Integrated mass-balance-based modeling of cadmium in agricultural soils incorporating erosion processes: Future trends, accumulation rates, and dynamic critical loads.

Predictive assessment of heavy metal contamination in agricultural soils is essential for food security and ecosystem protection, yet current models primarily focus on vertical transport while largely neglecting lateral erosion fluxes. To overcome this limitation, a spatially explicit dynamic mass-balance model was developed that integrated vertical processes with lateral transport via water and wind erosion, supported by a comprehensive field-based dataset. The model was used to simulate cadmium (Cd) dynamics and evaluate dynamic critical loads (DCL) over 50 years in an industrial-impacted watershed under four scenarios: baseline (A), reduced input (B), straw removal (C), and integrated measures (D). Under scenario A, atmospheric deposition contributed 88.8% of total Cd input, while rainfall erosion accounted for 34.7% of output, confirming its non-negligible role in lateral Cd redistribution. After 50 years, the mean soil Cd concentration increased by 22.0%, 4.8%, and 6.8% under scenarios A, B, and C, respectively, but decreased by 9.5% under scenario D, resulting in a final concentration 25.8% lower than that of the baseline scenario A. Accumulation rate and pollution acceleration were introduced to provide insight into spatiotemporal Cd trends. Scenario D exhibited the highest soil carrying capacity (mean DCL of 555 g ha-1 yr-1), 3.3 times that of scenario A. Atmospheric deposition flux (Ia) and straw bioconcentration factors (BCF) were the most sensitive parameters, followed by sediment delivery ratio (SDR), rainfall erosion modulus (Ar) and the partition coefficient (Kd). The model was validated against historical data, which confirmed its predictive robustness. This study presented an improved watershed-scale framework for the dynamic assessment and management of heavy metals, which aimed to facilitate integrated strategies that target pollution sources and optimize plant-based removal processes. It is designed to inform policy decisions and guide technological innovation in soil remediation.