joint ceip/msc-e technical report on emission … programme for monitoring ... relative difference...

Convention on Long-range Transboundary Air Pollution

Co-operative programme for monitoringand evaluation of the long-rangetransmission of air pollutants in Europe

TECHN

ICAL REPO

RT CEIP1/2017

Joint CEIP/MSC-E technical report on emission inventory improvementfor heavy metals modeling

MCS-EO. TravnikovI. IlyinO. Rozovskaya

CEIP:K. MareckovaM. Tista R. Wankmueller

MSC-E & CEIP

EMEP Technical Report 01/2017

Joint CEIP/MSC-E technical report on emission inventory improvement

for heavy metals modeling

CENTRE ON EMISSION INVENTORIES AND PROJECTIONS

K. Mareckova, M. Tista, R. Wankmüller

METEOROLOGICAL SYNTHESIZING CENTRE – EAST

O. Travnikov, I. Ilyin, O. Rozovskaya

MCS-E Meteorological Synthesizing Centre – East 2nd Roshchinsky proezd, 8/5 115419 Moscow Russia Phone.: +7 926 906 91 78 Fax: +7 495 956 19 44 E-mail: [email protected] Internet: www.msceast.org

CEIP Centre on emission inventories and projections Umweltbundesamt GmbH Spittelauer Lände 5 1090 Wien Österreich/Austria E-mail: [email protected] Internet: www.ceip.at

ISBN 978-3-99004-436-0

Authors – CEIP Mareckova K. Tista M. Wankmueller R.

Authors – MSC-E Travnikov O. Ilyin I. Rozovskaya O.

Layout and typesetting Manuela Kaitna Melanie Tista

Cover © ussatlantis – Fotolia.com; Completeness of 2016 submissions for Mercury

Imprint

Owner and Editor: Umweltbundesamt GmbH Spittelauer Lände 5, 1090 Vienna/Austria

Printed by: Umweltbundesamt GmbH

The Environment Agency Austria prints its publications on climate-friendly paper

© Umweltbundesamt GmbH, Vienna, 2017 All rights reserved ISBN 978-3-99004-436-0

Joint CEIP/MSC-E technical report on emission inventory improvement for heavy metals modeling

EMEP Technical Report 01/2017 3

Contents

INTRODUCTION ........................................................................................................................... 5

1 DATA ASSESSMENT, METHODOLOGIES AND IMPROVEMENTS TO DEAL WITH HEAVY METALS IN THE EMEP REGION (CEIP CONTRIBUTION) .................... 6

1.1 Gap-filling methods ......................................................................................................................... 6 1.1.1 Gap-filling of National Total data ..................................................................................................... 6 1.1.2 Gap-filling of sectoral data ................................................................................................................ 7 1.2 Identified sources of errors and reasons for replacement of reported data ............................... 7 1.3 Proposal to upgrade the current situation and practice .............................................................. 8 1.3.1 Method overview .............................................................................................................................. 8 1.3.2 Literature or expert data .................................................................................................................... 8 1.3.3 Completeness analysis of submissions .............................................................................................. 9 1.3.4 Key category analysis ..................................................................................................................... 10 1.3.5 Correlation with other pollutants ..................................................................................................... 12 1.3.6 Correlations with population or GDP data ...................................................................................... 13

2 EMISSION DATA FOR HEAVY METALS MODELING (MSC-E CONTRIBUTION) ......................................................................................................... 14

2.1 Current situation ........................................................................................................................... 14 2.2 Time-series of gridded annual emission ...................................................................................... 15 2.3 Chemical composition of emissions.............................................................................................. 16 2.3.1 Mercury ........................................................................................................................................... 16 2.4 Temporal variation of emissions .................................................................................................. 17 2.5 Vertical distribution of emissions ................................................................................................ 17 2.6 Global emission inventories .......................................................................................................... 18 2.7 Historical and natural emissions .................................................................................................. 20 2.8 Summary ........................................................................................................................................ 21

3 CONCLUSIONS AND RECOMMENDATIONS ...................................................................... 22 3.1 CEIP ............................................................................................................................................... 22 3.2 MSC-E ............................................................................................................................................ 22

4 RECOMMENDATION ................................................................................................................ 24

5 REFERENCES .............................................................................................................................. 25

ANNEX I – REPORTED EMISSIONS OF HEAVY METALS .......................................................... 28


4 EMEP Technical Report 01/2017

List of tables

Table 1: Overview of heavy metal key categories of the EMEP countries ............................................ 10 Table 2: Emission parameters for modeling generated by CEIP and MSC-E ........................................ 14 Table 3: Available global emission inventories for heavy metals .......................................................... 19 Table 4: Key emission parameters affecting quality of model estimates ................................................ 21 Table A.1: Lead trend table (reported until November 2016) .................................................................... 28 Table A.2: Cadmium trend table (reported until November 2016) ............................................................ 29 Table A.3: Mercury trend table (reported until November 2016) .............................................................. 30

List of figures

Figure 1: Overview of reported data and imputation methods for National Total data ............................. 6 Figure 2: Overview on gap-filled and replaced data .................................................................................. 8 Figure 3: Completeness of 2016 submissions accumulated over all EMEP countries ............................... 9 Figure 4: Completeness of 2016 submissions for Mercury ...................................................................... 10 Figure 5: Cadmium emissions of the individual countries

(the five most important key categories of all EMEP countries are shown) ............................. 11 Figure 6: Pearson correlation between heavy metals and particulate matter

(only original reported National Total values of 31 countries) ................................................. 12 Figure 7: Pearson correlation between heavy metals and population/GDP data

(Pb and Cd: 38 countries, Hg: 39 countries) ............................................................................. 13 Figure 8: Gridded emissions reporting by Parties in the EMEP area ....................................................... 15 Figure 9: Annual Hg deposition in the EMEP region simulated for different speciation of Hg

emissions: (a) – all Hg emissions are treated as GEM; (b) – all Hg emissions are treated as oxidized Hg (GOM and PBM). Total Hg emission was kept the same in both cases. ......... 16

Figure 10: Modelled and observed monthly mean concentrations of lead at Polish stations PL5, averaged over 2005-2012 period (a) and at German station DE7, averaged over 2000-2012 period (b) ........................................................................................ 17

Figure 11: Relative difference between Pb deposition, calculated with and without use of vertical distribution of emissions. Negative values mean decrease and positive – increase of deposition ............................................................................................................... 18

Figure 12: Global Hg emissions inventory, 2010 [AMAP/UNEP, 2013] .................................................. 19 Figure 13: Deposition fluxes of lead caused by secondary and anthropogenic sources within

EMEP region and by non-EMEP sources ................................................................................. 20



INTRODUCTION

According to the “2016-2017 workplan for the implementation of the Convention” (ECE/EB.AIR/ 133/Add.1, p.1.1.2.5) “Review and assess data, methodologies and competences available to deal with POPs and HM issues in the ECE region and propose a strategy to improve emission inven-tories”, CEIP and MSC-E have prepared “Joint CEIP/MSC-E technical report on emission in-ventory improvement for heavy metals modeling”. Technical report is focused on current situa-tion, gap filling and methodologies used for gap filling, gridding, discrepancies between report-ed and expert emission estimates (to the extent possible), identified sources of errors, etc. Report considers approaches used by CEIP and MSC-E and proposals to upgrade current situation and practice. Report should serve as a basis for EMEP strategy to alleviate existing problems and guide Parties.

