气候变化领域集成服务门户(CCPortal): Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees

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DOI	10.5194/acp-20-3273-2020
	Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees
	Wei J.; Li Z.; Cribb M.; Huang W.; Xue W.; Sun L.; Guo J.; Peng Y.; Li J.; Lyapustin A.; Liu L.; Wu H.; Song Y.
发表日期	2020
ISSN	1680-7316
起始页码	3273
结束页码	3289
卷号	20 期号:6
英文摘要	Fine particulate matter with aerodynamic diameters ≤ 2:5 μm (PM2:5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2:5 concentrations has made intensive use of satellitederived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2:5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space-time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2:5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2:5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low rootmean-square error of 10.33 (10.93) μgm-3, a small mean absolute error of 6.69 (7.15) μgm-3 and a small mean relative error of 21.28% (23.69 %). In particular, the model captured well the PM2:5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2:5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2 D 0:80), which can be used to estimate historical PM2:5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2:5 dataset across mainland China (i.e., ChinaHighPM2:5), important for air pollution studies focused on urban areas. © Author(s) 2020.
语种	英语
scopus关键词	accuracy assessment; atmospheric modeling; atmospheric pollution; concentration (composition); diameter; estimation method; machine learning; metropolitan area; particulate matter; satellite data; spatial resolution; China
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141469
作者单位	State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China; College of Geomatics, Shandong University of Science and Technology, Qingdao, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China; Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, United States; College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China; Facult...
推荐引用方式 GB/T 7714	Wei J.,Li Z.,Cribb M.,et al. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees[J],2020,20(6).
APA	Wei J..,Li Z..,Cribb M..,Huang W..,Xue W..,...&Song Y..(2020).Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees.Atmospheric Chemistry and Physics,20(6).
MLA	Wei J.,et al."Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees".Atmospheric Chemistry and Physics 20.6(2020).