Electroactive bacteria-established long-distance electron transfer to oxygen facilitates bio-transformation of dissolved organic matter for sediment remediation

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-11-22 DOI:10.1016/j.watres.2024.122829
Yinxiu Liang, Meijun Dong, Shan Yang, Lizhou Lin, Haobin Huang, Daobo Li, Min Ji, Meiying Xu
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Abstract

Electroactive bacteria (EAB) in sediment commonly establish long-distance electron transfer (LDET) to access O2, facilitating the degradation of organic contaminants, which we hypothesize is mediated by the bio-transformation of dissolved organic matter (DOM). This study confirmed that EAB-established LDET to O2 via a microbial electrochemical snorkel raised the electric potential of sediment by increasing HCl-extracted Fe(III) and NO— 3concentrations while reducing DOM concentrations, which further modified microbial diversity and composition, notably reduced the relative abundance of fermentative bacteria. As a result, DOM showed the highest SUVA254 value (3.88) and SUVA280 value (1.61), preliminarily suggesting their enhanced aromaticity, humification and average molecular weight. Additionally, these DOM exhibited the highest electron transfer capacity (174.14±3.62 μmol e /g C) and redox current. Based on these findings, we propose four possible avenues through which EAB-established LDET to O2 facilitates sediment remediation, mainly including DOM involved affinity, direct and indirect electron transfer, and induced photochemical reaction in degradation or humification process of organic contaminants. Although these proposed avenues require further verification, this work sheds light on deciphering the mechanisms underlying the augmented degradation of organic contaminants facilitated by EAB-established LDET to O2, offering fresh insights into sediment remediation.

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电活性细菌与氧气之间建立的长距离电子传递促进了溶解有机物的生物转化,有利于沉积物修复
沉积物中的电活性细菌(EAB)通常通过建立长距离电子传递(LDET)来获取氧气,从而促进有机污染物的降解。本研究证实,EAB 通过微生物电化学潜标建立的 LDET 向氧气的转移提高了沉积物的电势,增加了 HCl 萃取的铁(III)和 NO- 3 浓度,同时降低了 DOM 浓度,这进一步改变了微生物的多样性和组成,特别是降低了发酵菌的相对丰度。因此,DOM 的 SUVA254 值(3.88)和 SUVA280 值(1.61)最高,初步表明它们的芳香度、腐殖化程度和平均分子量都有所提高。此外,这些 DOM 表现出最高的电子转移能力(174.14±3.62 μmol e- /g C)和氧化还原电流。基于这些发现,我们提出了 EAB 建立的 LDET 对 O2 促进沉积物修复的四种可能途径,主要包括 DOM 参与亲和、直接和间接电子传递以及在有机污染物降解或腐殖化过程中诱导光化学反应。尽管这些拟议的途径还需要进一步验证,但本研究揭示了EAB建立的LDET对O2促进有机污染物降解的机制,为沉积物修复提供了新的见解。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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