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.

Conventional methods for predicting chlorine demand (HOCldem) 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 HOCldem based on eight constitutional descriptors. Model compounds with HOCldem 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 R2 = 0.86 and standard error of regression (StdEreg) = 1.24 mol HOCl per mole compound and p < 0.05. Internal cross-validation has average q2 = 0.85 and the external validation has q2 = 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 HOCldem comparable to reported measurements from natural water treatment.

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) ≈ Al(III) ≈ Pb(II) > Zn(II) ≈ Ni(II) > Ca(II) ≈ 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 ∼0.1 to ∼0.3 log units in free or total metal concentrations and typical biases of <0.2 log units in those concentrations.

An attenuated total reflectance Fourier transform infrared spectroscopy technique has been developed utilizing an oxide coated internal reflection element to quantitatively evaluate the concentrations of three inorganic oxoanions, arsenate, sulfate, and selenate, at environmentally significant levels. Two iron oxide coatings, goethite and an iron sol–gel, were used and compared to an uncoated internal reflection element which typically has a limit of detection around 1.0 mM. The goethite coating improved the limits of detection by factors of 45.6 and 137.0 for arsenate and sulfate as compared to an uncoated cell. The iron sol coating improved the limits of detection by factors of 481.2, 156.2, and 114.0 for arsenate, sulfate, and selenate, respectively.

Remediation of U-contaminated sites relies upon thermodynamic speciation calculations to predict U(VI) movement in the subsurface. However, reliability and applicability of geochemical speciation and reactive transport models may be limited by determinate (model) errors and random (uncertainty) errors in the equilibrium speciation calculations. This study examines propagated uncertainty in two types of subsurface calculations: I. Dissolved U(VI) speciation based on measured analytical constraints and solution phase equilibria and II. Overall U(VI) speciation which combined the dissolved phase equilibria with previously published adsorption reactions. Three levels of uncertainty, instrumental uncertainty, temporal variation and spatial variation across a site, were investigated using first-derivative sensitivity calculations and Monte Carlo simulations. Dissolved speciation calculations were robust, with minimal amplification of uncertainty and normal output distributions. The most critical analytical constraints in the dissolved system are pH, DIC, total U and total Ca, with some effect from dissolved SO42-. When considering adsorption equilibria, calculations were robust with respect to adsorbed U(VI) concentration prediction, but bimodal distributions of dissolved U(VI) concentrations were observed in simulations with background levels of total U(VI) and higher (spatial and temporal variability) estimates of input uncertainty. Consequently, sorption model predictions of dissolved U(VI) may not be robust with respect these higher levels of uncertainty.Highlights► Propagation of uncertainty in uranium speciation is examined by derivative and Monte Carlo methods. ► Predictions of solution speciation are robust with minimal amplification of input uncertainty. ► Predictions of sorption equilibria at low total U(VI) are not robust, with bimodal distributions of predicted speciation.

An agent-based biogeochemical model has been developed which begins with biochemical precursor molecules and simulates the transformation and degradation of natural organic matter (NOM). This manuscript presents an empirical quantitative structure activity relationship (QSAR) which uses the numbers of ligand groups, charge density and heteroatom density of a molecule to estimate Cu-binding affinity (KCu′) at pH 7.0 and ionic strength 0.10 for the molecules in this model. Calibration of this QSAR on a set of 41 model compounds gives a root mean square error of 0.88 log units and r2 = 0.93. Two simulated NOM assemblages, one beginning with small molecules (tannins, terpenoids, flavonoids) and one with biopolymers (protein, lignin), give markedly different distributions of logKCu′. However, calculations based on these logKCu′ distributions agree qualitatively with published experimental Cu(II) titration data from river and lake NOM samples.