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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Lv, Baolei Chang, Howard H. Bai, Yuqi Cai, Jun Xu, Bing Russell, Armistead G. Hu, Yongtao |
| Description | Country affiliation: China Author Affiliation: Lv B ( The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China); Hu Y ( School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.); Chang HH ( Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.); Russell AG ( School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States. Electronic address: ted.russell@gatech.edu.); Cai J ( The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China); Xu B ( The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China); Bai Y ( The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China) |
| Abstract | The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM ) 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 PM variations. In this study, we aim to estimate ground-level PM concentrations 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-PM relationships. 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 (R =0.75) and in the cross validation procedure (R =0.58 by random method and R =0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM concentrations. The gap-filled AOD values corresponded well with our understanding of PM pollution conditions in BTH. The prediction accuracy of PM concentrations 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 PM estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures. |
| ISSN | 00489697 |
| Journal | Science of The Total Environment |
| Volume Number | 580 |
| e-ISSN | 18791026 |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2017-02-15 |
| Publisher Place | Netherlands |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Environmental Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
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