One-stage anammox and thiocyanate-driven autotrophic denitrification for simultaneous removal of thiocyanate and nitrogen: Pathway and mechanism

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-08-21 DOI:10.1016/j.watres.2024.122268
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Abstract

The coupled process of anammox and reduced-sulfur driven autotrophic denitrification can simultaneously remove nitrogen and sulfur from wastewater, while minimizing energy consumption and sludge production. However, the research on the coupled process for removing naturally toxic thiocyanate (SCN-) is limited. This work successfully established and operated a one-stage coupled system by co-cultivating mature anammox and SCN--driven autotrophic denitrification sludge in a single reactor. In this one-stage coupled system, the average total nitrogen removal efficiency was 89.68±3.33 %, surpassing that of solo anammox (81.80±2.10 %) and SCN--driven autotrophic denitrification (85.20±1.54 %). Moreover, the average removal efficiency of SCN- reached 99.50±3.64 %, exceeding that of solo SCN--driven autotrophic denitrification (98.80±0.65 %). The results of the 15N stable isotope tracer labeling experiment revealed the respective reaction rates of anammox and denitrification as 106.38±10.37 μmol/L/h and 69.07±8.07 μmol/L/h. By analyzing metagenomic sequencing data, Thiobacillus_denitrificans was identified as the primary contributor to SCN- degradation in this coupled system. Furthermore, based on the comprehensive analysis of nitrogen and sulfur metabolic pathways, as well as the genes associated with SCN- degradation, it can be inferred that the cyanate (CNO) pathway was responsible for SCN- degradation. This work provided a deeper insight into coupling anammox with SCN--driven autotrophic denitrification in a one-stage coupled system, thereby contributing to the development of an effective approach for wastewater treatment involving both SCN- and nitrogen.

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同时去除硫氰酸盐和氮的单级anammox和硫氰酸盐驱动的自养反硝化作用:途径和机制
氨氧化和还原硫驱动的自养反硝化耦合过程可同时去除废水中的氮和硫,同时最大限度地降低能耗和污泥产量。然而,有关耦合工艺去除天然有毒硫氰酸盐(SCN)的研究还很有限。这项研究通过在一个反应器中共同培养成熟的厌氧菌和 SCN 驱动的自养反硝化污泥,成功建立并运行了一个单级耦合系统。在该单级耦合系统中,平均总氮去除率为 89.68±3.33%,超过了单体厌氧反应(81.80±2.10%)和 SCN 驱动的自养反硝化反应(85.20±1.54%)。此外,SCN 的平均去除率达到 99.50±3.64%,超过了单独 SCN 驱动的自养反硝化作用(98.80±0.65%)。N稳定同位素示踪标记实验结果显示,anammox和反硝化反应速率分别为106.38±10.37 μmol/L/h和69.07±8.07 μmol/L/h。通过分析元基因组测序数据,确定了该耦合系统中 SCN 降解的主要贡献者。此外,根据对氮和硫代谢途径以及与 SCN 降解相关的基因的综合分析,可以推断氰酸酯(CNO)途径是 SCN 降解的罪魁祸首。这项研究深入探讨了在单级耦合系统中将anammox与SCN驱动的自养反硝化作用耦合起来的问题,从而有助于开发一种同时处理SCN和氮的有效废水处理方法。
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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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