1. Satellite Remote Sensing of a Multipollutant Air Quality Health Index Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Lok Lamsal, Dalhousie University Xiong Liu, NASA Goddard

2. Multipollutant Air Quality Health Index (AQHI) Use Canadian AQHI (Stieb et al., JAWMA, 2008) AQHIExcess Mortality Risk (%) Satellite Observations Provide Context to Ground-Based Measurements Insufficient In Situ Measurements for Exposure Assessment

3. Challenging to Infer Boundary Layer Ozone Concentration S(z) = shape factor C(z) = concentration Ω = column NO 2 Aerosol Extinction O3O3 Martin, AE, 2008 0.300.360.43 0.52 0.62 2.2 4.7 O 3 Aerosol O 3 NO 2 0.75 9.6 Normalized GEOS-Chem Summer Mean Profiles over North America Strong Rayleigh Scattering Weak Thermal Contrast Vertical Profile Affects Boundary-Layer Information in Satellite Obs

4. General Approach to Estimate Surface Concentration Daily Observed Column S → Surface Concentration Ω → Tropospheric column In Situ GEOS-Chem Coincident GEOS-Chem Profile Actual approach (not shown) exploits sub-grid satellite information to improve profile estimate MODIS/MISR AOD OMI NO 2 (DOMINO) OMI O 3 (Xiong Liu)

5. Significant Spatial Correlation from NO 2 and PM 2.5 (OMI-derived NO 2, MODIS/MISR-derived PM 2.5 ) Mean over Jun – Aug 2005 Partial AQHI (NO 2 and PM 2.5 ) y=1.4x-0.57 r=0.87 In Situ Partial AQHI Satellite-derived Partial AQHI

6. Evaluation of Surface O 3 Estimate with AQ Network O 3 Mixing Ratio (ppbv) OMI-Derived Surface O 3 for North America (Jun – Aug 2005) GEOS-Chem simulates strong correlation (r=0.9) between tropospheric O 3 Column and surface O 3 concentration during summer r=0.77 y=0.89 + 20.0

7. Significant Spatial Correlation in Satellite-derived and In Situ AQHI (OMI-derived NO 2 and O 3, MODIS/MISR-derived PM 2.5 ) Mean values over June – August 2005 for North America AQHI 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 In Situ AQHI Satellite-derived AQHI r=0.85 y=1.1x+0.47

8. Significant Correlation of Satellite-derived and In Situ AQHI Jun – Aug 2005 Correlation Coefficient

9. Aerosol Size-Dependent Below-Cloud Scavenging Betty Croft, Randall Martin, Dalhousie University Ulrike Lohmann, Sylvaine Ferrachat, ETH Philip Stier, Oxford University Sabine Wurzler, LANUV, Germany Hans Feichter, Max Plank Rebecca Posselt, Meteoswiss

10. Below-Cloud Aerosol Scavenging by Precipitation Varies with Size Croft et al., ACPD, 2009 Aerosol Collection Efficiency Implemented into ECHAM5-HAM GCM Reduces global mean AOD by 15% Changes dust & sea-salt mass burdens by 10-30% vs fixed model approach

11. Modeling Challenges: Continue to develop simulation of vertical profile Comprehensive assimilation capability Encouraging Prospects for Satellite Remote Sensing of Air Quality Implications of Size-Resolved Aerosol-Scavenging for GEOS-Chem