of which are pollutions that affect the air we breathe and therefore our health and our climate. Credit: ESA.

thermal energy (geothermal, about 27 Celsius). So, images in these spectral bands represent maps of thermal energy emitted by the surface of the Earth. Consider now the spectral band beyond and extended after 12 mm. In this region (Fig. 3.3), observation of the transmittance of the atmosphere as a function of wavelength shows that the atmosphere is in reality nontransparent particularly for spectral bands larger than 12 mm to about 0.5 mm. Nonetheless, beyond the 0.5 mm wavelength, the atmosphere becomes transparent once again and can be used again in remote sensing. Detection of this spectral band, which extends from 1 to 1000 mm, is used for imaging radar (Dicke, 1946). Imaging radars provide information on geometry properties of the target as well as the dielectric properties of the surface (landscape topography, geomorphology, surface roughness, moisture content of the ground). Sensors operating on those spectral bands have the advantage of operating

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Table 3.4 Various imaging systems covering the thermal spectral bands with satellite platforms. Category

Instrument

Satellite platform

Altitude

Low spatial

Imager

GOES (USA)

35,800 km

Moderate spatial

AVHRR

NOAA (USA)

833 km

SLSTR

Sentinel-3A Sentinel-3B (Europe)

814.5 km

MODIS

Terra, Aqua

705 km

Spectral, spatial resolution

Temporal resolution

One VNIR band 3.80e4.00 mm (4 km) 6.50e7.00 mm (8 km) 10.20e11.20 mm (4 km) 11.50e12.50 mm (4 km) Two VNIR bands 1.58e1.64 mm (1 km) 3.55e3.93 mm (1 km) 10.30e11.30 mm (1 km) 11.50e12.50 mm (1 km) 6 VNIR bands 3.742 mm (0.398 mm) (1 km) 10.854 mm (0.776 mm) (1 km) 12.022 mm (0.905 mm) (1 km) 3.742 mm (0.398 mm) (1 km) 10.854 mm (0.776 mm) (1 km) 19 VNIR bands 3.660e3.840 mm (1 km) 3.929e3.989 mm (1 km) 3.939e3.989 mm (1 km) 4.020e4.080 mm (1 km) 4.433e4.498 mm (1 km) 4.482e4.549 mm (1 km) 6.535e6.895 mm (1 km) 7.175e7.475 mm (1 km)

30 min

In a few hours

0.01 SI) (Sharma, 1997). The most common processing technique is to remove diurnal variations using data collected at a base station. The base station data are plotted versus time (Fig. 5.67). The first reading at the base station is regarded as the base value, and any variations from this value at the base station for later times are subtracted or added to the field data (Fig. 5.67). The only other processing technique that may be applied is to remove the earth’s main magnetic field (DGRF). This is usually performed on surveys covering many kilometers and is not performed on small-scale environmental surveys because the earth’s main magnetic changes only 0e2 gammas eastewest and 2e5 gammas northesouth per kilometer. Gradiometer data do not require the removal of diurnal effects or the DGRF.

5.7.8 Material properties Density: The interpreter of gravity data is interested in determining the subsurface variations of mass. This process requires the density of the material of interest or the density contrast between the material of interest and the surrounding material to be known. The density can be determined in many ways. The best technique consists of acquiring rock samples within the study area and determining their average density. One can also use density logs obtained from drill holes, but these are not always available. Density can also be estimated from experimental relationships relating compressional seismic velocities (obtained from seismic refraction surveys) (Nafe and Drake, 1957; Birch, 1961).

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FIGURE 5.67 Typical diurnal variation curve used to remove diurnal variations from total field magnetic data. A, Amount of gammas to remove from field data. B, Amount of gammas to add to field data. Both values are at a given time.

Also, the interpreter can use average density values from tables obtained from measurements of numerous rock, soil, and mineral samples (Johnson, and Olhoeft, 1984; Telford et al., 1990). Table 5.8 shows the density range for common sediments and sedimentary rocks usually encountered in environmental surveys. Magnetic susceptibility: The magnetic characteristics of rocks and minerals is a complex subject whose details are still under investigation. However, for most environmental applications, the magnetic characteristics are usually controlled by the content of magnetic minerals (mainly magnetite) in a rock and the type of metal (mainly iron) composing buried drums, cables, and pipes. Table 5.9 shows the range of magnetic susceptibilities of common rock and mineral types.

Table 5.8 Density range of common sediments and sedimentary rocks. Earth material

Density (g cmL3)

Soil Gravel Alluvium Chalk Sand Sandstone Silt Loess Shale Gypsum Limestone

1.20e2.40 1.70e2.40 1.96e2.00 1.53e2.60 1.70e2.30 1.60e2.75 1.80e2.20 1.40e1.93 1.75e3.20 2.20e2.60 1.93e2.90

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Table 5.9 Magnetic susceptibilities of common rocks and minerals. Earth material

Magnetic susceptibility (SI units)

Dolomite Limestone Sandstone Shale Granite Basalt Hematite Clay Magnetite Quartz Pyrite

12 to 50 10e25,000 0e21,000 60e18,000 0.01e15 500e70,000 420e35,000 0.2 1200e2,000,000 0.11 50e50,000

Modified from Telford et al., 1990; Reynolds, J., 2011. An Introduction to Applied and Environmental Geophysics, Wiley-Blackwell, Oxford, UK.

The magnetic readings measured by a magnetometer are usually assumed to be caused by magnetization induced in a magnetizable material (those with a large magnetic susceptibility) by the earth’s main magnetic field. This magnetization is not permanent and is lost when the external field is removed. In addition, most rocks and minerals have a permanent magnetization called remnant magnetization, which is acquired by a variety of mechanisms (Telford et al., 1990) and is usually in a direction and magnitude different from that of the induced magnetization. In most cases, the magnitude of the remnant magnetization is small compared with the induced magnetization and can be ignored, although for detailed surveys (source anomalies less than 5 gammas) it can be important, requiring laboratory analyses to determine the remnant magnetization properties of the rock or soil samples. Most environmental magnetic studies involve taking readings over soils, which in most cases has low magnetic susceptibilities; the interpreter will not have to be concerned with anomalies caused by magnetic variations within the soil. However, the magnetic susceptibilities of a soil reflect those of their source rocks, because soils derived from mafic igneous rocks (e.g., basalts and gabbros) tend to have a high magnetite content (Sharma, 1997). Furthermore, magnetite is a heavy mineral that is resistant to weathering, so it tends to accumulate in river deposits, beach sands, and alluvial fans, and thus may create magnetic anomalies owing to areas of magnetite accumulation. Also, highly organic soils often contain maghemite, an iron mineral with a high magnetic susceptibility.

5.7.9 Gravity and magnetic interpretation techniques The object of the gravity and magnetic methods is to determine information about the earth’s subsurface. The interpretation techniques used for gravity and magnetic data are similar except for a couple of special cases (e.g., reduction to the pole for magnetic data), so I will treat the description of these methods as one. One can just examine the grid of gravity or magnetic values or gravity magnetic profiles to determine the lateral location of any gravity or magnetic variations, or one can perform a more detailed analysis to quantify the nature (depth, geometry, and density) of the subsurface feature causing the gravity or magnetic variations. To determine the latter, it is usually necessary to separate

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Observed Bouguer Gravity Anomalies

70 Bouguer Gravity Anomaly (mGal)

273

Estimated Regional Anomaly

60

50 0

0.5

1.0 km

Residual (mGal)

+10

0

1.0 km

–10

FIGURE 5.68 Example of regional-residual gravity anomaly separation using graphical smoothing (regional anomaly). Adapted from Reynolds, J., 2011. An Introduction to Applied and Environmental Geophysics,. Wiley-Blackwell, Oxford, UK.

the anomaly of interest (residual) from the remaining background anomaly (regional) (Fig. 5.68). Then, the residual anomaly is modeled to determine the depth, density, and geometry of the anomaly’s source. Next, I will describe some of the most commonly used methods to interpret gravity and magnetic data in environmental applications.

