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A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information

文献类型：期刊论文

作者Chen, Gongbo1; Li, Shanshan1; Knibbs, Luke D.2; Hamm, N. A. S.3,4; Cao, Wei5; Li, Tiantian6; Guo, Jianping7; Ren, Hongyan5; Abramson, Michael J.1; Guo, Yuming1
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2018-09-15
卷号636页码:52-60
ISSN号0048-9697
关键词PM2.5 Aerosol optical depth Random forests Machine learning China
DOI10.1016/j.scitotenv.2018.04.251
通讯作者Guo, Yuming(yuming.guo@monash.edu)
英文摘要Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter <= 2.5 mu m) at daily time scale in China at a national level. Objectives: To estimate daily concentrations of PM2.5 across China during 2005-2016. Methods: Daily ground-level PM2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM2.5 across China with a resolution of 0.1 degrees (approximate to 10 km) during 2005-2016. Results: The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM2.5 [10-fold cross-validation (CV) R-2 = 83%, root mean squared prediction error (RMSE) = 28.1 mu g/m(3)]. At the monthly and annual time-scale, the explained variability of average PM2.5 increased up to 86% (RMSE = 10.7 mu g/m(3) and 6.9 mu g/m(3), respectively). Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies. Capsule: Random forests approach can be used to estimate historical exposure to PM2.5 in China with high accuracy. (C) 2018 Elsevier B.V. All rights reserved.
WOS关键词AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; FINE PARTICULATE MATTER ; AIR-POLLUTION ; UNITED-STATES ; RANDOM FORESTS ; TERM EXPOSURE ; MULTI-CITY ; AOD ; REGRESSION
资助项目Career Development Fellowship of Australian National Health and Medical Research Council[APP1107107] ; Early Career Fellowship of NHMRC[APP1109193] ; NHMRC Centre of Research Excellence-Centre for Air quality and health Research and evaluation[APP1030259] ; China Scholarship Council (CSC)
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000436599000006
资助机构Career Development Fellowship of Australian National Health and Medical Research Council ; Early Career Fellowship of NHMRC ; NHMRC Centre of Research Excellence-Centre for Air quality and health Research and evaluation ; China Scholarship Council (CSC)
源URL[http://ir.igsnrr.ac.cn/handle/311030/54304]  
专题中国科学院地理科学与资源研究所
通讯作者Guo, Yuming
作者单位1.Monash Univ, Sch Publ Hlth & Prevent Med, Dept Epidemiol & Prevent Med, Level 2,553 St Kilda Rd, Melbourne, Vic 3004, Australia
2.Univ Queensland, Sch Publ Hlth, Dept Epidemiol & Biostat, Brisbane, Qld, Australia
3.Univ Nottingham, Fac Sci & Engn, Geospatial Res Grp, Ningbo, Zhejiang, Peoples R China
4.Univ Nottingham, Fac Sci & Engn, Sch Geog Sci, Ningbo, Zhejiang, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
6.Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth Sci, Beijing, Peoples R China
7.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Gongbo,Li, Shanshan,Knibbs, Luke D.,et al. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2018,636:52-60.
APA Chen, Gongbo.,Li, Shanshan.,Knibbs, Luke D..,Hamm, N. A. S..,Cao, Wei.,...&Guo, Yuming.(2018).A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.SCIENCE OF THE TOTAL ENVIRONMENT,636,52-60.
MLA Chen, Gongbo,et al."A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information".SCIENCE OF THE TOTAL ENVIRONMENT 636(2018):52-60.

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来源：地理科学与资源研究所

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