Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth
文献类型：期刊论文
| 作者 | Li, Lianfa1,2; Zhang, Jiehao1; Meng, Xia3; Fang, Ying1; Ge, Yong1; Wang, Jinfeng1; Wang, Chengyi4; Wu, Jun5; Kan, Haidong2 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 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. |
| 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 |
| 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研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000447570900042 |
| 出版者 | ELSEVIER SCIENCE INC |
| 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) |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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