5.7.10 Data presentation Gravity and magnetic data are usually collected along a profile or in a grid. The most basic interpretation technique is to collect data along a profile and plot the profile. Then, the profile is examined for obvious gravity or magnetic maxima or minima. Fig. 5.69 shows a magnetic profile in central Mexico across a region with known voids (producing magnetic minima) embedded in highly magnetic volcanic rocks. If further information on the voids is desired (e.g., depth, size), a residual magnetic anomaly can be determined and the residual anomaly can be modeled. To display gridded data, a variety of methods including contour maps, wire meshes, gray scale, or color images exist. Fig. 5.70 shows a wire mesh representation of magnetic data collected over a buried pipe and dolerite dikes. The linear, high-amplitude anomaly is characteristic of linear features including pipes and dikes. Fig. 5.71 shows magnetic and gradiometer (vertical gradient) data contoured over a buried storage drum. Both surveys indicate a magnetic field maximum over the drum with the magnetic field indicating a northenortheast orientation. The gradient anomaly better defines the orientation and indicates the ends of tanks, as well as suggests its length as 4 m. However, the most common method is to make a color image in which one can put in a sun angle to give the data a 3D sense to it. Fig. 5.72 shows a complete Bouguer gravity anomaly map from Tennessee, where several minima indicate the presence of sinkholes.

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FIGURE 5.69 Theoretical magnetic anomaly due to a cave within a highly magnetic lithology. Eppelbaum, L., 2011. Study of magnetic anomalies over archaeological targets in urban environments. Phys. Chem. Earth. https:// doi.org/10.1016/jj.pce2011.02.005.

5.7.11 Magnetic anomaly shapes The magnetic field intensities of buried magnetized objects produce anomaly patterns different from those of gravity anomalies produced by similarly shaped objects. Because of the dipolar nature of magnetic fields, a single magnetized body will produce both a positive and negative anomaly, and thus it is more difficult to interpret than gravity anomalies. The shape of the anomaly depends on the magnetic inclination (I) where the body is located. Fig. 5.73 shows the magnetic anomaly resulting from a positively magnetized sphere at different magnetic inclinations. Only at the magnetic north pole (I ¼ 90 degrees) is the maximum peak directly over the sphere. At other inclinations, it is offset toward the south until the magnetic equator (I ¼ 0 degrees) where the minimum is centered over the sphere. Thus, the body is not always directly under the maximum or minimum anomaly. Also, all anomalies

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FIGURE 5.70 Grid of total field magnetic data shown as a wire mesh with a high-amplitude, linear magnetic anomaly characteristic of anomalies caused by pipes and igneous dikes. Adapted from Stanely, J., Cattach, M., 1990. The use of high-definition magnetics in engineering site investigations. Explor. Geophys. 21, 91e103.

FIGURE 5.71 Contour maps of gridded total field magnetic data shown in gammas (left) and magnetic gradiometer data in gammas per kilometer (right). The total field maximum anomaly is due to a buried metallic drum, whereas the two gradiometer maximum anomalies marked the lateral extent of the drum. Adapted with permission from Schilinger, C.M., 1990. Magnetometer and gradiometer surveys for detection of underground storage tanks. Bull. Assoc. Eng. Geol. 27, 37e50.

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FIGURE 5.72 Complete Bouguer gravity anomaly map of a karst landscape in Tennessee. Purples (Blacks in print version) represent gravity minima and reds (dark grays in print version), gravity maxima. Adapted from Whitelaw, J., Mickus, K., Whitelaw, M., Nave, J., 2008. High-resolution gravity study of the gray fossil site, Geophysics 73, B25eB32.

FIGURE 5.73 Profiles of total-field magnetic data over a sphere at different inclinations (Inc) for north–south (N–S) and east–west (E–W) profiles. Modified from Breiner, S., 1973. Applications Manual for Portable Magnetometers, Geometerics Inc.

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shown in Fig. 5.73 are for northesouth profiles. Individual eastewest profiles will not show the positive and negative anomaly patterns, and misinterpretation of the location of the buried body is possible. Whenever possible, northesouth profiles should be collected.

5.7.12 Regional and residual gravity anomalies Many techniques can be used to accomplish regional-residual anomaly separation (Telford et al., 1990). In environmental gravity studies, the most common techniques are manual and polynomial surface fitting (Hinze, 1990). This is because of the small scale of the survey; the regional gravity and magnetic field over such an area usually have little lateral changes. The simplest methods are manual techniques such as graphical smoothing, in which a simple smooth regional anomaly is subtracted from the observed anomaly to obtain a residual anomaly (Fig. 5.68). An advantage of the manual techniques is that the interpreter may have information on the lateral location of the source bodies; this information can be used to select a correct regional anomaly. Most other regional-residual anomaly separation techniques involve mathematical operations using a computer. One problem with the mathematical techniques is that they do not accurately represent the true residual gravity or magnetic anomaly due to a specific body. Thus, they should not be used for quantitative interpretation of the subsurface, but only for qualitative interpretation (Ulyrch, 1968). The most common mathematical techniques are surface fitting and weighted averaging. Surface fitting involves a least-square fitting of a 2D polynomial (Beltrao et al., 1991) or 2D Fourier series (James, 1966) of different orders to the original gridded data to represent a regional gravity or magnetic anomaly map. The higher the surface order, the greater the fit to the original data; however, high-order surfaces are usually not desired because they will contain part of the anomaly that is desired. Fig. 5.74 shows a third-order polynomial surface that was removed from the original Bouguer gravity data to produce a third-order residual anomaly map over a landfill (Hinze, 1990).

5.7.13 Data enhancement Data enhancement techniques are used to increase the perceptibility of the gravity and magnetic anomalies that might be related to bodies of interest. This is important in environmental gravity and magnetic work, because most anomalies have small amplitudes and are easily obscured by the regional gravity or magnetic field. The most important techniques are derivative methods. The most commonly used derivatives are the first (gradient) (Fajklewicz, 1976; Butler, 1984a,b) and second (curvature) (Elkins, 1951), which are analytically calculated from a Bouguer gravity or magnetic anomaly grid. The first and second derivative methods both enhance near-surface anomalies at the expense of deeper anomalies and are good at locating the edges of a body. Traditionally, the second vertical derivative has been the most commonly used derivative because the amplitude and width of a second vertical derivative are higher and narrower than the first vertical gradient and thus supposedly easier to interpret. However, the second vertical derivative is more susceptible to data noise and topographic irregularities and should be used only for large-scale interpretations. Given the problems with second vertical derivatives, numerous authors (Butler, 1984a,b) developed methods of determining the vertical and horizontal gradients for shallow gravity applications. Numerous case studies by Butler (1984b) show that the horizontal gravity gradients do not contain topographic effects and are able to locate shallow objects better than the vertical gravity gradients. Newer methods include Euler deconvolution

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FIGURE 5.74 Bouguer gravity anomaly map (A) and third-order residual gravity anomaly map (B) of the Old Faithful region in Yellowstone national park constructed by removing a third-order polynomial surface from the Bouguer gravity anomaly data. Bright green lines (gray lines in printed version) represent the outline of the geothermal areas.

(Reid et al., 1990; Ravat, 1996), analytic signals (Salem et al., 2002), and tilt derivatives (Hinze et al., 2013). The Euler deconvolution method uses both vertical and horizontal derivatives to determine the edges of a source body and can be employed to determine a depth to the source, whereas the tilt derivative uses a ratio of the horizontal and vertical derivatives so that both shallow and deeper sources can be analyzed. Fig. 5.75 shows observed gravity, horizontal gradient, and second vertical derivative profiles over a cavern and limestone pinnacle.

