4.7 Article

Arsenic removal by iron-oxidizing bacteria in a fixed-bed coconut husk column: Experimental study and numerical modeling

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ENVIRONMENTAL POLLUTION
卷 272, 期 -, 页码 -

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ELSEVIER SCI LTD
DOI: 10.1016/j.envpol.2020.115977

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Reactive transport model; Arsenic removal; Arsenic oxidation; Iron oxidation; Iron-oxidizing bacteria; Groundwater

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This study investigated the biological oxidation of As(III) and Fe(II) using laboratory experiments and reactive transport modeling. By using Fe-oxidizing bacteria and coconut husk support media, the study successfully reduced the concentration of As(III) and Fe(II) in synthetic groundwater.
Groundwater in several parts of the world, particularly in developing countries, has been contaminated with Arsenic (As). In search of low-cost As removal methods, the biological oxidation of As(III) and Fe(II) followed by co-precipitation requires detailed investigation for the practical implementation of this technology. The present study investigated the biological oxidation of As(III) and Fe(II) through a combination of laboratory experiments and reactive transport modeling. Batch experiments were conducted to evaluate the As(III) oxidation by Fe-oxidizing bacteria, mainly Leptothrix spp. A fixed-bed down-flow biological column containing inexpensive and readily available coconut husk support media was used to evaluate the combined removal of As(III) and Fe(II) from synthetic groundwater. Oxidation and coprecipitation processes effectively reduced the concentration of As(III) from 500 mu g/L to < 10 mu g/L with a hydraulic retention time of 120 min. A one-dimensional reactive transport model was developed based on the microbially mediated biochemical reactions of As(III) and Fe(II). The model successfully reproduced the observed As(III) and Fe(II) removal trends in the column experiments. The modeling results showed that the top 20 cm aerobic layer of the column played a primary role in the microbial oxidation of Fe(II) and As(III). The model calibration identified the hydraulic residence time as the most significant process parameter for the removal of Fe and As in the column. The developed model can effectively predict As concentrations in the effluent and provide design guidelines for the biological treatment of As. The model would also be useful for understanding the biogeochemical behavior of Fe and As under aerobic conditions. (C) 2020 Elsevier Ltd. All rights reserved.

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