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中国不同类型城市大气重金属污染特征及健康风险评估

Atmospheric Heavy Metal Pollution Characteristics and Health Risk Assessment Across Various Type of Cities in China.

作者信息

Cha Zhichun, Zhang Xi, Zhang Kai, Zhou Guanhua, Gao Jian, Sun Sichu, Gao Yuanguan, Liu Haiyan

机构信息

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.

Faculty of Environmental Engineering, The University of Kitakyushu, 1-1 Hibikino, Wakamatsu, Kitakyushu 808-0135, Fukuoka, Japan.

出版信息

Toxics. 2025 Mar 17;13(3):220. doi: 10.3390/toxics13030220.

DOI:10.3390/toxics13030220
PMID:40137547
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11945492/
Abstract

This study investigates the spatiotemporal trends and health risks of nine atmospheric heavy metals (Pb, As, Mn, Ni, Cr, Cd, Zn, Cu, Fe) in PM across 50 Chinse cities, comparing resource-industrial cities (RICs) and general cities (GCs) before (2014-2018) and after (2019-2021) China's 2018 Air Pollution Prevention and Control Action Plan. Post-2018, concentrations of all metals except Fe declined significantly (33-77%), surpassing PM reductions (25%). Geospatial analysis revealed elevated heavy metal levels in northern and southern regions in China, aligning with industrial and mining hotspots. While RICs exhibited persistently higher metal concentrations than GCs, the inter-city gap narrowed post-2018, with RICs achieving greater reduction. Pre-2018, the combined non-carcinogenic hazard index (HI < 1) remained below safety thresholds, but the combined carcinogenic risk total (CRT) for children exceeded 10, driven primarily by As and Cr(VI). HIs were 1.5-2.0 times higher in RICs than in GCs. Post-2018, the CRT declined by 69.0-71.1%, aligning with reduced heavy metal levels. Despite improvements, CRTs necessitate targeted mitigation for As (contributing 81.1-86.2% to CRT) and Cr(VI) (11.7-14.0%). These findings validate the policy's effectiveness in curbing industrial and vehicular emissions but underscore the need for metal-specific controls in resource-intensive regions to safeguard child health.

摘要

本研究调查了中国50个城市细颗粒物(PM)中9种大气重金属(铅、砷、锰、镍、铬、镉、锌、铜、铁)的时空趋势和健康风险,比较了中国2018年《大气污染防治行动计划》实施前(2014 - 2018年)和实施后(2019 - 2021年)资源型工业城市(RICs)和一般城市(GCs)的情况。2018年后,除铁以外的所有金属浓度均显著下降(33% - 77%),降幅超过了细颗粒物(25%)。地理空间分析显示,中国北方和南方地区的重金属水平升高,与工业和采矿热点地区一致。虽然资源型工业城市的金属浓度一直高于一般城市,但2018年后城市间差距缩小,资源型工业城市的降幅更大。2018年前,综合非致癌危害指数(HI < 1)仍低于安全阈值,但儿童的综合致癌风险总量(CRT)超过10,主要由砷和六价铬驱动。资源型工业城市的HI比一般城市高1.5 - 2.0倍。2018年后,CRT下降了69.0% - 71.1%,与重金属水平降低一致。尽管有所改善,但CRT仍需要针对砷(对CRT的贡献率为81.1% - 86.2%)和六价铬(11.7% - 14.0%)进行有针对性的缓解。这些发现证实了该政策在抑制工业和车辆排放方面的有效性,但强调了在资源密集型地区进行特定金属控制以保障儿童健康的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/85195a85daf6/toxics-13-00220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/ae9d14bde88e/toxics-13-00220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/90ebe60ef550/toxics-13-00220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/98177dd87457/toxics-13-00220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/64b632e5f808/toxics-13-00220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/51672bdd7315/toxics-13-00220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/85195a85daf6/toxics-13-00220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/ae9d14bde88e/toxics-13-00220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/90ebe60ef550/toxics-13-00220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/98177dd87457/toxics-13-00220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/64b632e5f808/toxics-13-00220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/51672bdd7315/toxics-13-00220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba76/11945492/85195a85daf6/toxics-13-00220-g006.jpg

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本文引用的文献

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Huan Jing Ke Xue. 2024 Nov 8;45(11):6229-6237. doi: 10.13227/j.hjkx.202312063.
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Estimating the geographical patterns and health risks associated with PM-bound heavy metals to guide PM control targets in China based on machine-learning algorithms.基于机器学习算法估算与 PM 结合的重金属的地理分布模式及其对健康的影响,以指导中国的 PM 控制目标。
Environ Pollut. 2023 Nov 15;337:122558. doi: 10.1016/j.envpol.2023.122558. Epub 2023 Sep 13.
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Pollution evaluation and source identification of heavy metals in soil around steel factories located in Lanshan District, Rizhao City, eastern China.
中国东部日照市岚山区钢铁厂周边土壤重金属污染评价与来源识别
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