5.7.14 Modeling Gravity and magnetic modeling is usually the final step in gravity interpretation and involves determining the physical properties (density or magnetic susceptibility) depth, and geometry of the subsurface bodies. There are some important differences when modeling magnetic data. Because the shape and amplitude of the magnetic anomalies depend on the earth’s main magnetic field and the direction the profile was taken (not important for 3D models), one must know the DGRF at the survey’s location. The required values include the intensity of the earth’s main magnetic field, the magnetic inclination and declination, and the azimuth of the profile to be modeled. Many different techniques are available to perform the modeling procedure. They can be broken down into three main categories: (1) analytical solutions due to simple geometries, (2) forward

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FIGURE 5.75 Gravity, first vertical derivative of gravity and second vertical derivative of gravity profiles over a cave. Modified from Butler, D., 1983. Cavity Detection and Delineation Research. Report 1. Microgravimetric and Magnetic Surveys: Medford Cave Site, Florida. Army Engineer Waterways Experiment Station Geotechnical Lab Report No.Wes/tr/gl-83-1-1.

modeling using 2D, 2.5D, and 3D irregularly shaped bodies; and (3) inverse modeling using 2, 2.5, and 3D irregularly shaped bodies. Most of these techniques involve iterative modeling, in which the gravitational or magnetic field due to the model is calculated and compared with the observed or residual gravity or magnetic anomalies. If the calculated values do not match the observed anomalies, the model is changed and the procedure is performed again until the match between the calculated values and the observed anomalies is deemed close enough. Before the advent of computers, solutions to simple geometries (e.g., spheres, cylinders, prisms, thin sheets) were used to approximate subsurface mass distributions employing residual gravity anomalies (Grant and West, 1965; Telford et al., 1990). More commonly used are simplifications of the analytical solutions to obtain an approximation of a body’s depth. These simplifications are termed depth or half-width rules because they are based on the horizontal distance (x1/2) from the maximum anomaly value to one-half of that anomaly value. The half-width rules can be used in the field to determine a quick approximation to the depth of a given source. Several depth rules (see Telford et al., 1990; Reynolds, 2011) can be used to determine a body’s depth.

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The most common technique in gravity and magnetic modeling is computer forward modeling of polygonally shaped, multiple 2D, and 2.5D bodies (Cady, 1980) along profiles of data. The difference between 2D and 2.5D is that for 2.5D bodies, the cross-sectional shape extends out a finite distance (called strike lengths) in both directions perpendicular to the profile. 2D and 2.5D models can be used in environmental studies to determine the lateral position and offsets of shallow faults, the thickness of the soil layer and the bedrock topography (Adams and Hinze, 1990), and the size of and depth to subsurface voids (Cornwell and Carruthers, 1985). Fig. 5.76 shows a 2D gravity model of the soil thickness in a karst environment with cutters and pinnacles. 3D modeling is not commonly used in environmental studies because of the difficulty of setting up the model and the time involved in determining a model, and because a grid of data must be used as the observed data. Complicated models involving multiple bodies with varying densities are usually not attempted. More commonly, modeling of a few bodies (commonly one) using a residual gravity anomaly is the norm. 3D models are sometimes used to determine the total volume of subsurface voids (Hinze, 1990). The final method of gravity interpretation is inverse modeling, in which a set of observed data and a general starting model, a computer algorithm, will determine a set of parameters (body geometry and density) that best fit the observed data (Mickus and Peeples, 1992; Li and Oldenburg, 1996, 1998). Along with determining a model, the algorithm may determine how well that model fits the data and a range of models that equally fit the given observed data. Although these so-called automated techniques may seem attractive, there are problems in determining the inversion parameters, which have limited their use in environmental studies. Fig. 5.77 shows a 3D inversion model of residual gravity anomaly data to determine the extent of sinkholes of a region in Tennessee (Fig. 5.72). However, studies by Butler (1995) have shown that using gravity gradient inversion methods may be useful in shallow geophysical investigations.

FIGURE 5.76 Two-dimensional gravity model (lower figure) of the soil thickness in a karst limestone environment. The solid line is the calculated gravity values due to the model and the stars are the observed data.

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FIGURE 5.77 Three-dimensional gravity model showing two slices of the model determined by inverting residual gravity anomalies using the method of Li and Oldenburg (1996). The residual gravity anomalies in Fig. 5.72 were used in the inversion. Dark blue (dark gray in print version) regions are sinkholes.

5.8 Summary and concluding remarks Geophysical methods have been increasingly used to investigate subsurface pollution problems within the past several decades. This has been made possible because of the development of easy-to-use equipment for all geophysical methods, better data processing software, user-friendly 2D and 3D modeling algorithms, and high-speed computers with the memory to handle large-scale data sets and complicated models. Although all geophysical techniques (electrical resistivity, electromagnetics, GPR, seismology, gravity, and magnetics) are useful in helping identify physical properties of the subsurface, the electrical resistivity, electromagnetic, and GPR methods are probably best in locating subsurface pollution sources and tracking contamination. The electrical and electromagnetic methods can especially be well-suited for identifying groundwater and contaminated groundwater because of the large electrical resistivity contrasts between water and the surrounding soil, sediment, and/or bedrock. Seismology, gravity, and magnetics methods can aid in determining both shallow and deep subsurface structures (e.g., depth to bedrock), which can be used to determine the path and possible flows of subsurface pollutants. Because geophysical interpretations are nonunique, it is better to combine at least two methods when performing a study, to minimize this nonuniqueness and provide engineers and geologists with better images of the subsurface. With the enhanced modern equipment

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and computer modeling programs available, large amounts of geophysical data can be obtained and interpreted daily, which will allow engineers and geologists to determine where and whether subsurface pollutants exist. This, in turn, will allow the engineers and geologists to plan better where to perform costly drilling, trenching, and subsurface remediation.

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Reid, A., Allsop, J., Granser, H., Millett, A., Somerton, I., 1990. Magnetic interpretation in three dimensions using Euler deconvolution. Geophysics 55, 80e91. Reynolds, J., 2011. An Introduction to Applied and Environmental Geophysics. Wiley-Blackwell, Oxford, UK. Robinson, E., Caruh, C., 1988. Basic Exploration Geophysics. Wiley and Sons, NY, NY. Rybakov, M., Goldshmidt, V., Fleischer, L., Yostein, Y., 2001. Cave detection and 4-D monitoring: a microgravity case history near the dead sea. Lead. Edge 20, 896e900. Salem, A., Ravat, D., Gamey, T., Ushijima, K., 2002. Analytic signal approach and its applicability in environmental magnetic investigations. J. Appl. Geophys. 49, 231e244. Santos, F., Mateus, A., Figueiras, J., Goncalves, M., 2006. Mapping groundwater contamination around a landfill facility using the VLF-EM method-A case study. J. Appl. Geophys. 60, 115e125. Schilinger, C.M., 1990. Magnetometer and gradiometer surveys for detection of underground storage tanks. Bull. Assoc. Eng. Geol. 27, 37e50. Sinha, A., 1990. Interpretation of ground VLF-EM data in terms of vertical conductor models. Geoexploration 26, 213e231. Sharma, P.V., 1997. Environmental and Engineering Geophysics. Cambridge University Press, NY, NY. Sheriff, R., Geldart, L., 1995. Exploration Seismology, second ed. Cambridge University Press, NY, NY. Sjogren, B., 2013. Shallow Refraction Seismics. Elsevier Publ., Houten, The Netherlands. Stanely, J., Cattach, M., 1990. The use of high-definition magnetics in engineering site investigations. Explor. Geophys. 21, 91e103. Steeples, D., Miller, R., 1990. Seismic methods applied to engineering, environmental, and groundwater problems. In: Ward, S. (Ed.), Geotechnical and Environmental Geophysics, Vol. 1: Review and Tutorial, Soc. Explor. Geophys., vol. 5. Investigations in Geophysics, Tulsa, USA, pp. 1e30. Styles, P., 2012. Environmental Geophysics: Everything You Ever Wanted (Needed!) to Know but Were Afraid to Ask! EAGE, Houten, The Netherlands. Swift, C., 1991. Fundamentals of the electromagnetic method. In: Nabighian, M. (Ed.), Electromagnetic Methods in Applied Geophysics: Vol. 1, Theory, Soc. Explor. Geophys., vol. 5. Investigations in Geophysics, Tulsa, USA, pp. 4e11. Telford, W., Geldart, L., Sheriff, R., Keys, D., 1990. Applied Geophysics. Cambridge University Press, NY, NY. Ulyrch, T.J., 1968. Effect of wavelength filtering on the shape of the residual anomaly. Geophysics 33, 1015e1018. Upadhyay, S., 2004. Seismic Data Processing. Springer Publishing Co., NY, NY. Utsi, E., 2017. Ground Penetrating Radar: Theory and Practice. Elsevier Publ., The Netherlands. Van Nostrand, R., Cook, K., 1966. Interpretation of Electrical Resistivity Data, vol. 49. USGS Professional Paper, p. 326. Ward, S., 1990. Resistivity and induced polarization methods. In: Ward, S. (Ed.), Geotechnical and Environmental Geophysics, Vol. 1: Review and Tutorial, Soc. Explor. Geophys., vol. 5. Investigations in Geophysics, Tulsa, USA, pp. 147e190. Ward, S., Hohmann, G., 1988. Electromagnetic theory for geophysical applications. In: Nabighian, M. (Ed.), Electromagnetic Methods in Applied Geophysics: Vol. 1, Theory, Soc. Explor. Geophys., vol. 5. Investigations in Geophysics, Tulsa, USA, pp. 130e311. Weymouth, J.W., 1986. Archaeological Site Surveying Program at the University of Nebraska. Geophysics 51, 538e552. Whitelaw, J., Mickus, K., Whitelaw, M., Nave, J., 2008. High-resolution gravity study of the gray fossil site. Geophysics 73, B25eB32. Xia, J., 2006. Delineating Subsurface Features with the MASW Method at Maxwell AFB in Montgomery, Alabama. Kan. Geol. Surv. Open-file Rept. 2006-1. Yilmaz, O., 1987. Seismic Data Processing. Society of Exploration Geophysicists, Tulsa, USA.

