Discrete and continuous multiligand models for metal-humate interactions are compared and analyzed by using both synthetic and experimental data. Discrete ligands (typically two or three) are shown to be a simple and accurate means of predicting metal-humate binding within the range of calibrating titrations. Selection of discrete ligand parameters is best achieved via nonlinear regression. The continuous affinity spectrum model is highly sensitive to experimental error, and thus its usefulness as an aid for selection of discrete ligands is limited. As anticipated, only the weakest, most abundant ligands in a ligand mixture can be identified with the continuous stability function model. The continuous normal ligand distribution model is capable of fitting metal-humate binding data with only three parameters, but only the stronger ligands in the assumed distribution are important for fitting observed data. The discrete ligand approach is probably the most useful way to model metal-humate binding because of the ease with which discrete ligands can be incorporated into chemical equilibrium computer programs.
All Science Journal Classification (ASJC) codes
- Environmental Chemistry