No abstract

Millions of individuals worldwide are chronically exposed to hazardous concentrations of arsenic from contaminated drinking water. Despite massive efforts toward understanding the extent and underlying geochemical processes of the problem, numerical modeling and reliable predictions of future arsenic behavior remain a significant challenge. One of the key knowledge gaps concerns a refined understanding of the mechanisms that underlie arsenic mobilization, particularly under the onset of anaerobic conditions, and the quantification of the factors that affect this process. In this study, we focus on the development and testing of appropriate conceptual and numerical model approaches to represent and quantify the reductive dissolution of iron oxides, the concomitant release of sorbed arsenic, and the role of iron-mineral transformations. The initial model development in this study was guided by data and hypothesized processes from a previously reported,1 well-controlled column experiment in which arsenic desorption from ferrihydrite coated sands by variable loads of organic carbon was investigated. Using the measured data as constraints, we provide a quantitative interpretation of the processes controlling arsenic mobility during the microbial reductive transformation of iron oxides. Our analysis suggests that the observed arsenic behavior is primarily controlled by a combination of reductive dissolution of ferrihydrite, arsenic incorporation into or co-precipitation with freshly transformed iron minerals, and partial arsenic redox transformations.

[1] Water resources systems management often requires complex mathematical models whose use may be computationally infeasible for many advanced analyses, e.g., optimization, data assimilation, model uncertainty, etc. The computational demand of these analyses can be reduced by approximating the model with a simpler reduced model. Proper Orthogonal Decomposition (POD) is an efficient model reduction technique based on the projection of the original model onto a subspace generated by full-model snapshots. In order to implement this method, an appropriate number of snapshots of the full model must be taken at the appropriate times such that the resulting reduced model is as accurate as possible. Since confined aquifers reach steady state in an exponential manner, we present a simple exponential function that can be used to select snapshots for these types of models. This selection method is then employed to determine the optimal snapshot set for a unit, dimensionless model. The optimal snapshot set is found by maximizing the minimum eigenvalue of the snapshot covariance matrix, a criterion similar to those used in experimental design. The resulting snapshot set can then be translated to any complex, real world model based on a simple, approximate relationship between dimensionless and real-world times. This translation is illustrated using a basin scale model of Central Veneto, Italy. We show that this method is very easy to implement and produces an accurate reduced model that, in the case of Central Veneto, Italy, runs approximately 1,000 times faster than the full model.

Managed aquifer recharge of aerobic water into deep aquifers often induces the oxidation of pyrite, which can lead to groundwater acidification and metal mobilization. As circumneutral pH is often maintained by the dissolution of sedimentary calcite or high injectant alkalinity little attention is generally paid to potential alternative pH buffering processes. In contrast, this study analyzed water quality evolution from a 2 year long groundwater replenishment trial in an anaerobic, mostly carbonate free aquifer. While injection of aerobic, very low salinity water triggered pyrite oxidation, the comprehensive field data showed that in many aquifer zones pH was buffered without substantial release of inorganic carbon. A numerical analysis was performed to test and evaluate different conceptual models and suggested that either proton buffering or the dissolution of aluminosilicates, or a combination thereof, can explain the observed, rapid buffering at locations where carbonates were absent. In contrast to many previous managed aquifer recharge [MAR) studies, the oxidation of sedimentary pyrite by nitrate was found to be of minor importance or negligible. The study also highlights that the depositional history of the aquifer, and the associated differences in mineralogy and geochemistry, need to be considered when estimating groundwater quality evolution during the injection of various water types for aquifer replenishment or other management purposes. ] describe sequential buffering during ongoing pyrite oxidation in a carbonate-bearing mine waste. After the initial consumption of primary and secondary carbonates (calcite and siderite), they observed the dissolution of previously precipitated gibbsite at pH 4.9-5.2, and subsequent ferrihydrite dissolution at pH < 4.5. Salmon and Malmstr€ om [2004] found that the pH of 4.9 in the groundwater of a carbonate-free mine waste deposit could largely be explained by a balance of acid generation by pyrite oxidation and acid consumption by chlorite and ferrihydrite dissolution.