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Zhandov, M., 2017. Geophysical electromagnetic theory and methods. In: Methods in Geochemistry and Geophysics, third ed., vol. 43. Elsevier Publ., Houten, The Netherlands. Zhody, A., 1989. A new method for the automatic interpretation of schlumberger and wenner sounding curves. Geophysics 54, 245e253. Zhody, A., 1974. Use of Dar Zarrouk Curves in the Interpretation of Vertical Electrical Sounding Data. USGS Bulletin 1313-D, p. 41p. Zhou, H., 2014. Practical Seismic Data Analysis. Cambridge University Press, NY, NY. Zhou, W., Beck, B., Adams, A., 2002. Effective electrode array in mapping karst hazards in electrical resistivity tomography. Environ. Geol. (Berl.) 42, 922e928. Zonge, K., Wynn, J., Urquhart, S., 2005. Resistivity, induced polarization and complex resistivity. In: Butler, D. (Ed.), Near-Surface Geophysics, Soc. Explor. Geophys., vol. 13. Investigations in Geophysics, Tulsa, USA, pp. 265e300.

Further reading Butler, D., Wolfe, P., Hansen, R., 2001. Analytical modeling of magnetic and gravity signatures of unexploded ordinance. J. Environ. Eng. Geophys. 6, 33e46. Littleton, J., Jenkinson, B., Hopkins, D., Ulmer, M., Tuttle, W., 2006. Hydropedological investigations with ground-penetrating radar (GPR): estimating water-table depths and local ground-water flow pattern in areas of coarse-textured soils. Geoderma 131, 317e329.

CHAPTER

Site investigation

6

Abdel-Mohsen O. Mohamed1, 2, Fares M. Howari3, Habes Ghrefat4, Evan K. Paleologos5 1

Uberbinder, Inc., Seattle, WA, United States; 2EX Scientific Consultants, Abu Dhabi, United Arab Emirates; 3College of Natural and Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates; 4College of Science, King Saud University, Reyadh, Kingdom of Saudi Arabia; 5College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates

6.1 Introduction The establishment of an effective remedial action plan for uncontrolled hazardous waste sites must integrate all the pathways involved in the transport of pollutants through the environment settings and to receptors through a proper site characterization study. The basic aspects of a remedial action selection process are (Mohamed and Antia, 1998): 1. Assessment of the nature and extent of pollution: Prior to the selection of any remedial program for an existing site, it is necessary to determine the concentration and distribution of pollutants through the sampling of soil, water, and biological species. Monitoring of these media offers a comprehensive analysis of the nature and distribution of pollutants; 2. Collection of site-specific data: Site-specific characteristics are of high importance to the selection of appropriate prevention/remedial actions. It is therefore advisable to collect as much specific information as early as possible in the selection process. The data should also include the quantity and quality of waste material, characteristics of site cover, climate of the areas, subsurface geology, proximity to various receptors, existing land use, etc.; 3. Determination of remedial measures: Currently, we have several prevention/remedial-engineered methods for environmental media. Remedial methods range from passive and extraction systems to direct or indirect on-site and off-site treatment processes; and 4. Selection of an appropriate remedial measure: This can be established by doing a detailed environmental impact assessment. This chapter outlines the practical site investigation methods used by scientists and engineers at polluted sites.

Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering. https://doi.org/10.1016/B978-0-12-809582-9.00006-2 Copyright © 2021 Elsevier Inc. All rights reserved.

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6.2 Site investigation approach Field works conducted by geotechnical engineers, in general, start with planning and site investigation. From the initial development of soil mechanics engineering practices to Terzaghi’s founding theories in soil mechanics and exploratory procedures, we have reached an understanding that the methodical evaluation of the physical, chemical, and mineralogical properties of soils are fundamental to the currently approved site investigation procedures. Site evaluation has steadily advanced over the past five decades, from initial borehole investigation for civil infrastructures to hundreds of soil borings and groundwater monitoring wells for polluted site evaluations. There seems to be the belief that the larger the amount of data procured during a field investigation, the greater the probability that a project will be completed successfully without complication or delay. The procedures used for collecting information for subsurface characterization and evaluation through drilling, sampling, and logging have slightly altered within the last five decades. The basic methodologies developed by Hvorslev in the late 1940s for evaluation of subsurface conditions during field investigation are valid and currently being used by engineers. To establish a cost-effective approach to evaluate polluted sites, Sara (1993) developed a method, known as the observational method, based on work reported by Peck (1969). The observational method is divided into the following six steps: “(a) conduct an investigation of sufficient scope to establish the general characteristics of the site, (b) assess the most probable site conditions and the deviations from these probable conditions, (c) develop a design based upon the most probable site conditions, (d) determine what course of action should be taken if the conditions deviate from predictions, (e) measure and evaluate actual conditions during construction, and (f) modify the design as needed to suite actual situations.” Notably, this method can be used “. to define the proper location for monitoring systems or to establish the rate and extent of environmental pollution” (Sara, 1993). The implementation of this approach to field investigation relies upon the geotechnical engineer’s knowledge, experience, and judgment. Determination of site characteristics in the observational method is termed the phased approach to site investigation and is based upon (Sara, 1993): “(a) a thorough review of existing literature and available technical information for the site under investigation, (b) preliminary site aerial and ground reconnaissance, (c) development of initial regional and site-specific geologic conceptual models, and (d) design of field investigation based upon the initial conceptual models.”

6.3 Phase I investigations Investigations carried out during Phase I are preliminary in nature and are designed to provide a comprehensive overview of available site information. The objectives of Phase I investigations are to “determine the nature and characteristics of expected pollutants, characterize the environmental issues at the site, develop a conceptual model, and establish a framework for phase two investigations” (Mohamed and Antia, 1998).

6.3.1 Collecting information The following sources of information are generally required for identifying the potential subsurface pollution and various environmental concerns.

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6.3.1.1 Sources of information on site history At this stage, it is important to find out the history, land use, and activities at the site to determine the presence or the possibility of subsurface contamination. The types and possible sources of information are summarized in Table 6.1 (CCME, 1994). Key issues to be considered during Phase I investigations are (Mohamed and Antia, 1998): 1. Nature of known or suspected pollution: It is very important to identify the type of pollutants and their physicochemical, fate, and exposure parameters (Mohamed and Paleologos, 2018). In addition, it is required to determine the relevant environmental regulations for soil, air, and water media; 2. Sources or possible sources of pollution: Some of the obvious potential sources of pollution are waste management practices and disposal facilities. Also, the transfer of bulk chemicals and petroleum products to trains and trucks often generate uncontrolled spillage on the ground surface with a significant cumulative subsurface pollution. Although the pollution potential of some practices and facilities can be readily recognized, the potential of others may be masked and only available in reports such as inappropriate disposal practices of used solvents and solvent sludges, past chemical usages, and current processes at the facility; 3. Transport and migration of subsurface pollutants: The extent of pollution migration in the subsurface environment is highly dependent on the physicochemical and fate properties of pollutants, volume of chemicals introduced to the subsurface, and the natural conductions of the subsurface environment; and 4. Environmental health and safety: A detailed environmental health and safety plan should be specific to the site and consider risks likely to be present at the site. Some common risk sources are electrical utilities, unstable slopes, dangerous debris, aesthetic impact, temperature, radiation, disease, infection, explosion, fires, etc. Table 6.1 Sources of information for site history. Sources of information

