Blind prediction of Cu(II) sorption onto goethite: Current capabilities of surface complexation modeling

The paper presents examples illustrating the current blind predictive capabilities of surface complexation models (SCM) in combination with a respective database RES3T (Rossendorf Expert System for Surface and Sorption Thermodynamics).
The general strategy for the selection of numerical data is discussed. Based on the information about the minerals collected in the sorption database RES3T, first a set of relevant surface species must be formed. Then respective surface complexation parameters are taken from RES3T: the binding site density for the minerals, the surface protolysis constants, and the stability constants for all relevant surface complexes. To be able to compare and average thermodynamic constants originating from different sources a normalization concept is applied.
In a second part the current capabilities of SCM is demonstrated based on a blind prediction exercise. The system covered is the Cu(II) sorption onto goethite, with the predictions compared to raw data from three independent experimental investigations. To keep the number of parameters at a minimum, the Diffuse Double Layer model was selected to account for electrostatics. The calculations were performed with the FITEQL code, version 3.2.
In most cases the model prediction represented the experimental values for the sorbed amount of Cu(II), expressed as conventional distribution coefficients KD, within one order of magnitude or better.
We conclude, that the application of SCM can indeed be very valuable for estimating distribution coefficients for contaminants in well defined mineral systems. The SCM database so far assembled within the RES3T project is able to provide the respective parameter sets following the outlined stepwise strategy of species selection, data collection, normalization and averaging.