Submitted to: Vadose Zone Journal
Publication Type: Peer reviewed journal
Publication Acceptance Date: 6/1/2007
Publication Date: 8/1/2007
Citation: Goldberg, S.R., Criscenti, L.J., Turner, D.R., Davis, J.A., Cantrell, K.J. 2007. Adsorption-desorption processes in subsurface reactive transport modeling. Vadose Zone Journal. Vol 6(3):407-435. Interpretive Summary:
Technical Abstract: The use of equilibrium geochemical models to calculate the solubilities and aqueous speciation of contaminants is well established in the field of geochemical modeling. Surface complexation modeling is an extension of this thermodynamic modeling approach to include the reactions between dissolved species and the functional groups present on mineral surfaces. The adsorption reactions are included as part of the network of chemical reactions that require equilibration, rather than as a condition-dependent partitioning coefficient, like Kd. Once the model is calibrated, it may allow predictive calculations for a range of geochemical conditions without changing the values of the stability constants for ion adsorption. The adsorption equations can be included efficiently in transport simulations in which there are chemical gradients in the subsurface environment rather than constant chemical conditions (Curtis et al., 2006; Kent et al., 2000). This type of model also provides a sounder thermodynamic basis from which to examine uncertainty in transport parameters that result from spatial heterogeneity in the physical and chemical characteristics of the system of interest. The challenge in applying the surface complexation concept in the environment is to simplify the adsorption model, such that predicted adsorption is still calculated with mass laws that are coupled with aqueous speciation, while lumping parameters that are difficult to characterize in the environment in with other parameters. In order to be applied by solute transport modelers and within risk assessment applications, the complexity of the adsorption model needs to be balanced with the goal of using the simplest possible model that is consistent with observed data.