Type of information

Availability

Owner and regulatory agency files Land use and ownership history Aerial photographs

Site operational and environmental history Site activities and operations

Site owner and government agencies (environment and regulatory) Municipal tax records and directories, and title searches Government agencies (natural resources, national and local archives) National and local archives, corporate files Corporate and municipal files

Archival records Site plans and engineering drawings Historical maps and fire insurance plans Reports Industrial activities and processes

Land use history, physical and drainage features Historical photographs and operational history Site layout and features Land use and industrial process areas Site history and practices Manufacture, use, storage, and disposal of chemicals

National and local archives Present and former employees, residents, and local historians Corporate archives, historical and contemporary trade journals and texts

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6.3.1.2 Geologic and hydrogeologic information Reported studies at or near the site can provide information on the site geology and hydrogeology, and subsurface soil characteristic. Soil type can be useful to predict relevant properties of subsurface material such as soil permeability, strength, water-holding capacity, and potential chemical reactions. Aerial photographs can provide an excellent assessment of geology and surficial drainage features. Geophysical studies at the site can assist in the design of pore holes, location of the monitoring wells, and overall interpretation of ground pollution conditions at the site. The hydrogeologic setting of any site is a critical parameter since it controls the movement of water and pollutants in the subsurface. In Phase I investigation, guidelines for collecting and synthesizing information on the subsurface setting are developed. Potential recharge and discharge zones, depth to the water table, general groundwater flow directions, and surface drainage patterns are some of the features that can be identified in Phase I investigation.

6.3.1.3 Hydrologic information The focus of the investigation will be on surface water, its location, movement, quality, and connection to groundwater. Much information about sources and direction of flow of water can be estimated from topographic maps. More details can be found in more specialized water resources reports. Some of the existing information may also be useful at later stages of investigation. For example, the flow rates of nearby streams and rain fall data might be useful for calculating the amount of water moving through the subsurface.

6.3.2 Field reconnaissance Once the literature search has been completed, a site visit must be conducted to substantiate conclusions drawn from the literature findings. During this phase, the following important observations are made (Mohamed and Antia, 1998): (1) site terrain and its surroundings to assess accessibility for geological surveys and drilling equipment, (2) site logistical considerations to determine the presence of underground utilities, need for excavation/drilling clearance, access routes across private property, and availability of clean water, (3) site geological conditions to determine the consistency of the available background data with the regional pattern of geology and locations of bedrock, (4) site topography, drainage, and vegetation to determine locations of liquid waste discharge and stressed vegetation areas, (5) status of waste, particularly its mobility and degree of exposure, (6) status of monitoring devices in the study area, notably their condition, depth of penetration, and the groundwater level, and (7) site climate to determine precipitation and temperature at the study site. Based on precipitation conditions, surface water, and groundwater conditions, wind and erosion potentials are determined. Physical processes such as rates of reaction, volatilization, microbial degradation, and transformation processes are highly dependent on temperature conditions at the site.

6.3.3 Development of a conceptual model After collecting the necessary information, a conceptual model can be developed and restructured once we obtain new information. The basic components are (Mohamed and Antia, 1998): 1. The geologic setting at and near the site: The model will distinguish between various geologic layers in terms of their hydraulic characteristics (permeabilities), and will attempt to indicate the

6.4 Phase II investigations

2.

3.

4.

5.

6.

293

significance of the various layers in influencing the groundwater flow system, and the potential pathways of pollutant migration in the subsurface; The regional and local surface and groundwater flow systems: The model should identify the interaction between the groundwater and surface water systems near the site and indicate the interrelationships between the regional and local groundwater flow systems. It should also incorporate topographic and stratigraphic information into the schematic diagrams of the groundwater flow system; Identification of the impact of human activities on water flow and pollutant migration at the site: For examples: (1) buried pipelines, utilities, and sewers and their associated coarse-grained backfill often provide conduits for the preferred flow of nonaqueous phase liquids (NAPLs) and groundwater through the subsurface, and (2) groundwater pumping wells near a site may also alter hydraulic gradients and modify the groundwater flow system; Identification of the natural and preferential pathways for pollutant migration: These pathways might include high hydraulic conductivity layers and lenses in the geological materials or fractures in clays and rocks; Identification of the characteristics of pollutants: It is important to include the pollutant characteristics in the development of the model to ensure that potential areas of occurrence and migration can become the focus of a site monitoring and investigation program; and Identification of the potential receptors for evaluating the degree of environmental impact: Receptors may include people, plant, animals, and aquatic organisms.

6.3.4 Establishing the work plan The collation and evaluation of existing background information and field visits during Phase I investigation are essential for the development of a comprehensive work plan or program for subsequent stages of the site assessment process. The work plan should account for special physical features at the site. For example, low hydraulic conductivity layers on a site may protect deeper zones from near-surface pollution. Inappropriate drilling techniques may jeopardize the integrity of such features and create increased pollution. Notably, the professional decision for the type of the investigative technique used at a site is highly dependent on its geological formation. In addition, the special characteristics of the pollutants at a site should also be considered in the development of a work plan. Examples of these considerations are (Mohamed and Antia, 1998): “(a) suitability of the overall approach to site investigation, i.e., avoid making subsurface pollution worse, (b) suitability of using geophysical techniques during site investigation, (c) compatibility between pollutants and monitoring well materials, and (d) suitability of drilling, monitoring well installation or sampling techniques.”

6.4 Phase II investigations The geoengineering investigations during Phase II consist of the site characterization phase (Phase IIa), and the setting of the network of groundwater monitoring wells (Phase IIb). Various investigative techniques in the engineering profession do exist for data collection for site characterization. The actual site investigation will include direct and indirect methods. The former methods include, but are not limited to, installation of boreholes and monitoring wells, soil and water sampling, and

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characterization of soil and water samples. The later methods include geophysical techniques such as aerial photography, ground penetrating radar, and earth conductivity and resistivity. The investigator should combine direct and indirect methods to produce an efficient and complete characterization of the site under investigation. Readers who are interested in this subject matter are advised to read in this book Chapter 3 on remote sensing (authored by Mertikas et al., 2020), Chapter 4 on geographical information systems (authored by Howari and Ghrefat, 2020), and Chapter 5 on geophysical techniques (authored by Mickus, 2020). Irrespective of the method employed, the intended purpose of soil sampling at polluted sites is to determine the level of pollution at the site and whether it exceeds the regulatory standards, and the potential impact on the environmental ecosystems discussed in this book in Chapter 1, Sustainable Pollution Assessment Practices (authored by Mohamed and Paleologos, 2020). Specifically, soil sampling efforts are designed for the purpose of (Mohamed and Antia, 1998): “(a) determining soil characteristics and properties being physical, hydrological, chemical and mineralogical, (b) identifying sources of contamination, (c) determining the level of concentration of each pollutant in comparison with background levels and regulatory standards, (d) determining the spatial and temporal distribution of pollutants in the ground, (e) determining the role the soil will play on transporting pollutants to air or water media, i.e., the degree of saturation of the ground, (f) identifying pathways of pollutant transport, (g) identifying pollutant receptors in the area, (h) utilizing contaminant transport models to predict the potential migration of pollutants, (i) determining the toxicity and level of risks to humans, plants and animals, and (j) proposing mitigation measures that meet the regulatory standards.” The complication and variability of subsurface soils demand that multiple sampling and monitoring methods be included in the subsurface investigations. Both field testing, in which information regarding soil characteristics, groundwater flow regime, and pollutant migration is obtained, and laboratory testing, in which analytical data on the type and quantity of a pollutant present in the subsurface is determined, are essential to understand the nature and extent of pollution. In addition, in the field, groundwater level monitoring is essential to subsurface investigation to properly determine the variations in groundwater flow with respect to space and time in both saturated and unsaturated zones. Notably, correlation of the requirements into a phased investigation (Phase IIa and Phase IIb) for the determination of soil and water regime characteristics will facilitate an already difficult task by eliminating many of the variables consistent with the environmental conditions. A brief and basic outline of the various techniques is presented in the following sections, with the recognition that literally volumes of material could be and have been written on each precise method and technique.

