EMEP/MSC-E Technical Report 4/2006 2006

PROGRESS IN FURTHER DEVELOPMENT OF MSCE-HM AND MSCE-POP MODELS

(implementation of the model review recommendations)

A. Gusev, I. Ilyin, L.Mantseva, O.Rozovskaya, V. Shatalov, O. Travnikov

Meteorological Synthesizing Centre - East Leningradsky prospekt, 16/2, 125040 Moscow Russia Tel.: +7 495 614 39 93 Fax: +7 495 614 45 94 E-mail: msce@msceast.org Internet: www.msceast.org

CONTENTS

INTRODUCTION 5

1. VALIDATION OF INPUT METEOROLOGICAL DATA FOR MSCE-HM AND MSCE-POP MODELS 9

1.1. Analysis of parameterisations of physical processes in MM5 9

1.2. Validation of meteorological data generated by MM5 13

2. RESUSPENSION OF PARTICLE-BOUND HEAVY METALS FROM SOIL AND SEAWATER 17

2.1. Saltation 17

2.2. Sandblasting 20

2.3. Heavy metal concentration in soil 22

2.4. Sea-salt aerosol suspension 23

2.5. Estimates of heavy metals resuspension 25

3. MSCE-HM MODEL TESTING ON THE BASE OF DIFFERENT HM EMISSION SCENARIOS 27

3.1. Lead, cadmium, arsenic, chromium and nickel 28

3.2. Zinc, copper, selenium 35

3.3. Mercury 37

4. MSCE-POP MODEL IMPROVEMENT 39

4.1. Refinement of POP physical-chemical properties 39

4.1.1. Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans 40

4.1.2. Polycyclic aromatic hydrocarbons 46

4.1.3. Polychlorinated biphenyls 49

4.1.4. -Hexachlorocyclohexane (lindane) 53

4.1.5. Hexachlorobenzene 56

4.2. Refinement of process parameterization 58

4.2.1. Degradation of POPs in the atmosphere 58

4.2.2. Partitioning of POPs in soils 67

3

4.2.3. Removal of POPs with precipitation (snow scavenging) 82

4.2.4. Partitioning of POPs in seawater 84

5. DEVELOPMENT OF HEMISPHERIC/REGIONAL MODELLING APPROACH FOR POPs 87

5.1. Description of hemispheric/regional modelling approach 87

5.2. Evaluation of transboundary transport for European region 91

5.3. Analysis of input data for hemispheric and regional modelling 99

CONCLUSIONS 105

REFERENCES 109

Annex A. METEOROLOGICAL DATA ANALYSIS 115

Annex B. MODEL PARAMETERISATIONS FOR SELECTED POPs 125

Annex C. POP EMISSIONS 141

4

Introduction

This technical report reflects the progress in further development of MSC-E models of long-range transport of heavy metals (HMs) and persistent organic pollutants (POPs). The MSCE-HM and MSCE-POP models have been reviewed at the EMEP Task Force on Measurements and Modelling meeting in Zagreb and TFMM Workshop on model review in Moscow in 2005. It was concluded that the models are suitable for the evaluation of the long-range transboundary transport and deposition of HMs and POPs in Europe. Along with that the TFMM Workshop in Moscow has recommended to continue further improvement of modelling approaches for HMs and POPs.

It was recommended at the workshop that MSC-E consider the following issues.

General requirements:

Validation of meteorological fields generated by MM5;

Inclusion of a shallow lowest model layer;

Description of emission processes that are driven by meteorology such as resuspension and volatilisation from soils;

Extension of the HM and POP modelling domain to the global scale and employing meteorological fields with 1x1 resolution.

Specific requests for HM modelling:

extension of the MSCE-HM model to the consideration of other elements and heavy metals, including Ni, As, Cu, Cr, Zn and Se;

Improvement of deposition processes description;

Investigation of mercury dry deposition to forests ;

Further research and improvement of the description of mercury chemical transformations in the atmosphere.

Specific requests for POP modelling:

degradation of POPs in particle-bound and gaseous phase in the atmosphere including photodegradation;

seasonal dependence of soil volatilisation;

values of concentrations at boundaries of the EMEP grid;

seasonal variations of emissions;

application of the MSCE-POP model to screening of a wider range of POPs for their potential environmental significance;

inverse modelling using passive sampling campaign data;

ation of the potential influence of climate change on the fate and behaviour of POPs.

E has started its work on further improvement of MSC-E modelling approach for HMs and POPs.

investig

Following these recommendations MSC-

5

The verification of meteorological fields, generated by meteorological driver MM5, has been performed. Different parameterisations of atmospheric processes in MM5 were used and the obtained meteorological data were compared with ECMWF re-analysis. In Chapter 1 preliminary results of meteorological data verification generated by MM5 are presented. More detailed information is given in the Annex A of the report.

Parameterisation of emissions of metals to the atmosphere driven by meteorological processes has been developed. There is a number of natural mechanisms responsible for emission of aerosol-bound heavy metals to the atmosphere. In particular, they include emission with wind-blown dust and sea-salt aerosol. Since human activity has led to significant increase of concentrations of heavy metals in soils, compared to pre-industrial times, the meteorologically-driven emissions include both natural component and re-emission of previously deposited matter from anthropogenic sources, and further in the report will be called natural and historical emission. Brief description of these approaches is presented in Chapter 2.

According to the recommendations of TFMM, pilot parameterisations for arsenic, nickel, chromium, zinc, copper and selenium were elaborated. Besides, new approach to model natural and historical emissions of these new heavy metals together with lead and cadmium was developed. A number of model simulations aimed at evaluation of model performance after introduction of these changes were carried out. The simulations were performed on the base of two emission data sets. The first one includes data officially reported by Parties to the UNECE for 2000. For countries which did not report their national data, emission expert estimates of TNO were used [van der Gon et al., 2005]. The second one represents emission expert estimates for 2000 produced in the framework of ESPREME project [http://espreme.ier.uni-stuttgart.de/data.html]. The results of the numerical tests are described in Chapter 3.

With respect to the refinement of MSCE-POP model parameterisation essential attention at current stage of work was given to the harmonisation of physical-chemical properties of selected POPs and the improvement of process parameterisations used in the model. Further refinement of physical-chemical properties of POPs considered in modelling activities (PCDD/Fs, PAHs, PCBs, lindane and HCB) has been performed. Significant part of these improvements concerns the values of three partition coefficients: octanol/water partition coefficient, air/water partition coefficient or dimensionless Henry constant, and octanol/air partition coefficient. The values of these three coefficients should be harmonised as they are actually not independent. The description of adjustment procedure and the results of its application for the harmonisation of POP physical-chemical parameters are presented in the first section of Chapter 4. Updated model parameterisations for PCDD/Fs, PAHs, PCBs, lindane and HCB are given in the Annex B.

In order to improve the agreement of computed and observed seasonal variations of POP concentrations in the atmosphere a number of modifications was introduced into the MSCE-POP model. In course of the comparison of model results with measurements it was found that the model considerably underestimates seasonal variations of B[a]P air concentrations for a number of monitoring sites. Possible reasons of this disagreement could be connected with the neglecting of B[a]P degradation in particle-bound phase and underestimation of seasonal variations of B[a]P emissions. The possibility of improving the agreement between model results and measurements for B[a]P by the refinement of degradation process description and usage of different scenarios of seasonal variations of emission is considered in the second section of Chapter 4.

Additional modification of MSCE-POP model parameterisation is connected with the description of POP partitioning in soil. The processes of POP absorption in soil and volatilisation to the atmosphere are described in the model taking into account the effect of temperature variations. However the

6

parameterisation of POP partitioning in soil previously used in the model did not take into account temperature dependence of octanol/water partition coefficient KOW. It is expected that temperature dependence of KOW can affect the behaviour of POPs in soils and, as a consequence, the rate of POP volatilisation to the atmosphere. The sensitivity analysis of POP behaviour in soils with respect to KOW is presented below in the second section of Chapter 4.

Further development of EMEP/MSC-E modelling approach for POPs has been continued by improving the consistency of hemispheric and regional scale modelling for POPs. The developed modelling approach permits to evaluate POP pollution levels for European region and to provide estimates of transboundary fluxes between European countries accounting also for the contributions of non-European emission sources and of re-emission of POPs accumulated in environmental compartments. The later contributions for many of POPs can be significant as they are characterized by considerable long-range transport potential and essential residence time in the environmental media where they can be accumulated during a long period of time. Implemented modelling approach is based on the nesting of regional and hemispheric scale modelling using MSCE-POP model. The description of the approach is given in Chapter 5. The Annex C presents POP emission data used for modelling. A special attention is given to its computational aspects, compatibility of the input data used for hemispheric and regional modelling, and evaluation of transboundary transport of POPs between the European countries.

In close cooperation with the Task Force on POPs MSC-E has continued the activities on evaluating new substances by criteria of the long-range transport potential and overall persistence contributing to the preparatory work for the review of the POP Protocol. In particular, model evaluation of the Long-Range Transport Potential (LRTP) and overall persistence for a number of substances was carried out for PentaBDE, endosulfan, dicofol hexachlorobutadien (HCBD) pentachlorobenzene (PeCBz) and polychlorinated naphthalenes (PCN). On the basis of these calculations a technical report has been prepared by MSC-E [Vulykh et al., 2006] and delivered to the Task Force on POPs for the support of the work on peer review of new substances dossiers that may be proposed by Parties for inclusion into annexes to the Protocol.

