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Applications of Satellite Remote Sensing to Inform Air Quality Management

Applications of Satellite Remote Sensing to Inform Air Quality Management. Randall Martin with contributions from Aaron van Donkelaar , Brian Boys, Matthew Cooper, Shailesh Kharol , Colin Lee, Sajeev Philip.

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Applications of Satellite Remote Sensing to Inform Air Quality Management

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  1. Applications of Satellite Remote Sensing to Inform Air Quality Management Randall Martin with contributions from Aaron van Donkelaar, Brian Boys, Matthew Cooper, ShaileshKharol, Colin Lee, Sajeev Philip DavenHenze (UC Boulder), Yuxuan Wang (Tsinghua), Qiang Zhang (Tsinghua), Dan Crouse (Health Canada), Rick Burnett (Health Canada), Mike Brauer (UBC), Jeff Brook (Environment Canada), Aaron Cohen (HEI) Fall AGU San Francisco 3 Dec 2012

  2. Vast Regions Have Insufficient Measurements for Exposure Assessment to Fine Particulate Matter (PM2.5) Locations of Publicly-Available Long-Term PM2.5 Monitoring Sites Previous WHO Global Burden of Disease Project for the Year 2000Impaired by Insufficient Global Observations of PM2.5 Cohen et al., 2005

  3. General Approach to Estimate Surface Concentration Daily Satellite(MODIS, MISR, SeaWifs, OMI) Column of AOD or NO2 Coincident Model (GEOS-Chem) Profile Altitude Concentration • S→ Surface Concentration • Ω → Tropospheric column

  4. Climatology (2001-2006) of MODIS- and MISR-Derived PM2.5 Included in current Global Burden of Disease report Evaluation in North America: r=0.77 slope = 1.07 N=1057 Outside Canada/US N = 244 (84 non-EU) r = 0.83 (0.83) Slope = 0.86 (0.91) Bias = 1.15 (-2.64) μg/m3 EHP Paper of the Year van Donkelaar et al., EHP, 2010

  5. Significant Association of Long-term PM2.5 Exposure and Cardiovascular Mortality at Low PM2.5 Crouse et al., EHP, 2012

  6. Coherent PM2.5 Trends Inferred from MISR and SeaWiFS AOD Uses Coincident AOD/PM2.5 from GEOS-Chem 2 1 MISR 2000 - 2011 Tuesday, 5:24 3012 Moscone West ΔPM2.5 [µg m-3 yr-1] 0 -1 SeaWiFS 1998 - 2010 -2 Boys et al., in prep.

  7. SPARTAN: An Emerging Global Network to Evaluate and Enhance Satellite-Based Estimates of PM2.5Measures PM2.5 Mass & Composition at AERONET sites AOD from CIMEL Sunphotometer (AERONET) PM2.5 Sampling Station from Vanderlei Martins (Airphoton) Filter PM2.5 & PM10 3-λNephelometer www.spartan-network.org

  8. PM2.5 Nearly as Sensitive to Emissions of NOx as to SO2 GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions Supported by Comparison of GEOS-Chemvs IASI NH3 ΔSO2 Emissions ΔNOx Emissions ΔNH3 Emissions ΔPM2.5 (ug m-3) -0.5 0 1 2 34% 25% 41% IASI - GC GC (w/AK) IASI DJF Using NH3emissions from Streets et al. (2003) reduced by 30% following Huang et al. (2012) JJA Kharol et al., GRL, in prep

  9. Change in PM2.5 Exposure from Local Changes in EmissionsUse GEOS-ChemAdjoint & Satellite PM2.5 Distribution Uses relation of exposure and mortality from Global Burden of Disease Project Δ Anthropogenic SO2 Emissions Δ Anthropogenic NOx Emissions Change in Global Premature Mortality for 10% change in Emissions Lee et al., EHP, in prep

  10. Enhanced Algorithm to Infer PM2.5 from MODIS • Optimal Estimation AOD • CALIOP-adjusted AOD/PM2.5 Chemical Transport Model a priori AOD Observed TOA reflectance a posteriori AOD • Optimal Estimation allows: • Error-constrained AOD solution • Consistent optical properties • Local reflectance information MODIS Imaging Spectroradiometer CALIOP Space-borne LIDAR observational error a priori error Optimal Estimation constrains AOD retrieval by error: van Donkelaar et al., in prep

  11. Optimal Estimation Improves Global AOD Retrieval Used to Infer Global PM2.5 slope=1.47 r=0.65 slope=0.87 r=0.80 slope=0.55 r=0.53 Western North America n = 25,497 slope=1.25 r=0.85 slope=0.95 r=0.86 slope=0.70 r=0.72 Optimal Estimation AOD (Unitless) n = 15,554 Eastern North America slope=1.23 r=0.77 slope=1.11 r=0.77 slope=1.36 r=0.62 n = 29,976 Europe van Donkelaar et al., in prep

  12. Use CALIOP Observations (2006-2011) to Correct Bias in Simulated Aerosol Extinction Southeast US China η = PM2.5 / AOD van Donkelaar et al., in prep

  13. Optimal Estimation Retrieval Improves Accuracy and CoverageMODIS-Derived PM2.5 for 2005 van Donkelaar et al., in prep

  14. A Satellite-Based Multipollutant Index from PM2.5 & NO2OMI-derived NO2 Indicator of Combustion Sources 150 75 0 PM2.5 [μg/m3] Eastern China 15 7.5 0 Shanghai Beijing Delhi Karachi Seoul Cairo Lima Tehran Los Angeles Berlin Moscow Nairobi MPI [unitless] NO2 PM2.5 25 15 5 0 1 2 5 7 9 11 13 15 PM2.5 [μg/m3] 0 4 8 12 Satellite-Based Multipollutant Index (Unitless) MPI Moscow 2.5 1.5 0.5 Multipollutant Index MPI [unitless] AQG = WHO Air Quality Guideline Cooper et al., ES&T, 2012

  15. Numerous Opportunities to Inform Air Quality Management through Satellite Remote Sensing and Modeling • Particulate matter is major risk factor for global mortality • Evidence of no lower limit on the health effects of PM2.5 • Controls on Chinese NOx emissions reduce PM2.5 • SPARTAN and CALIOP evaluate AOD/PM2.5 simulation • Asian PM2.5 increasing by 1-2 ug/m3/yr • Optimal estimation improves retrieval of PM2.5 • Satellite-based indicator of air pollution from PM2.5 and NO2 Acknowledgements: NSERC, Environment Canada, Health Canada, NASA

  16. Optimal Estimation Improves Global AOD Retrieval Used to Infer Global PM2.5 East Asia Optimal Estimation AOD (Unitless) South Asia South America van Donkelaar et al., in prep

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