One of the main tasks of the EMEP Centre on Emission Inventories and Projections (CEIP) is the preparation of gridded emission data sets as input for long-range transport models. As data sub-mitted by parties is not always complete and as several parties do not submit data (see Annex I) it is necessary to fill in missing information before these emission data sets can be used by mod-elers. To gap-fill those missing data, CEIP applies different gap-filling methods. These methods are described in the Chapter 1. After the gap-filling, sector emissions are used for spatial emis-sion mapping, i.e. the EMEP grid. The gap-filled data sets are also published in the UNECE/ EMEP emission database (WebDab)1 which contains information on air pollutant emissions and projections from the Parties to the LRTAP Convention (UNECE 1979).

Information on pollutant’s emission to the atmosphere and other environmental media is one of the key parameters required for model assessment of pollution levels and transboundary fluxes. Completeness and uncertainties of emission data can significantly affect quality of the model es-timates. Sensitivity analysis of the modeling results has shown that, in many cases, emission un-certainties largely determine the overall uncertainty of the model assessment. Emission data for heavy metal (HMs) modeling are described in the Chapter 2.

Application of chemical transport models for assessment of HMs pollution levels in the EMEP countries requires anthropogenic emission data spatially distributed (or ‘gridded’) over the regu-lar EMEP grid. It should be noted that modeling of air concentration and deposition fluxes needs emission data covering the entire EMEP domain that includes not only territories of all EMEP countries but also adjacent areas (Northern Africa, Middle East etc.). Along with this, applica-tion of the gridded emission data for modeling requires evaluation of additional emission param-eters. They comprise chemical composition of emitted pollutants, vertical distribution of emis-sion height and temporal variation of anthropogenic emissions along the year.

Mercury is persistent in the environment and can travel over long distances. In many EMEP coun-tries pollution levels of these substances are significantly influenced by emissions from other parts of the globe. Therefore, emission inventories on a global scale are required for pollution assessment within the EMEP region. Besides, cycling of HMs in the environment has a complex character and includes not only atmospheric transport and transformations but also bi-directional exchange with the earth’s surface. Natural and secondary emission sources should be taken into account when assessing both effectiveness of environment protection policy and human expo-sure of these contaminants.

1 http://www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase/

http://www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase/



1 Data assessment, methodologies and improvements to deal with heavy metals in the EMEP region (CEIP contribution)

1.1 Gap-filling methods

Data used by CEIP were reported by the Parties to the LRTAP Convention as sectoral emissions (NFR14) and National Total emissions according to the UNECE guidelines for reporting emis-sions and projections data under the Convention on long-range transboundary air pollution, An-nex I (UNECE 2014). The nomenclature for reporting (NFR) foresees 140 different sectors. The reporting template contains guidance how these 140 sectors can be aggregated to 13 GNFR sec-tors which are used in EMEP models. As gap-filling on NFR level would require very detailed background data the datasets reported by parties were aggregated to the GNFR level before they were gap-filled.

1.1.1 Gap-filling of National Total data

The share of reported data ranged between 60% and 70% of National Total data (Figure 1). All reported data up to the 24th March 2016 were included for the CEIP gap-filled data set 2016.

The most common imputation method (39%) for gap-filling of National Totals was the inter- or extrapolation of emission projections from the dutch institute TNO (Denier van der Gon et al. 2005) (Figure 1). The study was published in 2005 and comprises emission data of lead, cadmi-um and mercury for the year 2000 and projections for lead, cadmium and mercury for the year 2010 and for lead for the year 2020. For National Totals, 33% of the gap-filled values originate from emission estimates done by MSC-E (MSC-E 2013) in 2013 and provided to CEIP.

Other gap-filling methods comprise amongst others: sum of the sectors, copy or extrapolation of previous reported gap-filled data.

Figure 1: Overview of reported data and imputation methods for National Total data



1.1.2 Gap-filling of sectoral data

The share of reported data is about 70% (Figure 1). All report data up to the 24th Match 2016 were included for the CEIP gap-filled data set 2016.

The most common imputation method (78 to 81%) to gap-fill sector data was to use the distri-bution ratio of sector emissions from similar countries. To identify which countries are similar to each other, for all countries where data were available a distance matrix using Euclidean dis-tances was generated using GDP per capita2 and gap-filled or reported National Total emissions from the individual pollutants as variables (z-transformed).

Other gap-filling methods were applied only in individual cases. These methods were sector distribution as in previous years, copy of reported data from previous years or linear extrapola-tion of sector data.

1.2 Identified sources of errors and reasons for replacement of reported data

Most countries submitted data that seem to be complete and plausible. Problems occurred where no data at all were available, or when submitted data were not plausible. Emissions were defined as not being plausible when no data for relevant sectors were reported, when the sum of the sec-tors differed from the National Total, when submitted data showed unusual trends or when they varied substantially from data of emission estimates or projections of other sources. In these cases, data were replaced. Several countries did not report heavy metal emission data for the last three years: Albania, Bosnia and Herzegovina, Greece, Montenegro, the Russian Federation and Tur-key. Further, no frequent reporting of heavy metal data over the last years was done by Azerbai-jan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Luxembourg and Monaco.

When no data were available, different imputation methods were applied. Apart from reported data of previous years, additional data sources are of high importance. For the heavy metals lead, cadmium and mercury, two data sources were available for the gap-filling in 2016: projec-tions from TNO (Denier van der Gon et al. 2005) and estimates from MSC-E (MSC-E 2013).

In four cases, data submitted by the countries in 2016 were replaced: Sectoral data for cadmium from Armenia, mercury emissions from Kyrgyzstan, National Total data of all heavy metals from Kazakhstan and sectoral data of all heavy metals from Liechtenstein. Reasons for the replace-ments were implausible data (Kyrgyzstan, Kazakhstan) and discrepancies in the sum of the sec-tors compared to the National Total (Armenia, Liechtenstein). 26% to 31% of the values in the total gap-filled data set were gap-filled and 1% to 4% were replaced (Figure 2).

2 Data source: The World Bank, World Development Indicators. Indicator name: GDP per capita (current US$), indicator code:

NY.GDP.PCAP.CD. Values for 2014 are taken, except for Liechtenstein (2012) and Monaco (2011), as no other data were available.



Figure 2: Overview on gap-filled and replaced data

1.3 Proposal to upgrade the current situation and practice

1.3.1 Method overview

To gap-fill missing data and also for plausibility checks of reported data, literature or expert data can be used, or data can be calculated using different (imputation) methods.

1.3.2 Literature or expert data

For future versions of the gap-filled WebDab database, the intention is to search for additional and updated data sources, estimates and projections to fill gaps and compare data. An example for an additional data source is the new version of the Global Mercury Assessment that is planned to be published in 2018 (UNEP, 2016).

Also, the cooperation and possible exchange of information with other organisations dealing with air pollution data, like the task force on Hemispheric Transport of Air Pollution (TF HTAP)3, shall be enhanced.

3 http://www.htap.org/

http://www.htap.org/



1.3.3 Completeness analysis of submissions

Annex I presents trends of reported heavy metals by 51 EMEP Parties. The table shows that 41-42 countries provided heavy metals national total emission data for the year 2014. More infor-mation concerning completeness can be found in the EMEP/EEA inventory review 2016 (EMEP/ EEA 2016a).

