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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Aquatic Microbiology
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1458853
This article is part of the Research Topic Biological Invasions in Aquatic Ecosystems: Detection, Assessment and Countermeasures View all 4 articles

A comprehensive analysis of microbial community differences in four morphologies of mainstream anaerobic ammonia oxidation systems using big-data mining and machine learning

Provisionally accepted
Shijie Zhou Shijie Zhou Zhu Weidi Zhu Weidi Yuhang He Yuhang He Tianxu Zhang Tianxu Zhang Ming Zeng Ming Zeng *Zhicheng Jiang Zhicheng Jiang
  • Tianjin University of Science and Technology, Tianjin, China

The final, formatted version of the article will be published soon.

    Achieving carbon neutrality in wastewater treatment plants relies heavily on mainstream anaerobic ammonia oxidation. However, the stability of this process is often compromised, largely due to the significant influence of microbial morphology. This study analyzed 208 microbial samples using bioinformatics and machine learning (ML) across four different morphologies: Suspended Sludge (SS), Biofilm, Granular Sludge (GS) and the Integrated Fixed-film Activated Sludge process (IFAS). The results revealed IFAS’s notably complex and stable community structure, along with the identification of endemic genera and common genera among the four microbial morphologies. Through co-occurrence network analysis, the interaction between microorganisms of various genera was displayed. Utilizing the Extreme Gradient Boosting (XGBoost) model, a ML modeling framework based on microbiome data was developed. The ML-based feature importance analysis identified LD-RB-34 as a key organism in SS and BSV26 was an important bacterium in IFAS. Additionally, functional bacteria KF-JG30-C25 occupied a higher proportion in GS, and Unclassified Brocadiaceae occupied a higher proportion in Biofilm. Furthermore, dissolved oxygen, temperature and pH were identified as the primary factors determining microbial communities and influencing anammox activity. Overall, this study deepens our understanding of bacterial communities to enhance the mainstream anammox nitrogen removal.

    Keywords: Anammox, bacterial community, Sludge morphology, machine learning, 16S rRNA gene

    Received: 03 Jul 2024; Accepted: 14 Aug 2024.

    Copyright: © 2024 Zhou, Weidi, He, Zhang, Zeng and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ming Zeng, Tianjin University of Science and Technology, Tianjin, China

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