HR: 11:50h
AN: H32C-07    [Abstracts]
TI: Predicting Arsenate Adsorption by Soils Using Soil Chemical Parameters in the Constant Capacitance Model
AU: * Goldberg, S R
EM: sgoldberg@ussl.ars.usda.gov
AF: USDA-ARS George E. Brown Jr. Salinity Laboratory, 450 W Big Springs Road, Riverside, CA 92507 United States
AU: Lesch, S M
EM: slesch@ussl.ars.usda.gov
AF: USDA-ARS George E. Brown Jr. Salinity Laboratory, 450 W Big Springs Road, Riverside, CA 92507 United States
AU: Suarez, D L
EM: dsuarez@ussl.ars.usda.gov
AF: USDA-ARS George E. Brown Jr. Salinity Laboratory, 450 W Big Springs Road, Riverside, CA 92507 United States
AB: Prediction of arsenate, As(V), adsorption and transport in soils requires detailed studies of As(V) adsorption and subsequent determination of model parameters. Arsenate adsorption on 49 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. 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 ability to describe As(V) 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) surface complexation constants from easily measured soil chemical characteristics using the As(V) adsorption data for 44 of the soils. These chemical properties were: cation exchange capacity (CEC), surface area (SA), inorganic carbon content (IOC), organic carbon content (OC), and iron oxide content (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 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) adsorption on most of the 44 soils using the As(V) surface complexation constants predicted from the regression equations. The prediction equations were used to obtain values for As(V) surface complexation constants for the remaining five 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 As(V) 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 prediction 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) concentrations under diverse environmental and agricultural management conditions without requiring soil specific adsorption data and subsequent parameter optimization.
DE: 1899 General or miscellaneous
DE: 1831 Groundwater quality
DE: 1871 Surface water quality
DE: 1875 Unsaturated zone
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting