1. Impact of various emission inventories on modelling results; impact on the use of the GMES products Laurence Rouïl (laurence.rouil@ineris.fr)laurence.rouil@ineris.fr

2.  What is the « optimal spatial resolution » of emission inventories feeding atmospheric chemisry transport models ?  What is the actual sensitivity to emission data resolution? Atmospheric chemistry-transport modelling: sensitivity to the emission inventory

3. Context : Do higher resolution emission inventories provide better quality AQ modelling results ?  More and more sensitive issue considering the increased use of air quality modelling for operational purposes (e.g. GMES/MACC platforms) :  Air quality forecasting  Air pollutants concentration fields mapping : nowcasting and analyses for general public and decision makers information  Source allocation and analysis of peaks and exceedances  Exposure assessment and hot spots qualification (downstream applications)  To be balanced with other sources of uncertainties : meteorology, boundary conditions, model parametrisations....  Depends on the pollutant (primary vs secondary), the geographical area (street, city, country...) and the emission sources considered

4. Some results from previous studies

5. Reasons for model underestimation in the case of PM modelling: SOA formation (  background issue) Uncertainties and missing elements in emission inventories Non standard emission sources : Wildfires (60% of the total PM10 emissions in Europe! including a part of Russia - AQMEII project) (  background issue) Domestic wood burning in wintertime Road traffic resuspension (~30% of traffic emissions for PM10) Resuspension from soil erosion (  background issue) Emission vertical profiles Meteorology (kz calculation, wet deposition)

6. Impact on GMES products  Uncertainties in air pollutant concentration forecasts : missing events…  Uncertainties in peak concentration detections  Potential uncertainties in background concentrations maps  Significant improvements can be obtained thanks to data assimilation processes but this is out of the scope of the present considerations (improvement of raw simulations data) EXCEPT if DA helps in improving emissions

7. Bessagnet et al., 2008 in JGR Simulated PM10 concentrations in µg/m 3 PM10 > 1400 µg/m 3 in Slovakia and 150 µg/m 3 in France (Birmili et al., 2008) 20070323 [12:00]20070324 [12:00] 20070324 [18:00] Dust event in March 2007: emission inventory adjusted with EO information Ukraine LIDAR CALIOP Source: H. Chepfer (LMD)

8. Underestimation of « more classical » PM events (winter episodes) with the PREV’AIR system : adjustements with in-situ observations Analysed PM10 daily mean concentrations on 10th January 2010 Underestimation of wood burning emissions that happened because of very cold conditions When the cause is understood, DA helps in quantifying the correction

9. Underestimated PM2.5 background concentrations : PollutantRange of concentration Criteria used for defining the representatiness area NO210 µg/m 3 Annual mean at the station  5 µg/m 3 PM1010 µg/m 3 Annual mean at the station  5 µg/m 3 PM1016 µg/m 3 Centile 90,4 of daily concentrations  8 µg/m 3 Ozone18 µg/m 3 Centile 93,2 of daily maximums of 8h- averages  9 µg/m 3 January 2009 PM2.5 50 km resolution EMEP emissions IFS meteorology CHIMERE model

10. How to improve emissions  « residential emissions » (SNAP2) reallocated with population density (+ wood burning share urban vs rural with french data)  « Crops » landuse proxy for Agricultural sector  « built-up » landuse proxy for the other anthropogenic sectors  « road map » proxy for road traffic emissions (in progress) PPM2.5 emission before PPM2.5 emission after

11. MACC emissions versus EMEP+proxy EMEP 2006 MACC 2006 Primary PM2.5

12. Updated PM2.5 background concentrations (from the EC4MACS LIFE project)…. January 2009 PM2.5 6 km resolution EMEP emissions IFS meteorology CHIMERE model

13. For a better simulation of urban background concentrations….  Use of the GMES products for policy-oriented applications related to the implementation of the AQ Directive can be foreseen Annual Urban increment for PPM2.5 concentrations -> Toward urban bacground exceedances detection

14. How to use and share national emission data?  Implementation by the end of 2011 of the French spatialised emission inventory (INS)  Reporting processes ? EMEP and/or E-PRTR?  How to ensure consistency with other national emission inventories? NOx (2004) PM10 (2004)

15. Necessary to simulate urban and suburban scales phenomena Especially if high resolution meteorological fields are used Or to correct analysed maps (ancilliary data)

16. Conclusion : main needs for improving GMES AQ products (i)  Various ways to use high resolution emission inventories: input data of CTMs, ancilliary variables for mapping processes…  High resolution emission inventories provide valuable data to derive useful products for the implementation of the AQ Directive (at lest for primary PM and NO2) : exceedances forecasting and monitoring, source analysis…  But consistency between various emission inventories must be ensured or assessed  Urban scale is a key issue for such applications : compilation and use of national high resolution information is necessary too. Use of appropriate proxys to refine inventories should be investigated

17. Conclusion : main needs for improving GMES AQ products (ii)  Beyond spatial high resolution, analysis and anticipation of air quality threshold exceedances require high resolution description of the temporal variability of the emissions … to develop time or weather dependant emission inventories !  In-situ and satellite information should play a key-role in the future : forest fire emissions, dust events, resuspension.....