Predicting Boron Adsorption Isotherms by Midwestern Soils using the Constant Capacitance Model

Abstract

Prediction of B adsorption and transport has required detailed studies of B adsorption and subsequent determination of model parameters. In this study we tested a general regression model previously developed for predicting soil B surface complexation constants from easily measured soil chemical characteristics. The constant capacitance model, a chemical surface complexation model, was applied to B adsorption isotherms on 22 soils from the A and B horizons of 16 soil series from Oklahoma and Iowa. The measured chemical properties were surface area, organic C (OC) content, inorganic C (IOC) content, and Al oxide content. The prediction equations of Goldberg et al. (2000) were used to obtain constant capacitance model values for B surface complexation constants thereby providing a completely independent evaluation of the ability of the constant capacitance model to describe B adsorption. The model was well able to predict B adsorption isotherms on the majority of the soils. The regression model was used to obtain the parameters for the constant capacitance model. Then the constant capacitance model was used to predict the soil specific B adsorption. This is in contrast to regression models that fit adsorption of a series of soils. The distinction is that using the combined regression equations and the constant capacitance model only soil properties and not adsorption are needed to predict soil specific B adsorption data. The prediction equations developed from a set of soils primarily from California, were able to predict B adsorption on a set of soils from different parts of the country. This result suggests wide applicability of the model prediction equations developed previously, for describing B adsorption both as a function of solution B concentration and solution pH.