Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM2.5 levels

作者:Yu, C (Yu, Chao)[ 1,2,3 ] ; Di Girolamo, L (Di Girolamo, Larry)[ 4 ] ; Chen, LF (Chen, Liangfu)[ 1 ] ; Zhang, XY (Zhang, Xueying)[ 2 ] ; Liu, Y (Liu, Yang)[ 2 ]

JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY

卷: 25  期: 5  页: 457-466

DOI: 10.1038/jes.2014.49

出版年: SEP-OCT 2015

摘要

The spatial and temporal characteristics of fine particulate matter (PM2.5, particulate matter <2.5 mu m in aerodynamic diameter) are increasingly being studied from satellite aerosol remote sensing data. However, cloud cover severely limits the coverage of satellite-driven PM2.5 models, and little research has been conducted on the association between cloud properties and PM2.5 levels. In this study, we analyzed the relationships between ground PM2.5 concentrations and two satellite-retrieved cloud parameters using data from the Southeastern Aerosol Research and Characterization (SEARCH) Network during 2000-2010. We found that both satellite-retrieved cloud fraction (CF) and cloud optical thickness (COT) are negatively associated with PM2.5 levels. PM2.5 speciation and meteorological analysis suggested that the main reason for these negative relationships might be the decreased secondary particle generation. Stratified analyses by season, land use type, and site location showed that seasonal impacts on this relationship are significant. These associations do not vary substantially between urban and rural sites or inland and coastal sites. The statistically significant negative associations of PM2.5 mass concentrations with CF and COT suggest that satellite-retrieved cloud parameters have the potential to serve as predictors to fill the data gap left by satellite aerosol optical depth in satellite-driven PM2.5 models.

通讯作者地址: Liu, Y (通讯作者)

Emory Univ, Dept Environm Hlth, Rollins Sch Publ Hlth, 1518 Clifton Rd NE, Atlanta, GA 30322 USA.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Emory Univ, Dept Environm Hlth, Rollins Sch Publ Hlth, Atlanta, GA 30322 USA

[ 3 ] Univ Chinese Acad Sci, Beijing, Peoples R China

[ 4 ] Univ Illinois, Dept Atmospher Sci, Urbana, IL USA