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总氮提标改造工程的微生物群落结构分析
摘要点击 1697  全文点击 498  投稿时间:2021-11-05  修订日期:2022-01-25
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中文关键词  污水处理厂  总氮提标改造工程  群落结构  生物标识  环境因素  功能预测
英文关键词  wastewater treatment plant  total nitrogen upgrading project  microbial community structure  biomarker  environmental factors  function prediction
作者单位E-mail
李海松 郑州大学生态与环境学院, 郑州 450001
知和环保科技有限公司, 郑州 450000 
lhs@zzu.edu.cn 
王柯丹 知和环保科技有限公司, 郑州 450000  
陈晓蕾 知和环保科技有限公司, 郑州 450000  
阎登科 知和环保科技有限公司, 郑州 450000  
许子聪 知和环保科技有限公司, 郑州 450000  
胡培基 知和环保科技有限公司, 郑州 450000  
中文摘要
      对出水ρ[总氮(TN)]<1.5 mg·L-1的市政污水处理厂(WWTP)的活性污泥进行微生物群落结构分析,揭示同一污水厂常规生化(WLK_OD)段和深度脱氮(WLK_AD)段的微生物群落多样性差异及功能差异.通过16S rRNA基因高通量测序及分析,结果表明相较于WLK_OD,WLK_AD中微生物群落具有较低均匀性,是因为特定的环境对微生物进行了选择.Proteobacteria在WLK_AD中富集且相对丰度达到70.11%.ThaueraFlavobacteriumHydrogenophagaZoogloea等反硝化菌为WLK_AD中新增的优势菌,在两工段不同的生态位和微生物生长动力学共同作用下,体系内的反硝化菌发生演替.通过组间比较(P<0.05),演替的优势反硝化菌在两个工段中有显著差异.此外,通过共现网络模块化分析结果表明,不同的水质环境对微生物群落结构具有影响.通过冗余分析发现COD和TN的去除率对微生物群落结构影响最大,SaccharibacteriaThermomarinilineaTerrimonasComamonas与COD的去除率呈正相关,而DokdonellaThaueraFlavobacteriumZoogloea等反硝化菌与TN的去除率呈正相关.在WLK_AD中,NO3--N和TN的去除率较高,NovosphingobiumDokdonellaThaueraSphingomonas等反硝化功能菌相对丰度也较高,它们协同去除TN,实现出水ρ(TN)<1.5 mg·L-1.功能预测结果表明WLK_AD中编码反硝化酶的基因丰度更高.
英文摘要
      The activated sludge of a biochemical unit (WLK_OD) and an advanced denitrification unit (WLK_AD) were collected from a municipal wastewater treatment plant (WWTP), in which the TN concentration of effluent was less than 1.5 mg·L-1, and their microbial community structure and function profiles were analyzed using 16S rRNA gene high-throughput sequencing. The microorganisms in WLK_AD had lower evenness compared with that in WLK_OD, which was attributed to environmental selection. Furthermore, PCoA revealed that different incoming wastewaters had an impact on microbial community structure. At the phylum level, Proteobacteria (70.11%) was enriched in WLK_AD. At the genus level, Thauera, Flavobacterium, Hydrogenophaga, and Zoogloea served as distinct-dominant denitrifying bacteria in WLK_AD; however, Trichococcus (3.50%) and Terrimonas (1.10%) were enriched in WLK_OD. Through the comparison between groups (P<0.05), the biomarkers detected in each WWTP were different. Furthermore, the results of the co-occurrence network showed that the bacteria from module I had a higher proportion in WLK_AD; the bacteria from module II had a higher proportion in WLK_OD, and they were common microorganisms in WWTPs, implying that wastewater environments drpve the differences in the microbial community structure. Among the types of environmental parameters, the removal efficiency of COD and TN had the greatest impact on the microbial community by the RDA. The removal efficiency of COD was positively correlated with the dominant bacteria from WLK_OD, such as Saccharibacteria, Thermomarinilinea, Terrimonas, and Comamonas; the removal efficiency of TN was positively correlated with the denitrifying bacteria from WLK_AD, such as Dokdonella, Thauera, Flavobacterium, and Zoogloea. WLK_AD was enriched with Novosphingobium, Dokdonella, Thauera, and Sphingomonas, which synergistically removed TN, leading to the TN of the effluent being less than 1.5 mg·L-1. Moreover, based on the results of function prediction, WLK_AD had a higher proportion of genes that could code the denitrification enzymes.

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