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Soil Chemistry

Predicting Arsenate Adsorption by Soils using Soil Chemical Parameters in the Constant Capacitance Model

USDA-ARS, George E. Brown Jr., Salinity Lab., 450 W. Big Springs Road, Riverside, CA 92507

^* Corresponding author (sgoldberg{at}ussl.ars.usda.gov)

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 by optimizing either three monodentate or two bidentate As(V) surface complexation constants. 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 (CEC), inorganic C (IOC) content, organic C (OC) content, iron oxide content, and surface area (SA). 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.

Abbreviations: ARMSE, average root mean squared error • CEC, cation exchange capacity • Cip, coefficient of imprecision • DF, degrees of freedom • ICP, inductively coupled plasma • IOC, inorganic carbon • MANOCOVA, multivariate analysis of covariance • MSE, mean square error • OC, organic carbon • SA, surface area

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