Country-Scale Assessment of Heavy Metal Pollution: a Case Study for Germany

EHIA REPORT TECHNICAL

0Jn 20 June 20 1 2 20 /20

Country-scale assessment of heavy metal pollution: A case study for Germany

TechnicalReport 1 /20 20

Joint MSC-E &German Environment Agency Report

MSC-E Technical Report 1/2020 June 2020

Country-scale assessment of heavy metal pollution: A case study for Germany

METEOROLOGICAL SYNTHESIZING CENTRE - EAST

I. Ilyin and O. Travnikov

German Environment Agency

G. Schütze, S. Feigenspan and K. Uhse

Meteorological Synthesizing Centre - East German Environment Agency

2nd Roshchinsky proezd, 8/5, 115419 Moscow, Russia Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany Phone.: +7 926 906 91 78, Fax: +7 495 956 19 44 Telephone: +49-340-2103-0, Fax: +49-340-2103-2285 E-mail: [email protected] Email: [email protected] www.msceast.org www.umweltbundesamt.de

CONTENTS

1. INTRODUCTION 5 2. NATIONAL DATA 7 2.1. Anthropogenic emissions 7 2.2. Measurement data 8 2.3. Land cover 14 2.4. Soil texture 15 2.5. Heavy metal concentration in soil 16 3. MODEL 17 3.1. Model setup 17 3.2. Model updates 18 3.2.1. Meteorological data 18 3.2.2. Wind re-suspension of particulate heavy metals 19 3.2.3. Natural and legacy emissions of Hg from soil 20 3.3. Multi-scale simulation procedure 21 3.4. Statistics applied in the analysis 21 4. SPATIAL PATTERNS OF HEAVY METAL POLLUTION IN GERMANY 23 4.1. Lead 23 4.2. Cadmium 25 4.3. Mercury 28 5. EVALUATION OF MODELING RESULTS VS. OBSERVATIONS 31 5.1. Lead 31 5.2. Cadmium 41 5.3. Mercury 49 6. COMPARISON WITH PREVIOUS EMEP ESTIMATES 59 6.1. Lead 59 6.2. Cadmium 64 6.3. Mercury 68 7. Conclusion and recommendations 72 References 74 Annex A. Stations used in the model evaluation 76 Annex B. Observed bulk deposition and modelled wet deposition fluxes 78 at German stations in 2014-2016 Annex C. Time series of Pb, Cd and Hg monthly mean modelled and observed concentrations 84 in air and monthly sums of wet deposition fluxes in 2014-2016 Annex D. Format of numerical data 119

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ACKNOWLEDGEMENTS

The project was jointly funded by the Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP) and the German Environment Agency (UBA). The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www.ready.noaa.gov) used in this publication. The authors also wish to thank Irina Strizhkina for preparation of the graphics and other editorial work.

1. INTRODUCTION

Heavy metals and their compounds are toxic pollutants, which can harmfully impact human health and ecosystems. These substances are dangerous at low exposure levels and have acute and chronic effects on human health. In particular, they are multi-organ toxicants and adversely affect neurological, cardiovascular, renal, gastrointestinal, hematological and reproductive systems. In the environment, heavy metals are toxic to plants, animals and micro-organisms. They can bioaccumulate in food webs and impact both terrestrial and aquatic wildlife.

A case study of heavy metal pollution in Germany is performed jointly by Meteorological Synthesizing Centre - East (MSC-E) of the Cooperative Programme for Monitoring and Evaluation of the Long- range Transmission of Air Pollutants in Europe (EMEP) and the German Environment Agency (UBA). The main objective of the study is generation of detailed information on levels and spatial distribution of air concentration and atmospheric deposition of three heavy metals – lead (Pb), cadmium (Cd), and mercury (Hg) – over the country’s territory for the period from 2014 to 2016. Results of the study will be used by the German Environment Agency (UBA) for integrative evaluation of available information about heavy metal pollution of various ecosystems (e.g. for comparison with metal concentrations in mosses) and for subsequent risk assessment.

The whole program of the study is divided into two stages. The first stage consisted of preparation of variety of input information required for the study (anthropogenic and secondary emissions, measurement data, meteorological fields, etc.), its analysis and integration into the MSC-E modeling system, model simulations of heavy metal transport and deposition over the country with fine spatial resolution, and testing the model performance. The second stage included detailed evaluation of the modelling results against observations, analysis of discrepancies, and formulation of recommendations for improvement of the assessment quality both on national and regional scales.

As a result of the study MSC-E has provided UBA with modelling results of heavy metal pollution in Germany. The numerical data include yearly maps of Pb, Cd, and Hg concentration in air and atmospheric deposition for Germany with a spatial resolution of 0.1°x0.1° for the period 2014-2016. The dataset also includes maps of heavy metal deposition to individual land cover types (CORINE classification).

This report presents main results of the study. An overview of national data collected for the project by UBA experts (anthropogenic emissions, measurement data, land cover information, soil texture, and heavy metal concentration in soils) is given in Chapter 2. A short description of the MSC-E modeling system (GLEMOS) and model updates performed for the study (generation of country-scale meteorological data, integration of national data for improvement of heavy metal secondary emissions) are given in Chapter 3. Chapter 4 describes the main output of the study – spatial patterns

5 of heavy metal concentration and deposition over the territory of Germany based on both model assessment and measurement data. Detailed comparison of the modeling results with observations and analysis of the revealed deviations are presented in Chapter 5. Chapter 6 includes comparison of the current assessment results with previous estimates of heavy metal pollution performed as a part of the EMEP activities. Main conclusions and recommendations of the study are formulated in Chapter 7.

2. NATIONAL DATA

Variety of detailed national scale data were prepared and provided by German national experts (UBA) for aims of the study. The data include an updated inventory of heavy metal anthropogenic emissions in the country, measurements of air concentration and wet deposition from the national monitoring network, information on land cover, soil texture and heavy metal concentration in top soil of the country.

2.1. Anthropogenic emissions

Information on anthropogenic emissions of Pb, Cd and Hg in 2014, 2015 and 2016 (submission 2018 - inland principle) were prepared with spatial resolution 0.1°x0.1° and vertical height level information of the model (0-83, 83-166, 166-301, 301-543, 543-861, 861-1228 meter). These data were generated as a part of the national project GRETA (Gridding Emission Tool for ArcGIS) [Schneider et al., 2016]. Emissions of other countries falling in the model domain were prepared by MSC-E using the EMEP gridded data produced by Centre on Emission Inventories and Projections (CEIP). The resulting maps of spatial distribution of Pb, Cd and Hg anthropogenic emissions in 2015 are shown in Fig. 2.1. Changes of both total values and spatial distributions of heavy metal emissions in Germany were insignificant during the considered period 2014-2016.

a b c

Fig. 2.1. Spatial distribution of anthropogenic emissions of Pb (a), Cd (b) and Hg (c) in Germany in 2015.

Emission data for Germany are split into several source categories in accordance with the GNFR (Gridded Nomenclature For Reporting) classification adopted in the Convention. The major part of Cd emission in Germany falls on sector ‘Industry’, which contributes about 70% to national total

7 emission (Fig. 2.2). This sector is also an important contributor to emissions of Pb (37%) and Hg (30%). About 60% of Hg national emission comes from the sector ‘Public power’. Sector ‘Road Transport’ is responsible for about 40% of Pb emissions.

Along with gridded emission data the GLEMOS modelling system requires additional parameters of Industry heavy metal emissions, namely, intra-annual variations, Public Power distribution of emissions with height and chemical Resid. Combustion speciation of Hg emissions. Necessary vertical and Solvents Cd temporal disaggregation of the emissions was Road Transport Pb generated using emission pre-processing tool, Other Hg developed by MSC-E for the GLEMOS modelling system 0 20 40 60 80 [Ilyin et al., 2018]. Chemical speciation of Hg emissions Contrib. to total emission, % assumes that 90% of Hg is emitted in elemental gaseous form (Hg0) and 10% is emitted as oxidized Hg Fig. 2.2. Contribution of GNFR emission sectors to total emissions of Pb, Cd and Hg in Germany in (HgII) equally divided between gaseous oxidized Hg and 2016 particulate Hg.

2.2. Measurement data

Information on measured levels of Pb, Cd and Hg in air and wet deposition fluxes in Germany is available from a number of stations of the EMEP monitoring network. Air concentrations and wet deposition of Pb and Cd are measured at 6 EMEP stations located in Germany, whereas Hg levels are observed at 5 EMEP stations. Besides, measurements of air concentration and wet deposition of Pb and Cd are available from about 120 measurement stations from national monitoring networks. Information on Hg wet deposition fluxes is reported by 65 national stations. However, in each of the considered years the number of stations reported the observed values differs.

EMEP monitoring stations represent pollution levels typical for locations remote from main anthropogenic sources. The set of national (non-EMEP) monitoring data includes observations from stations of different types. The classification according to the station location distinguishes urban, suburban and rural stations. Besides, predominant influence of particular emission sources defines splitting into industrial, traffic and background stations. Therefore, type of monitoring station is taken into account in the analysis of the discrepancies between modeled and observed levels. In particular, preliminary comparison of modelled and observed levels demonstrates that the model strongly underestimates concentrations of Pb and Cd observed at industrial stations. The data from these stations are not used in the further model evaluation, since the model was not developed for this purpose, but for accounting of long-range transport.

Non-EMEP measurement data for Pb, Cd and Hg stem from different networks of the German federal states (provinces) and the German Weather Service (DWD). The networks have been established historically for different purposes, with different intensity and methodologies of measurement.

8 In the framework of the European Directive 2008/50/EC on ambient air quality and cleaner air for Europe and the Directive 2004/107/EC relating to arsenic (As), cadmium, mercury, nickel (Ni) and polycyclic aromatic hydrocarbons (PAH) ambient air concentrations of different gaseous and particulate pollutants are measured at over 600 air monitoring stations (air quality network) throughout Germany. At a subset of those stations the concentrations of Cd, Pb, As and Ni in PM10 are monitored and stations’ annual means are compared with target values (Cd, Ni, As) or limit values (Pb) for the protection of human health. The directives provide reference methods for the sampling and analysis, minimum numbers of stations, their characterization and descriptions of stations and requirements of data quality and coverage. In Germany it is the task of the 16 individual federal states to monitor the air quality in regard to human health. The network of the German Environment Agency (UBA), which also delivers data to EMEP, is designed to measure air quality in very remote areas to provide basic information on background air quality. Within this network also Hg concentrations are measured. Beside the concentrations of Cd, Pb, As and Ni in PM10 some networks measure bulk deposition data in relation to the Directive. Due to the defined reporting parameter (unit: ng/m².day) these data on bulk deposition are not delivered with information on concentrations in precipitation and precipitation amount.

Most of the data on heavy metal deposition, which were used in the national case study for comparison with model output, are either from the German provinces or DWD. Data from the provinces are from environmental monitoring stations in forests (most of them ICP Forest, level II) or from hydrological observation networks, which are at some places completed by deposition measurements. Data from environmental monitoring in forests and hydrological networks are bulk, while DWD data are wet only measurements. UBA receives these data on a voluntary basis, but there is no coordinated reporting to UBA. The data are delivered as quality checked. Responsible for the data quality are the networks in the provinces, but there are no defined data quality objectives or reference methods.

Different methods in sampling as well as chemical analysis are used in the networks. This causes limitations of comparability. Therefore, more detailed information on the characteristics and equipment of the measurement stations and the methodology is needed to reduce uncertainties in the interpretation of spatial distribution of measured heavy metal deposition and comparison with modelled deposition rates. This detailed analysis together with the 16 provinces was not possible by UBA within the time frame of the national case study with EMEP, but should be subject to further work.

Temporal resolution of the measured concentrations in air or deposition fluxes differs widely among the stations. More than half of national stations measure wet deposition of Cd and Pb with one month resolution, and about quarter of stations – with weekly resolution. About 10% of stations, mainly in Rhineland-Palatinate and Saarland provinces, provide one measured value per 2 or 3 months. At several stations measurement period varies from one to three weeks throughout a year. Wet deposition fluxes of Hg are provided with resolution about a month. EMEP stations carry out wet deposition measurements of Pb, Cd and Hg on weekly basis. Most of concentrations in air (about 80%) are reported with daily temporal resolution. At other stations measurements are performed for

9 weekly, decadal, monthly or four-day periods. Weekly measurements of Pb and Cd and daily measurements of Hg0 concentrations in air are carried out at the EMEP stations.

In addition to observed concentration in air or in precipitation, the measurement data contain information on the detection limits. Currently all values are used in the model evaluation, even if they are close to the detection limit values. The exception is the measured values coded as erroneous.

Quality of national monitoring data was analyzed in cooperation with national experts. First of all, the analysis of time series of Pb, Cd and Hg concentrations in precipitation revealed that a number of monitoring stations report constant measured concentrations over the long periods of time (Fig. 2.3). In some cases these constant concentration values are lower than the reported detection limits. It was agreed with the national experts that the data with long periods of constant values are not relevant for the analysis and should not be used in the further evaluation of the modelling results. The list of national stations remaining after removing data with constant observed concentrations of Pb, Cd and Hg in precipitation is presented in Tables A1-A3 in Annex A.

0.20 Cd, DEBW201 0.008 Observed concentration in precip. Hg, DENI095 Observed concentration in precip. 0.007 Detection Limit Detection Limit 0.15 0.006 0.005 0.10 0.004 0.003 0.05 0.002

Conc. in Conc. precipitatin, ug/L 0.001 Conc. in precipitatin, in Conc. precipitatin, ug/L

0.00 0.000

20140127 20140226 20140520 20140813 20141105 20150209 20150309 20150504 20150601 20150727 20150824 20151019 20151116 20140325 20140424 20140617 20140716 20140910 20141007 20141203 20150112 20150407 20150630 20150921 20151214

20140226 20140325 20140617 20140716 20141007 20141105 20150209 20150309 20150601 20150630 20150921 20151019 20140424 20140520 20140813 20140910 20141203 20150112 20150407 20150504 20150727 20150824 a b 20140127

Fig. 2.3. Observed concentrations of Cd at station DEBW201 (a) and Hg at station DENI095 (b) and the corresponding detection limits.

Another aspect relating to the use of measured data is detection and filtration of outliers. In some cases observed levels in particular periods looks suspiciously high compared to other periods or compared to the same periods observed at nearby stations. To identify the outliers in time series of raw data the standard deviation method was applied [e.g., Bain and Engelhardt, 1992]. The method defines an outlier as a value falling outside the range of ±(3*SD), where is the mean value, and SD is the standard deviation. This approach was applied to time series of measured concentrations in air and in precipitation. Examples of times series of Cd and Hg concentrations in precipitation before (original data) and after the filtration are shown in Fig. 2.4.

Another issue related to the measurement data is inconsistent time series of Pb concentrations in air at several stations. Measured air concentrations at these stations demonstrate strong dip in 2015 (Fig. 2.5). This dip is not noted for Pb air concentration measurements at other German stations and for Cd concentrations in air. It was agreed with national experts not to use concentrations at these stations measured in 2015 in the analysis.

10 40 Filtered data 5 35 DETH105 DETH105 Data after filtration 30 4 25 3 20 15 2 10 1 5

0 0

Concentrations Concentrations in precip., ug/l

2016.Jul.28 2014.Jul.31 2015.Jul.16

2014.Jul.31 2015.Jul.16 2016.Jul.28

2014.Jan.02 2015.Jan.29

2015.Apr.23

2014.Jun.19 2016.Jun.16 2015.Jun.04

2014.Jun.19 2015.Jun.04 2016.Jun.16

2014.Oct.23 2015.Oct.08 2016.Oct.20

2015.Oct.08 2014.Oct.23 2016.Oct.20

2016.Feb.11 2014.Feb.13

2014.Feb.13 2016.Feb.11

2014.Sep.11 2016.Sep.08

2014.Dec.18 2016.Dec.01 2015.Nov.19 2015.Dec.31

2014.Dec.18 2015.Nov.19 2016.Dec.01 2015.Dec.31

2015.Aug.27

2015.Mar.12 2016.Mar.24 2014.Mar.27

2015.Aug.27

2015.Mar.12 2014.Mar.27 2016.Mar.24

2014.May.08 2016.May.05 2016.May.05 a b 2014.May.08

600 DESH054 Filtered data 25 DESH054 500 Data after filtration 20 400 15 300 10 200 100 5

Concentrations Concentrations in precip., ng/l 0 0

Concentrations in Concentrations precip., ng/l

20140107 20140429 20140819 20141209 20150331 20150721 20151110 20160105 20160426 20160816 20161206 20140304 20140624 20141014 20150203 20150526 20150915 20160301 20160621 20161011

20140107 20140429 20140819 20141014 20150203 20150526 20150915 20160105 20160301 20160426 20160621 20161011 20140624 20141209 20150331 20150721 20151110 20160816 20161206 c 20140304 d Fig. 2.4. Time series of measured concentration in precipitation of Cd at station DETH105 and Hg at station DESH054 before (left column) and after (right column) filtration of outliers.

60 3 50 DENW038 40 DENW082 DENW112 30 DENW116 DENW131 20 DENW207 DENW307 10 DENW338

Concentration in air, ng/mConcentration in air, 0

Jul, 2014 Jul, 2015 Jul, 2016 Jul,

Jan, 2014 Jan, Jan, 2015 Jan, 2016 Jan,

Apr, 2016 Apr, Apr, 2014 Apr, 2015 Apr,

Jun, 2014 Jun, 2015 Jun, Jun, 2016 Jun,

Oct, 2014Oct, 2015Oct, Oct, 2016Oct,

Feb, 2014 Feb, 2015 Feb, 2016 Feb,

Sep, 2014 Sep, 2015 Sep, 2016 Sep,

Dec, 2015 Dec, 2016 Dec, Dec, 2014 Dec,

Nov, 2014 Nov, 2015 Nov, 2016 Nov,

Aug, 2016Aug, Aug, 2014Aug, 2015Aug,

Mar, 2014 Mar, 2015 Mar, 2016 Mar,

May, 2014 May, May, 2016 May, May, 2015 May, Fig. 2.5. Monthly-mean time series of Pb concentrations in air with unexpectedly low levels in 2015.

Gaps in time series caused by missing measurements or filtering out of outliers can result to different real periods of accumulation of wet deposition fluxes through the year. Therefore, for comparability of measured wet deposition fluxes at different stations, observed wet deposition fluxes are adjusted to full-year period. Each accumulated annual value is converted to mean daily flux via dividing by actual number of days when measurements were available. Then daily flux is multiplied by a number of days in a year (365 or 366). Implicitly it means that for the remaining period of a year the same average concentration in precipitation and the same average daily precipitation sums is assumed. This approach is useful for further presentation of maps with modelled wet deposition covering full- year period combined with the observed values at particular stations. However, in other cases, e.g., analysis of time series, this adjustment is not used.

Location of measurement stations finally selected for the analysis is presented in Figs. 2.6 and 2.7. The figures show average air concentrations and wet deposition fluxes of Pb, Cd and Hg measured at German EMEP and national stations in the period 2014-2016.

11 a b c

Fig. 2.6. Measurements of three-year mean air concentration of Pb (a), Cd (b), and Hg (c). Circles show the EMEP observations and squares present German national measurements.

a b c

Fig. 2.7. Measurements of Pd (a), Cd(b) and Hg (c) three-year mean wet deposition. Circles show the EMEP observations and Squares present German national measurements.

Additionally, data on bulk deposition fluxes of Pb and Cd were available from about 90 national monitoring stations (Fig. 2.8). The reported data on bulk deposition fluxes do not contain information on concentrations in precipitation and precipitation amount. Therefore, it is not possible to carry out the analysis of concentration in precipitation similar to that performed for the main observations dataset. Besides, bulk deposition measurements include some contribution of dry deposition which makes their interpretation complicated. Preliminary comparison of modelled wet deposition with observed bulk deposition fluxes revealed that the model underestimates the observed fluxes by a factor of 3-5. Therefore, these data were not involved into the main analysis. However, comparison of modelled wet deposition with measured bulk deposition fluxes of Pb and Cd is presented in Table B1-B6 of Annex B.

12 a b

Fig. 2.8. Measurements of Pd (a) and Cd(b) three-year mean bulk deposition fluxes at German national stations.

Names of measurement stations contain the codes of German provinces, where they are situated. The list and location of the provinces are shown in Fig. 2.9. Exceptions are names of the German EMEP stations, which have both EMEP and national codes given in Table 2.1.

Code Province BB Brandenburg BE Berlin BW Baden-Wuerttemberg BY Bavaria HB Bremen HE Hesse HH Hamburg MV Mecklenburg-Western Pomerania NI Lower Saxony NW North Rhine-Westphalia RP Rhineland-Palatinate SH Schleswig-Holstein SL Saarland SN Saxony ST Saxony-Anhalt TH Thuringia

Fig. 2.9. Location and codes of German provinces used in the names of monitoring stations.

Table 2.1. Codes of the German EMEP stations

EMEP code National code Station DE0001 DEUB001 Westerland DE0002 DEUB005 Waldhof DE0003 DEUB004 Schauinsland DE0007 DEUB030 Neuglobsow DE0008 DEUB029 Schmücke DE0009 DEUB028 Zingst

13 2.3. Land cover

Information on land cover is highly important for modeling of ecosystem-dependent deposition fluxes because dry deposition velocities strongly depend on characteristics of the underlying surface. Besides, the land cover data are necessary for simulation of wind re-suspension of particle-bound heavy metals. Land cover information derived from the MODIS satellite dataset [Strahler et al., 1999] were used for simulations on the global and regional scales. This dataset provides information on 17 land cover categories with original spatial resolution of 30x30 arc seconds (about 1x1 km2). More detailed land cover data from the CORINE dataset (release v18_5) were used for the model simulations on the country scale. The CORINE dataset contains 44 land cover types of the underlying surface (Table 2.2). Technical descriptions of the recent CORINE releases are available in [Büttner et al., 2017]. Examples of spatial distribution of mixed forests, non-irrigated arable land and urban areas (discontinuous urban fabric) are shown in Fig. 2.10 as fractions of grid cell areas.

Table 2.2. List of land cover types of the CORINE classification

N Description N Description 1 Continuous urban fabric 23 Broad-leaved forest 2 Discontinuous urban fabric 24 Coniferous forest 3 Industrial or commercial units 25 Mixed forest 4 Road and rail networks and associated land 26 Natural grasslands 5 Port areas 27 Moors and heathland 6 Airports 28 Sclerophyllous vegetation 7 Mineral extraction sites 29 Transitional woodland-shrub 8 Dump sites 30 Beaches, dunes, sands 9 Construction sites 31 Bare rocks 10 Green urban areas 32 Sparsely vegetated areas 11 Sport and leisure facilities 33 Burnt areas 12 Non-irrigated arable land 34 Glaciers and perpetual snow 13 Permanently irrigated land 35 Inland marshes 14 Rice fields 36 Peat bogs 15 Vineyards 37 Salt marshes 16 Fruit trees and berry plantations 38 Salines 17 Olive groves 39 Intertidal flats 18 Pastures 40 Water courses 19 Annual crops associated with permanent crops 41 Water bodies 20 Complex cultivation patterns 42 Coastal lagoons 21 Land principally occupied by agriculture, with 43 Estuaries significant areas of natural vegetation 22 Agro-forestry areas 44 Sea and ocean

14 a b c

Fig. 2.10. Spatial distribution of mixed forests (a), non-irrigated arable land (b) and urban areas (discontinuous urban fabric) (c).

