Prediction and verification of arsenic phytoavailability in paddy soil based on CD-MUSIC model in the Xiangjiang River Basin.

Arsenic (As) accumulation in rice grains is primarily governed by its phytoavailability, which is major influenced by soil physicochemical properties and the surface activity of iron oxides, particularly goethite and ferrihydrite. Soil pH and the ionic strength of the soil solution are determinants of surface activity on iron oxides, which controlled the As adsorption and desorption dynamics. However, As activity is highly variable during the rice reproductive cycle, there is still a lack of approach to accurately predict and to the phytoavailability of As due to the paddy soil heterogeneity and complexity. The Charge Distribution-Multisite Complexation (CD-MUSIC) is a promising method to simulate ion adsorption behavior on iron oxide surfaces. This study applied the CD-MUSIC model to optimize soil pH and ionic strength parameters based on different scenario simulation. Subsequent linear regression analysis revealed a strong correlation (R² = 0.7664) between model-predicted dissolved As concentrations and As accumulation in rice grains. The R2 between predicted rice As with pot rice As increasing to 0.9208 after BCF (Bioconcentration Factor) corrected, demonstrating high homology between model-predicted dissolved As and bioavailable As assimilated by rice plants. To evaluate regional applicability, the model was simplified and validated across 293 sampling sites spanning the upper, middle, and lower reaches of the Xiangjiang River Basin, achieving a robust regional-scale prediction accuracy (R² = 0.6958). These results provide a feasible model for predicting rice As accumulation, which supports the safe development of regional agriculture and risk management.