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根据微生物社区测序和源分类器机器学习,识别导致水污染的人类活动.

Zhangmu Jing1, Yi Zhang2, Xiaoling Liu3

  • 1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore.

Environment international
|December 31, 2024
PubMed
概括
此摘要是机器生成的。

机器学习准确地追踪了影响河流微生物的人类活动. 这种微生物分析有助于管理水生生态系统并减轻环境健康的污染风险.

关键词:
16S rRNA 测序数据的数据人类活动 人类活动微生物群落就是微生物群落.污染源的追踪 污染源的追踪源代码分类器机器学习

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科学领域:

  • 微生物学 微生物学
  • 环境科学 环境科学
  • 电子货币信息学 (Ecoininformatics) 是一种电子货币信息学.

背景情况:

  • 人类活动通过环境污染对水生生态系统和人类健康构成重大威胁.
  • 机器学习 (ML) 为分析高通量数据集提供了强大的分析能力,以追踪人类对河流生态系统的影响.

研究的目的:

  • 采用ML框架和16SrRNA测序数据来揭示微生物动态和追踪中国各地的人类活动.
  • 研究微生物社区聚集,元社区结构和河流环境中的人类活动之间的关联.

主要方法:

  • 利用16S rRNA测序来分析水和沉积物样本中的微生物群落.
  • 应用了一个源分类器机器学习 (SCML) 算法,集成微生物指数 (MBIs),微生物相对丰度 (MRA) 和环境和地理指数 (EGIs).
  • 开发并优化了SCML模型,以分类五种不同的人类活动类型:低人力影响,农业投入,国内投入,工业投入和水建设.

主要成果:

  • 微生物聚集主要是由决定性因素驱动的,包括环境条件和物种相互作用.
  • 超社区结构在水和沉积物中显示出与人类活动的显著关联.
  • 发现人类活动增加了跨物种发生网络的易感性,并增强了环境因素对微生物群落相似性和遗传学距离的影响.
  • 优化的SCML模型 (MBIs + MRA + EGIs) 显示出高性能,水的R平方值为0.882,沉积物的R平方值为0.924.

结论:

  • 微生物社区结构是人类活动在河流系统中的敏感指标.
  • 开发的ML框架为识别和区分人类对水生环境的影响提供了一个强大的工具.
  • 研究结果支持改善生态系统管理,可持续利用水资源和有效的污染减缓战略.