Combinatorial Parameter Space As an Empirical Tool for Predicting Water Chemistry: Fe(II) Oxidation Across a Watershed

Abstract

Fe(II) oxidation kinetics in surface waters are a complex function of the concentration of several dissolved species that vary geographically and temporally across watersheds. This work reports an empirical, combinatorial investigation of Fe(II) oxidation that simultaneously evaluated these variations across the pH, Fe(II), PO₄³⁻, Cl⁻, Br(-), CO₃²⁻, and natural organic matter (NOM) axes. The work assayed the effects of independent and dependent variables through application of a novel experimental design that varied Fe(II), PO₄³⁻, Cl⁻, Br⁻, and CO₃²⁻ along the pH axis. Each factor was varied across concentration ranges corresponding to the natural variation between typical fresh and salt water. The system was designed to describe the oxidation of Fe(II) that occurs when Fe(II)-rich groundwaters are mixed rapidly with oxic overlaying waters as a result of tidal movement, bioturbation, dredging, and other mixing/resuspension events. Factors and interfactor interactions were statistically evaluated to determine their importance to Fe(II) oxidation at the 95% level of confidence. Significant factors were retained and used to construct predictive numerical models of Fe(II) oxidation rates. Two models (M1 and M2) were constructed to represent the conditional endmembers of unrestricted Fe cycling (M1) and restricted Fe cycling (due to forced precipitation of Fe(III), M2). The models were challenged to predict net Fe(II) oxidation rates across a watershed (the Congaree/Santee rivers, sampled at ten different locations in South Carolina). The models were generally capable of predicting Fe(II) oxidation rates to within the 95% confidence interval, although M2 consistently overpredicted the rate relative to M1. The minimum initial Fe(II) concentration needed to observe Fe cycling is estimated based on the model output.