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Predicting Arsenate Adsorption by Soils using Soil Chemical Parameters in the Constant Capacitance Model
Author(s) -
Goldberg Sabine,
Lesch S. M.,
Suarez D. L.,
Basta N. T.
Publication year - 2005
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2004.0393
Subject(s) - adsorption , arsenate , soil water , cation exchange capacity , chemistry , capacitance , environmental chemistry , soil science , analytical chemistry (journal) , geology , arsenic , organic chemistry , electrode
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.