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Randall Martin with contributions from

Global Fine Particulate Matter Concentrations and Trends Inferred from Satellite Observations, Modeling, and Ground-Based Measurements. Randall Martin with contributions from

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Randall Martin with contributions from

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  1. Global Fine Particulate Matter Concentrations and Trends Inferred from Satellite Observations, Modeling, and Ground-Based Measurements Randall Martin with contributions from Aaron van Donkelaar, Brian Boys, Matthew Cooper, Colin Lee, Ryan MacDonell, GraydonSnider, Crystal Weagle, Mark Gibson Michael Brauer (UBC), Aaron Cohen (HEI), DavenHenze (CU Boulder), Christina Hsu (NASA), Yang Liu (Emory), Zifeng Lu (Argonne), VanderleiMartins (AirPhoton), David Streets (Argonne), Siwen Wang (Tsinghua), QiangZhang (Tsinghua) EGU 30 April 2014

  2. Vast Regions Have Insufficient Measurements for Fine Particulate Matter (PM2.5) Exposure Assessment Locations of Publicly Accessible Long-Term PM2.5 Monitoring Sites Emerging Network Previous Global Burden of Disease Project for the Year 2000Impaired by Insufficient Global Observations of PM2.5

  3. General Approach to Estimate Daily PM2.5 Concentration Daily Satellite(MODIS, MISR, SeaWifs) Column of AOD Coincident Model (GEOS-Chem) Profile Altitude Concentration • Accounts for • relation of “dry” PM2.5 with ambient extinction • relation of aerosol during satellite-observation vs continuous

  4. Climatology (2001-2006) of MODIS- and MISR-Derived PM2.5 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. Used in Global Burden of Disease Study 2010 PM2.5Causal Role in 70 Million Disability Adjusted Life Years (~3%)>3 Million Excess Deaths (~5%) • Three-fold increase in premature mortality rate over previous GBD study for 2000 Lim et al., Lancet, 2012 Similar Conclusions Reached by WHO in 2014 Significant Association of Long-term PM2.5 Exposure with Cardiovascular Mortality at Low PM2.5 Benefits from Large Statistical Power Crouse et al., EHP, 2012

  6. Enhanced Algorithm to Infer PM2.5 from MODIS • Optimal Estimation AOD • CALIOP-adjusted AOD/PM2.5 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., JGR, 2013

  7. Optimal Estimation (OE) Can Improve Global AOD Retrieval Western North America Operational slope=1.47 r=0.65 slope=0.81 r=0.75 slope=0.56 r=0.53 Optimal Estimation AOD (Unitless) Number of Observations Eastern North America slope=1.25 r=0.85 slope=0.94 r=0.87 slope=0.71 r=0.71 Operational = NASA MODIS Collection 5 Best agreement van Donkelaar et al., JGR, 2013

  8. Use CALIOP Observations (2006-2011) to Correct Seasonal Bias in Simulated Aerosol Extinction East China Eastern US η = PM2.5 / AOD van Donkelaar et al., JGR, 2013

  9. Satellite-Derived PM2.5 Trends Inferred from SeaWifs & MISR AOD and GEOS-Chem AOD/PM2.5 MISR 2000 -2012 SeaWiFS 1998 -2010 PM2.5 Trend [μg m-3 yr-1] Boys et al., submitted

  10. Combine SeaWifs & MISR to Calculate 15-Year PM2.5Timeseries (1998-2012) East Asia Eastern North America PM2.5 (μg m-3) PM2.5 (μg m-3) 0 Middle East South Asia PM2.5 (μg m-3) 0.1 P- value 0.05 0.01 -1.5 -0.5 -1 1 2 1.5 -2 0.5 -0.25 0.25 PM2.5 Trend [µg m-3 yr-1] Boys et al., submitted

