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DOI	10.1016/j.atmosenv.2021.118302
	Review of satellite-driven statistical models PM2.5 concentration estimation with comprehensive information
	Xu X.; Zhang C.; Liang Y.
发表日期	2021
ISSN	1352-2310
卷号	256
英文摘要	Particulate matter pollution is increasingly serious due to the acceleration of urbanization, which has an adverse effect on human health. In order to offset that the nonuniform spatial distribution of PM2.5 and the short of long-term observational data from ground monitoring, optical products based on satellite retrieval, such as aerosol optical depth (AOD), had been the main data to estimate PM2.5 concentration. This paper summaries the principle and progress of statistical models for PM2.5 concentration estimation in recent decades. Specifically, we first reviews the characteristics and gap-filling methods of satellite-driven AOD products. Secondly, the auxiliary variables used to enhance the explanatory of PM2.5 changes are introduced and their impact on fine particles is analyzed. Third and most important, we summarizes the statistical models of PM2.5 concentration estimation, and discusses the model performance of each subtype separately (i.e., regression-based, machine learning-based and hybrid model). According to the summary and discussion of the above work, there are still problems and challenges that need to be paid attention and further improved. Finally, this paper also provides some feasible improvements about quality control of PM2.5 measurements, maximize the role of PM2.5 measurements in the model and the trend analysis of fine time scale PM2.5. © 2021 Elsevier Ltd
关键词	Aerosol optical depth Auxiliary variable Estimation model PM2.5 concentration
语种	英语
scopus关键词	Learning algorithms; Learning systems; Optical properties; Satellites; Adverse effect; Aerosol optical depths; Auxiliary variables; Comprehensive information; Concentration estimations; Estimation models; Particulate matter pollution; PM$-2.5$; PM2.5 concentration; Statistic modeling; Aerosols; atmospheric modeling; concentration (composition); literature review; particulate matter; pollution effect; public health; satellite altimetry; statistical analysis; urbanization; aerosol; article; attention; human; machine learning; optical depth; particulate matter 2.5; quality control
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/248399
作者单位	Department of Environmental Engineering, Kyoto University, Kyoto, Japan; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
推荐引用方式 GB/T 7714	Xu X.,Zhang C.,Liang Y.. Review of satellite-driven statistical models PM2.5 concentration estimation with comprehensive information[J],2021,256.
APA	Xu X.,Zhang C.,&Liang Y..(2021).Review of satellite-driven statistical models PM2.5 concentration estimation with comprehensive information.ATMOSPHERIC ENVIRONMENT,256.
MLA	Xu X.,et al."Review of satellite-driven statistical models PM2.5 concentration estimation with comprehensive information".ATMOSPHERIC ENVIRONMENT 256(2021).