Insight from the A-Train into Global Air Quality Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Lok Lamsal, Akhila Padmanabhan,

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Presentation transcript:

Insight from the A-Train into Global Air Quality Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Lok Lamsal, Akhila Padmanabhan, Dalhousie University Chulkyu Lee, Dalhousie  National Institute of Meteorological Research, Korea with contributions from Rob Levy, Ralph Kahn, Nick Krotkov, NASA Mark Parrington, Dylan Jones, University of Toronto OMI NO 2 Team A-Train Symposium, New Orleans October 26, 2010

Two Applications of Satellite Observations for Air Quality Key pollutants: PM 2.5, O 3, NO 2 (AQHI) Top-down Constraints on Emissions (to improve AQ simulations) Smog Alert Estimating Surface Concentrations (large regions w/o ground-based obs) PM 2.5 : fine aerosol

Column Observations of Aerosol and NO 2 Strongly Influenced by Boundary Layer Concentrations S(z) = shape factor C(z) = concentration Ω = column NO 2 Aerosol Extinction O3O3 Martin, AE, Aerosol O 3 NO Normalized GEOS-Chem Summer Mean Profiles over North America Strong Rayleigh Scattering Weak Thermal Contrast Vertical Profile Affects Boundary-Layer Information in Satellite Obs O 3 Wavelength (μm)

Temporal Correlation of AOD vs In Situ PM 2.5 Correlation over Aug-Oct 2010

Combined AOD from MODIS (and MISR) for Rejected Retrievals over Land Types with Monthly Error vs AERONET >0.1 or 20% van Donkelaar et al., EHP, 2010 Spatial Correlation (r) of AOD vs in situ PM 2.5 for North America MODIS: r = 0.39 MISR: r = 0.39 Simple Average:r = 0.44 Combined:r = 0.61

Chemical Transport Model (GEOS-Chem) Simulation of Aerosol Optical Depth Aaron van Donkelaar

Ground-level “dry” PM 2.5 = η AOD Ground-level “dry” PM 2.5 = η · AOD η affected by vertical structure, aerosol properties, relative humidity Obtain η from aerosol-oxidant model (GEOS-Chem) sampled coincidently with satellite obs GEOS-Chem Simulation of η for van Donkelaar et al., EHP, 2010

τ(z)/τ(z=0) Altitude [km] Evaluate GEOS-Chem Vertical Profile with CALIPSO Observations Coincidently sample model and CALIPSO extinction profiles –Jun-Dec 2006 Compare % within boundary layer Model (GC) CALIPSO (CAL) Optical depth above altitude z Total column optical depth van Donkelaar et al., EHP, 2010

Evaluation with measurements outside Canada/US Global Climatology ( ) of PM 2.5 from MODIS (& MISR) AOD and GEOS-Chem AOD/PM 2.5 Relationship Number sitesCorrelationSlopeOffset (ug/m 3 ) Including Europe Excluding Europe van Donkelaar et al., EHP, 2010 Evaluation for US/Canada r=0.77 slope=1.07 n=1057 Better than in situ vs model (GEOS-Chem): r= , slope = 0.63 – 0.71 US EPA standard

van Donkelaar et al., EHP, 2010

80% of global population exceeds WHO guideline of 10 μg/m 3 35% of East Asia exposed to >50 μg/m 3 in annual mean 0.61±0.20 years lost per 10 μg/m 3 [Pope et al., 2009] Estimate decreased life expectancy due to PM 2.5 exposure PM 2.5 Exposure [μg/m 3 ] Data Valuable to Assess Health Effects of PM 2.5 van Donkelaar et al., EHP, AQG IT-3 IT-2 IT-1 Population [%] WHO Guideline & Interim Targets

USA Today: Hundreds Dead from Heat, Smog, Wildfires in Moscow 9 Aug 2010: “Deaths in Moscow have doubled to an average of 700 people a day as the Russian capital is engulfed by poisonous smog from wildfires and a sweltering heat wave, a top health official said Monday.” MODIS/Aqua: 7 Aug 2010

PM 2.5 Estimate from MODIS AOD and GEOS-Chem AOD/PM 2.5 Aaron van Donkelaar Near Moscow In Situ MODIS-based Evaluation Near Moscow Regional Mean MODIS-based Estimate Before Fires During Fires

Impact of TES Assimilation on Surface Ozone (Aug. 2006) The model underestimates surface ozone in the west [Parrington et al., GRL, 2009] TES-based estimates of background O 3 are ppb GEOS-Chem before assimilationGEOS-Chem after assimilation Surface O3 difference (assim - no assim) AQS and NAPS surface O3 data Background O 3 at the surface before assim. Background O 3 at the surface after assim. Without assimilation the model underestimates background ozone by as much as 9 ppb (in western North America)

OMI Tropospheric NO 2 Column Proxy for Surface Concentration NO / NO2   with altitude OMI Standard Product: October 2004 – September 2007 Inclusive

General Approach to Estimate Surface Concentration Daily OMI Tropospheric Column S → Surface Concentration Ω → Tropospheric column In Situ GEOS-Chem Coincident Model (GEOS-Chem) Profile

Ground-Level NO 2 Inferred From OMI for 2005 Temporal Correlation with In Situ Over 2005 Lamsal et al., JGR, 2008 Spatial Correlation of Mean vs In Situ for North America = 0.78 ×  In situ —— OMI

Application of Satellite Observations for Timely Updates to NO x Emission Inventories Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions Walker et al., ACP, 2010 Local sensitivity of column changes to emissions changes Fractional Change in Emissions Fractional Change in Trace Gas Column Lamsal et al., GRL, in prep Apply to regions where anthropogenic emissions dominate (>50%)

Forecast Inventory for 2009 Based on Bottom-up for 2006 and OMI NO 2 for Lamsal et al., GRL, in prep 9% increase in global emissions 21% increase in Asian emissions

OMI SO 2 Column Retrievals Reflect Anthropogenic Emissions Lee et al., JGR, 2009 OMI Improved SO 2 Vertical Columns for 2006 Agreement with Aircraft Observations (INTEX-B): slope = 0.95, r=0.92

Use OMI SO 2 Columns to Map SO 2 Emissions Apply GEOS-Chem for the Inversion Combustion, Smelters, Volcanoes Emission SO 2 SO 4 2- ~day OH, cloud Tropospheric SO 2 column ~ E SO2 Over Land Phytoplankton DMS day Deposition

Global Sulfur Emissions Over Land for Tg S/yr 54.6 Tg S/yr r = 0.77 vs bottom-up SO 2 Emissions (10 11 molecules cm -2 s -1 ) Lee et al., JGR, submitted Top-Down (OMI) Bottom-Up in GEOS-Chem (EDGAR2000, NEI99, EMEP2005, Streets2006) Scaled to 2006 Volcanic SO 2 Columns (>10 DU) Excluded From Inversion Top-Down Minus Bottom-up

Challenges Develop retrievals to increase boundary-layer information Continue to develop simulation to relate retrieved and desired quantity Develop comprehensive assimilation capability (i.e. CALIPSO vertical profiles and OMI SO 2 to inform AOD/PM 2.5 relationship) A-Train Has Provided Unprecedented Insight Into Global Air Quality Chemical Transport Model Plays a Critical Role in Relating Retrieved and Desired Quantity NASA, NSERC, Health Canada, Environment Canada Acknowledgements