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Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
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Identifying human activities causing water pollution based on microbial community sequencing and source classifier

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.

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|December 31, 2024
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Summary
This summary is machine-generated.

Machine learning accurately traces human activities impacting river microbes. This microbial analysis aids in managing aquatic ecosystems and mitigating pollution risks for environmental health.

Keywords:
16S rRNA sequencing dataHuman activitiesMicrobial communitiesPollution source tracingSource classifier machine learning

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Area of Science:

  • Microbiology
  • Environmental Science
  • Ecoinformatics

Background:

  • Human activities pose significant threats to aquatic ecosystems and human health through environmental pollution.
  • Machine learning (ML) offers powerful analytical capabilities for analyzing high-throughput datasets to track human impacts on river ecosystems.

Purpose of the Study:

  • To employ an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.
  • To investigate the association between microbial community assembly, metacommunity structure, and human activities in riverine environments.

Main Methods:

  • Utilized 16S rRNA sequencing to analyze microbial communities in water and sediment samples.
  • Applied a source classifier machine learning (SCML) algorithm, integrating microbiological indices (MBIs), microbial relative abundance (MRA), and environmental and geographical indices (EGIs).
  • Developed and optimized the SCML model to categorize five distinct human activity types: low human-impact, agricultural inputs, domestic inputs, industrial inputs, and dam construction.

Main Results:

  • Microbial assembly was primarily driven by deterministic factors, including environmental conditions and species interactions.
  • Metacommunity structure showed a significant association with human activities in both water and sediment.
  • Human activities were found to increase the susceptibility of interspecific occurrence networks and enhance the influence of environmental factors on microbial community similarity and phylogenetic distance.
  • The optimized SCML model (MBIs + MRA + EGIs) demonstrated high performance, achieving R-squared values of 0.882 for water and 0.924 for sediment.

Conclusions:

  • Microbial community structure is a sensitive indicator of human activities in river systems.
  • The developed ML framework provides a robust tool for identifying and differentiating human impacts on aquatic environments.
  • Findings support improved ecosystem management, sustainable water resource utilization, and effective pollution mitigation strategies.