Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss: Measured Accumulation in Moss 3031256352, 9783031256356

Table of contents :
Contents
Abbreviations
List of Figures
List of Tables
1 Background and Aim
1.1 Background
1.2 Aim
References
2 Material and Methods
2.1 Data
2.2 Data Preparation
2.3 Methods
2.3.1 Methods of Descriptive Statistics
2.3.2 Correlation Analysis
2.3.3 Analysis of the Distances Between Measuring Points of the Technical and Biological Monitoring of Atmospheric Depositions
2.3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median
References
3 Results
3.1 Descriptive Statistics
3.1.1 Measurement Data on Heavy Metal Concentrations in Moss
3.1.2 Modelled Mean Heavy Metal Concentrations in the Atmosphere
3.1.3 Modelled Total Annual Atmospheric Depositions of Heavy Metals
3.1.4 Technical Measurement Data on Mean Atmospheric Heavy Metal Concentrations
3.1.5 Technical Measurement Data on Atmospheric Heavy Metal Depositions
3.2 Correlation Analysis
3.2.1 Correlations Between Modelled Mean Concentrations of Heavy Metals in the Atmosphere (EMEP) and Concentrations in Moss (MM2015)
3.2.2 Correlations Between Modelled Atmospheric Heavy Metal Deposition (EMEP) and Concentrations in Moss (MM2015)
3.2.3 Correlations Between Mean Concentration Values from Technical Measurements of Heavy Metals in the Atmosphere and Concentrations in Moss
3.2.4 Correlations Between Technical Measurements of Atmospheric Heavy Metal Deposition and Concentrations in Moss
3.2.5 Graphical Comparison of the Correlation Coefficients
3.3 Comparison of the Federal and State Air Quality Monitoring Network with the Moss Monitoring Network 2015
3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median
3.5 Summary with Regard to the Objectives and Key Questions of the Study
References
4 Conclusions
References
Appendices: Statistical Data Analyses—Tables, Diagrams, Maps
Appendix A: Descriptive Statistics
A.1 Measurement Data on Heavy Metal Concentrations in Moss
A.2 Modelled Mean Heavy Metal Concentrations in the Atmosphere
A.3 Modelled Total Annual Atmospheric Depositions of Heavy Metals
A.4 Technical Measurement Data on Atmospheric Heavy Metal Concentrations
A.5 Technical Measurement Data on Atmospheric Heavy Metal Depositions
Appendix B: Correlation Analysis
B.1 Correlations Between Modelled Mean Concentrations of Heavy Metals in the Atmosphere (EMEP) and Concentrations in Moss (MM2015)
B.2 Correlations Between Modelled Atmospheric Heavy Metal Deposition (EMEP) and Concentrations in Moss (MM2015)
B.3 Correlations Between Mean Concentration Values from Technical Measurements of Heavy Metals in the Atmosphere and Concentrations in Moss
B.4 Correlations Between Technical Measurements of Atmospheric Heavy Metal Deposition and Concentrations in Moss
B.5 Graphical Comparison of the Correlation Coefficients
Appendix C: Comparison of the Federal and State Air Quality Monitoring Network with the Moss Monitoring Network 2015
Appendix D: Map Annex
Reference

Citation preview

Stefan Nickel Winfried Schröder Ilia Ilyin Oleg Travnikov

Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss

Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss

Stefan Nickel · Winfried Schröder · Ilia Ilyin · Oleg Travnikov

Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss

Stefan Nickel PlanWerk Nidda Nidda, Germany Ilia Ilyin Meteorological Synthesizing Centre-East Moscow, Russia

Winfried Schröder Chair of Landscape Ecology University of Vechta Vechta, Germany Oleg Travnikov Meteorological Synthesizing Centre-East Moscow, Russia

ISBN 978-3-031-25635-6 ISBN 978-3-031-25636-3 (eBook) https://doi.org/10.1007/978-3-031-25636-3 This publication is based on text excerpts translated into English from the final report on the project “Updated Statistical Evaluation of Spatial Patterns of Lead, Cadmium, and Mercury Levels in Mosses in Conjunction with Atmospheric Exposure Data” (project number 146520, Schröder and Nickel 2021) on behalf of the German Environment Agency. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Background and Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4

2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Methods of Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Analysis of the Distances Between Measuring Points of the Technical and Biological Monitoring of Atmospheric Depositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 7 11 11 11 12

3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Measurement Data on Heavy Metal Concentrations in Moss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Modelled Mean Heavy Metal Concentrations in the Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Modelled Total Annual Atmospheric Depositions of Heavy Metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Technical Measurement Data on Mean Atmospheric Heavy Metal Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.5 Technical Measurement Data on Atmospheric Heavy Metal Depositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15

13

13 14

15 16 16 17 17 v

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Contents

3.2 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Correlations Between Modelled Mean Concentrations of Heavy Metals in the Atmosphere (EMEP) and Concentrations in Moss (MM2015) . . . . . . . . . . . . . . . . . . 3.2.2 Correlations Between Modelled Atmospheric Heavy Metal Deposition (EMEP) and Concentrations in Moss (MM2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Correlations Between Mean Concentration Values from Technical Measurements of Heavy Metals in the Atmosphere and Concentrations in Moss . . . . . . . . . . . . 3.2.4 Correlations Between Technical Measurements of Atmospheric Heavy Metal Deposition and Concentrations in Moss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Graphical Comparison of the Correlation Coefficients . . . . . . 3.3 Comparison of the Federal and State Air Quality Monitoring Network with the Moss Monitoring Network 2015 . . . . . . . . . . . . . . . 3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Summary with Regard to the Objectives and Key Questions of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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20 20 21

22 23 26

4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Appendices: Statistical Data Analyses—Tables, Diagrams, Maps . . . . . . . . 33

Abbreviations

As Cd CL CLC CLRTAP Cu EMEP

GIS Hg ICP

Level II

MM2015 MSC-E N Ni Pb V Zn

Arsenic Cadmium Critical Loads CORINE Land Cover Convention on Long-range Transboundary Air Pollution Copper Co-operative Programme for Monitoring and Evaluation of the Longrange Transmission of Air Pollutants in Europe (unofficially ‘European Monitoring and Evaluation Programme’ = EMEP) Geographic Information System Mercury International Cooperative Programmes reporting to the CLRTAP Working Group on Effects. Of the six existing ICPs, this report evaluates data collected or calculated in the ICP Vegetation (ICP on Effects of Air Pollution on Natural Vegetation and Crops) and the ICP Forests (ICP on Assessment and Monitoring of Air Pollution Effects on Forests). Reference is also made to the work of the ICP Modelling and Mapping (ICP on Modelling and Mapping of Critical Loads and Levels and Air Pollution Effects, Risks and Trends). The ICP Forests monitors forest conditions at two monitoring intensity levels: Level I and Level II. Level II intensive monitoring currently covers 68 sites in selected forest ecosystems in Germany with the aim of clarifying cause–effect relationships. Moss Monitoring 2015 Meteorological Synthesizing Centre East of EMEP Nitrogen Nickel Lead Vanadium Zinc vii

viii

Min P20 P50 P90 P98 Max MW SD VT

Abbreviations

Minimum 20. Percentile 50. Percentile 90. Percentile 98. Percentile Maximum Arithmetic Mean Standard Deviation Distribution; VT [1] = normal distribution; VT [2] = lognormal distribution; VT [3] = other distribution

List of Figures

Fig. A.1

Fig. A.2

Fig. B.1

Fig. B.2

Fig. B.3

Distributions of two modelling and technically measured atmospheric concentrations of Cd, Hg and Pb (left scales) and measured concentrations of Cd, Hg and Pb in moss in Germany (right scales). Graphical comparison of median values and distributions from Tables A.1, A.2, A.3 and A.6. Data basis: data sets 01, 03, 06, 10 in Table 2.1 . . . . . . . . . . . . . . . Distributions of two modelling and technically measured atmospheric depositions of Cd, Hg and Pb (left scales) and measured concentrations of Cd, Hg and Pb in moss in Germany (right scales). Graphical comparison of median values and distributions from Tables A.1, A.4, A.5 and A.7. Data basis: data sets 02, 04, 07, 10 in Table 2.1 . . . . . . . . . . . . . . . Correlations (Spearman) between modelled atmospheric concentrations and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.1. Data basis: data sets in 01, 03, 10 Table 2.1 . . . . Correlations (Pearson) between modelled atmospheric concentrations and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.1. Data basis: data sets in 01, 03, 10 Table 2.1 . . . . Correlations (Spearman) between modelled atmospheric concentrations and geostatistical surface estimations of MM2015—comparison of EMEP 50 km × 50 km and EMEP 0.1° × 0.1° spatial resolutions. Graphical comparison of correlation coefficients from Table B.2. Data basis: data sets in 01, 03, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . .

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Fig. B.4

Fig. B.5

Fig. B.6

Fig. B.7

Fig. B.8

Fig. B.9

Fig. B.10

List of Figures

Correlations (Pearson) between modelled atmospheric concentrations and geostatistical surface estimations of MM2015—comparison of EMEP 50 km × 50 km and EMEP 0.1° × 0.1° spatial resolutions. Graphical comparison of correlation coefficients from Table B.2. Data basis: data sets in 01, 03, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Spearman) between modelled atmospheric depositions and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.3. Data basis: data sets in 02, 04, 10 Table 2.1 . . . . Correlations (Pearson) between modelled atmospheric depositions and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.3. Data basis: data sets in 02, 04, 10 Table 2.1 . . . . Correlations (Spearman) between modelled atmospheric deposition and geostatistical surface estimations of MM2015—comparison of spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.4. Data basis: data sets in 02, 04, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlations (Pearson) between modelled atmospheric deposition and geostatistical surface estimations of MM2015—comparison of spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.4. Data basis: data sets in 02, 04, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlations (Spearman) between modelled atmospheric concentrations (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.1 and B.2. Data basis: data sets in 03, 10, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Pearson) between modelled atmospheric concentrations (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.1 and B.2. Data basis: data sets in 03, 10, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. B.11

Fig. B.12

Fig. B.13

Fig. B.14

Fig. B.15

Fig. B.16

Fig. B.17

Correlations (Spearman) between modelled atmospheric deposition (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.3 and B.4. Data basis: data sets in 04, 10, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Pearson) between modelled atmospheric deposition (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.3 and B.4. Data basis: data sets in 04, 10, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.1 and B.3. Data basis: data sets in 03, 04, 10 Table 2.1 . . . . . . . . . . Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.1 and B.3. Data basis: data sets in 03, 04, 10 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Spearman) between EMEP 0.1° × 0.1° and geostatistical surface estimations of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.2 and B.4. Data basis: data sets in 03, 04, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Pearson) between EMEP 0.1° × 0.1° and geostatistical surface estimations of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.2 and B.4. Data basis: data sets in 03, 04, 11 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015 (all sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients from Tables B.3, B.5 and B.6. Data basis: data sets in 04, 05, 10 Table 2.1 . . . . . . . . . .

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Fig. B.18

Fig. B.19

Fig. B.20

Fig. C.1

Fig. D.1

Fig. D.2

Fig. D.3

Fig. D.4

List of Figures

Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015 (all sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients from Tables B.3, B.5 and B.6. Data basis: data sets in 04, 05, 10 Table 2.1 . . . . . . . . . . Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015 (forest sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients calculated and from Tables B.2 and B.4. Data basis: data sets in 04, 05, 10 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015 (forest sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients calculated and from Tables B.2 and B.4. Data basis: data sets in 04, 05, 10 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of the smallest distances between the stations of the federal and state air quality monitoring network and the sites of MM2015. Data basis: data sets in 06, 07, 08, 09, 10 Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Cd median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets in 03, 04, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Hg median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets in 03, 04, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Pb median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 03, 04, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Cd median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. D.5

Fig. D.6

Fig. D.7

Fig. D.8

Fig. D.9

Fig. D.10

Fig. D.11

Spatial distributions of deviations from the nationwide Hg median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Pb median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Cd median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Hg median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Pb median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Cd median of technically measured bulk atmospheric deposition (Ilyin et al. 2020: Annex B) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 08, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of deviations from the nationwide Pb median of technically measured bulk atmospheric deposition (Ilyin et al. 2020: Annex B) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 08, 11 in Table 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Tables

Table 2.1 Table A.1

Table A.2

Table A.3

Table A.4

Table A.5

Table A.6

Table A.7

Table A.8

Table B.1

Overview of the data basis and methods used . . . . . . . . . . . . . . . Descriptive-statistical parameters of the concentration of Pb, Cd and Hg in moss of the MM (measured 2015 data and 2016 geostatistical surface estimations) . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere (EMEP 50 km × 50 km, 2013–2015) . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere (EMEP 0.1° × 0.1°, 2014–2016) . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg (EMEP 50 km × 50 km, 2013–2015) . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg (EMEP 0.1° × 0.1°, 2014–2016) . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of the mean values of concentrations from technical measurements of Pb, Cd and Hg in the atmosphere (2014–2016, data used for model validation in Ilyin et al. 2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristic values of the technical measurement data for the wet deposition of Cd in Hg and Pb (2014–2016, data used for model validation in Ilyin et al. 2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive-statistical characteristics of the technical measurement data on bulk deposition of Cd and Pb (2014–2016, data taken from Ilyin et al. 2020: Tables B1–B6) . . . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and measured concentrations in moss (MM2015) . . . .

8

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Table B.2

Table B.3

Table B.4

Table B.5

Table B.6

Table B.7

Table B.8

Table B.9

Table B.10

Table B.11

List of Tables

Correlation matrix (Spearman, Pearson) for modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and measured concentrations in moss (MM2015) . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (CLC-derived land use) . . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (land use surveyed in MM2015) . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use derived from CLC) . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use surveyed in MM2015) . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for mean values of concentrations from technical measurements of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for technical measurements of wet atmospheric deposition of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix (Spearman, Pearson) for technical measurements of bulk atmospheric deposition of Pb, Cd and Hg (data taken from Ilyin et al. 2020: Tables B1–B6) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) . . . . . . . . . . . . . . . . . . . . . . . .

49

51

52

53

54

55

56

57

58

59

Chapter 1

Background and Aim

1.1 Background Heavy metals can have harmful effects on human health and ecosystems (Schröder et al. 2018). Therefore, they are the subject of national and international regulations such as the Convention on Long-range Transboundary Air Pollution, Geneva Clean Air Convention (CLRTAP). For this purpose, the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (unofficially ‘European Monitoring and Evaluation Programme’ = EMEP) collects data on atmospheric deposition of e.g. Pb, Cd, Hg by technical measurements (Amodio et al. 2014; Hansen et al. 2013) or by modelling with chemical transport models (Jacob 1999; Jacobson 2005). The ICP Vegetation collects concentrations of, among others, heavy metals in moss at five-year intervals as a bioindication of atmospheric pollution (Frahm 1998; Frontasyeva et al. 2020; Rühling 1994; Schröder et al. 2017, 2019b; Steinnes 1997). These three approaches are complementary to each other and each has specific advantages and disadvantages. Existing uncertainties in emission data (emission level, completeness) are currently the main reason why atmospheric heavy metal deposition cannot be modelled with sufficient accuracy (Engardt et al. 2017; Schröder et al. 2014). Similarly, low grid resolutions (e.g. 50 km by 50 km; Tørseth et al. 2012) have so far meant that estimates of the risk of structural and functional changes in the receptors exposed to the deposition (plants, animals, soils, waters, ecosystems) or exceedances of critical loads (CL; ICP Modelling and Mapping 2004–2013; Nagel and Gregor 1999) have been fraught with uncertainty. Complementary to this, biomonitoring with poikilohydric (alternating moisture) mosses, which adapt their water content to the moisture state of their environment, provides an indirect measure derived from the accumulation of substances to characterise background pollution from atmospheric heavy metal and nitrogen deposition in spatially dense monitoring networks (Bealey et al. 2008; Hoodaji et al. 2012; Markert et al. 2003; Mohr 2007). In order to achieve the objectives of the Geneva Convention on Long-Range Transboundary Air Pollution (CLRTAP), the advantages of bioindication with mosses are used in the European Moss Monitoring © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Nickel et al., Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss, https://doi.org/10.1007/978-3-031-25636-3_1

1

2

1 Background and Aim

(European Moss Survey of the ICP Vegetation) to provide empirical evidence for the transboundary long-range transport of air pollutants in Europe, to determine spatial patterns and temporal trends of atmospheric substance deposition across Europe, to identify the location and extent of particularly polluted areas, and to identify existing uncertainties in deposition modelling and their causes (e.g. unaccounted emission sources) and, if necessary, localise them (ICP Vegetation 2020; Nickel 2019; Nickel and Schröder 2018; Nickel et al. 2018; Schröder and Nickel 2018, 2019). The complementary use of data from the European Moss Monitoring has already been carried out in Germany for comparisons of atmospheric deposition maps calculated with different chemical transport models (Schröder et al. 2017, 2019b), e.g. for cadmium (Cd), mercury (Hg) and lead (Pb) depositions (model: EMEP/MSC-E, 50 km × 50 km, 2005, 2015, Ilyin et al. 2016), Cd and Pb depositions (model: LOTOS-EUROS (LE), 7 km × 7 km, 2005, Schaap et al. 2018a), arsenic (As), copper (Cu), nickel (Ni), vanadium (V), zinc (Zn) depositions (LE, 25 km × 25 km, 2005, Schaap et al. 2018b). In the meantime, significantly higher resolution deposition data for the three priority heavy metals Pb, Cd and Hg of the CLRTAP are available from EMEP. The current resolution of the EMEP deposition grid is 0.1° × 0.1°, whereby both averaged non-use-specific and use-specific substance deposition were calculated. In the German moss monitoring 2015/2016, among other things, metal concentrations in moss were collected at 400 sites of a systematic monitoring network using harmonised methods (Schröder et al. 2019b). In comparative statistical evaluations of modelled deposition rates with concentrations in moss in this study, which was based on an expert’s report commissioned by the German Environment Agency (UBA) (Schröder and Nickel 2021), modelled deposition data (EMEP, CLRTAP) were used that no longer correspond to the state of knowledge. Therefore, selected aspects of the statistical evaluations carried out in Schröder et al. (2019b) will be repeated in this study with the current data on atmospheric deposition (Ilyin et al. 2020) and supplemented by additional evaluations. Since 2018, updated regionalised data on heavy metal emissions in Germany have been available, which were submitted to CLRTAP’s EMEP programme. The Meteorological Synthesizing Centre East (MSC-E) of the EMEP programme used these data in a joint national case study with UBA to develop nationwide maps of the concentrations of the three metals in the atmosphere as well as maps of total deposition for the years 2014, 2015, 2016. The latter are available as area-weighted averages per EMEP grid as well as use-specific (Ilyin et al. 2020). These results are to be included in the statistical evaluations of this study. The current measurement data on the concentration (in the atmosphere) and deposition of Pb, Cd and Hg at measuring stations of UBA and the federal states, which are available in a station database of UBA, were provided at the start of the study (Table 2.1).

