IGSNRR OpenIR
Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging
Shao, Yanchuan1; Ma, Zongwei1,2; Wang, Jianghao3,4; Bi, Jun1
2020-10-20
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume740Pages:12
Corresponding AuthorMa, Zongwei(zma@nju.edu.cn) ; Wang, Jianghao(wangjh@lreis.ac.cn)
AbstractAmbient fine particulate matter (PM2.5) plays an important role in cardiovascular- and respiratory-related death. Empirical statistical models have been widely applied to estimate ambient PM2.5 concentrations with correlated variables. However, empirical statistical models ignore the nonlinear relationship between PM2.5 and covariates and assume that residuals are independent and identically distributed random variables. Here, a hybrid approach, which integrates random forest (RF) model and spatiotemporal kriging, is proposed to estimate the daily PM2.5 concentration. The proposed RF-based spatiotemporal kriging (RFSTK) model effectively captures nonlinear interactions among different predictors and accounts for the detailed spatiotemporal dependence of the PM2.5 concentration. The RFSTK model performs well in predicting the daily PM2.5 concentration. The 10-fold overall cross-validation R-2 value is 0.881, the mean absolute error (MAE) is 6.89 mu g/m(3) and the root-mean-square error (RMSE) is 11.48 mu g/m(3), indicating better performance than the original RF model (R-2 = 0.848, MAE = 7.88 mu g/m(3) and RMSE = 13.26 mu g/m(3)). The spatiotemporal prediction of the PM2.5 concentration shows that approximately 90.04% of China had a daily exposure to PM2.5 in 2018 that was below the nation's air quality standard of 75 mu g/m(3). The proposed hybrid method is entirely general and can be applied to map the ambient PM2.5 concentration over a large spatiotemporal domain. (C) 2020 Elsevier B.V. All rights reserved.
KeywordAerosol optical depth PM2.5 Random forest Spatiotemporal kriging
DOI10.1016/j.scitotenv.2020.139761
WOS KeywordAEROSOL OPTICAL DEPTH ; SATELLITE ; MODEL ; PM10 ; REGRESSION ; EXPOSURE ; CLIMATE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41531174] ; National Natural Science Foundation of China[71921003] ; National Natural Science Foundation of China[91644220]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000562059800004
PublisherELSEVIER
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/158033
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMa, Zongwei; Wang, Jianghao
Affiliation1.Nanjing Univ, Sch Environm, State Key Lab Pollut Control & Resource Reuse, Nanjing 210023, Jiangsu, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
Recommended Citation
GB/T 7714
Shao, Yanchuan,Ma, Zongwei,Wang, Jianghao,et al. Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,740:12.
APA Shao, Yanchuan,Ma, Zongwei,Wang, Jianghao,&Bi, Jun.(2020).Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging.SCIENCE OF THE TOTAL ENVIRONMENT,740,12.
MLA Shao, Yanchuan,et al."Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging".SCIENCE OF THE TOTAL ENVIRONMENT 740(2020):12.
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