2.4. Soil texture

Information on soil texture is required for simulation of heavy metals wind re-suspension. For standard simulations with the GLEMOS model definition of soil texture is based on Harmonized World Soil Dataset (HWSD) [FAO, 2009]. Detailed national data on soil texture distribution over Germany (BÜK 1000 N, Version 2.3) were provided by national experts from UBA. This dataset was combined with the HWSD data for other territories of the model domain and was used for the country scale simulations. To harmonize the two soil texture datasets each texture type of the national data was identified in terms of the HWSD classification using the sand/silt/clay ratios. The resulting map of soil texture is shown in Fig. 2.11.

Fig. 2.11. Spatial distribution of soil texture types.

15 2.5. Heavy metal concentration in soil

Wind re-suspension flux of Pb and Cd as well as natural and secondary emission of Hg strongly depends on their concentration in topsoil. National data on heavy metal concentration in topsoil of Germany were provided by national experts. Original data for Germany given in a form of geospatial polygons were transformed into the regular model grid and combined with data from the pan- European FOREGS programme [Salminen, 2005] for other territories of the model domain. Spatial distribution of obtained Pb, Cd and Hg concentrations in topsoils are shown in Fig. 2.12.

a b c

Fig. 2.12. Spatial distribution of Pb (a), Cd (b) and Hg (c) concentration in topsoil.

3. MODEL

The model simulations of heavy metal atmospheric transport and deposition over the territory of Germany were performed with the Global EMEP Multi-media Modeling System (GLEMOS). A number of the model updates have been performed to adapt the national data and fit the model to the needs of the study.

3.1. Model setup

The model assessment of heavy metal pollution in Germany was performed with the standard version of the GLEMOS model (version v2.0) that was updated as described below (see Section 3.2). Description of the current stable version of the model is available at the MSC-E website (http://en.msceast.org/index.php/j-stuff/glemos). The modeling of transport and deposition were carried out using the multi-scale approach combining model simulations on a global, regional and national scales (see Section 3.3 for details). Meteorological information for all nested model domains was generated using a meteorological pre-processor based on the Weather Research and Forecast modelling system (WRF) [Skamarock et al., 2008] and fed by operational analysis data from the European Centre for Medium Range Weather Forecasts (ECMWF) [ECMWF, 2019]. Parameterizations of the preprocessor were updated to improve quality of the meteorological information for the national domain (Section 3.2.1). Anthropogenic emissions data were prepared based on the national gridded emissions fields for Germany and complemented by the EMEP emissions data for other countries (Section 2.1). Atmospheric concentrations of chemical reactants (O3, OH, SO2, and Br) and particulate matter (PM2.5), which are required for description of Hg chemistry, were imported from the MOZART and p-TOMCAT models [Emmons et al., 2010; Yang et al., 2005; 2010]. Data on wind re- suspension of particle-bound heavy metals (Pb and Cd) and evasion of gaseous elemental Hg from soil were updated using detailed national data on heavy metal concentrations in soil, soil texture and high-resolution land cover information (Sections 3.2.2 and 3.2.3). The updated land cover data were also used for assessment of ecosystem-specific deposition of heavy metals.

17 3.2. Model updates

3.2.1. Meteorological data

Particular attention of MSC-E was paid to generation of 500 meteorological data for the country-scale model Mod = 1.7 x Obs domain. The Centre undertook efforts to improve quality MRB = 100% 400 Rc = 0.43 of the generated data testing various parameterizations

of physical processes of the WRF pre-processor. In 300 particular, a number of numerical tests were performed

using different model schemes of cumulus convection, 200 Mod = 0.9 x Obs Modelled, mm . MRB = -4% cloud microphysics and the air-soil interactions in order Rc = 0.61 to refine simulated summer precipitation. Calculated 100 values of precipitation sums were compared with Original Used in modelling observations from national network of synoptic stations 0 0 100 200 300 400 500 of German Weather Service (DWD) [DWD, 2018]. The Observed, mm usage of updated set of WRF parameterizations Fig. 3.1. Comparison of observed and calculated (original and corrected for modelling favoured improvement of the calculated precipitation purposes) precipitation sums in July, 2016. sums (Fig. 3.1) decreasing the mean relative bias (MRB) Dashed line depicts 1:1 ratio, and dotted lines changed from 100% to -4% and increasing spatial indicate correlation (Rc) from 0.43 to 0.61. Resulting maps of 2-fold difference. annual precipitation and station-based observations in 2014, 2015 and 2016 are shown in Fig. 3.2.

a b c

Fig. 3.2. Spatial distribution of calculated (map) and observed (circles) precipitation sums in 2014 (a), 2015 (b) and 2016 (c).

18 3.2.2. Wind re-suspension of particulate heavy metals

Wind re-suspension of Pb and Cd deposited to the ground during previous years could be important contributor to atmospheric concentration and deposition of these metals. The re-suspension process depends on a number of factors including meteorological conditions, soil texture, land cover type and concentrations of heavy metals in dust particles suspended from ground. Detailed description of this process parameterization applied in the model is available in [Gusev et al., 2006, Ilyin et al., 2007].

Data on topsoil concentrations of heavy metals generated within the FOREGS programme [Salminen, 2005] are used for operational modeling of heavy metal pollution in the EMEP region. These data characterize baseline levels of heavy metals in soils of Europe. In order to take into account long- term accumulation over recent years or even decades, spatially distributed enrichment factors for concentrations in soils were utilized. It is assumed that the enrichment factor is proportional to the accumulated deposition.

National data on land-cover distribution (Section 2.3), soil texture (Section 2.4) and heavy metal concentrations in soils (Section 2.5), were used for modeling of Pb and Cd pollution levels in Germany within current study. The model parameterization of wind re-suspension was updated in order to integrate the national data into the modeling system. For example, annual wind re- suspension fluxes of Pb and Cd in 2015 are shown in Fig. 3.3.

a b

Fig. 3.3. Spatial distribution of lead (a) and cadmium (b) annual wind re-suspension flux in 2015.

19 3.2.3. Natural and legacy emissions of Hg from soil

The model parameterization of Hg natural and legacy emissions from soil was updated integrating the detailed national data on Hg concentration in topsoil of the country. For this purpose, the parameterization from [Zhang et al., 2001; Song et al., 2015] was applied. The flux of gaseous elemental Hg (Hg0) evasion from soil is described as a function of Hg soil concentration and solar radiation (ng m-2 h-1):

-1 -2 -2 -1 where is the Hg soil concentration (ng g ); g is the scaling factor (1.2 × 10 g m h ). is the solar radiation flux at the ground (W m-2):

Where SR is solar radiation at the top of the canopy (W m-2); LAI is the leaf area index of the canopy;  is the solar zenith angle; and  = 0.5 is an extinction coefficient assuming random leaf angle distributions.

Mercury evasion from soil in other countries and from seawater was parameterized using the model standard parameterization [Travnikov and Ilyin, 2009]. The resulting map of Hg natural and legacy emission is shown in Fig. 3.4.

Fig. 3.4. Spatial distribution of Hg natural and legacy emissions in 2015.

20 3.3. Multi-scale simulation procedure

A multi-scale modelling approach was used for simulations of heavy metal pollution in Germany to take into account influence of national, regional and global sources on the pollution levels in various locations of the country. This approach allows linking various spatial scales in an optimal manner combining wide coverage with fine resolution of the final domain for the country. The multi-scale model domains include the global domain, the regional EMEP domain, and the country-scale domain that covers the territory of Germany and adjacent regions of the surrounding countries (Fig. 3.5). The link between different scales was performed through setting up appropriate boundary conditions with 6-hour temporal resolution. The modeling results on the regional scale were evaluated against the EMEP measurement data, whereas the country-scale results were also compared with national observations.

Fig. 3.5. Modelled concentrations of Pb on a global, regional (EMEP) and country scales. Circles denote EMEP monitoring stations, and squares show national monitoring stations in Germany.

3.4. Statistics applied in the analysis

The analysis presented in the report includes evaluation of various statistical parameters. Agreement between modelled and observed levels is expressed by relative bias as follows:

th where and are observed and modelled values, respectively, related to monitoring station. Positive bias mean overestimation of the observed levels by the model and negative bias mean that the modelled levels lie below the observed ones.

Another parameter is the Pearson’s correlation coefficient ( ) which characterizes linear relationship between two sets of variables, for example, between modelled and observed time series or spatial distributions of modelled and measured values at monitoring stations:

where and are mean modelled and observed values, N – number of stations or samples in time series. The closer to unity, the higher correlation of time series or spatial distributions of modelled and measured values is.

In Chapter 6 modelling results produced in current study (new results) are compared with the results based on the previous version of the model and emission data (old results). The relative difference between spatial distributions from the new and old results is calculated as follows:

Positive value of this difference means that concentration or deposition from the new modelling result is higher than that from the old one.

Besides, the new and old modelling results are compared with measurement data. In order to identify whether the new or old modelled value is closer to the observed value, the change in the model performance is defined as a difference of biases:

where and are relative biases between modelled and observed values for the old and new modelling results, respectively. If is negative, the new modelling results are closer to the observed value than the old results, and vice versa.

4. SPATIAL PATTERNS OF HEAVY METAL POLLUTION IN GERMANY

Results of the model assessment of heavy metal pollution in Germany include maps of ground-level air concentration and atmospheric deposition of Pb, Cd, and Hg over territory of the country with fine spatial resolution in the period 2014-2016. The deposition maps are also specified for 44 land cover types in accordance with the CORINE classification (see Section 2.3). This chapter contains description of heavy metal pollution levels in the country based on the modeling results and measurement data involved in the study. More detailed comparison of the modeling results with measurements and analysis of deviations are presented in Chapter 5.

4.1. Lead

Spatial distribution of Pb air concentrations is mostly determined by the distribution of anthropogenic emission sources and wind re-suspension. Over most part of Germany the concentrations range from 3 to 6 ng/m3 (Fig. 4.1). Wide areas of relatively high modelled concentrations (8-12 ng/m3) are noted for the western part of the country (North Rhine-Westphalia and Hesse province). Besides, some hot spots take place in the vicinity of high emission sources or near cities (e.g., Berlin, Hamburg, Bremen, Stuttgart etc.). Relatively high concentrations are observed at stations in the eastern and central parts of Germany. The lowest levels take place in the southernmost part of the country. These are mountainous Alpine regions, where Pb surface air concentration tends to decline with altitude.

a b c

Fig. 4.1. Spatial distribution of Pb air concentration in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations and squares present German national measurements in the same colour palette.

23 The main factors controlling spatial distribution of Pb wet deposition are emissions and atmospheric precipitation. Thus, spatial pattern of wet deposition is mostly explained by location of emission sources. For example, relatively high levels in the western part of Germany are determined by high emissions in the North Rhine-Westphalia province (Fig. 4.2). The difference between wet deposition maps in the three considered years is caused by variability of meteorological conditions. Lower precipitation amounts in 2015 compared to 2014 and 2016 resulted in lower wet deposition fluxes over most part of Germany. The exception is the north of the country where increase of precipitation favoured growth of wet deposition in 2015. Spatial distribution of the observed wet deposition may differ from the distribution of the modelled levels. Relatively low levels measured at some stations in Thuringia agree with the modelled fluxes. However, observed fluxes in Schleswig-Holstein are higher, and in Lower Saxony are lower compared to the modelled wet deposition. High fluxes, compared to the modelled levels, are also noted for a number of stations in Brandenburg and Thuringia. More details regarding evaluation of the model performance against measurements are discussed in Chapter 5.

a b c

Fig. 4.2. Spatial distribution of Pb wet deposition in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations, squares present German national measurements in the same colour palette.

Total deposition is a sum of wet and dry deposition fluxes. Over most part of Germany the contribution of wet to total deposition exceeds 50%. Therefore, spatial distribution of total deposition is similar to that of wet deposition. Total deposition of lead varies from 0.5 to 1.5 kg/km2/y over most territory of the country (Fig. 4.3), whereas in the most polluted areas it exceeds 2 kg/km2/y.

Deposition fluxes were calculated for different land cover categories. Unlike wet deposition, dry deposition flux strongly depends on parameters of the underlying surface. Therefore, total deposition of Pb varies widely among types of the underlying surface considered in the model simulations. The highest deposition is noted for forests. In the central part of the country Pb deposition to forests ranges from 0.8 to 1.2 kg/km2/y, whereas in the western part it can exceed 2.5 kg/km2/y. Therefore, deposition flux to forests (Fig. 4.4a) can exceed grid-cell averaged deposition

24 presented in Fig. 4.3. Deposition to other land cover classes is smaller. For example, Pb deposition to non-irrigated arable lands and discontinuous urban fabric (Fig.4.4b,c) varies from 0.3 to 0.6 kg/km2/y over the major part of Germany.

a b c

Fig. 4.3. Spatial distribution of Pb total deposition in 2014 (a), 2015 (b), and 2016 (c).

a b c

Fig. 4.4. Spatial distribution of Pb deposition flux to mixed forest (a), non-irrigated arable land (b), and discontinuous urban fabric (c) in 2015.

4.2. Cadmium

Modelled and observed air concentrations of Cd vary from 0.06 to 0.2 ng/m3 over the German territory (Fig. 4.5). In the western part of the country higher values are noted (0.2-0.3 ng/m3). Besides, the levels exceeding 0.3 ng/m3 are predicted at a number of stations in the central and eastern parts of the country. Relatively low air concentrations (0.03-0.06 ng/m3) take place in the southern, eastern and south-eastern regions of Germany.

25 a b c

Fig. 4.5. Spatial distribution of Cd air concentration in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations and squares present German national measurements in the same colour palette.

Similar to air concentrations, the main factor governing spatial distribution of wet deposition is location of emission sources. Therefore, areas of the highest modelled wet deposition in the western part of Germany are explained by high anthropogenic emissions (Fig. 4.6). Besides, relatively high levels in the northern (Hamburg) and southern (Bavaria, Baden-Wuerttemberg) parts of the country are also determined by location of emission sources. However, comparison of wet deposition in different years reveals important role of atmospheric precipitation. Wet deposition of Cd in 2015 was lower than deposition in 2014 and 2016 over most of German territory because of lower precipitation (Fig. 4.6). However, in the northern part of Germany (Schleswig-Holstein) the modelled precipitation sums in 2015 were higher compared to other years, which led to higher wet deposition. Wet deposition fluxes observed at national wet deposition network are comparable with the modelled levels in Lower Saxony and are often higher than the calculated fluxes in Schleswig- Holstein, Brandenburg, Saarland and Rhineland-Palatinate.

a b c

Fig. 4.6. Spatial distribution of Cd wet deposition in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations, squares present German national measurements in the same colour palette.

26 Despite general similarity between spatial patterns of wet and total deposition, the latter is characterized by more mosaic distribution due to contribution of dry deposition. Besides, there are additional areas of large Cd deposition, which are not connected with precipitation. For instance, the area of high total deposition of Cd in Lower Saxony is explained by large contribution of dry deposition to forested area in the north-east of this province (Fig. 4.7).

a b c

Fig. 4.7. Spatial distribution of Cd total deposition in 2014 (a), 2015 (b), and 2016 (c).

Cd deposition to forested areas is much higher compared to deposition to other land cover classes. Cadmium deposition to forests in the northwestern part of Germany ranges from 50 to 80 g/km2/y and even exceeds 80 g/km2/y in some other regions (Fig. 4.8). Deposition declines to levels of 15-20 g/km2/y to the southeast of the country. Deposition of Cd to arable lands and urban areas vary mainly from 10 to 25 g/km2/y.

a b c

Fig. 4.8. Spatial distribution of Cd deposition flux to mixed forest (a), non-irrigated arable land (b), and discontinuous urban fabric (c) in 2015.

27 4.3. Mercury

Mercury occurs in the atmosphere predominantly in the gaseous elemental form (Hg0) that is characterized by long residence time with respect to deposition and, therefore, transported globally. It is emitted to the atmosphere from both anthropogenic and natural/secondary sources. Divalent oxidized Hg (HgII) is also released to the air from anthropogenic sources or can be formed in situ in the atmosphere by oxidation of Hg0, but due to short residence time it is promptly removed by wet II and dry deposition. Oxidized Hg occurs both in gaseous form (Hg gas) and in composition of II atmospheric aerosol (Hg part).

Spatial distribution of Hg0 air concentration over Germany is shown in Fig. 4.9. Levels of Hg0 concentration vary slightly over the country’s territory (±10%) due to the long residence time (months to year) that leads to mixing of this substance in the atmosphere. Near-ground Hg0 concentrations range from 1.5 ng/m3 in the remote mountain regions of the Alps to 1.8 ng/m3 in the industrial region of western Germany. The concentrations are elevated in the western (North Rhine- Westphalia) and eastern (Brandenburg, Saxony) parts of the country due to effect of national emissions and transboundary transport from neighboring Poland, respectively. Levels of Hg0 decrease from the central part to the north and south of the country, which generally agrees with available observations. Air concentrations are somewhat (by 5-7%) lower in 2015 in comparison with other years because of increased oxidation of Hg0 near the ground. It led to increase of Hg dry deposition and decrease of wet deposition in this year.

a b c

Fig. 4.9. Spatial distribution of Hg0 air concentration in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations in the same colour palette.

Mercury wet deposition is formed by scavenging of HgII with precipitation. Therefore, the spatial pattern of Hg wet deposition is governed by variation of HgII concentration and precipitation amount. High levels of Hg wet deposition are characteristics of mountain regions in the southern part of the country (Bavaria, Baden-Wuerttemberg) due to significant annual precipitation, as well as in the western part (North Rhine-Westphalia), where elevated precipitation amount is combined with high anthropogenic emissions (Fig. 4.10). Wet deposition in 2015 is lower than that in other years due to

28 smaller precipitation amount and increased dry deposition in this year. Available observations generally confirm the modeled spatial pattern. However, national measurement stations densely located in northern Germany demonstrate higher spatial variability of wet deposition in comparison with the modeling results. This aspect is more thoroughly considered in Chapter 5.

a b c

Fig. 4.10. Spatial distribution of Hg wet deposition in 2014 (a), 2015 (b), and 2016 (c). Circles show the EMEP observations and squares present German national measurements in the same colour palette.

Total deposition of Hg reflects spatial pattern of wet deposition that is supplemented by contribution of dry deposition. Mercury dry deposition depends on HgII concentration in the surface air and type of the underlying surface. Thus, it is larger for vegetated surfaces (forests, shrubs etc.) in the vicinity of anthropogenic emission sources. Elevated levels of Hg total deposition (up to 15 g/km2/y) are predicted in the western (North Rhine-Westphalia), southwestern (Baden-Wuerttemberg), eastern (Brandenburg, Saxony) parts and in some locations of the central part of the country (Fig. 4.11). Relatively small Hg deposition (below 7 g/km2/y) is typical for areas in central Germany, which characterized by low precipitation.

Mercury atmospheric deposition differs for different ecosystems. Dry deposition of HgII is larger for rough surfaces such as high vegetation or construction objects. Besides, dry deposition of Hg0 mostly takes place over vegetated surfaces. Simulated Hg deposition to three different types of land cover (mixed forests, arable lands, and urban areas) is shown in Fig. 4.12. Spatial patterns of Hg deposition are similar for all three land cover types and reflect fixed contribution of wet deposition. However, absolute levels of Hg deposition differ significantly. Mercury deposition to forests is above 10 g/km2/y over most territory of the country. Deposition flux to urban areas varies from 5 to 15 g/km2/y in different parts of Germany. Mercury atmospheric load to arable lands is mostly below 10 g/km2/y.

29 a b c

Fig. 4.11. Spatial distribution of Hg total deposition in 2014 (a), 2015 (b), and 2016 (c).

a b c

Fig. 4.12. Spatial distribution of Hg deposition flux to mixed forest (a), non-irrigated arable land (b), and discontinuous urban fabric (c) in 2015.

Model assessment of heavy metal pollution levels in Germany reveals common patterns of their spatial distribution in the country. The highest air concentrations of all metals are predicted for the western provinces because of large impact of anthropogenic emissions. Concentrations of Hg are more evenly distributed over the country due to significant contribution of regional and global emissions. Deposition patterns of all metals are more mosaic due to spatial variability of atmospheric precipitation and characteristics of the underlying surface. Beside the western part of the country, elevated deposition levels are also assessed in the eastern and southern provinces. Forests and other areas with high vegetation are the largest receptors of heavy metal pollution. Simulated spatial patterns generally agree with observations. However, there are discrepancies between modelling and measured values in some areas or at particular monitoring stations. More detailed comparison of the modelling results with measurements is given in Chapter 5.

5. EVALUATION OF MODELING RESULTS vs. OBSERVATIONS

Results of the model simulations are evaluated against measurements of heavy metals concentration in air and wet deposition at both EMEP and other national monitoring stations. The EMEP measurements are well documented and checked in regular inter-calibration campaigns performed under the guidance of the EMEP Chemical Coordination Centre (EMEP/CCC). Therefore measurements from the EMEP stations are used as a base for the evaluation of the model performance. Measurement data from other national networks are used for additional analysis. The chapter contains comparison of the modeling results with observations at a limited number of stations for the illustration purpose. A complete set of the model-to-measurement comparison for all monitoring stations is given in Annexes B-C.

5.1. Lead

Air concentrations

Comparison of mean annual modelled and observed concentrations of Pb in air for each year of the period 2014-2016 is presented in the form of scatter plots in Fig. 5.1. Average relative bias for all EMEP stations varies from -12% to +28%. In 2014 the model tends to slightly underestimate the observed concentrations, while in 2015 and 2016 some overestimation takes place. Besides, the overestimation is noted for the stations DE0008 (up to a factor of 1.7) and DE0003 (up to a factor of 2). Both these stations are located in mountainous regions at altitudes of 937 and 1205 m above sea level, respectively. Location of these stations challenges modelling the atmospheric transport of pollutants under specific high-altitude conditions.