1.2 Aim

3

1.2 Aim With these new, spatially clearly differentiated data on atmospheric exposure to Pb, Cd, Hg (Ilyin et al. 2020), the descriptive and correlation analyses of Schröder et al. (2019b) will be updated and supplemented on behalf of UBA. In detail, it will be analysed, • whether the spatial correlation has improved due to the more valid data basis for modelled atmospheric heavy metal pollution (EMEP, concentration/deposition) compared to the analysis in Schröder et al. (2019a) and • which key measures of atmospheric pollution (concentration/deposition) and which annual totals show the highest correlation with the concentrations in moss. Furthermore, results of technical measurements on metal concentrations in the atmosphere and on wet and bulk heavy metal deposition of UBA and the federal states are to be spatially linked with the maps of the geostatistical surface estimation of heavy metal concentrations from the 2015 moss survey and it is to be examined whether the database of measured concentration and deposition values is sufficient for correlation analyses with the concentrations in moss (2015). A suitable methodological concept is to be developed to evaluate these data together with moss data with regard to spatial correlation. In addition, for the land use type forest it is to be examined whether a higher correlation between modelled deposition and concentrations in moss results if only measured and modelled values reported for this land use type are used. The core questions of this study are: • Achievement of higher spatial correlation between the concentration/deposition of the metals and the concentrations in moss if the current deposition data (joint UBA case study with EMEP MSC-E, mean total deposition of the years 2014– 2016, 0.1° × 0.1° resolution) are used instead of the EMEP data used in previous studies (mean total deposition of Pb, Cd, Hg of the years 2013–2015 in a 50 km × 50 km grid)? • Does the consideration of land-use specific deposition maps of EMEP help to explain differences in spatial patterns of modelled deposition and concentrations in moss? • How can the results of technical measurements by UBA and the federal states be incorporated into joint evaluations of moss data and modelled deposition data? Which methods are recommended for spatial correlation analyses in order to achieve the best possible assessment of the actual pressures on terrestrial ecosystems? The results of this study are to contribute to a more differentiated assessment of the burden on ecosystems from atmospheric heavy metal deposition than has been the case to date as a prerequisite for the targeted further development of risk assessments for Germany. The results will also be made available to the ICP Vegetation.

4

1 Background and Aim

References Amodio M, Catino S, Dambruoso PR, de Gennaro G, Di Gilio A, Giungato P, Laiola E, Marzocca A, Mazzone A, Sardaro A, Tutino M (2014) Atmospheric deposition: sampling procedures, analytical methods, and main recent findings from the scientific literature. In: Advances in meteorology, vol 2014, Article ID 161730, 27 pp Bealey WJ, Long S, Spurgeon DJ, Leith I, Cape JN (2008) Review and implementation study of biomonitoring for assessment of air quality outcomes. In: Science report—SC030175/SR2. Environment Agency, Bristol, pp 1–170 Engardt M, Simpson D, Schwikowski M, Granat L (2017) Deposition of sulphur and nitrogen in Europe 1900–2050. Model calculations and comparison to historical observations model. Tellus B Chem Phys Meteorol 69:1–20 Frahm JP (1998) Moose als Bioindikatoren. Quelle & Meyer GmbH & Co., Wiesbaden, 187 p Frontasyeva M, Harmens H, Uzhinskiy A, Nickel S, Schröder W and the Participants of the Moss Survey (2020) Mosses as biomonitors of air pollution: 2015/2016 survey on heavy metals, nitrogen and POPs in Europe and beyond. Bangor, Dubna, pp 1–136. ISBN 978-5-9530-0508-1 Hansen K, Thimonier A, Clarke N, Staelens J, Zlinda D, Waldner P, Marchetto A (2013) Atmospheric deposition to forests. In: Ferretti M, Fischer R (eds) Forest monitoring. Methods for terrestrial investigations in Europe with an overview on North America and Asia. Develop Environ Sci 12:337–374 Hoodaji M, Ataabadi M, Najafi P (2012) Biomonitoring of airborne heavy metal contamination. In: Mukesh K (ed) Air pollution—monitoring, modelling, health, control. IntechOpen. https://www.intechopen.com/books/air-pollution-monitoring-modellinghealth-and-con trol/biomonitoring-of-airborne-heavy-metal-contamination. Downloaded on: 13 June 2018 ICP Modeling and Mapping (ed) (2004–2013) Manual on methodologies and criteria for modeling and mapping critical loads & levels. UBA-Texte 52/2004. Updated version 04/2013. www.icp mapping.org. Downloaded on: 20 July 2015 ICP Vegetation (International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops) (2020) Monitoring of atmospheric deposition of heavy metals, nitrogen and POPs in Europe using bryophytes. In: Monitoring manual 2020 survey. United Nations Economic Commission for Europe Convention on long-range transboundary air pollution. Bangor, Dubna, pp 1–27 Ilyin I, Rozovskaya O, Travnikov O, Varygina M, Aas W, Pfaffhuber KA (2016) Assessment of heavy metal transboundary pollution, progress in model development and mercury research. EMEP status report 2/2016. Meteorological Synthesizing Centre-East, Moscow, Russian Federation. http://www.msceast.org/reports/2_2016.pdf. Downloaded on: 28 Aug 2018 Ilyin I, Travnikov O, Schütze, G, Feigenspan S, Uhse K (2020) Country-scale assessment of heavy metal pollution: a case study for Germany. Technical report 1/2020. Meteorological Synthesizing Centre-East, Moscow, Russia; German Environment Agency, Dessau, Germany, pp 1–121 Jacob D (1999) Introduction to atmospheric chemistry, 1st edn. Princeton University Press, pp 75–85 Jacobson MZ (2005) Fundamentals of atmospheric modelling, 2nd edn. Cambridge University Press, Cambridge, UK, p 656 Markert BA, Breure AM, Zechmeister HG (eds) (2003) Bioindicators and biomarkers: principles, concepts, and applications. Amsterdam, Elsevier, p 1014 Mohr K (2007) Biomonitoring von Stickstoffimmissionen. Möglichkeiten und Grenzen von Bioindikationsverfahren. Umweltwiss Schadst Forsch 19:255–264 Nagel HD, Gregor HD (eds) (1999) Ökologische Belastungsgrenzen—critical loads & levels. Ein internationales Konzept für die Luftreinhaltepolitik. Springer, Berlin Nickel S (2019) Methodologie integrativer analyse, Modellierung und management umweltwissenschaftlicher Daten für landschaftsökologische Forschung und Lehre am Beispiel

References

5

der Exposition von Wäldern gegenüber atmosphärischen Stoffeinträgen und daraus resultierender Veränderungen der Ökosystemintegrität in Kombination mit dem Klimawandel. Habilitationsschrift, Universität Vechta Nickel S, Schröder W (2018) Kleinräumige Untersuchungen zum Einfluss des Kronentraufeffekts auf Element-konzentrationen in Moosen. In: Schröder W, Fränzle O, Müller F (Hg): Handbuch der Umweltwissenschaften. Grundlagen und Anwendungen der Ökosystemforschung. Kap. V-1.10. 25. Erg.Lfg., Wiley-VCH, Weinheim, pp 1–35 Nickel S, Schröder W, Schmalfuss R, Saathoff M, Harmens H, Mills G, Frontasyeva MV, Barandovski L, Blum O, Carballeira A, de Temmerman L, Dunaev AM, Ene A, Fagerli H, Godzik B, Ilyin I, Jonkers S, Jeran Z, Lazo P, Leblond S, Liiv S, Mankovska B, Nunez Olivera E, Piispanen J, Poikolainen J, Popescu IV, Qarri F, Santamaria JM, Schaap M, Skudnik M, Špiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Uggerud HT, Zechmeister HG (2018) Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe. Environ Sci Eur 30(53):1–17 ´ (ed) (1994) Atmospheric heavy metal deposition in Europe—estimation based on moss Rühling Å analysis. Nordic Council of Ministers. NORD 9:1–53 Schaap M, Hendriks C, Jonkers S, Builtjes P (2018a) Impacts of heavy metal emission on air quality and ecosystems across Germany—sources, transport, deposition and potential hazards, part 1: assessment of the atmospheric heavy metal deposition to terrestrial ecosystems in Germany, Texte 106/2018, Umweltbundesamt, Dessau Roßlau Schaap M, Hendriks C, Kranenburg R, Kuenen J, Segers A, Schlutow A, Nagel HD, Ritter A, Banzhaf S (2018b) PINETI-III: Modellierung und Kartierung atmosphärischer Stoffeinträge von 2000 bis 2015 zur Bewertung der ökosystemspezifischen Gefährdung von Biodiversität in Deutschland. Abschlussbericht FKZ 3714 64 2010149. Umweltbundesamt, Dessau-Roßlau, 149 p Schröder W, Nickel S (2018) Mapping percentile statistics of element concentrations in moss specimens collected from 1990 to 2015 in forests throughout Germany. Atmos Environ 190:161– 168 Schröder W, Nickel S (2019) Spatial structures of heavy metals and nitrogen accumulation in moss specimens sampled between 1990 and 2015 throughout Germany. Environ Sci Eur 31(33):1–15 Schröder W, Nickel S (2021) Aktualisierte statistische Evaluierung räumlicher Muster der Gehalte an Blei, Cadmium und Quecksilber in Moosen in Verbindung zur atmosphärischen Belastung. UBA-Texte 106/2021, pp 1–116 Schröder W, Pesch R, Schönrock S, Harmens H, Mills G, Fagerli H (2014) Mapping correlations between nitrogen concentrations in atmospheric deposition and mosses for natural landscapes in Europe. Ecol Ind 36:563–571 Schröder W, Nickel S, Schönrock S, Schmalfuß R, Wosniok W, Meyer M, Harmens H, Frontasyeva MV, Alber R, Aleksiayenak J, Barandovski L, Blum O, Carballeira A, Dam M, Danielsson H, de Temmermann L, Dunaev AM, Godzik B, Hoydal K, Jeran Z, Pihl Karlsson G, Lazo P, Leblond S, Lindroos J, Liiv S, Magnússon SH, Mankovska B, Núñez-Olivera E, Piispanen J, Poikolainen J, Popescu IV, Qarri F, Santamaria JM, Skudnik M, Špiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thöni L, Uggerud HT, Zechmeister HG (2017) Bioindication and modelling of atmospheric deposition in forests enable exposure and effect monitoring at high spatial density across scales. Ann For Sci 74(31):1–23 Schröder W, Nickel S, Schlutow A, Nagel H-D, Scheuschner T (2018) Auswirkungen der Schwermetall-Emissionen auf Luftqualität und Ökosysteme in Deutschland-Quellen, Transport, Eintrag, Gefährdungspotenzial. Teil 2: Integrative Datenanalyse, Erheblichkeitsbeurteilung und Untersuchung der gegenwärtigen Regelungen und Zielsetzungen in der Luftreinhaltung und Vergleich mit ausgewählten Anforderungen, die sich in Bezug auf den atmosphärischen Schadstoffeintrag aus den verschiedenen Rechtsbereichen ergeben. Abschlussbericht, Forschungskennzahl 3713 63 253, UBA-FB 002635. Im Auftrag des Umweltbundesamtes. UBA-Texte 107/2018, pp 1–257

6

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Schröder W, Nickel S, Jenssen M, Hofmann G, Schlutow A, Nagel H-D, Burkhard B, Dworczyk C, Elsasser P, Lorenz M, Meyerhoff J, Weller P, Altenbrunn K (2019a) Anwendung des Bewertungskonzepts für die Ökosystemintegrität unter Berücksichtigung des Klimawandels in Kombination mit Stoffeinträgen. UBA-Texte 97/2019, pp 1–504 Schröder W, Nickel S, Völksen B, Dreyer A, Wosniok W (2019b) Nutzung von Bioindikationsmethoden zur Bestimmung und Regionalisierung von Schadstoffeinträgen für eine Abschätzung des atmosphärischen Beitrags zu aktuellen Belastungen von Ökosystemen. UBA-Texte 91/2019 B1:1–189, Bd. 2, pp 1–296 Steinnes E (1977) Atmospheric deposition of trace elements in norway studied by means of moss analysis. Kjeller report, KR 154. Institute for Atomenegri, Kjeller, Norway Tørseth K, Aas W, Breivik K, Fjaeraa AM, Fiebig M, Hjellbrekke AG, Myhre CL, Solberg S, Yttri KE (2012) Introduction to the European monitoring and evaluation programme (EMEP) and observed atmospheric composition change during 1972–2009. Atmos Chem Phys 12:5447–5481

Chapter 2

Material and Methods

2.1 Data The data used in Ilyin et al. (2020) include measurements of air concentrations (Cd, Hg, Pb; 2014, 2015, 2016) and wet depositions (Cd, Pb; 2014, 2015, 2016) from the 6 EMEP stations of the UBA monitoring network and up to 196 other stations in Germany whose measurements were submitted to UBA by the federal states. These include, in particular, stations from hydrological monitoring networks and study sites of intensive forest environmental monitoring (ICP Forests Level II). These data were provided by MSC-East on 24.09.2020 for these investigations and correspond to the technical measurement data used in Ilyin et al. (2020). A consolidated data documentation [revised Appendices A.1 and A.2 of the Ilyin et al. (2020) report] was provided by MSC-East on 05.10.2020. Appendices B.1–B.5 of the report by Ilyin et al. (2020) also list numerous other stations with bulk deposition values that were not used by Ilyin et al. (2020) for model validation and are therefore not included in the MSC-East deposition data, but were evaluated as a separate data set in this study. Table 2.1 gives an overview of the data used in the present study. These include quantitative results of the atmospheric exposure methods mentioned in Chap. 1: technical measurements, biomonitoring—here with mosses—and by modelling with the Global EMEP Multimedia Modelling System, GLEMOS, version v2.0). The modelled exposure patterns break down into mean concentrations and total annual depositions of Cd, Hg and Pb at a resolution of 50 km × 50 km for the years 2013– 2015 and at a resolution of 0.1° × 0.1° for the years 2014–2016. The annual total depositions are available as deposition rates entry rates weighted according to the land use distribution and, at a resolution of 0.1° × 0.1°, additionally as land usespecific rates, i.e. calculated for each land use class of the CORINE Land Cover dataset (CLC).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Nickel et al., Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss, https://doi.org/10.1007/978-3-031-25636-3_2

7

Unit

X

µg m−2 a−1

06 Average concentration of Cd, Hg, Pbb

ng m−3 X

X

µg m−2 a−1

X

X

X

µg m−2 a−1

X

2014

ng m−3

X

ng m−3

2013

Technical measurement data on atmospheric concentration and deposition

05 Annual total deposition of Cd, Hg, Pb (EMEP 0.1° × 0.1°, land use specific)b

04 Annual total deposition of Cd, Hg, Pb (EMEP 0.1° × 0.1°)b

03 Average concentration of Cd, Hg, Pb (EMEP 0.1° × 0.1°)b

02 Annual total deposition of Cd, Hg, Pb (EMEP 50 km × 50 km)a

01 Average concentration of Cd, Hg, Pb (EMEP 50 km × 50 km)a

Modeled data on atmospheric concentration and deposition

Data sets

Table 2.1 Overview of the data basis and methods used

X

X

X

X

X

X

2015

X

X

X

X

2016

(continued)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.3 and B.5) Analysis of the distances between stations of the technical and biological monitoring of atmospheric concentration (Appendix C) Cartographic comparison with bioindication data (Appendix D)