6.5 Geophysical techniques Geophysical techniques are generally used at the initial stage of field investigation to evaluate subsurface conditions and determine the level to which pollutants have migrated to the site. The basis for using a geophysical approach to investigate groundwater pollution is because dissolved electrolytes form ions, which enable water to conduct electrical currents and, up to a limit, solution conductivity is proportional to the amount of dissolved electrolyte. They include ground penetrating radar, electromagnetic conductivity, electrical resistivity surveys, and seismic surveys. It is to be noted that a geophysical method that is successful at a specific site may or may not have the same success at another site because of its operational principles, the nature of subsurface soils,

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and the nature of contaminants. Therefore one should employ someone with a geophysical background who has enough experience and understanding of the limitations of each technique. Readers who are interested to know more about the geophysical techniques are advised to read in this book Chapter 5 authored by Mickus (2020).

6.6 Hydrogeological investigations Field hydrogeological investigations are performed to “detect and identify subsurface contaminants being organic or inorganic in nature, measure the concentrations of contaminants, characterize the geological environment through which contaminants migrate, and determine the subsurface groundwater flow direction” (Mohamed and Antia, 1998). And at a specific site, the suitability of a well installation procedure depends on “the expected geology of the site, type of liquid waste and its anticipated effect on the drilling mud and well materials, and the impact of adopted installation procedures on the reliability of the experimental results obtained for both water and soil samples” (Mohamed and Antia, 1998).

6.6.1 Drilling methods Several drilling methods for sampling and installing groundwater monitoring wells are available (GeoTrans, 1989). In choosing the best method, site geology, along with size and type of well materials, is critical. A required well material may influence available drilling methods. Well casing and screens constructed from polyvinyl chloride (PVC) or thin-wall stainless steel are best installed using rotary or auger drilling methods. Conversely, cable tool drilling requires the use of black steel or galvanized steel casing. Often, a combination of two or more drilling techniques is used to complete monitoring wells. Commonly used drilling methods are briefly discussed next.

6.6.1.1 Hollow-stem auger This is the preferable drilling method for monitoring well construction by geotechnical engineers and water scientists. It does not require drilling fluids and causes minimum disturbance to subsurface soils. From a practical viewpoint, one should consider the following: (1) when penetrating consolidated rock, auger rigs are not required, (2) in ground settings, where the borehole could not stand open, the monitoring well is constructed inside the hollow-stem auger prior to its removal from the hole, and (3) well diameter and depth should not exceed 0.1 and 50 m, respectively. The hollow-stem auger has an added advantage because it allows one to collect unremitting geological samples without auger section removal.

6.6.1.2 Solid-stem auger This is widely used in fine-grained soil formation, which does not collapse by itself. The method is like the hollow stem; however, the auger flight must be removed from the hole to allow the insertion of the well casing and screen. Notably, when using a solid stem auger, core samples cannot be collected; therefore samples will be collected from cuttings that come to the surface during the operation process. This, in turn, creates a possibility of cross-contamination and makes it difficult to have precise well logging.

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6.6.1.3 Cable-tool drilling In general, this method is advised for the installation of monitoring wells. During its operation, one would collect good quality soil samples, and be able to detect tinny permeable zones within soil formation.

6.6.1.4 Air-rotary drilling In this technique, air is pushed down the drill stem and back up the borehole to remove soil cuttings within the pore hole. This method is generally used for drilling in fractured rock formation and is not recommended for highly contaminated grounds because of the potential hazards caused by both the air and the soil coming out of the borehole. Care should be taken in the following situations: (1) for sites contaminated with organic compounds, injected air must be filtered to prevent cross-contamination, and (2) for sites contaminated with highly volatile organic compounds, the likelihood of contaminating the samples is high due to volatilization.

6.6.1.5 Air-percussion rotary or down-hole hammer This method is recommended for materials likely to cave, i.e., loose soils. The capability to construct a monitoring well inside the driven casing, prior to its being pulled, adds to the appeal of the airpercussion method. However, the problems with contamination and crew safety must be considered.

6.6.1.6 Reverse circulation drilling This technique has limited application for monitoring well installation because it requires circulation of large quantities of water in the borehole and up the drill stem to remove soil cuttings. Notably, in the case of permeable subsurface material, one would expect seepage of large water quantities into the subsurface; hence, altering the quantity of the water to be sampled.

6.6.1.7 Hydraulic rotary In this technique, soil cuttings are removed with the aid of a drilling mud (usually bentonite), which is circulated down the drill stem and up the borehole. Notably: (1) the screened areas within the borehole must be cleaned because drilling mud creates a tinny layer inside the borehole that blocks the screens, (2) because of the high reactivity of bentonite, ion exchange is expected to occur and would potentially reduce the concentration of pollutants in the water entering the borehole, and (3) in the case of using biodegradable organic drilling muds instead of bentonite, water samples would be contaminated with organic compounds.

6.6.2 Sampling methods There are many alternative techniques for the collection of samples. The utmost appropriate method will depend on both the nature of soil materials being sampled and the drilling technique being used.

6.6.2.1 Drill cutting samples During any drilling operation, the cutting action of the drill bit produces fragments of the geologic material being penetrated. In a preliminary drilling program, these cuttings can be used to provide basic information on mineralogy, grain size, and stratigraphy of the subsurface materials.

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6.6.2.2 Core samples For laboratory studies of hydraulic properties and diffusion of undistributed soil samples, continuous core sampling is preferable. The most common coring technique is the split spoon sampler, which consists of a hardened steel drive shoe screwed onto a hollow steel tube that is split down the middle. The top of the tube is connected to a head assembly that attaches to the drill rod. The core tube is 0.45 or 0.6 m in length and is commonly between 0.05 and 0.10 m in diameter. With the advancement of a borehole installation, a sample can be obtained at the specified depth. The sampler is driven into the subsurface material by a hammering device on the surface, and the number of blows used to fully penetrate the sampler by its full length is recorded. This technique is generally referred to as the standard penetration test (ASTM Dl586-84). The hollow-stem auger rig provides the most efficient and reliable method of collecting split spoon samples.

6.6.3 Well installation techniques After the test borehole is drilled and the subsurface material is sampled, a monitoring well device can be installed in the borehole for groundwater sampling and measuring piezometric levels. The most common techniques for monitoring well installations are discussed in the following sections.

6.6.3.1 Drive point wells The simplest and least expensive technique, to install a monitoring well, is to drive the well down to the specified depth with a hammering device. These wells have been successfully installed in soft soils up to 30 m, and their diameters range from 15 to 30 mm. There are several drive point monitoring techniques available in engineering practice (Desaulnier, 1983).

6.6.3.2 Individual wells The most common type of monitoring well is the individual well installed in a drilled borehole. These types of monitoring wells commonly range from 0.02 to 0.1 m in diameter. They are typically constructed of steel, stainless steel, PVC, or Teflon, depending upon the specific requirements for chemical sampling. The screen and filter pack should ensure that subsurface water can pass easily into the monitoring well. The placement technique is as follows (Mohamed and Antia, 1998): “(a) place the selected well screen and casing down into the borehole to the required depth, (b) install permeable filter pack material around and slightly above the well screen to allow groundwater from the adjacent formation to flow freely to the well screen, (c) place a sealing material above the filter pack to isolate the well screen from the rest of the borehole, (d) backfill the annulus above the seal with a grouting material, and (e) install a protective cover over the well casing at ground level for security and to prevent precipitation from entering the well.”

6.6.4 Monitoring well design components A monitoring well is built to be able to collect subsurface water samples for analysis. As discussed in the preceding sections, one should be aware of: (1) the design components of a monitoring well, which must not materially alter the quality of the water being sampled, and (2) the chemistry of suspected pollutants and the geological setting in which the monitoring well is to be constructed since they play a major role in the drilling technique and well construction materials used. The major components,

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FIGURE 6.1 Monitoring well components (Mohamed and Antia, 1998).

which are needed to be considered in monitoring well design, are shown in Fig. 6.1 (Mohamed and Antia, 1998) and described briefly in the following sections.

