Mapping the sources of PM 2.5 The EU urban air quality Atlas. P. Thunis, B. Degraeuwe, E. Pisoni, C. Belis, E. Peduzzi, J. Wilson and E.

Transcription

1 Mapping the sources of PM 2.5 The EU urban air quality Atlas P. Thunis, B. Degraeuwe, E. Pisoni, C. Belis, E. Peduzzi, J. Wilson and E. Vignati DG JRC Directorate: Energy, Transport and Climate EU Commission Prague 12 June 2018

2 premature deaths in the EU28 18 MS with exceedances to PM 10 (2015) 8 MS with exceedances to PM 25 (2015)

3 Main challenges and dedicated tools o Domain: Europe o Meteorology: ECMWF 2012 o AQ model: CHIMERE o Emissions: EC4MACS o Resolution: 7km o Urban & rural background o Yearly PM 2.5, PM 10, NO 2

4 Focus on three main questions

5 A few points before starting is designed for urban backround And for yearly average values (not for episodes) City definition is a key parameter As is the receptor definition Worst case receptor 5

6 Mapping the source of PM 2.5 in the EU (Urban Air Quality Atlas) City contribution

7 Mapping the source of PM 2.5 in the EU (Urban Air Quality Atlas) Contribution from transport Contribution from residential Contribution from agriculture

8 Mapping the source of PM 2.5 in the EU (Urban Air Quality Atlas)

9 Mapping the source of PM 2.5 in the EU (Urban Air Quality Atlas) Transport Measurements Industry Agriculture Residential Cz. R EU Emissions Prague Information for additional cities & regions can be produced with the JRC air quality integrated tool (freely available)

10 Mapping the source of PM 2.5 in the EU (Urban Air Quality Atlas) Local actions at the city scale are an effective means of improving air quality. Air quality plans (sectors, scales) should be city specific. Sectoral measures addressing agriculture at country or EU scale would have a clear benefit on urban air quality. 10

11 The urban Air Quality PM2.5 Atlas is available at: The Air Quality integrated tool is available at: 11

12 formulation, uncertainties and methodological approach Sectoral and/or spatial origins of pollution formulation & limitations CHIMERE Uncertainties of the input data EMISSIONS METEOROLOGY Methodological approach CAMX-PSAT LOTOS-PSAT

13 formulation, uncertainties and methodological approach Sectoral and/or spatial origins of pollution formulation & evaluation CHIMERE CAMX-PSAT LOTOS-PSAT EMISSIONS METEOROLOGY

14 formulation & evaluation CHIMERE

15 formulation, uncertainties and methodological approach Sectoral and/or spatial origins of pollution CHIMERE Uncertainties of the input data EMISSIONS METEOROLOGY CAMX-PSAT LOTOS-PSAT

16 Uncertainties of the input data Sectors (Transport-Industry-Residential-Agriculture CHIMERE EMEP 16 Emissions Meteorology Base year Resolution Emissions Meteorology Base year Resolution

17 formulation, uncertainties and methodological approach Sectoral and/or spatial origins of pollution CHIMERE Methods CAMX-PSAT LOTOS-PSAT EMISSIONS METEOROLOGY

18 Methodological approaches CAMx simulations over the Po-valley (Italy) Comparison of tagging species (PSAT) vs. scen. analysis

19 formulation, uncertainties and methodological approach Sectoral and/or spatial origins of pollution formulation 5-10% bias Uncertainties Priority changes CHIMERE EMISSIONS METEOROLOGY Methodology AGRI << CAMX-PSAT LOTOS-PSAT Fac 2/3 Methodology Urban Contrib. << 30-50%

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