Correlation analysis with bioindication data, group by land use type (Appendices B.2 and B.5)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.2 and B.5)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.1 and B.5)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.2 and B.5)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.1 and B.5)

Aim/method (the relevant sections of the text/appendix are given in brackets)

8 2 Material and Methods

µg m−2 a−1

Unit

09 Bulk deposition of Cd, Pb µg m−2 a−1 (three additional ICP Forests Level II sites and 1 Hessian site, Northwest German Forest Research Institute)

08 Bulk deposition of Cd, Hg, Pbc

07 Wet deposition of Cd, Hg, Pbb

Data sets

Table 2.1 (continued) 2013

X

X

X

2014

X

X

X

2015

X

X

X

2016

(continued)

Analysis of the distances between stations of the technical and biological monitoring of atmospheric concentration (Appendix C)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.4 and B.5) Analysis of the distances between stations of the technical and biological monitoring of atmospheric concentration (Appendix C) Cartographic comparison with bioindication data (Appendix D)

Descriptive statistics (Appendix A) Correlation analysis with bioindication data (Appendices B.4 and B.5) Analysis of the distances between stations of the technical and biological monitoring of atmospheric concentration (Appendix C) Cartographic comparison with bioindication data (Appendix D)

Aim/method (the relevant sections of the text/appendix are given in brackets)

2.1 Data 9

Descriptive statistics (Appendix A) Correlation analysis with modelled data on atmospheric concentration (Appendices B.1 and B.5), deposition (Appendices B.2 and B.5), and technical measurement data on atmospheric concentration (Appendices B.3 and B.5) and deposition (Appendices B.4 and B.5) Cartographic comparison with technical measurement data (Appendix D)

Descriptive statistics (Appendix A) Correlation analysis with modelled data on atmospheric concentration (Appendices B.1 and B.5), deposition (Appendices B.2 and B.5), and technical measurement data on atmospheric concentration (Appendices B.3 and B.5) and deposition (Appendices B.4 and B.5) Analysis of the distances between stations of the technical and biological monitoring of atmospheric concentration (Appendix C)

Aim/method (the relevant sections of the text/appendix are given in brackets)

a http://en.msceast.org/index.php/pollution-assessment/emep-domain-menu/data-hm-pop-menu (27.04.2018) b Data delivery MSC-East from (of 24.09.2020 which data sets = 06, 07 data from UBA and the federal state monitoring networks used in Ilyin et al. (2020) for model validation) c Values taken from Ilyin et al. (2020: Annex B), = data from monitoring networks of the federal states d http://www.mapserver.uni-vechta.de/mossEU/login.php (29.09.2020) x Data set available for the year indicated in the table header

X

2016

µg/g

2015

11 Concentration of Cd, Hg, Pb in Moosen (Moss Survey 2015, geostatistical surface estimation, 3 km × 3 km)

2014

X

2013

µg/g

Unit

10 Concentration of Cd, Hg, Pb in Moosen (Moss Survey 2015, survey year 2016)d

Biomonitoring data

Data sets

Table 2.1 (continued)

10 2 Material and Methods

2.3 Methods

11

2.2 Data Preparation The data compiled in Table 2.1 were prepared for the descriptive and correlation statistical analyses in a geographical information system (GIS) in the following four steps: 1. The technical measurement data on atmospheric concentration and wet deposition (datasets 06 and 07 in Table 2.1, data used in Ilyin et al. 2020 for model validation) and on bulk deposition (dataset 08 in documented in Table 2.1, Ilyin et al. 2020, Annex B, Tables B1–B6) were linked to the coordinates of the UBA1 station database. 2. The arithmetic mean values of modelled atmospheric concentrations (dataset 01 in Table 2.1) and annual total deposition were calculated for the annual spans: 2013–2014, 2014–2015, 2013–2015 (EMEP 50 km × 50 km) (dataset 01 in Table 2.1) and 2014–2015, 2015–2016, 2014–2016 (EMEP 0.1° × 0.1°) (data sets 04, 05 in Table 2.1). 3. Spatial linkage of the moss monitoring sites (dataset 10 in Table 2.1) or geostatistical area estimate (dataset 11 in Table 2.1) with the modelled concentration (datasets 01, 03 in Table 2.1) and deposition (datasets 02, 04, 05 in Table 2.1) and calculation of the median value per EMEP grid. The modelled atmospheric concentration, the atmospheric deposition weighted according to the land use distribution in each EMEP grid, and the land use-specific modelled concentration/deposition assigned based on the land use information available at each monitoring site [derived from (1) CLC and (2) mapped vegetation] were used. 4. The locations of the federal and state air monitoring networks with the coordinates listed under (1) from the UBA station database (datasets 06, 07, 08 in Table 2.1) were spatially linked with the geostatistical surface estimations of the concentrations in the moss (datasets 11 in Table 2.1).

2.3 Methods 2.3.1 Methods of Descriptive Statistics The data analysed with the statistical package R (R Core Team 2018) were tested for normal distribution according to Shapiro and Wilk (1965). In comparison to other tests for normal distribution (e.g., Kolmogorov–Smirnov test, chi-square test), the Shapiro–Wilk test is characterised by its high test strength in numerous test situations, especially when testing smaller samples with n < 50. In this study, the test for normal distribution according to Shapiro and Wilk (1965) will be regarded as a pre-test for further statistical analyses that are not provided for in the expert opinion mandate but are necessary and are recommended in Chap. 4. 1

UBA station database: https://www.env-it.de/stationen/public/open.do.

12

2 Material and Methods

The following distributions are distinguished in this study: VT [1] normal distribution, VT [2] lognormal distribution and VT [3] other distribution. In addition, the following position measures were determined for the data distributions: Minimum (Min), 20th percentile (P20), 50th percentile (P50), 90th percentile (P90), 98th percentile (P98), maximum (Max), arithmetic mean (MW), standard deviation (SD). The results are tabulated in Appendix A and summarised in Chap. 3.

2.3.2 Correlation Analysis The strength and direction of the correlation between two characteristics is determined by correlation analysis. In this study, the correlations were calculated according to Pearson (Eq. 2.2) and Spearman (Eq. 2.1). While Pearson’s correlation coefficient examines the linear relationship between two continuous variables and processes the metric distances, Spearman’s coefficient is based on the ranking of the measured values, independent of the distances between the values (Sachs and Hederich 2009). Uniform guidelines for evaluating correlation coefficients do not exist in the literature. The correlations calculated in this study according to Eqs. (2.1) and (2.2) are classified e.g. by Brosius (2013) as: very weak (< 0.2), weak (0.2 to < 0.4), medium (0.4 to < 0.6), strong (0.6 to < 0.8), very strong (≥ 0.8). Cohen (1988) classifies more coarsely: According to this, correlations between r = 0.1 and r = 0.3 are to be classified as low/weak, between r = 0.3 and r = 0.5 as medium/moderate and from r = 0.5 as high/strong. Nachtigall and Wirtz (2004) consider correlations up to 0.5 as low, 0.7 as moderate and 0.9 as high. In the empirical social sciences, whose methodology is similar to that of ecology (Jensen et al. 2019; Schröder and Nickel 2020), the strength of statistical relationships is classified according to Cohen (1988). The fact that only three categories are used here is conducive to a concise textual presentation. Correlation coefficient (Spearman) rs = 1 −

6

Σn

(

Rxi − R yi ( ) n n2 − 1

)2

i=1

Σ (xi − x)(yi − y) Correlation coefficient (Pearson) rp = /Σ Σ (xi − x)2 (yi − y)2

(2.1) (2.2)

In evaluations with consideration of land use specific deposition, a distinction must be made between two different allocations of land use to moss monitoring measurement points: On the one hand, the land use at these sites was assigned according to Corine Land Cover (marked with “CLC” in the statistical evaluations), on the other hand, the land use is taken into account that is determined directly in the field during moss sampling and recorded in the sampling protocol (marked with “Veg”).

2.3 Methods

13

2.3.3 Analysis of the Distances Between Measuring Points of the Technical and Biological Monitoring of Atmospheric Depositions The empirically collected data listed in the Table 2.1—these are the technical measurement data on atmospheric concentration and deposition (data sets 06: Mean concentration of Cd, Hg, Pb, 07: Annual wet deposition of Cd, Hg, Pb, 08 and 09: Bulk deposition of Cd, Pb; Table 2.1) and the bioindication data (data sets 10, 11: concentrations of Cd, Hg, Pb in moss; Table 2.1)—originate from measurement networks that are not spatially congruent. For the assessment of the results of the correlation analyses of data from spatially non-congruent monitoring networks, the spatial distance between their locations is significant. Therefore, the shortest distances between the stations of the federal and state monitoring networks with data on atmospheric concentration and deposition and the sites of the 2015 moss monitoring were calculated. The results of these calculations are compiled in Appendix C and summarised in Chap. 3.

2.3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median The comparison of the mapped spatial differentiation of the data of this study (Table 2.1) included the following four variants: 1. Spatial distributions of deviations from the nationwide Cd, Hg and Pb medians modelled atmospheric concentration and total deposition (EMEP 0.1° × 0.1°) (datasets 03, 04, 05 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) (Figs. D.1, D.2 and D.3), 2. Spatial distributions of deviations from the nationwide Cd, Hg and Pb medians technically measured atmospheric concentrations (Ilyin et al. 2020) (dataset 06 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) (Figs. D.4, D.5 and D.6), 3. Spatial distributions of deviations from the nationwide Cd, Hg and Pb medians of technically measured wet deposition (Ilyin et al. 2020) (dataset 07 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) (Figs. D.7, D.8 and D.9) and 4. Spatial distributions of deviations from the nationwide Cd and Pb medians of technically measured bulk deposition (Ilyin et al. 2020: Annex B) (dataset 08 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) (Figs. D.10 and D.11).

14

2 Material and Methods

The deviations were calculated on the basis of the relative deviations from the median value according to Eqs. (2.4) and (2.5), for the technical measurement data, the calculation of the deviations from the median value of the modelled concentration and deposition. Concdev = Depodev =

) ) (( Concx,y − Concmedian /Concmedian ∗ 100

(2.4)

) ) Depox,y − Depomedian /Depomedian ∗ 100

(2.5)

((

For the map comparisons, the mean values of the atmospheric concentration of the year interval 2015–2016 (data set 03 in Table 2.1) and the mean values of the deposition of the annual span 2014–2016 (data set 04 in Table 2.1) were used for all three heavy metals, as the correlations are somewhat stronger than for the corresponding individual annual values. The correlations were calculated according to Spearman, as log-normal distributions are present in the fewest cases (Appendix A). The results are illustrated in Figs. D.1, D.2, D.3, D.4, D.5, D.6, D.7, D.8, D.9, D.10 and D.11 and summarised in Chap. 3.

References Brosius F (2013) SPSS 21. Mitp/bhv, Heidelberg Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. L. Erlbaum Associates, Hillsdale, NJ Ilyin I, Travnikov O, Schütze, G, Feigenspan S, Uhse K (2020) Country-scale assessment of heavy metal pollution: a case study for Germany. Technical report 1/2020. Meteorological Synthesizing Centre-East, Moscow, Russia; German Environment Agency, Dessau, Germany, pp 1–12 Jensen U, Netscher S, Weller K (Hrsg) (2019) Forschungsdatenmanagement sozialwissenschaftlicher. Umfragedaten. Grundlagen und praktische Lösungen für den Umgang mit quantitativen Forschungsdaten. Verlag Barbara Burdich, Opladen, Berlin, Toronto. 233 p Nachtigall C, Wirtz MA (2004) Wahrscheinlichkeitsrechnung und Inferenzstatistik, 3rd edn. Juventa Verlag, Weinheim R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/, 14 Aug 2020 Sachs L, Hedderich J (2009) Angewandte Statistik. Methodensammlung mit R. Springer, Berlin Schröder W, Nickel S (2020) Research data management as an integral part of the research process of empirical disciplines using landscape ecology as an example. Data Sci J 19(26):1–14. https:// doi.org/10.5334/dsj-2020-026 Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3/4):591–611

Chapter 3

Results

The results, which are documented in detail in the appendices, are summarised in the following five blocks according to the methods used for their statistical analysis: Sects. 3.1, 3.2, 3.3, 3.4 and 3.5.

3.1 Descriptive Statistics All results of the descriptive-statistical evaluation of the data under investigation (Table 2.1) are documented in Appendix A.

3.1.1 Measurement Data on Heavy Metal Concentrations in Moss Table A.1 contains the descriptive-statistical characteristic values of the concentration of Pb, Cd and Hg in moss of the MM2015 (measured data 2016 from dataset 10 in Table 2.1 and geostatistical surface estimations from dataset 11 in Table 2.1). This shows that the medians of the empirically collected moss data largely agree with those of the geostatistical surface estimations calculated from these measurement data when there are differences between the metals. In contrast, the calculated percentiles (P20, P90, P98), the minima and maxima as well as the standard deviation of the measured values and the geostatistically calculated surface estimates differ considerably. Normal distribution is present neither in the measured data nor in the area estimates derived from them. Figure A.1 illustrates statistical distribution characteristics of the investigated data sets 01, 03, 06, 10 in Table 2.1, i.e. for distributions of two modellings (high and low spatial resolution) and technically measured atmospheric concentrations of Cd, Hg and Pb as well as measured concentrations of Cd, Hg and Pb in moss in Germany. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Nickel et al., Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss, https://doi.org/10.1007/978-3-031-25636-3_3

15

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3 Results

For Hg it becomes clear that both technical measurements and modelling show a low variance. On the one hand, this is due to the distribution characteristics of Hg in the environment. On the other hand, the technical Hg measurement network covers extremely few sites, so that potentially existing spatial concentration and deposition differences are not recorded. In this respect, the moss monitoring network is superior to the technical monitoring and should therefore be used to validate the modelling. Figure A.2 contains the statistical distributions of two modellings and technically measured atmospheric deposition as well as measured concentrations of Cd, Hg and Pb in moss collected throughout Germany (datasets 02, 04, 07 and 10 in Table 2.1. Here the variances correspond to the distributions shown in Fig. A.1.

3.1.2 Modelled Mean Heavy Metal Concentrations in the Atmosphere Table A.2 contains the descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere for the EMEP grid with 50 km × 50 km mesh size for the years 2013, 2014 and 2015, respectively, as well as for the annual intervals 2013–2014 and 2013–2015 (data set 01, Table 2.1). It is to be noted that all position and dispersion measures between the individual years and the annual intervals show only slight deviations from each other in substance-specific terms with differences between the metals. Cd and Hg decrease slightly over the years, in contrast to Pb. All data are neither normally nor lognormally distributed. Table A.3 contains the descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere in the EMEP grid with a spatial resolution of 0.1° × 0.1° for the years 2014, 2015 and 2016, respectively, as well as further for the year intervals 2014–2015, 2015–2016 and 2014–2016 (data set 03, Table 2.1). In general, it can be observed that all position and dispersion measures between the individual years and the annual intervals show only slight deviations from each other in the case of differences between the metals on a substance-specific basis. Cd and Hg show no trend, Pb shows slight decreases. The distribution of the data is without exception neither normal nor lognormal.

3.1.3 Modelled Total Annual Atmospheric Depositions of Heavy Metals Table A.4 documents the descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg in the EMEP grid with a mesh size of 50 km × 50 km for the years 2013, 2014 and 2015 as well as the intervals 2014–2015 and 2013–2015 (data set 02, Table 2.1). All parameters vary slightly between the individual years and the annual intervals with differences between the metals on a

3.1 Descriptive Statistics

17

substance-specific basis. For the three metals, slight decreases over the years can be observed. Without exception, the data are neither normally nor lognormally distributed. Table A.5 contains the descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg (EMEP 0.1° × 0.1°, 2014–2016) (dataset 04, Table 2.1). The statistical parameters are very similar across all individual years and annual intervals, with differences between the metals on a substance-specific basis. Cd and Hg are not subject to any trend, Pb decreases slightly over the years. The data are all neither normally nor lognormally distributed.

3.1.4 Technical Measurement Data on Mean Atmospheric Heavy Metal Concentrations The descriptive-statistical characteristic values of the technical measurement data for the mean concentration of Pb, Cd and Hg in the atmosphere (data set 06, Table 2.1) show little substance-specific variation over the individual years and annual intervals with differences between the metals. Cd and Pb decrease somewhat over time, for Hg this only applies to the minimum of the measured values (Table A.6).

