基于机器学习和非参数估计的PM<sub>2.5</sub>风险评估

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Abstract A systematic approach of regional PM_2.5 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 PM_2.5 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 PM_2.5 exposure risk by 0.30~0.97 times, even keeping PM_2.5 concentration unchanged. After combining PM_2.5 concentration and population mobility simultaneously, four major PM_2.5 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 14^th 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.

Key words： PM_2.5 machine learning non-parametric estimation exposure risk assessment feather recognition

Received: 27 December 2021

PACS:	X513
	X823

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	ZHOU Qi
	YU Yang
	LIU Miao-miao
	BI Jun

Cite this article:

ZHOU Qi,YU Yang,LIU Miao-miao等. Risk assessment of PM_2.5 pollution based on machine learning and nonparametric estimation[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(8): 3554-3560.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I8/3554

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