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Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from

Applications of Satellite Remote Sensing to Estimate Global Ambient Fine Particulate Matter Concentrations. Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from Aaron van Donkelaar, Dalhousie University Rob Levy, Ralph Kahn NASA Michael Brauer, UBC

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Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from

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  1. Applications of Satellite Remote Sensing to Estimate Global Ambient Fine Particulate Matter Concentrations Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from Aaron van Donkelaar, Dalhousie University Rob Levy, Ralph Kahn NASA Michael Brauer, UBC Michal Krzyzanowski, WHO Aaron Cohen, HEI 21st Annual International Society of Exposure Science Conference 24 October 2011

  2. Large Regions Have Insufficient Measurements for Air Pollution Exposure Assessment Locations of Publicly-Available Long-Term PM2.5 Monitoring Sites (2001-2006) Monitor locations can be driven by compliance objectives ~1 site / 10,000 km2 in continental US & southern Canada

  3. Aerosol Remote Sensing: Analogy with Visibility Effects of Aerosol Loading Waterton Lakes/Glacier National Park Pollution haze over East Coast PM2.5 = 7.6 ug m-3 PM2.5 = 22 ug m-3

  4. Combined AOD from MODIS and MISRRejected Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20% 0.3 0.25 0.2 0.15 0.1 0.05 0 Combined MODIS/MISR r = 0.61(vs. in-situ PM2.5) AOD [unitless] MODIS r = 0.39 (vs. in-situ PM2.5) MISR r = 0.39 (vs. in-situ PM2.5) van Donkelaar et al., EHP, 2010

  5. Calculate Coincident PM2.5/AOD with Chemical Transport Model (GEOS-Chem) Aaron van Donkelaar

  6. Significant Agreement with Coincident In situ Measurements Annual Mean PM2.5 [μg/m3] (2001-2006) Satellite Derived Satellite-Derived [μg/m3] In-situ In-situ PM2.5 [μg/m3] van Donkelaar et al., EHP, 2010

  7. Global Climatology (2001-2006) of PM2.5 Evaluation with measurements outside Canada/US Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71 van Donkelaar et al., EHP, 2010

  8. Error in Satellite-Derived PM2.5 has Three Primary Sources Satellite-derived PM2.5 =AOD Satellite • Error limited to 0.1 + 20% by AERONET filter • Implication for satellite PM2.5 determined by η • Model • Affected by aerosol optical properties, concentrations, vertical profile, relative humidity • Most sensitive to vertical profile [van Donkelaar et al., 2006] Sampling Biases Satellite retrievals are at specific time of day for cloud-free conditions

  9. Model (GC) CALIPSO (CAL) Evaluate GEOS-Chem Vertical Profile with CALIPSO Observations Altitude [km] • Coincidently sample model and CALIPSO extinction profiles • Jun-Dec 2006 • Compare % within boundary layer Optical depth above altitude z Total column optical depth τa(z)/τa(z=0)

  10. Error Estimate Satellite-Derived [μg/m3] • Estimate error from bias in profile and AOD ±(1 μg/m3 + 15%) • Contains 68% (1 SD) of North American data • Total uncertainty 25% (with sampling) • Global population-weighted mean uncertainty 7 μg/m3 In-situ PM2.5 [μg/m3] van Donkelaar et al., EHP, 2010

  11. van Donkelaar et al., EHP, 2010

  12. van Donkelaar et al., EHP, 2010

  13. Emerging Applications • Estimate global outdoor air pollution exposure for global burden of disease (WHO) (Brauer et al., ES&T, submitted) • Significant association of long-term PM2.5 exposure and cardiovascular mortality at low PM2.5 levels (Crouse et al., EHP, submitted) • Satellite dataset dominant contributor to Canada-wide PM2.5 model (Hystad et al., EHP, 2011) • Cigarette smoking is a negative confounder in epidemiological studies of long-term ambient air pollution and mortality outcomes in Canada (Villeneuve et al., OEM, 2011)

  14. Wildfires near Moscow in Summer 2010 MODIS/Aqua: 7 Aug 2010

  15. Relaxed Cloud Screening Needed for this Extreme Event van Donkelaar et al., AE, 2011

  16. Spatial and Temporal Variation in Satellite-Based PM2.5 during Moscow 2010 Fires van Donkelaar et al., AE, 2011

  17. Satellite-based Estimates of PM2.5 in Moscow During Fires Before Fires r2 =0.85, slope=1.06 MODIS-based In Situ from PM10 In Situ PM2.5 van Donkelaar et al., 2011

  18. Challenges Encouraging Prospects for Satellite Remote Sensing of Air Pollutants Remote Sensing: Improved algorithms to increase accuracy and resolution Modeling: Develop representation of vertical profile Measurements: More needed for evaluation throughout the world Health Applications: Close interaction to develop appropriate applications Acknowledgements: Health Canada NSERC NASA

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