3.1.5 Technical Measurement Data on Atmospheric Heavy Metal Depositions The descriptive-statistical characteristic values of the technical measurement data on the wet deposition of Cd, Hg and Pb (2014–2016) listed in Table A.7 (dataset 07; Table 2.1) vary considerably between the years and annual intervals. Cd and Pb show decreasing values over the years, whereas Hg shows no trend. Some data are lognormally distributed (Hg deposition 2015, 2016, 2014–2015, 2015–2016, 2014– 2016, Pb deposition 2016, 2015–2016, 2014-2016). The rest is neither normally nor lognormally distributed. The descriptive-statistical characteristics of the technical measurement data on the bulk deposition of Cd and Pb (2014–2016; data set 08 in Table 2.1) differ from the results presented previously (Table A.8): The Cd deposition—measured at P50— does not decrease. On the other hand, the minimum decreases, while the maximum and the standard deviation increase significantly. The situation is also unclear in the case of the Pb deposition: Min, P20, MW and P90 decrease, P50 and the other parameters increase. Normal distribution is not present, lognormal distribution for Cd deposition 2015–2016 as well as for Pb deposition 2015, 2015–2016 and 2014–2016.

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3 Results

3.2 Correlation Analysis The textual summary of the results only considers statistically significant correlations, whereby their strength is classified in three levels according to Cohen (1988). Accordingly, correlations between r = 0.1 and r = 0.3 are classified as low/weak, between r = 0.3 and r = 0.5 as medium/moderate and from r = 0.5 as high/strong. Further classifications are given in Chap. 2 (section correlation analysis).

3.2.1 Correlations Between Modelled Mean Concentrations of Heavy Metals in the Atmosphere (EMEP) and Concentrations in Moss (MM2015) The correlations (Spearman, Pearson) between modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and measured concentrations in moss (MM2015; datasets 01, 03; Table 2.1) are mostly weak (Cohen 1988) and mostly (very) significant (Table B.1). The current, spatially higher-resolution data show slightly weaker statistical correlations for Pb than the spatially lower-resolution data. The statistical correlations between modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and geostatistical surface estimations of Cd, Hg and Pb concentrations in moss (MM2015, 3 km × 3 km; datasets 01, 03, 11 in Table 2.1), which can be classified as weak, moderate and strong according to Cohen (1988), are predominantly (very) significant (Table B.2).

3.2.2 Correlations Between Modelled Atmospheric Heavy Metal Deposition (EMEP) and Concentrations in Moss (MM2015) According to Cohen (1988), the almost exclusively weak correlations (Spearman, Pearson) between modelled atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015; datasets 01, 03, 10 in Table 2.1) are mostly statistically (very) significant (Table B.3). In Table B.3, Cd deposition has the higher correlation in the rough grid in the cases where correlations were found. In the case of Pb it is the other way round. No correlations could be found for Hg. The statistically very significant correlations (Spearman, Pearson) between modelled atmospheric depositions of Cd, Hg, and Pb (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km; datasets 02, 04, 11 in Table 2.1) are to be classified as weak and moderate according to Cohen (1988) and predominantly more pronounced when taking into account the current,

3.2 Correlation Analysis

19

spatially higher-resolution data (Table B.4). For 4 of 6 pairs of values for Cd, the coarser resolution of the deposition shows the higher correlation in Table B.4. For Pb it is the other way round. No correlations could be found for Hg. The statistically (very) significant correlations (Spearman, Pearson) between land use-specific modelled atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015; datasets 04, 05, 10 in Table 2.1) (CLCderived land use; Table B.5) are weak according to Cohen (1988) and turn out to be lower for Cd and higher for Pb for the non-use-specific modelled deposition data. The correlation coefficients (Spearman, Pearson) compiled in Table B.6 for land use-specific modelled atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015; datasets 04, 05, 10 in Table 2.1) (land use surveyed in MM2015) are predominantly very significant and weak according to Cohen (1988). In contrast to the non-use-specific data, the use-specific data provide plausible correlation coefficients for Hg, which, like the values for Cd and Pb, are statistically very significant. The correlation coefficients based on use-specific data calculated correlations are somewhat higher compared to the undifferentiated data basis for Pb (exception: Pearson correlations for Cd). The correlations (Spearman, Pearson) between use-specific modelled atmospheric deposition of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use derived from CLC without differentiation of deciduous and coniferous forests) are weak for Cd and Pb and mostly statistically (very) significant. No statistically significant correlations can be determined for Hg (Table B.7; data sets 04, 05, 10 in Table 2.1). If instead of the CLC data the land use data collected in the moss monitoring, the strength of the statistical correlations increases somewhat, but remains weak. No statistically significant correlations can be found for Hg (Table B.8; datasets 04, 05, 10 in Table 2.1).

3.2.3 Correlations Between Mean Concentration Values from Technical Measurements of Heavy Metals in the Atmosphere and Concentrations in Moss Half of the correlations (Spearman, Pearson) between technical measurements of mean concentration of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) (datasets 06, 11 in Table 2.1) are weak and moderate (Pb) and (very) significant (Table B.3). No plausible relationship was found for the other combinations.

20

3 Results

3.2.4 Correlations Between Technical Measurements of Atmospheric Heavy Metal Deposition and Concentrations in Moss The correlations between technical measurements of wet deposition of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) (datasets 07, 11 in Table 2.1) were investigated but did not yield statistically significant relationships (Table B.10). In contrast, the correlations (Spearman, Pearson) between the results of technical measurements of the bulk atmospheric deposition of Pb, Cd and Hg (data taken from Ilyin et al. 2020: Tables B1–B6) and geostatistical surface estimations of the concentration in moss (MM2015, 3 km × 3 km) (datasets 08, 11 in Table 2.1) are statistically very significant and, according to Cohen (1988), occasionally moderate and otherwise strong in the case of Pb and Cd (here only for 2014; Table B.11).

3.2.5 Graphical Comparison of the Correlation Coefficients The correlations documented in tabular form in Sects. 3.2.1–3.2.4 were also prepared graphically. Figure B.1 shows the correlations (Spearman) between modelled atmospheric concentrations and measured data from MM2015 comparing the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1° (datasets 01, 03, 10 in Table 2.1). It can be seen that the higher spatial resolution yields very weak but significant correlations for Cd and Hg compared to the 50 km × 50 km resolution, but slightly stronger statistical correlations for Pb than in the case of the 50 km × 50 km grid. For the correlations calculated according to Pearson, a similar picture emerges for Cd and Hg and for Pb slightly stronger statistical correlations than in the case of lower spatial resolution (datasets 01, 03, 10 in Table 2.1) (Fig. B.2). The correlations (Spearman) between modelled atmospheric concentrations and MM2015 geostatistical surface estimations are neither higher nor lower for Cd at 0.1° × 0.1° spatial resolution and higher for Hg and Pb than at 50 km × 50 km resolution (datasets 01, 03, 11 in Table 2.1) (Fig. B.3). The Pearson coefficients are higher for Cd, Hg and Pb at finer resolution than at coarser (data sets 01, 03, 11 in Table 2.1) (Fig. B.4). The correlations (Spearman) between modelled atmospheric depositions and measured data from MM2015 are somewhat less pronounced for Cd at higher spatial differentiation than at the coarser resolution. The opposite is true for Pb Fig. B.5) (datasets 02, 04, 10 in Table 2.1). The Pearson correlations show an inconsistent picture when comparing the different spatial resolutions (Fig. B.6) (data sets 02, 04, 10 in Table 2.1). The Spearman correlations between modelled atmospheric depositions and geostatistical surface estimations of MM2015 comparing the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1° show higher correlations for Cd

3.3 Comparison of the Federal and State Air Quality Monitoring Network …

21

at the coarser resolution, for Hg at the coarser resolution partly very low but significant correlations and for Pb at the finer resolution higher correlations than at the coarser resolution (datasets 02, 04, 11 in Table 2.1) (Fig. B.7). The corresponding Pearson correlations (data sets 02, 04, 11 in Table 2.1) show no clear trend for Cd at the coarser resolution, no significant correlations for Hg and higher correlations for Pb again at the finer resolution (Fig. B.8). The correlations (Pearson and Spearman) between atmospheric concentrations (EMEP 0.1° × 0.1°) and MM2015 data are higher for Cd, Hg and Pb in the geostatistical surface estimations than in the pointmeasured concentrations in moss (datasets 03, 10, 11 in Table 2.1) (Figs. B.9 and B.10). The same finding applies to the correlations (Pearson and Spearman) between atmospheric deposition (EMEP 0.1° × 0.1°) and geostatistical surface estimations and measured concentrations in moss (MM 2015) at least for Cd and Pb, while Hg, on the other hand, shows no correlations at all (datasets in 04, 10, 11 in Table 2.1) (Figs. B.11 and B.12). The measured element concentrations in the mosses correlate more strongly with the modelled deposition than with the modelled concentrations in the air for Cd and Pb. In the case of Hg, significant correlations with the moss monitoring data can only be determined for the modelled concentrations in the air (data sets 03, 04, 10 in Table 2.1). This applies to the Spearman coefficients as well as to the Pearson coefficients (Figs. B.13 and B.14). This uniformity does not hold for the Spearman coefficients for the relationship between MM2015 geostatistical surface estimations and modelled atmospheric concentrations and depositions (datasets 03, 04, 11 in Table 2.1) (Fig. B.15). Invariably, the Pearson coefficient measures stronger relationships between modelled atmospheric concentrations of Cd and Pb to the geostatistical surface estimations of element concentrations in moss (2015) than between these and depositions (datasets 03, 04, 11 in Table 2.1) (Fig. B.16). The correlations (Spearman) between EMEP 0.1° × 0.1° and measured data from MM2015 (all sites) are more significant in the comparison of the use-specific and weighted atmospheric depositions modelled according to the use distribution are highest in the case of the allocation of the use-specific modelled deposition made on the basis of the land use data collected in the moss monitoring (Fig. B.17 based on datasets 04, 05, 10 in Table 2.1). The correlations (Pearson) follow this pattern only for Pb and Hg, while for Cd the weighted deposition shows the highest correlation (Fig. B.18 based on datasets 04, 05, 10 in Table 2.1). The statistical correlations for forest sites are more inconsistent (Figs. B.19 and B.20 based on the data sets in 04, 05, 10 Table 2.1).

3.3 Comparison of the Federal and State Air Quality Monitoring Network with the Moss Monitoring Network 2015 Figure C.1 shows the statistical distribution of the smallest distances between the stations of the federal and state air quality monitoring network and the MM2015 sites. This measuring point distance analysis shows that the measuring points of the

22

3 Results

empirical data sets 06, 07, 08, 09, and 10, compiled in Table 2.1, are not congruent. But 56 station pairs (= 112 sites) have a spatial distance of less than 5 km. This lies within the autocorrelation range of the substance deposition indexed with mosses (Schröder et al. 2019). This suggests that the data of spatially disparate monitoring networks can be meaningfully statistically correlated with each other, as in this study.

3.4 Mapping of Spatial Distributions of Deviations of Modelled Atmospheric Metal Concentration and Deposition (EMEP) and Concentration in Moss (MM2015) from the Respective Nationwide Median So-called average values such as median and arithmetic mean condense the information contained in several measured values into one statistical characteristic value. This central value of the data distribution can, for example, summarise the mean or average deposition of Cd, Hg and Pb for Germany, for individual federal states or natural units. However, it hides the spatial deviations from it. These are shown in the maps of Appendix D. Due to the nature of the task, their comparison cannot be made quantitatively, for example by means of correlation statistics, but must be limited to a few non-quantified descriptions. The spatial distributions of the deviations from the nationwide Cd and Pb medians of modelled atmospheric concentration and total deposition (EMEP 0.1° × 0.1°) (datasets 03, 04 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) are visualised in Figs. D.1, D.2 and D.3. The spatial distribution of deviations from the nationwide Cd, Hg and Pb medians of technically measured atmospheric concentrations (dataset 06 in Table 2.1) (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) is shown in Figs. D.4, D.5 and D.6. In the case of Hg (Fig. D.5), a serious assessment is not possible due to the low measuring point density. In the case of Cd (Fig. D.4), this is significantly higher and allow the observation that the pattern of deviations of the results from technical measurements from the respective median is not particularly similar to that of the pattern generated from the moss data. For Pb, Fig. D.6 show with sufficient measuring point density—except in Brandenburg and Schleswig–Holstein—a given similarity of the deviation patterns. Figures D.7, D.8 and D.9 illustrate the spatial distributions of deviations from the nationwide Cd, Hg and Pb median of technically measured wet atmospheric deposition (dataset in 07 Table 2.1) (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1). For Cd, a comparison of the deviation patterns is complicated by the fact that for the technically measured wet Cd deposition (dataset 07 in Table 2.1) only a few stations are available, which cover the area of Germany poorly. These show a clearly different deviation pattern

3.5 Summary with Regard to the Objectives and Key Questions of the Study

23

than the moss data (Fig. D.7). The same applies to Hg (data set 07 in Table 2.1) (Fig. D.8) and Pb (data set 07 in Table 2.1) (Fig. D.9) as well as to the technically recorded bulk deposition of Cd and Pb (data set 08 in Table 2.1) (Figs. D.10 and D.11).

3.5 Summary with Regard to the Objectives and Key Questions of the Study The descriptive-statistical evaluation shows that all data are not normally distributed. The distribution characteristics determined in detail would have to be taken into account in the choice of statistical parameters, such as the determination of the minimum number of samples recommended for a follow-up study. It is true that the measurement points of the empirical data sets are not spatially congruent. But they have a spatial distance that lies within the autocorrelation range of the substance deposition indexed with mosses (Schröder et al. 2019). Therefore, these data can be meaningfully statistically related to each other, as in this study, which was the main objective of this investigation. The spatial distributions of the deviations from the nationwide Cd and Pb median of modelled atmospheric concentration and total deposition EMEP (0.1° × 0.1°) (datasets 03, 04 in Table 2.1) and geostatistically estimated concentrations in moss (MM2015) (dataset 11 in Table 2.1) have a very similar spatial structure in some regions. In contrast, this is not the case for Hg. The spatial distribution of deviations from the nationwide Cd, Hg and Pb median of technically measured atmospheric concentrations and geostatistically estimated concentrations in moss (MM2015) shows that a serious assessment is not possible for Hg due to the low measurement point density. This is significantly higher for Cd and allow the observation that the pattern of deviations of the results from technical measurements from the respective median is not particularly similar to that of the pattern generated from the moss data. For Pb, a certain agreement of the deviation patterns is shown when the density of measuring points appears to be sufficient— except in Brandenburg and Schleswig–Holstein. The spatial distributions of the deviations from the nationwide Cd, Hg and Pb median of technically measured wet deposition and geostatistically estimated concentrations in moss (MM2015) show that for Cd a comparison of the deviation patterns is complicated by the fact that for the technically measured wet Cd deposition only a few stations are available that cover the area of Germany poorly. These show a clearly different deviation pattern than the moss data. The same applies to Hg and Pb as well as to the technically recorded bulk deposition of Cd and Pb. The main objective of this study was to update and complement the correlation statistical analyses of Schröder et al. (2019) with new, spatially higher resolved data on atmospheric exposure to Pb, Cd, Hg (Ilyin et al. 2020). For this purpose, Pearson

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and Spearman correlations between EMEP modelling (0.1° × 0.1°) of atmospheric metal concentration and atmospheric metal deposition on the one hand and moss monitoring data (measurements and area estimates) on the other hand were calculated and tested whether they are significantly larger or smaller or not significantly different than those correlations between EMEP modelling (50 km × 50 km) of concentration and deposition on the one hand and moss monitoring data (measurements and area estimates) on the other. The calculations showed no correlations for two of the 19 models, weak correlations for eight, low and medium correlations for five models, medium-strong correlations for one model and weak, medium and strong correlations for another, moderate and strong correlations for two: No correlation was found for 1. Modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at open land sites (land use surveyed in MM2015) and for 2. technical measurements of wet atmospheric deposition of Pb, Cd and Hg and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km). Low are the correlations for 3. modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and measured concentrations in moss (MM2015), 4. modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and measured concentrations in moss (MM2015), 5. modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (land use derived from CLC), 6. modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (land use surveyed in MM2015), 7. modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use derived from CLC) and for 8. modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use surveyed in MM2015). Low and medium correlations are pronounced for 9.

modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) and for 10. Mean values of concentrations from technical measurements of Pb, Cd and Hg and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km).

