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Emissions data for of heavy metal and POP modelling

This article discusses the processing of emissions data, including gridded sectoral emissions and emission speciation, for heavy metals and persistent organic pollutants (POPs) modelling. It also highlights the challenges and potential solutions for gridded emissions data for polychlorinated biphenyls (PCBs) and the sensitive nature of mercury and POPs modelling due to uncertain and outdated expert estimates. The article also explores the evaluation of emissions through case studies and model validation with measurements.

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Emissions data for of heavy metal and POP modelling

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  1. Emissions data for of heavy metal and POP modelling Oleg Travnikov, Alexey Gusev, Ilia Ilyin, Olga Rozovskaya, Victor Shatalov Meteorological Synthesizing Centre – East of EMEP

  2. Processing of emissions data Annual gridded data for the new EMEP domain (0.1°×0.1°) • Primary emissions data (prepared by CEIP): • Gridded sectoral emissions (Pb, Cd, Hg, PCDD/Fs, HCB) • Gridded sectoral emissions of 4 PAHs (BaP, BbF, BkF, IP) • No gridded data for PCBs (no congener composition reported) • Additional data and auxiliary parameters (prepared by MSC-E): • PCBs gridded emissions based on national data and expert estimates • Seasonal variation of emissions (all HMs and POPs) • Vertical distribution of emissions(all HMs and POPs) • Emission speciation (Hg) and congener composition (PCDD/Fs) • Global and historical emissions (Hg, PCBs, PCDD/Fs, HCB)

  3. - 1st priority - 2nd priority - 3rd priority Review of emission parameters Ranking of key emission parameters Joint CEIP / MSC-E technical reports on HM and POP emission inventory improvement (2017)

  4. Possible solution – National reporting of rough estimates of congener composition or updates of available expert estimates Preparation of PCB emissions • Available information on PCB emissions: • Reported national totals and gridded data without congener composition • Gridded global inventory of 22 PCB congeners (Breivik et al., 2007) • Emissions data for modelling: • Indicator congener: PCB-153 • Spatial distribution: Reported national data (or population density) • Country totals: Expert estimates (Breivik et al., 2007) • Limitations and requirements: • No congener composition is reported • Available expert estimates are quite outdated • Modelling also needs PCB emissions to other media (soil, water)

  5. Mercury PCDD/F Species: Hg0, Hg(II)gas, Hg(II)part Reported emissions: total Hg Expert estimates: UNEP GMA 2013 (AMAP/UNEP, 2013) Species: 17 toxic congeners Reported emissions: total toxicity equiv. Expert estimates: POPCYCLING-Baltic project (Pacyna et al., 2003) Average PCDD/F congener composition in the EMEP countries Average Hg emission speciation in the EMEP countries Hg and POPs modelling is very sensitive to chemical composition but available expert estimates are uncertain and outdated Hg(II)part Hg(II)gas Hg0 0 20 40 60 80 100 Fraction, % Data processing: Chemical composition

  6. Public power (A) Industry (B) Residential combustion (C) 2.5 2.5 2.5 2.0 2.0 2.0 1.5 1.5 1.5 Scaling factor Scaling factor Scaling factor 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 Jul Apr Jul Jun Aug Apr Jan Oct Jun Feb Mar Aug Oct Sep Nov Jan Dec Feb Mar May Jul Sep Nov Dec Apr May Jun Aug Jan Oct Feb Mar Sep Nov Dec May Transport (F, G, H, I) Fugitive (D), Waste (J), Livestock (K) Solvents (E) 2.5 2.5 2.5 2.0 2.0 2.0 1.5 1.5 Scaling factor 1.5 Scaling factor Scaling factor 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 Jul Apr Jun Aug Jan Oct Feb Mar Sep Nov Dec Jul May Apr Jun Aug Oct Jul Jan Feb Mar Sep Nov Apr Dec Jun Aug May Jan Oct Feb Mar Sep Nov Dec May Field burning in agriculture (L) 2.5 2.0 1.5 Scaling factor 1.0 0.5 0.0 Jul Apr Jun Aug Jan Oct Feb Mar Sep Nov Dec May Data processing: Seasonal variation Source: Parameterization of seasonal variations developed by TNO (van der Gon et al., 2011)

  7. Estimates of effective emissions height (Brigg’s approach) Prunéřov II Power Plant h Stack height - 300 m Data processing: Vertical distribution Required parameters: • Stack height • Stack diameter • Gas outflow velocity • Gas temperature

  8. Public power (A) Industry (B) Waste (J) Other (C,D,E,F,G,H,I,K,L) 1200 1200 1200 1200 1000 1000 1000 1000 800 800 800 800 Height, m Height, m Height, m Height, m 600 600 600 600 400 400 400 400 200 200 200 200 0 0 0 0 0 20 40 60 0 30 60 90 0 30 60 90 0 25 50 75 100 Fraction, % Fraction, % Fraction, % Fraction, % Data processing: Vertical distribution Source: Vertical emission profiles calculated by the SMOKE emission preprocessor (Bieser et al., 2011)

  9. Compilation of global emissions Further development of global inventories requires co-operation with other international bodies (UN Env., Minamata and Stockholm Conv.) Global Hg emissions (2015) Global PCDD/F emissions

  10. Belarus Poland Czech Republic Croatia Netherlands Spain Model evaluation of emissions: Case studies Objective: Evaluation of pollution levels in a country involving variety of national data Countries involved: Czech Republic, Croatia, the Netherlands, Belarus, UK, Poland, Spain, France, Germany • Evaluation of emissions: • Preliminary analysis based on comparison of modelling results with measurements • Development of emission scenarios (e.g. using statistical optimization) • Model evaluation of scenarios

