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Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

文献类型：期刊论文

作者Li, Lianfa1,2; Wu, Anna H.2; Cheng, Iona3,4; Chen, Jiu-Chivan2; Wu, Jun5
刊名ATMOSPHERIC ENVIRONMENT
出版日期2017-10-01
卷号166页码:182-191
关键词Particulate matter PM2.5 Historical concentration Spatiotemporal model
ISSN号1352-2310
DOI10.1016/j.atmosenv.2017.07.023
通讯作者Li, Lianfa(lianfali@usc.edu) ; Wu, Jun(junwu@uci.edu)
英文摘要Monitoring of fine particulate matter with diameter <2.5 mu m (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 mu m (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R-2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R-2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R-2 of 0.61 for the daily model when PM2.5 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available. (C) 2017 Elsevier Ltd. All rights reserved.
WOS关键词CALIFORNIA
资助项目Susan G. Komen[IIR13262718] ; Natural Science Foundation of China[41471376]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000411298800016
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Susan G. Komen ; Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/62279]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Lianfa; Wu, Jun
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.Univ Southern Calif, Dept Prevent Med, Los Angeles, CA 90089 USA
3.Stanford Canc Inst, Stanford, CA USA
4.Canc Prevent Inst Calif, Fremont, CA USA
5.Univ Calif Irvine, Program Publ Hlth, Irvine, CA USA
推荐引用方式
GB/T 7714		 Li, Lianfa,Wu, Anna H.,Cheng, Iona,et al. Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect[J]. ATMOSPHERIC ENVIRONMENT,2017,166:182-191.
APA Li, Lianfa,Wu, Anna H.,Cheng, Iona,Chen, Jiu-Chivan,&Wu, Jun.(2017).Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect.ATMOSPHERIC ENVIRONMENT,166,182-191.
MLA Li, Lianfa,et al."Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect".ATMOSPHERIC ENVIRONMENT 166(2017):182-191.

入库方式： OAI收割

来源：地理科学与资源研究所

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