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A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels

文献类型： 外文期刊

作者： Zhang, Ruixin 1 ; Di, Baofeng 1 ; Luo, Yuzhou 3 ; Deng, Xunfei 4 ; Grieneisen, Michael L. 3 ; Wang, Zhigao 5 ; Yao, Ga 1 ;

作者机构： 1.Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China

2.Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610200, Sichuan, Peoples R China

3.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA

4.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

5.State Grid Sichuan Elect Power Res Inst, Chengdu 610072, Sichuan, Peoples R China

6.Rhein Westfal TH Aachen, Inst Environm Engn, D-52072 Aachen, Germany

7.Sichuan Univ, Sinogerman Ctr Water & Hlth Res, Chengdu 610065, Sichuan, Peoples R China

8.Sichuan Univ, Med Big Data Ctr, Chengdu 610041, Sichuan, Peoples R China

关键词： Fine particulate matter; Aerosol optical depth; Missing data; Random forest; Sichuan basin

期刊名称：ENVIRONMENTAL POLLUTION （ 影响因子：8.071； 五年影响因子：8.35 ）

ISSN： 0269-7491

年卷期： 2018 年 243 卷

页码：

收录情况： SCI

摘要： Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM2.5), though it is important to mitigate the estimation bias of PM2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM2.5 levels during 2013-2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R-2 of 0.95. Subsequently, the second random-forest submodel (named PM2.5-submodel) was trained to estimate the PM2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM2.5 levels, and the covariates, and achieved a cross-validation R-2 of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM2.5 levels could be overestimated by 34.6% if the PM2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM2.5 from incomplete remote sensing data, which is essential for air quality management and human exposure assessment. (C) 2018 Elsevier Ltd. All rights reserved.

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