Integration of self-organizing map and Monte Carlo simulation for ecological risk prediction of heavy metal attenuation in groundwater
As the exploitation and utilization of metal mines continue, issues concerning the groundwater ecological environment have become increasingly prominent, attracting significant attention. However, few studies have predicted the ecological risks associated with the attenuation of heavy metals in mini...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325011066 |
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| Summary: | As the exploitation and utilization of metal mines continue, issues concerning the groundwater ecological environment have become increasingly prominent, attracting significant attention. However, few studies have predicted the ecological risks associated with the attenuation of heavy metals in mining areas. This research focuses on a typical lead-zinc mine in Jiangxi Province, China. We employed a Self-Organizing Map (SOM) to analyze groundwater pollution clustering and integrated the Domenico model with Monte Carlo simulation to predict heavy metal attenuation. To assess present and long-term ecological risks, we applied the potential ecological risk index. The research findings indicate that: Mn, Fe, and As exhibited significant enrichment during periods, while Cu, Cd, As, and Al showed the highest coefficients of variation. According to the SOM-based clustering analysis, Zn and Pb show a strong correlation with Cd, while Cu, As, and Al are strongly correlated with Fe. The assessment of potential ecological risks reveals that single-factor evaluations for both periods mostly indicate slight ecological risks (Eri<30). Cd, As, Cu, and Mn pose very high ecological risks. The comprehensive ecological risk is very high around the tailings pond and the waste dump, with 11.11 % of the sites showing very high ecological risk. Combining the SOM output with the potential ecological risk analysis, cluster 1 includes sites identified as very high ecological risk points, with As being the key ecological risk factor. The ecological risk prediction based on Monte Carlo simulation indicates that the ecological risk of As will decrease by 75.6 % to 13,565, yet it persists at very high ecological risk. This study will offer critical insights for controlling heavy metal pollution in groundwater and for guiding ecological restoration in mining areas and their surroundings. |
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| ISSN: | 0147-6513 |