气候变化领域集成服务门户(CCPortal): Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning

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DOI	10.1016/j.atmosres.2021.105516
	Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning
	Zeng Z.; Gui K.; Wang Z.; Luo M.; Geng H.; Ge E.; An J.; Song X.; Ning G.; Zhai S.; Liu H.
发表日期	2021
ISSN	0169-8095
卷号	254
英文摘要	The spatial-temporal variations of the ground-based and satellite-derived PM2.5 are crucial for studying air quality, human health, and climate change. However, the existing ground-based PM2.5 monitoring network has sparsely-distributed sites and satellite cannot give 24-h PM2.5, which make it difficult to grasp the spatial and sub-daily variation characteristics of PM2.5. This study aims to fill that gap by establishing a virtual network of hourly PM2.5 concentration using the LightGBM model, based on the high-density ground meteorological observations at ~2400 sites across China. The virtual network shows a desirable performance of hourly PM2.5 estimation across China, with R2 of 0.86, root-mean-square error values of 14.99 μg/m3, and mean absolute error of 9.48 μg/m3 (the results of Cross-Validation). It also exhibits high spatial-temporal consistencies with the observed PM2.5. Spatially, the heaviest PM2.5 pollution is mainly distributed in eastern China (especially the Beijing-Tianjin-Hebei, the Yangtze and Pearl river deltas, and the Sichuan-Chongqing areas). Temporarily, PM2.5 exhibits remarkable seasonal and diurnal changes characterized by higher concentration in winter and nighttime and lower in summer and daytime. Meanwhile, we found that visibility can be used as the primary predictor in the machine learning model to enhance the accuracy of estimated PM2.5. The established virtual hourly PM2.5 network (~2400 stations) provides a more intuitive and detailed PM2.5 data for us to understand the diurnal variation of PM2.5 and monitor inter-regional transport of haze over China. It thus is of benefit to the study of air pollution control and related diseases. © 2021
英文关键词	High temporal resolution; High-density meteorological observations; Hourly PM2.5 virtual network; LightGBM; Machine learning
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/236825
作者单位	Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, 430079, China; State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China; School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, 510275, China; School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, Hong Kong; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States; National Meteorological Center, CMA, Beijing, 100081, China
推荐引用方式 GB/T 7714	Zeng Z.,Gui K.,Wang Z.,et al. Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning[J],2021,254.
APA	Zeng Z..,Gui K..,Wang Z..,Luo M..,Geng H..,...&Liu H..(2021).Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning.Atmospheric Research,254.
MLA	Zeng Z.,et al."Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning".Atmospheric Research 254(2021).