各种消毒剂方案下全规模饮用水输水管网中阿米巴群落的动态和集结机制

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-11-26 DOI:10.1016/j.watres.2024.122861
Yuxing Hu, Kaiyang Jiang, Siqing Xia, Weixian Zhang, Jianhua Guo, Hong Wang
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引用次数: 0

摘要

自由生活阿米巴(FLA)在饮用水输配管网(DWDN)中十分普遍,但我们对其群落动态和聚集机制的了解仍然有限。本研究描述了阿米巴群落的发生模式,并确定了影响其在中国三个城市的四个全面的 DWDNs 中聚集的关键因素,每个 DWDNs 都使用了不同的消毒剂(氯、氯胺和二氧化氯)。全长 18S rRNA 基因的高通量测序揭示了 DWDNs 中高度多样化的 FLA 群落和一系列稀有 FLA 物种。在受氯污染的 DWDN 中观察到了独特的 FLA 群落结构和较高的三种阿米巴类群(疣状蛭阿米巴、棘阿米巴和福氏奈格氏虫)基因拷贝数,凸显了氯胺对阿米巴群落形成的独特影响。DWDN中的FLA群落主要由确定性过程驱动,消毒剂和氮化合物(硝酸盐、亚硝酸盐和氨)被认为是主要的影响因素。机器学习模型显示,优势变形虫属(如Vannella和Vermamoeba)的SHAPley Additive exPlanations(SHAP)值很高,表明它们在塑造更广泛的细菌和真核生物群落中发挥着关键的生态作用。阿米巴属与细菌类群之间的相关性分析表明,82%与阿米巴呈负相关的细菌类群是革兰氏阴性菌,这表明阿米巴喜欢捕食革兰氏阴性菌。网络分析显示,在不同的模块中只存在一到两种阿米巴虫,这表明单个阿米巴虫在吃草时可能具有选择性。这些发现有助于深入了解饮用水中变形虫的群落动态、聚集机制和生态作用,有助于在 DWDNs 中进行风险评估和制定缓解策略。
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Amoeba Community Dynamics and Assembly Mechanisms in Full-scale Drinking Water Distribution Networks under Various Disinfectant Regimens
Free-living amoebae (FLA) are prevalent in drinking water distribution networks (DWDNs), yet our understanding of FLA community dynamics and assembly mechanisms in DWDNs remains limited. This study characterized the occurrence patterns of amoeba communities and identified key factors influencing their assembly across four full-scale DWDNs in three Chinese cities, each utilizing different disinfectants (chlorine, chloramine, and chlorine dioxide). High-throughput sequencing of full-length 18S rRNA genes revealed highly diverse FLA communities and an array of rare FLA species in DWDNs. Unique FLA community structures and higher gene copy numbers of three amoeba taxa of concern (Vermamoeba vermiformis, Acanthamoeba, and Naegleria fowleri) were observed in the chloraminated DWDN, highlighting the distinct impact of chloramine on shaping the amoeba community. The FLA communities in DWDNs were primarily driven by deterministic processes, with disinfectant and nitrogen compounds (nitrate, nitrite, and ammonia) identified as the main influencing factors. Machine learning models revealed high SHapley Additive exPlanations (SHAP) values of dominant amoeba genera (e.g., Vannella and Vermamoeba), indicating their critical ecological roles in shaping broader bacterial and eukaryotic communities. Correlation analyses between amoeba genera and bacterial taxa revealed that 82% of the bacterial taxa exhibiting a negative correlation with amoebae were gram-negative, suggesting the preferred predation of amoebae toward gram-negative bacteria. Network analysis revealed the presence of only one to two amoebae in distinct modules, suggesting that individual amoebae might be selective in grazing. These findings provide insight into the amoeba community dynamics, assembly mechanisms and ecological roles of amoebae in drinking water, which can aid in risk assessments and mitigation strategies within DWDNs.
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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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