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An ensemble-based model of PM<sub>2.5</sub> concentration across the contiguous United States with high spatiotemporal resolution.

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第一作者： Qian,Di

第一单位： Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States; Research Center for Public Health, Tsinghua University, Beijing, China. Electronic address: qiandi@mail.harvard.edu.

作者： Qian,Di ^[1] ; Heresh,Amini ^[2] ; Liuhua,Shi ^[2] ; Itai,Kloog ^[3] ; Rachel,Silvern ^[4] ; James,Kelly ^[5] ; M Benjamin,Sabath ^[6] ; Christine,Choirat ^[6] ; Petros,Koutrakis ^[2] ; Alexei,Lyapustin ^[7] ; Yujie,Wang ^[8] ; Loretta J,Mickley ^[9] ; Joel,Schwartz ^[2]

作者单位： Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States; Research Center for Public Health, Tsinghua University, Beijing, China. Electronic address: qiandi@mail.harvard.edu. ^[1] Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States. ^[2] Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel. ^[3] Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States. ^[4] U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Research Triangle Park, NC, United States. ^[5] Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, MA, United States. ^[6] NASA Goddard Space Flight Center, Greenbelt, MD, United States. ^[7] University of Maryland, Baltimore County, Baltimore, MD, United States. ^[8] John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States. ^[9]

关键词 Ensemble model Fine particulate matter (PM(2.5))Gradient boosting Neural network Random forest

医学主题词空气污染物(Air Pollutants);空气污染(Air Pollution);算法(Algorithms);环境监测(Environmental Monitoring);模型, 统计学(Models, Statistical);颗粒物(Particulate Matter);美国(United States)

DOI 10.1016/j.envint.2019.104909

PMID 31272018

发布时间 2025-05-30

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