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| Titel |
A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions |
| VerfasserIn |
A. Chudnovsky, C. Tang, A. Lyapustin, Y. Wang, J. Schwartz, P. Koutrakis |
| 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. 21 ; Nr. 13, no. 21 (2013-11-07), S.10907-10917 |
| Datensatznummer |
250085802
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| Publikation (Nr.) |
 copernicus.org/acp-13-10907-2013.pdf |
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| Zusammenfassung |
| Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC)
algorithm was developed for the MODerate Resolution Imaging
Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km
resolution. The relationship between MAIAC AOD and PM2.5 as measured by
84 EPA ground monitoring stations in the entire New England and the Harvard
super site during 2002–2008 was investigated and also compared to the
AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval
from Aqua platform (MYD04) for the same days and locations. The correlations
for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that
AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly
higher correlation coefficient (r) for MAIAC can be related to its finer
resolution resulting in better correspondence between AOD and EPA monitoring
sites. Regardless of resolution, AOD–PM2.5 relationship varies daily,
and under certain conditions it can be negative (due to several factors such
as an EPA site location (proximity to road) and the lack of information
about the aerosol vertical profile). By investigating MAIAC AOD data, we
found a substantial increase, by 50–70% in the number of collocated
AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data
are more capable in capturing spatial patterns of PM2.5. Importantly,
the performance of MAIAC AOD retrievals is slightly degraded but remains
reliable under partly cloudy conditions when MYD04 data are not available,
and it can be used to increase significantly the number of days for
PM2.5 spatial pattern prediction based on satellite observations. |
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