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Human Health Risk Assessment of Heavy Metal(loid)s in Topsoil and Groundwater From a Typical Heavy‐Duty Enterprise Gathering Area of China Using Self‐Organizing Feature Map.

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Title: Human Health Risk Assessment of Heavy Metal(loid)s in Topsoil and Groundwater From a Typical Heavy‐Duty Enterprise Gathering Area of China Using Self‐Organizing Feature Map.
Authors: Zhao, Di1 (AUTHOR), Wang, Chan2 (AUTHOR), Zhou, Xiaoyan2 (AUTHOR), Wang, Ruzhen3 (AUTHOR), Guo, Zhengmeng3 (AUTHOR), Ding, Mou4 (AUTHOR), Yu, Bo5 (AUTHOR), Wang, Bo1 (AUTHOR) zealotking88@sina.com
Source: Water Environment Research (10614303). Jan2026, Vol. 98 Issue 1, p1-13. 13p.
Subjects: Health risk assessment, Pollution source apportionment, Self-organizing maps, Carcinogenicity, Cities & towns, Water table, Ecological risk assessment, Heavy metal toxicology
Geographic Terms: China
Abstract: Bayannur City in northern China, which includes Urad Rear Banner, has a high concentration of non‐ferrous metal mining activities and is a key region for the regulation of heavy‐duty enterprises. There are 14 heavy‐duty enterprises in Urad Rear Banner, involving a population of 48,000. The regulation of mining activities in this area necessitates effective ecological and human health risk assessments of the heavy metal(loid)s produced by the numerous lead‐zinc ore and copper ore smelting operations. In this study, the pollution levels and pollution sources of toxic heavy metal(loid)s (Cr, As, Pb, Cd, and Hg) in topsoil and groundwater were analyzed using a self‐organizing feature map (SOM) for the first time. So the pollution source impacts, site characteristics and geographic properties can be further evaluated. The results revealed significant Pb and Cd pollution, exceeding the standard established by China MEE, resulting from the high concentration of heavy industry in the study area. The distributions of toxic metals were linked to pollution source and site characteristics using the neural network‐based SOM. Based on the optimal neurons, k‐means clustering, and the Davies–Bouldin index (DBI), the SOM indicated five possible pollution sources: human factors, natural sources, natural settlement, wastewater leakage, and wind effect. Meanwhile, the ecological risk assessment showed that the ecological risk decreased in the order of Cd > Hg > As > Pb > Cr, which reflects the difference between ecotoxicological sensitivity and pollution level. That is, low‐polluting metals may still have high toxicity. In the health risk assessment of heavy metal(loid)s in topsoil and groundwater, the hazard quotient (HQ) and hazard index (HI) were all below the safety limit of 1, while the carcinogenic risk (CR) and total carcinogenic risk (TCR) values were 10−6 to 10−4 (within the range of human tolerance). Among the heavy metal(loid)s evaluated, Pb and As had relatively high carcinogenic risks. Due to contributions from multiple sources, the southeastern part of the study area was heavily polluted. This study represents an innovative use of SOM in pollution source apportionment. This novel approach has the advantages of high precision, high efficiency, good visualization, and little human interference. SOM can be used to quantify sources while also comprehensively considering the hydrogeochemical characteristics, and it is especially suitable for case studies with large sample sizes. In this study, we applied SOM in an innovative way to evaluate the ecological and human health risks of heavy metal pollution in an area with numerous heavy industries and revealed the potential risk pathways. The findings provide a basis for the prevention, control, and remediation of pollution along with associated policymaking. Summary: Quantified the level of heavy metal(loid) pollution around the metal ore processing, mining, and smelting factories in Bayannur City.Self‐organizing feature map (SOM) can provide a basis for inferring the source of heavy metal(loid) pollution.Heavy metal(loid) risks were assessed based on an optimized ecological and health risk model.Pb, Cd, and As posed a relatively serious threat to the region.Targeted emission reduction and soil remediation measures are required to reduce heavy metal(loid) pollution in factories. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Bayannur City in northern China, which includes Urad Rear Banner, has a high concentration of non‐ferrous metal mining activities and is a key region for the regulation of heavy‐duty enterprises. There are 14 heavy‐duty enterprises in Urad Rear Banner, involving a population of 48,000. The regulation of mining activities in this area necessitates effective ecological and human health risk assessments of the heavy metal(loid)s produced by the numerous lead‐zinc ore and copper ore smelting operations. In this study, the pollution levels and pollution sources of toxic heavy metal(loid)s (Cr, As, Pb, Cd, and Hg) in topsoil and groundwater were analyzed using a self‐organizing feature map (SOM) for the first time. So the pollution source impacts, site characteristics and geographic properties can be further evaluated. The results revealed significant Pb and Cd pollution, exceeding the standard established by China MEE, resulting from the high concentration of heavy industry in the study area. The distributions of toxic metals were linked to pollution source and site characteristics using the neural network‐based SOM. Based on the optimal neurons, k‐means clustering, and the Davies–Bouldin index (DBI), the SOM indicated five possible pollution sources: human factors, natural sources, natural settlement, wastewater leakage, and wind effect. Meanwhile, the ecological risk assessment showed that the ecological risk decreased in the order of Cd > Hg > As > Pb > Cr, which reflects the difference between ecotoxicological sensitivity and pollution level. That is, low‐polluting metals may still have high toxicity. In the health risk assessment of heavy metal(loid)s in topsoil and groundwater, the hazard quotient (HQ) and hazard index (HI) were all below the safety limit of 1, while the carcinogenic risk (CR) and total carcinogenic risk (TCR) values were 10−6 to 10−4 (within the range of human tolerance). Among the heavy metal(loid)s evaluated, Pb and As had relatively high carcinogenic risks. Due to contributions from multiple sources, the southeastern part of the study area was heavily polluted. This study represents an innovative use of SOM in pollution source apportionment. This novel approach has the advantages of high precision, high efficiency, good visualization, and little human interference. SOM can be used to quantify sources while also comprehensively considering the hydrogeochemical characteristics, and it is especially suitable for case studies with large sample sizes. In this study, we applied SOM in an innovative way to evaluate the ecological and human health risks of heavy metal pollution in an area with numerous heavy industries and revealed the potential risk pathways. The findings provide a basis for the prevention, control, and remediation of pollution along with associated policymaking. Summary: Quantified the level of heavy metal(loid) pollution around the metal ore processing, mining, and smelting factories in Bayannur City.Self‐organizing feature map (SOM) can provide a basis for inferring the source of heavy metal(loid) pollution.Heavy metal(loid) risks were assessed based on an optimized ecological and health risk model.Pb, Cd, and As posed a relatively serious threat to the region.Targeted emission reduction and soil remediation measures are required to reduce heavy metal(loid) pollution in factories. [ABSTRACT FROM AUTHOR]
ISSN:10614303
DOI:10.1002/wer.70242