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| Titel |
A robust calibration approach for PM10 prediction from MODIS aerosol optical depth |
| VerfasserIn |
X. Q. Yap, M. Hashim |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 13, no. 6 ; Nr. 13, no. 6 (2013-03-27), S.3517-3526 |
| Datensatznummer |
250018546
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| Publikation (Nr.) |
 copernicus.org/acp-13-3517-2013.pdf |
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| Zusammenfassung |
| Investigating the human health effects of atmospheric particulate
matter (PM) using satellite data are gaining more attention due to
their wide spatial coverage and temporal advantages. Such
epidemiological studies are, however, susceptible to bias errors and
resulted in poor predictive output in some locations. Current
methods calibrate aerosol optical depth (AOD) retrieved from MODIS
to further predict PM. The recent satellite-based AOD calibration
uses a mixed effects model to predict location-specific PM on
a daily basis. The shortcomings of this daily AOD calibration are
for areas of high probability of persistent cloud cover throughout
the year such as in the humid tropical region along the equatorial
belt. Contaminated pixels due to clouds causes radiometric errors in
the MODIS AOD, thus causes poor predictive power on air quality. In
contrary, a periodic assessment is more practical and robust
especially in minimizing these cloud-related contaminations. In this
paper, a simple yet robust calibration approach based on monthly AOD
period is presented. We adopted the statistical fitting method with
the adjustment technique to improve the predictive power of MODIS
AOD. The adjustment was made based on the long-term observation
(2001–2006) of PM10-AOD residual error
characteristic. Furthermore, we also incorporated the ground PM
measurement into the model as a weighting to reduce the bias of the
MODIS-derived AOD value. Results indicated that this robust approach
with monthly AOD calibration reported an improved average accuracy
of PM10 retrieval from MODIS data by 50% compared to
widely used calibration methods based on linear regression models,
in addition to enabling further spatial patterns of periodic PM
exposure to be undertaken. |
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