Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要:
A high degree of uncertainty associated with the emission inventory for China tends to degrade the performance of chemical transport models in predicting PM2.5concentrations especially on a daily basis. In this study a novel machine learning algorithm, Geographically-Weighted Gradient Boosting Machine (GW-GBM), was developed by improving GBM through building spatial smoothing kernels to weigh the loss function. This modification addressed the spatial nonstationarity of the relationships between PM2.5concentrations and predictor variables such as aerosol optical depth (AOD) and meteorological conditions. GW-GBM also overcame the estimation bias of PM2.5concentrations due to missing AOD retrievals, and thus potentially improved subsequent exposure analyses. GW-GBM showed good performance in predicting daily PM2.5concentrations (R2�=�0.76, RMSE�=�23.0�μg/m3) even with partially missing AOD data, which was better than the original GBM model (R2�=�0.71, RMSE�=�25.3�μg/m3). On the basis of the continuous spatiotemporal prediction of PM2.5concentrations, it was predicted that 95% of the population lived in areas where the estimated annual mean PM2.5concentration was higher than 35�μg/m3, and 45% of the population was exposed to PM2.5>75�μg/m3 for over 100 days in 2014. GW-GBM accurately predicted continuous daily PM2.5concentrations in China for assessing acute human health effects. � 2017 Elsevier Ltd
Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang, China; Department of Land, Air, and Water Resources, University of California, Davis, CA, United States; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
Recommended Citation:
Zhan Y,, Luo Y,, Deng X,et al. Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm[J]. Atmospheric Environment,2017-01-01,155