10 R = 0.77 10 R = 0.72 10 R = 0.88 corr corr corr

Bias = -12% Bias = 28% Bias = 23%

3 3

2 2 2

Model,ng/m Model,ng/m 1 Model,ng/m 1 1

0.3 0.3 0.3 0.3 1 2 10 0.3 1 2 10 0.3 1 2 10 Observed, ng/m3 Observed, ng/m3 Observed, ng/m3 a b c Fig. 5.1. Comparison of simulated and observed Pb air concentration at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2

31 Observed air concentrations demonstrate distinct seasonal variability with higher levels in cold period and lower levels in warm period. This seasonal variability is successfully reproduced by the model over the most part of the considered period. Some examples of the modelled and observed time series of Pb concentrations in air are shown in Fig. 5.2. The modelling results present source apportionment of air concentrations including contributions of German national sources (‘German’), other EMEP anthropogenic sources (‘EMEP’), emission sources located outside EMEP domain (‘non- EMEP’) and wind re-suspension in the EMEP region (‘natural/secondary’). The EMEP station DE0001 (Westerland) is located in the northern part of Germany on the coast of the North Sea. Due to its remote location the contribution of German and EMEP anthropogenic emission sources to concentration at this station is relatively low (23% and 7% on average, respectively), whereas the main contribution is made by secondary sources of the EMEP region (Fig. 5.2a). The station DE0002 (Waldhof) is located in the inner part of the country, and the contribution of national sources at this station is higher (about 50%) (Fig. 5.2b).

8 Natural/secondary non-EMEP EMEP German Observed 10 Natural/secondary non-EMEP EMEP German Observed 3 3 DE0001 DE0002 7 9 8 6 7 5 6 4 5 3 4 3

2 Concentration in air, ng/mConcentration in air, Concentration in air, ng/mConcentration in air, 2 1 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.2. Comparison of simulated and observed monthly Pb concentrations in air at German EMEP stations DE0001 (Westerland, a) and DE0002 (Waldhof, b) over the period 2014-2016.

At some EMEP stations (e.g. DE0002) the model underestimates the observed values in autumn and winter of 2014 (Fig. 5.2b). Analysis of weekly concentration time series shows that high observed concentrations are associated with atmospheric transport from eastern and south-eastern parts of Europe (Fig. 5.3). It is possible to suppose that emissions in this part of Europe could be underestimated contributing to the underestimation of observed Pb concentrations by the model.

a b Fig. 5.3. Monthly frequencies of 24-hour back trajectories calculated with HYSPLIT [Stein et al., 2015] for periods of high observed Pb concentrations at January 18-27, 2014 (a) and November 11-17, 2014 (b) at station DE0002.

32 Evaluation of the modelled air concentrations for the 2014-2016 period is also performed at about 100 national stations. Unlike the EMEP stations which are classified as background rural or remote, most of the national stations are background urban or traffic urban stations. Therefore, the model evaluation against measurements obtained at national stations is a more challenging task. Comparison of modelled and observed Pb air concentrations at German national stations are presented in Fig. 5.4. The stations were combined in several groups. The grouping is based on geographical location of the stations. The first group includes stations located in the province North Rhine – Westphalia. Simulated Pb concentrations in this province are the highest in the country and largely exceed observations. Another group includes stations from the Hesse province, where the modelled Pb levels are also high and overestimate measurements. The group “Southern Germany” includes stations located in southern provinces (Baden-Wurttemberg, Bavaria, Rhineland-Palatinate). At most of these stations the modelled concentrations slightly exceed the observed levels (falling within a factor of 1.5). The model reasonably agrees with measurements (within a factor of 1.5) or somewhat underestimate measurements at stations related to the group “North-Eastern Germany”. This group covers provinces in the northern and eastern parts of Germany (Lower Saxony, Berlin, Brandenburg, Saxony-Anhalt, Saxony, Mecklenburg-Western Pomerania). The remaining stations (provinces Saarland, Thuringia and Schleswig-Holstein) relate to the group “Other”.

40 40 40

3 3 10 10 3 10

DESL017

DENI016 Model,ng/m

DENI016 Model,ng/m Model,ng/m DENI016

1 1 1 0.6 0.6 0.6 0.6 1 10 40 0.6 1 10 40 0.6 1 10 40 Observed, ng/m3 Observed, ng/m3 Observed, ng/m3 a b c Fig. 5.4. Comparison of simulated and observed Pb air concentration at German national stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2. Orange circles depict stations in North Rhine-Westphalia, blue squares – Hesse, green circles - Lower Saxony, Berlin, Brandenburg, Saxony-Anhalt, Saxony, Mecklenburg-Western Pomerania, blue circles - Baden-Wurttemberg, Bavaria, Rhineland-Palatinate and violet rhombs – other provinces.

Relative bias between modelled and observed three-year mean Pb air concentrations varies from - 50% to 100%, which corresponds to the model-to-measurement ratio within a factor of 2, at three fourth of stations (Fig. 5.5). Substantial overestimation is noted for the stations in the western part of the country (Hesse and North Rhine-Westphalia). Underestimation takes place at some stations located in south-western (Saarland), eastern and central (Brandenburg, Saxony, Saxony-Anhalt and Lower Saxony) provinces of the country.

Fig. 5.5. Relative bias of modelled Pb air concentration at German national stations for the period 2014-2016.

For stations related to the group “North-Eastern Germany” the modelled mean levels agree with the observed ones at most of the stations within a factor of 1.5. Seasonal variability of air concentrations at these stations is also mainly reproduced by the model (Fig. 5.6a). At a number of stations of this group the model tends to underestimate measured values in autumn and winter of 2014. The reasons for this underestimation are similar to those discussed earlier in this section for the EMEP stations.

The modelled concentrations also well agree with the observed ones (within a factor of 1.5) at most of the stations in the group “Southern Germany”. The seasonal variability is also reasonably reproduced by the model. Temporal correlation coefficient exceeds 0.5 at the majority of stations within this group. In contrast to the previous case, the stations in this group are largely affected by German anthropogenic sources (Fig. 5.6b).

12 Natural/secondary non-EMEP EMEP German Observed 9 Natural/secondary non-EMEP EMEP German Observed DEMV004 3 DEBW029

3 8 10 7 8 6 5 6 4 4 3

Concentration in air, ng/mConcentration in air, 2

Concentration in air, ng/mConcentration in air, 2 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.6. Comparison of simulated and observed monthly Pb concentrations in air at German stations DEMV004 (a) (in North-East Germany) and DEBW029 (b) (in South-West Germany) over the period 2014-2016.

Underestimation of the observed concentrations in air is noted for a number of stations in the eastern (Brandenburg, Saxony-Anhalt) and south-western (Saarland) parts of the country. Most of stations in the Brandenburg province are classified as ‘traffic urban’. The model has not been designed to reproduce local traffic effects that can lead to the underestimation. Besides, the underestimation is often taking place in winter (Fig. 5.7). This tendency is observed for a number of stations in Brandenburg (DEBB060, DEBB083, DEBB087) as well as for stations in other eastern provinces (e.g. DEST092). Possible reasons for the underprediction in the cold season are local

34 30 Natural/secondary non-EMEP EMEP German Observed emission effects or underestimation of Pb 3 DEBB044 emissions from residential heating in 25 20 Germany or in neighbouring countries. 15

Concentration in air, ng/mConcentration in air, Large (2.7-fold) underestimation is also noted 5

for the background urban station DESN017. 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May For other stations located nearby (DESN011, May DESN074, DESN061 and DESN051) the mean Fig. 5.7. Comparison of simulated and observed monthly bias between the modelled and observed Pb concentrations in air at German national station DEBB044 over the period 2014-2016. levels is small (within ±10% range). Therefore, the underestimation at DESN017 could be caused by local source not accounted by the model. At the background suburban station DENI016 (Lower Saxony Province) the model underestimates the observed levels by an order of magnitude and temporal correlation is low. This underestimation can be caused by uncertainties of the measurements or due to effect of local unaccounted emission source. For comparison, at the neighboring traffic suburban station DENI071 the model performance is much better, in particular, mean relative bias is around -30% and coefficient of temporal correlation is about 0.7.

For most of the stations located in the North Rhine-Westphalia and Hesse provinces the model overestimates observed Pb concentrations by a factor of 1.5-3.7. At a number of stations the overestimation takes place even when only national anthropogenic emissions are taken into account (Fig. 5.8). National emissions are the main contributor to Pb concentrations in air at most stations of these provinces (35-85%), followed by re-suspension (10-45%). Contributions of non-EMEP sources and transboundary transport from EMEP countries are mostly below 10%.

18 North Rhine-Westphalia 12 Hesse 3 16 3 10 14 12 8 10 6 8 6 4 4

2 2

Concentration in air, ng/m air, in Concentration Concentration in air, ng/m air, in Concentration

0 0

DEHE005 DEHE008 DEHE009 DEHE013 DEHE018 DEHE022 DEHE037 DEHE042 DEHE043 DEHE052 DEHE053 DEHE054 DEHE056 DEHE057 DEHE001

DENW008 DENW038 DENW053 DENW064 DENW067 DENW081 DENW082 DENW100 DENW112 DENW189 DENW207 DENW212 DENW022 DENW040

Modelled (Re-susp.) Modelled (non-EMEP) Modelled (EMEP) Modelled (Re-susp.) Modelled (non-EMEP) Modelled (EMEP) Modelled (Germany) Observed Modelled (Germany) Observed a b Fig. 5.8. Comparison of modeled and observed Pb air concentrations at stations in North Rhine-Westphalia (a) and Hesse (b) for the period 2014-2016.

Both measurements and modelling results demonstrate weak seasonal variability of Pb air concentrations in North Rhine-Westphalia compared to Hesse, and much weaker comparing to the EMEP stations or stations located in other provinces (Fig. 5.9). It can be explained by large contribution of emissions from anthropogenic sources with low seasonal variability. Thus, the seasonal changes of Pb levels in this area are mostly caused by variability of atmospheric transport and meteorological conditions (stability of the boundary layer, precipitation amount etc.).

25 Natural/secondary non-EMEP EMEP German Observed 14 Natural/secondary non-EMEP EMEP German Observed 3 3 DENW053 DEHE043 12 20 10

15 8

10 6

4 Concentration in air, ng/mConcentration in air, Concentration in air, ng/mConcentration in air, 5 2

0 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a May b Fig. 5.9. Comparison of simulated and observed monthly Pb concentrations in air at German EMEP stations DENW053 (a) and DEHE043 (b) over the period 2014-2016.

Supplementary information on concentrations of heavy metals in mosses collected in survey for 2015/2016 [Harmens et al., 2016] was used for the analysis of pollution levels in Germany. Comparison of three-year mean modelled deposition fluxes and concentrations in mosses demonstrates that the modeled sharp gradient of Pb deposition levels between North Rhine- Westphalia and neighbouring provinces does not agree with spatial distribution of Pb concentrations in mosses (Fig. 5.10). For comparison, high deposition fluxes well agree with high measured Pb levels in mosses in the western part of Poland.

a b

Fig. 5.10. Three-year mean Pb deposition over the period 2014-2016 (a) and Pb concentrations in mosses from the 2015/2016 survey (b).

The overestimation of the observed Pb concentrations in North Rhine-Westphalia and Hesse can be caused by a number of factors including overprediction of anthropogenic emissions in these provinces or underestimation of simulated dispersion of the pollutant outside the provinces due to insufficient vertical distribution of emissions. In order to explore these assumptions we performed a number of sensitivity tests. In Test 1 we placed all emissions from industrial and public power production sources in North Rhine-Westphalia to the 5th model layer (540-860 m) instead of the 3rd and the 4th layers, respectively, in the original data. These two sectors make up 64% and 88% of total Pb and Cd emissions in this province, respectively. Test 2 considers an extreme and unrealistic case when all emissions in North Rhine-Westphalia are placed to the 6th model layer (860-1200 m). This case can hardly be realized in reality but illustrates the model sensitivity to very high updraft of emissions. Lead emissions in Hesse are largely dominated by contribution of road transport (50%),

36 whereas industrial sources contribute only 23% of total Pb emissions. Therefore, increase of emission heights of industrial sources can hardly have significant effect on near-ground Pb concentrations in this province. According to Test 3 Pb anthropogenic emissions were reduced in the North Rhine- Westphalia province by the factor 2.7 (46.5 t/y). Since Pb wind re-suspension flux depends on previous anthropogenic deposition, it was also re-calculated on the base of modified field of deposition. Test 4 prescribes reduction of Pb anthropogenic emission in Hesse by the factor of 4 (15.8 t/y) with consequent re-calculation of wind re-suspension fluxes. Emissions in other provinces remained the same as in the base case.

Results of the tests are shown in Fig. 5.11. Test 1 leads to decrease of Pb air concentration by 10% in North Rhine-Westphalia. Test 2 demonstrates strong (by a factor of two) decrease of Pb concentrations in the near-surface air in North Rhine-Westphalia and insignificant changes in other provinces. It means that large amount of emissions from this province outflows beyond the country’s territory. Thus, the observed levels in North Rhine-Westphalia can be reproduced only if all emissions from all sectors in this province are released at unrealistically high altitude (about 1 km). However, even in this case it does not affect significantly Pb concentration/deposition in other provinces of the country. Emissions reduction in Tests 3 and 4 allows reproduction of measured Pb air concentrations in North Rhine-Westphalia and Hesse, respectively, and improvement of the model-to-observation agreement in the nearest provinces (e.g., Rhineland-Palatinate and Lower Saxony). Thus, both overestimation of anthropogenic emissions and incorrect vertical distribution of emission sources (or their combination) can be responsible for the model/observation discrepancies in these provinces.

16 3 Pb Base case Test 1 Test 2 12 Test 3 Test 4 Observed

0 Concentration in air, ng/m air, in Concentration

Fig. 5.11 Comparison of measured and modelled Pb air concentration averaged over measurement stations in German provinces for different sensitivity tests (2015).

Wet deposition

Wet deposition fluxes of Pb were measured at six EMEP stations. Mean relative bias of modeled wet deposition with respect to measurements varies between -23% and 12% (Fig. 5.12). Similar to air concentrations, the modelled wet deposition fluxes are somewhat lower than the observed ones in 2014. Deviation of modelled values from observed ones does not exceed a factor of 1.5 for most of the stations.

2 2 2 R = 0.32 R = -0.25 R = 0.84 corr corr corr Bias = -23% Bias = -6% Bias = 12% 1

1 /y 1

/y /y

2 2

Model,kg/km

Model,kg/km Model,kg/km

0.1 0.1 0.1 0.1 1 2 0.1 1 2 0.1 1 2 Observed, kg/km2/y Observed, kg/km2/y Observed, kg/km2/y a b c Fig. 5.12. Comparison of simulated and observed Pb wet deposition at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3.

Simulation of seasonal variability of wet deposition fluxes is more challenging task compared to that of air concentrations. Nevertheless, most of observed monthly-mean peaks are captured by the model (Fig. 5.13). However, some of the peaks are underestimated (e.g. August, 2015 at DE0002; or July, September and December, 2014 at DE0008). In some cases the discrepancies can be explained by uncertainties in input meteorological parameters (e.g. precipitation amount). Besides, the analysis of back trajectories (Fig. 5.3) revealed potential source regions, where emissions can be underestimated, resulting in the underestimation of observed fluxes by the model.

160 DE0002 Natural/secondary non-EMEP EMEP German Observed 180 DE0008 Natural/secondary non-EMEP EMEP German Observed

140 160 /month

/month 140 2 2 120 120 100 100 80 80 60 60

40 40 Wet deposition, deposition, Wet g/km Wet deposition, deposition, Wet g/km 20 20

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.13. Comparison of simulated and observed monthly Pb wet deposition fluxes at German EMEP stations DE0002 (Waldhof) (a) and DE0008 (Schmücke) (b) over the period 2014-2016.

Comparison of simulated and observed Pb wet deposition is shown in Fig. 5.14. Besides, Fig. 5.15 illustrates the relative bias between the model and measurements at national monitoring stations. The model performance considerably varies for different provinces. For about 70% of national stations the model reproduces observed levels with the relative bias ranging from -50% to 100% limits. These stations are mostly located in the northern (Schleswig-Holstein), central (some stations in Thuringia and Lower Saxony) and south-western (Rhineland-Palatinate, Saarland) parts of the country. However, there are stations where the model significantly exceeds or underestimates the observed wet deposition fluxes. For example, significant overestimation (bias > 100%) takes place at most of stations located in Lower Saxony province. Underestimation often occurs in Thuringia, Brandenburg and Berlin provinces.

5 5 5

/y /y

2 2

1 1 1

Model,kg/km

Model,kg/km Model,kg/km

0.1 0.1 0.1

0.1 1 5 0.1 1 5 0.1 1 5 Observed, kg/km2/y Observed, kg/km2/y Observed, kg/km2/y a b c Fig. 5.14. Comparison of simulated and observed Pb wet deposition at German national stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3. Orange circles depict stations in Lower Saxony, green circles – Saarland, violet rhombs - Schleswig-Holstein, blue circles - Rhineland-Palatinate, blue squares – Brandenburg, yellow triangles – Thuringia.

It is worth to note a distinct difference between the observed wet deposition levels in two neighbouring provinces - Thuringia and Lower Saxony. For example, wet deposition fluxes observed at stations located in Lower Saxony are much lower (by a factor of 6 on average) than the fluxes in Thuringia at stations located close to the border between these two provinces (Fig. 5.16). At the same time the modelled fluxes do not exhibit such stepwise change between the provinces. Available emission data also do not exhibit this distinct gradient at the border of the provinces (Fig. 5.16a). A possible reason for the difference in the observed fluxes can be connected with peculiarities of the monitoring procedure in Fig. 5.15. Relative bias of modelled Pb different provinces or with local primary or secondary wet deposition at German national emission sources not reflected in the emissions inventory. stations for the period 2014-2016.

a b Fig. 5.16. Location of stations near border between Thuringia and Lower Saxony borders (a) and three-year mean modelled and observed wet deposition fluxes of Pb at stations located near border between Lower Saxony and Thuringia provinces (b). Layout map – anthropogenic emissions of Pb in 2015.

39 For more detailed analysis we compared precipitation amount and Pb concentration in precipitation at two sites DENI106 and DETH106 located within a distance of 30 km from each other in different sites of the Harz slopes ridge (Fig. 5.17). For many months precipitation amount does not differ significantly at these stations. However, Pb concentration in precipitation can differ by one or two orders of magnitude over long periods (from a few months to a year). Back trajectory analysis performed for both stations does not reveal considerable difference in transport pathways for both a period of comparable concentrations (March 2015) and a period, when concentrations differed by a factor of 35 (August 2015). Therefore, the effect of local sources seems to be more probable for explanation of very high measured levels at station DETH106.

300 6 Precipitation DTH106 DNI106 Pb concentration in precipitation DTH106 DNI106

200 4

100 2

Precipitation, Precipitation, mm/month Concentration Concentration in prec., ug/L

0 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a May b Fig. 5.17. Measured precipitation amount (a) and Pb concentration in precipitation (b) at stations DENI106 and DETH106 in the period 2014-2016.

Additionally, the measurement data from national stations in some cases can be characterized by the comparison with observations at closely located EMEP stations. For this purpose the following pairs of stations were selected: DE0002 - DENI099 and DE0007 - DEBB094 (Fig. 5.18). Deposition flux measured at station DENI099 is 3.8 times lower than that at neighbouring station DE0002 (Fig. 5.18a). The model reproduces deposition fluxes at DE0002 station with average bias of -5%, while for other stations in Lower Saxony large overestimation (by a factor of 2) takes place. It is possible that this overestimation is explained by peculiarities of measurements in the Lower Saxony province. Observed Pb wet deposition at station DE0007 is lower by a factor of 5 than deposition at station DEBB094 located in Brandenburg province (Fig. 5.18b). Simulated deposition flux at DE0007 well agrees with the observed value (mean bias about -3%), the observed value at DEBB094 is strongly underestimated by the model. Most likely, high observed levels at some stations located in Brandenburg are explained by influence of local emission sources or peculiarities of measurements in the province. Besides, in Berlin and Brandenburg provinces similar underestimation is noted for some stations measuring concentrations in air. Therefore, another possible reason of general underestimation of Pb pollution levels may be connected with underestimation of national emissions in the province or emissions in neighboring countries.

160 600 140 Observed (DENI099) Observed (DEBB094) 500

/month Observed (DE2) Observed (DE7) 2

120 /month 2 100 400 80 300 60 200 40

20 100

Wet deposition, deposition, Wet g/km Wet deposition, deposition, Wet g/km

0 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Apr Apr Apr

Sep Sep Sep

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a b May Fig. 5.18. Comparison of Pb wet deposition fluxes observed at national stations and nearby EMEP stations located in Lower Saxony (a) and Brandenburg (b) provinces

40 5.2. Cadmium

Air concentrations

Air concentrations of Cd in 2014-2016 were measured at six EMEP stations located in Germany. Mean relative bias for modelled concentrations of Cd in air at the EMEP stations ranges from -18% to 42% (Fig. 5.19). For 2014 the model tends to underestimate measurements, whereas in 2015 and 2016 – overestimate the observed Cd levels. Deviation between the modeling results and observations does not exceed a factor of 1.5 for most of the stations. Exceptions are the high-altitude stations DE3 and DE8 where the model-to-measurement difference exceeds a factor of 3 and 2, respectively.

0.8 0.7 0.7 R = 0.27 R = 0.23 R = 0.46 corr corr corr

Bias = -18% Bias = 28% Bias = 42%

3 3

0.1 0.1 0.1

Model,ng/m

Model,ng/m Model,ng/m

0.01 0.01 0.01

0.01 0.1 0.8 0.01 0.1 0.7 0.01 0.1 0.7 Observed, ng/m3 Observed, ng/m3 Observed, ng/m3 a b c Fig. 5.19. Comparison of simulated and observed Cd air concentration at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2.

Seasonal variability of the observed Cd concentrations at the EMEP stations is generally reproduced by the model. Maximum air concentrations take place in late autumn – winter, and minimum concentrations occur in summer (Fig. 5.20). Higher concentrations in winter time can be caused by two reasons. Firstly, contribution of emission from residential heating is higher in cold period than in warm period. Secondly, the stability of the near-surface atmospheric layer is stronger in winter that prevents mixing of pollutants along the vertical. From the presented examples it is seen that main contributors to Cd air concentration at the EMEP stations are national sources of Germany (25-60%), wind re-suspension (20-40%) and non-EMEP sources (20-30%).

0.20 Natural/secondary non-EMEP EMEP German Observed 0.25 Natural/secondary non-EMEP EMEP German Observed 3 3 DE0001 DE0009 0.18 0.16 0.20 0.14 0.12 0.15 0.10 0.08 0.10

0.06 Concentration in air, ng/mConcentration in air, Concentration in air, ng/mConcentration in air, 0.04 0.05 0.02

0.00 0.00

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a May b Fig. 5.20. Comparison of simulated and observed monthly Cd concentrations in air at German EMEP station DE0001 (a) and DE0009 (b) over the period 2014-2016.