Figure 1.3 shows the completeness of the aggregated data set for the whole EMEP area before the data set was gap-filled. The number of notations keys, values and empty cells (for missing submissions all source categories were counted as “no submission”) reported in the NFR report-ing template was summed up for all Parties in the EMEP area. The use of notation keys is given as percentage of all NFR source categories4. About 26-28% is missing data, this means the submissions contain empty cells, the value ‘0’, the notation key ‘NE’ (‘not estimated’) or no submission was made. The completeness for the three heavy metals is similar (Figure 3).

Figure 3: Completeness of 2016 submissions accumulated over all EMEP countries

The quality of the data submissions vary considerably between different countries. As an example, in Figure 4 the Mercury submissions of the individual countries are shown.

4 As example: In total all parties within the EMEP region should have reported 6 153 NFR source categories of Cadmium. Of these

reported values, 565 were “NE”, which means that 10% of the reported values were “NE”, see Figure 3.



Figure 4: Completeness of 2016 submissions for Mercury

However, in spite of this result gap-filling will be limited mostly to total emissions as before, as the reasons for missing data are often not very transparent, i.e. the notation key ‘NE’ is some-times used rightly but not documented. Nevertheless, for the review of submitted data such analyses as shown in Figure 4 can help to evaluate the quality and credibility of the data, e.g. for replacement decisions.

1.3.4 Key category analysis

Most previous imputation methods were limited to rather simple calculations like extrapolation of data (compare section 1.1). To improve the methods, it was assessed which sectors (catego-ries) are the most important emission sources for the specific heavy metals. For this assessment a key category analysis was made. A key category analysis identifies those source categories that have a significant influence on a country’s total inventory in terms of their absolute level of emis-sions. Table 1 shows the results of the analysis, and Figure 5 shows the share of total emissions of the five most important key categories of Cadmium as an example.

Table 1: Overview of heavy metal key categories of the EMEP countries

Cd Hg Pb Number of key categories (all EMEP countries):

38 Number of key categories (all EMEP countries):

34 Number of key categories (all EMEP countries):

35

Number of key categories per country (min-max):

1-11 Number of key categories per country (min-max):

1-13 Number of key categories per country (min-max):

1-10

Five most important categories

No and % of countries, having this category as a key category





1A4bi 27 71% 1A1a 32 82% 2C1 20 53%

1A1a 25 66% 2C1 21 54% 1A1a 19 50%

2C1 15 39% 1A2f 19 49% 1A4bi 16 42%

1A2gviii 13 34% 1A4bi 17 44% 1A3bvi 14 37%

1A2f 11 29% 1A2a 8 21% 1A3bi 11 29%



Figure 5: Cadmium emissions of the individual countries

(the five most important key categories of all EMEP countries are shown)

This analysis showed that reporting is very diverse between the countries, i.e. many different key categories were identified for the individual countries. The Cadmium example (Figure 5) shows that sources are quite different between the countries, even the distribution of the main key cat-egories differs much.

Anyway, for each heavy metal one or two main key categories seem to be a major source for sev-eral countries (compare Table 1). These are the categories ‘1A4bi – Residential: Stationary’ for cadmium, ‘1A1a – Public electricity and heat production’ for mercury, cadmium and lead, and ‘2C1 – Iron and steel production’ for lead and mercury.

The key category analysis together with activity data for the most important key categories (taken from databases or literature) would enable a solid expert estimate in cases of missing/incom-plete emissions by using emission factors from the air pollutant emission inventory guidebook (EMEP/EEA 2016b). For key categories, usually Tier 2 emission factors or better methods should be used. However, this would be too complex for the gap-filling procedure. In the guide-book, Tier 1 emission factors for the categories ‘1A1a’ ‘1A4bi’ and ‘2C1’ are given that could be used when activity data became available. In a next step, the calculated amount of the emis-sions from the key categories can be grossed up for the total emissions.

However, these calculations are very complex due to the gathering of activity data, the calcula-tions and the grossing up, and will involve high uncertainties. Therefore this method will not be implemented for future gap filling.



1.3.5 Correlation with other pollutants

Another idea to fill gaps or to compare reported National Total values is to use reported emissions of pollutants which have similar sources as a proxy to calculate emission values for missing emis-sions. Therefore, correlations between pollutants that are expected to have similar sources have been made, e.g. for heavy metals with particulate matter emissions (Figure 6).

Figure 6 shows that a strong relationship exists between emissions of particulate matter and heavy metals, especially mercury. Correlations for all heavy metals are stronger if compared with PM10 than using PM2.5. Especially for mercury, but also for cadmium and lead, the correlation with particulate matter (primarily: PM10) can be used to calculate emissions when particulate matter emission data are available.

Figure 6: Pearson correlation between heavy metals and particulate matter

(only original reported National Total values of 31 countries)

PM10

PM2.5

Linear (PM10)

Linear (PM2.5)

r=0.76, p<0.00001r=0.73, p<0.00001

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0 50 100 150 200 250 300

Cadm

ium

(Gg)

Particulate matter (Gg)

r=0.87, p<0.00001r=0.83, p<0.00001

0

0.002

0.004

0.006

0.008

0.01

0.012

0 50 100 150 200 250 300

Mer

cury

(Gg)


r=0.73, p<0.00001

r=0.69, p<0.0001

0

0.1

0.2

0.3

0.4

0.5

0.6

0 50 100 150 200 250 300

Lead

(Gg)




1.3.6 Correlations with population or GDP data

Within this analysis, the correlation of National Total heavy metal emissions with population or GDP data is studied (Figure 7). The intention is to investigate if such data can be used for calcu-lating heavy metal emissions, e.g. by extrapolating older emission data using current population or GDP data.

Figure 7: Pearson correlation between heavy metals and population/GDP data

(Pb and Cd: 38 countries, Hg: 39 countries)

Figure 7 shows that a strong relationship exists between heavy metals emissions and population data, especially for mercury. There are also significant correlations of heavy metal emissions with GDP data, and for mercury it is as well a strong correlation. However, to calculate heavy metal emissions (especially mercury emissions), it seem to be a good method to use population data and might be used rather than GDP data in the future.



2 EMISSION DATA FOR HEAVY METALS MODELING (MSC-E CONTRIBUTION)

2.1 Current situation

EMEP Centre on Emission Inventories and Projections (CEIP) is responsible for the preparation of emission data sets as input for long-range transport models. Emission data currently provided by the EMEP countries in their national inventories cover only part of the information that is required for model assessment of HM pollution. The gap filling is performed by the Centre on Emission Inventories and Projections to provide complete sets of gridded emissions over the EMEP domain. Along with this, application of the gridded emission data for modeling requires evaluation of additional emission parameters. They comprise chemical composition of emitted pollutants, vertical distribution of emission height and temporal variation of anthropogenic emis-sions along the year.

Emission parameters for model application prepared by CEIP and MSC-E are given in Table 2 Assumptions and methodologies used for evaluation of the parameters prepares by MSC-E are discussed below along with characteristics of their major uncertainties.

Table 2: Emission parameters for modeling generated by CEIP and MSC-E

Information on HM (Pb, Cd, Hg) emissions Emission data for modeling (CEIP)

Additional emission data for modeling

(MSC-E)

Time-series of national total emissions (annually) X

Gridded sectoral emissions (once in five years) X

Emissions of Large Point Sources (once in five years) X

Gridded total emissions for the latest reported year generated by CEIP (annually) X

Time-series of gridded annual emissions 1990-2014 X

Vertical distribution of emissions X

Temporal variation of emissions X

Speciation of Hg forms (Hg˚, Hg(II)gas, Hg(II)part) X

Emissions for the non-EMEP countries within the EMEP domain. (North Africa and Middle East). X

Natural emissions X

Re-suspension, re-emissions X

Global emission inventories (Hg) X



2.2 Time-series of gridded annual emission

Nowadays, the gridded data are available for the years of the period from 2009 to 2014. It should be noted that consistent time series of gridded emission data for the whole period from 1990 to 2014 (including the latest recalculations of all years of the period) are not available for modeling of long-term pollution trends.