In cooperation with national experts from the Parties to the Convention MSC-E has continued the work on POP model intercomparison study. In the current year the second stage of the study devoted to the comparison of mass balance estimates, calculated deposition and concentration fields of POPs in different environmental compartments and a number of sensitivity studies is completed. The updated results of this stage are presented in the revised EMEP/MSC-E Intermediate Technical Report 5/2006 POP Model Intercomparison Study. Stage II. Comparison of mass balances estimates and sensitivities. At present the third stage of the intercomparison is ongoing. This stage is aimed at the comparison of model predictions of long-range transport potential and overall persistence of 14 reference pollutants made by participating models. This activity assumes the application of these models, including MSCE-POP model, to screening of a wide range of POPs for their potential environmental significance.

The TFMM Workshop on MSC-E models review has pointed out a number of long-term strategic issues important for the evaluation of European region pollution by HMs and POPs. MSC-E has initiated implementation of these tasks and will continue to work in these directions in close co-operation with TFMM. It is planned to provide the detailed information on the modifications made and modelling results obtained using updated MSCE-HM and MSCE-POP model versions to the next TFMM.

7

Chapter 1

VALIDATION OF INPUT METEOROLOGICAL DATA FOR MSCE-HM AND MSCE-POP MODELS

Meteorological Synthesizing Cenrtre East uses MM5 as a meteorological preprocessor to prepare meteorological data for heavy metal and POP regional transport models. MM5 system is described in detail in [Grell et al., 1995; http://www.mmm.ucar.edu/mm5/overview.html]. The configuration of system used by MSC-E, and its input and output meteorological are overviewed in MSCE report [Travnikov and Ilyin, 2005].

TFMM workshop devoted to review of MSC-E models recommended to present validation of meteorological fields obtained by MM5 [TFMM Workshop minutes, 2005]. The process of validation is ongoing and so far not complete. This chapter deals with first results of the validation. In particular, the influence of different parameterisations of physical processes on the simulated meteorological parameters was tested. Additionally, first results of evaluation of meteorological parameters produced by MM5 via comparison with ECMWF re-analysis data are presented.

1.1. Analysis of parameterisations of physical processes in MM5

MM5 allows to use a wide scope of atmospheric parameterisations, including planetary boundary layer processes, soil-atmosphere interactions, parameterisation of cloud microphysics, convection etc. Different parameterisations of physical processes may lead to differences in output values of meteorological parameters. The influence of different parameterisations on output meteorological parameters was analysed. In Table 1.1 the list of sets of parameterisations applied in MM5 and involved in the analysis, is presented. The set of parameterisations currently used by MSCE is given in the first string of the table.

Table 1.1. Sets of parameterisations

N Cumulus clouds Planetary boundary layer Explicit Moisture

Schemes Soil Polar physics

11 Kain-Fritsch-22 MRF3 Reisner4 Five-Layer5 - 2 Kain-Fritsch-2 Gayno-Seaman6 Reisner Five-Layer - 3 Kain-Fritsch-2 ETA7 Reisner Five-Layer - 4 Kain-Fritsch-2 Pleim-Chang8 Reisner Pleim-Xiu9 - 5 Kain-Fritsch-2 MRF Reisner Noah10 - 6 Betts-Miller11 MRF Reisner Five-Layer - 7 Kain-Fritsch-2 MRF Dudhia12 Five-Layer - 8 Kain-Fritsch-2 MRF Goddard13 Five-Layer - 9 Kain-Fritsch-2 MRF Reisner-2 Five-Layer - 10 Kain-Fritsch-2 MRF Schultz14 Five-Layer - 11 Kain-Fritsch-2 MRF Reisner Five-Layer + 12 Betts-Miller ETA Reisner Noah -

Note: 1 - base case; 2 - Kain, 2004; 3 - Hong and Pan, 1996; 4 - Reisner et al, 1998; 5 - Dudhia, 1996; 6 - Ballard et al, 1991; Shafran et al, 2000; 7 - Janjic, 1990, 1994; 8 - Pleim and Chang, 1992; 9 - Xiu and Pleim, 2000; 10 - Chen and Dudhia, 2001; 11 - Betts, 1986; Betts and Miller, 1986; 1993; Janjic, 1994; 12 - Grell et al, 1995; 13 - Lin et al, 1983; Tao et al, 1989; Tao and Simpson, 1993; 14 Schultz, 1995.

9

http://www.mmm.ucar.edu/mm5/overview.html

Simulations of meteorological fields with the use of different parameterisations of MM5 were done for July and January, 2000. The calculations were performed with 150-km spatial resolution for the domain, covering almost entire northern hemisphere (Fig. 1.1). To prepare initial conditions and data assimilation (FDDA) the NCEP/NCAR re-analysis data were used.

The output data of each set were compared with ECMWF re-analysis data (so-called ERA-40), available for free for scientific purposes at website [http://data.ecmwf.int/data/d/era40_daily/ ]. Temporal resolution of these ECMWF data is 6 hours, and spatial resolution 2.5 x 2.5.

The results of computations were compared to the ECMWF re-analysis data and to each other both visually (plots, spatial distributions) and by statistical indices. Near-surface meteorological parameters were analyzed for a set of points located in various regions, climate conditions and at various heights above sea level (Table 1.2, Fig.1.1). Some of them correspond to the EMEP observation stations (lines 1-6 in Table 1.2). The rest are points which geographical coordinates are divisible by 2.5. In addition the results of computations and re-analysis were analyzed for five vertical levels approximately corresponding to 1000, 925, 850, 700 500 hP pressure levels. The sets of time-averaged meteorological parameters in 2.5x2.5 grid points were used at each level to compute statistical indices.

Table 1.2. Geographical position of points used to compute statistical indices of agreement between the

results of test computations and the ECMWF re-analysis data

N Conventional name Latitude Longitude 1 GB91 57.08 -2.53 2 DE4 49.76 7.05 3 DK20 55.11 14.91 4 CZ3 49.58 15.08 5 FI90 60.28 27.2 6 NO55 69.63 25.22 7 France 47.5 -2.5 8 Spain 40 -5 9 Italy 45 7.5 10 Serbia 42.5 22.5 11 Russia 57.5 40 12 Norway 62.5 7.5 13 USA 1 35 -95 14 USA 2 42.5 -72.5 15 USA 3 50 -127.5 16 Alert (Canaga) 82.5 -60 17 Chukotka 70 175 18 Novaya Zemlya 75 57.5 19 Spitsbergen 77.5 15 20 Far East 45 137.5 21 Siberia 60 115 22 China 37.5 115

Fig. 1.1. Position of points used to compare the results of MM5 computations with the EMCWF re-analysis data

10

The resulting statistical indices are given in Annex A, the required computing time (in relative units) is given is Table 1.3.

Table 1.3. MM5 computing time for various variants of physical processes parameterization (in relative units)

No of computing variant (Table) 1 2 3 4 5 6 7 8 9 10 11 12

Computing time 1.00 1.97* 1.05 2.41 1.00 1.01 0.94 1.12 1.34 0.94 1.02 -**

* Computation were made with a half time step ** Time not estimated

On the basis of the analysis the following preliminary conclusions can be made.

1. The best agreement between the results of computations and re-analysis data is seen for meteorological parameters that are involved in the assimilation procedure (temperature, air humidity, wind speed). Coefficients of correlation in time and space are normally within the range of 0.7 -0.9. For most of the points the total amount of monthly precipitation is fairly well reproduced by the model, however the coefficient of correlation in time is only 0.3 -0.4 on the average.

2. Statistical indices for various points may vary significantly. The best results were achieved for DE4 and Far East, the worst for Norway and Alert.

3. In most of the selected points the computations give less pronounced daily temperature variation than the ECMWF re-analysis data.

4. For the selected gridpoints MM5 tends to underestimate air temperature and to produce lower precipitation amounts, compared to ECMWF re-analysis data.

5. All meteorological parameters except precipitation amount are sensitive to the change in boundary layer and soil parameterization. Parameterization of clouds and microphysical processes influences air humidity and precipitation amount first of all.

6. The use of polar physics option improves the MM5 performance for polar regions. However, the effect of this option seems to be small.

7. There are several combinations of describing physical processes in MM5 that provide similar high quality of results in a reasonable computing time. Figures 1.2 and 1.3 show that the results of computations are in close agreement with each other as well as with the EMCWF re-analysis data. Conclusions on the capability of the considered parameterization variants to reproduce distributions of basic meteorological parameters are summarized in Table 1.4. Parameters for which adequate results have been achieved are marked with +.

11

calculation set 1 calculation set 3

calculation set 6 re-analysis ECMWF

Figure 1.2. Mean air temperature (0 ) at the height of 2 m above ground surface in January 2000

calculation set 1 calculation set 3

calculation set 6 re-analysis ECMWF

Fig. 1.3. Total amount of precipitation (sm) in July 2000

12

Table 1.4. Test results for MM5 parameterisation (qualitative assessment)

Capability to reproduce meteorological parameters No of computation

variant Air temperature Precipitation Wind speed Air humidity Computing

time

1 + + + + + 2 + 3 + + + + + 4 + 5 + + 6 + + + + + 7 + + + + 8 + + + + 9 + + + + 10 + + + + 11 + + + + + 12 + + + + +

The comparison of Tables 1.1 and 1.4 allows the following methods of describing physical processes to be recognized as most preferable

Cumulus clouds: Kain-Fritsch2, Betts-Miller; Boundary layer: MRF PBL, ETA PBL; Explicit moisture scemes: Reisner; Soil: Five-Layer.

