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Unravelling and Reconstructing the Nexus of Salinity, Electricity, and Microbial Ecology for Bioelectrochemical Desalination.

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Title: Unravelling and Reconstructing the Nexus of Salinity, Electricity, and Microbial Ecology for Bioelectrochemical Desalination.
Authors: Yuan, Heyang1, Sun, Shan2, Abu-Reesh, Ibrahim M.3, Badgley, Brian D.2 badgley@vt.edu, He, Zhen1 zhenhe@vt.edu
Source: Environmental Science & Technology. 11/7/2017, Vol. 51 Issue 21, p12672-12682. 11p.
Subject Terms: *Salinity, *Microbial ecology, *Saline water conversion, *Wastewater treatment, Bioelectrochemistry
Abstract: Microbial desalination cells (MDCs) are an emerging concept for simultaneous water/wastewater treatment and energy recovery. The key to developing MDCs is to understand fundamental problems, such as the effects of salinity on system performance and the role of microbial community and functional dynamics. Herein, a tubular MDC was operated under a wide range of salt concentrations (0.05-4 M), and the salinity effects were comprehensively examined. The MDC generated higher current with higher salt concentrations in the desalination chamber. When fed with 4 M NaC1, the MDC achieve a current density of 300 A M-3 (anode volume), which was one of the highest among bioelectrochemical system studies. Community analysis and electrochemical measurements suggested that electrochemically active bacteria Pseudomonas and Acinetobacter transferred electrons extracellularly via electron shuttles, and the consequent ion migration led to high anode salinities and conductivity that favored their dominance. Predictive functional dynamics and Bayesian networks implied that the taxa putatively not capable of extracellular electron transfer (e.g., Bacteroidales and Clostridiales) might indirectly contribute to bioelectrochemical desalination. By integrating the Bayesian network with logistic regression, current production was successfully predicted from taxonomic data. This study has demonstrated uncompromised system performance under high salinity and thus has highlighted the potential of MDCs as an energy-efficient technology to address water-energy challenges. The statistical modeling approach developed in this study represents a significant step toward understating microbial communities and predicting system performance in engineered biological systems. [ABSTRACT FROM AUTHOR]
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Abstract:Microbial desalination cells (MDCs) are an emerging concept for simultaneous water/wastewater treatment and energy recovery. The key to developing MDCs is to understand fundamental problems, such as the effects of salinity on system performance and the role of microbial community and functional dynamics. Herein, a tubular MDC was operated under a wide range of salt concentrations (0.05-4 M), and the salinity effects were comprehensively examined. The MDC generated higher current with higher salt concentrations in the desalination chamber. When fed with 4 M NaC1, the MDC achieve a current density of 300 A M<sup1>-3</sup1> (anode volume), which was one of the highest among bioelectrochemical system studies. Community analysis and electrochemical measurements suggested that electrochemically active bacteria Pseudomonas and Acinetobacter transferred electrons extracellularly via electron shuttles, and the consequent ion migration led to high anode salinities and conductivity that favored their dominance. Predictive functional dynamics and Bayesian networks implied that the taxa putatively not capable of extracellular electron transfer (e.g., Bacteroidales and Clostridiales) might indirectly contribute to bioelectrochemical desalination. By integrating the Bayesian network with logistic regression, current production was successfully predicted from taxonomic data. This study has demonstrated uncompromised system performance under high salinity and thus has highlighted the potential of MDCs as an energy-efficient technology to address water-energy challenges. The statistical modeling approach developed in this study represents a significant step toward understating microbial communities and predicting system performance in engineered biological systems. [ABSTRACT FROM AUTHOR]
ISSN:0013936X
DOI:10.1021/acs.est.7b03763