气候变化领域集成服务门户(CCPortal): Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model

CCPortal

DOI	10.1016/j.atmosenv.2021.118448
	Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model
	Li R.; Guo J.; Geng G.; Xiao Q.; Zhang Q.
发表日期	2021
ISSN	1352-2310
卷号	255
英文摘要	Fine particles with aerodynamic diameters less than 1 μm (PM1) often exert a greater threaten on human health, and thus it is highly imperative to accurately characterize the spatiotemporal variation of PM1 concentrations and to assess the potential health risks. Our study attempted to predict the long-term full-coverage PM1 concentrations across China during 2004–2018 using a stacking decision tree model based on satellite data, meteorological variables, and other geographical covariates. The result suggested that the stacking model captured strong prediction capability with a higher cross-validation (CV) R2 value (0.64), and the lower root-mean-square error (RMSE: 18.60 μg/m3) and mean absolute error (MAE: 11.96 μg/m3) compared with the individual model. The higher PM1 concentrations were mainly concentrated on North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin due to intensive anthropogenic activities and poor meteorological conditions especially in winter. The annual mean PM1 concentration in China exhibited a remarkable increase during 2004–2007 by 1.34 μg/m3/year (p < 0.05), followed by a gradual decrease during 2007–2018 by −1.61 μg/m3/year (p < 0.05). After 2013, the mean PM1 concentration at the national scale experienced a dramatic decrease by −2.96 μg/m3/year (p < 0.05). The persistent increase of PM1 concentration across China during 2004–2007 was mainly caused by the rapid increases of energy consumption and inefficient emission control measures, while the dramatic decrease since 2013 was attributed to increasingly strict control measures, particularly the implementation of the Air Pollution Prevention and Control Action Plan (the Action Plan). The long-term PM1 estimates obtained here provide a key scientific basis and data support for epidemiological research and air pollution mitigation. © 2021
关键词	AOD China Long-term trends PM1 Stacking model
语种	英语
scopus关键词	Air pollution; Emission control; Energy utilization; Health risks; Mean square error; Risk assessment; River pollution; Action plan; Aerodynamic diameters; AOD; China; Decision tree modeling; Fine-particles; Long-term trend; PM1; Stacking models; Stackings; Decision trees; aerodynamics; atmospheric pollution; concentration (composition); error analysis; estimation method; health risk; human activity; model validation; particle size; particulate matter; prediction; satellite data; spatiotemporal analysis; air pollution control; article; China; controlled study; cross validation; decision tree; energy consumption; human; meteorology; prediction; river; winter; China; North China Plain; Sichuan Basin; Yangtze River
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248422
作者单位	Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
推荐引用方式 GB/T 7714	Li R.,Guo J.,Geng G.,et al. Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model[J],2021,255.
APA	Li R.,Guo J.,Geng G.,Xiao Q.,&Zhang Q..(2021).Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model.ATMOSPHERIC ENVIRONMENT,255.
MLA	Li R.,et al."Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model".ATMOSPHERIC ENVIRONMENT 255(2021).