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Abstract The aerosol optical depth (AOD) derived via dark-target algorithm has been widely used as an effective tool for estimating PM2.5concentrations. However, this algorithm cannot effectively retrieve AOD on the bright surface. Therefore, the authors used a random forest model incorporating meteorological parameters to predict the missing AOD values, and then employed a second-stage random forest model combining the retrieved AOD with meteorological parameters, vegetation cover and road density to estimate the PM2.5concentrations in two districts of eastern coastal zone of China, i.e., YRD and PRD. The result shows that the proposed model performed very well, achieving R2 of 0.94 for AOD predictions and MODIS AOD and an overall R2 of 0.97 with RMSE being only 5.57 μg/m 3 between the estimated and observed PM2.5 concentrations. The spatial distribution of PM2.5concentrations suggests that the high values are mainly located in Jiangsu Province with low elevation (≥40 μg/m3). The results indicate that the proposed two-stage random forest model incorporated with satellite AOD and other variables could be effectively used for estimating the ground-level PM2.5 concentrations.
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| Keywords
random forest model
PM2.5 distribution
AOD retrieval
YRD
PRD
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Issue Date: 23 December 2020
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2020,
Vol. 32
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