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Suraj Gupta1, Diana Aga2, Amy Pruden3

  • 1The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, Virginia 24061, United States.

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Area of Science:

  • Environmental Science and Engineering (ESE)
  • Data Science
  • Environmental Monitoring

Background:

  • Traditional environmental monitoring methods are evolving with new data acquisition and handling techniques.
  • Machine learning (ML) is emerging as a powerful tool for analyzing complex environmental data.
  • There is a growing need for comprehensive data analytics frameworks in ESE research.

Purpose of the Study:

  • To provide an overview of data analytics frameworks applicable to Environmental Science and Engineering (ESE).
  • To showcase current applications of ML algorithms in ESE through case studies.
  • To propose future directions for integrating data analytics in ESE research and practice.

Main Methods:

  • Overview of data analytics frameworks.
  • Application of ML algorithms in three representative ESE case studies.
  • Analysis of metagenomic data, nontarget pollutant profiling, and anomaly detection in engineered water systems.

Main Results:

  • Demonstrated ML's utility in characterizing antimicrobial resistance.
  • Showcased ML's effectiveness in environmental pollutant profiling via nontarget analysis.
  • Highlighted ML's capability in detecting anomalies in engineered water systems data.

Conclusions:

  • Data analytics, particularly ML, offers enhanced environmental monitoring and management capabilities.
  • The presented case studies illustrate the practical benefits of ML in diverse ESE applications.
  • Advancing the incorporation of data analytics is crucial for future ESE research and application.