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Estimating the availability of phosphorus in soils and sediments is complicated by the diverse mineralogical properties of iron (hydr)oxides that control the environmental fate of phosphorus. Despite various surface complexation models have been developed, lack of generic phosphate affinity constants (logKPO4s) for iron (hydr)oxides hinders the prediction of phosphate adsorption to iron (hydr)oxides in nature. The aim of this work is to derive generic logKPO4s for the Charge Distribution-Multisite Complexation extended-Stern-Gouy-Chapman (CD-MUSIC-eSGC) model using a large phosphate adsorption database and previously derived generic protonation parameters. The optimized logKPO4s of goethite, hematite and ferrihydrite are located in a much narrower range than those in the RES3T database. Specifically, the logKPO4 ranges of FeOPO3, FeOPO2OH, FeOPO(OH)2, (FeO)2PO2, and (FeO)2POOH complexes were 17.40-18.00, 24.20-27.40, 27.90-29.80, 26.50-29.60, and 30.70-33.40, respectively. A simplified CD-MUSIC-eSGC model with species FeOPO2OH and (FeO)2PO2 and generic logKPO4 values 26.0 ± 0.9 and 27.9 ± 0.8, respectively, provides an accurate prediction of phosphate adsorption and dominant speciation to the iron (hydr)oxides at environmental pH and phosphate levels. For ferrihydrite at low pH and high phosphate levels the species FeOPO(OH)2 and (FeO)2POOH cannot be neglected. The simplified model expands the application boundaries of CD-MUSIC-eSGC model in predicting the phosphate adsorption on natural iron (hydr)oxides without laborious characterization.

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Illite plays an essential role in arsenic (As) transportation in the subsurface. Despite extensive investigations into As adsorption onto illite, debates persist due to the absence of direct evidence revealing the underlying processes. In this research, we conducted batch experiments and employed spherical aberration-corrected scanning transmission electron microscope, X-ray absorption spectroscopy, and density functional theory-based calculations to elucidate the mechanisms for the adsorption of two major inorganic As species (As(III) and As(V)) onto illite. Experimental results indicate adsorption capacities of 0.251 and 0.667 µmol/g for As(III) and As(V) onto illite, respectively. As(III) adsorption occurs within 300 min, whereas As(V) is rapidly adsorbed within 500 min, after which it tends to stabilize. Both As species can adsorbed onto the basal surface via electrostatic forces, where cations act as a bridge, leading to specific-cation effects. Conversely, As adsorption onto the edge surface can be ascribed to inner-sphere complexes via As-O-Al bonds, causing a negatively shifted isoelectric point of illite. These mechanisms collectively account for the partially reversible adsorption and two-stage kinetics pattern. Finally, a process-based surface complexation model was developed to predict As adsorption onto illite, which includes the inner/outer-sphere complexation and monodentate/bidentate complexes.

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The diversity of soil adsorbents for arsenic (As) and the often-overlooked influence of manganese (Mn) on As(III) oxidation impose challenges in predicting As adsorption in soils. This study uses Mössbauer spectroscopy, X-ray diffraction of oriented clay, and batch experiments to develop a kinetic coupled multi-surface complexation model that characterizes As adsorbents in natural soils and quantifies their contributions to As adsorption. The model integrates dynamic adsorption behaviors and Mn-oxide interactions with unified thermodynamic and kinetic parameters. The results indicate that As adsorption is governed by five primary adsorbents: poorly crystalline Fe oxides, well crystalline Fe oxides, Fe-rich clay, Fe-depletion clay, and organic carbon (OC). Fe oxides dominate As adsorption at low As concentrations. However, at higher As concentrations, soils from carbonate strata, with higher content of Fe-rich clay, exhibit stronger As adsorption capabilities than soils from Quaternary sediment strata. The enrichment in Fe-rich clay can enhance the resistance of adsorbed As to reduction processes affecting Fe oxides. Additionally, extensive redox cycles in paddy fields increase OC levels, enhancing their As adsorption compared to upland fields. This model framework provides novel insights into the intricate dynamics of As within soils and a versatile tool for predicting As adsorption across diverse soils.

