Risk assessment of PM2.5 pollution based on machine learning and nonparametric estimation
ZHOU Qi1,2, YU Yang3, LIU Miao-miao1, BI Jun1
1. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; 2. School of Environment, Tsinghua University, Beijing 100084, China; 3. Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
Abstract：A systematic approach of regional PM2.5 risk and characterization assessment was developed in this study by integrating random forest model, Quantile-Quantile plot model, and K-mean model, based on multi-source data including mobile phone signals, meteorological data, geographic data, etc. This new approach was further applied in a case study of Nanjing at a 0.3km resolution grid. On the one hand, this new approach effectively simulated the temporal and spatial distribution of the PM2.5 concentration with an10-fold cross-validation R2 of 0.76 and screened out four major pollution characteristics. On the other hand, it effectively captured the short-term population mobility risk. Short-term population mobility increased the PM2.5 exposure risk by 0.30~0.97 times, even keeping PM2.5 concentration unchanged. After combining PM2.5 concentration and population mobility simultaneously, four major PM2.5 exposure risk modes were identified. 6.5% of the areas of Nanjing were at high risk, and 23.0% were at low risk. During the 14th Five Year Plan, it is suggested that the government should speed up the application of modern science and technology in environmental protection and implement gridding and differentiated policies on air pollution risk control to promote human health.
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ZHOU Qi, YU Yang, LIU Miao-miao, BI Jun. Risk assessment of PM2.5 pollution based on machine learning and nonparametric estimation. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(8): 3554-3560.
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