1.2. Validation of meteorological data generated by MM5

For the parameterisation set 1, which is currently used for processing of meteorological data fro MSC-E regional models, more detailed analysis is anticipated. In the framework of meteorological data validation activity it is planned to compare in detail meteorological fields produced by MM5 with ECMWF re-analysis data. The compared parameters are horizontal wind velocity components, precipitation amounts and air temperature. These parameters were selected for the comparison because of two reasons. First of all, they are of primary importance for transport modelling of heavy metals and POPs. Secondly, these parameters are present in ECMWF re-analysis database. The comparison was performed for 2000. Spatial resolution of fields produced by MM5 and used in the validation, is 50 km.

Air temperatures and horizontal wind velocity components are three-dimensional parameters. That is why both their near-surface values and values from higher tropospheric layers were compared. It is important to note, that ECMWF data are available at isobaric levels, while MM5 data at p-sigma levels. Therefore, the comparison of upper tropospheric parameters is more correct over marine surfaces or lowlands, and less correct over mountains. Precipitation amounts simulated by MM5 are three-dimensional. However, ECMWF data are available only at surface, so only surface precipitation amounts were compared. Spatial analysis and investigation of temporal variability was performed for grid-points which geographical coordinates are divisible by 2.5. This approach allows to avoid errors of interpolation of 2.5x2.5 grid of ECMWF data to 50-km grid of MM5-derived data. Totally there were 386 such gridpoints, which is significant for statistical treatment.

13

In addition to ECMWF re-analysis, precipitation data prepared in the framework of project GPCP (Global Precipitation Climatology Project) were utilized. These data represent global set of gridded daily sums of precipitation amounts with resolution 1x1. The GPCP precipitation amounts used in the comparison, represent a combination of data, observed at meteorological stations and estimates of precipitation derived from satellite observations. More details about the project and its data are available through the Internet (http://cics.umd.edu/~yin/GPCP/main.html).

It is planned to include in the comparison the following steps:

1) Comparison of annual and monthly mean of MM5 and ECMWF/GPCP fields averaged over European domain

2) Analysis of spatial distributions of annual and monthly mean MM5 and ECMWF/GPCP fields

3) Analysis of temporal variability of MM5 and ECMWF/GPCP parameters over large number of gridpoints

4) Analysis of frequency distributions of the selected parameters in MM5 and ECMWF/GPCP data

The validation of the meteorological data set for long-range transport modelling is not complete. Only some preliminary results are demonstrated in this chapter. More detailed analysis of meteorological data will be prepared to the next TFMM meeting.

Analysis of spatial distributions

Ability of MM5 to simulate nearsurface temperature was evaluated by comparison of MM5 air temperature TMM5 at the middle 1st model layer (~40 m) with ECMWF temperatures at 2 meters above surface TECMWF. The difference between these to fields, expressed as TMM5 - TECMWF is within 1 (Fig. 1.4). The larger difference about -2 - -3 is noted mainly for mountainous regions Scandinavia, Balkans, Asia Minor, Pyrenees. Probably, this larger difference could be attributed to differences in orography used in MM5 and ECMWF.

Spatial correlation coefficients between monthly-mean and annual-mean air temperatures from MM5 and ECMWF were calculated. For every month and for the year as a whole the correlation coefficient is always higher than 0.97.

Annual sums of precipitation amounts, produced by MM5 were compared with those of ECMWF and derived from GPCP project. The measure of agreement between precipitation from MM5 and other data sources was expressed in terms of relative difference: (PMM5 Pref)/Pref x 100%, where PMM5 annual precipitation from MM5, and Pref - reference precipitation (ECMWF or GPCP). Over most of Europe the difference between MM5 and GPCP precipitation range within 25% (Fig. 1.5a). In high latitudes MM5 precipitation are essentially higher than GPCP ones. South Europe is characterized by large (>50%) both overestimation and underestimation of GPCP precipitation sums. In contrast to GPCP, the differences between MM5 and ECMWF precipitation over polar regions are not high (within 25%). Over southern, south-eastern and central parts of Europe MM5 tends to produce higher precipitation amounts compared to ECMWF. More detailed analysis of precipitation differences between MM5 and

Fig. 1.4. Difference between annual-mean near-surface air temperatures produced by MM5 and ECMWF (TMM5 - TECMWF)

14

EMCWF and GPCP is needed. In particular, seasonal changes and variability over shorter periods (day - week) should be analysed.

a b

Fig. 1.5. Relative difference between annual sums of precipitation amounts produced by GPCP and MM5 (a) and ECMWF and MM5 (b)

Spatial correlation coefficients for annual sums of precipitation processed by MM5 versus those prepared by ECMWF reanalysis is 0.80, and versus GPCP - 0.64. However, the correlations of monthly sums of precipitation vary considerably (Fig. 1.6). For every month MM5 precipitation are better correlated with ECMWF re-analysis data than with GPCP. The correlation is higher in winter and autumn seasons, and in July. The lowest correlation coefficients were obtained for May and August. Relatively low correlation coefficients (both with GPCP and ECMWF data) for spring and summer may be explained by complexity of modelling of convective precipitation which occur in warm season more often than in cold one.

0.0

0.2

0.4

0.6

0.8

1.0

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

MM5 vs. ECMWFMM5 vs. GPCP

Fig. 1.6. Spatial correlation coefficients of monthly-mean precipitation sums processed by MM5 with those from ECMWF and GPCP

Analysis of temporal variability

Temporal variability of air temperatures calculated by MM5 were evaluated by comparing 6-hour time series at each 2.5x2.5 gridpoint with similar data from ECMWF. The agreement between the time series was assessed by correlation coefficient. For each gridpoint the correlation is better than 0.88 (Fig. 1.7). The higher correlation was calculated for northern, central and eastern parts of Europe. The lower one was obtained for the south-eastern part.

15

Wind velocity is characterized by its magnitude and direction. Therefore the evaluation of temporal variability of near-surface winds included comparison of 6-hour times series of wind component along latitude (U), along longitude (V) and

magnitude of wind ( 22 VU + ). In order to validate near-surface wind the data from the middle of MM5 1st level were compared with ECMWF data at 10m height. It is important to note that within boundary layer magnitude of wind velocity tends to increase along vertical, so this comparison may result in some overestimation of ECMWF winds. Further it is planned to use in the comparison 10-m wind velocities from MM5.

Over most part of Europe correlation between ECMWF and MM5 two wind components and wind magnitude is relatively high greater than 0.65 (Fig. 1.8). Smaller correlation took place mainly over mountainous areas. Similar to air temperatures, this could be connected with differences in orography used in ECMWF reanalysis and MM5.

Fig. 1.7. Temporal correlation coefficients for near-surface air temperatures (whole year, 6-h time step) produced by MM5 and ECMWF

a b c

Fig. 1.8. Temporal correlation coefficients for near-surface wind (whole year, 6-h time step) produced by MM5 and ECMWF a): U-component; b): V-component; c): wind magnitude

Since the activity on validation of meteorological data produced by MM5 for modelling of heavy metals and POPs transport is not complete, only preliminary concluding remarks can be drawn.

Comparison of near-surface air temperatures, produced by MM5 and derived from ECMWF demonstrated good agreement between them. It is confirmed by high spatial and temporal correlation coefficients. Besides, the differences in annual-mean air temperatures are not high, with the exception of few gridpoints located in mountainous regions. Precipitation amounts modelled by MM5 were compared with precipitation derived from two different sources: ECMWF re-analysis and results of GPCP project. Spatial correlations of monthly sums of precipitation are higher for ECMWF, than for GPCP data. The agreement between annual sums of precipitation from MM5 and ECMWF on one hand, and between MM5 and GPCP on another hand significantly differs across Europe. Temporal correlations of wind components and wind magnitudes, derived from MM5 and ECMWF, are relatively high (>0.65) over most of Europe. In mountainous regions the correlation is lower, presumably because of different orography data.

16

Chapter 2

RESUSPENSION OF PARTICLE-BOUND HEAVY METALS FROM SOIL AND SEAWATER

Analysis of long-term trends of measured depositions and estimated emissions as well as the atmospheric balance for the Europe as a whole revealed significant inconsistencies between measured levels of lead and cadmium and their official European emissions [Ilyin and Travnikov, 2005]. These inconsistencies could be explained by either underestimation of the anthropogenic emissions data, or significant unaccounted influence of natural emissions and re-emissions of historic depositions, or by both reasons. That is why, beside improvement the official emissions estimates, the EMEP/TFMM Workshop on the review of MSC-E HM and POP models recommended development of emission algorithms and models for representations of meteorological processes driven emissions, such as resuspension of particle-bound heavy metals [TFMM Workshop minutes, 2005].

This chapter presents description of a progress in development of a tentative parameterisation for the resuspension of particle-bound heavy metals (Pb, Cd, As, Cr, Ni) from soil and seawater. This parameterisation is to be improved and refined further in future.

