Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bioremediation00:46

Bioremediation

18.2K
Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.
18.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Electrochemical Generation of Nonradical Hydrotrioxide for Sustainable Water Purification.

Environmental science & technology·2026
Same author

Profiling Active Low-Abundance Microbes in As/Sb-Contaminated Soils via d-Amino Acid-Based In Situ Labeling.

Environmental science & technology·2026
Same author

Function-Oriented and Waste-Derived Single-Atom Catalysts for Water Purification.

Chemical reviews·2026
Same author

Molecular-Level Perturbations of Dissolved Organic Matter Driven by Episodic Firecracker Residue Leaching.

Environmental science & technology·2026
Same author

Self-driven tandem alcoholysis for full upcycling of waste polycarbonate plastics.

Nature communications·2026
Same author

Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity.

Environmental science & technology·2026

Related Experiment Video

Updated: Jun 23, 2025

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.2K

Machine Learning-Assisted Optimization of Mixed Carbon Source Compositions for High-Performance Denitrification.

Yuan Pan1, Tian-Wei Hua1, Rui-Zhe Sun2

  • 1CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.

Environmental Science & Technology
|June 20, 2024
PubMed
Summary

Optimizing mixed carbon sources boosts wastewater denitrification. A new machine learning method rapidly identifies ideal blends, improving nitrogen removal and reducing costs in wastewater treatment plants (WWTPs).

Keywords:
heterotrophic denitrificationhigh-throughput methodmachine learningmixed carbon sourcesnitrate uptake ratewastewater treatment plant

More Related Videos

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis
10:44

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

9.9K
Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.4K

Related Experiment Videos

Last Updated: Jun 23, 2025

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.2K
Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis
10:44

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

9.9K
Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.4K

Area of Science:

  • Environmental Science
  • Microbiology
  • Machine Learning

Background:

  • Municipal wastewater treatment plants (WWTPs) require efficient denitrification to manage nitrogen pollution.
  • Optimizing carbon sources is crucial for enhancing denitrification but traditional methods are inefficient.
  • Mixed carbon sources offer potential for improved denitrification and cost reduction.

Purpose of the Study:

  • To develop a machine learning-assisted high-throughput method for rapid identification and optimization of mixed carbon sources for denitrification.
  • To assess the denitrification potential of optimized mixed carbon sources compared to single sources.
  • To investigate the microbial mechanisms underlying enhanced denitrification with mixed carbon sources.

Main Methods:

  • A high-throughput method was used to create a mixed carbon source denitrification dataset from a local WWTP.
  • An XGBoost machine learning model was trained using carbon source composition and sludge type as inputs.
  • Kinetic experiments, long-term reactor operations, and metagenomic analysis were performed to evaluate performance and microbial communities.

Main Results:

  • The machine learning model accurately predicted nitrogen removal rates and microbial growth, identifying optimal mixed carbon sources.
  • Predicted mixed carbon sources demonstrated significantly enhanced denitrification potential compared to single carbon sources.
  • Metagenomic analysis revealed increased diversity and complexity of denitrifying bacterial networks with mixed carbon sources.

Conclusions:

  • The developed machine learning-assisted method efficiently optimizes mixed carbon source compositions for WWTPs.
  • Mixed carbon sources enhance denitrification potential through synergistic effects and by promoting a more diverse microbial community.
  • This approach offers a novel strategy for improving wastewater treatment efficiency and understanding denitrification mechanisms.