Air Quality Applications of Satellite Remote Sensing Randall

Air Quality Applications of Satellite Remote Sensing Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie University Lok Lamsal, Dalhousie U NASA Goddard with contributions from Rob Levy, Ralph Kahn, NASA 1 st Workshop on Satellite Observations for Air Quality Management 9 May 2011

Two Major Air Quality Applications of Satellite Observations of Atmospheric Composition Estimating Pollution Concentrations (regions w/o ground-based obs) (AQHI) Smog Alert Top-down Constraints on Emissions (to improve AQ simulations)

Major Nadir-viewing Space-based Measurements of Tropospheric Trace Gases and Aerosols (Not Exhaustive) Solar Backscatter & Thermal Infrared Sensor GOES GOM MOPIT MISR MODI AIR SCIA- TE OMI PARA CALIO GOM IAS Imager E T S S MACH S -SOL P E-2 I Y Platfor GOES ERS- Terra m (varied 2 Aqua (launch ) (1995 (1999) ( ) ) 2002) Equato r Crossi ng n/a 10: 30 Typical Res (km) 4 x 4 320 x 40 22 x 22 Global Obs n/a 3 3. 5 X X Aerosol 10: 30 1: 30 Envisa t (2002) Aura (2004) 10: 00 1: 45 PARA Calips -SOL o (2004) (2006) 1: 30 Met. Op (2006) 9: 30 18 x 1 10 x 10 14 60 x 30 8 x 5 >24 18 x 16 40 x 40 80 x 4 12 8 x 14 x 13 0 x 12 7 2 X X 1 6 n/a 1 1 n/a 1 X X X NO 2 X X HCHO X X CO XX X 0. 5 X

Column Observations of Aerosol and NO 2 Strongly Influenced by Boundary Layer Concentrations Weak Thermal Strong Rayleigh Scattering O 3 HCHO SO 2 0. 30 Aerosol CO CO Contrast O 3 2. 2 4. 7 9. 6 NO 2 0. 36 0. 52 0. 62 0. 75 Wavelength (μm) 0. 43 Vertical Profile Affects Boundary-Layer Information in Satellite Obs Normalized GEOS-Chem Annual Mean Profiles over North America Aerosol Extinction O 3 CO HCHO SO 2 NO 2 S(z) = shape factor C(z) = concentration Ω = column Martin, AE, 2008

Temporal Correlation of AOD vs In Situ PM 2. 5 Correlation over Aug-Oct 2010

Aerosol Optical Depth (AOD) from MODIS and MISR over 2001 -2006 MODIS r = 0. 40 vs. in-situ PM 2. 5 1 -2 days for global coverage (w/o clouds) AOD retrievals at 10 km x 10 km Requires assumptions about surface reflectivity MISR 6 -9 days for global coverage (w/o clouds) AOD retrievals at 18 km x 18 km MISR r = 0. 54 vs. in-situ PM 2. 5 0 0. 1 0. 2 AOD [unitless] 0. 3 Simultaneous retrieval of surface reflectance and aerosol optical properties van Donkelaar et al. , EHP, 2010

July Agreement With AERONET Varies with Surface Type MODIS MISR 9 surface types, defined by monthly mean surface albedo ratios, evaluation against AERONET AOD van Donkelaar et al. , EHP, 2010

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

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

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 2001 -2006 van Donkelaar et al. , EHP, 2010

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)

Significant Agreement with Coincident In situ Measurements 0. 40 MISR AOD 0. 54 Combined AOD 0. 63 Combined PM 2. 5 0. 77 Satellite-Derived [μg/m 3] MODIS AOD 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 Bias (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

van Donkelaar et al. , EHP, 2010

Long-term Exposure to Outdoor Ambient PM 2. 5 • WHO Guideline & Interim Targets AQG IT-3 100 IT-2 IT-1 90 80% of global population exceeds WHO guideline of 10 μg/m 3 80 Significant association of PM 2. 5 and health at low PM 2. 5 levels (Crouse et al. , EHP, in prep) Population [%] 35% of East Asia exposed to 70 >50 μg/m 3 in annual mean 60 Global mortality from PM 2. 5 2 -8 million deaths/year (Evans 50 et al. , EHP, submitted) 40 Used in WHO Global Burden of Disease assessment 30 • 20 10 0 van Donkelaar et al. , EHP, 2010 5 10 15 25 35 PM 2. 5 Exposure 50 [μg/m 3] 100

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

Relaxed Cloud Screening Needed for Extreme Events van Donkelaar et al. , submitted

Application of Satellite-based Estimates to Moscow Smoke Event During Fires Before Fires MODIS-based In Situ from PM 10 In Situ PM 2. 5 van Donkelaar et al. , submitted

General Approach to Estimate Surface NO 2 Concentration NO 2 Column Model Profile In Situ GEOS-Chem S → Surface Concentration Ω → Tropospheric column

Ground-Level NO 2 Inferred From OMI for 2005 Works in Near-Real-Time! Values Estimated Using Monthly NO 2 Profiles for Different Year (2006) Temporal Correlation with In Situ Over 2005 × In situ —— OMI Insignificant change in results if profiles are daily coincident values from 2005 Lok Lamsal

Ground-Level NO 2 Inferred From OMI for 2005 Spatial Correlation vs In Situ for North America = 0. 78 Lok Lamsal

Bottom-Up Emission Inventories Take Years to Compile Bottom-up Anthropogenic NOx Emission Inventory from Land Sources for 2006 Based on EDGAR (2000), CAC (2005), NEI 2005, BRAVO (1999), EMEP (2006), Zhang (2006), scaled to 2006

Changes in Tropospheric NO 2 Column Reflect Changes in NOx Emissions Trend in Tropospheric NO 2 Column over 1996 -2002 from GOME 1996 - 2002 Richter et al. , 2005

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 Fractional Change in Trace Gas Column Local sensitivity of column changes to emissions changes Insensitive to changes in anthropogenic CO and VOCs Walker et al. , ACP, 2010 Lamsal et al. , GRL, 2011

Evaluate Hindcast Inventory Versus Bottom-up Hindcast for 2003 Based on Bottom-up for 2006 and Monthly NO 2 for 2003 -2006 Bottom-up Hindcast Lamsal et al. , GRL, 2011

Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly OMI NO 2 for 2006 -2009 Temporary Dataset Until Bottom-Up Inventory Available 9% increase in global emissions 19% increase in Asian emissions 6% decrease in North American emissions Lamsal et al. , GRL, 2011

Emerging Applications of Satellite Remote Sensing of Atmospheric Composition Chemical Transport Model Plays a Valuable Role in Relating Retrieved and Desired Quantity • Ground-level Estimates of PM 2. 5 & NO 2 • Simple Method for Timely Updates to NOx Emission Inventories Challenge • Continue to develop retrieval capability • Evaluate and improve simulation to relate retrieved and desired quantity (includes AOD/PM 2. 5, NO 2 / NOx emissions) Acknowledgements: NSERC, Environment Canada, Health Canada, NASA