气候变化领域集成服务门户(CCPortal): Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

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DOI	10.5194/acp-21-7863-2021
	Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
	Wei J.; Li Z.; Pinker R.T.; Wang J.; Sun L.; Xue W.; Li R.; Cribb M.
发表日期	2021
ISSN	1680-7316
起始页码	7863
结束页码	7880
卷号	21 期号:10
英文摘要	Fine particulate matter with a diameter of less than 2.5 μm (PM2.5) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polarorbiting satellites. This study exploits the use of the nextgeneration geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (Light-GBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samplesD1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV-R2 D0.85), with a root-meansquare error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 μgm-3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10.00 LT (GMTC8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring. © 2021 BMJ Publishing Group. All rights reserved.
语种	英语
scopus关键词	algorithm; atmospheric pollution; diurnal variation; geostationary satellite; ground-based measurement; machine learning; particulate matter; China; Satellites
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/246866
作者单位	State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, United States; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
推荐引用方式 GB/T 7714	Wei J.,Li Z.,Pinker R.T.,et al. Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)[J],2021,21(10).
APA	Wei J..,Li Z..,Pinker R.T..,Wang J..,Sun L..,...&Cribb M..(2021).Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM).ATMOSPHERIC CHEMISTRY AND PHYSICS,21(10).
MLA	Wei J.,et al."Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.10(2021).