Surface complexation modeling for predicting solid phase arsenic concentrations in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA

The potential health impact of As in drinking water supply systems in the Mississippi River Valley alluvial aquifer in the state of Arkansas, USA is significant. In this context it is important to understand the occurrence, distribution and mobilization of As in the Mississippi River Valley alluvial aquifer. Application of surface complexation models (SCMs) to predict the sorption behavior of As and hydrous Fe oxides (HFO) in the laboratory has increased in the last decade. However, the application of SCMs to predict the sorption of As in natural sediments has not often been reported, and such applications are greatly constrained by the lack of site-specific model parameters. Attempts have been made to use SCMs considering a component additivity (CA) approach which accounts for relative abundances of pure phases in natural sediments, followed by the addition of SCM parameters individually for each phase. Although few reliable and internally consistent sorption databases related to HFO exist, the use of SCMs using laboratory-derived sorption databases to predict the mobility of As in natural sediments has increased. This study is an attempt to evaluate the ability of the SCMs using the geochemical code PHREEQC to predict solid phase As in the sediments of the Mississippi River Valley alluvial aquifer in Arkansas. The SCM option of the double-layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from chemical extractions, calculated surface-site densities, published surface properties, and published laboratory-derived sorption constants for the sorbents. The model results are satisfactory for shallow wells (10.6 m below ground surface), where the redox condition is relatively oxic or mildly suboxic. However, for the deep alluvial aquifer (21–36.6 m below ground surface) where the redox condition is suboxic to anoxic, the model results are unsatisfactory.

Research highlights► Surface complexation models (SCMs) can be used to predict the distribution of Arsenic (As) in natural sediments with a moderate to high level of uncertainty. ► The accuracy of the SCMs depends on the qualitative and quantitative determination of accurate solid sorbent phases, selection of proper extraction methods, inclusion of accurate surface parameters, internally consistent surface reaction constants for each sorbent phases, and detailed chemical analysis of groundwater. ► The use of default surface-site densities for individual sorbent phases reported in the literature is not applicable for sediments collected through the entire depth profile having different redox conditions at varying depths. ► It is very important to develop consensus on the appropriate extraction methods and sorption databases for interested contaminants (e.g. arsenic) that can be used in SCMs for natural sediments.