Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure

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

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)

PM (Aerosol) Concentrations Sensitive to Climate ChangePM (Aerosol) Concentrations Sensitive to Climate Change

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

Satellite Observations Complement Ground-Based Satellite Observations Complement Ground-Based Measurements Measurements

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)

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

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

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

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


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


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

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

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


Sat

ellit

e-D

eriv

ed

[μg/

m3]

In-situ PM2.5 [μg/m3]

• 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


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

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)

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

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

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

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

http://www-as.harvard.edu/chemistry/trop/satellite/index.html

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

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

Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure

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