satellite remote sensing of global air pollution
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Satellite Remote Sensing of Global Air Pollution. Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Dalhousie University Lok Lamsal, Dalhousie University NASA Goddard with contributions from Michael Brauer, UBC Rob Levy, Ralph Kahn, NASA. - PowerPoint PPT PresentationTRANSCRIPT
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Satellite Remote Sensing of Global Air Pollution
Randall Martin, Dalhousie and Harvard-Smithsonian
Aaron van Donkelaar, Dalhousie University
Lok Lamsal, Dalhousie University NASA Goddard
with contributions from
Michael Brauer, UBC
Rob Levy, Ralph Kahn, NASA
Symposium on Air Quality and Health in Atlantic Canada: New Directions and Opportunities
16 February 2011
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Large Regions Have Insufficient Measurements for Air Large Regions Have Insufficient Measurements for Air Pollution Exposure AssessmentPollution Exposure Assessment
Locations of Publicly-Available Long-Term PM2.5 Monitoring Sites
Aaron van Donkelaar
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Aerosol Remote Sensing: Analogy with Visibility Aerosol Remote Sensing: Analogy with Visibility Effects of Aerosol LoadingEffects of Aerosol Loading
7.6 ug m-3
22 ug m-3
Pollution haze over East Coast
Waterton Lakes/Glacier National Park
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Combined Aerosol Optical Depth (AOD)Combined Aerosol Optical Depth (AOD) from MODIS and from MODIS and MISR Instruments for 2001-2006MISR Instruments for 2001-2006
CombinedMODIS/MISR
r = 0.63 (vs. in-situ PM2.5)
van Donkelaar et al., EHP, 2010
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Chemical Transport Model (GEOS-Chem) Chemical Transport Model (GEOS-Chem) Simulation of Aerosol Optical Depth Simulation of Aerosol Optical Depth
Aaron van Donkelaar
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Ground-level “Dry” PMGround-level “Dry” PM2.52.5 = = ηη · AODAOD
η affected by vertical structure, aerosol properties, relative humidityObtain 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
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Significant Agreement with Coincident In situ MeasurementsSignificant Agreement with Coincident In situ Measurements
SatelliteDerived
In-situ
Sat
ellit
e-D
eriv
ed
[μg/
m3]
In-situ PM2.5 [μg/m3]
Ann
ual M
ean
PM
2.5 [
μg/
m3]
(200
1-20
06)
r
MODIS τ 0.40
MISR τ 0.54
Combined τ 0.63
Combined PM2.5 0.77
van Donkelaar et al., EHP, 2010
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Evaluation with measurements outside Canada/US
Global Climatology (2001-2006) of PMGlobal Climatology (2001-2006) of PM2.52.5
Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71
Number sites Correlation Slope Bias (ug/m3)
Including Europe 244 0.83 0.86 1.15
Excluding Europe 84 0.83 0.91 -2.5
van Donkelaar et al., EHP, 2010
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van Donkelaar et al., EHP, 2010
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van Donkelaar et al., EHP, 2010
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• 80% of global population exceeds WHO guideline of 10 μg/m3
• 35% of East Asia exposed to >50 μg/m3 in annual mean
• Estimate health effects of PM2.5 exposure
PM2.5 Exposure [μg/m3]
Long-term Exposure to Long-term Exposure to Outdoor Ambient PMOutdoor Ambient PM2.52.5
van Donkelaar et al., EHP, 2010
100
90
80
70
60
50
40
30
20
10
0
AQG IT-3 IT-2 IT-1
Pop
ulat
ion
[%]
5 10 15 25 35 50 100
WHO Guideline & Interim Targets
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Emerging ApplicationsEmerging Applications
Villeneuve et al., OEM, submitted
Canadian non-smokers more likely to live in areas with higher concentrations of ambient PM2.5. Cigarette smoking will act as a negative confounder in epidemiological studies of long-term ambient air pollution and mortality outcomes in Canada
Hystad et al., EHP, submitted, Satellite dataset dominant contributor to national PM2.5 model
Evans et al. in prep: Estimate global mortality from PM2.5
Brauer et al. in prep; Estimate global burden of disease attributable to air pollution; uses satellite estimates and global model (TM5)
Burnett et al., in prep; appears that satellite estimates better than in situ at predicting mortality
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Application of Satellite-based Estimates to Moscow Application of Satellite-based Estimates to Moscow Smoke EventSmoke Event
Before Fires During Fires
van Donkelaar et al., in prep
In Situ
MODIS-based
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General Approach to Estimate Surface NOGeneral Approach to Estimate Surface NO22 Concentration Concentration
NO2 Column
S → Surface Concentration
Ω → Tropospheric column
In Situ
GEOS-Chem
Coincident ModelProfile
OM
MO S
S
Method: Solar backscatter
Scattering by Earth surface and atmosphere
IdealizedNO2
absorptionspectrum
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Ground-Level NOGround-Level NO2 2 Inferred From OMI for 2005 Inferred From OMI for 2005
Lamsal et al., JGR, 2008
Spatial Correlation vs In Situ for North America = 0.78
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ChallengesChallengesRemote Sensing: Improved algorithms to increase accuracy and observe
other pollutants
Modeling: Develop representation of processes
Measurements: More needed for evaluation
Encouraging Prospects for Satellite Remote Encouraging Prospects for Satellite Remote Sensing of Air PollutantsSensing of Air Pollutants
Acknowledgements:Acknowledgements: Health Canada Health Canada NSERC NSERC NASA NASA
Health Applications:Close interaction to develop appropriate applications