Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates for North America

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• 作者：Aaron van Donkelaar ; Randall V. Martin ; Adam N. Pasch ; James J. Szykman ; Lin Zhang ; Yuxuan X. Wang ; Dan Chen
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2012
• 出版时间：November 6, 2012
• 年：2012
• 卷：46
• 期：21
• 页码：11971-11978
• 全文大小：632K
• 年卷期：v.46,no.21(November 6, 2012)
• ISSN：1520-5851

文摘

We improve the accuracy of daily ground-level fine particulate matter concentrations (PM_2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM_2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM_2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-蟽 mean uncertainties are reduced across the United States and southern Canada from 卤 (1 渭g/m³ + 67%) to 卤 (1 渭g/m³ + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM_2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-蟽 uncertainty to 卤(1 渭g/m³ + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM_2.5 as compared to day-specific relationships.