Dynamic modeling of long-term remediation strategies for heavy metals in mining-impacted agricultural soil

The widespread contamination of heavy metals (HMs) in mining areas poses severe and persistent threats to ecosystem and human health, necessitating advanced tools for predictive risk assessment. In this study, we conducted quarterly sampling over one year across 62 farmland soil sites and 22 paired irrigation water and sediment sites in a mining area of South China, and measured concentrations of eight HMs (As, Cd, Pb, Zn, Cu, Ni, Hg, and Cr). A Monte Carlo-optimized Level IV fugacity model was employed to simulate HM transport and assess ecological risks across environmental compartments. The results showed that Hg and Cd posed the highest ecological risks among all HMs, with their potential ecological risk index values in sediment reaching 195.11 and 126.93, respectively. Transfer flux analysis revealed that atmospheric deposition dominated inputs to water (15.64%–95.31%) and soil (99.89%–99.99%), while water-to-sediment transfer accounted for 99% of water outputs, making sediment the predominant sink. Scenario analysis indicated that a 50% emission reduction would delay Hg risk in sediment from reaching the extremely high level (RI ⩾ 320) until 2040 and maintain Cd in soil within the low risk range (RI ⩽ 40). However, historical contamination would sustain Hg risk in sediment above RI ⩾ 160 under all scenarios. Overall, this research establishes a crucial dynamic modeling framework for forecasting long-term ecological risks and designing targeted, metal-specific control strategies in mining-affected regions.