Global Air Pollution Inferred from Satellite Remote Sensing
Global Air Pollution Inferred from Satellite Remote Sensing Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from Aaron van Donkelaar, Dalhousie University Lok Lamsal, Dalhousie U NASA Goddard Rob Levy, Ralph Kahn NASA Michael Brauer, UBC Michal Krzyzanowski, WHO Aaron Cohen, HEI Workshop on Atmospheric Chemistry and Health: current knowledge and future directions 12 October 2011
Aerosol Remote Sensing: Analogy with Visibility Effects of Aerosol Loading Waterton Lakes/Glacier National Park Pollution haze over East Coast 7. 6 ug m-3 22 ug m-3
Combined AOD from MODIS and MISR Rejected Retrievals for Land Types with Monthly Error vs AERONET >0. 1 or 20% 0. 25 Combined MODIS/MISR r = 0. 63 (vs. in-situ PM 2. 5) 0. 2 0. 15 0. 1 0. 05 MODIS r = 0. 40 MISR r = 0. 54 (vs. in-situ PM 2. 5) 0 van Donkelaar et al. , EHP, 2010 AOD [unitless] 0. 3
Calculate Coincident PM 2. 5/AOD with Chemical Transport Model (GEOS-Chem) Aaron van Donkelaar
Significant Agreement with Coincident In situ Measurements 0. 40 MISR τ 0. 54 Combined τ 0. 63 Combined PM 2. 5 0. 77 Satellite-Derived [μg/m 3] MODIS τ Satellite Derived In-situ PM 2. 5 [μg/m 3] van Donkelaar et al. , EHP, 2010 Annual Mean PM 2. 5 [μg/m 3] (2001 -2006) r
Global Climatology (2001 -2006) of PM 2. 5 Evaluation with measurements outside Canada/US Number sites Correlation Slope Offset (ug/m 3) Including Europe 244 0. 83 0. 86 1. 15 Excluding Europe 84 0. 83 0. 91 -2. 5 Better than in situ vs model (GEOS-Chem): r=0. 52 -0. 62, slope = 0. 63 – 0. 71 van Donkelaar et al. , EHP, 2010
Error in Satellite-Derived PM 2. 5 has Three Primary Sources Satellite-derived PM 2. 5 = Model • • Affected by aerosol optical properties, concentrations, vertical profile, relative humidity Most sensitive to vertical profile [van Donkelaar et al. , 2006] AOD Satellite • • Error limited to 0. 1 + 20% by AERONET filter Implication for satellite PM 2. 5 determined by η Sampling Biases Satellite retrievals are at specific time of day for cloud-free conditions
Model (GC) CALIPSO (CAL) • • Coincidently sample model and CALIPSO extinction profiles – Jun-Dec 2006 Compare % within boundary layer Altitude [km] Evaluate GEOS-Chem Vertical Profile with CALIPSO Observations Optical depth above altitude z Total column optical depth τa(z)/τa(z=0)
• • Estimate error from bias in profile and AOD ±(1 μg/m 3 + 15%) Contains 68% (1 SD) of North American data Total uncertainty 25% (with sampling) Global population-weighted mean uncertainty 7 μg/m 3 Satellite-Derived [μg/m 3] Error Estimate In-situ PM 2. 5 [μg/m 3] van Donkelaar et al. , EHP, 2010
van Donkelaar et al. , EHP, 2010
van Donkelaar et al. , EHP, 2010
Wildfires near Moscow in Summer 2010 MODIS/Aqua: 7 Aug 2010
Spatial and Temporal Variation in Satellite-Based PM 2. 5 during Moscow 2010 Fires van Donkelaar et al. , AE, 2011
Satellite-based Estimates of PM 2. 5 in Moscow During Fires Before Fires r 2 =0. 85, slope=1. 06 MODIS-based In Situ from PM 10 In Situ PM 2. 5 van Donkelaar et al. , 2011
Similar Technique to Infer Ground-Level NO 2 from OMI Lamsal et al. , JGR, 2008
Encouraging Prospects for Satellite Remote Sensing of Air Pollutants Challenges Remote Sensing: Improved algorithms to increase accuracy and resolution Modeling: Develop representation of processes Develop assimilation capability to inform AOD/PM 2. 5 Measurements: More needed for evaluation throughout the world Acknowledgements: Health Canada NSERC NASA
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