Numerical Modeling of Arsenic Mobility during Reductive Iron-Mineral Transformations

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.

Identification of Hydrobiogeochemical Processes Controlling Seasonal Variations in Arsenic Concentrations Within a Riverbank Aquifer at Jianghan Plain, China

“…The simulated groundwater levels in case 1 changed seasonally and showed a similar trend as the observed data (Figure 3). However, the simulated results are apparently lower than the observed results from Rawson et al (2016). c Kim and Nriagu (2000).…”

“…The Monod‐type rate expression for arsenic reduction proposed by Rawson et al () was modified by adding a term to account for oxygen inhibition: where k 2 is the rate constant, C DOC and C As(V) are the concentrations of DOC and As (V), K DOC and K As(V) are the half saturation constants with respect to DOC and As (V), and denotes the inhibition constant due to the competitive inhibition from oxygen.…”

“…The Monod-type rate expression for arsenic reduction proposed by Rawson et al (2016) was modified by adding a term to account for oxygen inhibition:…”

Processes governing arsenic retardation on Pleistocene sediments: Adsorption experiments and model‐based analysis

“…A PSO code was written within the PEST++ YAMR run manager [ Welter et al ., ], and linked with PHREEQC for the estimation of parameters, similar to the study conducted by Rawson et al . []. To further improve the calibration, the GLM algorithm was employed to conduct a local search in the neighborhood of the parameter set resulting from PSO calibration.…”

“…Therefore, heuristic, global-search methodologies were explored, and Particle Swarm Optimization (PSO) was chosen for this study [Coello et al, 2004;Eberhart and Kennedy, 1995;Kennedy et al, 2001]. A PSO code was written within the PEST11 YAMR run manager [Welter et al, 2015], and linked with PHREEQC for the estimation of parameters, similar to the study conducted by Rawson et al [2016]. To further improve the calibration, the GLM algorithm was employed to conduct a local search in the neighborhood of the parameter set resulting from PSO calibration.…”