1. Joint Research Centre the European Commission's in-house science service Progress in air quality modelling Rita Van Dingenen 2 nd Consultation Meeting on the Global Platform on Air Quality and Health Geneva, 18-20 August 2015

2. Why use global Chemical Transport Models for pollution exposure estimates? -In-situ observations are sparse outside US, EUR, Japan (although data coverage improving in Asia) -Observations mostly in urban areas (where exposure is highest) http:// www.epi.yale.edu/visuals/airmap / Satellite data: Boys et al., 2014; van Donkelaar et al., 2014 City-level data: WHO, 2006

3. Why use global Chemical Transport Models for pollution exposure estimates? -In-situ observations are sparse outside US, EUR, Japan (although data coverage improving in Asia) -Observations mostly in urban areas (where exposure is highest) -Still: also rural population exposed to dangerous PM2.5 levels USAEastern Europe SE-Asia Middle East TM5-FASST + ECLIPSE emissions, processed with CIESIN population 2005 Dust!

4. Why use global Chemical Transport Models for pollution exposure estimates? -In-situ observations are sparse outside US, EUR, Japan (although data coverage improving in Asia) -Observations mostly in urban areas (where exposure is highest) -Still: also rural population exposed to dangerous PM2.5 levels -Provide chemical speciation and linking to sources (in particular source-receptor models) -Adjoint model: sensitivity map of specific metric (e.g. global mortalities) to location of emissions

5. Adjoint modelling: reduction in global mortality per 10% reduction of local precursor emissions Colin J. Lee et al., EST, 2015

6. Published studies on global premature mortalities from OAP based on CTM (+ satellite) Millions

7. Recommendations after first meeting Improve spatial and temporal resolution for PM 2.5 and ozone estimates, e.g. by better description of urban areas and its emissions [HTAP regional models?] Consider the use of regional emission inventories (e.g. inventory mosaic) in global models, and the use of emission inventory by source sector. Consider the development of “ensemble” of models to improve the simulated concentration data, including exploration of the use of the regional/local modelling that is being conducted by the countries Explore the use of top-down constraints by satellite and in-situ observations for assessment of pollution trends, spatial and temporal variation and emission ratios such as NO 2 /reactive hydrocarbons ratios Advance understanding and description of parameterization of the sub-grid physical processes occurring in the atmosphere and their impact on the concentration estimates, e.g. secondary organic particles formation Explore feasibility of global models for other health and climate related air pollutants and of models describing pollution in cities (or megacities) Conduct retrospective analysis and source apportionment (integrating the air quality modelling data in order to provide information on main sources and sectors contributing to human exposure.

8. Take advantage of work done within HTAP! What is HTAP?  The UNECE Convention on Long-Range Transboundary Air Pollution (LRTAP) created the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) in December 2004.  Under the leadership of the European Union (F. Dentener, JRC/IES) and the United States (T. Keating, EPA, U.S.), TF HTAP aims at addressing the issue of the intercontinental transport (ICT) of atmospheric pollutants and studying the impact of specific emission reduction strategies on various regions of the Northern Hemisphere.  The multi-model global experiments performed in the first phase of HTAP (HTAP1) allowed to produce, in 2010, the first assessment of the ICT of air pollution  The second phase of HTAP (HTAP 2) multi-model experiments and analysis was launched in 2012.

9.  Improve the understanding of the transport of air pollution across the Northern Hemisphere. Air pollution includes: - Ozone and its precursors, including NOx, VOC, CO, and methane - Particulate Matter and its components, including black carbon - Mercury (Hg) - Persistent Organic Pollutants (POPs)  Assess potential emission mitigation options available inside and outside the UNECE region  Assess the impacts of the options on regional and global air quality, public health, ecosystems, and near-term climate change  Work in collaboration with other groups inside and outside the Convention. HTAP mandate

10. HTAP Phase 2 The TF HTAP general objectives are to: Examine the transport of air pollution across the Northern Hemisphere from source to downwind regions Assess the emission and transport impacts on regional and global air quality, ecosystems, public health, and climate Provide information on potential emission mitigation options HTAP Phase 2 specificities (compared to HTAP Phase 1) Harmonized emissions database (HTAPv2 2008-2010 emissions) More intercontinental source-receptor calculations, including more regions and both global and regional modelling More model outputs (PM speciation, O3 fluxes to vegetation,..) More model-observations comparison (e.g., profiling of O3 and PM, through comparison to satellite and aircraft data), development of evaluation tools and methods....

