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The dynamic interactions among iron (Fe) oxides, organic matter (OM) and heavy metals/metalloids play crucial roles in controlling the geochemical cycling of carbon (C) and heavy metals/metalloids in natural environments. Although the inhibitory effects of arsenate (As(V)) or OM on the ferrihydrite transformation process have been studied previously, there is still a lack of mechanistic and quantitative understanding on the kinetics of As(V) and C sequestration during the Fe(II)-induced ferrihydrite transformation. In this study, we employed a suite of techniques to elucidate the underlying mechanisms accounting for the temporal changes of As(V) and C distributions and speciation on Fe oxides during the Fe(II)-induced ferrihydrite transformation process. Characterizations with X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), and the spherical aberration corrected scanning transmission electron microscopy (Cs-STEM) at different times indicated that the presence of As and/or FA resulted in the formation of more lepidocrocite than goethite. Besides surface adsorption, a portion of As(V) may be incorporated into the lattice structures of newly formed crystalline Fe oxides, and the amount of As(V) sequestration within Fe oxides were correlated with the formation of crystalline Fe oxides. In comparison, FA molecules were either adsorbed on the surfaces of goethite or diffused into the defects or nano pore spaces of lepidocrocite. Our results shed the light on different nanoscale mechanisms accounting for the sequestration of C and As(V) on Fe oxides. The defects or nano pore spaces formed in the structures of lepidocrocite may provide an effective way to sequester C through physical isolation, while the crystal structures of Fe oxides may sequester As(V) through isomorphic substitution. The knowledge on the dynamic coupling between Fe oxide transformation and C/As(V) sequestration provided the basis for accurately predicting the geochemical cycling of trace elements and OM.

Ferrihydrite is ubiquitous in natural environments, and its transformation significantly influences the fate of heavy metals. Although Pb(II) adsorption on iron oxides has been extensively studied, there is still a knowledge gap on the fate of Pb during the dynamic processes of iron oxide transformation. In this study, a set of wet chemistry experiments, spherical aberration corrected scanning transmission electron microscopy (Cs-STEM) integrated with energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray absorption spectroscopy (XAS) were used to unravel Pb interactions with iron oxides during ferrihydrite transformation processes under abiotic conditions. Wet chemistry experiments, STEM-EDS elemental mapping and line scans, and quantitative analysis suggested that Pb penetrated into hematite nanoparticles during the transformation processes. STEM analysis at sub-nanoscales, XRD fine scans, and XAS analysis provided evidence of Pb incorporation into the crystal structures of hematite nanoparticles, in which Pb distributed along the zone axis and enlarged the lattice distance of hematite. These results advanced our understanding of the dynamic interactions of Pb with iron oxides by offering new perspectives about the critical roles of chemical speciation, nano-scale spatial distribution, and atomic coordination environments in controlling the geochemical dynamics of heavy metals.

The fractionation of humic substances (HS) at the mineral and water interface can change the constituents and reactivity of HS, but there is still a lack of the understanding of the effects of HS fractionation on the binding characteristics of heavy metals to HS. In this study, the binding characteristics of five heavy metals (Cd, Cu, Ni, Pb, and Zn) to humic acid (HA) before and after adsorption by ferrihydrite were investigated by employing two-dimensional correlation spectroscopy (2D COS) integrated with synchronous fluorescence and Fourier transform infrared (FTIR) spectroscopies. 2D COS analyses of the fluorescence results indicated that the susceptibility of the fluorescence of humic-like fraction to heavy metals significantly decreased after the adsorption of HA by ferrihydrite, which may be due to the fact that humic-like components were preferentially adsorbed by ferrihydrite. However, the fractionation processes did not alter the metal binding sequence and affinity to different HA components. 2D COS analyses of the FTIR results suggested that fractionation processes decreased the susceptibility of COO- groups to heavy metals, and changed the metal binding sequence to polysaccharides C-O and aryl groups, with the exception of Pb. Furthermore, model calculations showed that the binding ability of heavy metals to both humic-like and fulvic-like fractions decreased after the adsorption of HA by ferrihydrite. The results of this study contribute to predicting heavy metal behavior in the environment.