  11. 0.4 DE7 3 0.3 Cd air concentration in Poland 0.2 Air concentration, ng/m 0.1 0.0 Jul Mar Oct Jan Jun Apr Nov May Aug Dec Feb Sep Measurements Modelling 0.9 PL0505 0.3 3 CZ3 0.4 3 PL005 3 0.6 0.2 0.3 Air concentration, ng/m Air concentration, ng/m 0.3 0.2 Air concentration, ng/m 0.1 0.1 0.0 Jul 0.0 Mar Jan Jun Apr Oct Nov May Aug Dec Feb Sep 0.0 Jul Mar Jan Jun Apr Oct Nov May Aug Feb Sep Dec Jul Mar Jan Jun Apr Oct Nov May Aug Feb Sep Dec Poland: Cd from residential combustion Detailed analysis of Cd levels involving measurements and modelling

  12. Contribution of major sectors to Cd emissions in Poland Seasonal variation of emissions (TNO expert estimates) 18% Residential combustion 2.0 1.5 Normalized emissions 1.0 0.5 0.0 Jul Industrial emissions (B) Jan Mar Jun Oct Apr May Nov Aug Feb Sep Dec Residential combustion (C) Others Preliminary analysis of possible reasons Seasonal variation of anthropogenic emissions

  13. Cd emissions in Poland Measurement sites Cd air concentration 2.5 2.0 0.9 PL0505 3 1.5 Emission, tonnes/year 1.0 0.6 0.5 Air concentration, ng/m 0.0 Jul Jun Oct Jan Apr Nov Mar May Aug Feb Dec Sep 0.3 0.0 Jul Jan Mar Jun Apr Oct May Nov Aug Feb Dec Sep 3 Measurements 2 Modelling (original) Emission, tonnes/year 1 Modelling (scenario) Residential Others Residential (scenario) 0 Slaskie Lуdzkie Lubuskie Opolskie Lubelskie Podlaskie Pomorskie Malopolskie Dolnoslaskie Mazowieckie Podkarpackie Wielkopolskie Swietokrzyskie Zachodniopomorskie Kujawsko-Pomorskie Warminsko-Mazurskie Emission scenario Statistical optimizationof Cd emissions based on measurement data

  14. Probably, emissions of Cd from residential combustion are significantly underestimated in the south and southwest parts of Poland Emissions change Annual anthropogenic emissions of Cd in Poland in 2014 Original Scenario Total: 13.6 t/y Total: 17.2 t/y (26% increase)

  15. Annual air concentration of B(a)P in Spain in 2014 ES1799 10 Model Measurements 1 Air concentration, ng/m3 0.1 0.01 Jul Jan Mar Jun Apr Oct May Nov Aug Feb Sep Dec 10 ES1425 1 Air concentration, ng/m3 Measurements 0.1 Modelling 0.01 Jul Jan Mar Jun Apr Oct May Nov Aug Feb Sep Dec Spain: PAH emissions from agriculture

  16. Anthropogenic emissions of B(a)P in Spain in 2014 Sectoral composition of B(a)P emissions France B(a)P emissions (2014) Spain 7% 67% Public Power (A) Industry (B) Residential combustion (C) Road transport (F) Field burning in agriculture (L) Others Spain: PAH emissions from agriculture

  17. B(a)P emissions in Spain (2014) 80 67% 60 8% Emissions, t/y 40 20 Scenario emissions Original emissions 0 Original Scenario Field burning (L) Residential combustion (C) Industry (B) Others Emission scenario Annual anthropogenic emissions of B(a)P in Spain in 2014 Base case:Reported emission data (2014) Scenario: Field burning emissions (L) decreased from 67% to 8% to fit measurement data

  18. (scenario) Evaluation vs. measurements 10 ES1799 3 1 Emissions of B(a)P from field burning in agriculture are largely overestimated in southern Spain Air concentration, ng/m 0.1 0.01 Jul Apr Oct Jan Jun Feb Mar Aug Sep Nov Dec May Measurements Modelling (original) Modelling (scenario) Model evaluation Simulations of B(a)P in Spain based on scenario emissions (2014) B(a)P concentration (original)

  19. 1 0.1 Modelled, ng /m3 DE ES 0.01 FR PL Other 0.001 0.001 0.01 0.1 1 Observed, ng/m3 Further case studies for B(a)P Modelled vs. measured B(a)P concentrations (2016) • Spain and France (ongoing): • Analysis of national emissions and measurements • Modelling for Spain and France using GLEMOS and CHIMERE models • Analysis of sensitivity of model results to changes of national emissions • Analysis of factors affecting B(a)P transport: interaction with aerosols, reactants • Refinement of parameterizations for physical and chemical processes • Poland and Germany (proposed): • Proposal to perform country-specific assessment to refine estimates of B(a)P pollution

  20. Recommendations • Emissions reporting: • Chemical composition of emissions is critical for Hg, PCDD/Fs, and PCB modelling and requires update and refinement (possible co-operation with UN Env., Minamata and Stockholm Conventions) • B(a)P is a priority pollutant and needs particular attention in terms of sectoral composition and spatial distribution of emissions data • Evaluation of emission data: • Model evaluation of emission estimates can be relevant, particularly, on a national scale • It can be applied on a regular basis for evaluation of national emissions, e. g. as a part of the emissions review process

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