Tuesday, 11 July 2006

Prediction of Arsenate and Selenite Adsorption by Soils Using the Constant Capacitance Model.

Sabine Goldberg, USDA-ARS, George E. Brown, Jr., Salinity Laboratory, 450 W. Big Springs Road, Riverside, CA 92507, Scott M. Lesch, University of California, Riverside, Department of Environmental Sciences, Riverside, CA 92507, and Donald L. Suarez, USDA-ARS, George E. Brown Jr., Salinity Laboratory, 450 W. Big Springs Road, Riverside, CA 92507.

Arsenic and selenium are trace elements that can become elevated in soils and cause toxicity problems to plants and animals. Prediction of arsenate, As(V), and selenite, Se(IV) adsorption and transport in soils requires detailed studies of As(V) and Se(IV) adsorption and subsequent determination of model parameters. Arsenate adsorption on 49 soil samples and Se(IV) adsorption on 45 soil samples belonging to six different soil orders was investigated as a function of solution pH (3-10). The set of soils consisted of two subgroups: one from the Midwestern U.S. and one primarily from the southwestern U.S. For most soils, As(V) adsorption increased with increasing solution pH, reached a maximum around pH 6-7, and decreased with further increases in solution pH. Selenite adsorption on most soils was maximum at pH 2-3 and decreased with increasing solution pH. The constant capacitance model, a chemical surface complexation model, was well able to describe As(V) adsorption on the soil samples as a function of solution pH by simultaneously optimizing three As(V) surface complexation constants. The model was able to describe Se(IV) adsorption as a function of solution pH by simultaneously optimizing one Se(IV) surface complexation constant and the surface protonation constant. The ability of the model to describe As(V) and Se(IV) adsorption as a function of pH represents an advancement over the Langmuir and Freundlich adsorption isotherm approaches. A general regression model was developed for predicting soil As(V) and Se(IV) surface complexation constants from easily measured soil chemical characteristics using the As(V) adsorption data for 44 of the soils and the Se(IV) adsorption data for 36 of the soils. These chemical properties for As(V) adsorption were: cation exchange capacity (CEC), surface area (SA), inorganic carbon content (IOC), organic carbon content (OC), and iron oxide content (Fe). The chemical properties for Se(IV) adsorption were: OC and Fe. A preliminary analysis determined that the mean surface complexation constant values for the two soil subgroups were statistically different. For this reason, while the regression model equations for As(V) adsorption for each soil subgroup contained common intercepts and ln(CEC) terms, the ln(IOC), ln(OC), ln(Fe), and ln(SA) terms were different. The constant capacitance model was able to predict As(V) and Se(IV) adsorption on most of the soils using the As(V) and Se(IV) surface complexation constants predicted from the regression equations. The prediction equations were used to obtain values for As(V) and Se(IV) surface complexation constants for the remaining soils that had not been used to obtain the general regression model. This provided a completely independent evaluation of the ability of the constant capacitance model to describe adsorption. The model was able to accurately predict As(V) adsorption on three soils, qualitatively predict As(V) adsorption on one soil, and unable to predict As(V) adsorption on one soil. Incorporation of these regression prediction equations into chemical speciation-transport models will allow simulation of soil solution As(V) and Se(IV) concentrations under diverse environmental and agricultural management conditions without requiring soil specific adsorption data and subsequent parameter optimization.

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