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	16807316
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.
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
语种	英语
来源机构	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132159
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]. Atmospheric Chemistry and Physics,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.,20(6).
MLA	Wei J.,et al."Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees".20.6(2020).