Literature DB >> 29551616

Diversity and assembly patterns of activated sludge microbial communities: A review.

Yu Xia1, Xianghua Wen2, Bing Zhang1, Yunfeng Yang1.   

Abstract

Understanding diversity and assembly patterns of microbial communities in activated sludge (AS) is pivotal for addressing fundamental ecological questions and wastewater treatment engineering. Recent applications of molecular methods especially high-throughput sequencing (HTS) have led to the explosion of information about AS community diversity, including the identification of uncultured taxa, and characterization of low-abundance but environmentally important populations such as antibiotic resistant bacteria and pathogens. Those progresses have facilitated the leverage of ecological theories in describing AS community assembly. The lognormal species abundance curve has been applied to estimate AS microbial richness. Taxa-area and taxa-time relationships (TAR and TTR) have been observed for AS microbial communities. Core AS microbial communities have been identified. Meanwhile, the roles of both deterministic and stochastic processes in shaping AS community structures have been examined. Nonetheless, it remains challenging to define tempo-spatial scales for reliable identification of community turnover, and find tight links between AS microbial structure and wastewater treatment plant (WWTP) functions. To solve those issues, we expect that future research will focus on identifying active functional populations in AS using omics- methods integrated with stable-isotope probing (SIP) with the development of bioinformatics tools. Developing mathematic models to understand AS community structures and utilize information on AS community to predict the performance of WWTPs will also be vital for advancing knowledge of AS microbial ecology and environmental engineering.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Activated sludge; Assembly patterns; Ecology; Microbial diversity; Wastewater treatment engineering

Mesh:

Substances:

Year:  2018        PMID: 29551616     DOI: 10.1016/j.biotechadv.2018.03.005

Source DB:  PubMed          Journal:  Biotechnol Adv        ISSN: 0734-9750            Impact factor:   14.227


  16 in total

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