3.5 Summary with Regard to the Objectives and Key Questions of the Study

25

Weak, moderate and strong correlations are for 11. modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km). Moderate and strong correlations for 12. technical measurements of bulk atmospheric deposition of Pb, Cd and Hg and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km). Calculations of the correlations (Pearson, Spearman) between the measurement data from the technical and biological monitoring, taking into account information on the station character (all versus background) and the distance between the sites, which go beyond the scope of the study, yield the following findings, whereby the numbers of cases for the pairs of values investigated are predominantly very low, with n = 3 to n = 33: 1. There are no correlations between the technically measured air concentrations and the moss data for Cd and Hg, and strong significant correlations for Pb (r = 0.59–0.69; Pearson), whereby the correlation coefficient is significantly influenced by the sample size. The extent to which the surrounding land use at the air quality monitoring sites or the distance between the technical and biological monitoring stations has an influence on the correlation strength cannot be reliably assessed due to the given small number of cases. The comparison of the years 2015/16 with the moss data of the years 2015/16 yields higher correlations than the comparison with the data of the year 2014. 2. The measured wet deposition shows strong correlations with the concentrations in the mosses for Pb (r = 0.77–0.85; Pearson) and medium to strong correlations for Cd (r = 0.43–0.50; Spearman), but no significant correlations for Hg, which, compared to Pb, for example, is not only due to a too small sample. As the distance between the technical and biological monitoring stations is reduced, the correlation coefficient increases for Cd and Pb despite the reduction in the sample size. Comparisons with the wet deposition of 2016 yield the strongest correlations compared to the other years. The influence of the surrounding land use cannot be determined in more detail on the basis of the available data. 3. The measured bulk deposition does not correlate for Hg and Pb and strongly for Cd (r = 0.78–0.92; Pearson) with the moss data. The distance between the stations of the air monitoring networks and the moss monitoring has a decisive influence on the Correlation level: The closer the sites are together, the stronger the statistical correlations. For distances below 2000 m, a correlation coefficient of 1 (Spearman) is calculated, which, due to the small number of cases (n = 4) is not significant, however. If the comparison is restricted to the background stations of the air quality measurement networks, a mostly higher (but here not significant) correlation between the technical measurement data and the bioindication data is indicated in the range 5000–10,000 m distance despite a reduction in the number of cases.

26

3 Results

4. Overall, the correlations between the measured values seem to be higher than between the technical measurement data and the geostatistical surface estimations—despite all the uncertainty due to the small number of cases. Further statistical analyses and a conception of a measurement network optimisation, building on the approaches outlined in Chap. 4 and earlier work on the optimisation of nationwide deposition measurement (Fränzle et al. 1987), are necessary and promising.

References Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. L. Erlbaum Associates, Hillsdale, NJ Fränzle O, Klein A, Zölitz R (1987) Lokalisierung der Ergänzungsstationen des UBAImmissionsmessnetzes. Forschungsbericht 101 04 043/02 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Abschlussbericht Teilvorhaben II, Kiel Ilyin I, Travnikov O, Schütze, G, Feigenspan S, Uhse K (2020) Country-scale assessment of heavy metal pollution: a case study for Germany. Technical report 1/2020. Meteorological Synthesizing Centre-East, Moscow, Russia; German Environment Agency, Dessau, Germany, pp 1–121 Schröder W, Nickel S, Völksen B, Dreyer A, Wosniok W (2019) Nutzung von Bioindikationsmethoden zur Bestimmung und Regionalisierung von Schadstoffeinträgen für eine Abschätzung des atmosphärischen Beitrags zu aktuellen Belastungen von Ökosystemen. UBA-Texte 91/2019 B1:1–189, Bd. 2, pp 1–296

Chapter 4

Conclusions

The study suggests the following conclusions in addition to the findings and recommendations of Ilyin et al. (2020): 1. The updating and regionalisation of emission data as a basis for EMEP modelling and the increase in the spatial resolution of data on the concentration of metals in the atmosphere and their deposition on the earth’s surface (atmospheric deposition) do not consistently lead to an increase in correlations with the spatially highresolution element concentrations in moss. Even the higher-resolution modelling remains dependent on the quality of the input data (emissions, meteorology, land use, other boundary conditions) and intrinsic model uncertainties. If uncertainties persist, especially in the emission data used for modelling (emission level, completeness), refining the grid resolution does not necessarily lead to an increase in modelling accuracy. 2. The empirical basis for verifying the modelling of air concentration and deposition values is weak in that the database from technical measurement networks is spatially insufficient in terms of number and geographical distribution as well as in terms of the elements measured. 3. The conclusion to be drawn from the situation mentioned under point 2 is that the advantages of the data from moss monitoring (cost-effective procedure for observing large-scale trends in spatially dense measurement networks and thus for improving chemical transport models and emission inventories) and the data from technical air concentration measurements and deposition collection should be included in a complementary manner in the validation of concentration and deposition modelling. 4. The concrete proposal to examine the possibilities of integrative use of technical measurement data and bioindication data to improve the empirical basis for comparisons with modelling data on atmospheric concentration and deposition is derived from point 3 as follows:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Nickel et al., Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss, https://doi.org/10.1007/978-3-031-25636-3_4

27

28

4 Conclusions

(a) Integration initially of all available data from technical measurements of atmospheric concentration and deposition as well as land use data from the UBA station database with subsequent quality control, analysis of their methodological genesis/comparability and usability (it is expected that the surrounding land use has a decisive influence on the level of statistical correlations between the modelled depositions and the data from technical measurements); (b) Spatial linkage of data from technical measurements with area estimates of heavy metal concentration in moss (including distribution maps converted to different leaf area indices/uses); (c) Regression analysis (techn. measured data vs. area estimates) and calculation of regression maps of atmospheric concentration and/or deposition, if applicable; (d) Analysis of the residuals for spatial autocorrelation and, if necessary, calculation of residual maps, with preliminary variogram analyses; data used in this study already showed that the residuals are spatially weak, but sufficiently autocorrelated for spatial generalisation; (e) Calculation of regression kriging maps of atmospheric concentration and deposition based on the technical measurement data and bioindication data as a basis for correlation-analytical and cartographic comparisons with corresponding modelling—depending on the data situation for the entire area of Germany or subregions; (f) Examination of the possibilities of transferring the element concentrations measured in the mosses via regression-statistically derived conversion functions (technical measurement data vs. heavy metal concentrations in moss) into deposition rates, which—complementary to the technical measurements—could be compared with the modelled depositions in a comparatively dense moss measurement network; the prerequisite is a sufficiently high number of neighbouring station/value pairs (< 2000 m, better < 1000 m), which has not yet been achieved with the 31 or 16 station pairs investigated in this study. 16 station pairs investigated in this study (with numerous false values and outliers); therefore, for all technical monitoring sites found to be suitable, it should also be investigated whether the moss monitoring network can be expanded by adding new sites, i.e. determine on site whether any of the three moss species collected in Germany (Pleurozium schreberi, Pseudoscleropodium purum, Hypnum cupressiforme) occur at a distance of less than 2000 m (better < 1000 m) at the stations of the federal and state monitoring networks selected for the integrative analysis; (g) Calculation of the minimum number of samples for the measurement data of atmospheric deposition from technical collectors and mosses used for the validation of the chemical transport models for the calculation of atmospheric deposition and development of concrete proposals for the spatial supplementation of the measurement networks available in Germany for the recording of heavy metal concentrations in the atmosphere and atmospheric deposition.

4 Conclusions

5.

6.

7.

8.

29

The concept to be developed should specify the data and monitoring network requirements and describe the method modules for the integrative use of technical measurement data and bioindication data for the verification of modelled atmospheric concentrations and depositions in Germany. In addition to the approach for calculating minimum sample numbers, methods for the establishment and optimisation of environmental monitoring networks can be used, which were developed in several research projects on behalf of UBA. In these, Brümmer et al. (1987), Fränzle and Kuhnt (1983), Gawlik and Muntau (1999), Kuhnt et al. (1992) and Kuhnt and Muntau (1994) aimed at the selection of soils to be investigated and their localisation. Procedures for analysing and supplementing existing deposition measurement networks with technical collectors and bioindicators (mosses, spruce needles) were developed by Außenthal et al. (1991) for the federal state Schleswig-Holstein. For the federal territory, in addition to the selection of the main research areas for the ecosystem research programme of the Federal Republic of Germany (Fränzle et al. 1987b), methods were developed for the localisation of the supplementary stations of the UBA immission monitoring network (Fränzle et al. 1987a). Here, geostatistical procedures were combined with methods for determining the ecological representativeness with the aid of an ecological spatial classification/ecosystem typing as well as with a measure for the spatial distribution of measurement sites. This is based on the comparison of the actual mean distance of the sites to their nearest neighbour with the theoretical distribution in space. Linking exposure data (moss monitoring, measured and modelled data on atmospheric concentration and deposition of metal elements) with data on receptors, i.e. ecosystem types (Schröder et al. 2019a). In connection with the proposal under point 4, the modelling should also be harmonised in that the LOTOS EUROS and EMEP modelling is carried out with identical data sets (emissions, land use and meteorology as input data and data from moss monitoring and technical concentration and deposition measurements for validation). This allows an analysis of differences in spatial exposure patterns due to intrinsic model uncertainties better than if—as is currently the case— different input data sets are used. For the question of whether and to what extent LOTOS-EUROS can be a supplement or alternative to the EMEP model for Cd and Pb, in addition to direct comparisons of the modelling, it should also be determined which model results produce the better correlations to the empirical data from technical and biological monitoring. Only through an integrative investigation approach as outlined under numbers and 45 can it be achieved that the results of the exposure assessment with modelling, technical measurements and moss monitoring contribute to assessing the burden on ecosystems from atmospheric heavy metal deposition in a more differentiated way than before and thus enable a targeted further development of the classification of exposure-related ecological burdens and risks for Germany. A methodologically unproblematic overview of the results generated by different exposure characterisation methods can be obtained by classifying them into percentiles (e.g. P10, P20, …) and assigning rank values (e.g. from 1 to 10)

30

4 Conclusions

to the (e.g. 10) percentiles. These can be aggregated in the GIS spatially and measurement size-specific to form a synopsis. It is recommended to compare this pressure classification and the updated, spatially differentiated data with the results of the classification of ecosystem integrity of representative ecosystems in Germany (Jenssen et al. 2021a, b; Nickel et al. 2019; Schlutow and Schröder 2021; Schröder et al. 2019b) and critical loads (Schlutow et al. 2021a, b; Schröder et al. 2018b). This would be a significant step forward.

References Außenthal R, Fränzle O, Gliesmann S, Heinrich U, Jensen-Huß K, Kirchhoff M, Rudolph H-J (1991) Erarbeitung und Erprobung einer Konzeption für die ökologisch orientierte Planung auf der Grundlage der regionalisierenden Umweltbeobachtung am Beispiel Schleswig-Holsteins. Untersuchungen der atmosphärischen Deposition in Schleswig-Holstein. Forschungsbericht 109 02 033 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Teilband zum Abschlussbericht, Kiel Brümmer G, Fränzle, O, Kuhnt G, Kukowski H, Vetter L (1987) Fortschreibung der OECDPrüfrichtlinie ‘Adsorption/Desorption’ im Hinblick auf die Übernahme in Anhang V der EGRichtlinie 79/831: Auswahl repräsentativer Böden im EG-Bereich und Abstufung der Testkonzeption nach Aussagekraft und Kosten. Forschungsbericht 106 02 045 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Abschlussbericht, Kiel Fränzle O, Kuhnt G (1983) Regional repräsentative Auswahl der Böden für eine Umweltprobenbank. Exemplarische Untersuchung am Beispiel der Bundesrepublik Deutschland. Forschungsbericht 106 05 028 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Abschlussbericht, Kiel Fränzle O, Klein A, Zölitz R (1987a) Lokalisierung der Ergänzungsstationen des UBAImmissionsmessnetzes. Forschungsbericht 101 04 043/02 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Abschlussbericht Teilvorhaben II, Kiel Fränzle O, Kuhnt D, Kuhnt G, Zölitz R (1987b) Auswahl der Hauptforschungsräume für das Ökosystemforschungsprogramm der Bundesrepublik Deutschland. Forschungsbericht 101 04 043/02 im Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit. Im Auftrag des Umweltbundesamtes, Berlin. Abschlussbericht Teilvorhaben I, Kiel Gawlik BM, Muntau H (1999) Eurosoils II. Laboratory reference materials for soil-related studies. Office for Official Publications of the European Communities, Luxembourg, © European Communities, 1999. ISBN 92-828-7882-1 Ilyin I, Travnikov O, Schütze, G, Feigenspan S, Uhse K (2020) Country-scale assessment of heavy metal pollution: a case study for Germany. Technical report 1/2020. Meteorological Synthesizing Centre-East, Moscow, Russia; German Environment Agency, Dessau, Germany, pp 1–121 Jenssen M, Nickel S, Schütze G, Schröder W (2021a) Reference states of forest ecosystem types and feasibility of biocenotic indication of ecological soil condition as part of ecosystem integrity and services assessment. Environ Sci Eur 33(18):1–18. https://doi.org/10.1186/s12302-021-004 58-2 Jenssen M, Nickel S, Schröder W (2021b) Methodology for classifying the ecosystem integrity of forests in Germany using quantified indicators. Environ Sci Eur 33(46):1–28. https://doi.org/10. 1186/s12302-021-00478-y

References

31

Kuhnt G, Muntau H (eds) (1994) Euro-soils. Identification, collection, treatment and characterization, special publication no. 1.94.60. Joint Research Centre. European Commission, ISPRA Kuhnt G, Garniel A, Kothe P, Schröder W (1992) Umsetzung des Bodeninformationssystems: Begleitstudie zur bundesweiten Bodenzustandserhebung im Walde. Band 3: Standortbestimmung für die begleitende Bodenprobennahme und -analyse sowie Überprüfung der Meßnetzvalidität. Hannover (hrsg. v. Bundesanstalt für Geowissenschaften und Rohstoffe) Nickel S (2019) Methodologie integrativer analyse, Modellierung und management umweltwissenschaftlicher Daten für landschaftsökologische Forschung und Lehre am Beispiel der exposition von Wäldern gegenüber atmosphärischen Stoffeinträgen und daraus resultierender Veränderungen der Ökosystemintegrität in Kombination mit dem Klimawandel. Habilitationsschrift, Universität Vechta Schlutow A, Schröder W (2021) Rule-based classification and mapping of ecosystem services with data on the integrity of forest ecosystems. Environ Sci Eur 33(50):1–34.https://doi.org/10.1186/ s12302-021-00481-3 Schlutow A, Schröder W, Nickel S (2021a) Atmospheric deposition and element accumulation in moss sampled across Germany 1990–2015: trends and relevance for ecological integrity and human health. Atmosphere 12(2):193:1–30. https://doi.org/10.3390/atmos12020193 Schlutow A, Schröder W, Scheuschner T (2021b) Assessing the relevance of atmospheric heavy metal deposition with regard to ecosystem integrity and human health in Germany. Environ Sci Eur 33(7):1–34. https://doi.org/10.1186/s12302-020-00391-w Schröder W, Nickel S, Schlutow A, Nagel H-D, Scheuschner T (2018) Auswirkungen der Schwermetall-Emissionen auf Luftqualität und Ökosysteme in Deutschland-Quellen, Transport, Eintrag, Gefährdungspotenzial. Teil 2: Integrative Datenanalyse, Erheblichkeitsbeurteilung und Untersuchung der gegenwärtigen Regelungen und Zielsetzungen in der Luftreinhaltung und Vergleich mit ausgewählten Anforderungen, die sich in Bezug auf den atmosphärischen Schadstoffeintrag aus den verschiedenen Rechtsbereichen ergeben. Abschlussbericht, Forschungskennzahl 3713 63 253, UBA-FB 002635. Im Auftrag des Umweltbundesamtes. UBA-Texte 107/2018, pp 1–257 Schröder W, Nickel S, Jenssen M, Hofmann G, Schlutow A, Nagel H-D, Burkhard B, Dworczyk C, Elsasser P, Lorenz M, Meyerhoff J, Weller P, Altenbrunn K (2019a) Anwendung des Bewertungskonzepts für die Ökosystemintegrität unter Berücksichtigung des Klimawandels in Kombination mit Stoffeinträgen. UBA-Texte 97/2019, pp 1–504 Schröder W, Nickel S, Völksen B, Dreyer A, Wosniok W (2019b) Nutzung von Bioindikationsmethoden zur Bestimmung und Regionalisierung von Schadstoffeinträgen für eine Abschätzung des atmosphärischen Beitrags zu aktuellen Belastungen von Ökosystemen. UBA-Texte 91/2019 B1:1–189, B2: 1–296

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Abbreviations of the Data Sets Used (Legend to Appendices A–D) Modelled data on atmospheric concentration and deposition 01

Average concentration of Cd, Hg, Pb (EMEP 50 km × 50 km)a

02

Annual total deposition of Cd, Hg, Pb (EMEP 50 km × 50 km)a

03

Average concentration of Cd, Hg, Pb (EMEP 0.1° × 0.1°)b

04

Annual total deposition of Cd, Hg, Pb (EMEP 0.1° × 0.1°)b

05

Annual total deposition of Cd, Hg, Pb (EMEP 0.1° × 0.1°, land use specific)b

Technical measurement data on atmospheric concentration and deposition 06

Average concentration of Cd, Hg, Pbb

07

Wet deposition of Cd, Hg, Pbb

08

Bulk deposition of Cd, Hg, Pbc

09

Bulk deposition of Cd, Pb (three additional ICP Forests Level II sites and 1 Hessian site, Northwest German Forest Research Institute)

Biomonitoring data 10

Concentration of Cd, Hg, Pb in moss (Moss Survey 2015, survey year 2016)d

11

Concentration of Cd, Hg, Pb in moss (Moss Survey 2015, geostatistical surface estimation, 3 km × 3 km)

a

http://en.msceast.org/index.php/pollution-assessment/emep-domain-menu/data-hm-pop-menu (27.04.2018) b Data delivery MSC-East from (of 24.09.2020 which data sets = 06, 07 data from federal and state monitoring networks used in Ilyin et al. (2020) for model validation) c Values taken from Ilyin et al. (2020: Annex B), = data from monitoring networks of the federal states d http://www.mapserver.uni-vechta.de/mossEU/login.php (29.09.2020)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Nickel et al., Correlation of Modelled Atmospheric Deposition of Cadmium, Mercury and Lead with the Measured Enrichment of these Elements in Moss, https://doi.org/10.1007/978-3-031-25636-3

33

34

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Appendix A: Descriptive Statistics A.1 Measurement Data on Heavy Metal Concentrations in Moss See Table A.1 and Figs. A.1 and A.2.