41 For evaluation of Cd air concentrations in 2014-2016 observed levels of 105 national German stations were used. At around three fourth of these stations the bias between modelled and observed concentrations ranges from -50% to 100% (Fig. 5.21). Stations where the model tends to substantially overestimate Cd levels are located in the western and south-western parts of the country, i.e. in North Rhine-Westphalia and Hesse provinces. Stations where the model underestimates the observations are located mostly in the eastern part of the country. Fig. 5.21. Relative bias of modelled Cd air Similar to Pb, national monitoring stations measuring concentration at German national stations for Cd were clustered into several groups. Significant the period 2014-2016. overestimation is noted for stations in the North-Rhine Westphalia and Hesse provinces (Fig. 5.22). For most of stations in the south-western part of the country (Bavaria, Baden-Wurttemberg, Rhineland-Palatinate) reasonable fit (within a factor of 2) or some overestimation of the observed concentrations is noted. For stations located in the central, northern and eastern parts of the country (Lower Saxony, Berlin, Brandenburg, Saxony-Anhalt, Saxony, Mecklenburg-Western Pomerania) the model exhibits either good fit or some underestimation of the observations.

0.8 0.8 0.8

3 3

DEST092 DESN017 DSN017 0.1 0.1 0.1 DENI016

DENI016 DENI016 DST092

Model,ng/m

Model,ng/m Model,ng/m

0.02 0.02 0.02 0.02 0.1 0.8 0.02 0.1 0.8 0.02 0.1 0.8 Observed, ng/m3 Observed, ng/m3 Observed, ng/m3 a b c Fig. 5.22. Comparison of simulated and observed Cd air concentration at German national stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of two . Orange circles depict stations in North Rhine-Westphalia, blue squares – Hesse, green circles - Lower Saxony, Berlin, Brandenburg, Saxony-Anhalt, Saxony, Mecklenburg-Western Pomerania, blue circles - Baden-Wurttemberg, Bavaria, Rhineland-Palatinate and violet rhombs – other provinces.

For a number of national stations the model reproduces both magnitude and seasonal variability of observed Cd concentrations. For example, higher concentrations of Cd in winter and lower in summer observed at background rural station DEMV004 were captured by the model (Fig. 5.23a). On average, about 50% of Cd concentration in air is made up by national sources, around 10% is contributed by transboundary transport from EMEP sources and the remainder is by non-EMEP

42 sources and wind re-suspension. High concentrations in November 2014, which is underestimated by the model, are associated with the episodes of atmospheric transport from south-eastern part of Europe where emissions could be underestimated. Another example is a traffic urban station DEBW098 that is located in the southern part of the country (Fig. 5.23b). Much higher contribution of national sources (about 80%) is noted at this station in comparison with the rural station DEMV004.

0.40 Natural/secondary non-EMEP EMEP German Observed 0.45 Natural/secondary non-EMEP EMEP German Observed 3 3 DEMV004 DEBW098 0.35 0.40 0.30 0.35 0.30 0.25 0.25 0.20 0.20 0.15 0.15

0.10 Concentration in air, ng/mConcentration in air, Concentration in air, ng/mConcentration in air, 0.10 0.05 0.05

0.00 0.00

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a b May Fig. 5.23. Comparison of simulated and observed Cd air concentrations at national stations DEMV004(a) and DEBW098 (b) over the period 2014-2016.

Substantial overestimation of observed Cd concentrations takes place at stations located in the North Rhine-Westphalia and Hesse provinces. Relative bias for three-year mean air concentrations in North Rhine-Westphalia is 85% ranging from 10% to 140% at particular stations. In Hesse the bias is 93% varying from -20% to 270%. The overestimation occurs during the whole period of the study, as seen from the examples for stations DEHE005 and DENW067 shown in Fig. 5.24.

0.45 Natural/secondary non-EMEP EMEP German Observed 0.35 Natural/secondary non-EMEP EMEP German Observed DEHE005 3 DENW067 3 0.40 0.30 0.35 0.25 0.30 0.25 0.20 0.20 0.15 0.15 0.10

0.10 ng/mConcentration in air, Concentration in air, ng/mConcentration in air, 0.05 0.05

0.00 0.00

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a May b Fig. 5.24. Comparison of simulated and observed monthly Cd concentrations in air at German national stations DEHE005 (a) and DENW067 (b) over the period 2014-2016.

Analysis of contributions of different sources types to Cd air concentrations in North Rhine- Westphalia and Hesse demonstrates prevailing contribution of German national sources (Fig. 5.25). Their average contribution to modelled air concentrations varies from 70% to 80%. Similar to Pb, even the contribution of national anthropogenic sources taken alone provides Cd air concentrations, which considerably exceed the observed levels in these two provinces.

43 0.5 0.30

North Rhine-Westphalia Hesse 3 3 0.4 0.25 0.20 0.3 0.15 0.2 0.10 0.1 0.05

0.0 Concentration in air, ng/m air, in Concentration

Concentration in air, ng/m air, in Concentration 0.00

DENW008 DENW022 DENW038 DENW040 DENW053 DENW064 DENW067 DENW081 DENW082 DENW112 DENW207 DENW212 DENW100 DENW189

DEHE001 DEHE005 DEHE008 DEHE009 DEHE013 DEHE018 DEHE022 DEHE037 DEHE043 DEHE052 DEHE053 DEHE054 DEHE056 DEHE057 DEHE042 Modelled (Re-susp.) Modelled (non-EMEP) Modelled (EMEP) Modelled (Re-susp.) Modelled (non-EMEP) Modelled (EMEP) Modelled (Germany) Observed a b Modelled (Germany) Observed Fig. 5.25. Three of modeled and observed Cd air concentrations at stations in North Rhine-Westphalia (a) and Hesse (b) for the period 2014-2016.

Spatial pattern of three-year mean Cd deposition was compared with Cd concentrations in mosses available from the 2015/2016 survey [Harmens et al., 2016]. Cadmium deposition levels in North Rhine-Westphalia are much higher compared to the neighbouring provinces (Fig. 5.26a). However, concentrations in mosses do not exhibit such strong gradients of pollution levels in the province indicating possible overestimation of Cd emissions in this area (Fig. 5.26b).

To evaluate possible reasons of deviations between the modeling results and measurements a number of sensitivity tests are performed for Cd similar to those for Pb described above. In Test 1 emissions from industrial and public power production sources in North Rhine-Westphalia are placed to the 5th model layer (540-860 m), whereas in Test 2 all emissions in this province are released to the 6th model layer (860-1200 m). According to Test 3 Cd emissions in the North Rhine-Westphalia province were reduced by a factor of 3.3 (2.8 t/y). Test 4 prescribes the 5-fold reduction (1.1 t/y) of Cd emissions in Hesse. Wind re-suspension fluxes of Cd were also re-calculated on the base of modified fields of deposition.

a b Fig. 5.26. Three-year mean Cd deposition over the period 2014-2016 (a) and Pb concentrations in mosses from the 2015/2016 survey (b).

44 Results of the tests are shown in Fig. 5.27. Test 1 decreases Cd air concentrations in North Rhine- Westphalia by 20%, but the overestimation of the measurements by the model still remain. Lift up of all emissions to large altitude of about 1 km in Test 2 allows reproduction of observed surface concentration of Cd in the province but does not affect other provinces due to strong outflow of emissions across the country’s boundary. Tests 3 and 4 reproduces Cd concentrations in North Rhine- Westphalia and Hesse, respectively. Thus, as in the case of Pb, both considered factors or their combination can lead to the overprediction of observed Cd concentrations in these provinces.

0,4

3 Cd Base case Test 1 Test 2 0,3 Test 3 Test 4 Observed

0,2

0,1

0,0 Concentration in air, ng/m air, in Concentration

Fig. 5.27. Comparison of measured and modelled Cd air concentration averaged over measurement stations in German provinces for different sensitivity tests (2015).

Wet deposition

Modelled wet deposition fluxes agree with the observations at the German EMEP stations within a factor of two. In 2014 the average relative bias is somewhat larger compared to other years. Besides, more significant bias is noted for the station DE0008 in 2014 and 2015 (Fig. 5.28).

70 70 70 R = 0.33 R = 0.0 R = 0.88 corr corr corr

Bias = -28% Bias = -12% Bias = 17%

/y /y /y

2 2 2

10 10 10

Model,g/km Model,g/km Model,g/km

3 3 3 3 10 70 3 10 70 3 10 70 Observed, g/km2/y Observed, g/km2/y Observed, g/km2/y a b c Fig. 5.28. Comparison of simulated and observed Cd wet deposition at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3.

45 Seasonal changes of Cd wet deposition were reproduced at the most of the EMEP stations. For example, most of peaks of Cd wet deposition at stations DE0003 and DE0002 were captured by the model (Fig. 5.29). However, some underestimation of the observed deposition fluxes is noted for September 2014 and August 2015 at station DE2, which can be explained by discrepancies in monthly precipitation sums.

6 DE0003 Natural/secondary non-EMEP EMEP German Observed 5 DE0002 Natural/secondary non-EMEP EMEP German Observed

5 4

/month /month

2 2 4 3 3 2 2

1 1

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.29. Comparison of simulated and observed monthly Cd wet deposition fluxes at German EMEP stations DE0003(a) and DE0002 (b) over the period 2014-2016.

Evaluation of modelled wet deposition of Cd was also carried out for 72 German national stations. At 65% of the stations the bias between three-year mean levels varies between -50% and 100%. Reasonably good agreement takes place at stations located in the northern (Schleswig-Holstein), south-western (Rhineland-Palatinate), western and central (Lower Saxony) parts of the country. Observed wet deposition fluxes are underestimated by the model for about one-third of the total number of stations (Fig. 5.30). Fig. 5.30. Relative bias of modelled Cd wet Thus, the model performance for simulation of Cd deposition at German national stations for the wet deposition differs widely among the provinces of period 2014-2016. the country (Fig. 5.31). The model satisfactory (within a factor of two) captures Cd fluxes in Lower Saxony (mean bias is -13%). There is a tendency to underestimation (-40% - -70%) of measured levels in Schleswig-Holstein, Baden-Wuerttemberg and Rhineland-Palatinate. Large (around 100%) underestimation is obtained for stations in Thuringia, Brandenburg and Berlin provinces. In general, the modelled wet deposition is lower than the observed one at almost all stations in 2014, while in 2015 and 2016 the overestimation occurs at 20- 40% of stations, mostly located in the Lower Saxony province.

At a number of stations time series of wet deposition fluxes are reasonably well reproduced by the model (Fig. 5.32). Unlike Cd concentrations in air, which have distinct seasonal variability with maximums in winter and minimums in summer, periods of higher or lower deposition are explained by episodes of higher or lower precipitation and predominant atmospheric transport from more or less polluted regions.

1000 1000 1000

/y 2

100 100 2 100

Model,g/km Model,g/km

10 Model,kg/km 10 10

1 1 1 1 10 100 1000 1 10 100 1000 1 10 100 1000 Observed, g/km2/y Observed, kg/km2/y Observed, g/km2/y a b c Fig. 5.31. Comparison of simulated and observed Cd wet deposition at German national stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3. Orange circles depict stations in Lower Saxony, green circles – Saarland, violet rhombs - Schleswig-Holstein, blue circles - Rhineland-Palatinate, blue squares – Brandenburg, yellow triangles – Thuringia.

6 DENI103 Natural/secondary non-EMEP EMEP German Observed 12 DENI113 Natural/secondary non-EMEP EMEP German Observed

5 10

/month /month

2 2 4 8

3 6

2 4

1 2

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.32. Comparison of simulated and observed monthly Cd wet deposition at German national stations DENI103 (a) and DENI113 (b) over the period 2014-2016.

At station DENI103 strong underestimation of the observed wet deposition fluxes is noted for September and October, 2014. The peak in September can be explained by 9-fold underestimated precipitation amounts. However, difference in the modelled and observed precipitation in October is not high and hence cannot explain the underestimation. Significant underestimation of the observed wet deposition fluxes of Cd for the same period is also noted for a number of other stations of the same province (e.g., DENI104, DENI105, DENI106, DENI107, DENI108, DENI104, DENI123, DENI140, Annex C.3). All these stations are situated in close to each other (in the south-western corner of Lower Saxony). Therefore, the underestimation can hardly be explained by measurement uncertainties. For comparison, there are no significant differences between modelled and observed Pb fluxes in this period in the same location. Probably, high observed levels of Cd may be caused by effects of local emissions of this pollutant.

Substantial underprediction of the observed fluxes at stations in Thuringia and Brandenburg can be analysed via comparison with the Cd fluxes observed at the closely located EMEP stations. In particular, station DEBB094 is located near the EMEP station DE0007, and DET116 is close to the EMEP station DE0008. Comparison of the observed levels at these paired stations shows that the flux measured at national stations is higher than the flux measured at the EMEP stations by an order of magnitude (Fig. 5.33). Most likely, this drastic difference may be caused by uncertainties of measurements.

47 a b Fig. 5.33. Comparison of Cd wet deposition fluxes observed at national stations and nearby EMEP stations located in Brandenburg (a) and Thuringia (b) provinces

For stations located in the Schleswig-Holstein 14 DESH036 Natural/secondary non-EMEP EMEP German Observed

province the model tends to underestimate 12 /month 2 10 observed wet deposition fluxes by 40% on 8 average. Comparison of monthly-mean time 6 series of Cd wet deposition demonstrates that 4

Wet deposition, deposition, Wet g/km 2

there are periods with significant 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May underestimation, which take place at a number May of stations. For example, at station DESH036 Fig. 5.34. Comparison of simulated and observed the underestimation occurs in December of monthly Cd wet deposition fluxes at German station 2014, July-August of 2015 and summer of 2016 DESH036 over the period 2014-2016. (Fig. 5.34). Similar periods of discrepancies also take place for stations DESH036, DESH039, DESH040, DESH048, DESH049, DESH050, DESH054 (Annex C.3). The underestimation in summer 2016 can be explained by 2-5-fold underpredicted precipitation amount. Analysis of back trajectories undertaken for other months demonstrates predominant atmospheric transport from the west, but coarse temporal resolution of measurements (around a month) does not allow identifying location of potentially underestimated emission sources.

Another area with underestimated wet deposition of Cd is located in the south-western part of the country (Rhineland-Palatinate and Saarland provinces). For some stations (e.g., DERP058) of these provinces the model manages to reproduce both magnitude and seasonal changes of Cd wet deposition (Fig. 5.35a). However, at a number of other stations significant underestimation of wet deposition occurs over long periods of time. For example, at station DERP055, the long period of large underestimation results in significant overall bias (Fig. 5.35b). The periods of high observed wet deposition can be explained by local emissions or uncertainties of measurements, and could be studied in future in cooperation with national experts.

DERP058 Natural/secondary non-EMEP EMEP German Observed 12 DERP055 Natural/secondary non-EMEP EMEP German Observed

3 10

/month /month

2 2 8 2 6

1 4 Wet deposition, deposition, Wet g/km Wet deposition, deposition, Wet g/km 2

0 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Jul Jul Jul

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

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Jun Jun Jun

May May May

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May a b May Fig. 5.35. Comparison of simulated and observed monthly Cd wet deposition fluxes at German stations DERP059 (a) and DERP055 (b) over the period 2014-2016.

48 5.3. Mercury

Air concentrations

Measurements of Hg0 concentration in air are performed at German EMEP stations. No data is available from other national stations as well as no measurement data is available for other Hg forms II II 0 (Hg gas and Hg part). Comparison of simulated annual mean concentrations of Hg with measurements at German EMEP stations are shown in Fig. 5.36. The model successfully reproduces levels of Hg0 concentration in the country with a relative bias within the range ±5%. Spatial correlation of modeled and observed values is significant (above 0.6), however, the number of measurement stations is small to make conclusion about the spatial variation of Hg0 concentration over territory of the country.

R = 0.81 R = 0.61 R = 0.71 corr corr corr Bias = -0.7% Bias = -3.7% Bias = 2.1%

2 2 2

3 3 3

Model,ng/m Model,ng/m Model,ng/m

1 1 1 1 2 1 2 1 2 Observed, ng/m3 Observed, ng/m3 Observed, ng/m3 a b c Fig. 5.36 Comparison of simulated and observed annual mean air concentration of Hg0 at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2

Temporal variation of monthly mean Hg0 concentrations at all EMEP stations in the country is shown in Fig. 5.37. The figure also shows contribution of German, EMEP and non-EMEP anthropogenic sources as well as natural/secondary emissions to Hg0 concentrations. As seen both simulated and observed monthly levels of Hg0 vary insignificantly (±15%) over the whole period illustrating long lifetime of Hg0 in the atmosphere. About 40% of Hg mass in the surface air is determined by direct anthropogenic emissions and the remained 60% is from natural and secondary emissions. The long lifetime of Hg0 also explains significant contribution of non-EMEP anthropogenic sources (27%) in comparison with regional (8%) and national (5%) sources. It should be noted that impact of national and regional sources is larger for Hg deposition due to considerable contribution of deposition of short-lived HgII from emission sources located in Germany and other European countries.

49 2.5 DE0002 Natural/secondary non-EMEP EMEP German Observed 2.5 DE0003 Natural/secondary non-EMEP EMEP German Observed 2.0

/month 2.0

/month 2

1.5 1.5

1.0 1.0

0.5 0.5

Wet deposition, deposition, Wet g/km Wet deposition, deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Jan Jan Jan

Oct Oct Oct

Apr Apr Apr

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May May a b May 2.5 DE0008 Natural/secondary non-EMEP EMEP German Observed 2.5 DE0009 Natural/secondary non-EMEP EMEP German Observed

2.0 2.0

/month

2 2

1.5 1.5

1.0 1.0

0.5 0.5

Wet deposition, deposition, Wet g/km Wet deposition, deposition, Wet g/km

0.0 0.0

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Apr Apr Apr

Feb Feb Feb

Sep Sep Sep Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

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May May May

May May c d May Fig. 5.37. Comparison of simulated and observed monthly Hg0 air concentration at German EMEP stations Waldhof (DE0002, a), Schauinsland (DE0003, b), Schmücke (DE0008, c) and Zingst (DE0009, d) over the period 2014-2016.

Wet deposition

Evaluation of simulated wet deposition against measurements at German EMEP stations is illustrated in Fig. 5.38. The modeling results well agree with observations with deviations within a factor of 1.5 and significant spatial correlation (0.65-0.99). The model slightly (16-20%) overestimates measured annual deposition levels. Since Hg wet deposition is largely formed by scavenging oxidized Hg species, the overestimation can be connected with some overprediction of Hg0 oxidation in the atmosphere or with uncertainties in speciation of Hg anthropogenic emissions.

50 50 50 R = 0.98 R = 0.67 R = 0.99 corr corr corr

Bias = 20.4% Bias = 18.3% Bias = 16.7%

/y /y /y

2 2 2

10 10 10

Model,g/km Model,g/km Model,g/km

1 1 1 1 10 50 1 10 50 1 10 50 Observed, g/km2/y Observed, g/km2/y Observed, g/km2/y a b c Fig. 5.38. Comparison of simulated and observed Hg wet deposition at German EMEP stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3.

More detailed comparison of modeled and measured monthly wet deposition at four EMEP stations is shown in Fig. 5.39. As seen the model well reproduces observed temporal variation of Hg deposition (correlation coefficient is within the range 0.6-0.85). All EMEP stations reveal similar seasonal variation pattern of wet deposition with maximums during summer months and lower levels in winter. This seasonality is determined by more intensive photo-oxidation of Hg0 and scavenging of oxidized Hg species in summer than in winter. Such seasonal pattern is a characteristic of background locations, which are not strongly affected by local anthropogenic emissions. As shown

50 in the figure, contribution of German national sources to wet deposition at these stations varies from a few per cents in summer to 20-25% in winter when the effect of photochemistry is lower. The rest of wet deposition is almost equally divided between regional anthropogenic, non-EMEP anthropogenic and natural/secondary sources.

1.2 DE0001 Natural/secondary non-EMEP EMEP German Observed 2.5 DE0002 Natural/secondary non-EMEP EMEP German Observed

2.0 /month

/month 0.9

2 2

1.5 0.6 1.0

0.3

0.5

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b

5.0 DE0003 Natural/secondary non-EMEP EMEP German Observed DE0008 Natural/secondary non-EMEP EMEP German Observed 2.0

4.0

/month /month

2 2 1.5 3.0

2.0 1.0

1.0 0.5

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May c d May Fig. 5.39. Comparison of simulated and observed monthly Hg wet deposition at German EMEP stations Westerland (DE0001, a), stations Waldhof (DE0002, b), Schauinsland (DE0003, c), Schmücke (DE0008, d) over the period 2014-2016.

Further evaluation of the modelling results was performed using measurement data from other national monitoring networks. These data are less documented and require in depth analysis in terms of representativeness of the measurement stations and consistency of measured levels. Comparison of simulated and observed annual wet deposition at available national stations is shown in Fig. 5.40. The measurement stations are grouped in the figure by provinces – Lower Saxony (NI) and Schleswig-Holstein (SH). The model demonstrates relatively good agreement with observations in Schleswig-Holstein but tends to underestimate measured values at a number of stations in Lower Saxony.

50 50 50 R = 0.1 R = 0.0 R = 0.03 corr corr corr

Bias = -25.0% Bias = -35.0% Bias = -35.2%

/y /y

2 2

10 10 10

Model, g/km

Model,g/km Model,g/km

NI NI NI SH SH SH 1 1 1 1 10 50 1 10 50 1 10 50 Observed, g/km2/y Observed, g/km2/y Observed, g/km2/y a b c Fig. 5.40. Comparison of simulated and observed Hg wet deposition at German national stations in 2014 (a), 2015 (b), and 2016 (c). Lines depict different deviation levels: solid red – the 1:1 ratio; dashed red – by factor 1.5; dashed green – by factor of 2; dashed blue – by factor of 3.

51 The overall comparison with all measurement stations shows low correlation between the model and observations (0-0.1) and underestimation of measured values by the model by 25-35%. However, the situation differs significantly among the measurement stations. In general, variability of the modelled values (a factor of 2) is low in comparison with variation of the measured ones (an order of magnitude). This higher variability of measurements is probably determined by the effect of local conditions since stations of the national monitoring networks are not classified as regional background stations (but rather rural, suburban and urban), whereas the model is primarily applied for simulations of long-range transport of air pollution and can miss peculiarities of particular stations. Some details of comparison of the modelling results with national measurements are discussed below.

I III

a b Fig. 5.41. Relative bias of modelled Hg wet deposition at German national stations for the period 2014-2016 (a) and groups of stations with significant bias discussed in the text (b).

