A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM_2.5...

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM_2.5), though it is important to mitigate the estimation bias of PM_2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM_2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM_2.5 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 R² of 0.95. Subsequently, the second random-forest submodel (named PM_2.5-submodel) was trained to estimate the PM_2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM_2.5 levels, and the covariates, and achieved a cross-validation R² 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 PM_2.5 levels could be overestimated by 34.6% if the PM_2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM_2.5 from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.