A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels. (December 2018)

• Record Type: Journal Article
• Authors: Zhang, Ruixin; Di, Baofeng; Luo, Yuzhou; Deng, Xunfei; Grieneisen, Michael L.; Wang, Zhigao; Yao, Gang; Zhan, Yu
• Is Part Of: Environmental pollution. Volume 243(2018)Part B
• Page Start: 998
• Page End: 1007
• Publication Date: 2018-12
• Subjects: Fine particulate matter -- Aerosol optical depth -- Missing data -- Random forest -- Sichuan basin; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2018.09.052
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Low AOD coverage in regions like Sichuan Basin (11.7%) caused PM2.5 estimation bias. Novel approach employed two random forests to fill AOD gap and predict PM2.5 High cross-validation R 2 achieved for AOD- (0.95) and PM2.5 -submodels (0.86). Monthly PM2.5 in Sichuan Basin could be overestimated by 34.6% due to missing AOD. Annual average PM2.5 in Sichuan decreased from 67.7 to 50.1 μg/m 3 during 2013–2015. Abstract : A two-stage and random-forest-based approach is proposed to fill gaps in satellite-retrieved AOD for estimating full-coverage ambient PM2.5 concentrations.