A.2 Modelled Mean Heavy Metal Concentrations in the Atmosphere See Tables A.2 and A.3.

A.3 Modelled Total Annual Atmospheric Depositions of Heavy Metals See Tables A.4 and A.5.

A.4 Technical Measurement Data on Atmospheric Heavy Metal Concentrations See Table A.6.

A.5 Technical Measurement Data on Atmospheric Heavy Metal Depositions See Tables A.7 and A.8.

Min

400 1.115

0.0167 0.470

39,718

39,718

0.0786 0.0047

397

0.0350

398

39,718

n

P20

1.545

1.228

0.0281

0.0246

0.1140

0.0944

P50

1.843

1.830

0.0343

0.0336

0.1329

0.1360

P90

2.616

4.334

0.0436

0.0540

0.1760

0.2619

P98

3.291

9.094

0.0487

0.0702

0.2210

0.4743

4.017

19.340

0.0682

0.1960

0.2366

1.7600

Max

MW

1.951

2.370

0.0347

0.0367

0.1384

0.1656

SD

0.471

1.944

0.0069

0.0169

0.0292

0.1257

VT

[3]

[3]

[2]

[3]

[3]

[3]

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data sets 10 and 11 in Table 2.1

Pb conc. in moss 2016 area estimates

Pb conc. in moss 2016

Hg conc. in moss 2016 area estimates

Hg conc. in moss 2016

Cd conc. in moss, 2016 area estimates

Cd conc. in moss 2016

Feature*

Table A.1 Descriptive-statistical parameters of the concentration of Pb, Cd and Hg in moss of the MM (measured 2015 data and 2016 geostatistical surface estimations)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps 35

36

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Fig. A.1 Distributions of two modelling and technically measured atmospheric concentrations of Cd, Hg and Pb (left scales) and measured concentrations of Cd, Hg and Pb in moss in Germany (right scales). Graphical comparison of median values and distributions from Tables A.1, A.2, A.3 and A.6. Data basis: data sets 01, 03, 06, 10 in Table 2.1

Fig. A.2 Distributions of two modelling and technically measured atmospheric depositions of Cd, Hg and Pb (left scales) and measured concentrations of Cd, Hg and Pb in moss in Germany (right scales). Graphical comparison of median values and distributions from Tables A.1, A.4, A.5 and A.7. Data basis: data sets 02, 04, 07, 10 in Table 2.1

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

37

Table A.2 Descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere (EMEP 50 km × 50 km, 2013–2015) Feature*

n

Min

P20

P50

P90

P98

Max

MW

SD

VT

Cd 202 0.0512 concentration 2013 (50 km × 50 km)

0.0952 0.1121 0.1622 0.1862 0.2249 0.1205 0.0294 [3]

Cd 202 0.0510 concentration 2014 (50 km × 50 km)

0.0928 0.1163 0.1651 0.1767 0.1933 0.1213 0.0304 [3]

Cd 202 0.0587 concentration 2015 (50 km × 50 km)

0.0857 0.0986 0.1189 0.1326 0.1504 0.0990 0.0162 [3]

Cd 202 0.0556 concentration 2014–2015 (50 km × 50 km)

0.0959 0.1074 0.1376 0.1585 0.1591 0.1107 0.0194 [3]

Cd 202 0.0539 concentration 2013–2015 (50 km × 50 km)

0.0963 0.1066 0.1455 0.159

0.1807 0.1139 0.0222 [3]

Hg 202 1.57735 1.6174 1.6553 1.6797 1.6931 1.7194 1.6481 0.0306 [3] concentration 2013 (50 km × 50 km) Hg 202 1.6059 concentration 2014 (50 km × 50 km)

1.6364 1.6672 1.6877 1.7012 1.722

1.6636 0.0237 [3]

Hg 202 1.4418 concentration 2015 (50 km × 50 km)

1.4724 1.4995 1.5185 1.5354 1.5528 1.4958 0.0222 [3]

202 1.52385 1.5567 1.5831 1.6031 1.6171 1.6374 1.5797 0.0229 [3] Hg concentration 2014–2015 (50 km × 50 km) (continued)

38

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table A.2 (continued) Feature*

n

Min

P20

P50

P90

P98

Max

MW

SD

VT

Hg 202 1.5416 concentration 2013–2015 (50 km × 50 km)

1.5769 1.6085 1.6288 1.6415 1.6647 1.6026 0.0252 [3]

Pb 202 1.8716 concentration 2013 (50 km × 50 km)

3.8571 4.5919 6.2417 6.8754 8.3992 4.7492 1.0292 [3]

Pb 202 2.0937 concentration 2014 (50 km × 50 km)

4.1071 4.8959 5.9109 7.193

Pb 202 2.5935 concentration 2015 (50 km × 50 km)

3.8266 5.1229 6.5431 7.0697 7.2369 5.1116 1.1118 [3]

8.0699 4.9057 0.9784 [3]

Pb 202 2.32915 4.1506 4.9084 6.1319 6.8976 7.6534 5.0234 0.9584 [3] concentration 2014–2015 (50 km × 50 km) 202 2.2117 Pb concentration 2013–2015 (50 km × 50 km)

4.1493 4.9588 5.8894 7.1653 7.5655 4.9543 0.9200 [3]

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 01 in Table 2.1

1.4826 1.4743

4818

4818

4818

4818

4818

4818

4818

4818

4818

4818

Cd concentration 2015 (0.1° × 0.1°)

Cd concentration 2016 (0.1° × 0.1°)

Cd concentration 2014–2015 (0.1° × 0.1°)

Cd concentration 2015–2016 (0.1° × 0.1°)

Cd concentration 2014–2016 (0.1° × 0.1°)

Hg concentration 2014 (0.1° × 0.1°)

Hg concentration 2015 (0.1° × 0.1°)

Hg concentration 2016 (0.1° × 0.1°)

Hg concentration 2014–2015 (0.1° × 0.1°)

Hg concentration 2015–2016 (0.1° × 0.1°)

Min

1.4736

1.4604

1.4841

0.0213

0.0217

0.0211

0.0216

0.0213

0.0195

n

4818

Feature*

Cd concentration 2014 (0.1° × 0.1°)

1.5782

1.5864

1.6032

1.5532

1.6193

0.0907

0.0914

0.0912

0.0896

0.0929

0.0890

P20

1.6024

1.6124

1.6254

1.579

1.6437

0.1092

0.1107

0.1091

0.1096

0.1117

0.1070

P50

1.6522

1.6644

1.6789

1.6264

1.7053

0.1755

0.1774

0.1746

0.1775

0.1774

0.171

P90

1.7112

1.7246

1.7413

1.6793

1.7665

0.2647

0.2649

0.2615

0.2707

0.2637

0.2595

P98

1.9229

1.9558

2.0020

1.8929

2.0187

0.4386

0.4368

0.4344

0.4469

0.4267

0.4422

Max

1.6065

1.6159

1.6308

1.5822

1.6496

0.1213

0.1227

0.1210

0.1218

0.1236

0.1184

MW

0.0393

0.0409

0.0416

0.0375

0.0454

0.0472

0.0478

0.0464

0.0490

0.0467

0.0464

SD

(continued)

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

VT

Table A.3 Descriptive-statistical characteristics of modelled mean concentrations of Pb, Cd and Hg in the atmosphere (EMEP 0.1° × 0.1°, 2014–2016)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps 39

4818

4818

4818

4818

4818

4818

4818

Pb concentration 2014 (0.1° × 0.1°)

Pb concentration 2015 (0.1° × 0.1°)

Pb concentration 2016 (0.1° × 0.1°)

Pb concentration 2014–2015 (0.1° × 0.1°)

Pb concentration 2015–2016 (0.1° × 0.1°)

Pb concentration 2014–2016 (0.1° × 0.1°) 0.7462

0.7357

0.7651

0.6959

0.7494

0.7216

1.4771

Min

3.0936

3.0884

3.1906

2.9089

3.2661

3.0954

1.592

P20

3.9719

3.9451

4.0849

3.7378

4.1385

3.9971

1.6168

P50

6.2964

6.2518

6.4738

6.0169

6.5026

6.5624

1.6688

P90

10.1097

10.0079

10.2874

9.6819

10.3397

10.2785

1.7304

P98

19.646

18.7756

20.0496

18.8387

18.7125

21.3868

1.9515

Max

4.349

4.3087

4.4718

4.1033

4.5142

4.4295

1.6209

MW

1.858

1.8225

1.8987

1.7859

1.8654

1.9541

0.0410

SD

[3]

[3]

[3]

[3]

[3]

[3]

[3]

VT

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 03 in Table 2.1

n

Feature*

Hg concentration 2014–2016 (0.1° × 0.1°)

Table A.3 (continued)

40 Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

41

Table A.4 Descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg (EMEP 50 km × 50 km, 2013–2015) Feature*

n

Cd deposition 2013 (50 km × 50 km)

202

Min 22.480

P20 29.153

P50 32.116

P90 40.408

P98 43.453

Max 72.315

MW 33.633

SD 5.679

VT [3]

Cd deposition 2014 (50 km × 50 km)

202

20.778

25.300

32.905

42.455

44.974

62.150

33.085

7.521

[3]

Cd deposition 2015 (50 km × 50 km)

202

20.735

24.057

26.663

30.438

32.986

40.453

26.958

2.981

[3]

Cd deposition 2014–2015 (50 km × 50 km)

202

20.528

25.951

28.474

36.620

39.720

51.302

30.013

4.472

[3]

Cd deposition 2013–2015 (50 km × 50 km)

202

20.931

26.951

29.685

37.045

41.081

58.306

31.196

4.744

[3]

Hg deposition 2013 (50 km × 50 km)

202

10.672

14.118

16.108

17.687

19.765

22.071

15.672

1.925

[3]

Hg deposition 2014 (50 km × 50 km)

202

11.993

13.126

14.890

17.109

18.436

19.793

14.871

1.619

[3]

Hg deposition 2015 (50 km × 50 km)

202

10.777

12.324

13.313

14.772

16.805

18.724

13.409

1.254

[3]

Hg deposition 2014–2015 (50 km × 50 km)

202

11.472

12.880

13.823

15.917

16.920

19.259

14.121

1.413

[3]

Hg deposition 2013–2015 (50 km × 50 km)

202

11.124

13.550

14.508

16.397

17.868

20.196

14.649

1.508

[3]

(continued)

42

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table A.4 (continued) Feature*

n

Min

P20

P50

P90

P98

Max

MW

SD

VT

Pb deposition 2013 (50 km × 50 km)

202

791.25

990.47

1137.66

1409.55

1495.02

1963.62

1161.60

185.70

[3]

Pb deposition 2014 (50 km × 50 km)

202

671.72

919.12

1059.17

1225.82

1281.16

1448.09

1053.89

143.22

[3]

Pb deposition 2015 (50 km × 50 km)

202

770.64

905.85

1067.08

1224.11

1354.49

1477.58

1061.89

153.69

[3]

Pb deposition 2014–2015 (50 km × 50 km)

202

741.45

945.04

1084.62

1210.07

1253.34

1323.68

1057.07

126.61

[3]

Pb deposition 2013–2015 (50 km × 50 km)

202

738.03

958.26

1121.31

1276.71

1347.51

1478.28

1102.08

149.61

[3]

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 02 in Table 2.1

4.874

4.403

4818

4818

4818

4818

4818

4818

4818

4818

4818

4818

4818

Cd deposition 2015 (0.1° × 0.1°)

Cd deposition 2016 (0.1° × 0.1°)

Cd deposition 2014–2015 (0.1° × 0.1°)

Cd deposition 2015–2016 (0.1° × 0.1°)

Cd deposition 2014–2016 (0.1° × 0.1°)

Hg deposition 2014 (0.1° × 0.1°)

Hg deposition 2015 (0.1° × 0.1°)

Hg deposition 2016 (0.1° × 0.1°)

Hg deposition 2014–2015 (0.1° × 0.1°)

Hg deposition 2015–2016 (0.1° × 0.1°)

Hg deposition 2014–2016 (0.1° × 0.1°)

4.560

4.528

4.016

4.478

9.148

8.799

8.902

9.641

7.958

9.845

Min

n

4818

Feature*

Cd deposition 2014 (0.1° × 0.1°)

7.930

7.880

7.730

8.226

7.422

7.908

18.413

18.321

18.001

19.024

17.373

18.421

P20

9.005

9.032

8.814

9.464

8.588

9.032

22.446

22.436

21.929

23.555

21.385

22.634

P50

12.057

12.006

11.734

12.775

11.383

12.203

33.181

33.359

32.813

34.627

32.493

33.216

P90

14.879

15.046

14.446

15.935

14.254

14.851

53.523

53.086

52.641

54.232

51.877

53.344

P98

23.040

22.422

22.585

24.311

20.895

24.304

86.868

88.601

86.484

88.467

89.565

85.982

Max

9.441

9.412

9.202

9.919

8.906

9.499

24.436

24.395

23.982

25.346

23.445

24.518

MW

Table A.5 Descriptive-statistical characteristics of modelled atmospheric depositions of Pb, Cd and Hg (EMEP 0.1° × 0.1°, 2014–2016)

2.001

2.004

1.953

2.206

1.944

2.107

8.644

8.750

8.596

8.942

8.816

8.567

SD

VT

(continued)

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

[3]

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps 43

4818

4818

4818

4818

4818

4818

Pb deposition 2014 (0.1° × 0.1°)

Pb deposition 2015 (0.1° × 0.1°)

Pb deposition 2016 (0.1° × 0.1°)

Pb deposition 2014–2015 (0.1° × 0.1°)

Pb deposition 2015–2016 (0.1° × 0.1°)

Pb deposition 2014–2016 (0.1° × 0.1°)

256.42

232.10

256.19

241.70

207.31

286.37

Min

520.57

508.70

520.38

508.72

499.85

537.49

P20

631.14

619.76

639.39

620.67

619.20

656.99

P50

960.35

933.06

984.84

930.05

956.77

1027.83

P90

1404.51

1332.53

1453.61

1275.87

1401.37

1539.80

P98

3245.14

3131.56

3158.11

3419.21

3037.93

3472.30

Max

690.16

673.03

700.40

669.67

676.40

724.40

MW

237.51

228.79

249.43

221.10

245.29

261.90

SD

[3]

[3]

[3]

[3]

[3]

[3]

VT

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 04 in Table 2.1

n

Feature*

Table A.5 (continued)

44 Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

45

Table A.6 Descriptive-statistical characteristics of the mean values of concentrations from technical measurements of Pb, Cd and Hg in the atmosphere (2014–2016, data used for model validation in Ilyin et al. 2020) Feature*

n

Min

P20

P50

MW

SD

VT

Cd concentration 2014

96

0.0289

0.1118

0.1504

0.2503

0.3336

0.4384

0.1659

0.0707

[3]

Cd concentration 2015

99

0.0320

0.0876

0.1161

0.1859

0.2376

0.3217

0.1258

0.0500

[2]

Cd concentration 2016

96

0.0209

0.0848

0.1109

0.1853

0.2808

0.2987

0.1228

0.0536

[3]

Cd concentration 2014–2015

94

0.0305

0.1008

0.1350

0.2134

0.2675

0.3681

0.1449

0.0559

[2]

Cd concentration 2015–2016

92

0.0265

0.0870

0.1072

0.1694

0.2513

0.2982

0.1221

0.0492

[2]

Cd concentration 2014–2016

88

0.0273

0.0960

0.1222

0.1929

0.2764

0.3449

0.1359

0.0544

[3]

Hg concentration 2014

17

0.7906

1.3618

1.4428

1.6726

1.7252

1.7361

1.4290

0.2229

[3]

