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Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

文献类型： 外文期刊

作者： Zhan, Yu 1 ; Luo, Yuzhou 2 ; Deng, Xunfei 1 ; Chen, Huajin 2 ; Grieneisen, Michael L. 2 ; Shen, Xueyou 1 ; Zhu, Lizhon 1 ;

作者机构： 1.Zhejiang Univ, Dept Environm Sci, Hangzhou 310058, Zhejiang, Peoples R China

2.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA

3.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

关键词： Fine particulate matter;Human exposure;Spatial nonstationarity;Geographically weighted;Machine learning

期刊名称：ATMOSPHERIC ENVIRONMENT （ 影响因子：4.798； 五年影响因子：5.295 ）

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收录情况： SCI

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 PM2.5 concentrations 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 PM2.5 concentrations and predictor variables such as aerosol optical depth (AOD) and meteorological conditions. GW-GBM also overcame the estimation bias of PM2.5 concentrations due to missing AOD retrievals, and thus potentially improved subsequent exposure analyses. GW-GBM showed good performance in predicting daily PM2.5 concentrations (R-2 = 0.76, RMSE = 23.0 g/m(3)) even with partially missing AOD data, which was better than the original GBM model (R-2 = 0.71, RMSE = 25.3 g/m(3)). On the basis of the continuous spatiotemporal prediction of PM2.5 concentrations, it was predicted that 95% of the population lived in areas where the estimated annual mean PM2.5 concentration was higher than 35 g/m(3), and 45% of the population was exposed to PM2.5 > 75 g/m(3) for over 100 days in 2014. GW-GBM accurately predicted continuous daily PM2.5 concentrations in China for assessing acute human health effects. (C) 2017 Elsevier Ltd. All rights reserved.

  • 相关文献

[1]Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Zhan, Yu,Di, Baofeng,Luo, Yuzhou,Grieneisen, Michael L.,Zhang, Minghua,Deng, Xunfei. 2018

[2]Baseline map of organic carbon stock in farmland topsoil in East China. Deng, Xunfei,Chen, Xiaojia,Ma, Wanzhu,Ren, Zhouqiao,Long, Wenli,Lv, Xiaonan,Deng, Xunfei,Chen, Xiaojia,Ma, Wanzhu,Ren, Zhouqiao,Lv, Xiaonan,Zhang, Minghua,Grieneisen, Michael L.,Ni, Zhihua,Zhan, Yu. 2018

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