干旱区生态环境知识资源中心(Arid): High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model

Arid

DOI	10.1016/j.jclepro.2021.126493
	High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model
	Wang, Zhige; Zhou, Yue; Zhao, Ruiying; Wang, Nan; Biswas, Asim; Shi, Zhou
通讯作者	Shi, Z (corresponding author), Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China.
来源期刊	JOURNAL OF CLEANER PRODUCTION
ISSN	0959-6526
EISSN	1879-1786
出版年	2021
卷号	297
英文摘要	The concentration of fine particulate matter (PM2.5) has a significant impact on the environment and human health. However, strong spatial heterogeneity and spatiotemporal dependence increases the difficulty of prediction. Moreover, due to the lag of the update of auxiliary variables at national scale in the prediction application, it is still difficult to achieve the timely nationwide PM2.5 prediction at present. To better model and predict real time concentrations and spatial distributions of PM2.5, this study developed a workflow of future PM2.5 concentrations prediction based on long short-term memory (LSTM) model. Using ground-based station PM2.5 data in 2014-2018, the 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product and other auxiliary data to predict PM2.5 concentrations in the next year and generate a high-resolution national PM2.5 concentration spatial distribution map. The LSTM model outperformed random forest (RF) and Cubist approaches for prediction PM2.5 because of its recurrent neural network structure that can capture time dependence and nonlinear relationships among PM2.5 concentrations and other independent variables, and exhibited a stable accuracy with an R-2 of 0.83, by applying the annual time series, with an improvement of 0.04-0.09, compared to daily and monthly data. The results indicated that PM2.5 pollution had gradually decreased in 2019 after application of pollution controls, with annual mean PM2.5 concentrations of 27.33 +/- 15.56 mu g m(-3), although there were still some areas with severe pollution, including the North China Plain, parts of the Loess Plateau, and the Taklimakan Desert. The LSTM model makes it possible to predict fine-scale PM2.5 spatial distributions nationwide in the future and may thus be useful for sustainable management and control of air pollution at a national scale. (C) 2021 Elsevier Ltd. All rights reserved.
英文关键词	PM2.5 prediction AOD LSTM Time series data modeling
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000659113300001
WOS关键词	FINE PARTICULATE MATTER ; AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; NEURAL-NETWORK ; AIR-POLLUTION ; SATELLITE ; HAZE ; TRENDS ; MODIS ; TERRAIN
WOS类目	Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350761
作者单位	[Wang, Zhige; Zhao, Ruiying; Wang, Nan; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China; [Wang, Zhige; Zhao, Ruiying; Wang, Nan; Shi, Zhou] Zhejiang Univ, Coll Environm & Resource Sci, Key Lab Environm Remediat & Ecol Hlth, Minist Educ, Hangzhou 310058, Peoples R China; [Zhou, Yue] Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, B-1348 Louvain La Neuve, Belgium; [Biswas, Asim] Univ Guelph, Sch Environm Sci, Alexander Hall 135,50 Stone Rd East, Guelph, ON N1G 2W1, Canada
推荐引用方式 GB/T 7714	Wang, Zhige,Zhou, Yue,Zhao, Ruiying,et al. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model[J],2021,297.
APA	Wang, Zhige,Zhou, Yue,Zhao, Ruiying,Wang, Nan,Biswas, Asim,&Shi, Zhou.(2021).High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model.JOURNAL OF CLEANER PRODUCTION,297.
MLA	Wang, Zhige,et al."High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model".JOURNAL OF CLEANER PRODUCTION 297(2021).