Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model

•Surface species content was used to construct species-informed model.•The equilibrium and kinetic data were used to train species-informed model.•The optimal species-informed model showed excellent predictability.•LIME values helped explain species-informed model's surface species content.•Species-informed modeling framework could be applied to other adsorption systems. This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model. [Display omitted]