Multi-factor interaction perspective: machine learning-based analysis of Ni⁺ adsorption onto soil.

With the rapid development of industry and agriculture, the ecological and health impacts of nickel (Ni) have gained increasing attention. While previous experimental studies have identified factors influencing Ni adsorption behavior in soils, their nonlinear relationships and interactive effects remain underexplored. Through combining machine learning (CatBoost/XGBoost) models with SHapley Additive exPlanations (SHAP), this study analyzed 662 experimental datasets to reveal these nonlinear interactions between factors that affect the adsorption behavior of Ni in soil. The modeling results demonstrated CatBoost's superior performance over XGBoost (test R = 0.85 vs 0.83). Both feature importance analysis from the model and SHAP values identified the initial Ni concentration (C) as the most critical factor, followed by ionic strength (IS), solid-to-liquid ratio (SL), clay content, and cation exchange capacity (CEC). SHAP dependence plots revealed a nonlinear SL effect that maximum adsorption occurred at low SL ratios with subsequent fluctuations attributable to ionic competition and pore accessibility constraints. Notably, SHAP interaction analysis uncovered a key finding which C exhibited synergistic interactions with both CEC and clay content to enhance Ni immobilization, whereas elevated IS substantially diminished these cooperative effects. This work quantitatively characterizes multifactorial coupling in Ni adsorption processes, advancing theoretical foundations for risk assessment while informing targeted remediation strategies and enhancing mechanistic understanding of heavy metal interactions in soil systems.