IGSNRR OpenIR
A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information
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
2018-09-15
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume636Pages:52-60
Corresponding AuthorGuo, Yuming(yuming.guo@monash.edu)
AbstractBackground: 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.
KeywordPM2.5 Aerosol optical depth Random forests Machine learning China
DOI10.1016/j.scitotenv.2018.04.251
WOS KeywordAEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; FINE PARTICULATE MATTER ; AIR-POLLUTION ; UNITED-STATES ; RANDOM FORESTS ; TERM EXPOSURE ; MULTI-CITY ; AOD ; REGRESSION
Indexed BySCI
Language英语
Funding ProjectCareer 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)
Funding OrganizationCareer 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)
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000436599000006
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54305
Collection中国科学院地理科学与资源研究所
Corresponding AuthorGuo, Yuming
Affiliation1.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
Recommended Citation
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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