Literature DB >> 32836105

Spatiotemporal evolution of iron and sulfate concentrations during riverbank filtration: Field observations and reactive transport modeling.

Woonghee Lee1, Etienne Bresciani2, Seongnam An3, Ilka Wallis4, Vincent Post5, Seunghak Lee6, Peter K Kang7.   

Abstract

Riverbank filtration is a commonly-used technology that improves water quality by passing river water through aquifers. In this study, a riverbank filtration site in Busan, South Korea, was investigated to understand the spatiotemporal evolution of high iron and sulfate concentrations observed in the riverbank-filtered water. Discrepancies between the nonreactive transport results and field measurements suggest that iron-sulfate-related geochemical reactions play a major role in the spatiotemporal evolution of the hydrochemical properties. Pyrite oxidation was hypothesized to be the main process driving the release of iron and sulfate. To test this hypothesis, a reactive transport model was developed, that implemented pyrite oxidation as a kinetic process and subsequent ferrous iron oxidation and ferric iron precipitation as equilibrium processes. The model accurately captured the temporal evolution of sulfate; however, iron concentrations were underestimated. Sensitivity tests revealed that adjusting reaction constants significantly improved the prediction of iron concentrations. The results of this study suggest that pyrite oxidation can affect the hydrochemistry of riverbank-filtered water and highlight the potential limitations of using theoretical reaction constants in field modeling applications.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Groundwater; Iron and Sulfate; Pyrite oxidation; Reaction constant; Reactive transport modeling; Riverbank filtration

Year:  2020        PMID: 32836105     DOI: 10.1016/j.jconhyd.2020.103697

Source DB:  PubMed          Journal:  J Contam Hydrol        ISSN: 0169-7722            Impact factor:   3.188


  1 in total

1.  Machine learning to predict effective reaction rates in 3D porous media from pore structural features.

Authors:  Min Liu; Beomjin Kwon; Peter K Kang
Journal:  Sci Rep       Date:  2022-03-31       Impact factor: 4.379

  1 in total

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