Adam J. Siade

Numerical Modeling of Arsenic Mobility during Reductive Iron-Mineral Transformations.

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.

Heat and mass transport during a groundwater replenishment trial in a highly heterogeneous aquifer

Changes in subsurface temperature distribution resulting from the injection of fluids into aquifers may impact physiochemical and microbial processes as well as basin resource management strategies. We have completed a 2 year field trial in a hydrogeologically and geochemically heterogeneous aquifer below Perth, Western Australia in which highly treated wastewater was injected for large-scale groundwater replenishment. During the trial, chloride and temperature data were collected from conventional monitoring wells and by time-lapse temperature logging. We used a joint inversion of these solute tracer and temperature data to parameterize a numerical flow and multispecies transport model and to analyze the solute and heat propagation characteristics that prevailed during the trial. The simulation results illustrate that while solute transport is largely confined to the most permeable lithological units, heat transport was also affected by heat exchange with lithological units that have a much lower hydraulic conductivity. Heat transfer by heat conduction was found to significantly influence the complex temporal and spatial temperature distribution, especially with growing radial distance and in aquifer sequences with a heterogeneous hydraulic conductivity distribution. We attempted to estimate spatially varying thermal transport parameters during the data inversion to illustrate the anticipated correlations of these parameters with lithological heterogeneities, but estimates could not be uniquely determined on the basis of the collected data.

Quantifying Reactive Transport Processes Governing Arsenic Mobility after Injection of Reactive Organic Carbon into a Bengal Delta Aquifer

Over the last few decades, significant progress has been made to characterize the extent, severity, and underlying geochemical processes of groundwater arsenic (As) pollution in S/SE Asia. However, comparably little effort has been made to merge the findings into frameworks that allow for a process-based quantitative analysis of observed As behavior and for predictions of its long-term fate. This study developed field-scale numerical modeling approaches to represent the hydrochemical processes associated with an in situ field injection of reactive organic carbon, including the reductive dissolution and transformation of ferric iron (Fe) oxides and the concomitant release of sorbed As. We employed data from a sucrose injection experiment in the Bengal Delta Plain to guide our model development and to constrain the model parametrization. Our modeling results illustrate that the temporary pH decrease associated with the sucrose transformation and mineralization caused pronounced, temporary shifts in the As partitioning between aqueous and sorbed phases. The results also suggest that while the reductive dissolution of Fe(III) oxides reduced the number of sorption sites, a significant fraction of the released As was rapidly scavenged through coprecipitation with neo-formed magnetite. These secondary reactions can explain the disparity between the observed Fe and As behavior.

Processes governing arsenic retardation on Pleistocene sediments: adsorption experiments and model-based analysis

In many countries of south/south-east Asia, reliance on Pleistocene aquifers for the supply of low-arsenic groundwater has created the risk of inducing migration of high-arsenic groundwater from adjacent Holocene aquifers. Adsorption of arsenic onto mineral surfaces of Pleistocene sediments is an effective attenuation mechanism. However, little is known about the sorption under anoxic conditions, in particular the behavior of arsenite. We report the results of anoxic batch experiments investigating arsenite (1–25 µmol/L) adsorption onto Pleistocene sediments under a range of field-relevant conditions. The sorption of arsenite was non-linear and decreased with increasing phosphate concentrations (3–60 µmol/L) while pH (range 6–8) had no effect on total arsenic sorption. To simulate the sorption experiments, we developed surface complexation models of varying complexity. The simulated concentrations of arsenite, arsenate and phosphate were in good agreement for the isotherm and phosphate experiments while secondary geochemical processes affected the pH experiments. For the latter, the model-based analysis suggests that the formation of solution complexes between organic buffers and Mn(II) ions promoted the oxidation of arsenite involving naturally-occurring Mn-oxides. Upscaling the batch experiment model to a reactive transport model for Pleistocene aquifers demonstrates strong arsenic retardation and could have useful implications in the management of arsenic-free Pleistocene aquifers.

Identifying and Quantifying the Intermediate Processes during Nitrate-Dependent Iron(II) Oxidation

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.

Model-Based Analysis of Arsenic Immobilization via Iron Mineral Transformation under Advective Flows

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.

A Practical, Robust Methodology for Acquiring New Observation Data Using Computationally Expensive Groundwater Models

Regional groundwater flow models play an important role in decision-making regarding water resources; however, the uncertainty embedded in model parameters and model assumptions can significantly hinder the reliability of model predictions. One way to reduce this uncertainty is to collect new observation data from the field. However, determining where and when to obtain such data is not straightforward. There exist a number of data-worth and experimental design strategies developed for this purpose. However, these studies often ignore issues related to real-world groundwater models such as, computational expensive, existing observation data, high parameter dimension, etc. In this study, we propose a methodology, based on existing methods and software, to efficiently conduct such analyses for large-scale, complex regional groundwater flow systems for which there is a wealth of available observation data. The method utilizes the well-established d-optimality criterion, and the minimax criterion for robust sampling strategies. The so-called Null-Space Monte Carlo method is used to reduce the computational burden associated with uncertainty quantification. And, a heuristic methodology, based on the concept of the greedy algorithm, is proposed for developing robust designs with subsets of the posterior parameter samples. The proposed methodology is tested on a synthetic regional groundwater model, and subsequently applied to an existing, complex, regional groundwater system in the Perth region of Western Australia. The results indicate that robust designs can be obtained efficiently, within reasonable computational resources, for making regional decisions regarding groundwater level sampling.