Hg concentration 2015

16

0.4680

1.403

1.4948

1.6658

1.8306

1.8912

1.4570

0.2989

[3]

Hg concentration 2016

18

0.4743

1.2004

1.4268

1.6696

2.3425

2.625

1.3964

0.4595

[3]

Hg concentration 2014–2015

16

0.6293

1.3521

1.4764

1.6682

1.6997

1.7127

1.4453

0.2525

[3]

Hg concentration 2015–2016

13

0.4711

1.3741

1.4844

1.6460

1.9273

2.0140

1.4506

0.3428

[3]

Hg concentration 2014–2016

13

0.5776

1.3587

1.4781

1.6671

1.7978

1.8348

1.4377

0.2934

[3]

Pb concentration 2014

88

1.3159

3.7923

5.1095

10.3374

17.7393

27.9645

6.1339

4.1818

[3]

Pb concentration 2015

95

1.2259

3.0159

4.4200

7.5599

14.3919

43.2000

5.1825

4.6842

[3]

Pb concentration 2016

88

1.0199

3.0339

3.8249

7.9559

15.2480

30.8999

5.0407

3.9524

[3]

Pb concentration 2014–2015

86

1.2709

3.3944

4.7934

8.8172

16.4351

34.0657

5.7059

4.3111

[3]

Pb concentration 2015–2016

84

1.1229

3.0930

4.1399

7.4454

15.4629

37.0500

5.1085

4.3889

[3]

Pb concentration 2014–2016

80

1.1873

3.2641

4.4162

7.9092

17.0436

33.0104

5.4230

4.2452

[3]

P90

P98

Max

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 06 in Table 2.1

46

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table A.7 Descriptive-statistical characteristic values of the technical measurement data for the wet deposition of Cd in Hg and Pb (2014–2016, data used for model validation in Ilyin et al. 2020) Feature*

n

Cd deposition 2014

55

Min 13.800

P20 21.640

P50 28.500

P90 303.000

P98 450.760

Max 780.000

MW 83.966

SD 145.299

[3]

VT

Cd deposition 2015

70

6.060

15.180

22.650

89.190

246.820

431.000

45.790

68.469

[3]

Cd deposition 2016

45

8.250

10.280

18.300

37.360

183.880

645.000

35.167

94.691

[3]

Cd deposition 2014–2015

53

11.530

18.370

26.950

206.900

399.600

475.500

65.177

101.020

[3]

Cd deposition 2015–2016

44

7.640

11.748

19.450

38.675

243.450

538.000

36.752

82.315

[3]

Cd deposition 2014–2016

33

11.800

16.264

24.430

47.204

282.799

483.330

43.254

83.656

[3]

Hg deposition 2014

30

2.510

3.410

4.555

14.337

15.959

17.600

7.070

4.588

[3]

Hg deposition 2015

32

2.520

4.770

6.720

15.208

28.365

37.690

8.698

6.877

[2]

Hg deposition 2016

30

2.400

4.100

7.810

14.266

17.831

19.310

8.551

4.757

[2]

Hg deposition 2014–2015

29

2.530

4.676

6.410

13.574

19.544

20.910

8.092

4.589

[2]

Hg deposition 2015–2016

32

2.460

4.324

6.790

14.966

21.645

22.730

8.260

5.104

[2]

Hg deposition 2014–2016

29

2.490

4.492

6.870

13.702

17.507

18.750

8.093

4.337

[2]

Pb deposition 2014

69

120.00

232.00

570.00

2212.00

3051.60

14,800.00

991.99

1833.36

[3]

Pb deposition 2015

77

90.00

230.00

410.00

1644.00

4466.80

7110.00

778.69

1123.02

[3]

Pb deposition 2016

55

60.00

118.00

300.00

968.00

2120.80

3530.00

472.40

579.99

[2]

(continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

47

Table A.7 (continued) Feature*

n

Min

P20

P50

P90

P98

Max

MW

SD

VT

Pb deposition 2014–2015

65

115.00

239.00

525.00

1931.00

4293.60

10,455.00

924.89

1436.07

[3]

Pb deposition 2015–2016

53

85.00

184.00

380.00

1049.00

4049.00

5320.00

628.78

898.15

[2]

Pb deposition 2014–2016

41

96.66

250.00

586.66

1193.33

5070.00

7696.66

865.35

1305.04

[2]

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 07 in Table 2.1

Table A.8 Descriptive-statistical characteristics of the technical measurement data on bulk deposition of Cd and Pb (2014–2016, data taken from Ilyin et al. 2020: Tables B1–B6) Feature*

n

Cd deposition 2014

85

13.000

20.340

29.600

73.840

173.532

201.60

41.597

37.301

[3]

Cd deposition 2015

85

9.700

19.560

33.300

116.020

188.484

203.40

50.273

44.843

[3]

Cd deposition 2016

84

7.200

22.800

31.750

80.880

212.550

665.50

49.956

78.303

[3]

Cd deposition 2014–2015

80

14.400

24.040

35.350

101.550

138.352

194.45

46.185

34.103

[3]

Cd deposition 2015–2016

83

9.950

22.850

35.700

94.830

182.930

371.20

50.189

50.762

[2]

Cd deposition 2014–2016

79

12.260

24.784

36.100

77.514

134.265

229.630

44.857

33.181

[3]

Pb deposition 2014

85

385.00

665.80

1144.00

3210.80

8874.48

12,146.0

1740.94

1902.10

[3]

Pb deposition 2015

85

341.00

737.20

1176.00

2817.20

4525.48

9395.0

1489.49

1267.60

[2]

Pb deposition 2016

84

211.00

576.20

1219.00

2293.70

5780.28

9030.0

1394.49

1343.24

[3]

Pb deposition 2014–2015

80

415.50

786.40

1203.75

2846.15

6141.61

10,770.5

1612.17

1539.07

[3]

Min

P20

P50

P90

P98

Max

MW

SD

VT

(continued)

48

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table A.8 (continued) Feature*

n

Min

P20

P50

P90

P98

MW

SD

VT

Pb deposition 2015–2016

83

302.50

705.80

1237.50

2378.20

5128.20

Max 9212.5

1444.09

1274.21

[2]

Pb deposition 2014–2016

79

347.33

704.93

1227.33

2700.40

5638.85

10,190.3

1542.37

1459.27

[2]

n sample size; characteristic values (µg g−1 ): Min minimum; P20 20th percentile; P50 50th percentile; P90 90th percentile; P98 98th percentile; Max maximum; MW arithmetic mean; SD standard deviation; VT distribution; [1] normal distribution; [2] lognormal distribution; [3] other distribution; * for annual ranges: arithmetic mean of annual values; Data basis: data set 08 in Table 2.1

Appendix B: Correlation Analysis B.1 Correlations Between Modelled Mean Concentrations of Heavy Metals in the Atmosphere (EMEP) and Concentrations in Moss (MM2015) See Tables B.1 and B.2. Table B.1 Correlation matrix (Spearman, Pearson) for modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and measured concentrations in moss (MM2015) Atmospheric concentration (EMEP)*

Conc. in moss

Data set (EMEP modellings)

50 km × 50 km

Sample size

181

Cd

Hg

Pb

Cd

Hg

Pb

394

393

396

n.a.

n.a.

0.1° × 0.1°

182

182

–

0.29

**

n.a.

Correlation (Spearman) Concentration 2013

0.18

Concentration 2014

–

–

0.23

**

0.10

*

0.17

**

0.19

**

Concentration 2015

–

–

0.22

**

0.13

**

0.16

**

0.20

**

Concentration 2016

n.a.

n.a.

n.a.

0.12

*

0.17

**

0.20

**

Concentration 2013–2015

–

–

0.26

**

n.a.

Concentration 2014–2015

–

–

0.23

**

0.12

*

n.a. *

0.17

n.a. **

0.19

**

(continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

49

Table B.1 (continued) Atmospheric concentration (EMEP)*

Conc. in moss Cd

Hg

Pb

Cd

Concentration 2015–2016

n.a.

n.a.

n.a.

0.13

*

0.17

**

0.20

**

Concentration 2014–2016

n.a.

n.a.

n.a.

0.12

*

0.17

**

0.20

**

Hg

Pb

Correlation (Pearson) Concentration 2013

–

–

0.22

**

n.a.

Concentration 2014

–

–

0.21

**

0.1

*

0.11

*

0.23

**

Concentration 2015

–

–

0.24

**

0.11

*

0.10

*

0.25

**

Concentration 2016

n.a.

n.a.

n.a.

0.12

*

0.10

*

0.25

**

Concentration 2013–2015

–

–

0.24

**

n.a.

Concentration 2014–2015

–

–

0.23

**

0.11

*

0.11

*

0.24

**

Concentration 2015–2016

n.a.

n.a.

n.a.

0.11

*

0.10

*

0.25

**

Concentration 2014–2016

n.a.

n.a.

n.a.

0.11

*

0.11

*

0.24

**

n.a.

n.a.

n.a.

n.a.

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 01, 03, 10 in Table 2.1 Table B.2 Correlation matrix (Spearman, Pearson) for modelled mean concentrations of Cd, Hg and Pb in the atmosphere (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) Atmospheric concentration (EMEP)*

Conc. in moss (3 km × 3 km)

Data set (EMEP modellings)

50 km × 50 km

Sample size

201

Cd

Hg

Pb

Cd

Hg

Pb

4018

4018

4018

n.a.

n.a.

0.1° × 0.1°

201

201

Correlation (Spearman) Concentration 2013

0.35

**

–

0.35

**

n.a.

Concentration 2014

0.29

**

–

0.35

**

0.20

**

0.31

**

0.40

**

(continued)

50

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table B.2 (continued) Atmospheric concentration (EMEP)*

Conc. in moss (3 km × 3 km) Cd

Hg

Pb

Cd

Concentration 2015

–

–

0.18

Concentration 2016

n.a.

n.a.

n.a.

Concentration 2013–2015

0.30

**

–

0.32

**

n.a.

Concentration 2014–2015

0.22

**

–

0.27

**

0.22

**

0.30

**

0.41

**

Concentration 2015–2016

n.a.

n.a.

n.a.

0.24

**

0.30

**

0.42

**

Concentration 2014–2016

n.a.

n.a.

n.a.

0.23

**

0.31

**

0.41

**

*

Hg

Pb

0.25

**

0.28

**

0.42

**

0.23

**

0.32

**

0.42

**

n.a.

n.a.

Correlation (Pearson) Concentration 2013

0.35

**

–

0.35

**

n.a.

Concentration 2014

0.31

**

–

0.34

**

0.42

**

0.28

**

0.57

**

Concentration 2015

–

–

0.18

*

0.45

**

0.25

**

0.59

**

Concentration 2016

n.a.

n.a.

n.a.

0.46

**

0.26

**

0.59

**

Concentration 2013–2015

0.34

**

–

0.30

**

n.a.

Concentration 2014–2015

0.28

**

–

0.27

**

0.44

**

0.27

**

0.59

**

Concentration 2015–2016

n.a.

n.a.

n.a.

0.45

**

0.26

**

0.59

**

Concentration 2014–2016

n.a.

n.a.

n.a.

0.44

**

0.27

**

0.59

**

n.a.

n.a.

n.a.

n.a.

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 01, 03, 11 in Table 2.1

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

51

B.2 Correlations Between Modelled Atmospheric Heavy Metal Deposition (EMEP) and Concentrations in Moss (MM2015) See Tables B.3, B.4, B.5, B.6, B.7 and B.8. Table B.3 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and measured concentrations in moss (MM2015) Atmospheric deposition (EMEP)*

Conc. in moss

Data set (EMEP modellings)

50 km × 50 km

Sample size

181

Cd

Hg

Pb

Cd

Hg

Pb

394

393

396

n.a.

n.a.

0.1° × 0.1°

182

182

Correlation (Spearman) Deposition 2013

0.18

*

–

0.34

**

n.a.

Deposition 2014

0.17

*

–

0.18

*

0.16

**

–

0.26

**

Deposition 2015

0.15

*

–

0.19

*

0.14

**

–

0.23

**

Deposition 2016

n.a.

n.a.

n.a.

0.18

**

–

0.24

**

Deposition 2013–2015

0.16

*

–

0.27

**

n.a.

n.a.

n.a.

Deposition 2014–2015

0.16

*

–

0.21

**

0.15

**

–

0.25

**

Deposition 2015–2016

n.a.

n.a.

n.a.

0.16

**

–

0.24

**

Deposition 2014–2016

n.a.

n.a.

n.a.

0.16

**

–

0.25

**

–

0.3

**

n.a.

n.a.

n.a.

–

0.18

*

0.16

**

–

0.25

**

–

0.29

**

0.12

*

–

0.25

**

n.a.

n.a.

0.16

**

–

0.28

**

–

0.3

n.a.

n.a.

Correlation (Pearson) Deposition 2013

0.17

Deposition 2014

–

Deposition 2015

0.15

Deposition 2016

n.a.

Deposition 2013–2015

0.16

*

*

*

**

n.a.

(continued)

52

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table B.3 (continued) Atmospheric deposition (EMEP)*

Conc. in moss Cd *

Hg

Pb

–

0.25

Deposition 2014–2015

0.15

Deposition 2015–2016

n.a.

n.a.

Deposition 2014–2016

n.a.

n.a.

Cd **

Hg

Pb

0.14

**

–

0.26

**

n.a.

0.14

**

–

0.27

**

n.a.

0.15

**

–

0.27

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 02, 04, 10 in Table 2.1

Table B.4 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg and Pb (EMEP) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) Atmospheric deposition (EMEP)*

Conc. in moss (3 km × 3 km)

Data set (EMEP modellings)

50 km × 50 km

Sample size

201

Cd

Hg

Pb

Cd

Hg

Pb

4018

4018

4018

n.a.

n.a.

0.1° × 0.1°

201

201

Correlation (Spearman) Deposition 2013

0.44

**

–

0.43

**

n.a.

Deposition 2014

0.31

**

–

0.35

**

0.24

**

–

0.38

**

Deposition 2015

0.21

**

0.15

–

0.19

**

–

0.32

**

Deposition 2016

n.a.

n.a.

n.a.

0.24

**

–

0.32

**

Deposition 2013–2015

0.39

**

–

0.43

**

n.a.

n.a.

n.a.

Deposition 2014–2015

0.35

**

0.15

0.31

**

0.21

**

–

0.36

**

Deposition 2015–2016

n.a.

n.a.

n.a.

0.22

**

–

0.33

**

Deposition 2014–2016

n.a.

n.a.

n.a.

0.22

**

–

0.35

**

n.a.

n.a.

*

*

Correlation (Pearson) Deposition 2013

0.44

**

–

0.41

**

n.a.

Deposition 2014

0.30

**

–

0.34

**

0.36

**

–

0.45

**

Deposition 2015

0.26

**

–

0.14

*

0.3

**

–

0.42

**

Deposition 2016

n.a.

n.a.

n.a.

0.37

**

–

0.44

** (continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

53

Table B.4 (continued) Atmospheric deposition (EMEP)*

Conc. in moss (3 km × 3 km)

Deposition 2013–2015

0.40

Deposition 2014–2015

0.35

Deposition 2015–2016 Deposition 2014–2016

Hg

Pb

**

–

0.39

**

–

0.30

n.a.

n.a.

n.a.

n.a.

Cd

Cd

Hg

Pb

**

n.a.

n.a.

n.a.

**

0.33

**

–

0.45

**

n.a.

0.34

**

–

0.44

**

n.a.