For model reproduction of air concentration and deposition fluxes needs emission data covering the entire EMEP domain that includes not only territories of all EMEP countries but also adjacent areas (Northern Africa, Middle East etc.)

It should be also mentioned that further improvement of completeness of officially reported data and expert emission estimates are needed (Figure 8).

Figure 8: Gridded emissions reporting by Parties in the EMEP area

Besides, it is important to have quantitative estimates of emission data uncertainty in a form of error intervals of the reported emission values. This is needed for the evaluation of possible max-imum and minimum scenarios of pollution levels in the EMEP region. Range of uncertainties of the officially reported emission data is submitted only by 12 EMEP countries and can differ from 13% (Cyprus) to 488% (Denmark).



2.3 Chemical composition of emissions

2.3.1 Mercury

The character of Hg dispersion in the atmosphere and transport from one region to another are largely affected by physicochemical properties of Hg atmospheric species. Poorly soluble and rel-atively inert gaseous elemental mercury (GEM) can drift in the air for months providing transport of Hg mass between different regions of the planet. In contrast, oxidized Hg species – gaseous oxidized mercury (GOM) and particle bound mercury (PBM) – are easily removed from the air by precipitation scavenging or the surface uptake [Selin, 2009; Travnikov, 2011; AMAP/UNEP, 2013]. Therefore, proportion of anthropogenic emissions that deposits locally or regionally de-pends on speciation of Hg emissions that differs significantly for different emission sectors. Fig-ure 9 shows model estimates of mercury deposition in the EMEP region performed with differ-ent assumptions on speciation of Hg anthropogenic emissions.

Figure 9: Annual Hg deposition in the EMEP region simulated for different speciation of Hg emissions:

(a) – all Hg emissions are treated as GEM; (b) – all Hg emissions are treated as oxidized Hg (GOM and PBM). Total Hg emission was kept the same in both cases.

Information on speciation of Hg anthropogenic emissions is not reported by the Parties to CLRTAP. Therefore, expert estimates of the mercury emission speciation have been made by MSC-E. The current approach is based on utilizing the AMAP/UNEP global Hg emission inventory for 2010 [AMAP/UNEP, 2013]. This inventory is used to evaluate average speciation of Hg emissions in different EMEP countries. These country-specific ratios are applied to the EMEP gridded emis-sion data to obtain speciation of Hg emissions for modeling.

This approach contains considerable uncertainties. First of all, the original global AMAP/UNEP inventory was developed using consistent statistical approach on a global scale and, probably, did not reflect peculiarities of individual EMEP countries. Secondly, sector-specific speciation of Hg emissions is not available from this inventory. And finally, some recent researches involving both Hg modeling and contemporary measurements of Hg species in the ambient air suppose possible systematic bias in speciation of existing inventories overestimating proportion of oxi-dized Hg species in anthropogenic emissions [Amos et al., 2012; Zhang et al., 2012; Kos et al., 2013]. Therefore, involvement of national data and experts to evaluation of Hg emission specia-tion could significantly improve quality of pollution assessment in the EMEP countries.

a. b.

g/km2/y



2.4 Temporal variation of emissions

The contamination levels (air concentrations and deposition fluxes) of HMs are subject to essen-tial temporal variability [Shatalov et al., 2015]. It has been found that average seasonality of modelled and observed Pb and Cd levels is about 40-50% of the annual average.

Temporal variations of HM emissions are not included at present in the modelling because in-formation on seasonal variability of emissions is not available. However, both observations and modelling results show that seasonal variability of Pb and Cd levels makes up around 30-40% on average (Figure 10).

Figure 10: Modelled and observed monthly mean concentrations of lead at Polish stations PL5,

averaged over 2005-2012 period (a) and at German station DE7, averaged over 2000-2012 period (b)

This variability in modelling results is explained entirely by meteorological factors. Introduction of temporal variability of HM emissions could favor improvement of agreement between calcu-lated and observed pollution levels. However, further research is required to quantify the effect of temporal variability of emissions.

2.5 Vertical distribution of emissions

Vertical distribution of emissions is important for modeling of atmospheric transport of pollutants. Since wind velocity tends to increase with altitude, pollutants emitted at higher altitudes tend to transport over longer distances compared to those at lower layers. Besides, contributions of wet and dry components to total deposition depend on distribution of pollutants in the column of at-mosphere along the vertical. When vertical distribution of emissions is taken into account, simu-lated deposition flux decreases nearby large emission sources and increases in other regions of the EMEP domain (Figure 2.4). This effect is expected to be even stronger when moving to a finer resolution grid.

a. b.

PL5

0

2

4

6

8

10

12

Jan

Feb

Mar

Apr

May Ju

n

Jul

Aug

Sep Oct

Nov

Dec

Con

cent

ratio

n in

air,

ng/

m3 Observed

Modelled

DE7

0

2

4

6

8

10

12

Jan

Feb

Mar

Apr

May Ju

n

Jul

Aug

Sep Oct

Nov

Dec

Con

cent

ratio

n in

air,

ng/

m3 Observed

Modelled



Figure 11: Relative difference between Pb deposition, calculated with and

without use of vertical distribution of emissions. Negative values mean decrease and positive – increase of deposition

When applying vertical distribution of HM emissions it has been assumed that each source cat-egory is characterized by specific emission height, following [Berdowski et al., 1997]. Emis-sions are distributed along the vertical between three lowest model layers, based on contribu-tions of emission source categories to total emission in the EMEP region officially reported for 2000 [Travnikov and Ilyin, 2005]. It should be noted that the approach does not take into ac-count possible rise of emission plume.

Vertical distribution of HM emissions could be updated by linking atmospheric releases from particular source categories to their emission heights (or range of heights). Information on large point sources (LPS) such as physical stack height, gas outflow velocity, top diameter of a stack and gas temperature as well as meteorological information can facilitate improvement of verti-cal distribution due to considering rise or emission plumes [Briggs, 1984; Houyoux, 1998]. However, for regular operational calculations it seems feasible to use aggregated information of emission height for different source categories [Bieser et al., 2011].

2.6 Global emission inventories

Atmospheric dispersion of Hg is not limited by a regional scale. Given their persistence in the environment and long residence time in the atmosphere these substances can travel over long dis-tances. The potential to long-range transport of these pollutants is also enhanced by their ability to be re-emitted after deposition to the ground that leads to multi-hop dispersion. As a result pollu-tion levels in the EMEP region can be significantly affected by emissions from distant sources located in other regions or even continents. It has been estimated that deposition of Hg from direct anthropogenic sources in Europe is derived almost equally from domestic and foreign emissions [AMAP/UNEP, 2015].

Besides, even deposition of pollutants with relatively short residence time in the atmosphere (Pb and Cd) is influenced by sources located outside the EMEP region, particularly, in the Asian countries with growing economies. Therefore, correct consideration of these sources is required through evaluation appropriate boundary conditions.



Available emission inventories for HMs on a global scale are summarized in Table 3. Relatively favorable situation is with global emission data for mercury (Figure 12). There are two recent inventories of gridded Hg emissions developed for a number of years. The only gridded emis-sion dataset for Pb relates to 1989. It has been updated for mid-nineties but without spatial dis-tribution. Only aggregated emission estimates without spatial distribution of emission sources are available for Cd and also relate to mid-nineties.

