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