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This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.

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In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.

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This study aimed to obtain a better understanding on the environmental behavior of As in paddy soil and to reveal the influence mechanisms of different environmental factors on the availability of As in the soil solution. The effects of pH, calcium, and phosphate on the solubility and speciation distribution of As in the paddy soil collected from Zhuzhou of Hunan province were studied by combining the adsorption experiments with the NOM-CD model. The results showed that the minimum concentration of soluble As in the soil was at approximately a pH of 6.0, which was mainly affected by both electrical interactions and site competition between Ca2+, PO43-, As(Ⅲ), and As(Ⅴ). The adsorption of As onto soil particles could be increased by an increase in Ca2+ in the soil system, leading to the decrease in soluble As concentration. This effect became significant at a higher pH, because adsorbed Ca2+ increased the positive charge on (hydr)oxide surfaces. With phosphate addition, the reduction in As(Ⅴ) in the soil was inhibited at pH<5.5, whereas it was promoted at pH>5.5. Moreover, the concentration of soluble As(Ⅲ) and As(Ⅴ) in the soil solution was dramatically increased with the addition of phosphate owing to the competitive adsorption between As and phosphate. At a lower background of Ca2+, there was a higher fraction of As(Ⅲ) in the soil either with or without phosphate addition. This phenomenon might be caused by the higher bioavailability of phosphorus in soil at a lower concentration of Ca2+, which favors the dissimilatory reduction of As or iron (hydr)oxides. The results indicated that the NOM-CD model could predict the influence of pH, calcium, and phosphate on the solubility and speciation distribution of As in paddy soil and reveal its main mechanisms. Therefore, the NOM-CD model would provide the quantitative and scientific method for evaluating the risk of As in soils or other solid-water systems.

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The surface reactivity of iron (hydr)oxides plays a crucial role in controlling their interfacial reactions, for which various surface complexation models have been developed. The diversity of mineralogical properties of iron (hydr)oxides has resulted in a redundancy of model parameters, which hampers the modeling of iron (hydr)oxides in soils and sediments, where goethite, hematite and ferrihydrite dominate the iron (hydr)oxide mass fraction. To capture their combined surface reactivity, optimized generic protonation parameters of the Charge Distribution-Multisite Complexation (CD-MUSIC) extended-Stern-Gouy-Chapman (eSGC) model were derived by reanalyzing literature datasets and tested with some newly synthesized iron (hydr)oxides. It was observed that the proton and monovalent ion affinity constants of the different iron (hydr)oxides were located in a narrow range. For the singly- and triply-coordinated hydroxyl sites the obtained generic log(affinity constants) were 8.3 and 11.7 for the protonation reaction and -0.5 for the reaction with the monovalent background ions. Their combination with fixed site densities of singly-/triply-coordinated hydroxyl sites of 3.45/2.70, 5.00/2.50, and 5.80/1.40 sites/nm2 for goethite, hematite, and ferrihydrite, respectively, provided good results. The Stern layer capacitances of the inner and outer Stern layers were set equal and could be acquired by an empirical correlation with the sample specific surface area (SSA). The CD-MUSIC-eSGC model with the generic model parameters enables good quality predictions of the proton reactivity of iron (hydr)oxides in 1:1 electrolyte solutions regardless of the sample heterogeneity. The advantages of the generic CD-MUSIC-eSGC model are twofold: (1) protonation of iron (hydr)oxides can be described without making use of spectroscopic measurements and proton titrations, and (2) the model calculations are greatly simplified.

