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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental

Binbin Hu1, Yaodan Dai2, Hai Zhou1

  • 1College of Electronic and Information, Southwest Minzu University, Chengdu 610225, China; Key Laboratory of Electronic Information Engineering, Southwest Minzu University, Chengdu 610225, China.

Journal of Hazardous Materials
|June 11, 2024
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) offer powerful tools for analyzing microplastics (MPs) in the environment. This review details ML methods for MP identification, highlighting future research directions for more effective environmental monitoring.

Keywords:
Artificial intelligenceEnvironmental healthMachine learningMicroplastics

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Microplastic (MP) pollution is a growing global concern, necessitating advanced analytical techniques.
  • Current research on MPs requires robust methods for chemical composition, shape, distribution, and impact assessment.

Purpose of the Study:

  • To provide a comprehensive overview of machine learning (ML) methods applied to microplastic (MP) analysis.
  • To discuss data sources, preprocessing, algorithms, and limitations of ML in MP research.
  • To identify future research prospects in ML-driven MP analysis.

Main Methods:

  • Review of existing literature on machine learning applications for microplastic identification and analysis.
  • Detailed discussion of data preprocessing, algorithm principles, and limitations.

Main Results:

  • Machine learning methods show excellent performance in analyzing MPs in soil and water.
  • Current ML methods have limitations in various MP task analyses.

Conclusions:

  • Future research should focus on developing large, generalized MP datasets.
  • Designing high-performance, low-complexity algorithms and evaluating model interpretability are crucial for advancing MP research.