Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth

doi:10.1016/j.rse.2018.09.001

	Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth
	Li, Lianfa 1,2; Zhang, Jiehao 1; Meng, Xia 3; Fang, Ying 1; Ge, Yong 1; Wang, Jinfeng 1; Wang, Chengyi 4; Wu, Jun 5; Kan, Haidong 2
刊名	REMOTE SENSING OF ENVIRONMENT
	2018-11-01
卷号	217 页码:573-586
关键词	PM2.5 MAIAC AOD High spatiotemporal resolution Temporal variation AOD-PM2.5 associations Spatial effects Missingness Machine learning
ISSN号	0034-4257
DOI	10.1016/j.rse.2018.09.001
通讯作者	Li, Lianfa(lilf@lreis.ac.cn)
英文摘要	Exposure estimation of fine particulate matter with diameter < 2.5 mu m (PM2.5) at high spatiotemporal resolution is crucial to epidemiological studies that examine acute or sub-chronic health outcomes of PM2.5. However, exposure assessment of PM2.5 has been negatively affected by sparsely distributed monitoring stations. In addition, several limitations exist among the existing methods for high spatiotemporal resolution PM2.5 estimation, including ignorance or limited use of spatial autocorrelation, single-model methods, and use of aerosol optical depth data with non-random missingness. These limitations probably introduce bias or high uncertainty in model estimation. In this paper, we proposed an approach of constrained mixed-effect bagging models to leverage advanced algorithm of the high-resolution AOD retrieved by Multi-Angle Implementation of Atmospheric Correction (MAIAC), with other spatiotemporal predictors and spatial autocorrelation to reliably estimate PM2.5 at a high spatiotemporal resolution. Our base model was a daily mixed-effect spatial model that accounted for spatial autocorrelation using embedded structured and unstructured spatial random effects. Point estimates from the base models were then averaged based on the bootstrap aggregating (bagging) to reduce variance in prediction. Then, constrained optimization was developed to minimize the impact of missing AOD and to capture a full time-series of PM2.5 concentration. Our daily-level bagging allowed AOD-PM2.5 association and spatial autocorrelation to vary daily, which substantially improved the model performance. As a case study of daily PM2.5 predictions in 2014 in Shandong Province, China, our approach achieved R-2 of 0.87 (RMSE: 18.6 mu g/m(3)) in cross validation, and R-2 of 0.75 (RMSE: 20.6 mu g/m(3)) in an independent test, similar to or better than most existing methods. We further extended the 2014 models to simulate 2014-2016 full time-series of biweekly average PM2.5 concentrations with no use of covariates in 2015-2016 but constrained optimization over 2014 daily point estimates; the results showed well-captured temporal trend with a total correlation of 0.81 between the simulated and observed values from 2015 to 2016. Our approach can be applied for other regions for exposure estimation of PM2.5 when measurements alone are not able to capture the desirable spatial and temporal resolutions.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDA19040501] ; Natural Science Foundation of China[41471376] ; opening project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3)
WOS关键词	GROUND-LEVEL PM2.5 ; LAND-USE REGRESSION ; PARTICULATE MATTER ; AIR-QUALITY ; METEOROLOGICAL VARIABLES ; WEIGHTED REGRESSION ; SATELLITE DATA ; MODIS AOD ; POLLUTION ; STATES
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000447570900042
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences ; Natural Science Foundation of China ; opening project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3)
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/52745]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Lianfa
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.Fudan Univ, Shanghai Key Lab Atmospher Particle Pollut & Prev, Shanghai, Peoples R China 3.Emory Univ, Dept Environm Hlth, Rollins Sch Publ Hlth, Atlanta, GA 30322 USA 4.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China 5.Univ Calif Irvine, Susan & Henry Samueli Coll Hlth Sci, Program Publ Hlth, Irvine, CA 92697 USA
推荐引用方式 GB/T 7714	Li, Lianfa,Zhang, Jiehao,Meng, Xia,et al. Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth[J]. REMOTE SENSING OF ENVIRONMENT,2018,217:573-586.
APA	Li, Lianfa.,Zhang, Jiehao.,Meng, Xia.,Fang, Ying.,Ge, Yong.,...&Kan, Haidong.(2018).Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth.REMOTE SENSING OF ENVIRONMENT,217,573-586.
MLA	Li, Lianfa,et al."Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth".REMOTE SENSING OF ENVIRONMENT 217(2018):573-586.