气候变化领域集成服务门户(CCPortal): Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model

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DOI	10.1016/j.atmosres.2020.105146
	Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model
	Jiang T.; Chen B.; Nie Z.; Ren Z.; Xu B.; Tang S.
发表日期	2021
ISSN	0169-8095
卷号	248
英文摘要	Fine particulate matter such as PM2.5 has been the focus of increasing public concerns because of its adverse effect on environment and health risks. However, existing efforts of mapping PM2.5 concentrations are always limited by coarse spatial resolutions and temporal frequencies. Addressing this shortcoming, here we explicitly estimated hourly PM2.5 concentrations at 1-km spatial resolution in China from March 2018 to February 2019 using a two-stage random forest model. In the first stage, we used a gap-filling method to generate full-coverage Aerosol Optical Depth (AOD) by fusing AOD data from satellite (Himawari-8 and MODIS) and weather forecast model (CAMS), and additional meteorological and geographical variables. Gap-filled AOD was subsequently used to estimate hourly PM2.5 in the Stage II. Results showed that our model achieved accurate and robust estimations of PM2.5 concentrations, with an overall cross-validated R2 of 0.85, root mean squared error of 11.02 μg/m3, and mean absolute error of 6.73 μg/m3. CAMS-simulated PM2.5, elevation, and gap-filled AOD were identified to be important variables contributing to the model performance of PM2.5 estimation. The model performance varied over the daily temporal scale. Specifically, daily estimation model performed better in spring and winter but worse in summer and autumn. We provide an alternative to generate spatially and temporally explicit mapping of PM2.5 concentrations with fine resolutions, making it possible to achieve real-time monitoring of air pollutions. The detailed spatial heterogeneity and diurnal variability of PM2.5 concentrations will also be valuable for environmental exposure assessments. © 2020
英文关键词	AOD; China; Fine spatiotemporal resolution; PM2.5; Random forest
语种	英语
scopus关键词	Decision trees; Health risks; Mapping; Mean square error; Public risks; Random forests; Aerosol optical depths; Environmental exposure; Fine particulate matter; Random forest modeling; Real time monitoring; Root mean squared errors; Spatial heterogeneity; Weather forecast models; Weather forecasting; aerosol composition; algorithm; atmospheric modeling; concentration (composition); estimation method; particulate matter; spatial resolution; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141721
作者单位	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; Department of Land, Air and Water Resources, University of California, Davis, CA 95616, United States; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China; National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China; Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, 100081, China
推荐引用方式 GB/T 7714	Jiang T.,Chen B.,Nie Z.,et al. Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model[J],2021,248.
APA	Jiang T.,Chen B.,Nie Z.,Ren Z.,Xu B.,&Tang S..(2021).Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model.Atmospheric Research,248.
MLA	Jiang T.,et al."Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model".Atmospheric Research 248(2021).