A Scalable Surface Complexation Modeling Framework for Predicting Arsenate Adsorption on Goethite-Coated Sands

This article presents a scalable surface complexation modeling framework for predicting arsenate adsorption onto various types of goethite-coated sands (GCS) and goethite. Four types of GCS with their iron content and surface area varying by nearly an order of magnitude were synthesized. Arsenate adsorption onto these four sands was studied by conducting pH edge and adsorption isotherm experiments. A diffuse double layer surface complexation model was calibrated to fit the arsenate adsorption data of one of the sands. A modeling framework was developed to scale the calibrated model to other sands based on their measured surface-site-density values. The modeling framework was validated by using the scaled models to predict adsorption onto the other three sands. The scalability of the model was further tested by using the scaled models to make predictions for several arsenate-goethite adsorption datasets available in the literature. Results show that the scaled models were able to predict the laboratory data obtained for the three other GCS. Scaled models also successfully predicted the literature-derived datasets. Average error of the predictions was less than 5%, as quantified using root-mean-squared error (RMSE) values. In contrast, our simulation results indicate that the models that did not use the scaling approach failed to make good predictions. The proposed scaling approach offers a promising method for developing generic, scalable, surface complexation models.