0.35

**

–

0.45

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 02, 04, 11 in Table 2.1

Table B.5 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (CLC-derived land use) Atmospheric deposition (EMEP)*

Conc. in moss

Data set (EMEP modellings)

0.1° × 0.1°, not land use specific

0.1° × 0.1°, land use specific

Sample size

394

391

Cd

Hg

Pb

393

396

Cd

Hg

Pb

390

393

Correlation (Spearman) Deposition 2014

0.16

**

–

0.26

**

0.21

**

0.10

Deposition 2015

0.14

**

–

0.23

**

0.20

**

Deposition 2016

0.18

**

–

0.24

**

0.22

Deposition 2014–2015

0.15

**

–

0.25

**

Deposition 2015–2016

0.16

**

–

0.24

Deposition 2014–2016

0.16

**

–

*

0.21

**

–

0.19

**

**

–

0.21

**

0.21

**

–

0.21

**

**

0.21

**

–

0.20

**

0.25

**

0.21

**

–

0.21

**

*

–

0.19

**

–

0.19

**

–

0.23

**

Correlation (Pearson) Deposition 2014

0.16

**

–

0.25

**

0.13

Deposition 2015

0.12

*

–

0.25

**

0.10

Deposition 2016

0.16

**

–

0.28

**

0.13

**

(continued)

54

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table B.5 (continued) Atmospheric deposition (EMEP)*

Conc. in moss Cd

Hg

Pb

Cd

Hg

Pb

Deposition 2014–2015

0.14

**

–

0.26

**

0.11

*

–

0.19

**

Deposition 2015–2016

0.14

**

–

0.27

**

0.11

*

–

0.21

**

Deposition 2014–2016

0.15

**

–

0.27

**

0.12

*

–

0.20

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 04, 05, 10 in Table 2.1 Table B.6 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) in all land use types (land use surveyed in MM2015) Atmospheric deposition (EMEP)*

Conc. in moss

Data set (EMEP modellings)

0.1° × 0.1°, not land use specific

0.1° × 0.1°, land use specific

Sample size

394

359

Cd

Hg

Pb

393

396

Cd

Hg

Pb

356

359

Correlation (Spearman) Deposition 2014

0.16

**

–

0.26

**

0.24

**

0.18

**

0.27

**

Deposition 2015

0.14

**

–

0.23

**

0.22

**

0.12

*

0.24

**

Deposition 2016

0.18

**

–

0.24

**

0.25

**

0.16

**

0.26

**

Deposition 2014–2015

0.15

**

–

0.25

**

0.23

**

0.15

**

0.26

**

Deposition 2015–2016

0.16

**

–

0.24

**

0.23

**

0.15

**

0.25

**

Deposition 2014–2016

0.16

**

–

0.25

**

0.24

**

0.16

**

0.26

**

Correlation (Pearson) Deposition 2014

0.16

**

–

0.25

**

0.15

**

0.16

**

0.27

**

Deposition 2015

0.12

*

–

0.25

**

0.11

*

0.16

**

0.26

**

Deposition 2016

0.16

**

–

0.28

**

0.15

**

0.16

**

0.30

**

(continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

55

Table B.6 (continued) Atmospheric deposition (EMEP)*

Conc. in moss Cd

Hg

Pb

Cd

Hg

Pb

Deposition 2014–2015

0.14

**

–

0.26

**

0.13

*

0.16

**

0.27

**

Deposition 2015–2016

0.14

**

–

0.27

**

0.13

*

0.16

**

0.28

**

Deposition 2014–2016

0.15

**

–

0.27

**

0.14

**

0.16

**

0.28

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 04, 05, 10 in Table 2.1 Table B.7 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use derived from CLC) Atmospheric deposition (EMEP)*

Conc. in moss

Data set (EMEP modellings)

0.1° × 0.1°, not land use specific

0.1° × 0.1°, land use specific

Sample size

350

350

Cd

Hg

Pb

349

352

Cd

Hg

Pb

349

352

Correlation (Spearman) Deposition 2014

0.14

**

–

0.25

**

0.16

**

0.11

Deposition 2015

0.13

*

–

0.22

**

0.15

**

Deposition 2016

0.16

**

–

0.24

**

0.17

Deposition 2014–2015

0.14

*

–

0.24

**

Deposition 2015–2016

0.14

**

–

0.23

Deposition 2014–2016

0.15

**

–

*

0.20

**

–

0.17

**

**

–

0.20

**

0.16

**

–

0.19

**

**

0.16

**

–

0.19

**

0.24

**

0.16

**

–

0.19

**

0.19

**

Correlation (Pearson) Deposition 2014

0.14

**

–

0.26

**

–

0.11

Deposition 2015

0.11

*

–

0.26

**

–

–

0.19

**

Deposition 2016

0.15

**

–

0.29

**

–

–

0.23

**

*

(continued)

56

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table B.7 (continued) Atmospheric deposition (EMEP)*

Conc. in moss Cd

Hg

Pb

Cd

Hg

Pb

Deposition 2014–2015

0.13

*

–

0.26

**

–

–

0.20

**

Deposition 2015–2016

0.13

*

–

0.28

**

–

–

0.21

**

Deposition 2014–2016

0.13

*

–

0.27

**

–

–

0.21

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 04, 05, 10 in Table 2.1 Table B.8 Correlation matrix (Spearman, Pearson) for modelled total annual atmospheric depositions of Cd, Hg, and Pb (EMEP) and measured concentrations in moss (MM2015) at forest sites (land use surveyed in MM2015) Atmospheric deposition (EMEP)*

Conc. in moss

Data set (EMEP modellings)

0.1° × 0.1°, not land use specific

0.1° × 0.1°, land use specific

Sample size

339

320

Cd

Hg

Pb

Cd

336

339

–

0.25

**

0.17

–

0.22

**

Hg

Pb

317

320

**

–

0.23

**

0.15

**

–

0.2

**

Correlation (Spearman) Deposition 2014

0.12

*

Deposition 2015 Deposition 2016

0.14

*

–

0.24

**

0.17

**

–

0.23

**

Deposition 2014–2015

0.11

*

–

0.24

**

0.16

**

–

0.22

**

Deposition 2015–2016

0.12

*

–

0.23

**

0.16

**

–

0.21

**

Deposition 2014–2016

0.12

*

–

0.24

**

0.16

**

–

0.22

**

*

–

0.24

**

–

0.24

**

–

0.28

**

Correlation (Pearson) Deposition 2014

0.15

**

–

0.25

**

0.13

Deposition 2015

0.11

*

–

0.25

**

–

Deposition 2016

0.15

**

–

0.28

**

0.13

*

(continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

57

Table B.8 (continued) Atmospheric deposition (EMEP)*

Conc. in moss Cd

Hg

Pb

Cd

Deposition 2014–2015

0.13

*

–

0.25

**

0.11

Deposition 2015–2016

0.13

*

–

0.27

**

–

Deposition 2014–2016

0.14

*

–

0.26

**

0.12

*

*

Hg

Pb

–

0.24

**

–

0.26

**

–

0.25

**

** very significant; * significant; – not significant or implausible; n.a. not specified (missing EMEP information); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 04, 05, 10 in Table 2.1

B.3 Correlations Between Mean Concentration Values from Technical Measurements of Heavy Metals in the Atmosphere and Concentrations in Moss See Table B.9. Table B.9 Correlation matrix (Spearman, Pearson) for mean values of concentrations from technical measurements of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) Atmospheric concentration (measured)*

Conc. in moss (3 km × 3 km) n

Cd

n

Hg

n

Pb

Correlation (Spearman) Concentration 2014

95

–

4

–

87

0.36

**

Concentration 2015

98

–

4

–

94

0.21

*

Concentration 2016

96

–

4

–

88

0.29

**

Concentration 2014–2015

94

–

4

–

86

0.35

**

Concentration 2015–2016

92

–

4

–

84

0.31

**

Concentration 2014–2016

88

–

4

–

80

0.32

** (continued)

58

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Table B.9 (continued) Atmospheric concentration (measured)*

Conc. in moss (3 km × 3 km) n

Cd

n

Hg

n

Pb

4

–

87

0.28

4

–

94

–

Correlation (Pearson) Concentration 2014

95

0.27

**

Concentration 2015

98

–

Concentration 2016

96

0.22

*

4

–

88

0.25

*

Concentration 2014–2015

94

0.22

*

4

–

86

0.25

*

Concentration 2015–2016

92

–

4

–

84

0.23

*

Concentration 2014–2016

88

0.22

4

–

80

0.26

*

*

**

** very significant; * significant; – not significant or implausible; * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 06, 11 in Table 2.1

B.4 Correlations Between Technical Measurements of Atmospheric Heavy Metal Deposition and Concentrations in Moss See Tables B.10 and B.11. Table B.10 Correlation matrix (Spearman, Pearson) for technical measurements of wet atmospheric deposition of Pb, Cd and Hg (data used for model validation in Ilyin et al. 2020) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) Atmospheric deposition (measured)*

Conc. in moss (3 km × 3 km) n

Cd

n

Hg

n

Pb

Correlation (Spearman) Deposition 2014

53

–

30

–

68

–

Deposition 2015

68

–

32

–

76

–

Deposition 2016

45

–

30

–

55

–

Deposition 2014–2015

51

–

29

–

64

–

Deposition 2015–2016

44

–

32

–

53

–

Deposition 2014–2016

33

–

29

–

41

– (continued)

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

59

Table B.10 (continued) Atmospheric deposition (measured)*

Conc. in moss (3 km × 3 km) n

Cd

n

Hg

n

Pb

Correlation (Pearson) Deposition 2014

53

–

30

–

68

–

Deposition 2015

68

–

32

–

76

–

Deposition 2016

45

–

30

–

55

–

Deposition 2014–2015

51

–

29

–

64

–

Deposition 2015–2016

44

–

32

–

53

–

Deposition 2014–2016

33

–

29

–

41

–

** very significant; * significant; – not significant or implausible; * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 07, 11 in Table 2.1 Table B.11 Correlation matrix (Spearman, Pearson) for technical measurements of bulk atmospheric deposition of Pb, Cd and Hg (data taken from Ilyin et al. 2020: Tables B1–B6) and geostatistical surface estimations of concentration in moss (MM2015, 3 km × 3 km) Atmospheric deposition (measured)*

Conc. in moss (3 km × 3 km) n

Cd

N

Hg

n

Pb

n.a.

n.a.

65

0.54

Correlation (Spearman) Deposition 2014 65

0.63

**

**

Deposition 2015 67

–

n.a.

n.a.

67

–

Deposition 2016 66

–

n.a.

n.a.

66

0.50

**

Deposition 2014–2015

62

–

n.a.

n.a.

62

0.41

**

Deposition 2015–2016

65

–

n.a.

n.a.

65

0.34

**

Deposition 2014–2016

61

–

n.a.

n.a.

61

0.45

**

Correlation (Pearson) Deposition 2014 65

0.56

Deposition 2015 67

–

**

n.a.

n.a.

65

0.55

**

n.a.

n.a.

67

0.32

**

Deposition 2016 66

–

n.a.

n.a.

66

0.59

**

Deposition 2014–2015

62

–

n.a.

n.a.

62

0.50

**

Deposition 2015–2016

65

–

n.a.

n.a.

65

0.49

**

Deposition 2014–2016

61

–

n.a.

n.a.

61

0.55

**

** very significant; * significant; – not significant or implausible; n.a. no information (missing technical measurements); * for annual ranges: arithmetic mean of the annual values; Data basis: data sets 08, 11 in Table 2.1

60

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

B.5 Graphical Comparison of the Correlation Coefficients See Figs. B.1, B.2, B.3, B.4, B.5, B.6, B.7, B.8, B.9, B.10, B.11, B.12, B.13, B.14, B.15, B.16, B.17, B.18, B.19 and B.20.

Fig. B.1 Correlations (Spearman) between modelled atmospheric concentrations and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.1. Data basis: data sets in 01, 03, 10 Table 2.1

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

61

Fig. B.2 Correlations (Pearson) between modelled atmospheric concentrations and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.1. Data basis: data sets in 01, 03, 10 Table 2.1

Fig. B.3 Correlations (Spearman) between modelled atmospheric concentrations and geostatistical surface estimations of MM2015—comparison of EMEP 50 km × 50 km and EMEP 0.1° × 0.1° spatial resolutions. Graphical comparison of correlation coefficients from Table B.2. Data basis: data sets in 01, 03, 11 Table 2.1

62

Appendices: Statistical Data Analyses—Tables, Diagrams, Maps

Fig. B.4 Correlations (Pearson) between modelled atmospheric concentrations and geostatistical surface estimations of MM2015—comparison of EMEP 50 km × 50 km and EMEP 0.1° × 0.1° spatial resolutions. Graphical comparison of correlation coefficients from Table B.2. Data basis: data sets in 01, 03, 11 Table 2.1

Fig. B.5 Correlations (Spearman) between modelled atmospheric depositions and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.3. Data basis: data sets in 02, 04, 10 Table 2.1

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Fig. B.6 Correlations (Pearson) between modelled atmospheric depositions and measured data of MM2015—comparison of the spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.3. Data basis: data sets in 02, 04, 10 Table 2.1

Fig. B.7 Correlations (Spearman) between modelled atmospheric deposition and geostatistical surface estimations of MM2015—comparison of spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.4. Data basis: data sets in 02, 04, 11 Table 2.1

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Fig. B.8 Correlations (Pearson) between modelled atmospheric deposition and geostatistical surface estimations of MM2015—comparison of spatial resolutions EMEP 50 km × 50 km and EMEP 0.1° × 0.1°. Graphical comparison of correlation coefficients from Table B.4. Data basis: data sets in 02, 04, 11 Table 2.1

Fig. B.9 Correlations (Spearman) between modelled atmospheric concentrations (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.1 and B.2. Data basis: data sets in 03, 10, 11 Table 2.1

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Fig. B.10 Correlations (Pearson) between modelled atmospheric concentrations (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.1 and B.2. Data basis: data sets in 03, 10, 11 Table 2.1

Fig. B.11 Correlations (Spearman) between modelled atmospheric deposition (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.3 and B.4. Data basis: data sets in 04, 10, 11 Table 2.1

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Fig. B.12 Correlations (Pearson) between modelled atmospheric deposition (EMEP 0.1° × 0.1°) and MM2015 data—comparison of geostatistical surface estimations and measured concentrations in moss. Graphical comparison of correlation coefficients from Tables B.3 and B.4. Data basis: data sets in 04, 10, 11 Table 2.1

Fig. B.13 Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015— comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.1 and B.3. Data basis: data sets in 03, 04, 10 Table 2.1

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Fig. B.14 Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015— comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.1 and B.3. Data basis: data sets in 03, 04, 10 Table 2.1

Fig. B.15 Correlations (Spearman) between EMEP 0.1° × 0.1° and geostatistical surface estimations of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.2 and B.4. Data basis: data sets in 03, 04, 11 Table 2.1

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Fig. B.16 Correlations (Pearson) between EMEP 0.1° × 0.1° and geostatistical surface estimations of MM2015—comparison of modelled atmospheric concentrations and depositions. Graphical comparison of correlation coefficients from Tables B.2 and B.4. Data basis: data sets in 03, 04, 11 Table 2.1

Fig. B.17 Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015 (all sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients from Tables B.3, B.5 and B.6. Data basis: data sets in 04, 05, 10 Table 2.1

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Fig. B.18 Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015 (all sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients from Tables B.3, B.5 and B.6. Data basis: data sets in 04, 05, 10 Table 2.1

Fig. B.19 Correlations (Spearman) between EMEP 0.1° × 0.1° and measured data of MM2015 (forest sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients calculated and from Tables B.2 and B.4. Data basis: data sets in 04, 05, 10 Table 2.1

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Fig. B.20 Correlations (Pearson) between EMEP 0.1° × 0.1° and measured data of MM2015 (forest sites)—comparison of the land use-specific and according to the use distribution weighted modelled atmospheric depositions. Graphical comparison of correlation coefficients calculated and from Tables B.2 and B.4. Data basis: data sets in 04, 05, 10 Table 2.1

Appendix C: Comparison of the Federal and State Air Quality Monitoring Network with the Moss Monitoring Network 2015 See Fig. C.1.

Fig. C.1 Distribution of the smallest distances between the stations of the federal and state air quality monitoring network and the sites of MM2015. Data basis: data sets in 06, 07, 08, 09, 10 Table 2.1

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Appendix D: Map Annex See Figs. D.1, D.2, D.3, D.4, D.5, D.6, D.7, D.8, D.9, D.10 and D.11.

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Fig. D.1 Spatial distributions of deviations from the nationwide Cd median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets in 03, 04, 11 in Table 2.1

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Fig. D.2 Spatial distributions of deviations from the nationwide Hg median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets in 03, 04, 11 in Table 2.1

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Fig. D.3 Spatial distributions of deviations from the nationwide Pb median of modelled atmospheric concentration and deposition EMEP (0.1° × 0.1°) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 03, 04, 11 in Table 2.1

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Fig. D.4 Spatial distributions of deviations from the nationwide Cd median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1

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Fig. D.5 Spatial distributions of deviations from the nationwide Hg median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1

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Fig. D.6 Spatial distributions of deviations from the nationwide Pb median of technically measured atmospheric concentrations (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 06, 11 in Table 2.1

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Fig. D.7 Spatial distributions of deviations from the nationwide Cd median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1

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Fig. D.8 Spatial distributions of deviations from the nationwide Hg median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1

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Fig. D.9 Spatial distributions of deviations from the nationwide Pb median of technically measured atmospheric wet deposition (Ilyin et al. 2020) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 07, 11 in Table 2.1

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Fig. D.10 Spatial distributions of deviations from the nationwide Cd median of technically measured bulk atmospheric deposition (Ilyin et al. 2020: Annex B) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 08, 11 in Table 2.1

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Fig. D.11 Spatial distributions of deviations from the nationwide Pb median of technically measured bulk atmospheric deposition (Ilyin et al. 2020: Annex B) and geostatistically estimated concentrations in moss (MM2015). Data basis: data sets 08, 11 in Table 2.1

Reference Ilyin I, Travnikov O, Schütze G, Feigenspan S, Uhse K (2020) Country-scale assessment of heavy metal pollution: a case study for Germany. Technical report 1/2020. Meteorological Synthesizing Centre-East, Moscow, Russia; German Environment Agency, Dessau, Germany, pp 1–121