Simulation of Absorbing Aerosol Index & Understanding the Relation of NO 2 Column Retrievals with Ground-based Monitors Randall Martin (Dalhousie, Harvard-Smithsonian)

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Presentation transcript:

Simulation of Absorbing Aerosol Index & Understanding the Relation of NO 2 Column Retrievals with Ground-based Monitors Randall Martin (Dalhousie, Harvard-Smithsonian) with contributions from Melanie Hammer, Shailesh Kharol, Jeff Geddes, Aaron van Donkelaar (Dalhousie U) TEMPO Science Team Meeting 22 May 2014 Michael Brauer (UBC), Dan Crouse (Health Canada), Greg Evans (U Toronto), Mike Jerrett (Berkeley), Lok Lamsal (NASA), Rob Spurr (RT Solutions), Yushan Su (Ontario MoE), Omar Torres (NASA)

Growing Use of Remote Sensing for Exposure Assessment Looking backward: Use of (A) remote sensing data to supplement (B) available routine air quality monitoring Looking forward: Use of (B) available routine air quality monitoring to supplement (A) remote sensing data Wu J, et al (2006). Exposure assessment of PM air pollution before, during, and after the 2003 Southern California wildfires. Henderson SB, et al (2008). Use of MODIS products to simplify and evaluate a forest fire plume dispersion model for PM 10 exposure assessment. Significant Association of Satellite-derived Long-term PM 2.5 Exposure with Cardiovascular Mortality at Low PM 2.5 & Associations with Diabetes and Hypertension Crouse et al., EHP, 2012; Brook et al., Diabetes Care, 2013; Chen et al., EHP, 2013; Chen et al., Circulation, 2013 Some Groups Using Remote Sensing for Exposure Assessment: WHO, World Bank, OECD, Environmental Performance Index, Global Burden of Disease

Develop Assimilation System of Suite of TEMPO Observations to Estimate PM 2.5 Composition, Ground-level Ozone, and Ground-level NO 2 Absorbing Aerosol Index (aerosol composition) NO 2 (ozone and aerosol composition) Aerosol optical depth Ozone profile SO 2 (aerosol composition) HCHO (ozone and aerosol composition) Vegetation (VOC emissions) Assimilation System Could Also be Useful for AMF Calculation

Simulation of Absorbing Aerosol Index (AAI) GEOS-Chem Simulation of Aerosol Composition Coincident with OMI LIDORT Radiative Transfer Model Simulated Absorbing Aerosol Index TOMS UV Surface Reflectance (from Omar Torres) OMI Viewing Geometry A measure of the aerosol-induced spectral dependence of back-scattered UV Example observed AAI showing a smoke plume over the United States

Initial GEOS-Chem & LIDORT Simulation of OMI Absorbing Aerosol Index (July 2008) Will be Useful to Interpret AAI from TEMPO Melanie Hammer OMI GEOS-Chem & LIDORT OMI Cloud Fraction < 5%

General Approach to Estimate Surface Concentration S → Surface Concentration Ω → Tropospheric column Coincident Model (GEOS-Chem) ProfileDaily OMI NO 2 Column Concentration Altitude Also uses OMI to inform subpixel variation following Lamsal et al. (2008, 2013)

Kharol et al., in prep In SituOMI-Derived Slope with BEHR ~0.5 y = 0.40x r = 0.73 n = 102

Why is Satellite-Derived Surface NO 2 Biased vs In Situ? Kharol et al., in prep Molybdenum converter measurements corrected for NO z following Lamsal et al. (2008, 2010) Urban areas included y = 0.40x r = 0.80 n = 215 In situ sampled at OMI overpass time Slope with BEHR over US ~0.5

Use Land Use Regression (LUR) Datasets to Examine Effects of Monitor Placement Kharol et al., in prep LUR from Jerrett et al Toronto Hamilton

Monitor Placement Contributes to Bias Versus Area Average Kharol et al., in prep LUR NO 2 at Measurement Site Area Average LUR NO 2

Consistent Relative Trends in Ground-level NO 2 Indicate Both Observe Changes in Large-Scale Processes In situ OMI Kharol et al., in prep

Remote Sensing Offers Observational Estimate of Area- Average Concentrations & Changes in Surface NO 2 ΔNO 2 (ppbv yr -1 ) Trend Shailesh Kharol 2005 to 2011 Concentration NO 2 (ppbv) Lamsal et al. (2013)

Conclusions Initial simulation of Absorbing Aerosol Index Spatial bias in surface NO 2 from satellite and in situ monitors partially arises from monitor placement Ambiguity remains about long-term area-average NO 2 in urban areas Consider for TEMPO validation a dense collection (>10) of long-term monitors of ground-level NO 2 and column NO 2 within a TEMPO footprint for multiple urban areas Acknowledgements: NSERC, Environment Canada, Health Canada