Risk assessment of heavy metals in agricultural soil based on the coupling model of Monte Carlo simulation-triangular fuzzy number

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_geo) 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.