Figure 12: Global Hg emissions inventory, 2010 [AMAP/UNEP, 2013]

Table 3: Available global emission inventories for heavy metals

Chemical Period Spatial resolution Data source Reference

Heavy metals

Pb 1989 1°×1° Pacyna et al., 1995

1995 n/a Pacyna and Pacyna, 2001

Cd 1995 n/a Pacyna and Pacyna, 2001

Hg 1990-2010 0.5°× 0.5° AMAP(a) AMAP/UNEP, 2013

1970-2008 0.1°× 0.1° EDGAR/JRC(b) Muntean et al., 2014

(a) http://www.amap.no/mercury-emissions/ (b) http://edgar.jrc.ec.europa.eu/edgar_v4tox1/

Further improvements of global emission data for HMs should include update of existing inven-tories with more recent data and development of absent gridded data for Cd.

http://www.amap.no/mercury-emissions/

http://edgar.jrc.ec.europa.eu/edgar_v4tox1/



2.7 Historical and natural emissions

HM pollution levels are controlled by various emission sources, particularly, primary anthropo-genic emissions to the atmosphere and other media, as well as natural and secondary emissions (re-mobilization of previously deposited pollutants from soils, water bodies, etc.). Along with anthropogenic emissions, the contribution of secondary sources is an important factor that needs to be addressed in studies of contemporary levels of pollution. While primary emissions are de-creasing or ceasing due to emission control measures, the role of secondary emissions can in-crease or they can even dominate (Figure 13).

Figure 13: Deposition fluxes of lead caused by secondary and anthropogenic

sources within EMEP region and by non-EMEP sources

In particular, along with primary anthropogenic emissions, lead and cadmium are emitted to the atmosphere as a result of wind re-suspension of dust, sea salt emission, volcanic activity, wild forest fires, and biogenic emissions [Nriagu, 1989]. Anthropogenic emissions to the atmosphere over long period of time (decades – hundreds of years) lead to long-term accumulation of depos-ited HMs and increase of their concentrations in the top layer of soils, water bodies, etc. Thus, wind re-suspension of metals from surfaces of terrestrial and aquatic media can significantly con-tribute to the pollution in the EMEP region [Dore et al., 2014, Amato et al., 2014, Laidlaw et al., 2012]. It is likely that estimates of wind re-suspension currently used for model assessment of HM pollution are characterized by considerable uncertainties and may partly compensate possi-ble underestimation of the anthropogenic emissions. For improvement of estimation of second-ary emissions information on contemporary concentrations of Pb and Cd in top soils of the EMEP region is needed.

Experience of the Working Group on Effects (WGE) in dynamic modelling of HMs in soils could be highly valuable for establishing link between historical anthropogenic emissions, HM concen-trations in soils and their re-suspension.

Pb

0.0

0.5

1.0

1.5

2.0

2.5

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Dep

ositi

on fl

ux, k

g/km

2 /y

Secondary

Anthrop. and non-EMEP



Current Hg emissions to the atmosphere include, along with direct anthropogenic emissions, con-tribution of natural sources (weathering of Hg containing rocks, volcanic and geothermal activi-ties) and secondary emissions. The latter comprise re-emission to the atmosphere of previously deposited Hg which originally comes from anthropogenic and natural sources. According to the recent estimates natural and secondary sources contribute around 75% in sum to the total Hg emissions on a global scale [Mason et al., 2012; AMAP/UNEP, 2013]. Therefore, these source types can significantly affect levels of Hg air concentration and deposition in the EMEP domain [Ilyin et al., 2015; AMAP/UNEP, 2015]. The current modeling approach to Hg simulation both on global and regional scales applies expert estimates of Hg emissions from natural and secondary sources based on literature data [Travnikov and Ilyin, 2005; Travnikov and Ilyin, 2009; Travnikov et al., 2009]. Besides, to improve the estimates of these source types and their contribution to pollution levels in the EMEP countries a multi-media version of the GLEMOS model for Hg is currently under development. Along with model parameterizations, application of this multi-media approach will require information on historical Hg emissions in the EMEP and other re-gions of the world.

2.8 Summary

Emission data for HMs have been characterized from the viewpoint of their use for the opera-tional modeling within EMEP. Various emission parameters which affect quality of the model assessment have been discussed along with characterizing of their uncertainties. Importance of different emission parameters and their influence on quality of the assessment results varies considerably for simulation of different pollutants. This fact should be taken into account when planning improvement of the emission data. Table 4 characterizes the key emission parameters for HMs in terms of their priority for the further improvement. It should be noted that lower priority level does not mean that no improvements are needed for this emission parameter but rather determines order of their implementation.

Table 4: Key emission parameters affecting quality of model estimates

Emission parameter Pb and Cd Hg

Quality of gridded anthropogenic emissions 1 2

Chemical composition - 1

Temporal variation 2 6

Vertical distribution 3 7

Global emissions inventory 4 3

Historical emissions 5 4

Emissions to other media 6 5

– First priority; – Second priority; – Third priority



3 CONCLUSIONS AND RECOMMENDATIONS

3.1 CEIP

The quality of the data submissions vary greatly between different countries. The reasons for missing data are often not very transparent but for the review of submitted data the analysis of the submissions can help to evaluate the quality and credibility of the data, e.g. for re-placement decisions.

Key category distribution is diverse between the countries. To calculate emissions using key category distribution and activity data would be very resource demanding. Calculations are very complex due to the gathering of activity data, the calculations itself and the grossing up of emissions. Further, results would contain high uncertainties. Therefore this method will not be implemented for future gap filling.

Strong relationships exist between emissions of particulate matter and heavy metals. Especial-ly for mercury, but also for cadmium and lead, the correlation with particulate matter (pri-marily: PM10) can be used to calculate emissions when particulate matter emission data are available.

Strong relationships exist as well between emissions of heavy metals (especially mercury) and population or GDP data. Especially population data (rather than GDP data) could be used as indicator for e.g. extrapolation of previous reported emission data.

Additional and updated data sources, estimates and projections will be searched to fill gaps and compare data. Cooperation with other organisations dealing with air pollution data will be enhanced.

The quality of the data will increase the best, when countries frequently report complete emission inventories.

3.2 MSC-E

Emission data currently provided by the EMEP countries in their national inventories cover only part of the information that is required for model assessment of HM pollution. Consistent time series of gridded emission data for the whole period from 1990 to 2014 (including the latest recalculations of all years of the period) are not available for modeling of long-term pol-lution trends.

Modeling of air concentration and deposition fluxes needs emission data covering the entire EMEP domain that includes not only territories of all EMEP countries but also adjacent are-as (Northern Africa, Middle East etc.). Further improvement of completeness of officially re-ported data and expert emission estimates are needed.

Application of the gridded emission data for modeling requires evaluation of additional emis-sion parameters. They comprise chemical composition of emitted pollutants, vertical distribu-tion of emission height and temporal variation of anthropogenic emissions along the year.

Information on speciation of Hg anthropogenic emissions is not reported by the Parties to CLRTAP. Involvement of national data and experts to evaluation of Hg emission speciation could significantly improve quality of pollution assessment in the EMEP countries.



Temporal variations of HM emissions are not included at present in the modeling because information on seasonal variability of emissions is not available. Further research is required to quantify the effect of temporal variability of emissions.

