using satellite remote sensing to estimate global outdoor air pollution exposure
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Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure. Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Dalhousie University Lok Lamsal, Dalhousie University NASA Goddard. - PowerPoint PPT PresentationTRANSCRIPT
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Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure
Randall Martin, Dalhousie and Harvard-Smithsonian
Aaron van Donkelaar, Dalhousie University
Lok Lamsal, Dalhousie University NASA Goddard
Workshop on Space Technology for Public Health Actions in the Context of Climate Change Adaptation
20 June 2011
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Large Health Effects of Fine Particulate Matter (PMLarge Health Effects of Fine Particulate Matter (PM2.52.5))
Regulation of fine particulate matter achieved the largest estimated benefits of all U.S. Federal Regulations
~ 1 year increase of life expectancy for decreasing long-term exposure of PM2.5 by 10 ug/m3
(e.g. moving from Southeast US to most of Canada)
Long-term exposure to urban outdoor PM2.5 causes 800,000 deaths/yr (Cohen et al., 2004)
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PM (Aerosol) Concentrations Sensitive to Climate ChangePM (Aerosol) Concentrations Sensitive to Climate Change
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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
Monitor locations can be driven by compliance objectives
~1 site / 10,000 km2 in continental US & southern Canada
Lee et al., ACPD, 2011
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Satellite Observations Complement Ground-Based Satellite Observations Complement Ground-Based Measurements Measurements
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Column Observations of Aerosol and NOColumn Observations of Aerosol and NO2 2 Strongly Influenced Strongly Influenced
by Boundary Layer Concentrationsby Boundary Layer Concentrations
S(z) = shape factor C(z) = concentration Ω = columnNO2
Aerosol Extinction
O3
Martin, AE, 2008
0.30 0.36 0.43 0.52 0.62 2.2 4.7
Aerosol O3 NO2
0.75 9.6
Normalized GEOS-Chem Normalized GEOS-Chem Summer Mean Profiles Summer Mean Profiles over North Americaover North America
Strong Rayleigh Scattering
( )( )
C zS z
Weak Thermal Contrast
Vertical Profile Affects Boundary-Layer Information in Satellite ObsVertical Profile Affects Boundary-Layer Information in Satellite Obs
O3
Wavelength (μm)
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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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Calculate AOD/PMCalculate AOD/PM2.5 2.5 with Chemical Transport Model with Chemical Transport Model
(GEOS-Chem) Simulation(GEOS-Chem) Simulation
Aaron van Donkelaar
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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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Error in Satellite-Derived PMError in Satellite-Derived PM2.52.5 has Three Primary Sources has Three Primary Sources
Satellite• Error of 0.1 + 20% vs
independent observations
• Implication for satellite PM2.5
determined by η
Satellite-derived PM2.5 = η· AOD
Model• Affected by aerosol optical
properties, concentrations, vertical profile, relative humidity
• Most sensitive to vertical profile [van Donkelaar et al., 2006]
Sampling Biases
Satellite retrievals are at specific time of day for cloud-free conditions
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τa(z)/τa(z=0)
Alti
tud
e [k
m]
Evaluate Simulated Evaluate Simulated (GEOS-Chem) Vertical (GEOS-Chem) Vertical Profile with Satellite Profile with Satellite
(CALIPSO) Observations(CALIPSO) Observations
• Coincidently sample model and CALIPSO extinction profiles
– Jun-Dec 2006
• Compare % within boundary layer
Model (GC)CALIPSO (CAL)
Optical depth above altitude zTotal column optical depth
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Error EstimateError Estimate• Estimate error from bias in profile and
AOD ±(1 μg/m3 + 15%) • Contains 68% (1 SD) of North
American data
• Total uncertainty 25% (with sampling)• Global population-weighted mean
uncertainty 7 μg/m3
van Donkelaar et al., EHP, 2010
Sat
ellit
e-D
eriv
ed
[μg/
m3]
In-situ PM2.5 [μg/m3]
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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
• ~1 year life expectancy lost for 10 μg/m3
• 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
Estimate global burden of disease (WHO) attributable to air pollution (Cohen et al. in prep)
Significant association of PM2.5 and health at low PM2.5 levels (Crouse et al., EHP, in prep)
Satellite dataset dominant contributor to national PM2.5 model (Hystad et al., EHP, in press)
Estimate global mortality from PM2.5 (Evans et al. in prep)
Air pollution and adverse birth outcomes: An international analysis of WHO Global Survey on Maternal and Perinatal Health (Fleischer et al., ISEE, 2011)
Cigarette smoking is a negative confounder in epidemiological studies of long-term ambient air pollution and mortality outcomes in Canada (Villeneuve et al., OEM, 2011)
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USA Today: Hundreds Dead from Heat, Smog, USA Today: Hundreds Dead from Heat, Smog, Wildfires in MoscowWildfires 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
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Spatial and Temporal Variation in Satellite-Based PMSpatial and Temporal Variation in Satellite-Based PM2.52.5
during Moscow 2010 Firesduring Moscow 2010 Fires
van Donkelaar et al., AE, submitted
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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., submitted
MODIS-based
In Situ PM2.5
In Situ PM2.5 from PM10
r2 =0.85, slope=1.06
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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 Afternoon NOGround-Level Afternoon NO2 2 Inferred From OMI for 2005 Inferred From OMI for 2005
Lok Lamsal
Spatial Correlation vs In Situ for North America = 0.78
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ChallengesChallengesRemote Sensing: Improved algorithms to increase accuracy, resolution,
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 to Inform Air Pollution ExposureSensing to Inform Air Pollution Exposure
Acknowledgements:Acknowledgements: Health Canada Health Canada NSERC NSERC NASA NASA
Health Applications:Close interaction to develop appropriate applications