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Abstract

Enhanced reductive dehalogenation is an attractive in situ treatment technology for chlorinated contaminants. The process includes two acid-forming microbial reactions: fermentation of an organic substrate resulting in short-chain fatty acids, and dehalogenation resulting in hydrochloric acid. The accumulation of acids and the resulting drop of groundwater pH are controlled by the mass and distribution of chlorinated solvents in the source zone, type of electron donor, alternative terminal electron acceptors available and presence of soil mineral phases able to buffer the pH (such as carbonates). Groundwater acidification may reduce or halt microbial activity, and thus dehalogenation, significantly increasing the time and costs required to remediate the aquifer. In previous work a detailed geochemical and groundwater flow simulator able to model the fermentation-dechlorination reactions and associated pH change was developed. The model accounts for the main processes influencing microbial activity and groundwater pH, including the groundwater composition, the electron donor used and soil mineral phase interactions. In this study, the model was applied to investigate how spatial variability occurring at the field scale affects dechlorination rates, groundwater pH and ultimately the remediation efficiency. Numerical simulations were conducted to examine the influence of heterogeneous hydraulic conductivity on the distribution of the injected, fermentable substrate and on the accumulation/dilution of the acidic products of reductive dehalogenation. The influence of the geometry of the DNAPL source zone was studied, as well as the spatial distribution of soil minerals. The results of this study showed that the heterogeneous distribution of the soil properties have a potentially large effect on the remediation efficiency. For examples, zones of high hydraulic conductivity can prevent the accumulation of acids and alleviate the problem of groundwater acidification. The conclusions drawn and insights gained from this modeling study will be useful to design improved in-situ enhanced dehalogenation remediation schemes.

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