Publications

Chen, GB; Li, YX; Zhou, Y; Shi, CX; Guo, YM; Liu, YW (2021). The comparison of AOD-based and non-AOD prediction models for daily PM2.5 estimation in Guangdong province, China with poor AOD coverage. ENVIRONMENTAL RESEARCH, 195, 110735.

Abstract
The large amount of missing values has challenged the application of satellite-retrieved aerosol optical depth (AOD) in mapping surface PM2.5 concentrations. In this study, we developed a non-AOD random forest model to estimate daily concentrations of PM2.5 in Guangdong Province, China, where more than 80% of AOD data were missing. The predictive ability of the non-AOD model was compared with that of a AOD-based model. Daily ground-based measurements of PM2.5 were obtained from 148 ground-monitoring sites in Guangdong with a 60 km rectangle buffer from January 2016 to December 2018. Daily MODIS MAIAC AOD were downloaded from NASA at a resolution of approximately 1 km. Two random forest models were developed to predict ground-level PM2.5 with one included AOD as a predictor and the other one without AOD. The two random forest models were developed using the same dataset and their predictive abilities were compared. The results of 10-fold cross validation (CV) showed that the non-AOD and AOD-based random forest models yielded similar performance. The CV R-2 (RMSE) for the non-AOD model in 2016-2018 were 0.82 (8.4 mu g/m(3)), 0.81 (9.5 mu g/m(3)) and 0.78 (9.4 mu g/m(3)), and those for AOD-based model were 0.83 (8.2 mu g/m(3)), 0.82 (9.2 mu g/m(3)) and 0.80 (9.0 mu g/m(3)), respectively. Higher consistency of estimated PM2.5 were observed in areas close to monitoring sites than those far away from sites, and in southern coastal than northern areas. As the non-AOD random forest model is not affected by AOD missingness, it can be used for epidemiological studies to estimate individual-level exposure to PM2.5 at a high resolution without spatial or temporal gaps.

DOI:
10.1016/j.envres.2021.110735

ISSN:
0013-9351