Firstly, there are many national stations where the model relatively well reproduces the measurement data (Fig. 5.41a, bias within the range -33% - 50%). These stations are mostly located in the northern and western parts of the two provinces providing national measurement data on Hg wet deposition. Examples of these stations are shown in Fig. 5.42. As seen from the figure the model successfully simulates both average levels and seasonal variation of Hg wet deposition. Similar to the EMEP stations, wet deposition at these locations is higher in summer and lower in winter indicating predominant role of the oxidation chemistry. The modelling results also demonstrate relatively small contribution of national emissions in comparison with long-range transport from regional, non-EMEP and secondary sources.

However, there are a number of stations where the modelling results significantly differ from the measurements. These stations are collected geographically in three groups (Fig. 5.41b). The Group I consists of two measurement stations (DESH036 and DESH050), where the model significantly overestimates observations. The stations are located in the eastern part of Schleswig-Holstein, close to the Baltic coast (Fig. 5.43a). Levels of Hg wet deposition at these stations are among the lowest measured in the province and even lower than wet deposition observed at some background EMEP stations (Figs. 5.43b and 5.43c vs. 5.39). On the contrary, the model simulates significant deposition levels, which are comparable with other locations of the country.

52 Natural/secondary non-EMEP EMEP German Observed DENI084 2.0 DESH040 Natural/secondary non-EMEP EMEP German Observed

3.0

/month /month 2 2 1.5 2.0 1.0

1.0

0.5

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b

2.0 DESH046 Natural/secondary non-EMEP EMEP German Observed 2.5 DESH054 Natural/secondary non-EMEP EMEP German Observed

2.0 /month

/month 1.5

2 2

1.5 1.0 1.0

0.5

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May c May d Fig. 5.42. Comparison of simulated and observed monthly Hg wet deposition at German national stations DENI084, DESH040, DESH046, DESH054 over the period 2014-2016

1.5 DESH036 Natural/secondary non-EMEP EMEP German Observed

/month 2 1.0

0.5 Wet deposition, deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May b May

1.8 DESH050 Natural/secondary non-EMEP EMEP German Observed

/month 2 1.2

0.6

a Wet deposition, deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May c May Fig. 5.43. Location of measurement stations from Group I (a) and comparison of simulated and observed monthly Hg wet deposition at stations DESH036 and DESH050. Layout map – anthropogenic emissions of Hg in 2015.

Since the modelled precipitation amounts reasonably well agree with measured ones over the most part of the considered period, the deviations are largely determined by difference of Hg concentration in precipitation (Fig. 5.44). Moreover, the difference is significant over the summer period and negligible in winter. The model predicts higher levels of Hg in precipitation in summer due to the stronger effect of photochemistry, which leads to formation of oxidized Hg species in the free atmosphere with following scavenging by precipitation. This seasonal variation is typical for other background locations as it was shown above. The reasons of untypical temporal variability of Hg wet deposition at these particular stations can be connected with incomplete current understanding of Hg redox chemistry in the atmosphere and require further investigation. In particular, possible mechanisms of photoreduction of Hg oxidized forms are currently studied, which can lead to depletion of HgII in the atmosphere and, as a consequence, to decrease of Hg wet deposition. Another possible reason of decreased Hg wet deposition at the seashore sites is scavenging of oxidized Hg by sea-salt aerosol in air masses coming from the marine boundary layer.

53 30 150 DESH036 Observed Modelled DESH036 Observed Modelled 24 100 18

12 50

Hg in precipitation, in precipitation, Hg ng/L Precipitation, Precipitation, mm/month

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b

40 150 DESH050 Observed Modelled DESH050 Observed Modelled 30 100

Hg in precipitation, in precipitation, Hg ng/L Precipitation, Precipitation, mm/month

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May c May d Fig. 5.44. Comparison of simulated and observed monthly Hg concentration in precipitation and precipitation amount at stations DESH036 (a, b) and DESH050 (c, d) in the period 2014-2016.

The Group II includes stations DENI096 and DESH037 located in the vicinity of Hamburg (Fig. 5.45), where the model considerably underestimates measured wet deposition. The measurements at DENI096 are characterised by multiple monthly peaks, which are not captured by the model (Fig. 5.45b). Some of these peaks can be explained by uncertainties of meteorological data used for the modelling. For instance, the model significantly underestimates precipitation amount in May-June, 2014 and May-June, 2016 at station DENI096 (Fig. 5.46b), that led to underprediction of wet deposition in these periods. However, in other periods the model relatively well reproduces observed precipitation amount at the station and large difference between modelling results and observations are determined by high measured Hg concentrations in precipitation, which are missed by the model (Fig. 5.46a). Since the station is located in a few kilometres from Hamburg, the most probable explanation for these high pollution episodes is impact of pollution plumes from emission sources located in the city, which cannot be resolved with the model spatial grid.

4.0 DENI096 Natural/secondary non-EMEP EMEP German Observed

/month 3.0 2

2.0

1.0 Wet deposition, deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May b May

Natural/secondary non-EMEP EMEP German Observed

12.0 DESH037 /month 2 9.0

6.0

a 3.0 Wet deposition, deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May c May Fig. 5.45. Location of measurement stations from Group II (a) and comparison of simulated and observed monthly Hg wet deposition at stations DENI096 (b) and DESH037 (c). Layout map – anthropogenic emissions of Hg in 2015.

54 In contrast, the model successfully reproduces Hg wet deposition levels at station DESH037 except for two periods of extremely high deposition (May-June, 2015 and September-December, 2015) (Fig. 5.45c). These periods are characterized by very high Hg concentration in precipitation (up to 200 ng/L), which were not observed in other months (Fig. 5.46c). Probable explanations for these episodes are impact of a temporal emission source located closely to the station or measurement uncertainties caused by sample contamination or other sampling problems. Thus, measurements during this period cannot be considered as representative and used for the model evaluation.

50 250 DENI096 Observed Modelled DENI096 Observed Modelled 40 200

30 150

20 100

10 50

Hg in precipitation, Hg ng/L Precipitation, mm/monthPrecipitation,

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b

250 200 DESH037 Observed Modelled DESH037 Observed Modelled 200 150

150 100 100

Hg in precipitation, Hg ng/L Precipitation, mm/monthPrecipitation,

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May c May d Fig. 5.46. Comparison of simulated and observed monthly Hg concentration in precipitation and precipitation amount at stations DENI096 (a, b) and DESH037 (c, d) in the period 2014-2016.

Group III presents measurement stations located in the northern part of Lower Saxony in the vicinity of Bremen (Fig. 5.47). The stations are densely situated within a radius of thirty kilometres and are affected by emission sources in Bremen and Wilhelmshaven. At all stations the model demonstrates typical seasonal variation with elevated wet deposition in summer and small contribution of German national emissions (Fig. 5.48). In contrast, the measurements show significant monthly deposition peaks not only in summer but also in other seasons, which are absent in the modelling results. Besides, the measured peaks poorly correlate between the stations. This station-specific variation of wet deposition can be partly explained by difference in meteorological conditions (precipitation amount) at particular locations. Fig. 5.47. Location of measurement stations DENI085, Indeed, two of the five measurement stations DENI086, DENI087, DENI121, DENI126. Layout map (DENI121 and DENI126) are located at the coast, shows anthropogenic emissions of Hg in 2015. whereas the others are deeper inland.

4.0 DENI085 Natural/secondary non-EMEP EMEP German Observed 4.0 DENI086 Natural/secondary non-EMEP EMEP German Observed /month

/month 3.0 3.0

2 2

2.0 2.0

1.0 1.0

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b

4.0 Natural/secondary non-EMEP EMEP German Observed Natural/secondary non-EMEP EMEP German Observed

DENI087 5.0 DENI121 /month

/month 3.0 2 2 4.0

3.0 2.0 2.0 1.0

1.0

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May c May d

DENI126 Natural/secondary non-EMEP EMEP German Observed

3.0

/month 2

2.0

1.0 Wet deposition, deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May e May Fig. 5.48. Comparison of simulated and observed monthly Hg wet deposition at German national stations DENI085, DENI086, DENI087, DENI121, DENI126 over the period 2014-2016.

To separate the effect of precipitation on wet deposition we compared modelled and observed Hg concentration in precipitation at the stations. Figure 5.49 shows monthly variation of weighted mean Hg concentration in precipitation and average precipitation amount at the considered stations. As seen the model relatively well reproduces precipitation amount in the majority of simulated months. In contrast, the simulated concentrations in precipitation are considerably lower than the measurements in some periods, in particular, during winter and spring. In addition, measured concentrations exhibit much higher variability among the stations, which is not the case for the modelling results. This inter-station variability of measured Hg concentration in precipitation can be explained by effect of local emission sources, which are missing in the emissions inventory or located at a sub-grid distance from measurement stations. Indeed, even insignificant source of emissions situated in a short distance (within hundreds of meters) from a monitoring station can significantly affect the measurements. Resolving this sort of local effects require application of a special sub-grid parameterisation (e.g. plume-in-grid), which are not commonly used in regional or national scale modelling.

56 80 Observed Modelled 160 Observed Modelled 60 120

40 80

20 40

Hg in precipitation, in precipitation, Hg ng/L Precipitation, mm/monthPrecipitation,

0 0

Jul Jul Jul

Jan Jan Jan

Jun Jun Jun

Oct Oct Oct

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Apr Apr Apr

Feb Feb Feb

Sep Sep Sep

Dec Dec Dec

Aug Aug Aug

Nov Nov Nov

Mar Mar Mar

May May May

May May a May b Fig. 5.49. Comparison of simulated and observed monthly Hg concentration in precipitation (a) and precipitation amount (b) averaged over German national stations DENI085, DENI086, DENI087, DENI121, DENI126 for the period 2014-2016. Whiskers show variation among the stations. Shaded areas depict periods considered in the text.

To investigate potential effect of emission sources we plotted HYSPLIT backward trajectories [Stein et al, 2015] for two monthly episodes (April, 2015 and December, 2015), which are characterized by strong underestimation of observations by the model and high variability of measurement data among the stations (Fig. 5.50). In both cases the air masses mostly came from the northern and western directions from Denmark and aquatic areas of the Northern Sea and the Channel. The trajectories do not cover the major territory of the country and have high density in immediate proximity to the measurement stations. It means that high Hg concentrations in precipitation are most probably caused by local emission sources, which are not taken into account by the model. Another potential source that can affect considered measurement stations is shipping emissions, which are probably underestimated in the emissions inventory used in the study.

a b Fig. 5.50. Monthly frequencies of 24-hour back trajectories calculated with HYSPLIT [Stein et al., 2015] for selected stations: DENI087 in April 2015 (a) and DENI121 in December 2015 (b).

To investigate possible effect of local emission sources of Hg levels at considered stations we performed scenario simulations with modified anthropogenic emissions. For this purpose we increase Hg emissions in a grid cell located close to the station DENI121, which includes large emission source in port Wilhelmshaven (see Fig. 5.47), by a factor of 10 (from 64 to 643 kg/y) and define all Hg emissions from this source as HgII to increase effect on wet deposition. It leads to increase of total national Hg emissions in the country by 6%. Results of the scenario simulations are shown in Fig. 5.51. The emissions increase does not change significantly Hg wet deposition at DENI121 except for a number of monthly peaks (Fig. 5.51a). It means that high Hg levels at this station are mostly determined by other emission sources (e.g. shipping emissions). In contrast, Hg wet deposition at DENI126 considerably increases in the scenario simulation (Fig. 5.51b). In particular, it allows reproducing some of the measured monthly peaks (June - August 2015,

57 December 2015 - February 2016, Aplil - June 2016) and improves agreement of annual levels. Thus, observed levels of Hg wet deposition at this station can be affected by closely located unaccounted emissions source.

Natural/secondary non-EMEP EMEP German Observed Natural/secondary non-EMEP EMEP German Observed 5.0 DENI121 DENI126

3.0

/month /month 2 2 4.0

3.0 2.0

2.0 1.0

1.0

Wet deposition, deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Jun Jun Jun Jun Jun Jun

Oct Oct Oct Oct Oct Oct

Apr Apr Apr Apr Apr Apr

Feb Feb Feb Feb Feb Feb

Sep Sep Sep Sep Sep Sep

Dec Dec Dec Dec Dec Dec

Aug Aug Aug Aug Aug Aug

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May a May b Fig. 5.51. Comparison observed monthly Hg wet deposition at stations DENI121, DENI126 with scenario simulations discussed in the text.

5.4. Concluding remarks

The evaluation of modeling results against observations shows satisfactory agreement of modelled and measured levels of heavy metal air concentration and wet deposition at the background EMEP stations. The model well reproduces both levels and seasonal variation of heavy metal pollution. Measurement data from other national monitoring networks significantly expand the scope of the analysis. However, selection of the measurement sites for evaluation of the long-range modeling requires thorough analysis in terms of their representativeness. Combined use of the monitoring data and model simulations allow revealing uncertainties of measurements, emission estimates and modelling approaches. In particular, revealed deviations between observations and the modeling results can be explained by a number of factors including effect of local emission sources, uncertainties in spatial distribution of emissions, height distribution of emission sources, insufficient spatial resolution of the model grid and chemical speciation of Hg emissions.

6. COMPARISON WITH PREVIOUS EMEP ESTIMATES

Testing the new national inventory of heavy metal emissions developed for the country (Section 2.1) is one of the priority tasks of the study. For this purpose the modeling results obtained with the new emissions data for 2015 were compared with the previous model estimates performed as a part of the EMEP pollution assessment [Ilyin et al., 2017]. It should be noted that difference between the two sets of modeling results is determined by a number of factors including the difference of the anthropogenic emissions in Germany from two inventories, the difference of the model versions (spatial grids, parameterizations), and input data (meteorological fields, wind re-suspension, boundary conditions) used for the current and previous simulations. These factors are also taken into account in the analysis. Both sets of modeling results are evaluated against observations. Only measurements, which are classified as reliable and not affected by local emission sources (see Chapter 5) are used in the comparison.

6.1. Lead

National total Pb emission reported to EMEP by Germany and used in the calculations for 2015 is 220 t/y. Comparable emission total value (237 t/y) is used in the current study to simulate Pb levels. Although spatial distribution of new emissions is more detailed compared to the old one, general pattern is similar (Fig. 6.1). Relatively high old and new emissions take place in industrial (North Rhine-Westphalia) and highly populated (Hamburg, Berlin, Stuttgart, Munich etc.) regions of the country. The main differences in the emissions are noted for Berlin and Hamburg provinces.

a b

Fig. 6.1. Emissions of Pb in old (a) grid (50x50 km2) and new (b) grid (0.1°x0.1°) in 2015.

59 Spatial distribution of Pb concentrations in air are demonstrated in Fig. 6.2. The concentrations simulated by previous emissions (old results) range from 3 to 8 ng/m3 over most of German territory (Fig. 6.2a). Higher (>8 ng/m3) levels take place in North Rhine-Westphalia, Hesse and Baden- Wuerttemberg provinces. The results produced in current study and based on new emissions (new results) exhibit similar spatial pattern but the gradients are sharper. Lead concentrations in North Rhine-Westphalia exceed 12 ng/m3, while the levels in some regions of Thuringia are below 3 ng/m3 (Fig. 6.2b). The highest increase of concentrations caused by the transition from the old to new calculations is noted in Berlin and north-western sector of the country (North Rhine-Westphalia, Lower Saxony, Hessen, Hamburg) (Fig. 6.2c). The highest decline is simulated for the southern part of the country (Baden-Wuerttemberg, Bavaria) and southern Saxony.

a b c

Fig. 6.2. Spatial distribution of Pb concentration in air in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

From viewpoint of the verification of the modelled Pb concentrations in air, there are no marked changes in the country-mean levels. Relative bias improved insignificantly (from 40% to 36%), and spatial correlation remained almost the same (Fig. 6.3a). However, significant differences are noted for particular stations or provinces. Stations with the improved model performance of air concentrations are located mostly in the southern provinces of the country (Baden-Wurttemberg and Bavaria provinces). Besides, numerous stations, where improvement is noted, are scattered across other parts of the country (Fig. 6.3b). In the northern and eastern parts of Germany some decline of the model performance is noted. In North Rhine-Westphalia and Hesse also no improvement takes place. However, the model overestimation of measurements in these two provinces is probably connected with overprediction of anthropogenic emissions that is present in both emission inventories (see Chapter 5).

30 R = 0.29 corr Bias = 39.5%

3 10

Model,ng/m

R = 0.28 Old corr New Bias = 36.1% 0.7 0.7 10 30 Observed, ng/m3 a b Fig. 6.3. Comparison of old and new simulations of Pb concentration in air with observations (a) and change of relative bias (b).

Modelled (50x50) Modelled (0.1x0.1) Observed

An example of the difference between the new and 3 15 old simulations of Pb air concentrations in the 12 Baden-Wurttemberg province is shown in Fig. 6.4. At 9 12 of 14 stations in this province the relative bias considerably improved (from 100-240% to -10- 6 130%). 3

Concentrations in air, ng/mConcentrations in air, 0

Wet deposition of Pb from the old modelling results

DBW013 DBW019 DBW029 DBW033 DBW080 DBW098 DBW099 DBW112 DBW118 DBW122 DBW147 DBW152 DBW156 is rather smooth compared to that derived from the DBW125 Fig. 6.4. Modelled and observed concentrations of new modelling results (Fig. 6.5). In the both fields Pb in air at stations in Baden-Wuerttemberg higher fluxes are noted for North Rhine-Westphalia province province and lower – for the central part of the country (Thuringia, Saxony-Anhalt provinces). Over major part of Germany Pb wet deposition from the new results are lower than the deposition from the old results (Fig. 6.5c). It is explained mainly by changes in simulated atmospheric precipitation (see Section 3.2.1). However, there are some regions such as central parts of North Rhine-Westphalia and Hamburg province, where increase of wet deposition takes place. This increase is explained by changes in the emission data.

Mean relative bias for wet deposition fluxes changed from low positive value in case of old modelling results to low negative value in case of the new modelling results (Fig. 6.6a). Small improvement can be noted for spatial correlation. More substantial changes in the model performance are noted for particular stations or provinces. Improvements in the modelling of wet deposition are noted at most of stations in Germany. The most significant improvement (> 50%) takes place in Lower Saxony and Saarland (Fig. 6.6b). More contradictory situation is found for Rhineland-Palatinate and Thuringia provinces. At a number of stations obvious improvement of the model performance is noted, whereas at the others deviation between modelled and observed fluxes increased. In the northern part of the country the changes in the model-observation biases are relatively low.

61 a b c

Fig. 6.5. Spatial distribution of Pb wet deposition in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

3 R = 0.43 corr

Bias = 10.3% /y

2 1

Model,kg/km

R = 0.49 0.1 Old corr New Bias = -14.3% 0.05 0.05 0.1 1 3 Observed, kg/km2/y a b Fig. 6.6. Comparison of old and new simulations of Pb wet deposition with observations (a) and change of relative bias (b).

Total deposition maps based on the old and new model simulations differ substantially. Over major part of the country deposition fluxes based on the old simulations ranges from 0.5 to 2 kg/km2/y (Fig. 6.7a). The fluxes based on new simulations are lower, varying from 0.2 to 1.2 kg/km2/y over most part of Germany (Fig. 6.7b). Comparable levels (0.3-0.5 kg/km2/y) take place in the northern parts of Lower Saxony and Schleswig-Holstein provinces. There are two areas of high deposition fluxes in the old modelling results. The first area is related to North Rhine-Westphalia province where deposition ranges from 0.8 to 2 kg/km2/y. The second is located in the Baden-Wurttemberg province, where deposition exceeds 2 kg/km2/y (Fig. 6.7a). Area of high deposition in North Rhine-Westphalia is also revealed in the results based on the new emissions (Fig. 6.7b).

In Fig. 6.7c relative difference between modelled deposition from new and old model results is demonstrated. Most significant decrease (more than 50%) of Pb new deposition takes place in southern provinces of the country (Bavaria, Baden-Wurttemberg). In the northern (Mecklenburg- Western Pomerania, Schleswig-Holstein, Lower Saxony) and western (North Rhine-Westphalia) provinces areas with decreased deposition are alternating with patches of the increase of deposition.

62 These patches of increased deposition are correlated with sources of strong emissions which are resolved in the new data but smoothed over 50-km grid cells in the old emission data.

a b c

Fig. 6.7. Modelled Pb total deposition based on old (a, 50x50 km2) and new (b, 0.1°x0.1°) modelling results and relative difference between new and old deposition (c) in 2015

Large difference between total deposition based on the old and new model simulations is explained by the influence of multiple factors governing deposition. The main factor is the update of wind re- suspension estimates used the new model version. Contributions of national sources and re- suspension to German provinces are shown in Fig. 6.8. The contributions of national sources to total deposition in provinces are quite similar, whereas the contributions of wind re-suspension differ markedly.

1.6 Pb, Old emission (50x50 km2)

/y 1.6

2 Pb, New emission (0.1°x0.1°) /y

1.4 2 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0

Mean deposition deposition Mean flux, kg/km 0.0 Mean deposition deposition Mean flux, kg/km

Re-suspension Re-suspension Other Other Germany a b Germany Fig. 6.8. Contributions of different types of sources to Pb deposition based on old (a) and new (b) modelling results in German provinces in 2015.

63 6.2. Cadmium

Total emission of Cd used in calculations over old EMEP grid for 2015 is 6.6 t/y . New emissions used in the current study are almost two-fold higher amounting to 12.9 t/y. Obviously, spatial distribution of the new emissions is more detailed compared to the old one. Nevertheless, regions with higher or lower emissions across the country are similar in case of the old and new data (Fig. 6.9). In both emission maps areas with relatively high emissions such as North Rhine-Westphalia, Hamburg, Hesse, west part of Rhineland-Palatinate, central regions of Bavaria and southern part of Saxony-Anhalt are clearly seen.

a b

Fig. 6.9. Emissions of Cd in old (a) grid (50x50 km2) and new (b) grid (0.1°x0.1°) in 2015.

Spatial distributions of Cd modelled concentrations in air based on the old and new modelling results are similar from viewpoint of location of areas with higher or lower levels. Both calculation results demonstrate higher levels in North Rhine–Westphalia province exceeding 0.25 ng/m3. Besides, less intensive areas of relatively high levels are noted for Hamburg, Baden-Wuerttemberg and some regions of Bavaria province (Fig. 6.10). However, in the old modelling results areas of relatively high concentrations are seen in Bremen, Berlin and Hesse provinces. Over most part of the country the increase of the concentrations takes place as the modelling results changes from the old to new ones (Fig. 6.10c). The highest changes occur in the central part of Germany, Berlin province and numerous regions in the norther part of the country. The western (North Rhine –Westphalia), southern (Bavaria, Baden-Wuerttemberg) and eastern (Saxony, Saxony-Anhalt) parts of Germany are characterized by the decline of the modelled concentrations.

