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Related Concept Videos

Overview of Nitrogen Metabolism01:20

Overview of Nitrogen Metabolism

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Nitrogen is a very important element for life because it is a major constituent of proteins and nucleic acids. It is a macronutrient, and in nature, it is recycled from organic compounds and stored in the form of  ammonia, ammonium ions, nitrate, nitrite, or  nitrogen gas by many metabolic processes. Many of these metabolic processes are carried out only by prokaryotes.
The largest pool of nitrogen available in the terrestrial ecosystem is gaseous nitrogen (N2) from the air, but this...
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Updated: Jun 29, 2025

Measurement of the Potential Rates of Dissimilatory Nitrate Reduction to Ammonium Based on 14NH4+/15NH4+ Analyses via Sequential Conversion to N2O
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Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach.

Yuying Jia1, Xiangang Hu1, Weilu Kang1

  • 1Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

Environmental Science & Technology
|April 3, 2024
PubMed
Summary

This study identifies key drivers of microbial nitrogen metabolism in rivers using advanced machine learning. Findings reveal critical thresholds and concentration ranges for regulating nitrogen pollution and improving river health.

Keywords:
causal analysisconcentration windowecological safetymachine learningnitrogen metabolismtipping point

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Area of Science:

  • Environmental microbiology
  • River ecology
  • Biogeochemical cycles

Background:

  • Microbial nitrogen metabolism critically influences river pollution and greenhouse gas emissions.
  • The interactive drivers of microbial nitrogen metabolism in rivers remain poorly understood.
  • Existing models often fail to capture complex relationships between environmental factors and microbial processes.

Purpose of the Study:

  • To identify and analyze the interactive drivers of microbial nitrogen metabolism in Chinese rivers.
  • To apply an interpretable causal machine learning (ICML) framework for a deeper understanding of these drivers.
  • To propose precise regulation strategies for microbial nitrogen metabolism, focusing on tipping points and concentration windows.

Main Methods:

  • Analysis of microbial nitrogen metabolism patterns across 105 rivers in China.
  • Utilizing an interpretable causal machine learning (ICML) framework to model complex relationships.
  • Incorporating 26 environmental and socioeconomic factors into the analysis.
  • Identifying tipping points and concentration windows for key substances like dissolved organic carbon (DOC) and nitrate-nitrogen (NO₃⁻-N).

Main Results:

  • ICML models demonstrated superior performance in recognizing complex relationships compared to traditional linear regression.
  • Identified specific tipping points for dissolved organic carbon (DOC) influencing bacterial denitrification (6.2 mg/L) and nitrification (4.2 mg/L).
  • Determined optimal concentration windows for NO₃⁻-N (15.9-18.0 mg/L) and DOC (9.1-10.8 mg/L) to maximize denitrifying bacteria abundance.

Conclusions:

  • The study successfully clarifies the primary drivers of microbial nitrogen metabolism in rivers.
  • The findings support precise regulation of nitrogen pollution through identified tipping points and concentration windows.
  • The integration of ICML and field data provides a robust framework for effective river ecological management.