广州市多污染物联合暴露的健康效应评估

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Abstract This study attempted to use the Bayesian kernel machine regression (BKMR) to analyze the health effects of multiple air pollutants (i.e, SO₂、NO₂、PM₁₀、PM_2.5、CO and O₃) on non-accidental mortality in Guangzhou during 2015~2018. We found that all the pollutants presented significant effects on non-accidental mortality. The absolute values of effect estimates associated with the change in SO₂, O₃ or NO₂ concentration from the 25th percentile to the 75th percentile gradually increased with the increment of the fixed percentile of other pollutants; On the contrary, the absolute values of effect estimates decreased gradually when the concentration of PM_2.5, CO or PM₁₀ changes from the 25th percentile to the 75th percentile. The cumulative effect estimates associated with exposure to multiple pollutants on non-accidental mortality at lag 0~1 day was positive. Compared with the lowest concentration, when the concentration of all pollutants increased to the 90th percentile, the number of non-accidental deaths will increase by 21.98% (95% CI: 3.04%, 44.41%). With the application of BKMR model, this study found that exposure to multiple pollutants could produce a combined impact on public health in Guangzhou. In terms of air pollution prevention and control, it is necessary to not only target the main local air pollutants, but also strengthen the policies on protecting the public from exposure to multi-pollutants.

Key words： Bayesian kernel machine regression multi air pollutant exposure health risk assessment

Received: 26 April 2022

PACS:

X503.1

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	Articles by authors
	HUANG Lin
	LIU Di
	CAI Dong-jie
	CHEN Su-juan
	DONG Hang
	LIN Guo-zhen
	WANG Bo-guang
	YANG Jun

Cite this article:

HUANG Lin,LIU Di,CAI Dong-jie等. Health risk assessment of exposure to multiple pollutants in Guangzhou[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(11): 5418-5426.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I11/5418

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