Mean relative bias for Cd air concentrations increased from 16% to 37% due to transition from the old to new modelling results (Fig. 6.11a). Therefore, no improvement of the model performance was reached. Nevertheless, some improvements can be mentioned at particular stations located in Saxony, Saxony-Anhalt, Saarland and North Rhine-Westphalia (Fig. 6.11b).

64 a b c

Fig. 6.10. Spatial distribution of Cd concentration in air in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

1 R = 0.41 corr

Bias = 16.4%

0.1 Model,ng/m

R = 0.34 Old corr New Bias = 37.3% 0.02 0.02 0.1 1 Observed, ng/m3 a b Fig. 6.11. Comparison of old and new simulations of Cd concentration in air with observations (a) and change of relative bias (b).

General patterns of Cd wet deposition fluxes from the old and new model simulations are quite similar. Wide area of relatively high deposition levels (> 20 g/km2/y) takes place over the northwestern part of the country (Fig. 6.12a,b). Besides, similar, but somewhat lower levels occur in the eastern (Berlin, Brandenburg) and southern (Baden-Wuerttemberg, Bavaria) parts of Germany. Areas of lower wet deposition are located in the central (Thuringia, Saxony-Anhalt) and south-western (Rhineland-Palatinate) parts of the country. Areas with the highest increase of wet deposition fluxes due to transition from the old to new calculations are central parts of Hesse and North Rhine- Westphalia provinces, Saarland, Hamburg and some regions in Lower Saxony, Saxony and Bavaria (Fig. 6.12c).

Mean relative biases between wet deposition fluxes based on the new and old model simulations and the observed fluxes at German stations are almost the same (Fig. 6.13a). However, it is worth mentioning increase of spatial correlation coefficient from 0.35 to 0.48. Relative changes of the model performance at particular stations are shown in Fig. 6.13b. At most of the stations the relative change in the model performance lies within ±10%. Moderate improvement is noted for a number of

65 stations in Lower Saxony and Saarland. In total, a number of stations where noticeable (10-50%) improvement takes place is two-fold larger than a number of stations with the 10-50% regress of the model performance of Cd wet deposition.

a b c

Fig. 6.12. Spatial distribution of Cd wet deposition in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

70 R = 0.35 corr

Bias = -26.4%

10 Model,g/km

R = 0.48 Old corr New Bias = -25.8% 3 3 10 70 Observed, g/km2/y a b Fig. 6.13. Comparison of old and new simulations of Cd wet deposition with observations (a) and change of relative bias (b).

Total deposition of Cd in 2015 based on the old model simulations is in general higher than those based on the new model simulations (Fig. 6.14). Over the most part of the country deposition fluxes based on the old results exceed 20 g/km2/y. In the current study deposition flux varies from 15 to 20 g/km2/y over large part of the country. The highest fluxes ranging from 40 to 60 g/km2/y or even exceeding 60 g/km2/y are simulated for North Rhine Westphalia using both sets of emissions. Area of elevated modelled deposition derived from the old results takes place in Baden-Wurttemberg province. Areas of high deposition fluxes produced in the new model simulations take place in the south-east and the north-east of Lower Saxony and the south of Schleswig-Holstein. On the map based on the old model results these areas are smoothed.

66 Relative difference of the new and old modelled deposition is the highest in the central and northern parts of the country (Fig. 6.14c). In a number of provinces the increase exceeds 50%. As a rule, gridcells with significant increase are correlated with locations of significant local emission sources. In the southern part of the country decline of modelled deposition with fine spatial resolution compared to deposition based on the old emissions is observed.

a b c

Fig. 6.14. Spatial distribution of Cd total deposition in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

Comparison of province-averaged deposition fluxes demonstrates that contribution of national emission sources to deposition substantially increased when the new emission is used. This increase is caused by higher national anthropogenic emissions used in the new calculations. At the same time, the update of the wind re-suspension parameterization resulted in decline of Cd re-suspension flux, even in spite of the increase of national emissions. Hence, the contribution of re-suspension reduced in the new results (Fig. 6.15). The increase of contribution from national emissions is compensated by the reduction of re-suspension contribution. Overall contribution of other sources (EMEP and non- EMEP) shows relatively low change. As a result, the change of Cd deposition fluxes is not so large as the change of Pb fluxes.

45 Cd, Old emission (50x50 km2) 45 Cd, New emission (0.1°x0.1°)

/y /y 2 2 40 40 35 35 30 30 25 25 20 20 15 15 10 10

5 5 Mean deposition deposition Mean flux, kg/km Mean deposition deposition Mean flux, kg/km 0 0

Re-suspension Re-suspension Other Other Germany a Germany b Fig. 6.15. Contributions of different types of sources to Cd deposition based on old (a) and new (b) modelling results in German provinces in 2015.

67 6.3. Mercury

Total anthropogenic emissions of Hg in Germany in 2015 were 9.1 and 10 t/y according to the old and new emission inventories, respectively. Spatial distributions of Hg emissions for both inventories are shown in Fig. 6.16. The spatial pattern and average levels of emissions are similar for both inventories. However, the new dataset provides much higher spatial resolution of the emissions map. Both inventories estimate elevated emissions in industrial regions of the western and southwestern parts of the country and in large cities of northern and eastern Germany (e.g. Berlin, Hamburg). Generally, northern Germany is characterized by less Hg emissions than other parts of the country.

a b

Fig. 6.16. Spatial distribution of Hg anthropogenic emissions in 2015 according to old (a) and new (b) national emission inventories for Germany.

Comparison of the standard simulations Hg0 air concentrations based on previously reported emissions with the new modeling results is shown in Fig. 6.17. As seen the new simulations provide Hg0 concentrations, which are 5-10% higher than the previous estimates and better agree with the observations. Background levels of long-lived Hg0 are largely determined by global atmospheric transport and contribution of emission sources located not only in Europe but also in other regions (see Chapter 5). Therefore, the changes in Hg0 concentration are mostly caused by updated simulations of Hg transport on a global scale, which were used for defining new boundary conditions for regional and national scale simulations.

Comparison of the modeling results with measurements show some reduction of the relative bias of the new modeling results in comparison with previous ones (Fig. 6.18a). It more affects measurement stations located in the northern part of the country (Fig. 6.18b). In addition, there is significant increase of spatial correlation (from -0.9 to 0.61) that implies better reproduction of the north-to-south gradient of Hg0 concentration within the country. This effect can result from improved spatial distribution of national emissions and updated emissions in neighboring regions (e.g. Poland), which increase the global Hg0 background levels in industrial regions, but also eastern parts of the country.

68 a b c

Fig. 6.17. Spatial distribution of Hg0 concentration in air in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

R = -0.9 corr Bias = -8.6%

Model, ng/m

R = 0.61 Old corr New Bias = -3.7% 1 1 2 Observed, ng/m3 a b Fig. 6.18. Comparison of old and new simulations of Hg0 concentration in air with observations (a) and change of relative bias (b).

In contrast to air concentration, the new results for Hg wet deposition are lower than the previous estimates over most part of the country (Fig. 6.19). The reduction is more pronounced in western and central Germany (up to 60%) and is less significant in the southern part (below 20%). Wet deposition of Hg is composed of precipitation scavenging oxidized Hg species, which are emitted from anthropogenic sources or in situ produced in the atmosphere. The effect of atmospheric chemistry is lower in regions with significant emissions. Therefore, the decrease of wet deposition is largely connected with the revision of chemical speciation of Hg anthropogenic emissions in the new simulations (Section 2.1). Decrease in relative contribution of oxidized forms to Hg emissions from anthropogenic sources results in reduced wet deposition, particularly, in industrial regions. It should be noted that information on speciation of Hg emissions is not included in both previous and the new emissions inventory for Germany (Section 2.1) that introduces additional uncertainties to the model estimates.

69 a b c

Fig. 6.19. Spatial distribution of Hg wet deposition in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

As seen from Fig. 6.20, the new simulations better reproduce measured wet deposition levels in the country when compared with the previous estimates. Both spatial correlation and the relative bias with respect to observation are considerably improved in the new results (Fig. 6.20a). At majority of measurement stations the model-to-measurement bias decreased by 10-50% except for a few stations where the bias did not change or slightly increased. However, it should be mentioned that the measurements are mostly available in northern Germany that restricts evaluation of the modelling results in other parts of the country.

Changes between the new and previous modeling results for Hg total deposition to large extent follow the changes of wet deposition. Decrease of oxidized Hg in anthropogenic emissions led to reduction of both wet and dry deposition, which contributes to total Hg deposition flux. As a result, levels of total Hg deposition decreased by 20% in some areas in eastern and southeastern Germany up to 60% in the western part of the country (Fig. 6.21). Thus, the revised estimates predict somewhat lower overall Hg pollution of Germany.

20 R = 0.26 corr

Bias = 34.9% /y

2 10

Model,g/km

R = 0.53 Old corr New Bias = 3.8% 2 2 10 20 Observed, g/km2/y a b Fig. 6.20. Comparison of old and new simulations of Hg wet deposition with observations (a) and change of relative bias (b).

70 a b c

Fig. 6.21. Spatial distribution of Hg total deposition in 2015 according to old (a) and new (b) modelling results, and relative change between the old and new estimates (c).

The changes are more clearly illustrated by diagrams of average deposition flux for various German provinces (Fig. 6.22). Contribution of German anthropogenic sources to Hg deposition largely decreased in all provinces due to revised speciation of Hg anthropogenic emissions. Total Hg deposition from all other source types changed insignificantly. However, relative contributions of global and other (EMEP anthropogenic and natural) sources were redistributed due to more correct evaluation of Hg inflow trough the boundaries of the region in the new simulation. In particularly, Hg mass returned back to the region from the global circulation is explicitly taken into account.

25 25

2 /y

/y Hg, old simulations (50x50 km ) Hg, new simulations (0.1°x0.1°)

2 2 20 20

15 15

10 10

5 5 Mean deposition deposition Mean flux, g/km Mean deposition deposition Mean flux, g/km 0 0

Global Global Other Other Germany Germany a b Fig. 6.22. Contributions of different types of sources to Hg deposition based on old (a) and new (b) modelling results in German provinces in 2015.

Modelled pollution levels of Pb, Cd and Hg based on the old and new results exhibit similar spatial patterns. However, the spatial distributions of the new modelling results are more detailed due to finer spatial resolution. The model performance does not change significantly with respect to comparison with the whole set of national observations but demonstrates considerable improvement in individual provinces or particular locations. The improvements are connected with refinement of emissions data, update of secondary emissions and improved estimates of long-range transport from other regions. The new model estimates predict somewhat lower atmospheric load of heavy metals in the country.

7. CONCLUSIONS

Model assessment of heavy metal (Pb, Cd, and Hg) pollution in Germany has been performed involving detailed national information on anthropogenic emissions and other geophysical data. The modeling results have been evaluated against observations from both EMEP and national monitoring networks as well as compared with previous model estimates carried out as a part of operational EMEP activities.

The fine resolution modelling on a country scale provides detailed patterns of heavy metal pollution in the country. The highest concentrations and deposition levels of all three metals are predicted for the western provinces because of large impact of anthropogenic emissions. Beside the western part of the country, elevated deposition levels are also characteristics of the eastern and southern provinces. Deposition patterns of all metals are more mosaic than distribution of concentrations due to spatial variability of atmospheric precipitation and characteristics of the underlying surface. Among various ecosystems, forests and other areas covered with high vegetation are the largest receptors of heavy metal pollution. Simulated spatial patterns generally agree with observations. In particular, the model well agrees with background measurements at the EMEP stations reproducing both levels and seasonal variation of heavy metal pollution.

Refined national emissions inventory for heavy metals and updates of other input information favour general improvement of the model assessment in comparison with previous EMEP operational modeling. Finer spatial resolution of emissions data allows more detailed evaluation of pollution patterns and leads to better agreement with observations. Even though the model performance does not change significantly over the whole territory of the country, it demonstrates considerable improvement in individual provinces or particular locations.

Measurement data from national monitoring networks significantly expand the scope of the analysis. However, observations from some monitoring stations may not reflect background pollution levels (e.g. due to effect of local sources) that complicates their use for evaluation of the modeling results. Therefore, more detailed information on the methodology and sensitivity of measurements as well as on the physical conditions (such as geographical height, exposition, land cover) and pollution level/local sources in the surrounding of the monitoring stations would be needed to improve their applicability for evaluation of modeling results.

Evaluation of the simulated country-scale patterns of heavy metal pollution against national monitoring data and measurements in moss reveals discrepancies in some locations or provinces of Germany, particularly, in the western part of the country. Performed analysis showed that the differences between the modelled and measured data can result from:

 Total values and spatial distribution of anthropogenic emissions;  Height distribution of anthropogenic emissions (especially relevant for large point sources);

72  Absence of information on chemical speciation of Hg anthropogenic emissions;  Use of measurements from monitoring stations situated close to local emission sources, which are not representative for the country-scale pollution assessment;  Insufficient spatial resolution of the long-range model grid to resolve local scale pollution levels.

Further in depth analysis of the revealed discrepancies requires assessment on a local scale involving national experts, collection of more detailed information on emissions (parameters of point sources, detailed structure of emission sectors, size distribution of heavy metal emissions, Hg emissions speciation), measurement data (higher temporal resolution, detailed meta data) and local scale modeling (including both EMEP and national models as well as back trajectory analysis).

As shown, the country-scale assessment of heavy metal pollution for Germany identified a series of starting points for further in depth analysis by German experts in order to improve the knowledge about atmospheric pollution by Pb, Cd, Hg as basis for national risk assessment. Close cooperation of German experts with experts of EMEP/MSC-E is recommended to foster the overcoming of remaining discrepancies and clarification of still open questions.

73 REFERENCES

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Annex A STATIONS USED IN THE MODEL EVALUATION

Table A.1. Measurement stations (Pb) used in the model evaluation Code Period Code Period Code Period DEBB094 2014-2016 DERP062 2014-2016 DEUB005 2014-2016 DEBB095 2014-2016 DESH036 2014-2016 DEUB028 2014-2016 DEBB096 2014-2016 DESH037 2014-2016 DEUB029 2014-2016 DEBB097 2014-2016 DESH038 2014-2016 DEUB030 2014-2016 DEBB100 2014-2016 DESH039 2014-2016 DENI080 2015 DEBE070 2014-2016 DESH040 2014-2016 DENI101 2014, 2015 DENI079 2014-2016 DESH042 2014-2016 DENI103 2014, 2016 DENI082 2014-2016 DESH043 2014-2016 DENI110 2016 DENI084 2014-2016 DESH044 2014-2016 DENI114 2016 DENI090 2014-2016 DESH045 2014-2016 DENI123 2016 DENI096 2014-2016 DESH046 2014-2016 DENI124 2015 DENI099 2014-2016 DESH047 2014-2016 DENI125 2015 DENI104 2014-2016 DESH048 2014-2016 DENI126 2016 DENI105 2014-2016 DESH049 2014-2016 DENI133 2014, 2015 DENI106 2014-2016 DESH050 2014-2016 DENI138 2014, 2016 DENI107 2014-2016 DESH054 2014-2016 DENI140 2016 DENI108 2014-2016 DESL021 2014-2016 DENI142 2016 DENI109 2014-2016 DESL023 2014-2016 DETH103 2014, 2015 DENI111 2014-2016 DESL024 2014-2016 DETH104 2014, 2015 DENI112 2014-2016 DESL025 2014-2016 DETH106 2014, 2015 DENI113 2014-2016 DESL026 2014-2016 DETH107 2014, 2015 DERP048 2014-2016 DESL027 2014-2016 DETH108 2014, 2015 DERP054 2014-2016 DESL028 2014-2016 DETH109 2014, 2015 DERP055 2014-2016 DESL029 2014-2016 DETH111 2014, 2015 DERP056 2014-2016 DETH105 2014-2016 DETH112 2014, 2015 DERP057 2014-2016 DETH110 2014-2016 DETH113 2014, 2015 DERP058 2014-2016 DETH116 2014-2016 DETH114 2014, 2015 DERP059 2014-2016 DEUB001 2014-2016 DETH115 2014, 2015 DERP061 2014-2016 DEUB004 2014-2016

76 Table A.2. Measurement stations (Cd) used in the model evaluation Code Period Code Period Code Period DEBB094 2014-2016 DENI113 2014-2016 DESH045 2014-2016 DEBB095 2014-2016 DENI114 2014-2016 DESH046 2014-2016 DEBB096 2014-2016 DENI123 2014-2016 DESH047 2014-2016 DEBB097 2014-2016 DENI126 2014-2016 DESH048 2014-2016 DEBB100 2014-2016 DENI138 2014-2016 DESH049 2014-2016 DEBE070 2014-2016 DENI140 2014-2016 DESH050 2014-2016 DEBW161 2014-2016 DENI142 2014-2016 DESH054 2014-2016 DEBW205 2014-2016 DERP048 2014-2016 DETH105 2014-2016 DENI079 2014-2016 DERP054 2014-2016 DETH110 2014-2016 DENI082 2014-2016 DERP055 2014-2016 DETH116 2014-2016 DENI083 2014-2016 DERP056 2014-2016 DEUB001 2014-2016 DENI084 2014-2016 DERP057 2014-2016 DEUB004 2014-2016 DENI096 2014-2016 DERP058 2014-2016 DEUB005 2014-2016 DENI099 2014-2016 DERP059 2014-2016 DEUB028 2014-2016 DENI103 2014-2016 DERP061 2014-2016 DEUB029 2014-2016 DENI104 2014-2016 DERP062 2014-2016 DEUB030 2014-2016 DENI105 2014-2016 DESH036 2014-2016 DENI080 2015 DENI106 2014-2016 DESH037 2014-2016 DENI090 2014, 2015 DENI107 2014-2016 DESH038 2014-2016 DENI101 2014, 2015 DENI108 2014-2016 DESH039 2014-2016 DENI125 2015 DENI109 2014-2016 DESH040 2014-2016 DENI133 2015 DENI110 2014-2016 DESH042 2014-2016 DESL021 2015 DENI111 2014-2016 DESH043 2014-2016 DESL025 2015 DENI112 2014-2016 DESH044 2014-2016 DESL029 2015

Table A.3. Measurement stations (Hg) used in the model evaluation Code Period Code Period Code Period DENI082 2014-2016 DESH036 2014-2016 DESH049 2014-2016 DENI083 2014-2016 DESH037 2014-2016 DEUB001 2014-2016 DENI084 2014-2016 DESH038 2014-2016 DEUB004 2014-2016 DENI085 2014-2016 DESH039 2014-2016 DEUB005 2014-2016 DENI086 2014-2016 DESH040 2014-2016 DEUB028 2014-2016 DENI087 2014-2016 DESH042 2014-2016 DEUB029 2014-2016 DENI096 2014-2016 DESH043 2014-2016 DESH048 2014-2016 DENI097 2014-2016 DESH044 2014-2016 DESH050 2014-2016 DENI098 2014-2016 DESH045 2014-2016 DESH054 2014-2016 DENI119 2014-2016 DESH046 2014-2016 DENI121 2014-2016 DENI128 2014-2016 DESH047 2014-2016 DENI126 2014-2016

77 Annex B

OBSERVED BULK DEPOSITION AND MODELLED WET DEPOSITION FLUXES AT GERMAN STATIONS IN 2014-2016

Table B.1 Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Pb at national German stations in 2014. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 549 539 DEHE073 3933 519 DESN052 2214 381 DEBB021 906 471 DEHE074 1295 536 DESN059 1434 457 DEBB029 468 451 DEHE075 895 526 DESN061 4091 706 DEBB032 942 745 DEHE076 1144 525 DESN091 2477 428 DEBB053 627 634 DEHE077 1009 356 DESN092 2451 700 DEBB055 617 387 DEHE078 1774 536 DEST002 1052 435 DEBB056 519 380 DEHE079 666 534 DEST006 1302 453 DEBB063 665 439 DEHE080 1955 456 DEST011 1358 503 DEBB064 1531 542 DEHE081 1038 488 DEST015 1013 412 DEBB065 1007 378 DEHE082 1603 493 DEST028 1397 386 DEBB066 675 561 DEHE083 1310 496 DEST029 1744 402 DEBB067 416 407 DEHE084 600 420 DEST044 1093 415 DEBB076 1285 551 DEHE085 4245 436 DEST066 845 457 DEBB077 385 353 DEHE086 861 431 DEST072 2121 403 DEBB078 525 406 DEHE087 2235 447 DEST075 3350 394 DEBB083 890 639 DEHE088 12146 443 DEST076 2964 403 DEBB086 538 524 DEHE089 8470 506 DEST080 1188 398 DEBB088 772 375 DEHE090 3002 511 DEST089 710 397 DEBB092 467 628 DEHE091 2044 404 DEST090 881 404 DEBW033 849 436 DEHE092 1374 459 DEST091 3002 427 DEBW087 396 424 DESN006 2984 481 DEST098 673 345 DEHE065 1401 441 DESN011 1832 417 DEST105 671 395 DEHE066 1542 522 DESN017 9734 510 DEUB001 449 462 DEHE067 2391 439 DESN019 1528 381 DEUB004 679 508 DEHE068 4416 601 DESN020 2991 750 DEUB005 458 484 DEHE069 990 581 DESN025 3398 480 DEUB028 437 366 DEHE070 1007 492 DESN045 1779 582 DEUB029 950 486 DEHE071 1007 490 DESN051 1610 519 DEUB030 473 383 DEHE072 1265 497

78 Table B.2. Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Pb at national German stations in 2015. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 718 443 DEHE072 1368 438 DESN051 798 380 DEBB021 1324 414 DEHE073 3178 439 DESN052 1176 233 DEBB029 563 348 DEHE074 1481 344 DESN059 739 362 DEBB032 1201 455 DEHE075 911 446 DESN061 1583 444 DEBB053 730 398 DEHE076 695 331 DESN091 1326 325 DEBB055 996 415 DEHE077 873 452 DESN092 1557 451 DEBB063 1039 357 DEHE078 1594 459 DEST002 944 352 DEBB064 1523 460 DEHE079 1836 459 DEST006 1616 306 DEBB065 868 358 DEHE080 2153 465 DEST011 1168 344 DEBB066 1121 445 DEHE081 835 368 DEST015 1178 407 DEBB067 775 388 DEHE082 1764 371 DEST028 1315 287 DEBB078 764 402 DEHE083 1097 338 DEST029 1489 294 DEBB083 901 508 DEHE084 462 279 DEST044 1128 272 DEBB086 646 405 DEHE085 3793 328 DEST066 1178 431 DEBB092 658 353 DEHE086 749 326 DEST072 1894 306 DEBB099 2302 393 DEHE087 3121 324 DEST075 3459 296 DEBW015 1837 350 DEHE088 9395 338 DEST080 1540 269 DEBW026 1275 439 DEHE089 1255 263 DEST089 726 314 DEBW033 1133 324 DEHE090 1117 284 DEST090 958 299 DEBW087 616 363 DEHE091 1758 284 DEST091 3647 405 DEBW190 1789 270 DEHE092 1170 294 DEST098 585 297 DEHB005 3512 559 DESN006 1479 337 DEST105 2582 318 DEHE065 1716 487 DESN011 1256 335 DEUB001 425 507 DEHE066 1675 496 DESN017 6082 406 DEUB004 341 358 DEHE067 2974 362 DESN019 976 315 DEUB005 418 372 DEHE068 1441 490 DESN020 1248 529 DEUB028 394 482 DEHE069 665 437 DESN025 1759 405 DEUB029 657 396 DEHE070 1156 426 DESN045 985 458 DEUB030 369 361 DEHE071 1109 432

