Paper No. 5
Presentation Time: 2:35 PM
FIELD-SCALE APPLICATION OF SURFACE COMPLEXATION MODELING USING PHREEQC FOR PREDICTING SOLID-PHASE ARSENIC OCCURRENCES IN THE SEDIMENTS OF THE MISSISSIPPI RIVER VALLEY ALLUVIAL AQUIFER, ARKANSAS, USA
The application of surface complexation models (SCM) to predict the sorption behavior of Arsenic (As) in natural sediments has not been often reported, and such applications are greatly constrained by the lack of site-specific model parameters, including limited availability of spectroscopically reliable and internally consistent sorption databases for As and potential sorbents. The geochemical code PHREEQC was used to predict solid phase As occurrence in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA. The double layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from wet chemical extractions; calculated surface site concentrations; exclusive consideration of inner-sphere monodentate surface complexes; published surface properties; and normalized published laboratory-derived sorption constants for the sorbents. The model over predicts extracted As in deeper (21-36.6 m) reduced coarse-sediments 4- to 24-fold. The model predicts 57-92% of extracted As in shallow (0-17 m) relatively oxidized fine-sediments. The use of default surface-site concentrations proposed in the literature to calculate modeled surface-site concentration of individual sediment samples is not applicable for the entire depth profile because redox environments and the relative status of aging of hydrous Fe oxides (HFO) are different at different depths, which controls the sorption capacity of HFO. Determining individual surface properties of natural sorbents at different depths with different redox environments is necessary to improve model predictions and overall geochemical assessments. In addition, developing consensus on the appropriate extraction methods and spectroscopically valid sorption databases that can be used in SCM for natural sediments is also very important to improve reliability, consistency, and comparability of disparate study results.