6.6.4.1 Diameter The diameter of a monitoring well was generally based on the size of the device (bailer, pump, etc.) used to withdraw water samples. This practice worked well in very permeable formations, where an aquifer capable of furnishing large volumes of water was present. However, monitoring wells are quite often completed in very marginal water-producing zones. Hence, pumping one or more well volumes of water from a well built in low-yielding materials may present a serious problem if the well has a large diameter. In addition, when hazardous liquid wastes are present in the groundwater, the purged water must be properly disposed of. Therefore the quantity of water pumped from the well should be minimized for reasons of safety, as well as disposal cost. For these reasons, 50 mm inner diameter wells have become the standard in monitoring well technology. For cases where monitoring is followed by treatment of groundwater and polluted soils, large diameter wells used for monitoring can be used as a supply well to remove polluted water for treatment. Also, since large diameter wells have higher strength, they are often used for deep monitoring.

6.6.4.2 Casing and screen material As previously discussed, the materials used to construct a monitoring well can have a major effect on the quality of collected water samples. The materials to be used should neither adsorb nor leach chemical constituents that would change the characteristics of the samples collected for contaminant characterization and evaluation of the extent of pollution in the site. Fig. 6.2 (Mohamed and Antia, 1998) is a schematic representation of material selection procedures for a plume detection program. The types of materials used in well construction are (Mohamed and Antia, 1998): 1. PVC: Because of PVC’s low cost and easy installation, it has been used extensively for casing and well screens. PVC is inert to chemical reactions in nearly natural environments. However, PVC

6.6 Hydrogeological investigations

299

Material selection

Subsurface pollution condition

Unknown

Polytetrafluoroethylene Stainless steel Polyvinyl chloride

High inorganic Suspected

High organic or inorganic suspected

NO

Is detection limit important ?

YES Polytetrafluoroethylene Stainless steel

FIGURE 6.2 Schematic representation of material selection procedures for plume detection (Mohamed and Antia, 1998).

deteriorates when it comes in direct contact with low molecular weight ketones, aldehydes, and chlorinated solvents. Generally, as the organic content of a solution increases, direct attack on the polymer matrix or solvent adsorption or leaching may occur. 2. Teflon: Teflon is the most inert well material. However, because it is expensive, it is used where no chemical interferences can be tolerated. 3. Galvanized steel casing: Galvanized casing can be superior to PVC because it does not interact with organic chemicals and is tougher if the well must be driven into the formation. The galvanic coating inhibits rust formation, which otherwise decreases the life of the well. Also, galvanized casing can increase iron, manganese, zinc, and cadmium concentrations in water. Steel casing may contribute to sample contamination due to an increase of iron and manganese concentrations. Therefore PVC casing is preferred for monitoring groundwater polluted by heavy metals. 4. Stainless steel casing: Stainless steel does not interact with contaminants. However, at low pH it may release Cr into groundwater and catalyze some organic chemical reactions. The primary disadvantage is its high cost.

6.6.4.3 Sealing materials When drilling a hole using rotary, auger, or jetting methods, the final borehole diameters are bigger than the well casing. To prevent the flow of contaminated groundwater into the well, the spacing between the well casing and the borehole wall is grouted with either bentonite, cement, or a bentonite/ cement mixture. Some concerns related to each grouted material are discussed next (Mohamed and Antia, 1998): 1. Bentonite grout: Bentonite slurry mixture is used generally in drilling mud and to act as a borehole seal material after the well is completed. The bentonite structure is aluminosilicate

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sheets bonded through cation bridging. Bentonite clay has significant ion exchange capacity, which might interact with collected water samples and change its water chemistry when the seal is adjacent to the well screen. 2. Cement grout: This is used to seal the annulus, particularly after setting the casing in a hole drilled with rotary methods. Since cement is more permeable to groundwater than bentonite it is sometimes considered inappropriate as a material used for grouting in well construction. However, cement is firm and provides stability to the well casing. When improperly placed, cement grout has been known to seriously affect the pH of sampled water. Upon curing and weathering, cement grout may undergo shrinkage and cracking. 3. Bentonite cement mixture: Bentonite and cement mixtures are often used to create a slurry for grouting. After setting, the grout is slightly weaker than pure cement and a little more permeable than bentonite. Variations in the mix can enhance the structural strength or impermeability of the grout.

6.6.4.4 Screen length and depth of placement The length of well screen and the placement depth in the ground depend on the nature of contaminants as it moves thorough subsurface materials, and the purpose of the polluted site monitoring framework. “When monitoring an aquifer system for water supply, the entire thickness of the water-bearing formation must be screened. However, when specific depth intervals must be sampled at one location, vertical nesting of wells is common, which is often necessary when one encounters a thick layer of saturated zone that could not effectively be monitored with a ‘long-screened’ section. Typical screen lengths of 0.3e0.6 m are common when detailed spatial distribution of subsurface contaminants is needed” (Mohamed and Antia, 1998). Monitoring of NAPLs demands special attention. For example, light NAPLs, i.e., with densities less than water, will rise to the groundwater surface and spread over it. Therefore monitoring wells constructed to detect light NAPLs should contain screens that extend above the zone of saturation to allow these chemical substances to be collected inside the well. In addition, both screen length and position must be designed to allow for the water table level variations within subsurface ground.

6.6.4.5 Location and number Monitoring well locations and numbers in the monitoring program are closely linked and determined by the monitoring program. A typical monitoring well placement scheme is shown in Fig. 6.3A and B (Mohamed and Antia, 1998). Well “A” is the background monitoring well and is located far enough upgradient from the site to ensure that the landfill has no effect on the soil permeability at the well. Well “B” is located inside the site and is placed in a location where migrating pollutant can be detected. The “B” well will also serve as a first indicator of the effectiveness of the remedial action program. If water quality in the monitoring well does not indicate a steady improvement over time it will indicate the need for further remedial action considerations. This well must be very carefully constructed and sealed to prevent vertical migration of pollutants down the well casing. Well “C” is located down gradient from the site at a position close enough to detect changes in groundwater quality as soon as possible. These wells should also, over a period, show a similar trend of improvement of groundwater quality. Site geology, site hydrology, pollutant characteristics, and the size of the area under investigation all help determine where and how many wells should be constructed. Certainly, the larger the area being investigated, the greater is the required number of monitoring wells.

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FIGURE 6.3 Typical monitoring well network (Mohamed and Antia, 1998).

6.6.5 Well decontamination procedures Prevention of contamination of monitoring wells must be considered at all phases of water well construction, from the initial soil boring stages to the final water-sampling and water-measuring stages. The most used techniques for well decontamination are (Mohamed and Antia, 1998): (1) steam cleaning: the pressurized steam frees residual soil materials, washes them from the augers, and strips organic chemicals from the metal surface. Surfactants are added to the makeup water to more completely remove oil and grease from the drilling equipment, (2) heat treatment: heat is occasionally used to remove residual organic pollutants. Equipment such as augers, bits, and wrenches are stripped of organic chemicals using an open flame, which is less preferred for safety aspects, and (3) sand blasting: sand blasting is sometimes used to strip soil materials from augers, bits, and tools. After sand blasting, equipment is normally decontaminated using a steam cleaner and solvent rinses.

6.7 Hydrogeochemical investigation 6.7.1 Subsurface environment Sampling of subsurface tends to change the chemical equilibrium, hence new reactions could be developed. “The possible chemical interactions of drilling methods, materials used for constructing wells, and sampling collection methodologies must be considered when designing a sampling program” (Mohamed and Antia, 1998). The potential changes and disturbances of the environmental ecosystems are dependent on the physical and chemical conditions existing in the subsurface. The major geochemical parameters that characterize the subsurface ecosystem are: (1) pH and alkalinity, (2) redox potential, (3) salinity and dissolved constituents, (4) soil matrix, (5) temperature and pressure, and (6) microbial population.

