Simulating and Predicting Arsenic Mineralogy Controlling Arsenic Solubilization Into Groundwater in Semi-Arid Environments.

Elevated arsenic (As) concentrations in groundwater used for drinking water have been seen throughout the United States, with generally higher concentrations in the Rocky Mountain and Interior Plains regions. Long term exposure to arsenic concentrations over the Maximum Contaminant Level (MCL) of 0.01 µg/L can cause skin and lung cancer, along with several other adverse health effects. Previous studies have looked into the tragic As poisoning of millions of people in West Bengal, Bangladesh, and Southeast Asia; focusing only in these humid regions. There are limited data evaluating the fate of As and its mineral speciation in groundwater in semi-arid environments. At our study site, groundwater concentrations of As exceed the MCL, mineralogy is dominated by carbonates, and groundwater recharge is controlled by high evapotranspiration rates. The objective of this study is to determine As mineralogy that controls the solubility of As. Two cores were collected from the soil surface to depth of groundwater. Cores were sectioned under anaerobic conditions based on observed redoximorphic features. Pore water was analyzed for As and Fe redox species and general water quality parameters and solid phase As, Fe and Mn have been described using sequential extractions. We have evidence that surface soils contain As associated with pyritic material and As in the redox transition zone is associated with iron oxides and carbonates. Most of the As in all zones is associated with mineral surfaces or incorporated into non-crystalline minerals as defined by sequential extractions. To evaluate solid phase associations of As, parameters from pore water analysis from the various layers within the cores will be modeled using MINEQL+. The model results will be compared to bulk As concentration data from soil sequential extractions and different imaging techniques. Results will be used to simulate field conditions and understand and predict As retention and solubilization.