11. -HTAP_v2.2 Global emissions mosaics compiled from EDGAR4.3 and regional inventories used for U.S., Europe (consistent with AQMEII) and Asia (consistent with MICS-Asia). -Source Categories: power, industry, transport, buildings, agriculture, ships, aviation -0.1º x 0.1º spatial resolution (some data at 0.25º); -2008 and 2010 annual (and monthly) files per sector -CO, NMVOC, NO X, NH 3, SO 2, PM 10, PM 2.5, OC, BC -Global emissions files calculated/adapted from RETRO ratios (TNO) and HTAP_v2.2 -Required to calculate HTAP NMVOC emission for industrial sub-sectors - 0.1º x 0.1º spatial resolution -23 VOCs species -Volcano emissions provided by T. Diehl -Biomass burning : GFEDv3 daily -Dust and sea salt : model calculated -DMS emissions : Lana et al. 2011 Anthropogenic emissions : Mandatory (G. Maenhout et al.) Individual VOC emissions : Speciation guidance (LATMOS and B. Koffi) Other emissions : Recommended HTAP2 status EMISSION INVENTORIES

12. HTAP anthropogenic emissions mosaics for 2008 and 2010 HTAP2 2008 NOx Emissions from Road Transportation

13. Other available gridded emission inventories / ancillary data: ECCAD http://eccad.sedoo.fr

14. NA EU EA SA HTAP1 focused on source-receptor relationships between 4 large continental regions HTAP2 is looking at 16 “source” regions separated mostly along national boundaries and ~60 receptor regions. HTAP2 Source Receptor Modeling Experiments MICS Asia AQMEII And linking with higher resolution regional model intercomparisons

15. HTAP2 Participating Modeling Groups Current status of model runs (base run, perturbation run of all precursors)

16. HTAP2 Participating Modeling Groups Current status of model runs (perturbation by single pollutant precursors)

17. HTAP2 Participating Modeling Groups Current status of model runs (perturbation by sector)

18. Inorganic secondary PM2.5 (SO4, NO3, NH4) + carbonaceous and other primary PM2.5 except from traffic and domestic sector Carbonaceous and other primary PM2.5 from traffic and domestic sector Improve spatial and temporal resolution for PM 2.5 estimates (also for NO 2 )

19. Global CTM resolution = 1°x1° to 0.5°x0.5° Grid-mean PM not adequately representing population exposure when emission / concentration gradients are present within grid (urban vs. rural area)  Parameterization adjusting grid-mean concentration to urban incremented population- weighted exposure  Based on urban population fraction f up and urban area fraction f ua within grid cell Urban increment subgrid parametrization Gridded population

20. Urban areaUrban & Rural population Area-mean PM2.5Population-adjusted PM2.5 Population-weighted mean PM2.5

21. How important is dust? (based on emissions Tegen et al., 2002)

22. Variability and uncertainty in dust emissions

23. How important is residual water in PM 2.5 ? Potential residual water at measuring RH=50% (based on SO 4 +NO 3 +NH 4 +seasalt in PM 2.5 )

24. Population-weighted PM2.5 including urban increment, dust, potential residual water

25. Thank you!

26. Progress in emission inventories (HTAP v2) Premise: AQ Models need gridded pollutant and pollutant precursor emissions for modelling resulting spatial distribution of pollutants.  Spatial resolution?  Temporal resolution?  Speciation detail?  Validation? General approach: STEP 1: country-total emissions  From official national statistics where available  Or/complementary (gap-filling): bottom-up from economic drivers/energy use/activities/emission factors/control technologies/penetration rates… STEP 2: gridding procedure based on high resolution proxy data (population, land use, road networks, location of large point sources,…)

27. EDGARv4.2: 1970-2008: All GHG, Air Pollutants, PM10 IEA & FAO activity data EF of Corinair a.o. & EOP Online (edgar.jrc.it), Nov. 2011 EDGARv4FT2010: 1990-2010: All GHG (and CO2 till 2012) Online (edgar.jrc.it), Oct. 2013 Olivier & Janssens-Maenhout, IEAIII(2012) EDGARv4.3: 1970-2010: All GHG, Air Pollutants, Aerosols Forthcoming Feb. 2014 HTAPv1: for 2000-2005: CH4, Air Pollutants, Aerosols sector-specific totals, gapfilled with EDGARv4.2 gridded with EDGAR proxy Online (ECCAD), May 2010 Streets et al., HTAP, Ch. 3 (2010) HTAPv2: for 2008, 2010: Air Pollutants, Aerosols gridmaps gapfilled with EDGARv4.3 Online (ECCAD), November 2013 Technology-based calculations Emissions DB for global atmospheric modellers EDGARv4 EDGARv4 =ADxEF Emissions DB of HTAP Community Sum of inventories Sum of inventories ΣEM i Collecting official inventories HTAP versus EDGARv4

28. PM2.5, µg/m³ Comparison between measured and modelled urban incr. mean PM2.5 Measured and modelled median (5%ile, 95%ile) urban PM2.5 concentrations in North America (left), Europe (middle) and China (right). Measurements are from routine monitoring programs. TM5-URB values include the urban increment correction, and TM5- GRID refers to the unadjusted grid-cell average PM2.5 concentrations. Primary PM (EC+POM) applied correction factor as a function of urban area fraction and urban population fraction per grid cell.