Soil organic matter (SOM) is a key soil sorbent with a large number of reactive binding sites that may complex with metals, controlling their fate, transport, and bioavailability in soil. However, due to the complex and heterogeneous nature of SOM, it is not easy to probe its physical and chemical properties at the molecular level. In this study, an a priori method was developed to predict the molecular properties of SOM, which incorporated computational molecular modeling, SPARC Performs Automated Reasoning in Chemistry's (SPARC's) chemical reactivity models, and linear free energy relationships (LFERs). Specifically, the method uses SOM models simulated by molecular dynamics modeling based on the experimental elemental composition and functional group information of SOM. For the molecular characteristics, the molecular H/C and O/C ratios, molecular weight, aromatic index, and double bond equivalence of the SOM molecules were calculated. For the proton binding constants, the SPARC was used to calculate the microscopic pKa values of every binding sites of individual molecules of the SOM model. Based on the pKa values, the metal binding constants for individual monodentate binding sites were calculated using the Irving-Rossotti LFERs for different heavy metals. The results agreed reasonably with the default values used in the Windermere Humic Aqueous Model (WHAM) (VI) for the investigated metals. The theoretical SOM models, to some extent, represented the average properties of the investigated SOM. Overall, this study gives new quantitative and molecular insight into the structure and chemical properties of SOM. Detailed deprotonation and metal-SOM complexation information was gained for individual SOM binding sites. Such feasible and straightforward predictive scheme is useful to assess the risk of heavy metals in various aquatic and terrestrial environment involving heterogeneous natural organic matter.

Arsenate [As(V)] soil contamination is a great concern worldwide because of its high toxicity, carcinogenicity, and mobility. The adsorption of As(V) on soil minerals such as ferrihydrite and δ-MnO₂ controls the transport and bioavailability of As(V) in the soil environment. A large number of studies have investigated the equilibrium and mechanisms of As(V) adsorption onto ferrihydrite and δ-MnO₂ individually. However, both ferrihydrite and δ-MnO₂, commonly coexisting within soils has been overlooked and little information is available for the kinetics of As(V) adsorption and desorption in this mixed mineral system. Therefore, in this work, kinetics of As(V) adsorption and desorption onto ferrihydrite and δ-MnO₂ mixed minerals is studied using the stirred-flow method. A kinetics model for the mixed mineral system was developed using the component additivity approach, which incorporated the nonlinear binding of As(V) to both ferrihydrite and δ-MnO₂ simultaneously. The kinetics model successfully predicted the kinetics of As(V) adsorption and desorption in the mixed mineral system under varying solution chemistry conditions and properly accounted for the heterogeneity of the binding sites of both ferrihydrite and δ-MnO₂. For As(V) adsorption kinetics, the ferrihydrite bidentate non-protonated sites (Fh-bi-np) played a dominant role, followed by the δ-MnO₂ binding sites and the ferrihydrite bidentate protonated sites (Fh-bi-p). After the 4-h desorption, As(V) was mainly retained on ferrihydrite binding sites. This study helps to quantitatively elucidate the kinetic behavior and mechanisms of As(V) in the Fe oxide and Mn oxide systems, thus enhancing our understanding of the fate and transport of As in soil environments.

