Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging
文献类型：期刊论文
| 作者 | Shao, Yanchuan2; Ma, Zongwei2,3; Wang, Jianghao1,4; Bi, Jun2 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2020-10-20 |
| 卷号 | 740页码:12 |
| 关键词 | Aerosol optical depth PM2.5 Random forest Spatiotemporal kriging |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2020.139761 |
| 通讯作者 | Ma, Zongwei(zma@nju.edu.cn) ; Wang, Jianghao(wangjh@lreis.ac.cn) |
| 英文摘要 | Ambient 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. |
| WOS关键词 | AEROSOL OPTICAL DEPTH ; SATELLITE ; MODEL ; PM10 ; REGRESSION ; EXPOSURE ; CLIMATE |
| 资助项目 | National Natural Science Foundation of China[41531174] ; National Natural Science Foundation of China[71921003] ; National Natural Science Foundation of China[91644220] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000562059800004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/158032]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ma, Zongwei; Wang, Jianghao |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Nanjing Univ, Sch Environm, State Key Lab Pollut Control & Resource Reuse, Nanjing 210023, Jiangsu, Peoples R China 3.Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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