title: Daily estimation of ground-level PM2.5concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations.
abstract: The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM2.5) concentrations to evaluate their health effects. The associated estimation accuracy is often reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5variations. In this study, we aim to estimate ground-level PM2.5concentrations at a fine resolution with improved accuracy by fusing fine-scale satellite and ground observations in the populated and polluted Beijing-Tianjin-Hebei (BTH) area of China in 2014. We employed a Bayesian-based statistical downscaler to model the spatio-temporal linear AOD-PM2.5relationships. We used a 3km MODIS AOD product, which was resampled to a 4km resolution in a Lambert conic conformal projection, to assist comparison and fusion with predictions by atmospheric chemistry models. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a good performance in the fitting procedure (R2=0.75) and in the cross validation procedure (R2=0.58 by random method and R2=0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5pollution conditions in BTH. The prediction accuracy of PM2.5concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.
authors.affiliation: The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies, Beijing 100875, China.
authors.forename: Baolei
authors.initials: B
authors.lastname: Lv
authors.affiliation: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
authors.forename: Yongtao
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.affiliation: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States. Electronic address: ted.russell@gatech.edu.
authors.forename: Armistead G
authors.initials: AG
authors.lastname: Russell
authors.affiliation: The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies, Beijing 100875, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Cai
authors.affiliation: The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies, Beijing 100875, China. Electronic address: bingxu@tsinghua.edu.cn.
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.affiliation: The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies, Beijing 100875, China. Electronic address: yuqibai@tsinghua.edu.cn.
authors.forename: Yuqi
authors.initials: Y
authors.lastname: Bai
keywords: Aerosol optical depth; Downscaling; MODIS; PM(2.5)