The properties and composition of soil dissolved organic matter (DOM) are highly affected by the adsorption and desorption of organic matter (OM) on soil minerals and heterotrophic microbial respiration. Organic acids (e.g., oxalic acid), components of root exudates, have been revealed to liberate organic matter (OM) by the dissolution of protective mineral phases and stimulate microbial degradation of OM. However, the effects of organic acids on the properties and composition of soil DOM molecules and the related mechanisms are still poorly understood. In this study, we conducted microcosm incubation experiments with and without oxalic acid addition, and aimed to elucidate the variations of DOM properties and composition, employing a combination of Fourier transform ion cyclotron resonance mass spectrometry, optical spectroscopy, and bacterial community composition analysis. Our results indicated that the released OM from the direct dissolution of protective mineral phases by oxalic acid further stimulated the microbial reductive release of Fe mineral-associated OM under anoxic conditions. Furthermore, the addition of oxalic acid enhanced the degradation of aliphatic compounds and lignins with low O/C ratios, and increased the accumulation of lignins with high O/C ratios, tannins, and condensed aromatics. Linking the bacterial community composition to DOM molecular properties and composition further suggested that the enhanced reductive release of Fe mineral-associated OM was highly related to the increased abundances of Proteobacteria and Actinobacteria. Overall, oxalic acid induced long-lasting impacts on soil DOM properties and composition under anoxic soil conditions in our study. We expect that our results will contribute to understanding the dynamics of soil DOM molecules in the environment.

Sorptive fractionation of organic matter (OM) at mineral–water interfaces alters the composition of OM and therefore affects the fate, transport, and bioavailability of nutrients and contaminants in terrestrial and aquatic environments. It is essential to identify the fractions of OM that are selectively sorbed on minerals and to elucidate the mechanisms of sorptive fractionation. In this study, the partition between mineral and water for a large set of organic molecules covering the most relevant OM functional groups and building blocks were predicted using the mechanistic Polyparameter Linear Free Energy Relationships (pp-LFERs). The results were used to infer the sorptive fractionation tendency of OM moieties. It is found that for a OM moiety belonging to the chemical classes of alkanes, alkenes, or benzenes, its sorption to bentonite is the strongest, followed by kaolinite, talc or hematite, and Al₂O₃ or quartz, relative to water. For a moiety with either double bond, or –OH, or –COOH, or R–COO–R′, or R–O–R′, or R–COH, or R–CO–R′, or –NH₂, its partition to water is enhanced as compared to the respective alkane moiety. Opposite is found for thiols and sulfides. Meanwhile, a correlation is developed, that is, as the double bond equivalence decreases and the number of C atoms increases, the degree of sorption increases. Overall, the key factors controlling the sorptive fractionation are polarity, H-bonding, van de Waals dispersion, and the energy for cavity formation. This work provides insights into the OM sorption at the mineral and water interface at the molecular level.

Allophane, a nano-size alumino-silicate mineral, has a peculiar hollow and spherical nanostructure with diameters of 3.5–5 nm and some defects or open pores. The adsorptive fractionation of dissolved organic matter (DOM) within allophane nanostructures is a key soil process affecting carbon cycling in soil environments, but the underlying mechanisms are poorly understood. In this study, we employed both double spherical-aberration-corrected scanning transmission electron microscopy (Cs-STEM) and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to elucidate the spatial distribution of DOM on allophane at sub-nanoscales and the mechanisms controlling the adsorptive fractionation of DOM molecules. Copper titration experiments and WHAM 7 were used to assess the role of carboxylic groups in the fractionation of DOM on allophane. Cs-STEM results revealed that within allophane aggregates, C mainly distributed on the edges of the pore spaces between allophane aggregates while some DOM molecules localized at some small pore spaces. Moreover, the Cs-STEM results suggested that DOM molecules were able to enter the interior spaces within tiny allophane nanoparticles/aggregates. FT-ICR-MS analysis indicated that molecules with high aromaticity, oxygen numbers and amounts of COO groups were preferentially adsorbed. The results of Cu titration and WHAM 7 calculations indicated that the carboxylic functional groups of DOM were selectively removed from solutions during adsorptive fractionation. Overall, both chemical adsorption and physical isolation may be responsible for the strong sequestration of DOM molecules on allophane. Our results contribute to prediction of C cycling in the environment.

Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environmental behavior and ecological risks of organic pollutants and SWNTs. In this study, two models for predicting logK were developed with multiple linear regression (MLR) and support vector machine (SVM) algorithms. The two models have satisfactory goodness-of-fit, robustness and predictive ability, and the SVM model performs slightly better than the MLR model. The two models are based on the up-to-date experimental dataset consisting of 61 logK values, and the applicability domains cover diverse organic compounds with functional groups > CC CO, COOH, C(O)O, OH, O, F, Cl, Br, NH2, NH, >N, >NN NC(O)NH2, >NC(O)NH, S and S(O)(O). The adsorption of organic compounds toward SWNTs is mainly determined by van der Waals forces and hydrophobic interactions. Since only in silico molecular descriptors were employed for the modeling, the developed models are beneficial for prediction purposes.

Dissolved organic matter (DOM) plays a key role in many biogeochemical processes, but the drivers controlling the diversity of chemical composition and properties of DOM molecules (chemodiversity) in soils are poorly understood. It has also been debated whether environmental conditions or intrinsic molecular properties control the accumulation and persistence of DOM due to the complexity of both molecular composition of DOM and interactions between DOM and surrounding environments. In this study, soil DOM samples were extracted from 33 soils collected from different regions of China, and we investigated the effects of climate and soil properties on the chemodiversity of DOM across different regions of China, employing a combination of Fourier transform ion cyclotron resonance mass spectrometry, optical spectroscopy, and statistical analyses. Our results indicated that, despite the heterogeneity of soil samples and complex influencing factors, aridity and clay can account for the majority of the variations of DOM chemical composition. The finding implied that DOM chemodiversity is an ecosystem property closely related to the environment, and can be used in developing large-scale soil biogeochemistry models for predicting C cycling in soils.

Predicting the kinetics of heavy metal adsorption and desorption in soil requires consideration of multiple heterogeneous soil binding sites and variations of reaction chemistry conditions. Although chemical speciation models have been developed for predicting the equilibrium of metal adsorption on soil organic matter (SOM) and important mineral phases (e.g. Fe and Al (hydr)oxides), there is still a lack of modeling tools for predicting the kinetics of metal adsorption and desorption reactions in soil. In this study, we developed a unified model for the kinetics of heavy metal adsorption and desorption in soil based on the equilibrium models WHAM 7 and CD-MUSIC, which specifically consider metal kinetic reactions with multiple binding sites of SOM and soil minerals simultaneously. For each specific binding site, metal adsorption and desorption rate coefficients were constrained by the local equilibrium partition coefficients predicted by WHAM 7 or CD-MUSIC, and, for each metal, the desorption rate coefficients of various binding sites were constrained by their metal binding constants with those sites. The model had only one fitting parameter for each soil binding phase, and all other parameters were derived from WHAM 7 and CD-MUSIC. A stirred-flow method was used to study the kinetics of Cd, Cu, Ni, Pb, and Zn adsorption and desorption in multiple soils under various pH and metal concentrations, and the model successfully reproduced most of the kinetic data. We quantitatively elucidated the significance of different soil components and important soil binding sites during the adsorption and desorption kinetic processes. Our model has provided a theoretical framework to predict metal adsorption and desorption kinetics, which can be further used to predict the dynamic behavior of heavy metals in soil under various natural conditions by coupling other important soil processes.

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods. pp-LFERs system parameters are estimated for solvent-water, plant cuticle-water systems, and for novel compounds using QCAP solute parameters and experimental partition coefficients. Refitting the system parameter improves the calculation accuracy and eliminates the bias. Refitted models for solvent-water partition coefficients using QCAP solute parameters give better results (RMSE = 0.278 to 0.506 log units for 24 systems) than those based on ABSOLV (0.326 to 0.618) and QSPR (0.294 to 0.700) solute parameters. For munition constituents and munition-like compounds not included in the calibration of the refitted model, QCAP solute parameters produce pp-LFER models with much lower RMSEs for solvent-water partition coefficients (RMSE = 0.734 and 0.664 for original and refitted model, respectively) than ABSOLV (4.46 and 5.98) and QSPR (2.838 and 2.723). Refitting plant cuticle-water pp-LFER including munition constituents using QCAP solute parameters also results in lower RMSE (RMSE = 0.386) than that using ABSOLV (0.778) and QSPR (0.512) solute parameters. Therefore, for fitting a model in situations for which experimental data exist and system parameters can be re-estimated, or for which system parameters do not exist and need to be developed, QCAP is the quantum chemical method of choice.