Vertical distribution of HM emissions could be updated by linking atmospheric releases from particular source categories to their emission heights (or range of heights). Information on large point sources (LPS) such as physical stack height, gas outflow velocity, top diameter of a stack and gas temperature as well as meteorological information can facilitate improve-ment of vertical distribution due to considering rise or emission plumes. However, for regu-lar operational calculations it seems feasible to use aggregated information of emission height for different source categories.

Pollution levels in the EMEP region can be significantly affected by emissions from distant sources located in other regions or even continents. Therefore, emission inventories on a glob-al scale are required for pollution assessment within the EMEP region.

Cycling of HMs in the environment has a complex character and includes not only atmos-pheric transport and transformations but also bi-directional exchange with the earth’s surface. Natural and secondary emission sources should be taken into account when assessing both effectiveness of environment protection policy and human exposure of these contaminants. Along with anthropogenic emissions, the contribution of secondary sources is an important factor that needs to be addressed in studies of contemporary levels of pollution.



4 RECOMMENDATION

Quality of gridded emission data (including completeness of reported data and quality

of expert estimates used for gap filling) is among the first priority parameters. Other parameters with the highest priority include chemical composition of emissions

(for Hg). Lower priority parameter, which is still important for Hg, is global emissions

inventory. On the other hand, predominantly airborne substances (Pb, Cd) can be more affected by temporal variation of emissions and vertical distribution of emission sources.

It should be noted that lower priority level does not mean that no improvements are needed for this emission parameter but rather determines order of their

implementation.



5 REFERENCES

AMAP/UNEP [2013] Technical Background Report for the Global Mercury Assessment 2013. Arctic Monitoring and Assessment Programme, Oslo, Norway/UNEP Chemicals Branch, Geneva, Switzerland. vi + 263 pp.

AMAP/UNEP [2015] Global Mercury Modelling: Update of Modelling Results in the Global Mercury Assessment 2013. Arctic Monitoring and Assessment Programme, Oslo, Norway/UNEP Chemicals Branch, Geneva, Switzerland. iv + 32 pp.

Amato F., Alastuey A.., de la Rosa J., Castanedo G.Y., de la Campa S. A. M., Pandolfi M., Lozano A., González J. C., and Querol X. [2014] Trends of road dust emissions contributions on ambient air particulate levels at rural, urban and industrial sites in southern Spain. Atmos. Chem. Phys., 14, 3533–3544.

Amos H.M., Jacob D.J., Holmes C.D., Fisher J.A., Wang Q., Yantosca R.M., Corbitt E.S., Galarneau E., Rutter A.P., Gustin M.S., Steffen A., Schauer J.J., Graydon J.A., St. Louis V.L., Talbot R.W., Edgerton E.S., Zhang Y., Sunderland E. M. [2012] Gas-particle partitioning of atmospheric Hg(II) and its effect on global mercury deposition. Atmos. Chem. Phys., 12, 591–603.

Berdowski J.J.M., Baas J., Bloos J.P.J., Visschedijk A.J.H. and P.Y.J.Zandveld [1997] The European Emission Inventory of Heavy Metals and Persistent Organic Pollutants for 1990. TNO Institute of Environmental Sciences, Energy Research and Process Innovation, UBA-FB report 104 02 672/03, Apeldoorn, 239 p.

Bieser J., Aulinger A., Matthias V., Quante M., and Denier van der Gon H. [2011] Vertical emission profiles for Europe based on plume rise calculations., Environ. Pollut., 159, 2935–2946, doi:10.1016/j.envpol.2011.04.030.

Briggs G. A. [1984] Plume rise and buoyancy effects, Atmospheric Sciences and Power Production, D. Randerson, ed., DOE/TIC27601 (DE84005177), TN, 850 pp., Technical Information Center, U.S. Dept. of Energy, Oak Ridge, USA

CEIP 2016: ‘WebDab - EMEP database’. CEIP website; http://www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase.

Denier van der Gon, H.A.C., Bolscher, M. van het, Visschedijk, A.J.H., Zandveld, P.Y.J. 2005: Study to the effectiveness of the UNECE Heavy Metals Protocol and costs of possible additional measures. Phase I: Estimation of emission reduction resulting from the implementation of the HM Protocol. TNO report (B&O-A R 2005/193), TNO Industrie en Techniek TNO Milieu, Energie en Procesinnovatie, Apeldoorn, NL; http://publications.tno.nl/publication/105263/njvBTt/B&O-A-Ra2005-193.pdf.

Dore A. J., Hallsworth S., McDonald A. G., Werner M., Kryza M., J. Abbot, Nemitz E., Dore C. J., Malcolm H., Vieno M., Reis S., Fowler D. [2014] Quantifying missing annual emission sources of heavy metals in the United Kingdom with an atmospheric transport model. Science of the Total Environment, 479 –480 171–180.

ECE/EB.AIR/133/Add.1 Report of the Executive Body on its thirty-fourth session. Addendum. 2016–2017 workplan for the implementation of the Convention.

EMEP/EEA, 2016a: Inventory review 2016, Review of emission data reported under the LRTAP Convention and NEC Directive Stage 1 and 2 review, EEA Technical Report CEIP 1/2016; http://www.ceip.at/fileadmin/inhalte/emep/pdf/2016/DP-148_InventoryReport_2016.pdf.

EMEP/EEA, 2016b: EMEP/EEA air pollutant emission inventory guidebook — 2016, EEA Technical Report No 21/2016, European Environment Agency; http://www.eea.europa.eu/publications/emep-eea-guidebook-2016.

http://www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase/

http://publications.tno.nl/publication/105263/njvBTt/B&O-A-Ra2005-193.pdf

http://www.ceip.at/fileadmin/inhalte/emep/pdf/2016/DP-148_InventoryReport_2016.pdf

http://www.eea.europa.eu/publications/emep-eea-guidebook-2016



Houyoux M.R. [1998] Technical report: plume rise algorithm summary for the Sparse Matrix Operator Modeling System (SMOKE). Prepared for North Carolina Department of Environment and Natural Resources, UNC, Chapel Hill, North Carolina, ENV 98TR004eTR0v1.0

Ilyin I., Rozovskaya O., Travnikov O., Varygina M. [2015] Heavy metals: Analysis of long-term trends, country-specific research and progress in mercury regional and global modelling. EMEP Status Report 2/2015.

Kos G., Ryzhkov A., Dastoor A., Narayan J., Steffen A., Ariya P.A., Zhang L. [2013] Evaluation of discrepancy between measured and modelled oxidized mercury species. Atmospheric Chemistry and Physics, 13 (9), pp. 4839-4863.

Laidlaw M.A.S., Zahran S., Mielke H.W., Taylor M.P., Filippelli G.M. [2012]. Re-suspension of lead contaminated urban soil as a dominant source of atmospheric lead in Birmingham, Chicago, Detroit and Pittsburgh, USA. Atmospheric Environment, 49, 302-310.

Mason, R.P., A.L. Choi, W.F. Fitzgerald, C.R. Hammerschmidt, C.H. Lamborg, A.L. Soerensen and E.M. Sunderland [2012] Mercury biogeochemical cycling in the ocean and policy implications. Environmental Research, 119:101-117.

MSC-E 2013: Data provided to CEIP. MSC-E website: http://www.msceast.org.

Muntean M, Janssens-Maenhout G., Song S., Selin N.E., Olivier J.G.J., Guizzardi D, Maas R. and F. Dentener [2014] Trend analysis from 1970 to 2008 and model evaluation of EDGARv4 global gridded anthropogenic mercury emissions. Sci.Total Environ. 494–495, pp.337–350.

