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Satellite Remote Sensing of a Multipollutant Air Quality Health Index

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. Satellite Observations Provide Context to Ground-Based Measurements.

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Satellite Remote Sensing of a Multipollutant Air Quality Health Index

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

  3. Challenging to Infer Boundary Layer Ozone Concentration Strong Rayleigh Scattering Weak Thermal Contrast O3 Aerosol NO2 O3 9.6 2.2 4.7 0.52 0.62 0.75 0.30 0.36 0.43 Vertical Profile Affects Boundary-Layer Information in Satellite Obs Normalized GEOS-Chem Summer Mean Profiles over North America O3 Aerosol Extinction S(z) = shape factor C(z) = concentration Ω = column NO2 Martin, AE, 2008

  4. In Situ GEOS-Chem General Approach to Estimate Surface Concentration Coincident GEOS-Chem Profile Daily Observed Column MODIS/MISR AODOMI NO2 (DOMINO) OMI O3 (Xiong Liu) Actual approach (not shown) exploits sub-grid satellite information to improve profile estimate • S→ Surface Concentration • Ω → Tropospheric column

  5. Significant Spatial Correlation from NO2 and PM2.5 (OMI-derived NO2, MODIS/MISR-derived PM2.5) Mean over Jun – Aug 2005 y=1.4x-0.57 r=0.87 Satellite-derived Partial AQHI In Situ Partial AQHI Partial AQHI (NO2 and PM2.5)

  6. Evaluation of Surface O3 Estimate with AQ Network GEOS-Chem simulates strong correlation (r=0.9) between tropospheric O3 Column and surface O3 concentration during summer OMI-Derived Surface O3 for North America (Jun – Aug 2005) r=0.77 y=0.89 + 20.0 O3 Mixing Ratio (ppbv)

  7. Significant Spatial Correlation in Satellite-derived and In Situ AQHI (OMI-derived NO2 and O3, MODIS/MISR-derived PM2.5) Mean values over June – August 2005 for North America Satellite-derived AQHI r=0.85 y=1.1x+0.47 In Situ AQHI 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 AQHI

  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 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 Croft et al., ACPD, 2009

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

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