Time Series Analysis of Satellite-Derived PM2.5

Brian Boys, Randall V. Martin, Aaron van Donkelaar, Ryan MacDonell, Nai-Yung C. Hsu*

NASA/Goddard Space Flight Ctr*

~ 1 year increase of life expectancy for decreasing long-term exposure of PM2.5 by 10 ug/m3

Negative health outcomes from fine particulate (PM2.5) exposure

Increased morbidity and mortality from bothacute and chronic exposure to ambient fineparticulate matter (PM2.5)

Satellite-derived surface PM2.5: from satellite retrieved column AOD and modeled ‘surface PM2.5/column AOD’

X

‘η’

Satellites used:MISR on TERRA, Dec 1999 - ?• Designed for multianlge viewing of aerosols • ~400 km swath width, ~7 day coverage

SeaWiFS on SeaStar, Aug 1997 – Dec 2010• Designed to measure ocean colour• ~1500 km swath width, ~2 day coverage

Model used:GEOS-Chem (GC) chemical transport model*

• Model version 9.01.03 @ 2x2.5 deg horizontal resolution with MERRA meteorological fields

www.geos.chem.org*

Trend [µg m-3 yr-1]0 0.5 1 1.5 2-0.5-1-1.5-2

MISR2000 - 2011

SeaWiFS1998 - 2010

Spatially coherent regional “trends” in satellite-derived PM2.5

Trend [µg m-3 yr-1]0 0.5 1 1.5 2-0.5-1-1.5-2

MISR2000 - 2011

SeaWiFS1998 - 2010

Spatially coherent regional “trends” in satellite-derived PM2.5

PM 2.

5 [µg

m-3

]

GEOS- Chem

van Donkelaar et al, in prep*

JJA DJF

Calip

so o

bs. C

orr.

Fac.

E. US Chicago

ηmodel

ηinsitu

PM2.

5 [µg

m-3

] / A

OD

Evaluation of modeled ‘PM2.5 /column AOD’ [η]

Improving modeled η with region specific Calipso correction factor*

GEOS-Chem,coinciently sampled with MISR

0 0.25

-0.25 1-1 1.5-1.5 2-2

0.01

0.05

0.1

P- v

alue

Trend [µg m-3 yr-1]

Significant trends in modeled ‘PM2.5 /column AOD’ [η]

GEOS-Chem,coinciently sampled with SeaWiFS

0 0.25

-0.25 1-1 1.5-1.5 2-2

0.01

0.05

0.1

MIS

R P

- val

ue

Trend [µg m-3 yr-1]

MISR2000-2011

GEOS-Chem,coinciently sampled with MISR

Significant trends in satellite-derived PM2.5: MISR

0.25 1-1 1.5-1.5 2-2

0.01

0.05

0.1

SeaW

iFS

P- v

alue

Trend [µg m-3 yr-1]

SeaWiFS1998 -2010

GEOS-Chem,coincidently sampled with SeaWiFS

Significant trends in satellite-derived PM2.5: SeaWiFS

0-0.25

MISR

Regional average of satellite-derived PM2.5: E. US trend represented by GEOS-Chem secondary inorganics (SI) fraction

PM 2.

5 [µg

m-3

]

SeaWiFS

GEOS-Chem secondary inorganics

GEOS- ChemGEOS-Chem secondary inorganics

GEOS- Chem

MISR

Regional average of satellite-derived PM2.5: P. Gulf trend represented by GEOS-Chem fine dust fraction

PM 2.

5 [µg

m-3

]

SeaWiFS

GEOS-Chem fine dustGEOS- Chem

GEOS-Chem fine dust

GEOS- Chem

MISR

Regional average of satellite-derived PM2.5: India trend represented by GEOS-Chem SI, OC, and BC

PM 2.

5 [µg

m-3

]

SeaWiFS

GEOS- ChemGEOS- Chem

GEOS-Chem secondary inorganics

GEOS-Chem BC & OCGEOS-Chem secondary inorganics

GEOS-Chem BC & OC

MISR

Regional average of satellite-derived PM2.5: E. Asia trend represented by GEOS-Chem SI, OC, and BC

PM 2.

5 [µg

m-3

]

SeaWiFS

GEOS- ChemGEOS- Chem

GEOS-Chem secondary inorganics

GEOS-Chem secondary inorganics

GEOS-Chem BC & OCGEOS-Chem BC & OC

GEOS- Chem

E. US

India E. Asia

PM2.

5 [µg

m-3

] MISR

GCMISR

GCSeaWiFS

SeaWiFS

Comparison of MISR and SeaWiFS: sampling effectsP. Gulf

MISR

GC MISR

GC SeaWiFS

SeaWiFS

MISR

GCMISR

GCSeaWiFS

SeaWiFS

MISR

GCMISR

GC SeaWiFS

SeaWiFS

PM2.

5 [µg

m-3

]

Conclusions• Decadal(+) time series of AOD retrieving satellites

• Satellite-derived surface PM2.5 concentrations show spatially coherent regional change

• Evolution of surface to column relationship important for satellite-derived surface PM estimates

• Representation of regional “trend” components through GEOS-Chem

Acknowledgments