One-dimensional reactive transport and interface flux modeling of Pb, Cd, and As remobilization from contaminated River Tano sediments under hydrodynamic and redox disturbance.

Contaminated sediments can shift from long-term sinks to secondary sources of heavy metals when hydrodynamic forcing and redox conditions change, creating transient release to overlying water. This study developed a site and scenario-based one-dimensional reactive transport and interface flux model for Pb, Cd, and As remobilization from contaminated River Tano sediments under hydrodynamic and redox disturbance. The model coupled advection-diffusion-dispersion transport, sediment diagenesis, redox-sensitive reaction networks, Freundlich sorption/desorption, mineral dissolution-precipitation, and a sediment-water interface formulation that separates diffusive, advective, erosion/resuspension, and reaction enhanced exchange. Calibration and evaluation used porewater profiles, overlying water concentrations, interfacial flux observations, sequential extraction constraints, and an eight-site benthic chamber disturbance dataset. Scenario simulations indicated that stronger shear and turbulence increased sediment-water exchange by thinning the diffusive boundary layer and activating shear dependent particulate export, while redox transitions increased dissolved inventories through Fe/Mn oxide reduction and destabilization of sulfide-associated phases. Sensitivity analysis identified critical shear stress, boundary layer scaling, and Fe/Mn oxide reduction kinetics as the dominant controls on peak dissolved metal flux. Performance was strong for Pb and Cd but weaker for As. Including all validation points, the overall model performance was NSE = 0.919, R2 = 0.937, PBIAS = 4.12%, and 95% predictive coverage = 92.64%; after excluding t = 0 initialization points, overall performance remained good but lower (NSE = 0.910, R2 = 0.931, PBIAS = 4.71%, and coverage = 90.80%). The framework is therefore best interpreted as a mechanistic tool for site-specific scenario analysis and hypothesis testing, not as a fully general management predictor without site calibration.