Microbially driven nitrate-dependent iron (Fe) oxidation (NDFO) in subsurface environments has been intensively studied. However, the extent to which Fe(II) oxidation is biologically catalyzed remains unclear because no neutrophilic iron-oxidizing and nitrate reducing autotroph has been isolated to confirm the existence of an enzymatic pathway. While mixotrophic NDFO bacteria have been isolated, understanding the process is complicated by simultaneous abiotic oxidation due to nitrite produced during denitrification. In this study, the relative contributions of biotic and abiotic processes during NDFO were quantified through the compilation and model-based interpretation of previously published experimental data. The kinetics of chemical denitrification by Fe(II) (chemodenitrification) were assessed, and compelling evidence was found for the importance of organic ligands, specifically exopolymeric substances secreted by bacteria, in enhancing abiotic oxidation of Fe(II). However, nitrite alone could not explain the observed magnitude of Fe(II) oxidation, with 60-75% of overall Fe(II) oxidation attributed to an enzymatic pathway for investigated strains: Acidovorax ( A.) strain BoFeN1, 2AN, A. ebreus strain TPSY, Paracoccus denitrificans Pd 1222, and Pseudogulbenkiania sp. strain 2002. By rigorously quantifying the intermediate processes, this study eliminated the potential for abiotic Fe(II) oxidation to be exclusively responsible for NDFO and verified the key contribution from an additional, biological Fe(II) oxidation process catalyzed by NDFO bacteria.

Coal seam gas production involves generation and management of large amounts of co‐produced water. One of the most suitable methods of management is injection into deep aquifers. Field injection trials may be used to support the predictions of anticipated hydrological and geochemical impacts of injection. The present work employs reactive transport modeling (RTM) for a comprehensive analysis of data collected from a trial where arsenic mobilization was observed. Arsenic sorption behavior was studied through laboratory experiments, accompanied by the development of a surface complexation model (SCM). A field‐scale RTM that incorporated the laboratory‐derived SCM was used to simulate the data collected during the field injection trial and then to predict the long‐term fate of arsenic. We propose a new practical procedure which integrates laboratory and field‐scale models using a Monte Carlo type uncertainty analysis and alleviates a significant proportion of the computational effort required for predictive uncertainty quantification. The results illustrate that both arsenic desorption under alkaline conditions and pyrite oxidation have likely contributed to the arsenic mobilization that was observed during the field trial. The predictive simulations show that arsenic concentrations would likely remain very low if the potential for pyrite oxidation is minimized through complete deoxygenation of the injectant. The proposed modeling and predictive uncertainty quantification method can be implemented for a wide range of groundwater studies that investigate the risks of metal(loid) or radionuclide contamination.

Coal seam gas (CSG) extraction generates large volumes of coproduced water. Injection of the excess water into deep aquifers is often the most sustainable management option. However, such injection risks undesired sediment–water interactions that mobilize metal­(loid)­s in the receiving aquifer. This risk can be mitigated through pretreatment of the injectant. Here, we conducted a sequence of three push–pull tests (PPTs) where the injectant was pretreated using acid amendment and/or deoxygenation to identify the processes controlling the fate of metal­(loid)­s and to understand the treatment requirements for large-scale CSG water injection. The injection and recovery cycles were closely monitored, followed by analysis of the observations through reactive transport modeling. While arsenic was mobilized in all three PPTs, significantly lower arsenic concentrations were observed in the recovered water when the injectant was deoxygenated, regardless of pH adjustment. The breakthrough of arsenic was commensurate with molybdenum, but distinct from phosphate. This allowed for the observed and modeled arsenic and molybdenum mobilization to be attributed to a stoichiometric codissolution process during pyrite oxidation, whereas phosphate mobility was governed by sorption. Understanding the nature of these hydrochemical processes explained the greater efficiency of pretreatment by deoxygenation on minimizing metal­(loid) mobilization compared to the acid amendment.

Recent laboratory studies have demonstrated that coinjection of nitrate and Fe(II) (as ferrous sulfate) to As-bearing sediments can produce an Fe mineral assemblage containing magnetite capable of immobilizing advected As under a relatively wide range of aquifer conditions. This study combined laboratory findings with process-based numerical modeling approaches, to quantify the observed Fe mineral (trans)formation and concomitant As partitioning dynamics and to assess potential nitrate-Fe(II) remediation strategies for field implementation. The model development was guided by detailed solution and sediment data from our well-controlled column experiment. The modeling results demonstrated that the fate of As during the experiment was primarily driven by ferrihydrite formation and reductive transformation and that different site densities were identified for natural and neoformed ferrihydrite to explain the observations both before and after nitrate-Fe(II) injection. Our results also highlighted that when ferrihydrite was nearing depletion, As immobilization ultimately relied on the presence of magnetite. On the basis of the column model, field-scale predictive simulations were conducted to illustrate the feasibility of the nitrate-Fe(II) strategy for intercepting advected As from a plume. The predictive simulations, which suggested that long-term As immobilization was feasible, favored a scenario that maintains high dissolved Fe(II) concentration during injection periods and thereby converts ferrihydrite to magnetite.