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Surface complexation models (SCM), based mainly on the diffuse double layer (DDL) theory, have been used to predict zeta potential at the crude oil-brine-rock (COBR) interface with limited success. However, DDL is inherently limited in accurately predicting zeta potential by the assumptions that all the brine ions interact with the rock surface at the same plane and by the double layer collapse at higher brine ionic strength (>1M). In this work, a TLM-based SCM captured zeta potential trends at the calcite-brine interface with ionic strength up to 3 M. An extended DDL and TLM-based SCMs were used to predict the electrokinetic properties of a composite carbonate rock showing a different mineralogical composition. The extended TLM-based SCM captured the zeta potential prediction trends and magnitude, highlighting the contribution of the inorganic minerals and organic impurities on the composite carbonate surface. In contrast, the extended DDL-based SCM captured the zeta potential trends but failed to capture the magnitude of the measured zeta potential. Interestingly, the TLM-based SCM predicted a positive SP for the rock-brine interface, which could explain the oil-wet nature of composite carbonate rocks due to electrostatic adsorption of negatively charged carboxylic acids. Conversely, the DDL-based SCM predicted a negative SP, leading to an inaccurate interpretation of the electrokinetic properties at the rock-brine interface. Thus, the use of extended TLM-based SCM was required to accurately predict the zeta potential and account for the adsorption of carboxylic acids on the reservoir composite carbonate surface.

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Iron (Fe) oxides and fulvic acid (FA) are the key components affecting the fate of cadmium (Cd) in soil. The presence of FA influences Fe mineral transformation, and FA may complicate phase transformation and dynamic behavior of Cd. How varying Fe minerals and FA affect Cd immobilization during the ferrihydrite transformation induced by various Fe(II) concentrations, however, is still lack of quantitative understanding. In this study, we built a model for Cd species quantification during phase transformation based on mechanistic insights obtained from batch experiments. Spectroscopic analysis showed that Fe(II) concentrations affected secondary Fe minerals formation under the condition of co-existence of Cd and FA, and ultimately changed the distribution of Cd and FA. Microscopic analysis revealed that besides surface adsorption, part of Cd was sequestrated by magnetite, whereas FA was able to diffuse into lepidocrocite defects. The model revealed that adsorbed Cd was mainly controlled by FA and ferrihydrite, and direct complexation of Cd by FA had a strong impact on the continuous change in Cd at lower Fe(II) concentration. The results contribute to an in-depth understanding of the mobility of Cd in the environment and provide a method for quantifying the dynamic behavior of heavy metals in multi-reactant systems.

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Low-lying paddy fields in estuaries can be affected by salt water intrusion; however, it remains unclear how salt water intrusion influences the availability of heavy metals in paddy soil. In this study, batch adsorption and incubation experiments of soil were conducted with different salt water sampled along the estuary to investigate the effects of salt water intrusion on cadmium (Cd) availability. The surface complexation model (SCM) was established to assess the effects of pH on Cd adsorption behavior, which presented typical pH-dependent characteristics. The results of SCM also showed that Cd-chloro complexes became the dominant species when the ionic strength increased. The results of Cd fractions in the incubation experiments revealed a significant increase in dissolved Cd with increasing ionic strength. This may be attributed to the increased point of zero charge (pHpzc) in the presence of salt water with higher salinity, which likely formed more positive charges on soil surfaces, causing an inhibition of Cd adsorption via electrostatic repulsion. Moreover, higher concentrations of Cl- in salt water favored the formation of Cd-chloro complexes, facilitating Cd release from soil particles. This study provides mechanistic insights into the impact of salt water intrusion on Cd availability at the soil-water interface of paddy soil along the estuary.

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In this study, the nano-scale spatial distribution of natural organic matter (NOM) on the surface of iron (hydr)oxides and its relevance to oxyanion (PO43-) and metal cation (Cd2+ and Cu2+) adsorption to the assemblage of oxide (goethite) and NOM (humic acids (HA) or fulvic acids (FA)) was investigated with experiments and advanced surface complexation modeling. Both the linear additive Multi-Surface model (MSM) and the more sophisticated Natural Organic Matter-Charge Distribution (NOM-CD) model were used. The MSM model ignores the effects of NOM-mineral interaction on ion adsorption, whereas the NOM-CD model considers this effect. The results showed that with the increase of NOM loading on oxides, deviation between the MSM and NOM-CD model became bigger for PO43-, but smaller for Cd2+ and Cu2+. Oxyanions bind mainly to oxides and therefore the competitive effect of NOM cannot be neglected, which explains the large difference between these two models for PO43-. On the contrary, at a relatively high NOM loading, a large fraction of NOM extends further away from the surface of oxides. Thus for metal cations that bind mainly to NOM, the influence of NOM-mineral interaction on their adsorption is small and the results of the MSM and NOM-CD model are similar. In top soils, the NOM loading on oxides is often high, therefore the linear additive MSM is applicable for metal cation speciation calculations as reported in many literatures. An approach based on the NOM-CD model was proposed, which can not only calculate the macroscopic solid-solution distribution of both cations and anions, but can also provide information regarding their microscopic surface speciation.

