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基于机器学习的污水处理厂特定的微生物指标的识别,使用16S rRNA基因测序

Jaana Jurvansuu¹, Annika Länsivaara¹, Marja Palmroth²

¹Faculty of Medicine and Health Technology, Tampere University, P.O. Box 541, 33014, Tampere, Finland.

Scientific reports

|July 3, 2025

概括

机器学习识别了废水废水中的微生物指标. 这可以准确地预测净化厂的来源,有助于环境监测和工艺管理.

科学领域:

环境微生物学环境微生物学
污水处理污水处理污水处理
生物信息学是一种生物信息学.

背景情况:

城市污水处理厂 (WWTP) 拥有微生物群落,对于污染物去除至关重要.
废水监测提供了关于WWTP效率,环境影响和新出现的污染物的见解.
废水的来源归因对于有效的环境管理至关重要.

研究的目的:

开发一种机器学习 (ML) 框架,用于识别WWTP废水中的微生物指标.
根据其废水微生物群区分不同的市政净化厂.
为了使废水废水的快速和经济有效的来源归因.

主要方法:

从六个芬兰的水处理厂 (2016-2018) 收集了57个废水样本.
测序了16S rRNA基因的V4区域,以表征微生物群落.
采用ANOVA-F进行特征选择,并使用高斯纯朴贝叶斯进行ML分类.

主要成果:

在WWTP中确定了独特的微生物概况,具有相似的整体多样性和丰富性.
精选的属级微生物指标,以获得最佳的预测准确性.
通过使用九种细菌属和一个archaeon,在预测废水来源方面取得了92%的准确性.

结论:

该研究提出了一个强大的ML框架,用于识别废水的来源和监测WWTP过程.
已识别的微生物特征为环境管理提供了成本效益高且快速的工具.
证明了ML在推进环境监测和废水处理研究方面的巨大潜力.

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