title: Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM2.5 levels.
abstract: The spatial and temporal characteristics of fine particulate matter (PM2.5, particulate matter <2.5 μm 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.
authors: 1] State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China [2] Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA [3] University of the Chinese Academy of Sciences, Beijing, China.
authors: Chao
authors: C
authors: Yu
authors: Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
authors: Larry
authors: L
authors: Di Girolamo
authors: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors: Liangfu
authors: L
authors: Chen
authors: Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
authors: Xueying
authors: X
authors: Zhang
authors: Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
authors: Yang
authors: Y
authors: Liu
mesh_terms: D000336:Aerosols / Q000032:analysis; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D064751:Ammonium Compounds / Q000032:analysis; D004784:Environmental Monitoring / Q000379:methods; D006801:Humans; D009566:Nitrates / Q000032:analysis; D052638:Particulate Matter / Q000032:analysis*; D012044:Regression Analysis; D012424:Rural Population; D017215:Satellite Communications; D012621:Seasons; D015144:Southeastern United States; D013431:Sulfates / Q000032:analysis; D014481:United States; D018557:United States National Aeronautics and Space Administration; D014505:Urban Population; D014887:Weather
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D064751:Ammonium Compounds; D009566:Nitrates; D052638:Particulate Matter; D013431:Sulfates