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The purpose of this study was to investigate Cobalt (Co) removal from wastewater using synthesized manganese oxides from the recovered LiMn2O4. An efficient ultrasonication leaching method was utilized to recycle LiMn2O4 from spent lithium-ion batteries (LIBs). The recovered LiMn2O4 was used to synthesize tunnel λ-MnO2, γ-MnO2 and ß-MnO2 by acid leaching and hydrothermal methods. Meanwhile, Li+ in the supernatant was recycled by the precipitation of Li3PO4. Subsequently, for the synthesized tunnel MnO2, various characterizations and sodium hydroxide titration in NaNO3 solution were performed. The effect of sorption studies presented the uptake of Co increased with the pH increasing from pH ~1 to pH ~8 and the isothermal sorption at pH ~6 showed that γ-MnO2 possessed the highest uptake amount 0.44 meq/g, and the highest distribution coefficient 2.5 × 105 mL/g. Moreover, γ-MnO2 was found without Mn3+/Mn2+ leached during the sorption process. The ion exchange-surface complexation model was adopted to study the titration, effect of pH and isotherm sorption on the ion exchange reaction mechanism of Co adsorption. Overall, this work provides an economically feasible and environmentally friendly method to recycle the spent LIBs and the γ-MnO2 synthesized from the recovered LiMn2O4 was proved to be promising adsorbents for Co removal.

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Electrochemical interactions at calcite-water interface are characterized by the zeta potential and play an important role in many subsurface applications. In this work we report a new physically meaningful surface complexation model that is proven to be efficient in predicting calcite-water zeta potentials for a wide range of experimental conditions. Our model uses a two-stage optimization for matching experimental observations. First, equilibrium constants are optimized, and the Stern layer capacitance is optimized in the second stage. The model is applied to a variety of experimental sets that correspond to intact natural limestones saturated with equilibrated solutions of low-to-high salinity, and crushed Iceland Spar sample saturated with NaCl at non-equilibrium conditions. The proposed linear correlation of the Stern layer capacitance with the ionic strength is the main novel contribution to our surface complexation model without which high salinity experiments cannot be modelled. Our model is fully predictive given accurately known conditions. Therefore, the reported parameters and modelling protocol are of significant importance for improving our understanding of the complex calcite-water interfacial interactions. The findings provide a robust tool to predict electrochemical properties of calcite-water interfaces, which are essential for many subsurface applications including hydrology, geothermal resources, CO2 sequestration and hydrocarbon recovery.

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Calcite is the most stable polymorph of calcium carbonate (CaCO3) under ambient conditions and is ubiquitous in natural systems. It plays a major role in controlling pH in environmental settings. Electrostatic phenomena at the calcite-water interface and the surface reactivity of calcite in general have important environmental implications. They may strongly impact nutrient and contaminant mobility in soils and other subsurface environments, they control oil recovery from limestone reservoirs, and they may impact the safety of nuclear waste disposal sites. Besides the environmental relevance, the topic is significant for industrial applications and cultural heritage preservation. In this study, the structure of the calcite(104)-water interface is investigated on the basis of a new extensive set of crystal truncation rod data. The results agree with recently reported structures and resolve previous ambiguities with respect to the coordination sphere of surface Ca ions. These structural features are introduced into an electrostatic three-plane surface complexation model, describing ion adsorption and charging at the calcite-water interface. Inner surface potential data for calcite, as measured with a calcite single-crystal electrode, are used as constraints for the model in addition to zeta potential data. Ion adsorption parameters are compared with molecular dynamics simulations. All model parameters, including protonation constants, ion-binding parameters, and Helmholtz capacitances, are within physically and chemically plausible ranges. A PhreeqC version of the model is presented, which we hope will foster application of the model in environmental studies.