2.1. Saltation

In mineral dust production models the process of wind erosion and suspension of dust aerosol from the ground is commonly parameterized as combination of two major processes: saltation and sandblasting [e.g. Gomes et al., 2003; Zender et al., 2003; Gong et al., 2003]. The first process (saltation) presents horizontal movement of large soil aggregates driven by wind stress. Indeed, in natural soils small particles (below 20 m) never occur in free state, but are embedded in larger soil aggregates by cohesion forces (up to a few centimeters). These aggregates are too heavy to be directly suspended by wind in usual conditions. Instead, they are moved by wind stress close to the surface jumping from one place to another. When the saltating aggregates impact the ground they can eject much smaller particles (few micrometers), which can be easily suspended by wind and transported far away from the source region. This process is called the sandblasting.

The saltation process is characterized by the critical wind stress value, over which movement of soil particles can be initiated. This critical wind stress can be described by the threshold wind friction velocity, which depend on the soil particle size, soil wetness, and protection of the erodible soil by roughness elements (drag partitioning). In order to characterize this threshold friction velocity (Ut*) in the model we used a simplified empirically based parameterization proposed by Marticorena and Bergametti [1995]:

( )( )( )

>=

=

10Re,10Re0617.0exp858.01129.0

10Re,)1Re928.1(

129.0

*

5.0092.0*

t

t

U

U (2.1)

where

+=

2/5

7106s

sa

s

DgD

, . 38.0755.1Re 56.1 += sD

Here Ds is the soil particle size, a and s are air and soil mass densities, respectively; g is the gravity acceleration.

17

Dependence of the threshold friction velocity on soil particle size is shown in Fig.2.1. The threshold value is higher for very small and very large particles and has a minimum corresponding approximately to 75 m.

A parameterization of the threshold friction velocity dependence on soil wetness was proposed by Fcan et al. [1999] based on empirical data. According to this work the threshold is a function of volumetric soil moisture and clay content in soil. Taking into account that soil moisture data produced by the meteorological pre-processor (MM5) contain significant uncertainties and require transformation from gravimetric to volumetric values, we have chosen to use a simplified approach suggested by Grini et al. [2005]. It is based on rainfall events and implements the following assumptions:

10 100 10000

50

100

150

200

U* t (

cm/s

)

Ds (m) Fig. 2.1. Threshold friction velocity as a function of soil particles size

The dust production is stopped if precipitation during the last 24 hours exceeds 0.5 mm

The period without the dust production (in days) is equal to precipitation amount (in mm) during the last 24 hours

The dust production is resumed if no rain has fallen in the last 5 days

A phenomenological drag partition scheme was also proposed by Marticorena and Bergametti [1995]. It modifies the threshold friction velocity in order to take into account the effect of surface roughness elements hampering the transfer of wind momentum to the erodible surface. The scheme uses the soil roughness length (Z0) as a surrogate for the presence of these roughness elements. However, the large-scale roughness lengths commonly used in atmospheric transport models are deduced the standard deviation of the topography or from the vegetation height and cannot adequately represent the small-scale roughness to describe a surface process like aeolian erosion [Marticorena and Bergametti, 1997]. For this reason drag partition parameterization was not included to the current version of the resuspension model.

Ones the wind friction velocity exceeds the threshold value, the vertically integrated size-resolved saltation flux is given in the following form [Gomes et al., 2003]:

( )( 2****)( ttash UUUUgKDF += ) . (2.2)

The constant K in this expression reflects possibility of the limitation of soil aggregates supply because of depletion of loose material on the surface. Following Gomes et al. [2003] we adopted K=1 for deserts and K=0.02 for other erodible surfaces.

An example of the saltation flux dependence on the soil particle size is presented in Fig. 2.2a. As seen from the figure the wind of a given stress is able to involve into the movement soil aggregates with the particle size from the certain interval (35-200 m). The integrated saltation flux as a function of the wind friction velocity is illustrated in Fig.2.2b.

18

a

20 50 100 3000

2

4

6

8

10

12

14U* = 0.25 m/s

dFh/d

Ds (

mg

cm-1 s

-1

m-1)

Ds (m) b

0.0 0.2 0.4 0.6 0.8 1.00.0

0.3

0.6

0.9

1.2

1.5

F h (g

cm

-1 s

-1)

U* (m/s)

Fig. 2.2. Density of the saltation flux as a function of soil particles size (a) and dependence of the integrated saltation flux on wind friction velocity

In general the integrated saltation flux strongly depends on size distribution of soil aggregates occurring in natural conditions. The continuous multi-modal distribution can be presented by a combination of lognormal functions:

( )

=j j

jss

j

j

ss

DDDdD

dM

2

2

,

ln2lnln

expln2

1 , (2.3)

where jsD , is mass median diameter of the jth mode; and j is its geometric standard deviation.

Most pedological data on soil texture contain classification of soils according to their content of three major components (sand, silt, and clay). However, these data were obtained using the wet sedimentation technique, which results in the breakage of soil aggregates by water. Since wind erosion acts particularly on the soil aggregates, such data are not applicable to characterize the properties of erodible soil [Marticorena and Bergametti, 1997]. Instead, data based on the dry sieving technique can be used for this purpose [Chatenet et al., 1996]. Four soil populations were derived by Chatenet et al. [1996] using this technique as typical components of desert soils (Table 2.1). A size distribution of any soil can be presented as a combination of these populations according to its mineralogical type. Unfortunately, there is no any spatially resolved Europe-wide dataset on soil properties obtained using this technique is available at the moment.

Table 2.1. Soil populations identified in soils from arid and semiarid regions [Chatenet et al., 1996]

Typology Mineralogical type Ds, m Alumino-silicated-silt (ASS) Clay minerals dominant 125 1.6 Find sand (FS) Quartz dominant 210 1.8 Coarse sand (CS) Quartz 690 1.6 Salts (Sa) Salt and clay minerals 520 1.6

19

2.2. Sandblasting

The sandblasting model for dust suspension was developed by Alfaro et al. [1997; 1998]. Based on the wind tunnel experiments they derived that the aerosol particles released by sandblasting from the saltating aggregates of different natural soils can be sorted into three lognormal populations. Characteristics of the dust populations are presented in Table 2.2.

Table 2.2. Characteristics of tree dust aerosol particle populations released by the sandblasting [Alfaro and

Gomes, 2001]

Mode ei, kg m2/s2 di, m i

1 3.6110-7 1.5 1.7 2 3.5210-7 6.7 1.6 3 3.4610-7 14.2 1.5

According to the sandblasting model the vertical dust flux can be presented in the following form [Alfaro and Gomes, 2001]:

3,1,)()(, == idDdDdMDDFF s

ssis

Dhiv

s

, (2.4)

where the efficiency of the sandblasting process is given by:

i

iissi e

dpD3

6)( = . (2.5)

Here = 163 m/s2 is an empirical constant; pi is the fraction of kinetic energy of a soil aggregate required to release aerosol particles of mode i; di is the aerosol mass median diameter of mode i; and ei is binding energy of aerosol particles for mode i.

The fraction pi of the aerosol modes release depends on the kinetic energy of an individual soil aggregate

2*3

3100 UDe ssc = . (2.6)

A scheme of the dependence is presented in Table 2.3. The binding energies ei correspond to values presented in Table 2.2.

Table 2.3. Fractions (pi) of the dust aerosol modes release as a function of the kinetic energy ec of an individual soil aggregate

ec

An example of calculated relative fractions of the dust aerosol modes as a function of soil aggregates size are shown in Fig. 2.3a for given wind friction velocity. As seen from the figure sandblasting of larger soil aggregates, which have higher kinetic energy, releases smaller dust particles. Figure 2.3b presents an example of size distribution of the vertical dust flux for given wind friction velocity and size distribution of saltating soil aggregates. As seen the Mode 1 corresponds mostly to fine particles (below 2 m), where as two other modes present coarse particles (5-20 m).

a

0.0

0.2

0.4

0.6

0.8

1.0

160 180 200 220

Ds, m

Mod

es fr

actio

ns (

p i)

Mode 1Mode 2Mode 3

U* = 0.5 m/s

b

0.00

0.02

0.04

0.06

0.08

0.1 1 10 100

Dp, m

dFv/d

Dp,

kg

m-2

s-1

Dp-

1

Totalmode 3mode 2mode 1

U* = 0.5 m/s

Fig. 2.3. Fractions of dust aerosol modes as functions of soil aggregates size (a) and size distribution of vertical dust flux (b)

In general the size distribution of the dust suspension flux strongly depends on the size distribution of soil aggregates. As it was mentioned above, at the moment there is no spatially resolved Europe-wide dataset applicable to characterize the properties of erodible soils in Europe. However, in some cases one can neglect distinctions between different soil types. Indeed, Figure 2.4 shows the dust suspension flux as a function of the wind friction velocity for different dust particle modes and soil populations from Table 2.1. As seen from the figure the vertical dust flux of the coarse Modes 2 and 3 strongly depends on the soil populations (Figs.2.4b and c). The difference can reach two orders of magnitude. On the other hand, for the fine mode, which is the most important from the point of view of the long-range atmospheric transport, the vertical dust flux only slightly depends on the soil type (Fig. 2.4a). One can hardly expect that dust particles of the coarse modes can significantly contribute to the long-range transport of heavy metals in Europe. Therefore, in the first approximation it is possible to restrict further consideration only by the fine particles mode and neglect distinctions between different soil populations.