  11. Consistent Trends in Satellite-Derived and In Situ PM2.5 1999-2012 Eastern US PM2.5 Anomaly (ugm-3) SeaWifs & MISR In Situ In Situ In Situ (1999-2012) 0.37 ± 0.06 μg m-3 yr-1 Satellite-Derived (1999-2012) 0.36 ± 0.13 μg m-3 yr-1 Satellite-Derived Boys et al., submitted

  12. Interpret Satellite-derived PM2.5 Trends with GEOS-Chem Eastern North America Middle East SeaWifs & MISR 0.81±0.21 μg m-3 yr-1 SeaWifs & MISR -0.39±0.10 μg m-3 yr-1 PM2.5 [ug/m3] GEOS-Chem Mineral Dust 0.7μg m-3 yr-1 GEOS-Chem Secondary Inorganic -0.4 μg m-3 yr-1 East Asia South Asia SeaWifs & MISR 0.93±0.22 μg m-3 yr-1 SeaWifs & MISR 0.79±0.27 μg m-3 yr-1 PM2.5 [ug/m3] GEOS-Chem Secondary Inorganic 0.7 μg m-3 yr-1 GEOS-Chem Secondary Inorganic 0.8 μg m-3 yr-1 GEOS-Chem Organic 0.2μg m-3 yr-1 GEOS-Chem Organic 0.04μg m-3 yr-1 Year Year Boys et al., submitted

  13. Changes in Long-term Population-Weighted Ambient PM2.5Clean Areas are Improving; High PM2.5 Areas are Degrading WHO Guideline & Interim Targets van Donkelaar et al., submitted

  14. Changes in Long-term Population-Weighted Ambient PM2.5Clean Areas are Improving; High PM2.5 Areas are Degrading WHO Guideline & Interim Targets 1998 (51%) 2012 (70%) Exceedance of WHO IT1 increases from 51% to 70% WHO IT1 1998 Exceedance of WHO AQG drops from 62% to 19% 2012 WHO AQG van Donkelaar et al., submitted

  15. SPARTAN: An Emerging Global Network to Evaluate and Enhance Satellite-Based Estimates of PM2.5Measures PM2.5 Mass & Composition at Sites Measuring AOD Testing Deployed Committed Prospective Semi-Autonomous PM2.5 & PM10 Impaction Sampling Station (AirPhoton) Ions & metals 3-λNephelometer AOD from CIMEL Sunphotometer (e.g. AERONET) www.spartan-network.org Snider et al., in prep

  16. Nonlinear Relation Between PM2.5 and SourcesWhich Local Sources Should be Reduced to Decrease Mortality from PM2.5? PM2.5 Primary Chemistry Precursors Nitrogen Oxides (NOx) Sulfur Dioxide (SO2) Ammonia (NH3)

  17. Adjoint Model: Calculate Sensitivity of Global Premature Mortality to Local Emissions GEOS-Chem Global Mortality Health Impact Function Emissions Chemistry & Transport Concentrations GEOS-ChemAdjoint ∂ ∂Emissions Chemistry & Transport ∂ ∂Concentrations Colin Lee

  18. Sensitivity of Global Premature Mortality to SO2 Emissions ΔMortalityglobal / 10% ΔEmissions PM2.5subgrid variability resolved using satellite AOD Exposure-response function from Global Burden of Disease Project Lee et al., in prep

  19. Sensitivity of Global Premature Mortality to: SO2 Emissions NH3 Emissions ΔMortalityglobal / 10% ΔEmissions Lee et al., in prep

  20. Insight into Global PM2.5 through Satellite Remote Sensing Modeling, and Ground-based Instruments • Particulate matter is major risk factor for global premature mortality • Regions with high PM2.5 have increasing concentrations • Regions with low PM2.5 have decreasing concentrations • Asian PM2.5 increasing by 1-2 ug/m3/yr • SPARTAN and CALIOP evaluate AOD/PM2.5 simulation • Adjoint allows efficient calculation of sensitivity of premature mortality to emissions changes • Controls in South Asia on SO2 much more effective than on NH3 Acknowledgements:NSERC, Health Canada, Environment Canada

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