6.7.1.1 pH and alkalinity Solution pH and alkalinity are the main controlling parameters that affect ionic composition and precipitation reactions in an environmental setting. According to Barcelona et al. (1988), “sampling

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Table 6.2 Classes of soil pH. Class

pH

Class

pH

“Ultra” acidic “Extremely” acidic “Very strongly” acidic “Strongly” acidic “Medium” acidic “Slightly” acidic

Less than 3e5 3.5e4.5 4.5e5.0 5.1e5.5 5.6e6.0 6.1e6.5

“Neutral” “Mild” alkalinity “Moderate” alkalinity “Strong” alkalinity “Very strong” alkalinity

6.6e7.3 7.4e7.8 7.9e8.4 8.5e9.0 Greater than 9.0

Modified after Soil Survey Staff. (2006). Keys to soil taxonomy (10th ed.). Washington, D.C: U.S. Gov. Print. Office.

methods and material may affect subsurface pH.” For example, “when using cement as a grouting material, pH may increase by 4 to 5 units. Also, during well purging, pH may increase or decrease by 0.1 to 5 units.” These changes in pH will in turn affect geochemical processes in the subsurface environment. Table 6.2 (Soil Survey Staff, 2006) lists the soil pH classes. Subsurface geochemical processes that may be affected by pH are (Mohamed and Paleologos, 2018; and Mohamed and Antia, 1998): 1. Acidebase reactions: Strong acids and bases tend to flocculate and disperse clay structures, respectively; 2. Adsorptionedesorption: pH strongly influences adsorption because hydrogen ions play an active role in both chemical and physical bonding processes. Mobility of heavy metals is strongly influenced by pH. Adsorption rates of organic and natural clays are also pH dependent; 3. Precipitationedissolution: Acidic solutions tend to dissolve carbonates and clays, while highly alkaline solutions tend to dissolve silica. Generally, higher pH values increase cation exchange capacity of clays; 4. Complexation: Complexation strongly influences position of equilibria involving complex ions and metal chelate formation; 5. Oxidationereduction: Redox systems generally become more reducing with increasing pH; 6. Biodegradation: High to medium pH and low redox potential (Eh) environments will generally restrict bacterial populations to sulfate reducers and heterotrophic anaerobes (Baas-Becking et al., 1960); 7. Salinity: pH-induced dissolution increases salinity, while pH-induced precipitation decreases salinity; 8. Alkalinity: Alkalinity indicates the buffer capacity or resistance to change in pH. A solution with a high buffer capacity has a large resistance to change in pH. Since carbonate buffering is common to most natural waters, the solution pH may be quite sensitive to volatilization of CO2 during sampling operations; 9. Temperature: pH-driven exothermic (heat-releasing) reactions increase fluid temperature, while pH-driven endothermic (heat-consuming) reactions decrease fluid temperature; and 10. Pressure: Pressure is affected only when pH-induced reactions result in a significant change in the volume of reacting products.

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6.7.1.2 Redox potential The oxidation-reduction potential, or Eh, is a term used to denote the intensity of redox conditions in a system. It is measured as a result of the potential difference between a working electrode and the standard hydrogen electrode, and is expressed in volts or millivolts (mV). In natural water, positive readings are indicative of oxidizing (aerobic) environments, while negative readings are suggestive of reducing (anaerobic) environments. The US EPA (1991a,b,c) identified the following “redox potential classes: (a) highly oxidized for Eh greater than þ400 mV, (b) intermediate for Eh ranging from þ400 to 100 mV, and (c) highly reduced for Eh less than 100 mV.” Notably, Near-surface soils and sediments have maximum Eh values of 299 mV; whilst, surface water bodies have values in the range from 499 to 600 mV because they are in equilibrium with atmospheric oxygen” (Mohamed and Antia, 1998). Most redox reactions in the subsurface are microbially mediated. Notably, Eh measurements and/or calculated “equilibrium constants” do not indicate the “strength of the reducing environment” as do the actual measurements of the “reacting products.”

6.7.1.3 Salinity and dissolved constituents In the field, dissolved solids are estimated thorough measuring specific conductance of the water samples with conductivity meters. As discussed earlier, care should be taking during well development to avoid mixing of existing soil water solutions of different chemical compositions with that purged or groundwater sampled. For example, if such mixing takes place, the organic chemistry of soil water samples would be different due to redox reactions taking place during well installation. The US EPA (1991a,b,c) developed various salinity classes based on electrical conductivity of a saturation extract, as shown in Table 6.3.

6.7.1.4 Soil matrix Soil mineralogy, organic matter content, particle size distribution, and degree of saturation greatly impact the chemical composition of subsurface waters. As particle size decreases and organic matter increases, particle surface area increases; hence, an increase of potential chemical reactions between

Table 6.3 Salinity classes. Classes Solutions Solutions Solutions Solutions Solutions

Electrical conductivity (dS/m or mmho/cm) with with with with with

no salinity slight salinity moderate salinity very high salinity extreme salinity

0e2 2e4 4e8 8e16 Greater than 16

Modified after US EPA., 1991a. Handbook: groundwater, Methodology, United States Environmental Protection Agency, vol. II. Washington, D.C., EPA/625/6-90/016b; US EPA., 1991b. Site Characterization for Subsurface Remediation, United States Environmental Protection Agency, Washington, D.C., EPA/625/4-91/026; US EPA., 1991c. Description and Sampling of Contaminated Soils: A Field Pocket Guide, United States Environmental Protection Agency, Washington, D.C., EPA/625112-91/002.

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soil constituents and water. Also, soil mineralogy, particle size, and previous geochemical history have important effects of the cation exchange capacity of soils (Mohamed and Paleologos, 2018; Mohamed and Antia, 1998). The ability of soils to retain pollutants is directly related to their ability to exchange/ adsorb ions. Therefore evaluation of clay mineralogy and specific surface area could provide a significant understanding of subsurface geochemistry (Mohamed and Paleologos, 2018; Mohamed and Antia, 1998).

6.7.1.5 Temperature and pressure The rate of chemical reactions is a function of both temperature and pressure. The amount of dissolved gases in a solution increases as pressure increases. Notably, sampling techniques that allow gases and volatile organic compounds to escape to the atmosphere might underestimate the actual concentrations in tested samples. Since pressure increases with depth there is a likelihood of inducing errors to actual concentrations of pollutants in tested samples.

6.7.1.6 Microbial activity Groundwater “encompasses varied microorganisms, which depend on the available amount of nutrients and dissolved organic carbon for their growth” (Mohamed and Antia, 1998). Under aerobic conditions, most organic pollutants are readily degraded. The degradation process will highly depend on the subsurface geochemical conditions, the available microorganisms, and the subsurface microbial activity. Notably, nitrogen, ammonia, hydrogen sulfide, and methane are good initiators of microbial activity.

6.7.2 Sampling considerations A sampling program is intended to evaluate the geochemical and hydrological variations of the subsurface ground and determine pollutant concentrations and their distribution within the study area. Determinations of various contaminants and associated geochemical parameters are essentials to characterize the extent of pollution, the impact of pollutants on human health and the environment, and the specific remedial actions to be taken at the site. Since sampling locations and frequency are important aspects of a sampling program it is important to determine the hydrogeologic conditions via geophysical surveys prior to designing the sampling network, as discussed earlier.

6.7.2.1 Sampling location The three-dimensional distribution of subsurface contaminants is one of the main parameters in a sampling program. The number of samples and frequency depend on variability of pollutants and their concentration in the subsurface environment. According to Mohamed and Antia (1998), “there are two broad designs for sampling: (a) grids in which samples are taken from a matrix of squares or quadrants at a site, and (b) transects in which samples are taken at specified intervals along a line. Grids presume an aerial or dispersed source of some kind and transects presume a preferential source. Grids can be used to estimate short range correlation. Transects along the path of groundwater or pollutant movement provide the best way to look at long range correlation. The combination of the two strategies coupled with the initial analysis of selected solid samples at alternate grid or transect locations can be quite effective. For soils, at least 5 percent of sampling points should be duplicated to help determine the sampling variability.”

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6.7.2.2 Sampling frequency Table 6.4 lists the proposed sampling frequency as defined by US EPA (1991a,b,c). In addition, according to Barcelona et al. (1984), sampling frequency is calculated as per Eq. (6.1): fs ¼

nd kw  i

(6.1)

where fs is the frequency of sampling, n is the effective porosity, d is the distance along the flow path, kw is the soil hydraulic conductivity with reference to water permeation, and I is the hydraulic gradient.

6.7.2.3 Sample type and size The nature of the subsurface condition is the basic controlling parameter in relation to the choice of sampling program. For example, if the subsurface has obvious fractures and channels, samples should be obtained from both affected and apparently nonfractured areas for comparison.

Table 6.4 Estimated ranges of sampling frequency in months.

Parameter

Contaminated

Pristine background conditions

Up-gradient

Down-gradient

2e7

1e2

2e10

2e7

2e38

2e10

1e2