The kinetics of Cd(ii) adsorption/desorption on ferrihydrite is an important process affecting the fate, transport, and bioavailability of Cd(ii) in the environment, which was rarely systematically studied and understood at quantitative levels. In this work, a combination of stirred-flow kinetic experiments, batch adsorption equilibrium experiments, high-resolution transmission electron microscopy (HR-TEM), and mechanistic kinetic modeling were used to study the kinetic behaviors of Cd(ii) adsorption/desorption on ferrihydrite. HR-TEM images showed the open, loose, and sponge-like structure of ferrihydrite. The batch adsorption equilibrium experiments revealed that higher pH and initial metal concentration increased Cd(ii) adsorption on ferrihydrite. The stirred-flow kinetic results demonstrated the increased adsorption rate and capacity as a result of the increased pH, influent concentration, and ferrihydrite concentration. The mechanistic kinetic model successfully described the kinetic behaviors of Cd(ii) during the adsorption and desorption stages under various chemistry conditions. The model calculations showed that the adsorption rate coefficients varied as a function of solution chemistry, and the relative contributions of the weak and strong ferrihydrite sites for Cd(ii) binding varied with time at different pH and initial metal concentrations. Our model is able to quantitatively assess the contributions of each individual ferrihydrite binding site to the overall Cd(ii) adsorption/desorption kinetics. This study provided insights into the dynamic behavior of Cd(ii) and a predictive modeling tool for Cd(ii) adsorption/desorption kinetics when ferrihydrite is present, which may be helpful for the risk assessment and management of Cd contaminated sites.

Understanding the effect of ferrihydrite morphology on the kinetic reactions of metals with ferrihydrite is essential for predicting the dynamic behavior of metals in soil. In this study, kinetics of Pb(II) and Cu(II) adsorption and desorption on two types of ferrihydrite, the freshly precipitated gellike ferrihydrite and freeze-dried dense ferrihydrite representing two typical morphologies under extreme soil conditions, were studied. The high-resolution transmission electron microscopy (TEM) images revealed that the gellike ferrihydrite loosely aggregated with open structure, while the dense ferrihydrite compactly aggregated with more consolidated and thicker structure. The energy dispersive spectroscopy (EDS), at the nanometer scale, showed that Pb(II) and Cu(II) distributed evenly on gellike ferrihydrite but localized on dense ferrihydrite after adsorption. The stirred-flow kinetic experiments showed that higher pH and higher influent metal concentrations increased metal adsorption for both types of ferrihydrite. The dense ferrihydrite adsorbed slower and much less metals than gellike ferrihydrite because of less reactive sites. The desorption of heavy metals from ferrihydrite was affected by combined factors of the ferrihydrite morphology, adsorbed metal concentrations, pH, and metal re-adsorption rates. The mechanic kinetics model based on the CD-MUSIC model successfully described the adsorption and desorption kinetics of Pb(II) and Cu(II) on both gellike and dense ferrihydrite under various chemistry conditions. Our model provided quantitative tools for considering the effects of ferrihydrite morphology and heterogeneous binding sites under varying solution chemistry conditions when predicting the dynamic behavior of Pb(II) and Cu(II) in soil environment.