79 Table B.3. Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Pb at national German stations in 2016. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 588 505 DEHE071 1185 561 DESN045 1494 555 DEBB021 651 446 DEHE072 1371 565 DESN051 1539 531 DEBB029 573 347 DEHE073 1978 485 DESN052 2425 382 DEBB032 1003 614 DEHE074 2147 505 DESN059 838 425 DEBB053 220 479 DEHE075 906 494 DESN061 2376 666 DEBB055 539 398 DEHE076 1392 492 DESN091 1923 427 DEBB063 381 362 DEHE077 930 500 DESN092 1598 690 DEBB064 769 550 DEHE078 1147 505 DEST002 977 375 DEBB065 756 391 DEHE079 1254 503 DEST006 1389 355 DEBB066 305 548 DEHE080 1420 508 DEST011 1158 422 DEBB067 465 387 DEHE081 575 422 DEST015 1318 422 DEBB076 696 561 DEHE082 1173 420 DEST028 1214 376 DEBB078 1320 385 DEHE083 1465 426 DEST029 1744 355 DEBB083 577 652 DEHE084 596 423 DEST044 1302 374 DEBB086 383 491 DEHE085 1416 438 DEST066 1297 451 DEBB092 342 480 DEHE086 630 435 DEST072 1423 370 DEBB099 972 482 DEHE087 1756 456 DEST075 4542 357 DEBW015 1467 532 DEHE088 9030 453 DEST089 688 355 DEBW026 1230 669 DEHE089 1472 412 DEST090 1086 383 DEBW033 764 544 DEHE090 1119 415 DEST091 2170 408 DEBW087 364 539 DEHE091 2516 417 DEST098 545 321 DEHB005 2448 466 DEHE092 1138 425 DEST105 861 348 DEHE065 1254 590 DESN006 1702 415 DEUB001 228 326 DEHE066 1785 599 DESN011 1753 449 DEUB004 516 567 DEHE067 2941 581 DESN017 8184 580 DEUB005 349 346 DEHE068 1224 582 DESN019 1634 426 DEUB028 211 335 DEHE069 1231 563 DESN020 2306 600 DEUB029 574 470 DEHE070 1390 549 DESN025 2265 508 DEUB030 254 325

80 Table B.4. Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Cd at national German stations in 2014. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 33.1 14.8 DEHE073 46.0 20.4 DESN052 96.0 12.7 DEBB021 22.2 14.1 DEHE074 17.5 22.2 DESN059 33.3 15.5 DEBB029 22.3 14.1 DEHE075 26.3 21.3 DESN061 67.0 20.6 DEBB032 24.2 15.5 DEHE076 27.4 21.1 DESN091 87.3 14.1 DEBB053 45.9 16.5 DEHE077 16.1 15.3 DESN092 41.5 20.9 DEBB055 27.8 12.7 DEHE078 27.7 22.2 DEST002 22.3 13.6 DEBB056 22.0 11.9 DEHE079 200.8 21.9 DEST006 65.0 14.0 DEBB063 13.2 11.5 DEHE080 78.4 19.2 DEST011 34.6 19.4 DEBB064 36.9 14.6 DEHE081 201.6 20.1 DEST015 28.8 13.8 DEBB065 26.1 11.6 DEHE082 40.1 20.0 DEST028 28.6 12.8 DEBB066 18.3 15.5 DEHE083 123.9 20.4 DEST029 35.2 15.2 DEBB067 13.0 12.7 DEHE084 30.3 17.5 DEST044 32.8 15.2 DEBB076 24.7 14.8 DEHE085 54.8 16.0 DEST066 20.5 13.8 DEBB077 14.1 8.8 DEHE086 57.3 15.9 DEST072 34.6 12.7 DEBB078 16.2 11.2 DEHE087 29.6 16.5 DEST075 51.3 12.5 DEBB083 23.3 20.8 DEHE088 138.3 16.4 DEST076 53.1 12.6 DEBB086 38.1 14.3 DEHE089 41.2 17.0 DEST080 32.4 13.3 DEBB088 25.4 12.1 DEHE090 22.6 17.2 DEST089 24.0 13.0 DEBB092 14.2 14.4 DEHE091 32.3 14.2 DEST090 48.4 13.6 DEBW033 24.9 17.0 DEHE092 38.5 15.8 DEST091 31.6 14.2 DEBW087 18.5 15.6 DESN006 51.8 15.6 DEST098 23.5 12.6 DEHE065 20.7 14.9 DESN011 129.9 14.4 DEST105 19.7 12.5 DEHE066 24.1 17.2 DESN017 160.7 16.7 DEUB001 16.1 13.9 DEHE067 17.0 14.9 DESN019 43.6 12.1 DEUB004 19.3 15.7 DEHE068 37.6 19.4 DESN020 51.0 14.4 DEUB005 16.6 15.6 DEHE069 16.4 18.6 DESN025 46.7 16.8 DEUB028 15.3 11.1 DEHE070 22.0 16.0 DESN045 37.2 14.1 DEUB029 30.6 15.4 DEHE071 27.6 15.9 DESN051 39.5 15.6 DEUB030 17.4 11.7 DEHE072 28.0 16.2

81 Table B.5. Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Cd at national German stations in 2015. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 183.8 13.7 DEHE072 21.3 13.7 DESN051 22.3 12.0 DEBB021 97.9 13.3 DEHE073 36.0 16.6 DESN052 30.3 7.4 DEBB029 64.5 11.5 DEHE074 23.3 14.6 DESN059 19.4 13.4 DEBB032 118.3 14.4 DEHE075 19.6 17.4 DESN061 29.2 14.0 DEBB053 156.7 13.0 DEHE076 17.5 13.8 DESN091 41.4 10.2 DEBB055 80.0 14.2 DEHE077 14.1 17.9 DESN092 22.4 14.3 DEBB063 53.1 9.9 DEHE078 21.0 18.3 DEST002 27.5 12.1 DEBB064 67.3 12.8 DEHE079 188.1 18.2 DEST006 29.2 11.6 DEBB065 65.6 11.9 DEHE080 86.8 18.5 DEST011 45.1 13.9 DEBB066 189.3 12.7 DEHE081 81.6 14.0 DEST015 30.0 14.5 DEBB067 112.6 12.8 DEHE082 48.7 14.1 DEST028 41.8 10.3 DEBB078 58.1 12.8 DEHE083 129.6 13.4 DEST029 33.3 12.5 DEBB083 203.4 14.5 DEHE084 26.6 11.4 DEST044 53.0 11.4 DEBB086 49.2 12.8 DEHE085 47.1 12.6 DEST066 38.6 14.2 DEBB092 138.0 11.6 DEHE086 27.3 12.6 DEST072 31.6 11.7 DEBB099 98.1 13.2 DEHE087 51.1 12.4 DEST075 44.2 11.4 DEBW015 28.4 12.4 DEHE088 133.7 13.0 DEST080 35.9 10.8 DEBW026 35.9 15.5 DEHE089 12.7 9.2 DEST089 26.1 11.0 DEBW033 52.0 12.5 DEHE090 13.8 9.8 DEST090 20.7 11.7 DEBW087 17.6 12.8 DEHE091 13.2 9.8 DEST091 49.8 14.6 DEBW190 40.7 8.6 DEHE092 9.7 10.2 DEST098 24.1 11.4 DEHB005 76.9 17.4 DESN006 31.1 11.7 DEST105 35.8 10.5 DEHE065 17.5 15.4 DESN011 39.5 11.5 DEUB001 13.5 15.7 DEHE066 53.7 15.6 DESN017 79.5 13.8 DEUB004 12.9 10.9 DEHE067 34.6 12.3 DESN019 27.4 9.8 DEUB005 14.4 12.5 DEHE068 23.2 15.5 DESN020 26.3 12.0 DEUB028 16.0 14.8 DEHE069 14.2 13.7 DESN025 25.5 15.4 DEUB029 19.1 12.8 DEHE070 16.5 13.4 DESN045 23.4 11.9 DEUB030 11.4 11.7 DEHE071 31.6 13.5

82 Table B.6. Observed bulk deposition (Fobs) and modelled (Fmod) wet deposition fluxes of Cd at national German stations in 2016. Units: g/km2/y

Code Fobs Fmod Code Fobs Fmod Code Fobs Fmod DEBB007 36.7 17.0 DEHE071 49.3 18.9 DESN045 31.6 14.8 DEBB021 27.7 14.0 DEHE072 23.0 19.2 DESN051 28.1 17.5 DEBB029 25.8 12.3 DEHE073 30.3 21.1 DESN052 42.0 13.3 DEBB032 34.6 17.6 DEHE074 79.9 23.2 DESN059 15.6 16.0 DEBB053 28.0 15.2 DEHE075 21.2 22.2 DESN061 33.7 21.2 DEBB055 57.2 14.6 DEHE076 28.5 21.9 DESN091 54.1 15.3 DEBB063 28.3 12.5 DEHE077 23.7 22.8 DESN092 29.4 22.0 DEBB064 83.9 15.8 DEHE078 39.1 23.2 DEST002 38.9 13.0 DEBB065 33.7 13.2 DEHE079 300.0 22.8 DEST006 28.1 13.0 DEBB066 32.8 16.0 DEHE080 45.6 23.2 DEST011 22.5 18.6 DEBB067 29.4 12.8 DEHE081 54.8 18.2 DEST015 57.1 15.8 DEBB076 61.5 15.8 DEHE082 25.9 18.1 DEST028 37.4 14.1 DEBB078 42.6 12.5 DEHE083 167.5 18.5 DEST029 39.3 14.5 DEBB083 43.5 19.3 DEHE084 27.7 18.3 DEST044 35.5 14.1 DEBB086 28.1 15.1 DEHE085 24.3 18.3 DEST066 37.1 16.0 DEBB092 46.6 14.1 DEHE086 18.2 18.2 DEST072 31.9 13.1 DEBB099 102.3 15.4 DEHE087 33.0 19.0 DEST075 66.2 13.7 DEBW015 27.8 18.3 DEHE088 116.5 18.9 DEST089 62.3 13.2 DEBW026 28.5 22.9 DEHE089 45.6 15.3 DEST090 20.1 14.9 DEBW033 16.4 19.2 DEHE090 21.0 15.5 DEST091 22.5 15.6 DEBW087 11.9 19.1 DEHE091 104.3 15.4 DEST098 37.4 13.5 DEHB005 665.5 16.6 DEHE092 43.4 15.8 DEST105 81.3 11.9 DEHE065 25.5 20.1 DESN006 29.2 15.6 DEUB001 7.2 11.3 DEHE066 51.3 20.4 DESN011 61.2 16.1 DEUB004 14.7 17.3 DEHE067 23.4 20.0 DESN017 111.4 19.7 DEUB005 12.4 12.6 DEHE068 26.2 20.0 DESN019 35.8 15.1 DEUB028 10.3 11.2 DEHE069 15.0 19.2 DESN020 31.0 14.4 DEUB029 17.6 17.2 DEHE070 16.7 18.6 DESN025 31.2 19.4 DEUB030 8.5 10.8

Annex C TIME SERIES OF Pb, Cd AND Hg MONTHLY MEAN MODELLED AND OBSERVED CONCENTRATIONS IN AIR AND MONTHLY SUMS OF WET DEPOSITION FLUXES IN 2014-2016

C.1. Monthly mean modelled and observed concentrations of Pb in air in 2014-2016

7 Observed 12 Observed 3 DE0001 Modelled 3 DE0002 Modelled 6 10 5 8 4 6 3 4

2 Air concentrations, concentrations, Air ng/m 1 concentrations, Air ng/m 2

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

4 Observed 14 Observed 3 DE0003 3 DE0007 3.5 Modelled 12 Modelled 3 10 2.5 8 2 6 1.5

1 4 Air concentrations, concentrations, Air ng/m 0.5 concentrations, Air ng/m 2

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

6 Observed 12 Observed 3 3 DE0008 Modelled DE0009 Modelled 5 10 4 8 3 6

2 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 1 2

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

30 Observed 14 Observed 3 3 DBB044 Modelled DBB045 Modelled 25 12 10 20 8 15 6 10

4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 20 Observed 3 3 DBB049 DBB054 12 Modelled Modelled 10 15 8 10 6

4 5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

15 Observed 15 Observed 3 3 DBB060 Modelled DBB083 Modelled 12 12

9 9

6 6

3 3

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

30 Observed 14 Observed 3 3 DBB087 Modelled DBB092 Modelled 25 12 10 20 8 15 6 10

4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

25 Observed 25 Observed 3 3 DBE034 Modelled DBE065 Modelled 20 20

15 15

10 10

5 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 10 Observed 3 3 14 DBW013 Modelled DBW019 Modelled 8 12 10 6 8 6 4 4

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

8 Observed 10 Observed 3 3 DBW029 Modelled DBW033 Modelled 8 6 6 4 4 2

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 16 Observed 3 3 DBW080 DBW098 12 Modelled 14 Modelled 10 12 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 10 Observed 3 3 14 DBW099 Modelled DBW112 Modelled 8 12 10 6 8 6 4 4

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 7 Observed 3 3 DBW118 DBW122 14 Modelled 6 Modelled 12 5 10 4 8 3 6

4 2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 8 Observed 3 3 DBW125 Modelled 7 DBW147 Modelled 8 6 6 5 4 4 3 2

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 25 Observed 3 3 14 DBW152 Modelled DBW156 Modelled 20 12 10 15 8 6 10 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 8 Observed 3 3 DBY006 Modelled 7 DBY099 Modelled 8 6 6 5 4 4 3 2

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

5 Observed 20 Observed 3 3 DBY109 Modelled DBY115 Modelled 4 15 3 10 2 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 12 Observed 3 3 DBY119 Modelled DBY120 Modelled 8 10 8 6 6 4 4

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 14 Observed 3 3 DHB005 DHE001 14 Modelled 12 Modelled 12 10 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 20 Observed 3 3 DHE005 Modelled DHE008 Modelled 15 15

10 10

5 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 12 Observed 3 3 DHE009 Modelled DHE013 Modelled 10 15 8 10 6 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 20 Observed 3 3 DHE018 Modelled DHE022 Modelled 15 15

10 10

5 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 12 Observed 3 3 DHE037 Modelled DHE042 Modelled 10 15 8 10 6 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 14 Observed 3 3 DHE043 DHE052 12 Modelled 12 Modelled 10 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 16 Observed 3 3 DHE053 Modelled 14 DHE054 Modelled 15 12 10 10 8 6

5 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed Observed 3 3 DHE056 Modelled 15 DHE057 Modelled 15 12

10 9 6 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

25 Observed 12 Observed 3 3 DHE059 Modelled DMV004 Modelled 20 10 8 15 6 10 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 12 Observed 3 3 DMV019 Modelled DMV020 Modelled 8 10 8 6 6 4 4

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 100 Observed 3 3 DMV022 Modelled DNI016 Modelled 8 80

6 60

4 40

2 20

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

12 Observed 14 Observed 3 3 DNI31 Modelled DNI048 Modelled 10 12 10 8 8 6 6 4

4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

25 Observed 10 Observed 3 3 DNI067 Modelled DNI068 Modelled 20 8

15 6

10 4

5 2

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 14 Observed 3 3 DNI071 DNI143 Modelled 12 Modelled 8 10 6 8 4 6 4

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

35 Observed 30 Observed 3 3 DNW008 Modelled DNW022 Modelled 30 25 25 20 20 15 15 10

10 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

30 Observed 30 Observed 3 3 DNW038 Modelled DNW040 Modelled 25 25 20 20 15 15

10 10 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

25 Observed 8 Observed 3 3 DNW053 Modelled 7 DNW064 Modelled 20 6 15 5 4 10 3 2

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 20 Observed 3 3 14 DNW067 Modelled DNW081 Modelled 12 15 10 8 10 6

4 5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

25 Observed 20 Observed 3 3 DNW082 Modelled DNW100 Modelled 20 15 15 10 10 5

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

30 Observed 20 Observed 3 3 DNW112 Modelled DNW189 Modelled 25 15 20 15 10 10

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 25 Observed 3 3 14 DNW207 Modelled DNW212 Modelled 20 12 10 15 8 6 10 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 12 Observed 3 3 DRP01 Modelled DRP022 Modelled 14 10 12 10 8 8 6 6 4

4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

16 Observed 14 Observed 3 3 DRP023 DRP053 14 Modelled 12 Modelled 12 10 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 14 Observed 3 3 DSH033 DSH035 12 Modelled 12 Modelled 10 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

12 Observed 30 Observed 3 3 DSH053 Modelled DSL010 Modelled 10 25 8 20 6 15

4 10 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

120 Observed 10 Observed 3 3 DSL017 Modelled DSL019 Modelled 100 8 80 6 60 4 40

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 20

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 30 Observed 3 3 DSN011 Modelled DSN017 Modelled 25 15 20 10 15 10

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 30 Observed 3 3 DSN025 Modelled DSN051 Modelled 25 15 20 10 15 10

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

35 Observed 14 Observed 3 3 DSN061 DSN074 30 Modelled 12 Modelled 25 10 20 8 15 6

10 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 16 Observed 3 3 DST002 DST075 12 Modelled 14 Modelled 10 12 10 8 8 6 6

4 4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

35 Observed 10 Observed 3 3 DST092 DTH020 30 Modelled Modelled 8 25 20 6 15 4 10

2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 5

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

20 Observed 12 Observed 3 3 DTH036 Modelled DTH061 Modelled 10 15 8 10 6 4

5 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

8 Observed

3 7 DTH072 Modelled 6 5 4 3 2

Air concentrations, concentrations, Air ng/m 1

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May Fig.C.1. Monthly mean modelled and observed Pb air concentrations in 2014-2016

92 C.2. Monthly mean modelled and observed wet deposition fluxes of Pb in 2014-2016

120 Observed 160 Observed DE0001 Modelled DE0002 Modelled 100 140

120

/month

/month 2 2 80 100 60 80 40 60 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

May May May 200 Observed 100 Observed DE0003 Modelled DE0007 Modelled 80

150

/month /month

2 2 60 100 40 50

20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 200 Observed 100 Observed DE0008 Modelled DE0009 Modelled 80

150

/month /month

2 2 60 100 40 50

20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 600 Observed 800 Observed DBB094 Modelled DBB095 Modelled

500 700 /month

2 600 /month 400 2 500 300 400 200 300 200

100 100 Wet deposition, Wet g/km

0 deposition, Wet g/km 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 700 DBB096 Observed 700 DBB097 Observed

600 Modelled 600 Modelled /month

/month 500 500

2 2 400 400 300 300 200 200

100 100 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 800 Observed 1000 Observed 700 DBB100 Modelled DBE070 Modelled 800

600

/month /month

2 2 500 600 400 300 400 200 200

100 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

93 120 Observed 120 Observed DNI079 Modelled DNI080 Modelled

100 100

/month /month 2 2 80 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100 Observed 120 Observed DNI082 Modelled DNI083 Modelled

80 100

/month /month 2 2 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

120 Observed 100 Observed DNI084 Modelled DNI090 Modelled

100 80

/month /month 2 2 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

700 DNI096 Observed 70 DNI099 Observed

600 Modelled 60 Modelled /month

/month 500 50

2 2 400 40 300 30 200 20

100 10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

80 Observed 100 Observed 70 DNI101 Modelled DNI103 Modelled 80

/month /month

2 2 50 60 40 30 40 20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

70 Observed Observed DNI104 60 DNI105 60 Modelled Modelled

50 /month

/month 50

2 2 40 40 30 30 20 20

10 10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

94 120 Observed 100 Observed DNI106 Modelled DNI107 Modelled

100 80

/month /month 2 2 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

Observed 60 Observed 80 DNI108 Modelled DNI109 Modelled

/month /month 2 2 60 40 30 40 20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100 Observed 80 Observed DNI111 Modelled 70 DNI112 Modelled 80

/month /month

2 2 60 50 40 40 30 20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

80 Observed 120 Observed DNI113 Modelled DNI124 Modelled 70 100

/month /month 2 2 50 80 40 60 30 40 20

10 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100 DNI125 Observed 70 DNI133 Observed Modelled 60 Modelled

80 /month

/month 50

2 2 60 40 40 30 20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

70 Observed 60 Observed DNI138 Modelled DRP048 Modelled

60 50 /month

/month 50 2 2 40 40 30 30 20 20

10 10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

95 70 DRP054 Observed 200 DRP055 Observed 60 Modelled Modelled

150 /month

/month 50

2 2 40 100 30 20 50

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

120 Observed 250 Observed DRP056 Modelled DRP057 Modelled

100 200

/month /month 2 2 80 150 60 100 40

20 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100 Observed 250 Observed DRP058 Modelled DRP059 Modelled

80 200

/month /month

2 2 60 150

40 100

20 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

120 Observed Observed DRP061 Modelled 100 DRP062 Modelled 100

/month /month 2 2 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

80 Observed 250 Observed DSL021 Modelled DSL023 Modelled

70 200 /month

/month 60

2 2 50 150 40 30 100 20 50

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

70 DSL024 Observed 80 DSL025 Observed

60 Modelled Modelled /month

/month 50 60

2 2 40 40 30

20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

96 70 DSL026 Observed 100 DSL027 Observed 60 Modelled Modelled

80 /month

/month 50

2 2 40 60 30 40 20 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100 Observed 80 Observed DSL028 Modelled DSL029 Modelled

80 /month

/month 60

2 2 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

350 DSH036 Observed DSH037 Observed

300 Modelled 150 Modelled /month

/month 250 120

2 2 200 90 150 60 100

50 30 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

200 Observed 200 Observed DSH038 Modelled DSH039 Modelled

150 150

/month /month

2 2

100 100

50 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

400 Observed 200 Observed 350 DSH040 Modelled DSH042 Modelled

300 150

/month /month 2 2 250 200 100 150 100 50

50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

140 DSH043 Observed 200 DSH044 Observed 120 Modelled Modelled

150 /month

/month 100

2 2 80 100 60 40 50

20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

97 500 Observed 500 Observed DSH045 Modelled DSH046 Modelled

400 400

/month /month

2 2 300 300

200 200

100 100 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

140 DSH047 Observed 400 DSH048 Observed 120 Modelled 350 Modelled

300 /month

/month 100 2 2 250 80 200 60 150 40 100

20 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

120 Observed 160 Observed DSH049 Modelled DSH050 Modelled 100 140

120

/month /month 2 2 80 100 60 80 40 60 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

