Forward Modeling of Metal Complexation by NOM: I. A priori Prediction of Conditional Constants and Speciation

Abstract

An agent-based simulation of the transformations of natural organic matter (NOM) is combined with quantitative structure-property relationships (QSPRs) for conditional metal-ligand binding constants (K'ML at pH 7.0 and ionic strength = 0.10 M) in order to predict metal binding by NOM. The resulting a priori predictions do not rely upon calibration to environmental data, but vary with the precursor molecules and transformation conditions used in the simulation. Magnitudes and distributions of K'ML are consistent with previously reported values. In a simulation starting with tannin, terpenoid, and flavonoid precursors, metal binding decreases in the order Cu(II) approximately equal to Al(III) approximately equal to Pb(II) > Zn(II) approximately equal to Ni(II) > Ca(II) approximately equal to Cd(II), whereas in simulations containing protein precursors (and thus amine-containing ligands), Al(III) is relatively less and Ni(II) and Cd(II) relatively more strongly bound. Speciation calculations are in good agreement with experimental results for a variety of metals and NOM samples, with typical root-mean-square error (RMSE) of approximately 0.1 to approximately 0.3 log units in free or total metal concentrations and typical biases of <0.2 log units in those concentrations.