基于Fe(II)驱动自养反硝化和厌氧氨氧化的海洋厌氧氨氧化细菌对含盐废水氮转化的跟踪研究

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Na Zhao , Panqing Qi , Jin Li , Bowei Tan , Weichuan Kong , Hui Lu
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引用次数: 0

摘要

首次研究了基于海洋厌氧氨氧化菌的Fe(II)驱动自养反硝化厌氧氨氧化(MFeADA)对含盐废水的脱氮效果。研究表明,参与铁循环的不同进水剂量的铁(II)通过改变亚硝酸盐的命运显著影响氮的去除性能。当投加50 mg/L Fe(II)时,脱氮主要通过厌氧氨氧化和Fe(II)驱动的自养脱硝(FeAD)进行。当Fe(II)升高到100 ~ 150 mg/L时,厌氧氨氧化、FeAD和Feammox主要发生。当进水Fe(II)浓度为150 mg/L时,脱氮效率最高,达到93%。当Fe(II)达到250 mg/L时,过量的Fe(II)通过Fe(II)驱动的自养反硝化作用(FeADN)将硝酸盐直接还原为二氮气体。Scalindua Candidatus(4.1%)、Marinicella(5.3%)和SM1A02(31.8%)是优势功能微生物。此外,归一化硝酸盐还原酶丰度约为亚硝酸盐还原酶的3.1倍,导致FeAD的发生,为海洋厌氧氨氧化菌提供了稳定的亚硝酸盐供应。本研究可促进MFeADA工艺在含氮含盐废水处理中的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Tracking the nitrogen transformation in saline wastewater by marine anammox bacteria-based Fe(II)-driven autotrophic denitratation and anammox

Tracking the nitrogen transformation in saline wastewater by marine anammox bacteria-based Fe(II)-driven autotrophic denitratation and anammox

Tracking the nitrogen transformation in saline wastewater by marine anammox bacteria-based Fe(II)-driven autotrophic denitratation and anammox
Marine anammox bacteria-based Fe(II)-driven autotrophic denitratation and anammox (MFeADA) was investigated for nitrogen removal from saline wastewater for the first time. The study demonstrated that varying influent doses of Fe(II), which participate in the Fe cycle, significantly influenced nitrogen removal performance by altering the fate of nitrite. When 50 mg/L Fe(II) was added, the nitrogen removal was mainly performed by the anammox and Fe(II)-driven autotrophic denitratation (FeAD). As the Fe(II) rose to 100–150 mg/L, the anammox, FeAD and Feammox mainly occurred. Optimal nitrogen removal efficiency, reaching 93 %, was achieved at an influent Fe(II) concentration of 150 mg/L. As the Fe(II) reached 250 mg/L, however, nitrate was directly reduced to dinitrogen gas by the excessive Fe(II) through the Fe(II)-driven autotrophic denitrification (FeADN). Candidatus Scalindua (4.1 %), Marinicella (5.3 %) and SM1A02 (31.8 %) were the dominant functional microbes. In addition, the normalized nitrate reductase abundance was about 3.1 times that of nitrite reductase, leading to the occurrence of FeAD, which achieved a stable nitrite supply for marine anammox bacteria. This novel study can promote the practical implementation of the MFeADA process in nitrogen-laden saline wastewater treatment.
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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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