Cr(vi) has posed a serious risk for the environment and human beings because of its pollution and toxicity. It is essential to understand the equilibrium behavior of Cr(vi) in soils. In this study, the adsorption of Cr(vi) on fourteen soils was studied with batch experiments and quantitative modeling. The batch experiments included the adsorption edge and adsorption isotherm experiments, investigating the adsorption of Cr(vi) with varying soil properties, solution pH, and initial Cr(vi) concentrations. The experimental data were then modeled using the surface complexation models in Visual MINTEQ of CD-MUSIC by considering the adsorption of Cr(vi) and ions onto Fe (hydr)oxides and Al (hydr)oxides, and the Stockholm Humic Model and the fixed charge site model by accounting for the adsorption of the cations to soil organic matter and clay, respectively. Particularly, the modeling method of this study introduced an important parameter RO- to account for the amount of soil organic matter irreversibly adsorbed on soil minerals. Overall, the model predicted reasonably well for the equilibrium partition of Cr(vi) under various conditions with a root-mean-square-error of 0.35 for the adsorption edge data and 0.19 for the adsorption isotherm data. According to the model calculations, ferrihydrite dominated the binding of Cr(vi) at pH of 3.0-7.0. The content of ferrihydrite and reactive soil organic matter was found to be the main factor influencing RO-. The modeling results help to understand and predict Cr(vi) adsorption on different soils and are beneficial to environmental risk assessment and pollution remediation.

There is concern about the environmental fate and effects of munition constituents (MCs). Polyparameter linear free energy relationships (pp-LFERs) that employ Abraham solute parameters can aid in evaluating the risk of MCs to the environment. However, poor predictions using pp-LFERs and ABSOLV estimated Abraham solute parameters are found for some key physico-chemical properties. In this work, the Abraham solute parameters are determined using experimental partition coefficients in various solvent-water systems. The compounds investigated include hexahydro-1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX), hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX), hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX), hexahydro-1,3-dinitroso-5- nitro-1,3,5-triazine (DNX), 2,4,6-trinitrotoluene (TNT), 1,3,5-trinitrobenzene (TNB), and 4-nitroanisole. The solvents in the solvent-water systems are hexane, dichloromethane, trichloromethane, octanol, and toluene. The only available reported solvent-water partition coefficients are for octanol-water for some of the investigated compounds and they are in good agreement with the experimental measurements from this study. Solvent-water partition coefficients fitted using experimentally derived solute parameters from this study have significantly smaller root mean square errors (RMSE = 0.38) than predictions using ABSOLV estimated solute parameters (RMSE = 3.56) for the investigated compounds. Additionally, the predictions for various physico-chemical properties using the experimentally derived solute parameters agree with available literature reported values with prediction errors within 0.79 log units except for water solubility of RDX and HMX with errors of 1.48 and 2.16 log units respectively. However, predictions using ABSOLV estimated solute parameters have larger prediction errors of up to 7.68 log units. This large discrepancy is probably due to the missing R2NNO2 and R2NNO2 functional groups in the ABSOLV fragment database.

Predicting the kinetics of heavy metal release from the field-contaminated soils requires the consideration of the chemical and physical heterogeneity of soil composition, such as multiple soil adsorbents, heterogeneous binding sites, and varying soil particle sizes. In this study, we conducted kinetic experiments to investigate kinetics of copper (Cu) release from different size fractions of two field-contaminated soils. The morphology and elemental distribution of different size fractions of soil particles were measured with scanning electron microscopy (SEM) and spherical aberration corrected scanning transmission electron microscopy (Cs-STEM). A kinetics model considering both variations of soil adsorbents and particle sizes was developed. Overall, different size fractions of soil particles showed highly heterogeneous physical and chemical properties. The reactive soil adsorbents (e.g., soil organic matter (SOM)) and Cu concentrations increased with the decrease of the particle size, and the rate of Cu release from soil particles also increased with the decrease of the particle size. The kinetics model described the experimental kinetics data from different size fractions of soil particles reasonably well. More significantly, the model was able to predict Cu release from the bulk soil samples based on Cu kinetic reactions with different size fractions of soil particles. SOM appeared to be the most dominant adsorbent for Cu under our experimental conditions, and, among different SOM binding sites, bidentate sites involving carboxylic and phenolic functional groups were the most significant. Our results suggested that a unified modeling framework, which specifically considers the chemical heterogeneity of soil binding sites and variations of particle sizes, is promising for predicting kinetic reactions of metals with heterogeneous soil particles in the field.