Nriagu J.O. [1989] A global assessment of natural sources of atmospheric trace metals. Nature 338, 47-49.

Pacyna J. M., Scholtz M. T. and Y.-F.Li [1995] Global Budgets of Trace Metal Sources, Environmental Reviews, 3, 145-159.

Pacyna J.M. and E.G.Pacyna [2001] An assessment of global and regional emissions of trace metals to the atmosphere from anthropogenic sources worldwide. Environmental Reviews, 9, pp.269-298.

Selin N.E. [2009] Global biogeochemical cycling of mercury: A review. Annu. Rev. Environ. Resour. 34, 43-63.

Shatalov V., I. Ilyin, A. Gusev, O. Travnikov [2015] Heavy Metals and Persistent Organic Pollutants: Multi-scale modelling and trend analysis methodology. MSC-E Technical Report 1/2015.

Travnikov O. [2011] Atmospheric Transport of Mercury, in Environmental Chemistry and Toxicology of Mercury (eds G. Liu, Y. Cai and N. O’Driscoll), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118146644.ch10.

Travnikov O. and I. Ilyin [2005] Regional Model MSCE-HM of Heavy Metal Transboundary Air Pollution in Europe. EMEP/MSC-E Technical Report 6/2005.

Travnikov O. and I. Ilyin [2009] The EMEP/MSC-E mercury modeling system. In: Mason, R. and N. Pirrone (Eds.). Mercury Fate and Transport in the Global Atmosphere, pp. 571-587. Springer.

Travnikov O., J.E. Jonson, A.S. Andersen, M. Gauss, A. Gusev, O. Rozovskaya, D. Simpson, V. Sokovyh, S. Valiyaveetil and P. Wind [2009]. Development of the EMEP global modelling framework: Progress report.

UNECE 1979: The 1979 Geneva Convention on Long-range Transboundary Air Pollution. United Nations Economic Commission for Europe; http://www.unece.org/env/lrtap/lrtap_h1.html.

UNECE 2014: Guidelines for Reporting Emissions and Projections Data under the Convention on Long-range Transboundary Air Pollution. United Nations Economic Commission for Europe (ECE/EB.AIR/125); http://www.ceip.at/fileadmin/inhalte/emep/2014_Guidelines/ ece.eb.air.125_ADVANCE_VERSION_reporting_guidelines_2013.pdf.

http://www.msceast.org/

http://www.unece.org/env/lrtap/lrtap_h1.html

http://www.ceip.at/fileadmin/inhalte/emep/2014_Guidelines/ece.eb.air.125_ADVANCE_VERSION_reporting_guidelines_2013.pdf

http://www.ceip.at/fileadmin/inhalte/emep/2014_Guidelines/ece.eb.air.125_ADVANCE_VERSION_reporting_guidelines_2013.pdf



UNEP, 2016: Global Mercury Assessment. United Nations Environment Programme; http://www.unep.org/chemicalsandwaste/Mercury/ReportsandPublications/ GlobalMercuryAssessment/tabid/1060889/Default.aspx.

Zhang, L., Blanchard, P., Johnson, D., Dastoor, A., Ryzhkov, A., Lin, C.-J., Vijayaraghavan, K., Gay, D., Holsen, T. M., Huang, J., Graydon, J.A., St. Louis, V. L., Castro, M. S., Miller, E. K., Marsik, F., Lu, J., Poissant, L., Pilote, M. and Zhang, K. M. [2012] Assessment of modeled mercury deposition over the Great Lakes region. Environ. Pollut. 161, 272–283.

http://www.unep.org/chemicalsandwaste/Mercury/ReportsandPublications/GlobalMercuryAssessment/tabid/1060889/Default.aspx

http://www.unep.org/chemicalsandwaste/Mercury/ReportsandPublications/GlobalMercuryAssessment/tabid/1060889/Default.aspx



ANNEX I – REPORTED EMISSIONS OF HEAVY METALS

In 2016, 41 out of 51 countries reported data of the year 2014 for Lead and Cadmium, and 42 countries for Mercury. Table A.1, Table A.2 and Table A.3 show the trends of the national total emissions.

Table A.1: Lead trend table (reported until November 2016)

Country/Pb [Mg] 1990 1995 2000 2005 2010 2014 Albania 62.0 67.0 76.0 60.0

Armenia 11.0 0.3

5.9

Austria 215.0 16.0 12.0 13.0 15.0 15.0 Azerbaijan NE 0.1 0.1 1.4 2.4 2.7 Belarus 794.0 147.0

50.0 70.0 8.8

Belgium 253.0 186.0 107.0 74.0 40.0 23.0 Bosnia & Herzegovina

Bulgaria 321.0 323.0 256.0 126.0 70.0 198.0 Canada (national) 1,234.0 881.0 535.0 246.0 200.0 126.0 Croatia 538.0 328.0 276.0 52.0 7.0 7.0 Cyprus 35.0 41.0 43.0 29.0 31.0 24.0 Czech Republic 269.0 180.0 328.0 57.0 20.0 23.0 Denmark 128.0 26.0 19.0 17.0 12.0 11.0 Estonia 206.0 86.0 37.0 36.0 39.0 37.0 Finland 338.0 67.0 45.0 22.0 23.0 14.0 France 4,296.0 1,477.0 282.0 173.0 135.0 117.0 Georgia

0.4 2.9

Germany 2,070.0 702.0 393.0 277.0 220.0 213.0 Greece

Hungary 644.0 195.0 22.0 9.2 7.3 6.8 Iceland NR NR NR NR NR NR Ireland 124.0 78.0 19.0 18.0 16.0 13.0 Italy 4,418.0 2,032.0 948.0 284.0 266.0 260.0 Kazakhstan

127.0

Kyrgyzstan

0.01 NE Latvia 97.0 61.0 6.4 4.3 3.8 3.3 Liechtenstein 0.65 0.24 0.02 0.02 0.02 0.02 Lithuania 150.0 91.0 6.9 4.2 4.5 3.8 Luxembourg 19.0 9.1 1.4 1.5 1.3 1.5 Malta

0.7 0.8 3.4 4.3

Moldova, Republic of 16.0 6.2 8.2 10.0 3.5

Monaco 2.76 0.73 0.01 0.01 0.01 0.01 Montenegro 309.0 141.0 142.0 48.0 46.0

Netherlands 334.0 156.0 29.0 31.0 39.0 10.0 Norway 186.0 23.0 8.4 7.1 4.7 4.7 Poland 605.0 605.0 485.0 498.0 545.0 517.0 Portugal 548.0 753.0 37.0 36.0 33.0 34.0 Romania NE NE NE 72.0 42.0 37.0 Russia (European part) 3,591.0 2,426.0 2,352.0 355.0 NE NE Serbia 390.0 297.0 201.0 236.0 107.0 29.0 Slovakia 99.0 79.0 89.0 61.0 50.0 57.0 Slovenia 600.0 388.0 130.0 129.0 10.0 7.8 Spain 2,764.0 955.0 596.0 228.0 205.0 209.0 Sweden 359.0 38.0 27.0 15.0 13.0 11.0 Switzerland 376.0 169.0 33.0 22.0 19.0 18.0 The fYR of Macedonia 108.0 95.0 100.0 23.0 6.4 4.5 Turkey

Ukraine

304.0 159.0 88.0

United Kingdom 2,891.0 1,533.0 152.0 108.0 62.0 66.0 United States (national) 2,996.0 3,577.0