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Adsorption of uranium onto goethite is an important partitioning process that controls uranium mobility in subsurface environments, for which many different surface complexation models (SCMs) have been developed. While individual models can fit the data for which they are parameterized, many perform poorly when compared with experimental data covering a broader range of conditions. There is an imperative need to quantitatively evaluate the variations in the models and to develop a more robust model that can be used with more confidence across the wide range of conditions. We conducted an intercomparison and refinement of the SCMs based on a metadata analysis. By seeking the globally best fit to a composite dataset with wide ranges of pH, solid/sorbate ratios, and carbonate concentrations, we developed a series of models with different levels of complexity following a systematic roadmap. The goethite-uranyl-carbonate ternary surface complexes were required in every model. For the spectroscopically informed models, a triple-plane model was found to provide the best fit, but the performance of the double-layer model with bidentate goethite-uranyl and goethite-uranyl-carbonate complexes was also comparable. Nevertheless, the models that ignore the bidentate feature of uranyl surface complexation consistently performed poorly. The goodness of fitting for the models that ignore adsorption of carbonate and the charge distributions was not significantly compromised compared with that of their counterparts that considered those. This approach of model development for a large and varied dataset improved our understanding of U(VI)-goethite surface reactions and can lead to a path for generating a single set of reactions and equilibrium constants for including U(VI) adsorption onto goethite in reactive transport models.

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The accumulation of arsenic (As) in agrarian soils poses a potential long-term risk to human health, and this accumulation largely depends on the adsorption behavior of As onto soil minerals. This study considered the adsorption of As(III) onto natural soils from the Datong Basin, focusing on the quantification of the adsorption capacities of soil minerals and further the prediction of As(III) adsorption isotherms of the bulk soils. Linear programming calculations show that Fe-bearing minerals, illite, dolomite, and soil organic matter all contribute to As(III) adsorption, on average accounting for 73.9, 11.4, 8.2, and 6.5% of the overall adsorption capacity of soil to As(III), respectively. However, not all the Fe-bearing minerals in soils can adsorb As(III). Evidence from the sequential chemical extractions shows that 90.1% of the soil Fe is associated with silicates (FeSi), while results of the linear programming calculations suggest that FeSi cannot adsorb As(III). Based on the above results, a surface complexation model well predicts the experimental As(III) adsorption isotherms for aeolian and riverine soils. However, the adsorption of As(III) onto lacustrine soils is underestimated in both linear programming calculations and surface complexation modeling. This study highlights the importance of both Fe-bearing minerals and non-Fe minerals for As(III) adsorption and the difference in the adsorption capacity between various soil minerals. It further suggests that more comprehensive considerations are necessary when building a reactive transport model for As(III) in soil systems.

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The mobility of heavy metal contaminants and radionuclides in the environment is directly controlled by their interactions with charged mineral surfaces, hence an assessment of their potential toxicity, e.g. in the context of radioactive waste disposal sites, requires understanding of sorption processes on the molecular level. Here, we investigate the sorption of a variety of rare earth elements (REE) and trivalent actinides (Am, Cm) on K-feldspar using batch sorption, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and a surface complexation model. Initially, a reliable pKa for K-feldspar's surface deprotonation reaction was determined as 2.5 ± 0.02 by column titration experiments, in excellent agreement with a measured pHIEP of 2.8. Batch sorption experiments over a broad range of experimental conditions in terms of mineral grain size, pH, [M3+], ionic radius, solid/liquid ratio, ionic strength, and equilibration procedures were carried out to quantify macroscopic retention. The trivalent d-block element Y, early, mid, and late lanthanides (La, Eu, Nd, Lu), as well as two minor actinides (Am, Cm) were used for batch sorption experiments and showed similar pH dependent uptake behavior, underlining their chemical analogy. In parallel, spectroscopic investigations provided insight into surface speciation. Cm TRLFS spectra indicate the formation of three inner-sphere sorption complexes with increasing hydrolysis. Additionally, a ternary K-feldspar/Cm/silicate complex was found for pH > 10, and batch and spectroscopic data at low pH (<4) point to small amounts of outer sphere sorption complexes. Based on TRLFS data, batch sorption, and titration data, a generic geochemical sorption model was developed, that describes sorption edges for all investigated M3+/K-feldspar systems satisfactorily. The derived stability constants for the binary sorption complexes (logK1-4 = -3.6, -7.7, -11.5, and -17.4, respectively) could successfully be used to reproduce literature data. The stability constants obtained for the surface complexes were included into the database for the Smart Kd-concept, which will further improve the safety assessment of potential repositories for radioactive waste.