0.001

0.01

0.1

1

10

0.2 0.4 0.6 0.8 1.0

U*, m/s

F v, m

g/m

2 /s

SaCSFSASS

Mode 1

0.001

0.01

0.1

1

10

0.2 0.4 0.6 0.8 1.0

U*, m/s

Fv, k

g/m

2 /s

SaCSFSASS

Mode 2

0.001

0.01

0.1

1

10

0.2 0.4 0.6 0.8 1.0

U*, m/s

F v, k

g/m

2 /s

SaCSFSASS

Mode 3

a b c

Fig. 2.4. Vertical dust flux as a function of the wind friction velocity for different soil populations and dust particle modes: (a) mode 1; (b) mode 2; (c) mode 3

21

Implementing the discussed above assumptions we performed calculations of the dust suspension flux in Europe and adjacent territories in 2000. The dust suspension was estimated for the following types of land cover:

deserts and bare soils;

agricultural soils (during the cultivation period);

urban areas.

The calculated mean annual flux of dust suspension from soil is shown in Figure 2.5. High suspension fluxes are characteristics of the Sahara desert and also of deserts in Central Asia. Elevated fluxes were also obtained for urbanized areas of Western Europe and agricultural regions of Southeastern and Eastern Europe.

Fig. 2.5. Spatial distribution of calculated mean annual dust suspension flux in 2000

2.3. Heavy metal concentration in soil

To estimate particle-bound heavy metal emission with dust suspension from soil it is necessary to know content of these metals in erodible soils. For this purpose we used detailed measurement data on heavy metals concentration in topsoil from the Geochemical Atlas of Europe developed under the auspices of the Forum of European Geological Surveys (FOREGS) [www.gtk.fi/publ/foregsatlas/]. The data cover most parts of Europe (excluding Eastern European countries) with more than 2000 measurement sites. The kriging interpolation was applied to obtain spatial distribution of heavy metal concentration in soil. For Eastern Europe as well as for the rest of the model domain (Africa, Asia) we used default concentration values based on the literature data (Table 2.4).

Table 2.4. Default concentrations of heavy metals in soil

Metal Soil concentration, mg/kg Reference As 5 Beyer & Cromartie, 1987 Cd 0.2 Nriagu, 1980a Cr 50 Shacklette et al., 1970 Ni 15 Nriagu, 1980b Pb 15 Reimann and Cariat, 1998

The resulting spatial distributions of Pb, Cd, As, Cr, and Ni concentration in topsoil of Europe and adjusted territories are presented in Figure 2.6. These concentrations reflect both natural content of these metals in the Earths crust and accumulation of anthropogenic depositions during long-term period of human industrial activity.

22

a b

c d e

Fig. 2.6. Spatial distribution of heavy metal concentration in topsoil of Europe and adjacent territories: (a) Pb; (b) Cd; (c) As; (d) Cr; (e) - Ni

2.4. Sea-salt aerosol suspension

The model description of the generation and wind suspension of sea-salt aerosol we applied the empirical Gong-Monahan parameterization of the vertical number flux density [Gong, 2003]:

)exp(6.145.341.310

210)057.01(373.1 Bp

Ap

p

n RRUdRdF += , (2.7)

where , 44.1017.0)1(7.4

+= pRpRA 433.0log

1 pR

B = .

Here Rp is the sea-salt aerosol radius; U10 is wind speed at 10 m height; and = 30 is an adjustable parameter that controls the shape of the sub-micron size distribution.

The size distribution of the vertical sea-salt aerosol mass flux based on this parameterization is shown in Fig. 2.7a for different wind speeds. Figure 2.7b illustrates dependence of the integral sea-salt aerosol flux on wind speed at 10 m height for different cut-off aerosol diameters. In the following calculations we used the cut-off value of aerosol diameter equal to 10 m, since larger particles can hardly be transported far from the ocean coastal areas.

23

a

1.E-13

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

0.1 1 10Dp, m

dFm

/dD

p, kg

m-2

s-1

m-1

U = 5 m/sU = 10 m/sU = 15 m/s

b

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

0 10 20 30 40 5

U10, m/s

Fm, k

g/m

2 /s

0

Dmax = 10 umDmax = 20 umDmax = 30 um

Fig. 2.7. Sea-salt aerosol mass flux as a function of particle size (a) and dependence of the sea-salt flux on wind speed at 10 m height for different cut-off aerosol diameters (b)

In order to estimate suspension with sea-salt aerosol we used the emission factors derived from the literature (Table 2.5). These emission factors depend on measured heavy metal bulk concentration in seawater and on the estimated enrichment factor of the heavy metal in sea-salt particles comparing to its bulk concentration in seawater. The sea-salt enrichment is commonly connected with elevated concentrations of heavy metals in the sea surface micro-layer.

Table 2.5. Emission factors of heavy metals for suspension with sea-salt aerosol

Metal Emission factor (g/kg) Reference As 300 Nriagu, 1989 Cd 40 Richardson et al., 2001 Cr 80 Nriagu, 1989 Ni 180 Nriagu, 1989 Pb 4000 Richardson et al., 2001

2.5. Estimates of heavy metals resuspension

Estimates of resuspension of particle-bound heavy metals from soil and seawater were performed for Europe and adjacent territories in 2000. Spatial distributions of the mean annual resuspension flux of Pb, Cd, As, Cr, and Ni are presented in Fig. 2.8. In general, the resuspension fluxes from soil are significantly higher those frim seawater for all the metals. High resuspension fluxes were obtained from desert areas of Africa and Central Asia because of significant dust production in these regions. Elevated fluxes are also characteristics of some countries of Western, Central, and Southeastern Europe, which are conditioned by combination of relatively high concentration in soil and significant dust suspension from urban and agricultural areas.

24

a b

c d e

Fig. 2.8. Spatial distribution of annual resuspension flux of heavy metals in Europe in 2000: (a) Pb; (b) Cd; (c) As; (d) Cr; (e) Ni

Aggregated values of lead resuspension from soil in different European countries are presented in Figure 2.9a along with total anthropogenic emissions based on official data. As seen the estimated contribution of lead resuspension is comparable or even higher than anthropogenic emissions in such countries as Italy, France, Germany, Greece, Spain, the United Kingdom etc., where observed concentration of this metal in soil considerably exceeds its natural content in the Earths crust (Fig. 2.9b). The most probable reason for this is long-term accumulation of historical depositions.

Contrary to lead, cadmium resuspension from soil insignificantly contribute to total emission of this metal in most European countries (Fig. 2.10a). The exceptions are France, Italy and Greece. The reason for this is in relatively low cadmium concentrations measured in European soils. Only in a few countries of Europe (France, Italy, Greece, Belgium etc.) mean topsoil concentration noticeably exceeds cadmium natural content in the crust (Fig. 2.10b).

25

a

0

500

1000

1500

2000

2500

3000

Rus

sia

Italy

Fran

ce

Ukr

aine

Por

tuga

l

Ger

man

y

Gre

ece

Spa

in

Turk

ey

Pol

and

Uni

ted

Kin

gdom

Rom

ania

Kaz

akhs

tan

Ser

bia&

Mon

tene

gro

Bul

garia

Bel

gium

Cze

ch R

ep.

Cro

atia

Sw

itzer

land

Hun

gary

Net

herla

nds

Slo

vaki

a

Bos

nia&

Her

zego

vina

Mac

edon

ia

Pb

tota

l em

issi

ons,

t/y Re-suspension

Anthoropogenic emissions

b

0

10

20

30

40

50

60

Rus

sia

Italy

Fran

ce

Ukr

aine

Por

tuga

l

Ger

man

y

Gre

ece

Spa

in

Turk

ey

Pol

and

Uni

ted

Kin

gdom

Rom

ania

Kaz

akhs

tan

Ser

bia&

Mon

tene

gro

Bul

garia

Bel

gium

Cze

ch R

ep.

Cro

atia

Sw

itzer

land

Hun

gary

Net

herla

nds

Slo

vaki

a

Bos

nia&

Her

zego

vina

Mac

edon

iaPb

soil

conc

entra

tions

, mg/

kg

Top soil Crust

Fig. 2.9. Lead total anthropogenic emissions and resuspension from soil (a) and average topsoil concentration (b) in some European countries

a

0

20

40

60

Rus

sia

Pol

and

Ger

man

y

Spa

in

Fran

ce

Turk

ey

Ukr

aine

Italy

Bul

garia

Uni

ted

Kin

gdom

Rom

ania

Mol

dova

Ser

bia&

Mon

tene

gro

Slo

vaki

a

Kaz

akhs

tan

Gre

ece

Bel

gium

Cze

ch R

ep.

Hun

gary

Aze

rbai

jan

Por

tuga

l

Net

herla

nds

Bos

nia&

Her

zego

vina

Sw

itzer

land

Cd

tota

l em

issi

ons,

t/y

Re-suspensionAnthoropogenic emissions

b

0

0.2

0.4

0.6

0.8

Rus

sia

Pol

and

Ger

man

y

Spa

in

Fran

ce

Turk

ey

Ukr

aine

Italy

Bul

garia

Uni

ted

Kin

gdom

Rom

ania

Mol

dova

Ser

bia&

Mon

tene

gro

Slo

vaki

a

Kaz

akhs

tan

Gre

ece

Bel

gium

Cze

ch R

ep.

