Emission MAPper (EMAP): Enhancing Air Quality Modeling from Country to Grid Level

1. Atmospheric Emission Modelling:From Low Resolution Inventories to Air Quality Model Grids Nele Veldeman, Wim Peelaerts, Stijn Janssen

2. Solution: Emission MAPper (EMAP): Disaggregation of emissions data on country level on grid cells of specific air quality model domains (BelEUROS, OPS, Chimere, …) Introduction • Problem: Emission data: available on country level, gridded, linked to geographic information (roads), … Air quality models: specific gridded domain

3. General concept • Concept: • Top down approach • Surrogate variables Points, lines, surfaces with or without additional information EL = ET x VL / VT Spatial allocator

4. Spatial Allocator (SA) • MIMS SA • Generates spatial surrogates for emissions • Developed @ University of North Carolina • Licensed as open source code (sponsored by EPA Office of R&D) • Input: two shapefiles and grid description • Polygons on which emission data are available • Shapefile with info on surrogate • Grid description • Output: list containing percentages: xx % of country xx attributed to grid cell xx

5. Tables Emission-inventories Keys (= spatial surrogates generated by SA) Grids Additional data Queries Emission inventory Model grid Additional information Keys Data base • E-Map: Data base programmed in MySQL

6. Applications • E-MAP played an important role in the MIRA-S 2009 project, which is glancing ahead on the environment of tomorrow. Focus on Flanders, Belgium. • Within he European Space Agency PROMOTE (PROtocol MOniToring for the GMES Service Element: Atmosphere) project, E-MAP provided high resolution emissions for a study on a regional-scale air-quality management system for Prague, Czech Republic. • Within the AMFIC framework (Air Quality Monitoring and Forecasting in China) the effect of local measures to improve air quality during the Olympic Games in Beijing was studied. High resolution emission maps for different Chinese cities, were obtained with E-MAP. • Within the Market Based Instruments project the influence of including maritime emissions in an ETS is studied. The disaggregation of maritime emissions on European seas is performed with E-MAP.

7. MIRA-S 2009, Europe (Focus: Flanders, Belgium) BelEUROS grid 60 x 60 km² BelEUROS grid 7.5 x 7.5 km² Input BelEUROS = Point sources in entire model domain Areas sources diaggregated on 60 x 60 km² grid Areas sources disaggregated on 7.5 x 7.5 km² grid

8. Point sources EPER pointsource map FLOW 1 BelEUROS 60 x 60 km² grid Area sources FLOW 2 60 x 60 km² grid Gridded EMEP data EPER pointsource map Point sources FLOW 3 7.5 x 7.5 km² grid Emissions by SNAP sector Detailed Flemish emissions Flanders? FLOW 4 BelEUROS 7.5 x 7.5 km² grid Proxy data Area sources FLOW 5 Brussels, Wallonia? Proxy data FLOW 6 Non Belgium? Workflow

9. Workflow 1 and 3: Point sources Fraction attributed to point sources Spatial surrogates: point sources EPER

10. Workflow 2: Area sources on 60 x 60 km² grid Fraction attributed to area sources Spatial surrogates: EMEP gridded emissions

11. Point sources EPER pointsource map FLOW 1 BelEUROS 60 x 60 km² grid Area sources FLOW 2 60 x 60 km² grid Gridded EMEP data EPER pointsource map Point sources FLOW 3 7.5 x 7.5 km² grid Emissions by SNAP sector Detailed Flemish emissions Flanders? FLOW 4 BelEUROS 7.5 x 7.5 km² grid Proxy data Area sources FLOW 5 Brussels, Wallonia? Proxy data FLOW 6 Non Belgium? Workflow

12. Workflow 4: Area sources on Flemish part 7.5 x 7.5 km² grid • Fraction attributed to • Area sources • Flanders Spatial surrogates: detailed (1x1 km²) Flemish emissions

13. Workflow 5 and 6: Area sources on 7.5 x 7.5 km² grid CLC2000 CLC2000+EUROSTAT Population disaggregated over CLC2000 TREMOVE/CLC2000/UN traffic census • Fraction attributed to • Area sources • non Flanders TREMOVE/CLC2000/traffic networks /EUROSTAT … Spatial surrogates: proxy data

14. MIRA-S 2009 MIRA-S 2009, Europe (Focus: Flanders, Belgium) Database can be accessed via web interface • Choice grid: • OPS • BelEUROS • Chimere 1-5 • Choice Emission-inventory • EMEP • EMEP-Scenarios • Gains • + Year/scenario • Year • Scenario • Year + scenario

15. MIRA-S 2009 Emission maps • Starting from different emission inventories • 7.5 x 7.5 km² 60 x 60 km² Pointsources • Per SNAP sector Total emissions

16. MIRA-S 2009 Custom scenario: Per sector, per country, per pollutant: scaling factors

17. MIRA-S 2009 Scenario Upload: Flanders: per OPS-sector, per pollutant: emission data

18. ACESS emission inventory (2006) Air quality monitoring in China • SO2, NOx, CO, VOC, PM10, PM2.5, BC, OC • Emission sectors: powerplants, industry, residential en transport • Resolution : 0.5° • Spatial proxy data • Landuse • Population density • Road network • …

19. + Asian traffic emissions (Acess) Population density and Road network 4.034 Ton 6.074 Ton 9.832 Ton 8.961 Ton Air quality monitoring in China E-MAP NOx traffic: Yangzhou

20. E-MAP NOx traffic: Yangzhou

21. % Total Industry 13 Powerplants 72 Residential 2 Traffic 13 E-MAP NOx total: Yangzhou

23. Summary • EMAP is a straight forward tool developped to disaggregate low resolution emissions on gridded model domains • It is based on a top down approach in which the freely available MIMS Spatial Allocator plays an important role • Due to its simplicity it is widely applicable • Validated! In each of the projects the E-MAP tool was (indirectly) validated by comparing air quality model results with local measurement data. The overall quality of the validation provides confidence in the methodology and the tool.

24. We are looking forward to welcoming you at the i-SUP2010 conference!