基于模糊认知图组合赋权的酸性矿山废水污染土壤生态风险评价

洪梅 ,  王瀚霆 ,  张春鹏 ,  徐万洲 ,  宋博宇

吉林大学学报(地球科学版) ›› 2026, Vol. 56 ›› Issue (4) : 1362 -1371.

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吉林大学学报(地球科学版) ›› 2026, Vol. 56 ›› Issue (4) : 1362 -1371. DOI: 10.13278/j.cnki.jjuese.20250010
地质工程与环境工程

基于模糊认知图组合赋权的酸性矿山废水污染土壤生态风险评价

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Ecological Risk Assessment of Soil Contaminated by Acidic Mine Wastewater Based on Fuzzy Cognitive Map Combination Weighting

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摘要

摘要:酸性矿山废水是造成矿山土壤环境退化的主要因素之一,对其进行生态风险评价至关重要.在这一评估过程中,指标权重分配起关键作用.本研究针对现有赋权方法中对逆向指标考虑不足及人为赋权主观性强的问题,提出了一种基于模糊认知图(FCM)的赋权方法.筛选8项含逆向指标的核心风险因子,采用双曲正切函数构建模糊认知图,通过迭代收敛计算权重;结合敏感性分析与皮尔逊相关性检验进行验证,并将该方法应用于安徽省某矿区开展实地评价.结果表明:污染物潜在生态危害指数权重最高(0.856),裂隙率、巷道规模等权重均高于0.770,生态恢复措施为负权重(-0.749);敏感性分析显示模型稳定性高;皮尔逊相关系数均在0.85以上;实地评价中,引入FCM权重后低风险区减少5.3%、中高风险区增加6.5%,成功识别出矿区混合井处被忽视的高风险区域,评价结果更符合实际.

Abstract

Abstract: Acid mine drainage is one of the major factors causing soil environmental degradation in mining areas, and it is crucial to conduct ecological risk assessment on it. In this evaluation process, indicator weight allocation plays a key role. To address the problems of insufficient consideration of reverse indicators and high subjectivity in manual weighting in existing weighting methods, this study proposes a weighting method based on fuzzy cognitive map (FCM). Eight core risk factors (including reverse indicators) were screened, the hyperbolic tangent function was used to construct the fuzzy cognitive map, weights were calculated through iterative convergence, and sensitivity analysis and Pearson correlation test were integrated for validation, and finally the method is applied to a field evaluation in a mining area of Anhui Province. The results show that the potential ecological risk index of pollutants has the highest weight (0.856), the weights of fracture rate, roadway scale, etc. are all higher than 0.770, and the ecological restoration measure has a negative weight (-0.749). Sensitivity analysis shows that the model has high stability. The Pearson correlation coefficients are all above 0.85. In the field evaluation, after introducing the FCM-based weights, the low-risk area decreased by 5.3%, and the medium-high-risk area increased by 6.5%. A previously overlooked high-risk area at the mixed shaft in the mining area was successfully identified, and the evaluation results are more consistent with the actual situation.

关键词

酸性矿山废水 / 模糊认知图 / 权重分配方法 / 土壤生态风险

Key words

acid mine drainage / fuzzy cognitive map / weighting allocation method / soil ecological risk

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洪梅,王瀚霆,张春鹏,徐万洲,宋博宇. 基于模糊认知图组合赋权的酸性矿山废水污染土壤生态风险评价[J]. 吉林大学学报(地球科学版), 2026, 56(4): 1362-1371 DOI:10.13278/j.cnki.jjuese.20250010

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基金资助

国家重点研发计划项目(2022YFC3702200)

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