1,083.0

EU28 22,941.5 11,001.3 4,918.9 2,845.6 1,912.0 1,925.5



Table A.2: Cadmium trend table (reported until November 2016)

Country/Cd [Mg] 1990 1995 2000 2005 2010 2014

Albania 0.31 0.08 0.04 0.06

Armenia

0.00

Austria 1.60 1.00 0.92 1.10 1.20 1.10

Azerbaijan NE 0.05 0.07 0.12 0.37 0.17

Belarus 2.10 1.10

2.10 3.20 0.73

Belgium 6.10 5.00 2.80 2.20 2.40 2.00

Bosnia & Herzegovina

Bulgaria 5.20 3.70 3.50 2.90 1.10 2.10

Canada (national) 91.00 29.00 38.00 35.00 17.00 7.60

Croatia 0.88 0.42 0.51 0.58 0.51 0.80

Cyprus 0.08 0.08 0.10 0.09 0.06 0.05

Czech Republic 4.30 3.60 2.70 2.90 0.84 0.72

Denmark 1.10 0.62 0.55 0.61 0.65 0.56

Estonia 4.50 2.20 0.77 0.76 0.90 0.90

Finland 6.30 1.70 1.30 1.30 1.40 0.82

France 21.00 18.00 14.00 5.70 3.10 2.90

Georgia

0.01 0.29

Germany 20.00 14.00 12.00 9.10 7.10 6.50

Greece

Hungary 3.70 2.90 1.40 0.93 1.10 0.93

Iceland NR NR NR NR NR NR

Ireland 0.50 0.52 0.55 0.37 0.30 0.31

Italy 10.00 10.00 8.90 8.20 7.00 6.50

Kazakhstan

2.80

Kyrgyzstan

0.01 NE

Latvia 0.49 0.60 0.52 0.63 0.56 0.62

Liechtenstein 0.0025 0.0025 0.0026 0.0029 0.0025 0.0022

Lithuania 0.83 0.45 0.46 0.58 0.51 0.49

Luxembourg 0.08 0.06 0.05 0.08 0.07 0.05

Malta

0.48 0.59 0.05 0.01


Monaco 0.0003 0.0008 0.0009 0.0008 0.0008 0.0010

Montenegro 0.08 0.02 0.06 0.06 0.06

Netherlands 2.10 1.10 1.00 1.80 2.60 0.64

Norway 1.50 1.20 0.92 0.70 0.62 0.41

Poland 22.00 27.00 19.00 15.00 15.00 14.00

Portugal 6.30 6.50 6.20 6.80 4.30 4.30

Romania NE NE NE 3.80 3.30 2.90

Russia (European part) 79.00 57.00 51.00 59.00 NE NE

Serbia 3.80 2.20 1.90 1.80 1.80 1.60

Slovakia 10.00 10.00 9.10 5.80 1.00 1.10

Slovenia 1.40 0.93 0.66 0.78 0.54 0.47

Spain 25.00 23.00 19.00 15.00 10.00 8.10

Sweden 2.30 0.80 0.59 0.61 0.62 0.57

Switzerland 3.60 2.50 1.70 1.30 1.50 1.40

The fYR of Macedonia 0.48 0.39 0.51 0.35 0.40 0.14

Turkey

Ukraine

6.80 2.80 2.40

United Kingdom 23.00 11.00 6.10 3.90 2.90 3.10

United States (national) 180.00

56.00

EU28 185.52 151.86 119.86 95.50 69.11 62.48



Table A.3: Mercury trend table (reported until November 2016)

Country /Hg [Mg] 1990 1995 2000 2005 2010 2014

Albania 0.27 0.04 0.02 0.06

Armenia 0.010 0.001

0.34

Austria 2.14 1.20 0.89 0.98 1.00 0.96

Azerbaijan 0.04 0.10 0.12 0.21 0.49 0.30

Belarus 1.07 0.51

0.65 0.85 0.22

Belgium 5.67 2.98 3.03 1.99 1.70 1.54

Bosnia & Herzegovina

Bulgaria 2.45 1.95 1.50 1.60 0.91 0.78

Canada (national) 34.69 14.37 9.30 7.90 5.69 3.89

Croatia 1.15 0.30 0.49 0.59 0.53 0.50

Cyprus 0.10 0.11 0.11 0.10 0.07 0.10

Czech Republic 7.52 7.40 3.07 3.71 3.61 2.57

Denmark 3.17 2.33 1.01 0.70 0.44 0.33

Estonia 1.15 0.64 0.55 0.55 0.65 0.68

Finland 1.02 0.72 0.58 0.86 0.87 0.63

France 24.82 20.58 11.72 6.44 4.53 3.89

Georgia

0.15

Germany 32.24 19.55 17.65 13.54 10.75 9.14

Greece

Hungary 3.00 2.33 2.06 1.50 1.26 0.89

Iceland NR NR NR NR NR NR

Ireland 0.91 0.80 0.65 0.69 0.60 0.54

Italy 11.69 10.44 9.24 9.95 8.73 8.16

Kazakhstan

8.87

Kyrgyzstan

0.54 0.0001

Latvia 0.27 0.11 0.07 0.08 0.07 0.07

Liechtenstein 0.0002 0.0002 0.0002 0.0003 0.0003 0.0002

Lithuania 1.26 0.46 0.24 0.43 0.21 0.19

Luxembourg 0.39 0.21 0.25 0.18 0.05 0.05

Malta

0.63 0.60 0.01 0.01


Monaco 0.0011 0.0016 0.0018 0.0014 0.0013 0.0015

Montenegro 0.07 0.02 0.07 0.06 0.09

Netherlands 3.58 1.48 1.10 0.95 0.62 0.55

Norway 1.47 0.82 0.70 0.62 0.47 0.35

Poland 14.18 12.51 10.46 9.79 9.59 9.59

Portugal 3.24 3.39 3.11 2.61 1.68 1.50

Romania NE NE NE 5.39 2.24 1.99

Russia (European part) 15.60 10.40 10.00 14.00 NE NE

Serbia 1.84 1.46 1.63 1.66 1.61 1.10

Slovakia 12.76 4.31 6.15 2.60 1.04 1.18

Slovenia 0.31 0.22 0.20 0.19 0.19 0.16

Spain 14.44 15.95 12.91 11.14 6.89 5.47

Sweden 1.55 0.97 0.76 0.69 0.53 0.45

Switzerland 6.57 4.00 1.77 0.72 0.73 0.67

The fYR of Macedonia 0.85 0.63 0.78 0.56 0.51 0.27

Turkey

Ukraine

5.96 6.79 6.06

United Kingdom 37.53 19.89 8.27 7.52 6.57 5.44

United States (national) 187.00 146.00

95.00

EU28 211.25 153.77 117.42 98.36 65.31 57.36

CEIPUmweltbundesamtSpittelauer Lände 5, 1090 Vienna, Austriaemep

CEIPUmweltbundesamt GmbHSpittelauer Lände 5A-1090 ViennaAustriaPhone: +43-(0)1-313 04Fax: +43-(0)1-313 04/5400E-mail: [email protected]: www.umweltbundesamt.at

umweltbundesamtu

MSC-EMeteorological Synthesizing Centre-East2nd Roshchinsky proezd, 8/5115419 MoscowRussiaPhone +7 926 906 91 78Fax: +7 495 956 19 44E-mail: [email protected]: www.msceast.org

joint ceip/msc-e technical report on emission … programme for monitoring ... relative difference...

Documents