Hun

gary

Aze

rbai

jan

Por

tuga

l

Net

herla

nds

Bos

nia&

Her

zego

vina

Sw

itzer

land

Cd

soil

conc

entra

tions

, mg/

kg

Top soil Crust

Fig. 2.10. Cadmium total anthropogenic emissions and resuspension from soil (a) and average topsoil concentration (b) in some European countries

26

Chapter 3

MSCE-HM MODEL TESTING ON THE BASE OF DIFFERENT HM EMISSION SCENARIOS

In accordance to EMEP working plan [EB.AIR/GE.1/2005/10] and recommendations of TFMM workshop [TFMM Workshop minutes, 2005] MSC-E continued activity on model development. First of all, the model pilot parameterisations for arsenic, nickel, chromium, zinc, copper and selenium were prepared. Secondly, MSC-E revised the approaches to estimate emissions of aerosol-borne metals driven by natural mechanisms, as it was overviewed in Chapter 2 of this report. This emission includes both natural component, existed in the environment before any anthropogenic activity and re-emission of previously deposited metals from anthropogenic sources. Further we will refer this nature-driven emission to as natural and historical. The set of model tests were performed in order to evaluate the model performance after these changes were introduced. Following the EMEP working plan, the model tests were performed on the base of different emission scenarios. One scenario is based on officially reported emission data for 2000. For countries, which have not provided their emission data for 2000, TNO emission expert estimates were applied [van der Gon et al., 2005]. An alternative emission data set for 2000, developed in the framework of ESPREME project [http://espreme.ier.uni-stuttgart.de/data.html] was used for deposition modelling of lead, cadmium, mercury, arsenic, nickel and chromium.

The aim of this chapter is to evaluate performance of MSCE-HM model on the base of available emission data sets for 2000. At present only some results of the model exercises will be discussed in this chapter. Detailed overview of the results will be prepared by the next meeting of TFMM.

Total emissions of the available emission data sets from Europe are presented in Table 3.1. As seen from the table, ESPREME emissions are mainly much higher than official/TNO ones (except fro lead). Therefore, higher depositions based on ESPREME are expected. Besides, natural and historical emissions of all metals make essential contribution to total emission in Europe. The exception is cadmium, which natural and historical emissions, according to currently used parameterizations, are lower than anthropogenic ones by 1 2 orders of magnitude. Some amount of heavy metals enters the modelling domain through its lateral and top boundaries. However, as will be shown below, the influence of boundary concentrations on pollution levels measured at most of stations, is small. The exception is mercury, which air concentrations are dominated by the boundary concentrations.

Table 3.1. Total emissions from Europe in 2000, t/y

Pb Cd As Ni Cr Zn Cu Se Official/TNO 11180 280 440 3840 1780 16700 2490 420 ESPREME 13160 580 760 4800 2700 - - - Natural and historical* 6400 11 340 990 1450 3570 1410 32

*Includes emissions from seas surrounding Europe: the North, Baltic, Mediterranean, Black and Caspian Seas.

Official/TNO and ESPREME emissions from individual countries vary significantly (Fig. 3.1). Significant differences in countrys emissions affect the modelling results. It is exhibited particularly clear for countries where monitoring stations are situated.

27

a

0

500

1000

1500

2000

2500

Italy

Rus

sia

Ukr

aine

Ger

man

ySp

ain

Rom

ania

Uni

ted

King

dom

Fran

ce

Turk

eyP

olan

dG

reec

eB

elgi

umS

erbi

a&M

onte

negr

oC

zech

Rep

.P

ortu

gal

Pb

emis

sion

s, t

/y

ESPREME

Off&TNO

b

0

10

20

30

4050

60

70

Italy

Rus

sia

Ukr

aine

Ger

man

ySp

ain

Rom

ania

Uni

ted

King

dom

Fran

ce

Turk

eyP

olan

dG

reec

eBe

lgiu

mS

erbi

a&M

onte

negr

oC

zech

Rep

.Po

rtuga

l

Cd

emis

sion

s, t

/y

ESPREME

Off&TNO

c

0

200

400

600

800

1000

1200

Ger

man

yIta

lyR

ussi

aFr

ance

Sp

ain

Uni

ted_

King

dom

Ukr

aine

Pola

ndTu

rkey

Net

herla

nds

Belg

ium

Gre

ece

Rom

ania

Cze

ch_R

ep.

Portu

gal

Ni e

mis

sion

s, t

/y

ESPREME

O&T

d

0

40

80

120

160

Rus

sia

Ukr

aine

Pol

and

Ger

man

yS

pain

Italy

Uni

ted_

King

dom

Fran

ceTu

rkey

Cze

ch R

ep.

Bel

gium

Rom

ania

Net

herla

nds

Bel

arus

Gre

ece

As

emis

sion

s, t

/y

ESPREME

O&T

e

0

200

400

600

800

1000

1200

Rus

sia

Ger

man

yU

krai

nePo

land

Fran

ceIta

lyU

nite

d_ K

ingd

omTu

rkey

Spai

nBe

lgiu

mC

zech

Rep

.R

oman

iaN

ethe

rland

sG

reec

eFi

nlan

d

Cr e

mis

sion

s, t

/y

ESPREME

Off&TNO

Fig. 3.1. First 15 countries with the largest Official/TNO and ESPREME emissions for 2000. a) lead, b) cadmium, c) nickel, d) arsenic, e) chromium

3.1. Lead, cadmium, arsenic, chromium and nickel

This section deals with the results obtained for lead, cadmium, arsenic, chromium and nickel. For lead and cadmium three numerical tests were carried out. The first one aimed at evaluation of model performance by comparison with measurements based on official/TNO emissions only. Official data on anthropogenic emissions are reported by Parties to UN ECE. Some countries do not submit their national emissions so that modellers have to use expert estimates to fill gaps in the emission data. Information on natural and historical emissions is not provided by countries. Hence, the aim of this test was to demonstrate the comparison results if only officially submitted information supplemented by TNO expert estimates is used. In other tests natural and historical emissions and alternative ESPREME emission data sets were used.

Lead

Concentrations of lead in air and precipitation, calculated on the base of only anthropogenic official/TNO emissions are about 3 times underestimated compared to measurements (Fig. 3.2a). Therefore, the use of only anthropogenic emissions provided by Parties to the Convention is not enough to explain the existing levels. Hence, two other tests were carried out. In first one natural and historical emission was added, and in the second one official/TNO emission was replaced by ESPREME emission.

28

a

Mod = 0.32 x ObsRc = 0.86

0

5

10

15

20

0 5 10 15 20

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.35 x ObsRc = 0.70

0

1

2

3

4

0 1 2 3

Observed, g/L

Mod

el, g

/L

4

Fig. 3.2. Comparison of modelled and measured concentrations of lead in air (a) and in precipitation (b) based on official/TNO emissions

The addition of natural emission leads to much better agreement with observations (Fig. 3.3b). Measured concentrations both in air and in precipitation are underestimated by about 40%. If official/TNO emission is replaced by ESPREME emission, the agreement between measured and modelled quantities improves, although some underestimation (~20 25%) of observations still remains (Fig. 3.4). Therefore, both official/TNO and ESPREME emissions, in combination with natural and historical emissions, do not allow us to reach measured levels of lead. The reasons of the underestimation can be connected with too low anthropogenic emissions, or underestimated natural and historical emission. Uncertainties the MSCE-HM model can also contribute to the underestimation.

a

Mod = 0.57 x ObsRc = 0.80

0

5

10

15

20

0 5 10 15 20

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.62 x ObsRc = 0.59

0

1

2

3

4

0 1 2 3

Observed, g/L

Mod

el, g

/L

4

Fig. 3.3. Comparison of modelled and measured concentrations of lead in air (a) and in precipitation (b) based on official/TNO emissions and natural and historical emissions

a

Mod = 0.75 x ObsRc = 0.72

0

5

10

15

20

0 5 10 15 20

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.79 x ObsRc = 0.62

0

1

2

3

4

0 1 2 3

Observed, g/L

Mod

el, g

/L

4

Fig. 3.4. Comparison of modelled and measured concentrations of lead in air (a) and in precipitation (b) based on ESPREME emissions and natural and historical emissions

29

Despite the fact that for the entire set of stations the observations are underestimated, the situation for individual stations may differ. Example showing comparison of modelled concentrations of lead in air based on ESPREME emissions with measurements is present in Fig. 3.5. For each station contributions of anthropogenic emissions, natural and historical emissions and boundary concentrations are singled out. As seen, the contribution of boundary concentrations is minor at all (with few exceptions) stations. Instead, the contribution of natural emissions to air concentrations at monitoring stations is considerable, ranging from almost 20 to about 50%. As seen from the figure, at most stations addition of natural emissions significantly improves the comparison results. For example, at DE1, DE9, and Danish stations the modelled and measured concentrations became almost the same. However, at some stations (DE4, NL9) the use of anthropogenic emission alone provides better agreement with measurements. If official/TNO emissions are used, the model demonstrated better agreement with measurements at DE4, NL9 (Fig. 3.6). For some stations, e.g. those located in Czech Republic, Slovakia, Latvia, the use of natural emissions improves the comparison results, but modelled concentrations still remains well below the observed values. Possibly, inadequate spatial allocation of emissions over countrys area could contribute to discrepancies between measurements and model. Further activity should be aimed at more detailed analysis of the comparison results based on different emission data sets.

