Stephen E. Cabaniss

The effects of pH, ionic strength, and iron–fulvic acid interactions on the kinetics of non-photochemical iron transformations. I. Iron(II) oxidation and iron(III) colloid formation

Abstract Flow injection analysis was used to study the effect of a fulvic acid on the kinetics of iron(II) oxidation and iron colloid formation under conditions approximating fresh natural waters. While iron(II) oxidation in high-carbonate inorganic solutions is predicted well by a recently proposed homogeneous model, it overestimates the oxidation rate in low-carbonate solutions, possibly due to the formation of an intermediate iron(II) colloid or surface species. Results in fulvic acid solutions are consistent with the formation of an iron(II)–fulvic acid complex at both pH 6.0 and 8.0 which accelerates the overall oxidation rate relative to inorganic solutions. However, iron(III) complexation by fulvic acid greatly slows the formation of iron colloids, stabilizing dissolved iron(III). Decreased pH and increased ionic strength slow and decrease iron colloid formation. Evidence of a kinetic control on the distribution of iron(III) between organically complexed and colloidal forms is presented.

Rank Analysis of the pH-Dependent Synchronous Fluorescence Spectra of Six Standard Humic Substances.

Synchronous fluorescence spectra of standard humic substances (HS) at variable pH can only be adequately explained by positing large (≥7) numbers of fluorescent factors. Although most of the variation (99.9%) can be explained using only two or three major factors, careful representation of the experimental error shows numerous additional minor factors that have been overlooked in previous work. The experimental error in these spectra was measured by repeated spectral acquisition and estimated by a linear regression equation relating the standard deviation, σ , to fluorescence intensity. Each of the six HS examined gave a unique synchronous spectral shape at pH 7.0, although most showed similar patterns of pH dependence. These results indicate that these synchronous fluorescence measurements have a high information content, suitable for tracer studies of HS.

Forward Modeling of Metal Complexation by NOM: II. Prediction of Binding Site Properties

An a priori model of metal complexation by natural organic matter (NOM) has previously been shown to predict experimental data at pH 7.0 and 0.1 M ionic strength (Cabaniss, S. E. Environ. Sci. Technol. 2009). Unlike macroscopic models based only on stoichiometry and thermodynamics, this a priori model also predicts the ligand groups and properties of complexed (occupied) molecules. Ligand molecules with strong binding sites form complexes at low metal concentrations and have average properties (molecular weight, charge, aromaticity) which can differ significantly from the average properties of bulk NOM. Cu(II), Ni(II) and Pb(II) preferentially bind to strong amine-containing sites which are often located on small (MW < 1000), lower-aromaticity molecules. Cd(II) and Zn(II) show generally weaker binding, although they also prefer amine-containing sites to pure carboxylates and bind to smaller, less aromatic molecules. Ca(II) shows no real preference for amine over carboxylate ligand groups, preferentially binding to larger and more negatively charged molecules. Al(III) has a unique preference for phenol-containing sites and larger, more aromatic molecules. While some predictions of this model are consistent with a variety of experimental data from the literature, others await validation by molecular-level analysis.

The effects of pH, ionic strength, and iron-fulvic acid interactions on the kinetics of non-photochemical iron transformations. II. The kinetics of thermal reduction

Abstract A combination of flow-injection analysis and kinetic analysis was used to examine the speciation of iron(II) and iron(III) in fulvic acid solutions as a function of pH, ionic strength, and time. This methodology was used to follow a shift in iron speciation from faster to slower reacting species over a timescale of several days. This speciation data shows that both iron(II) and iron(III)–fulvic acid complexes are important iron species in humic-containing natural waters and that their amounts and their rates of transformation to colloidal iron are controlled primarily by the kinetics of thermal (dark) reduction and iron(II) oxidation. The kinetic analysis methodology also yielded the rate constants for the thermal reduction of iron by the fulvic acid. These rate constants decrease with increasing pH and are independent of ionic strength. While thermal reduction was found to be too slow to produce large amounts of steady state iron(II) at circumneutral pH, it does provide a mechanism for iron redox cycling in the absence of photochemical or biochemical processes.

Mercury in Natural Waters: A Mini-Review

Mercury in fish is a concern as for human health. Understanding mercury toxicity, however, requires an understanding of mercury speciation. Monomethylmercury is known to be the most concerning mercury species. This mini-review first covers an introductory toxicology of mercury. As human exposure to monomethylmercury is mainly through fish and as monomethylmercury concentrations in fish are related to inorganic mercury loads, health and environmental preventive regulations concerning mercury in natural waters are addressed. Further, mercury geochemistry in natural waters is briefly reviewed, and the biogeochemical processes which affect mercury toxicity in natural waters are discussed.

Quantitative structure-property relationship for predicting chlorine demand by organic molecules.

Conventional methods for predicting chlorine demand (HOCl(dem)) due to dissolved organic matter (DOM) are based on bulk water quality parameters and ignore structural features of individual molecules that may better indicate reactivity toward the disinfectant. The Quantitative Structure-Property Relationship (QSPR) modeling approach can account for structural properties of individual molecules. Here we report a QSPR for HOCl(dem) based on eight constitutional descriptors. Model compounds with HOCl(dem) ranging from 0.1 to 13.4 mol chlorine per mole compound were divided into a calibration and cross-validation data set (N = 159) and an external validation set (N = 42). The QSPR was calibrated using multiple linear regression in a 5-way leave-many-out approach and has average R(2) = 0.86 and standard error of regression (StdE(reg)) = 1.24 mol HOCl per mole compound and p < 0.05. Internal cross-validation has average q(2) = 0.85 and the external validation has q(2) = 0.88, indicating a robust model. The leverage of 7 of 42 compounds in the external validation data set exceeded the critical value, suggesting that these compounds may be overextrapolated. However, root-mean-square error of prediction in the external validation was 1.17 mol HOCl per mole compound, and all compounds were predicted with +/-2.5 standardized residuals (Sresid). Application of the QSPR to model structures of NOM predicts HOCl(dem) comparable to reported measurements from natural water treatment.

Geometry of Zn(II) complexes with dissolved organic matter: X-ray studies at variable pH

The structure of Zn(II) complexes with dissolved organic matter (DOM) is an important consideration in developing molecular-level models of Zn(II) speciation, but recent reports favoring the tetrahedral geometry differ from earlier findings that geometry was largely octahedral. In general, the presence of thiolate ligands favors the tetrahedral geometry, while O and N ligands favor the octahedral geometry. This work presents extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES) spectroscopic results, indicating an octahedral geometry over the pH range of 5–9 for a freshwater DOM isolate. Changes in XANES derivatives as a function of pH can be explained in terms of ligand protonation and/or changing ligand groups. Tetrahedral Zn(II)–DOM geometry may be restricted to binding environments containing thiol groups.