200 Observed 80 Observed DSH054 Modelled 70 DTH103 Modelled

150 60

/month /month 2 2 50 100 40 30 50 20

10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

250 Observed 120 Observed DTH104 Modelled DTH105 Modelled

200 100

/month /month 2 2 80 150 60 100 40

50 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

500 Observed 1000 Observed DTH106 Modelled DTH107 Modelled

400 800

/month /month

2 2 300 600

200 400

100 200 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

98 3000 Observed 300 Observed DTH108 Modelled DTH109 Modelled

2500 250

/month /month 2 2 2000 200 1500 150 1000 100

500 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 2000 Observed 100 Observed DTH110 Modelled DTH111 Modelled 80

1500

/month /month

2 2 60 1000 40 500

20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 200 Observed 200 Observed DTH112 Modelled DTH113 Modelled

150 150

/month /month

2 2

100 100

50 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

70 Observed 300 Observed DTH114 Modelled DTH115 Modelled

60 250

/month /month 2 2 50 200 40 150 30 100 20

10 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 200 Observed DTH116 Modelled

150

/month 2

100

Wet deposition, Wet g/km 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May Fig. C.2. Monthly sums of modelled and observed Pb wet deposition fluxes in 2014-2016

99 C.3.Monthly mean modelled and observed concentrations of Cd in air in 2014-2016

0.2 Observed 0.35 Observed 3 3 DE0001 DE0002 Modelled 0.3 Modelled 0.15 0.25 0.2 0.1 0.15

0.05 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.16 Observed 0.35 Observed 3 3 DE0003 DE0007 0.14 Modelled 0.3 Modelled 0.12 0.25 0.1 0.2 0.08 0.15 0.06

0.04 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.02 0.05

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

0.16 Observed 0.3 Observed 3 3 DE0008 Modelled DE0009 Modelled 0.14 0.25 0.12 0.1 0.2 0.08 0.15 0.06 0.1

0.04 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.02 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.4 Observed 3 3 DBB044 Modelled 0.35 DBB045 Modelled 0.4 0.3 0.3 0.25 0.2 0.2 0.15 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.6 Observed 0.5 Observed 3 3 DBB049 Modelled DBB054 Modelled 0.5 0.4 0.4 0.3 0.3 0.2 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.5 Observed 3 3 DBB060 DBB083 0.3 Modelled Modelled 0.4 0.25 0.2 0.3 0.15 0.2 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

100

0.8 Observed 0.4 Observed 3 3 0.7 DBB087 Modelled 0.35 DBB092 Modelled 0.6 0.3 0.5 0.25 0.4 0.2 0.3 0.15

0.2 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.6 Observed 3 3 DBE034 Modelled DBE065 Modelled 0.4 0.5 0.4 0.3 0.3 0.2 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.25 Observed 3 3 0.35 DBW013 Modelled DBW019 Modelled 0.2 0.3 0.25 0.15 0.2 0.15 0.1 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.25 Observed 0.3 Observed 3 DBW029 Modelled 3 DBW033 Modelled 0.2 0.25 0.2 0.15 0.15 0.1 0.1

0.05 Air concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

0.35 Observed 0.5 Observed 3 3 DBW080 DBW098 0.3 Modelled Modelled 0.4 0.25 0.2 0.3 0.15 0.2 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.25 Observed 3 3 0.35 DBW099 Modelled DBW112 Modelled 0.2 0.3 0.25 0.15 0.2 0.15 0.1 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

101

0.4 Observed 0.25 Observed 3 3 0.35 DBW118 Modelled DBW122 Modelled 0.2 0.3 0.25 0.15 0.2 0.15 0.1 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.25 Observed 3 3 DBW125 DBW147 0.3 Modelled Modelled 0.2 0.25 0.2 0.15 0.15 0.1 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.5 Observed 3 3 0.35 DBW152 Modelled DBW156 Modelled 0.4 0.3 0.25 0.3 0.2 0.15 0.2 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.25 Observed 0.25 Observed 3 3 DBY006 Modelled DBY099 Modelled 0.2 0.2

0.15 0.15

0.1 0.1

0.05 0.05

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.2 Observed 0.5 Observed 3 DBY109 Modelled 3 DBY115 Modelled 0.4 0.15 0.3 0.1 0.2 0.05

0.1

Air concentrations, Air ng/m Air concentrations, concentrations, Air ng/m

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

0.25 Observed 0.35 Observed 3 3 DBY119 DBY120 Modelled 0.3 Modelled 0.2 0.25 0.15 0.2 0.1 0.15 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

102

0.3 Observed 0.4 Observed 3 3 DHB005 Modelled DHE001 Modelled 0.25 0.35 0.3 0.2 0.25 0.15 0.2 0.1 0.15

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.5 Observed 3 3 DHE005 Modelled DHE008 Modelled 0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.25 Observed 3 3 DHE009 Modelled DHE013 Modelled 0.4 0.2

0.3 0.15

0.2 0.1

0.1 0.05

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.5 Observed 3 3 0.35 DHE018 Modelled DHE022 Modelled 0.4 0.3 0.25 0.3 0.2 0.15 0.2 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.4 Observed 3 3 DHE037 Modelled 0.35 DHE042 Modelled 0.4 0.3 0.3 0.25 0.2 0.2 0.15 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.35 Observed 3 3 DHE043 DHE052 0.3 Modelled 0.3 Modelled 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

103

0.5 Observed 0.4 Observed 3 3 DHE053 Modelled 0.35 DHE054 Modelled 0.4 0.3 0.3 0.25 0.2 0.2 0.15 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.5 Observed 3 3 DHE056 Modelled DHE057 Modelled 0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.7 Observed 0.4 Observed 3 3 DHE059 DMV004 0.6 Modelled 0.35 Modelled 0.5 0.3 0.25 0.4 0.2 0.3 0.15

0.2 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.25 Observed 0.25 Observed 3 3 DMV019 Modelled DMV020 Modelled 0.2 0.2

0.15 0.15

0.1 0.1

0.05 0.05

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.3 Observed 1.4 Observed 3 3 DMV022 Modelled DNI016 Modelled 0.25 1.2 1 0.2 0.8 0.15 0.6 0.1

0.4 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.3 Observed 0.35 Observed 3 3 DNI31 Modelled DNI048 Modelled 0.25 0.3 0.25 0.2 0.2 0.15 0.15 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

104

0.5 Observed 0.25 Observed 3 3 DNI067 Modelled DNI068 Modelled 0.4 0.2

0.3 0.15

0.2 0.1

0.1 0.05

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.3 Observed 0.3 Observed 3 3 DNI071 Modelled DNI143 Modelled 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.6 Observed 0.6 Observed 3 DNW008 Modelled 3 DNW022 Modelled 0.5 0.5 0.4 0.4 0.3 0.3

0.2 0.2 Air concentrations, Air ng/m 0.1 concentrations, Air ng/m 0.1

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

0.6 Observed 0.7 Observed 3 3 DNW038 Modelled DNW040 Modelled 0.5 0.6 0.5 0.4 0.4 0.3 0.3 0.2

0.2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.6 Observed 0.25 Observed 3 3 DNW053 Modelled DNW064 Modelled 0.5 0.2 0.4 0.15 0.3 0.1 0.2

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.5 Observed 3 3 DNW067 DNW081 0.3 Modelled Modelled 0.4 0.25 0.2 0.3 0.15 0.2 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

105

0.6 Observed 0.5 Observed 3 3 DNW082 Modelled DNW100 Modelled 0.5 0.4 0.4 0.3 0.3 0.2 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.7 Observed 0.5 Observed 3 3 DNW112 DNW189 0.6 Modelled Modelled 0.4 0.5 0.4 0.3 0.3 0.2 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.6 Observed 3 3 DNW207 Modelled DNW212 Modelled 0.35 0.5 0.3 0.25 0.4 0.2 0.3 0.15 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.3 Observed 3 3 DRP01 Modelled DRP022 Modelled 0.35 0.25 0.3 0.25 0.2 0.2 0.15 0.15 0.1

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.35 Observed 3 3 DRP023 DRP053 0.35 Modelled 0.3 Modelled 0.3 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.35 Observed 3 3 DSH033 DSH035 0.3 Modelled 0.3 Modelled 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

106

0.3 Observed 0.3 Observed 3 3 DSH053 Modelled DSL010 Modelled 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.35 Observed 0.25 Observed 3 3 DSL017 DSL019 0.3 Modelled Modelled 0.2 0.25 0.2 0.15 0.15 0.1 0.1

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.35 Observed 3 3 DSN006 DSN011 0.35 Modelled 0.3 Modelled 0.3 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.05 0.05

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.8 Observed 0.7 Observed 3 3 DSN017 DSN020 0.7 Modelled 0.6 Modelled 0.6 0.5 0.5 0.4 0.4 0.3 0.3

0.2 0.2 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.6 Observed 3 3 DSN025 Modelled DSN045 Modelled 0.4 0.5 0.4 0.3 0.3 0.2 0.2

0.1 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.1

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.5 Observed 0.5 Observed 3 3 DSN051 Modelled DSN061 Modelled 0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

107

0.25 Observed 0.5 Observed 3 3 DSN074 Modelled DSN077 Modelled 0.2 0.4

0.15 0.3

0.1 0.2

0.05 0.1

Air concentrations, concentrations, Air ng/m concentrations, Air ng/m

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.4 Observed 0.4 Observed 3 0.35 DST002 Modelled 3 0.35 DST075 Modelled 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15

0.1 0.1 Air concentrations, Air ng/m 0.05 concentrations, Air ng/m 0.05

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

1.2 Observed 0.25 Observed 3 3 DST092 Modelled DTH020 Modelled 1 0.2 0.8 0.15 0.6 0.1 0.4

0.05 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.2

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

0.2 Observed 0.16 Observed 3 3 DTH036 Modelled 0.14 DTH061 Modelled 0.15 0.12 0.1 0.1 0.08 0.06

0.05 0.04 Air concentrations, concentrations, Air ng/m Air concentrations, concentrations, Air ng/m 0.02

0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 0.25 Observed

3 DTH072 Modelled 0.2

0.15

0.1

0.05 Air concentrations, concentrations, Air ng/m

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May Fig. C.3. Monthly mean modelled and observed Cd air concentrations in 2014-2016

108 C.4. Monthly mean modelled and observed wet deposition fluxes of Cd in 2014-2016

3.5 DE0001 Observed 5 DE0002 Observed 3 Modelled Modelled

4 /month

/month 2.5

2 2 2 3 1.5 2 1 1

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

May May May 5 DE0003 Observed 3.5 DE0007 Observed Modelled 3 Modelled

4 /month

/month 2.5

2 2 3 2 2 1.5 1 1

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 7 DE0008 Observed 4 DE0009 Observed 6 Modelled 3.5 Modelled

3 /month

/month 5 2 2 2.5 4 2 3 1.5 2 1

1 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 160 Observed 80 Observed

140 DBB094 Modelled 70 DBB095 Modelled /month

2 120 60 /month 100 2 50 80 40 60 30 40 20

20 10 Wet deposition, Wet g/km

0 deposition, Wet g/km 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 140 DBB096 Observed 140 DBB097 Observed

120 Modelled 120 Modelled /month

/month 100 100

2 2 80 80 60 60 40 40

20 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May 300 Observed 100 Observed DBB100 Modelled DBE070 Modelled

250 80

/month /month 2 2 200 60 150 40 100

50 20 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

109 20 Observed 25 Observed DBW161 Modelled DBW205 Modelled 20

/month /month

2 2 15 10 10 5

5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

6 Observed 4 Observed DNI079 Modelled DNI080 Modelled 5 3.5

/month /month 2 2 4 2.5 3 2 2 1.5 1

1 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

3.5 DNI082 Observed 5 DNI083 Observed 3 Modelled Modelled

4 /month

/month 2.5

2 2 2 3 1.5 2 1 1

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

6 Observed 4 Observed DNI084 Modelled DNI090 Modelled 5 3.5

/month /month 2 2 4 2.5 3 2 2 1.5 1

1 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

30 Observed 2.5 Observed DNI096 Modelled DNI099 Modelled

25 2

/month /month 2 2 20 1.5 15 1 10

5 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

5 Observed 6 Observed DNI101 Modelled DNI103 Modelled

4 5

/month /month 2 2 4 3 3 2 2

1 1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

110 5 Observed 10 Observed DNI104 Modelled DNI105 Modelled

4 8

/month /month

2 2 3 6

2 4

1 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

7 Observed 30 Observed DNI106 Modelled DNI107 Modelled

6 25 /month

/month 5 2 2 20 4 15 3 10 2

1 5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

12 Observed 4 Observed DNI108 Modelled DNI109 Modelled 10 3.5

/month /month 2 2 8 2.5 6 2 4 1.5 1

2 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

4 DNI110 Observed 3.5 DNI111 Observed 3.5 Modelled 3 Modelled

3 /month

/month 2.5 2 2 2.5 2 2 1.5 1.5 1 1

0.5 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

3.5 Observed 12 Observed DNI112 Modelled DNI113 Modelled

3 10 /month

/month 2.5 2 2 8 2 6 1.5 4 1

0.5 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

2.5 Observed 5 Observed DNI114 Modelled DNI123 Modelled

2 4

/month /month

2 2 1.5 3

1 2

0.5 1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

111 5 Observed 12 Observed DNI125 Modelled DNI126 Modelled

4 10

/month /month 2 2 8 3 6 2 4

1 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

2 Observed 5 Observed DNI133 Modelled DNI138 Modelled 4

1.5

/month /month

2 2 3 1 2 0.5

1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

4 Observed 5 Observed 3.5 DRP140 Modelled DRP142 Modelled 4

/month /month

2 2 2.5 3 2 1.5 2 1 1

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

5 DRP048 Observed 14 DRP054 Observed Modelled 12 Modelled

4 /month

/month 10

2 2 3 8 2 6 4 1

2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 DRP055 Observed 3.5 DRP056 Observed Modelled 3 Modelled

8 /month

/month 2.5

2 2 6 2 4 1.5 1 2

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 DRP057 Observed 3.5 DRP058 Observed Modelled 3 Modelled

8 /month

/month 2.5

2 2 6 2 4 1.5 1 2

0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

112 5 Observed 2.5 Observed DRP059 Modelled DRP061 Modelled

4 2

/month /month

2 2 3 1.5

2 1

1 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

5 Observed 12 Observed DRP062 Modelled DSL021 Modelled

4 10

/month /month 2 2 8 3 6 2 4

1 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 6 Observed DSL025 Modelled DSL029 Modelled

8 5

/month /month 2 2 4 6 3 4 2

2 1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 Observed 12 Observed DSH036 Modelled DSH037 Modelled

12 10 /month

/month 10 2 2 8 8 6 6 4 4

2 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 6 Observed DSH038 Modelled DSH039 Modelled

8 5

/month /month 2 2 4 6 3 4 2

2 1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

14 DSH040 Observed 10 DSH042 Observed 12 Modelled Modelled

8 /month

/month 10

2 2 8 6 6 4 4 2

2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

113 5 Observed 12 Observed DSH043 Modelled DSH044 Modelled

4 10

/month /month 2 2 8 3 6 2 4

1 2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

10 Observed 8 Observed DSH045 Modelled 7 DSH046 Modelled 8

/month /month

2 2 6 5 4 4 3 2 2

1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

5 Observed 16 Observed DSH047 Modelled 14 DSH048 Modelled 4

/month /month

2 2 3 10 8 2 6 4 1

2 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

6 Observed 7 Observed DSH049 Modelled DSH050 Modelled

5 6 /month

/month 5 2 2 4 4 3 3 2 2

1 1 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

12 Observed 300 Observed DSH054 Modelled DTH105 Modelled

10 250

/month /month 2 2 8 200 6 150 4 100

2 50 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

12 Observed 70 Observed DTH110 Modelled DTH116 Modelled

10 60 /month

/month 50 2 2 8 40 6 30 4 20

2 10 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0 0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May Fig. C.4. Monthly sums of modelled and observed Cd wet deposition fluxes in 2014-2016

114 C.5.Monthly mean modelled and observed concentrations of Hg in air in 2014-2016

2 2

3 3

1.5 1.5

1 1

0.5 Observed 0.5 Observed

Modelled Modelled Air concentrations, ng/m concentrations, Air DE0001 ng/m concentrations, Air DE0003

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

2 2.5

3 3 2 1.5 1.5 1 1 0.5 Observed 0.5 Observed

Modelled Modelled Air concentrations, ng/m concentrations, Air DE0008 ng/m concentrations, Air DE0009

0 0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

May May May Fig. C.5. Monthly mean modelled and observed Hg air concentrations in 2014-2016

C.6.Monthly mean modelled and observed wet deposition fluxes of Hg in 2014-2016

1.0 Observed Observed DE0001 Modelled 2.0 DE0002 Modelled

0.8

/month

/month 2 2 1.6 0.6 1.2 0.4 0.8

0.2 0.4 Wet deposition, Wet g/km

0.0 deposition, Wet g/km 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar Nov Nov Nov

Mar Mar Mar

May May May

May May May 6.0 Observed 2.0 Observed DE0003 Modelled DE0008 Modelled

5.0

/month

/month 2

2 1.5 4.0 3.0 1.0 2.0 0.5

1.0 Wet deposition, Wet g/km

0.0 deposition, Wet g/km 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

Nov Nov Nov

Mar Mar Mar

May May May

May May May 1.2 Observed 3.0 Observed DE0009 Modelled DNI082 Modelled

1.0 2.5

/month

/month 2 2 0.8 2.0 0.6 1.5 0.4 1.0

0.2 0.5 Wet deposition, Wet g/km

Wet deposition, Wet g/km 0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

115 2.5 DNI083 Observed 3.5 DNI084 Observed Modelled 3.0 Modelled

2.0

/month

/month 2 2 2.5 1.5 2.0 1.0 1.5 1.0 0.5

0.5

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

3.5 Observed 4.0 Observed DNI085 DNI086 Modelled

3.0 Modelled 3.5

/month /month 2 2 2.5 3.0 2.5 2.0 2.0 1.5 1.5 1.0 1.0

0.5 0.5

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

3.5 Observed Observed 4.0 DNI087 Modelled DNI096 Modelled

3.0 3.5

/month

/month 2 2 2.5 3.0 2.0 2.5 2.0 1.5 1.5 1.0 1.0

0.5 0.5

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

5.0 Observed Observed 3.5 DNI097 Modelled DNI098 Modelled

4.0 3.0

/month

/month 2 2 2.5 3.0 2.0 2.0 1.5 1.0 1.0

0.5

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

Observed Observed 3.5 DNI119 Modelled 3.0 DNI128 Modelled

3.0 /month

/month 2.5

2 2 2.5 2.0 2.0 1.5 1.5 1.0 1.0

0.5 0.5

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

Observed 12.0 Observed 1.2 DSH036 Modelled DSH037 Modelled

10.0

/month /month

2 2 0.9 8.0

0.6 6.0 4.0 0.3

2.0

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

116 1.6 Observed 5.0 Observed DSH038 Modelled DSH039 Modelled

4.0

/month /month 2 2 1.2 3.0 0.8 2.0 0.4

1.0

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

Observed 1.4 Observed 2.0 DSH040 DSH042

Modelled 1.2 Modelled

/month /month 2 2 1.6 1.0 1.2 0.8 0.6 0.8 0.4

0.4 0.2

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

Observed Observed

2.0 DSH043 Modelled 1.5 DSH044 Modelled

/month /month 2 2 1.5 1.2 0.9 1.0 0.6 0.5

0.3

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

2.0 Observed 2.0 Observed

DSH045 Modelled DSH046 Modelled

/month /month 2 2 1.5 1.5

1.0 1.0

0.5 0.5

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

2.0 Observed 5.0 Observed

DSH047 Modelled DSH048 Modelled /month

/month 4.0 2 1.5 2 3.0 1.0 2.0 0.5

1.0

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

Observed Observed 4.0 1.6 DSH049 Modelled DSH050 Modelled

3.5

/month

/month 2 2 3.0 1.2 2.5 2.0 0.8 1.5 1.0 0.4

0.5

Wet deposition, Wet g/km Wet deposition, Wet g/km

0.0 0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May

117 2.5 Observed 5.0 Observed DSH054 Modelled DNI121 Modelled

2.0 4.0

/month /month

2 2 1.5 3.0

1.0 2.0

0.5 1.0

Wet deposition, Wet g/km deposition, Wet g/km

0.0 0.0

Jul Jul Jul Jul Jul Jul

Jan Jan Jan Jan Jan Jan

Sep Sep Sep Sep Sep Sep

Nov Nov Nov Nov Nov Nov

Mar Mar Mar Mar Mar Mar

May May May May May May

3.0 Observed DNI126 Modelled

2.5

/month 2 2.0 1.5 1.0

0.5 Wet deposition, Wet g/km

0.0

Jul Jul Jul

Jan Jan Jan

Sep Sep Sep

Nov Nov Nov

Mar Mar Mar

May May May Fig. C.6. Monthly sums of modelled and observed Hg wet deposition fluxes in 2014-2016

118 Annex D

FORMAT OF NUMERICAL DATA

The modeling data is provided in a form of 2d geographical maps in agreed numerical format (ArcGIS compatible shape-files). The original data were produced at the latitude-longitude projection with spatial resolution 0.1°×0.1°. The dataset consists of yearly maps Pb, Cd, and Hg air concentration, total deposition, and deposition to individual types of land cover (44 types according to the CORINE classification). Spatial distributions of the land cover types (fraction of a grid cell area) are also provided.

Units of the data are:

ng m-3 - for air concentrations;

g km-2 year-1 - for deposition fluxes;

fraction (0-1) - for land cover types .

The data archive has the following structure:

/MET_Conc/filename - directory containing air concentrations maps;

/MET_Totdep/filename - directory containing total deposition maps;

/MET_Totdep_LC/filename - directory containing maps of deposition to land cover types;

/LandCover/filename - directory containing fractions of the land cover types; where MET is particular heavy metal (Pb, Cd or Hg).

Filenames are in the following form:

MET_conc_YEAR.EXT - for air concentrations maps;

MET_totdep_YEAR.EXT - for total deposition maps;

MET_corine##_YEAR.EXT - for maps of deposition to land cover types;

landcover_corine##.EXT - directory containing fractions of the land cover types; where MET is particular heavy metal or species (pb, cd, hg0 or hgii); YEAR is the year of the map (2014, 2015, 2016); ## is the number of the CORINE land cover type (see Table 1); EXT is an extension of the shape-files (shp, shx, dbf).

119