Predicting the fate and transport of contaminants in the subsurface is of utmost importance in terms of risk assessment. Reactive transport models account for the physical and geochemical processes that control the move-ment of inorganic contaminants in the environment, including adsorption which is one of the most significant processes, especially for the transport of heavy metals. Iron oxides are highly reactive surfaces and various models have been developed to describe the reactivity of various phases, such as goethite and ferrihydrite. Adsorption has been traditionally described using empirical relationships, such as distribution factors (Kd) and Frendlich isotherms, that, however, do not account for the impact of variable chemical conditions on adsorption. A more robust description of adsorption is provided by surface complexation models (SCMs) that simulate adsorption mechanistically under various conditions. SCMs have been proven superior tools to estimate the partitioning of ions in the solid – solution interface. However, there are still two main limiting factors associated with the application of SCMs: a) a significant number of parameters is required, and thus their application is limited to pure mineral studies, and b) the parameters are interdependent with each other and cannot easily estimated experimentally, and therefore are usually fitted or adjusted to describe adsorption data, exhibiting a great variabil-ity among different studies. In this study, we propose a SCM tied with a multi - start global optimization algorithm to estimate the fitted param-eters and predict iron oxides reactivity. This tool enables the simultaneous optimization of different parameters revealing unified parameters that were previously scattered by the interference of other variable parameters. Chromate was used as a model contaminant in this study, however, this approach can be easily extended to any inorganic compound. Specifically, a 3–site model was used to describe surface protonation using site densi-ties derived from the structure and morphology, and protonation constants derived from the literature or fitted to mineral-specific charging curves. For chromate adsorption, insights from spectroscopy and batch adsorption experiments are incorporated to build the model. Spectroscopy profiles for chromate surface complexes were used to extract the inner – sphere equilibrium constants while a sensitivity analysis was performed for the batch adsorption data.
|Title of host publication
|Abstracts of the 38th Annual Meeting, Society for Environmental Toxicology and Chemistry
|Published - 2017