0

4

8

12

16

20

AT2

AT4

AT5

CZ1

CZ3

DE

1

DE

3

DE

4

DE

5

DE

7

DE

8

DE

9

DK

10

DK

3

DK

31

DK

5

DK

8

FI96

GB

14

GB

90

GB

91

IS91

LT15

LV10

LV16

NL9

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3 Anthropogenic Natural Bound Observed

Fig. 3.5. Contribution of anthropogenic (ESPREME) emissions, natural emission and boundary concentrations to modelled annual mean air concentrations of lead

0

4

8

12

16

20

AT2

AT4

AT5

CZ1

CZ3

DE

1

DE

3

DE

4

DE

5

DE

7

DE

8

DE

9

DK

10

DK

3

DK

31

DK

5

DK

8

FI96

GB

14

GB

90

GB

91

IS91

LT15

LV10

LV16

NL9

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3

Mod Obs

Fig. 3.6. Comparison of modelled and measured concentrations of lead in air. Official/TNO emissions and natural and historical emission.

30

Cadmium

Similar set of simulations was carried out for cadmium. Annual mean concentrations of cadmium in air and in precipitation are underestimated by about 3 times if only official/TNO emissions were used (Fig. 3.7a). Similar to lead, in other model test run natural and historical emissions were included, and in another one official/TNO emission data were replaced by ESPREME emission estimates.

a

Mod = 0.33 x ObsRc = 0.72

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.26 x ObsRc = 0.76

0

0.05

0.1

0.15

0.2

0 0.05 0.1 0.15 0.2

Observed, g/L

Mod

el, g

/L

Fig. 3.7. Comparison of modelled and measured concentrations of cadmium in air (a) and in precipitation (b) based on official/TNO emissions

The use of official/TNO emissions together with natural and historical emissions only slightly improves the model performance (Fig. 3.8). Although correlation coefficients increased, regression coefficients improved insignificantly: from 0.33 to 0.39 for air concentrations, and from 0.26 to 0.32 for concentrations in precipitation. This small increase of regression coefficients is explained by small contribution of cadmium natural and historical emissions (see Table 3.1). ESPREME emissions of cadmium are significantly larger than official/TNO ones. Therefore, the use of these data led to much smaller underestimation of measured concentrations in air and precipitation (Fig. 3.9). However, the scatter of the results is high and correlation coefficients are much lower compared to those obtained for official/TNO emissions. More detailed analysis of model results based on different cadmium emissions should be further continued.

a

Mod = 0.39 x ObsRc = 0.86

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.32 x ObsRc = 0.84

0.00

0.05

0.10

0.15

0.20

0.00 0.05 0.10 0.15 0.20

Observed, ug/L

Mod

el, u

g/L

Fig. 3.8. Comparison of modelled and measured concentrations of cadmium in air (a) and in precipitation (b) based on official/TNO emissions and natural and historical emissions

31

a

Mod = 0.88 x ObsRc = 0.56

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Observed, ng/m3

Mod

el, n

g/m

3

b

Mod = 0.71 x ObsRc = 0.53

0

0.05

0.1

0.15

0.2

0 0.05 0.1 0.15 0.2

Observed, g/L

Mod

el, g

/L

Fig. 3.9. Comparison of modelled and measured concentrations of cadmium in air (a) and in precipitation (b) based on ESPREME emissions and natural and historical emissions

Comparison of modelled air concentrations of cadmium, computed on the base of ESPREME and natural and historical emissions, against measurements at individual stations is demonstrated in Fig. 3.10. For bars indicating modelled values the contributions of anthropogenic emissions, natural and historical emissions and background concentrations are marked. The modelled concentrations are mainly determined by anthropogenic component. The contribution of natural and historical emissions at most of stations does not exceed 13%, and background concentrations 6%. At some of stations, e.g. the Dutch and British the model considerably overpredicts the observed values. Moreover, even the use of anthropogenic emission only would result in the overprediction. The ESPREME emissions in the United Kingdom are as much 5 times larger than those of official/TNO (Fig. 3.1b). For the Netherlands, and neighbouring Belgium and Germany the ESPREME emissions are larger 9, 6 and 3 times, respectively (Fig. 3.1b). Therefore, these emissions may be too large, resulting to the overestimation of observed concentrations by the model. Besides, the comparison of modelled air concentrations based on official/TNO plus natural and historical emissions shows that for stations NL9 and GB91 the model agree well with measurements (Fig. 3.11). On some other stations (e.g., located in Latvia, Slovakia) the situation is opposite: despite the use of relatively high ESPREME emissions and natural and historical emissions the observed concentrations are significantly (up to three times) underestimated. The reasons of the discrepancies between the model and measurements can be connected with uncertainties of emission magnitude and its spatial allocation, and uncertainties of the model parameterisations. More detailed investigation of these reasons is needed.

0

0.2

0.4

0.6

0.8

AT2

CZ1

CZ3

DK

3

DK

31

DK

5

DK

8

GB

14

GB

90

GB

91

IS91

LT15

LV10

LV16

NL9

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3

Anthropogenic Natural Bound Observed

Fig. 3.10. Contribution of anthropogenic emissions (ESPREME estimates), natural emission and boundary concentrations to modelled air concentrations of cadmium

32

0

0.2

0.4

0.6

0.8

AT2

CZ1

CZ3

DK

3

DK

31

DK

5

DK

8

GB

14

GB

90

GB

91

IS91

LT15

LV10

LV16

NL9

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3 Mod Obs

Fig. 3.11. Comparison of modelled and measured concentrations of cadmium in air. Modelling results are based on official/TNO emissions and natural and historical emission.

Separate models runs without natural emissions for arsenic, nickel and chromium have not been performed so far. Therefore, model results based on official/TNO emissions are compared with the results based on ESPREME emissions with the same natural and historical emission. In both cases natural and historical emissions were the same. It is important to note, that the results for Ni, As and Cr could be considered only as preliminary.

Arsenic

Air concentrations of arsenic based on official/TNO emissions are underestimated by 2 2.5 times (Fig. 3.12a). Similar degree of discrepancy took place for concentrations in precipitation (Fig. 3.12b). The use of ESPREME emissions gives better agreement between modelled and measured air concentrations as well as concentrations in precipitation. It is worth noting that correlation coefficients for concentrations in precipitation are lower than those for concentrations in air. Besides, concentrations in air, based on ESPREME emissions are somewhat overestimated compared to measurements, whereas concentrations in precipitation are underestimated (Fig. 12). It is planned to pay more attention to wet scavenging parameters of arsenic and to analysis of quality of monitoring data.

a

Mod = 0.44 x OBSRc = 0.80

b

Mod = 0.34 x OBSRc = 0.41Mod = 1.24 x OBS

Rc = 0.85

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Observed, ng/m3

Mod

elle

d, n

g/m

3

Mod = 0.72 x OBSRc = 0.37

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

Observed, g/L

Mod

elle

d,

g/L

Fig. 3.12. Comparison of modelled and measured arsenic concentrations: in air (a) and concentrations in precipitation (b) (official/ TNO blue; ESPREME red). Natural and historical emissions are included.

33

Nickel

Concentrations of nickel in air and in precipitation were underestimated by ~30% when official/TNO emission scenario was used. On the contrary, ESPREME emissions led to some (~25%) overestimation of measured concentrations in air and in precipitation (Fig 3.13). Comparison of modelled (ESPREME) and measured concentrations in air at individual stations demonstrates that the overall overestimation is caused by overestimation at Austrian, Danish sites and British site GB91 (Fig. 3.14). It is worth noting that even the use of anthropogenic emission alone would lead to substantial overestimation at these stations. If official/TNO emissions are used, the modelled concentrations at GB91 fit well measurements, and concentrations at Austrian and Danish sites are underestimated (Fig. 3.15). Probably, ESPREME emission magnitudes and/or their spatial allocation should be specified. Similar to results for arsenic, correlation coefficients for concentrations in precipitation are relatively low (0.41 0.51).

a

Mod = 0.69 x OBSRc = 0.87

Mod = 1.27 x OBSRc = 0.83

0

1

2

3

4

0 1 2 3 4

Observed, ng/m3

Mod

elle

d, n

g/m

3

b

Mod = 0.70 x OBSRc = 0.41

Mod = 1.24 x OBSRc = 0.51

0

0.3

0.6

0.9

1.2

0 0.3 0.6 0.9 1.2

Observed, g/L

Mod

elle

d,

g/L

Fig. 3.13. Comparison of modelled and measured nickel concentrations: in air (a) and concentrations in precipitation (b) (official and TNO blue; ESPREME red). Natural and historical emissions are included

0

1

2

3

4

AT2

DK

3

DK

5

DK

8

DK

10

DK

31

FI96

GB

14

GB

91

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3 Anthrop Natural Bound Obs

Fig. 3.14. Contribution of anthropogenic (ESPREME) emissions, natural emission and boundary concentrations to modelled annual mean air concentrations of nickel

0

1

2

3

AT2

DK

3

DK

5

DK

8

DK

10

DK

31

FI96

GB

14

GB

91

NO

42

NO

99

SK

2

SK

4

SK

5

SK

6

SK

7

Air

conc

entra

tion,

ng/

m3

Mod Obs

Fig. 3.15. Comparison of modelled and measured concentrations of nickel in air. Modelling results are based on official/TNO emissions and natural and historical emission.

