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Insight into Global Air Quality using Satellite Remote Sensing and Modeling

Insight into Global Air Quality using Satellite Remote Sensing and Modeling. Randall Martin with contributions from Aaron van Donkelaar , Shailesh Kharol , Matthew Cooper, Sajeev Philip, Colin Lee Lok Lamsal (Dalhousie  NASA ), Chulkyu Lee (Dalhousie  KMA)

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Insight into Global Air Quality using Satellite Remote Sensing and Modeling

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  1. Insight into Global Air Quality using Satellite Remote Sensing and Modeling Randall Martin with contributions from Aaron van Donkelaar, ShaileshKharol, Matthew Cooper, SajeevPhilip, Colin Lee LokLamsal (Dalhousie  NASA), Chulkyu Lee (Dalhousie  KMA) Siwen Wang (Tsinghua & Dalhousie), Qiang Zhang (Tsinghua) 10 Oct 2012

  2. Two Major Applications of Satellite Observations of Atmospheric Composition Estimating Pollution Concentrations (large regions w/o ground-based obs) Top-down Constraints on Emissions (to improve air quality and climate simulations)

  3. Column Observations of Aerosol and NO2 Strongly Influenced by Boundary Layer Concentrations Weak Thermal Contrast Strong Rayleigh Scattering Aerosol O3 NO2 O3 9.6 2.2 4.7 0.52 0.62 0.75 0.30 0.36 0.43 Wavelength (μm) Vertical Profile Affects Boundary-Layer Information in Satellite Obs Normalized Model Summer Mean Profiles over North America O3 Aerosol Extinction S(z) = shape factor C(z) = concentration Ω = column NO2 Martin, AE, 2008

  4. WHO Global Burden of Disease Report for 2000Impaired by Insufficient Global Observations of Fine Particulate Matter (PM2.5) ~ 1 year increase of life expectancy for decreasing long-term exposure of PM2.5 by 10 ug/m3 • Estimated air pollution in urban areas causes ~1 million excess deaths (~1%) • Could not estimate effects outside of urban areas for 2000 report • Could this be improved with satellite remote sensing? Cohen et al., 2005

  5. Aerosol Optical Depth (AOD) for 2001-2006Rejected 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

  6. Chemical Transport Model (GEOS-Chem) Simulation of Aerosol Optical Depth Use GEOS-Chem to Calculate AOD/PM2.5 Ratio for Each Obs Aaron van Donkelaar

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

  8. Significant Agreement of Satellite-Derived PM2.5 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

  9. 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

  10. van Donkelaar et al., EHP, 2010

  11. WHO Guideline & Interim Targets Long-term Exposure to Outdoor Ambient PM2.5 AQG IT-3 IT-2 IT-1 100 90 80 70 60 50 40 30 20 10 0 • 80% of global population exceeds WHO guideline of 10 μg/m3 • 35% of East Asia exposed to >50 μg/m3 in annual mean van Donkelaar et al., EHP, 2010 Forthcoming Global Burden of Disease (WHO) Study PM2.5 Causal Role in ~70 Million Disability Adjusted Life Years (~3%)~3 Million Excess Deaths (~5%) Population [%] 5 10 15 25 35 50 100 PM2.5 Exposure [μg/m3]

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

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

  14. Satellite-based Estimates of PM2.5 in Moscow During Fires Before Fires r=0.92, slope=1.06 MODIS-based In Situ from PM10 In Situ PM2.5 van Donkelaar et al., AE, 2011

  15. Daily Satellite-based Estimates for North America>30M People Live in Regions w/o Local Air Quality InfoGOCI and GEMS Provide Opportunities for Asia In Situ Satellite-derived van Donkelaar et al., ES&T, in press

  16. SPARTAN: An Emerging Global Network to Evaluate and Improve 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 (UMBC)

  17. In Situ GEOS-Chem General Approach to Estimate Surface NO2 Concentration NO2 Column Model Profile • S→ Surface Concentration • Ω → Tropospheric column

  18. NO2:Indicator of Population Exposure to Combustion SourcesOMI-Derived NO2 Lamsal et al., ES&T, in prep

  19. Multiple Pollutants Affect Air Quality • Mortality not well explained by PM2.5 and O3 alone • Mortality can be more strongly associated with NO2 than either PM2.5 or O3 (e.g. Stieb et al. 2007) • NO2 is an indicator of exposure to combustion sources that increase airmass toxicity (Brook et al., 2007)

  20. A Satellite-Based Multipollutant Index from PM2.5 & NO2 Shanghai Beijing Delhi Karachi Seoul Cairo Lima Tehran Los Angeles Berlin Moscow Nairobi NO2 MPI (unitless) PM2.5 0 1 2 5 7 9 11 13 15 0 4 8 12 Satellite-Based Multipollutant Index (Unitless) MPI Multipollutant Index PM2.5 (ug/m3) AQG = WHO Air Quality Guideline Cooper et al., ES&T, 2012

  21. Emission Inventories are Notoriously Difficult to Determineand Take Years to Compile

  22. Top-Down Constraints on NOx & SOx Emissions Inverse Modeling 2004-2005 SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1) Martin et al., 2006 49.9 Tg S yr-1 2006 OMI SO2 (1016molec cm-2) SOx emissions (1011 atoms N cm-2 s-1) Lee et al., 2011

  23. Global Anthropogenic Sulfur Emissions Over Land for 2006Volcanic SO2 Columns (>1x1017molec cm-2) Excluded From Inversion Top-Down (OMI) 49.9 Tg S/yr Bottom-Up in GEOS-Chem (EDGAR2000, NEI2005, EMEP2005, Streets2006) Scaled to 2006 r = 0.77 54.6 Tg S/yr SO2 Emissions (1011 molecules cm-2 s-1) Lee et al., JGR, 2011 Cloud Radiance Fraction < 0.2

  24. Anthropogenic Emissions Differences (2006) -2.2 Tg S/yr -4.7 Tg S/yr Cloud Radiance Fraction < 0.2, SZA < 50o Lee et al., JGR, 2011

  25. OMI Observations of Changes in NOxEmissionsfrom Chinese Power Plants Ratio of OMI NO2 Columns for 2007 / 2005Open Circles Indicate New Power Plants Wang et al., ACP, 2012

  26. Application of Satellite Observations for Timely Updates to NOx Emission Inventories Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly OMI NO2 for 2006-2009 9% increase in global emissions 19% increase in Asian emissions 6% decrease in North American emissions Lamsal et al., GRL, 2011

  27. PM2.5 Nearly as Sensitive to NOx as to SO2 and NH3 GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions ΔNOx Emissions ΔSO2 Emissions ΔNH3 Emissions Kharol et al., GRL, in prep

  28. Increasing NOx Emissions Increases NO3-, NH4+, and SO42-Increasing SO2 Emissions Decreases NO3- Kharol et al., GRL, in prep

  29. Preliminary GEOS-ChemAdjoint Calculation of the Change in Annual Mortality to Local Changes in EmissionsEffects of PM2.5 on Mortality from WHO Global Burden of Disease Project ΔM = Change in Global Mortality Lee et al., EHP, in prep

  30. Emerging Applications of Satellite Remote Sensing of Atmospheric Composition Chemical Transport Model Plays a Valuable Role in Relating Retrieved and Desired Quantity • Estimates of Ground-level Air Pollutants • Top-down Constraints on Emission Inventories • Challenges • Continue to develop retrieval capability • Evaluate and improve simulation to relate retrieved and desired quantity • (e.g. AOD/PM2.5, NO2 / NOx emissions) Acknowledgements: NSERC, Environment Canada, Health Canada, NASA

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