Jiangxi Provincial Key Laboratory of Mining and Metallurgy Environmental Pollution Control, Jiangxi University of Science and Technology, Ganzhou, People's Republic of China.
Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources, Jiangxi University of Science and Technology, Ganzhou, People's Republic of China.
Environ Geochem Health. 2024 Jan 31;46(2):62. doi: 10.1007/s10653-024-01866-y.
Soils in areas wherein agriculture and mining coexist are experiencing serious heavy metal contamination, posing a great threat to the ecological environment and human health. In this study, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in agricultural soil samples from mining areas were analyzed to explore pollution status, bioavailability, potential sources, and ecological/health risks. Particularly, the coupling model of Monte Carlo simulation-triangular fuzzy number (MCS-TFN) was established to quantify ecological/health risks accurately. Results showed that Cd was heavily enriched in soil and had the highest bioavailability based on both geo-accumulation index (I) and chemical speciation analysis. Pollution sources apportioned with the absolute principal component score-multiple linear regression (APCS-MLR) model demonstrated that heavy metals were mainly derived from agricultural activities, followed by mining activities and natural sources. The MCS-TFN ecological risk assessment classified Cd into the high-risk category with a probability of 40.96%, whereas other heavy metals were categorized as the low risk. Cd was regarded as the major pollutant for the ecosystem. Moreover, the MCS-TFN health risk assessment indicated that As showed high noncarcinogenic risk (0.07% probability) and moderate carcinogenic risk (1.87% probability), and Cd presented low carcinogenic risk (80.19% probability). As and Cd were identified as the main heavy metals that pose a threat to human health. The MCS-TFN risk assessment is superior to the traditional deterministic risk assessment since it can obtain the risk level and the corresponding probability, and significantly reduce the uncertainty in risk assessment.
农业和矿业共存地区的土壤正遭受严重的重金属污染，对生态环境和人类健康构成巨大威胁。本研究分析了矿区农业土壤样本中的重金属（As、Cd、Cr、Cu、Ni、Pb 和 Zn），以探讨污染状况、生物可利用性、潜在来源和生态/健康风险。特别是，建立了蒙特卡罗模拟-三角模糊数（MCS-TFN）耦合模型来准确量化生态/健康风险。结果表明，Cd 在土壤中高度富集，根据地质累积指数（I）和化学形态分析，具有最高的生物可利用性。基于绝对主成分得分-多元线性回归（APCS-MLR）模型的污染源分配表明，重金属主要来源于农业活动，其次是矿业活动和自然来源。MCS-TFN 生态风险评估将 Cd 归类为高风险类别，概率为 40.96%，而其他重金属则归类为低风险。Cd 被认为是生态系统的主要污染物。此外，MCS-TFN 健康风险评估表明，As 表现出高非致癌风险（0.07%的概率）和中度致癌风险（1.87%的概率），而 Cd 则表现出低致癌风险（80.19%的概率）。As 和 Cd 被确定为对人类健康构成威胁的主要重金属。MCS-TFN 风险评估优于传统的确定性风险评估，因为它可以获得风险水平和相应的概率，并显著降低风险评估中的不确定性。