34

Chromium

Observed concentrations of chromium in air and in precipitation were underestimated by the model if official/TNO emissions were used (Fig. 3.16). The underestimation made up about 2.5 times for concentrations in air and about two times for concentrations in precipitation. In case of ESPREME emissions, the observed concentrations in air and in precipitation were well reproduced: regression coefficients were 0.93 and 1.06, and correlation coefficients 0.83 and 0.63, respectively. Total European ESPREME emission of chromium is about 1.5 times higher than that of official/TNO. However, the emissions in Russia, which influence on stations concentrations is relatively small, according to TNO is about 1000 t/y, while ESPREME estimate is about 400 t/y (see Fig. 3.1c). Therefore, the ratio of total ESPREME to official/TNO emissions, excluding Russia is 3 times. The result of this large difference in emissions is that the modelled concentrations, modelled with ESPREME emissions, are higher and better fitting measurements.

a

Mod = 0.39 x OBSRc = 0.80

Mod = 0.93 x OBSRc = 0.83

0

1

2

3

0 1 2 3

Observed, ng/m3

Mod

elle

d, n

g/m

3

b

Mod = 0.56 x OBSRc = 0.54

Mod = 1.06 x OBSRc = 0.63

0

0.1

0.2

0.3

0.4

0 0.1 0.2 0.3 0.4

Observed, g/L

Mod

elle

d,

g/L

Fig. 3.16. Comparison of modelled and measured chromium concentrations: in air (a) and concentrations in precipitation (b) (official and TNO blue; ESPREME red). Natural and historical emissions are included.

3.2. Zinc, copper, selenium

Preliminary model calculations of concentrations and depositions were performed also for zinc, copper and selenium. The calculations were performed on the base of official emissions for 2000, supplemented with expert estimates of TNO [van der Gon et al., 2005]. Natural and historical emissions of zinc, copper, selenium were also included.

Zinc

Comparison of modelled air concentrations and concentrations in precipitation for Zn with observations are demonstrated in Fig. 3.17. Both for concentrations in air and in precipitation the model undepredicts the measured parameters by an order of magnitude. Attempts to model long-range transport of zinc have been undertaken earlier by other researches [e.g., Nijenhuis et al., 2001; Alcamo et al., 1992; Sofiev et al., 2001, Bartnicki et al., 1998]. The comparisons of modelled zinc concentrations and depositions with measured values, published in these papers, indicate essential underestimation of measurements. Wet depositions are underestimated by a factor 4 13, air concentrations 3.5 10 times. The researches explain the underestimation by low available emission estimates of zinc. Besides, J.Bartnicki et al. [1998] assumed that quality of zinc measurements can also contribute to the underestimation. The underestimation of zinc measurements by MSCE-HM could be connected with underestimated emissions (natural or anthropogenic or both), with quality of measurements and with uncertainties of the model. These assumptions need more detailed investigation.

35

a

Mod = 0.21 x OBSRc = 0.66

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Observed, ng/m3

Mod

elle

d, n

g/m

3

b

Mod = 0.12 x ObsRc = 0.57

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Observed, g/L

Mod

el, g

/L

Fig. #.17. Comparison of modelled and measured zinc concentrations: in air (a) and concentrations in precipitation (b). Modelling results are based on official/TNO emissions and natural and historical emission.

Copper

Similar to zinc, copper concentrations in air and in precipitation were significantly underpredicted by the model (Fig. 3.18): regression coefficients were 0.34 and 0.14, respectively. W.A.S. Nijenhuis et al. [2001] simulated copper transport and depositions over the North Sea, and their modelled air concentrations underestimate measurements also by a factor of 3. Possible reasons of the underestimation are similar to those for zinc.

a

Mod = 0.34 x OBSRc = 0.66

0

2

4

6

8

10

0 2 4 6 8 1

Observed, ng/m3

Mod

elle

d, n

g/m

3

0

b

Mod = 0.14 x ObsRc = 0.50

0

1

2

3

4

5

0 1 2 3 4 5

Observed, g/L

Mod

el, g

/L

Fig. 3.18. Comparison of modelled and measured concentrations in air (a) and concentrations in precipitation (b) of copper. Modelling results are based on official/TNO emissions and natural and historical emission

Selenium

Measurements of selenium at EMEP stations were not available for 2000. At station DK3 selenium concentrations in air were measured from 1979 to 1996, and at station IS91 from 1995 to 1997 (Fig. 3.19). Averaged concentrations at these stations are 0.61 and 0.18 ng/m3, respectively. Modelled concentrations in 2000 were 0.13 ng/m3 at DK3 and 0.02 ng/m3 at IS91. Concentrations in precipitation have not been measured at all at EMEP network. As the measurements are almost absent, it is not possible to evaluate the model performance for this metal. Modelled concentrations in precipitation at monitoring stations ranged from 6 to 80 ng/L. In order to increase measurement database to validate the modelling results for selenium, the data from other sources, e.g., results of other monitoring programs, should be drawn.

36

0

0.2

0.4

0.6

0.8

1

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

ng/m

3

DK3IS91

Fig. 3.19. Measured concentrations of selenium at EMEP stations DK3 and IS91

3.3. Mercury

Concentrations and depositions of mercury were simulated on the base of official/TNO and ESPREME emissions. Modelled Total Gaseous Mercury (TGM) concentrations based on ESPREME and official/TNO at measurement stations are almost the same and well reproduce measured values (Fig. 3.20). Since TGM concentrations are mainly controlled by incoming air masses through model domain boundaries, the minor differences in TGM concentrations derived from two different emission scenarios are not surprising.

0

0.4

0.8

1.2

1.6

2

DE9 DK10 DK15 FI96 IE31 NO42 NO99 SE2

ng/m

3

Observed Modelled ESPREME

Fig. 3.20. Comparison of observed and modelled TGM concentrations

Concentrations of mercury in precipitation are mostly determined by scavenging of particulate mercury and reactive gaseous mercury (RGM). These mercury forms to large extent are produced by anthropogenic emissions. That is why the difference in mercury concentrations in precipitation at monitoring stations, derived from different emission scenarios, is noticeable (Fig. 3.21). Scenario based on official/TNO emissions resulted in some overestimation of measurements. Regression coefficient is 1.27. The use of ESPREME emissions lead to smaller (~10%) overestimation. In this scenario the overestimation is mainly occurs for Swedish stations. More detailed analysis of the modelling results will be prepared to the next TFMM meeting.

0

4

8

12

16

20

DE1 DE9 NL91 NO99 SE11 SE2 SE5

ng/L

Obs Off&TNO ESPREME

Mod = 1.27 x OBSRc = 0.97

Mod = 1.10 x OBSRc = 0.93

0

4

8

12

16

20

0 4 8 12 16 20

Observed, ng/L

Mod

elle

d, n

g/L

Fig. 3.21. Comparison of observed and modelled mercury concentrations in precipitation

(official and TNO blue; ESPREME red)

37

Concluding remarks

The detailed analysis of the results obtained in the model testing exercises is not fully complete. Therefore, only preliminary concluding remarks can be formulated. 1. The addition of natural and historical emissions of lead resulted to significant improvement of modelling results against measurements. The positive effect of the use of cadmium natural and historical emissions is much smaller compared to one for lead. 2. Annual mean concentrations in air and in precipitation of Pb, Cd, As, Ni and Cr based on ESPREME emission estimates are generally higher than those based on combination of official data and TNO expert estimates. 3. The results computed on the base of official/TNO emissions are always underestimated compared to measurements. The use of ESPREME emission estimates may result in overestimation of measured quantities. In particular, concentrations of arsenic in air, chromium in precipitation, and nickel concentrations in both media were somewhat overestimated. 4. Concentrations in air and in precipitation of zinc and copper, modelled on the base of official/TNO emissions, were severely (up to an order of magnitude) underpredicted compared to measurements. The attempts to simulate transport and depositions of these metals by other modelling groups resulted in similar extent of the underestimation. The most probable reason for this is too low emission data. 5. Measurement data of selenium at EMEP network are insufficient to perform verification of the modelling results. Information from other national or international monitoring programmes is needed to improve the situation.

6. MSCE-HM model tends to somewhat overestimate mercury concentrations in precipitation. However, the use of ESPREME emission estimates resulted in smaller overestimation compared to official/TNO emissions.

38

Chapter 4

MSCE-POP MODEL IMPROVEMENT

Following the recommendations of TFMM meeting on model review MSC-E has started its work on further improvement of the modelling approach for POPs. At current stage essential attention was given to harmonization of physical-chemical properties of selected POPs and refinement of process parameterisations used in MSCE-POP model. In particular the following activities were carried out:

refinement of physical-chemical properties of POPs used in modelling;

improvement of model description of POP degradation processes in the atmosphere (photodegradation of particle-bound B[a]P);

improvement of model description of POP partitioning in soil and seawater;

improvement of model description of POP removal with precipitation (snow scavenging).

This chapter in the following three sectio