|Lesch, Scott - UC RIVERSIDE, CA|
|Basta, N - OHIO STATE UNIV.|
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 28, 2005
Publication Date: August 4, 2005
Citation: Goldberg, S.R., Lesch, S.M., Suarez, D.L., Basta, N.T. 2005. Predicting arsenate adsorption by soils using soil chemical parameters in the constant capacitance model. Soil Science Society of America Journal. 69(5):1389-1398. Interpretive Summary: Arsenate is a specifically adsorbing anion that is toxic to animals at elevated concentrations. Toxic concentrations can occur in agricultural soils and irrigation waters. A better understanding of the adsorption behavior of this ion is necessary. Adsorption of arsenate by 49 soil samples was investigated under changing conditions of solution pH. The adsorption behavior was evaluated and predicted using a chemical model and easily measured soil chemical characteristics. Our results will benefit scientists who are developing models of arsenate movement in arid zone soils. The results can be used to improve predictions of arsenate behavior in soils and thus aid action and regulatory agencies in the management of soils and waters which contain elevated concentrations of arsenate.
Technical Abstract: The constant capacitance model, a chemical surface complexation model, was applied to arsenate, As(V), adsorption on 49 soils selected for variation in soil properties. The constant capacitance model was able to fit arsenate adsorption on all soils. A general regression model was developed for predicting soil As(V) surface complexation constants from easily measured soil chemical characteristics. These chemical properties were cation exchange capacity, inorganic carbon content, organic carbon content, iron oxide content, and surface area. The prediction equations were used to obtain values for the As(V) surface complexation constants for five additional soils, thereby providing a completely independent evaluation of the ability of the constant capacitance model to describe As(V) adsorption. The model's ability to predict As(V) adsorption was quantitative on three soils, semi-quantitative on one soil, and poor on another soil. Incorporation of these prediction equations into chemical speciation-transport models will allow simulation of soil solution As(V) concentrations under diverse agricultural and environmental conditions without the requirement of soil specific adsorption data and subsequent parameter optimization.