1. Evaluation of pollution levels in urban areas of selected EMEP countries Alexey Gusev, Victor Shatalov Meteorological Synthesizing Centre - East

2. 17 th TFMM Meeting, 17 – 20 May, 2016 Pollution of urban areas  Significant proportion of urban population in the UNECE countries live in areas with exceedances of WHO guideline levels for fine particles. CLRTAP Assessment Report, 2016  Substantial experience is gained in studies of air quality in urban areas/megacities WMO GURME, EU MEGAPOLI, …  Evidence of link between fine particles emissions as well as of carcinogenic pollutants (e.g. B(a)P) from wood/coal heating and serious health effects to humans. WHO/CLRTAP Task Force, 2015  B(a)P emissions have increased in the past decade as a result of an increase of emissions from domestic combustion. EEA report, 2015; EMEP Status Report, 2015  In 2013 about half of B(a)P monitoring stations (mostly urban/suburban) in Europe continued measuring air concentrations above the EU target level 1 ng/m 3. EEA report, 2015

3. 17 th TFMM Meeting, 17 – 20 May, 2016 50 x 50 km 2 EMEP results indicate levels of B(a)P concentrations near or above EU target level 1 ng/m 3 for some of EMEP countries Modelled annual mean B(a)P air concentrations for 2013 Fraction of population in areas with exceeded EU target level for B(a)P Assessment of B(a)P pollution in EMEP countries (MSC-E/CCC report 2015)

4. 17 th TFMM Meeting, 17 – 20 May, 2016 Model vs. background urban/suburban sites (AirBase) Modelling results provide smoothed levels of concentrations and do not capture sharp gradients observed in urban areas MOD = 0.17 OBS R corr = 0.6 F2 = 20% MOD = 0.73 OBS R corr = 0.6 F2 = 51% Model vs. rural/background sites (EMEP, AirBase) Underestimation of B(a)P pollution in urban areas of EMEP countries

5. 17 th TFMM Meeting, 17 – 20 May, 2016 A study of B(a)P pollution in urban areas Objectives:  Estimate B(a)P pollution levels in urban areas including their spatial distribution to provide information for exposure studies  Analyze contributions of various types of emission sources and major source categories to urban concentrations Initial stage:  Fine resolution modelling and analysis of pollution levels in the Czech Republic Methods:  Nested modelling of B(a)P pollution levels  Regression analysis to determine relationship between measured urban concentrations and modelling results, emissions, meteorological variables Further activity:  Refinement of the approach and application for other countries

6. 17 th TFMM Meeting, 17 – 20 May, 2016 Assessment of B(a)P with fine resolution EMEP domain 50 x 50 km 2 5 x 5 km 2 Annual mean modelled B(a)P air concentrations for 2013 Fine resolution domain (Czech Republic)

7. 17 th TFMM Meeting, 17 – 20 May, 2016 B(a)P air concentrations in urban areas 5 x 5 km 2 MOD = 0.41 OBS R corr = 0.54 Modelling results vs. EEA/AirBase measurements for 2013 Ostrava Prague 5 x 5 km 2 Measurements selected for the analysis:  background suburban and background urban sites (23 sites),  fulfilling criteria of temporal coverage (>75%). Ostrava Prague x2 Modelled B(a)P air concentrations for 2013 and locations of sites

8. 17 th TFMM Meeting, 17 – 20 May, 2016 Comparison of observed and modelled values of air concentrations for background suburban sites in Ostrava and Prague  Modelled concentrations agree with measured for Prague, and underpredict measured concentrations for Ostrava (about a factor of 3) B(a)P air concentrations in Ostrava and Prague

9. 17 th TFMM Meeting, 17 – 20 May, 2016 Seasonal variations of observed and modelled values of air concentrations at background suburban sites  Modelled concentrations agree with measured for Prague, and underpredict measured concentrations for Ostrava (about a factor of 3)  Model closely described seasonal variations of B(a)P in air for both cities B(a)P air concentrations in Ostrava and Prague

10. 17 th TFMM Meeting, 17 – 20 May, 2016 Analysis of contributions to B(a)P concentrations Contributions of major source categories and internal/external sources to the pollution of Prague and Ostrava Contributions of internal and external emission sources Total concentrations, 2013 Ostrava Residential combustrion 73 % Industrial sources 23 % Road Transport 4.5 % Prague Road Transport 11 % Residential combustion 88 % Industrial sources 0.8 % Contributions of major emission source categories

11. 17 th TFMM Meeting, 17 – 20 May, 2016 Available approaches: Multi-parameter regression models to determine relationship between urban and background concentrations with help of secondary parameters  CityDelta project (Cuvelier et al., 2007, AtmEnv; Amann et al., 2007, IIASA): parameterization of functional relationship connecting urban and regional emissions with urban concentrations of PM 2.5  MEGAPOLI project (Moussiopoulos et al., 2012): functional relationship between concentration increment and the local meteorological situation, city characteristics, urban emissions and background concentrations (using paired rural/urban stations)  (Ortiz and Friedrich, 2013, APR): predictions of urban increment from measured background and urban concentrations of PM 10, NO 2 concentrations in Germany  (Guerreiro et al., 2014, ETC/ATM): estimation of B(a)P air concentrations using linear regression based on primary data (measurements) and secondary data (CTM model, temperature, etc.) followed by spatial interpolation of its residuals Regression model to estimate urban concentrations

