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

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Satellite remote sensing of global air pollution

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)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-Chem) Simulation of Aerosol Optical Depth

Aaron van Donkelaar


Satellite remote sensing of global air pollution

Ground-level “Dry” PM2.5 = 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

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 PM2.5

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


Satellite remote sensing of global air pollution

van Donkelaar et al., EHP, 2010


Satellite remote sensing of global air pollution

van Donkelaar et al., EHP, 2010


Long term exposure to outdoor ambient pm 2 5

WHO Guideline & Interim Targets

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

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

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 NO2 Inferred From OMI for 2005

Spatial Correlation vs In Situ for North America = 0.78

Lamsal et al., JGR, 2008


Challenges

Challenges

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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