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Numerous experimental studies have identified a multi-step reaction mechanism to control arsenite (As(III)) oxidation by manganese (Mn) oxides. The studies highlighted the importance of edge sites and intermediate processes, e.g., surface passivation by reaction products. However, the identified reaction mechanism and controlling factors have rarely been evaluated in a quantitative context. In this study, a process-based modeling framework was developed to delineate and quantify the relative contributions and rates of the different processes affecting As(III) oxidation by Mn oxides. The model development and parameterization were constrained by experimental observations from literature studies involving environmentally relevant Mn oxides at circumneutral pH using both batch and stirred-flow reactors. Our modeling results highlight the importance of a transitional phase, solely evident in the stirred-flow experiments, where As(III) oxidation gradually shifts from fast reacting Mn(IV) to slowly reacting Mn(III) edge sites. The relative abundance of these edge sites was the most important factor controlling the oxidation rate, whereas surface passivation restricted oxidation only in the stirred-flow experiment. The Mn(III) edge sites were demonstrated to play a crucial role in the oxidation and therefore in controlling the long-term fate of As. This study provided an improved understanding of Mn oxide reactivity and the significance in the cycling of redox-sensitive metal(loid)s in the environment.

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Novel composite materials are increasingly developed for water treatment applications with the aim of achieving multifunctional behaviour, e.g. combining adsorption with light-driven remediation. The application of surface complexation models (SCM) is important to understand how adsorption changes as a function of pH, ionic strength and the presence of competitor ions. Component additive (CA) models describe composite sorbents using a combination of single-phase reference materials. However, predictive adsorption modelling using the CA-SCM approach remains unreliable, due to challenges in the quantitative determination of surface composition. In this study, we test the hypothesis that characterisation of the outermost surface using low energy ion scattering (LEIS) improves CA-SCM accuracy. We consider the TiO2/Fe2O3 photocatalyst-sorbents that are increasingly investigated for arsenic remediation. Due to an iron oxide surface coating that was not captured by bulk analysis, LEIS significantly improves the accuracy of our component additive predictions for monolayer surface processes: adsorption of arsenic(V) and surface acidity. We also demonstrate non-component additivity in multilayer arsenic(III) adsorption, due to changes in surface morphology/porosity. Our results demonstrate how surface-sensitive analytical techniques will improve adsorption models for the next generation of composite sorbents.

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Evaluating speciation of arsenic (As) is essential to assess its risk in paddy soils. In this study, effects of phosphate on speciation of As in six paddy soils differing in redox status were studied over a range of pH (pH 3-9) and different background calcium (Ca) levels by batch adsorption experiments and speciation modeling. Contrasting effects of phosphate on As speciation were observed in suboxic and anoxic soils. Under suboxic conditions, phosphate inhibited Fe and As reduction probably due to stabilization of Fe-(hydr)oxides, but increased soluble As(V) concentration as a result of competitive adsorption between As(V) and phosphate. In anoxic soils, phosphate stimulated Fe and As reduction and caused increases of As(III) in soil solution under both acidic and neutral/alkaline pH. The LCD (Ligand and Charge Distribution) and NOM-CD (Natural Organic Matter-Charge Distribution) model can describe effects of pH, calcium and phosphate on As speciation in these paddy soils. The results suggest that phosphate fertilization may decrease (at low pH) or increase (at neutral/alkaline pH) As mobility in paddy soils under (sub)oxic conditions, but under anoxic conditions and in phosphorus deficient soils phosphate fertilization may strongly mobilize As by promoting microbial activities.

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