12. 17 th TFMM Meeting, 17 – 20 May, 2016 Regression model to estimate urban concentrations Multi-parameter regression analysis to determine relationship between observed urban/suburban concentrations, modelling results and additional parameters: j- urban/suburban monitoring site j C obs,j - B(a)P air concentrations observed at site j Var j - variability of modelled concentrations near corresponding grid cell C mod,j - modelled B(a)P concentration in the corresponding grid cell E ind, E res, E rot - emissions of B(a)P from major source categories W av,j - average wind speed in the grid cell; w j - residual component; a, b ind, b res, b rot - multiple-regression parameters. Variability of modelled B(a)P concentrations near the corresponding grid cell is estimated using 9-gridcell moving matrix: Var j = C max / C min

13. 17 th TFMM Meeting, 17 – 20 May, 2016 Modelling results for urban sites ModelledRegression RelBias60%0.01% NRMSE85%41% F239%96% Corr0.520.79 Observed vs. modelled B(a)P air concentrations (background urban/suburban sites)

14. 17 th TFMM Meeting, 17 – 20 May, 2016 Preliminary results of regression analysis Observed vs. modelled and regression model estimates of B(a)P air concentrations (background urban/suburban sites) ModelledRegression RelBias60%0.01% NRMSE85%41% F239%96% Corr0.520.79

15. 17 th TFMM Meeting, 17 – 20 May, 2016 Verification of regression model results  Application of regression model to additional monitoring sites in Czech Republic – generally reasonable results  Analysis of uncertainties of regression model results (on further stage) Test set of sites (industrial, traffic) Training set of sites (background urban/suburban sites)

16. 17 th TFMM Meeting, 17 – 20 May, 2016 Spatial distribution of B(a)P concentrations Estimation of spatial distribution of B(a)P concentrations in urban areas based on regression model results for selected monitoring sites Regression model results for urban areas EMEP model results f urb,j - fractions of urban areas in grid cell j (5x5 km 2 ), (adapted from GRUMPv1 project, http://sedac.ciesin.columbia.edu/) Combined map of B(a)P air concentrations ng/m 3

17. 17 th TFMM Meeting, 17 – 20 May, 2016 Combined map of B(a)P air concentrations for 2013 and locations of monitoring sites Factors influencing results of regression analysis  Uncertainties in spatial distribution and values of emissions  Effects of parameters of atmospheric conditions and geophysical characteristics which is not currently included ng / m 3 Monitoring sites near Ostrava Significant difference between observed and modelled concentrations

18. 17 th TFMM Meeting, 17 – 20 May, 2016 Factors influencing results of regression analysis Uncertainties in estimates of contribution of long range transport from distant emission sources Non-CZ sources: 30-60% Contribution of non-CZ emission sources to B(a)P air concentrations Significant difference between observed and modelled concentrations

19. 17 th TFMM Meeting, 17 – 20 May, 2016 Factors influencing results of regression analysis Uncertainties in estimates of contribution of long range transport from distant emission sources Modelled vs. observed B(a)P air concentrations for 2013 and locations of sites Modelled vs. observed B(a)P air concentrations: monitoring sites of Poland and Czech Republic Indication of possible underprediction of B(a)P transport from Polish emission sources to Czech Republic 5 x 5 km 2

20. 17 th TFMM Meeting, 17 – 20 May, 2016 Modelled (5x5 km 2 ) annual mean B(a)P air concentrations for 2013 ng / m 3 Source: Graphic Yearbook for 2013 (CHMI report) B(a)P concentrations in Czech Republic Estimated B(a)P concentrations for urban areas in comparison with data published in CHMI report ng / m 3

21. 17 th TFMM Meeting, 17 – 20 May, 2016 Implications for further activity  Exceedances of EU target value (1 ng/m 3 ) in 2013 were indicated for Poland, Bulgaria, Lithuania, Hungary, … (EEA report, 2015)  WHO estimated reference level for B(a)P 0.12 ng/m 3 is significantly lower than EU target value (EEA SOER, 2015) Observed B(a)P air concentrations in 2013 (EEA AirBase) Presented approach can be applied for other EMEP countries in case availability of data on emissions and measurements (e.g. Poland, the UK)  About 88% of European population lives in areas exceeded WHO estimated reference level (ETC/ACM, 2014 ) EU target value

22. 17 th TFMM Meeting, 17 – 20 May, 2016 Concluding remarks  Developing approach can reasonably describe B(a)P air concentrations in urban areas and provide input information for exposure studies  Discrepancies between observed and estimated concentrations may be due to: uncertainties of emissions and effect of meteorological parameters not currently taken into account  Further improvement of regression model can include application of:  additional factors influencing urban air concentrations (e.g. altitude, atmospheric conditions, etc.)  different methods of evaluation of sub-grid variability of B(a)P air concentrations  Fine resolution modelling in new EMEP grid will be used at further stages  Presented approach can be applied for other EMEP countries in case availability of data on emissions and measurements  Further cooperation is required with national experts on monitoring of pollution levels and evaluation of emissions

23. 17 th TFMM Meeting, 17 – 20 May, 2016 B(a)P emission data used for model assessment EMEP domain 50 x 50 km 2 5 x 5 km 2 Annual B(a)P emission data for 2007/2013 (EMEP/CEIP) B(a)P emissions based on EMEP and national data Fine resolution domain Major source categories of B(a)P emissions include residential combustion, road transport, and industry