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.

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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


Large regions have insufficient measurements for air pollution exposure assessment
Large Regions Have Insufficient Measurements for Air Pollution Exposure Assessment

Locations of Publicly-Available Long-Term PM2.5 Monitoring Sites

Aaron van Donkelaar


Aerosol remote sensing analogy with visibility effects of aerosol loading
Aerosol Remote Sensing: Analogy with Visibility Effects of Aerosol Loading

Waterton Lakes/Glacier National Park

Pollution haze over East Coast

7.6 ug m-3

22 ug m-3


Combined aerosol optical depth aod from modis and misr instruments for 2001 2006
Combined Aerosol Optical Depth (AOD) Aerosol from MODIS and MISR Instruments for 2001-2006

Combined

MODIS/MISR

r = 0.63(vs. in-situ PM2.5)

van Donkelaar et al., EHP, 2010


Chemical transport model geos chem simulation of aerosol optical depth
Chemical Transport Model (GEOS- Aerosol Chem) Simulation of Aerosol Optical Depth

Aaron van Donkelaar


Ground-level “Dry” PM Aerosol 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


Significant agreement with coincident in situ measurements
Significant Agreement with Coincident In situ Measurements Aerosol

Annual Mean PM2.5 [μg/m3] (2001-2006)

Satellite

Derived

Satellite-Derived [μg/m3]

In-situ

In-situ PM2.5 [μg/m3]

van Donkelaar et al., EHP, 2010


Global climatology 2001 2006 of pm 2 5
Global Climatology (2001-2006) of PM Aerosol 2.5

Evaluation with measurements outside Canada/US

Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71

van Donkelaar et al., EHP, 2010




Long term exposure to outdoor ambient pm 2 5

WHO Guideline & Interim Targets Aerosol

Long-term Exposure to Outdoor Ambient PM2.5

AQG IT-3 IT-2 IT-1

100

90

80

70

60

50

40

30

20

10

0

  • 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

Population [%]

5 10 15 25 35 50 100

PM2.5 Exposure [μg/m3]

van Donkelaar et al., EHP, 2010


Emerging applications
Emerging Applications Aerosol

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


Application of satellite based estimates to moscow smoke event
Application of Satellite-based Estimates to Moscow Smoke Event

During Fires

Before Fires

MODIS-based

In Situ

van Donkelaar et al., in prep


General approach to estimate surface no 2 concentration

In Situ Event

GEOS-Chem

General Approach to Estimate Surface NO2 Concentration

Method: Solar backscatter

NO2 Column

Coincident ModelProfile

l1

l2

Scattering by

Earth surface

and atmosphere

Idealized

NO2

absorption

spectrum

  • S→ Surface Concentration

  • Ω → Tropospheric column

l1

l2


Ground level no 2 inferred from omi for 2005
Ground-Level NO Event2 Inferred From OMI for 2005

Spatial Correlation vs In Situ for North America = 0.78

Lamsal et al., JGR, 2008


Challenges
Challenges Event

Encouraging Prospects for Satellite Remote Sensing of Air Pollutants

Remote Sensing:

Improved algorithms to increase accuracy and observe other pollutants

Modeling:

Develop representation of processes

Measurements:

More needed for evaluation

Health Applications:

Close interaction to develop appropriate applications

Acknowledgements: Health Canada NSERC NASA


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