The interactions between soil organic matter (SOM) and minerals are key processes controlling the organic carbon (OC) stability in the environment, but the underlying mechanisms responsible for OC distribution on minerals are still not clear. In this study, a common crystalline iron oxide, hematite, and a typical SOM component, humic acid (HA), were chosen to explore, at the nano and even sub-nano scales, HA adsorption and distribution on hematite under the impact of Pb(II) ions. Adsorption experiments and spherical aberration corrected scanning transmission electron microscopy (Cs-STEM) coupled with energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS) demonstrated that Pb(II) enhanced the adsorption of HA on hematite, but the nano scale distributions of HA varied between experimental conditions. The Cs-STEM, EDS, and the EELS analyses clearly unveiled that, with the presence of Pb(II), the hematite aggregates became more dispersed, and HA was not only adsorbed on the edges hematite nanoparticles, but also was localized in the extended areas away from the hematite particles/aggregates, which resulted in a heterogeneous distribution of OC in hematite-HA systems. Our imaging methods provided direct visualization of the mineral-SOM-metal interactions at nano scales, which shed lights on the underlying mechanisms for OC stabilization in natural environments and can enable more robust constraints in the models of organic matter-mineral interactions.

The speciation and mobility of As are controlled by both Fe and Mn (oxyhydr)oxides through a series of surface complexation and redox reactions occurring in the environment, which is also complicated by the solution chemistry conditions. However, there is still a lack of quantitative tools for predicting the coupled kinetic processes of As reactions with Fe and Mn (oxyhydr)oxides. In this study, we developed a quantitative model for the coupled kinetics of As adsorption/desorption and oxidation in ferrihydrite-Mn (oxyhydr)oxides and ferrihydrite-Mn(II)-O2 systems. This model also accounted for the variations in solution chemistry conditions and binding site heterogeneity. Our model suggested that Mn (oxyhydr)oxide and ferrihydrite mainly served as an oxidant and an adsorbent, respectively, when they coexisted. Among the three types of binding sites of ferrihydrite, the adsorbed As(V) was mainly distributed on the nonprotonated bidentate sites. Our model quantitatively showed that the oxidation rates of different reaction systems varied significantly. The rates of As(III) oxidation were enhanced with higher pH values and higher molar ratios of Mn(II)/As(III) in the ferrihydrite-Mn(II)-O2 system. This study provides a modeling framework for predicting the kinetic behavior of As when multiple adsorption/desorption and oxidation reactions are coupled in the environment.

Understanding the kinetics of Ni dissociation from dissolved organic matter (DOM) helps to better predict the fate and bioavailability of Ni ions in the environment. However, there is still a lack of predictive models for the kinetics of Ni dissociation from DOM under different reaction conditions. In this study, kinetics of Ni dissociation from humic acids (HA) and fulvic acids (FA), two most important components in DOM for Ni binding, was studied with a competing ligand exchange method at varying Ni concentrations, reaction pH, and ionic strength. The kinetic data were analyzed using a mechanistic kinetics model developed based on the Windermere Humic Aqueous Model (WHAM). Experimental results showed that Ni dissociation rates were affected by both Ni concentrations and pH, and Ni dissociation from FA had faster rates than that from HA. The kinetics model can reproduce the experimental data well, with only two model fitting parameters for different reaction conditions. The modeling results showed that various HA and FA binding sites played different roles in controlling Ni dissociation rates, with bidentate binding sites and the weak tridentate sites being the most significant. At high pH values the Ni re-association reactions were significant for controlling the overall rates of Ni dissociation but had minimal impact at lower pH values. Our model provided a modeling framework based on WHAM 7 for predicting Ni dissociation rates when humic substances were the dominant binding ligands.

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.