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Accumulation and Distribution of Fluorescent Microplastics in the Early Life Stages of Zebrafish
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Leveraging machine learning for microplastic detection, global distribution analysis, and management.

Sheetal Kumari1, Prabhat Kumar Patel2, Manish Kumar3

  • 1School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, PR China.

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|January 28, 2026
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Summary
This summary is machine-generated.

Artificial intelligence (AI) combined with analytical techniques enhances microplastic (MP) detection and characterization. This study explores AI

Keywords:
Aquatic environmentMachine learningMicroplasticMicroplastic managementMicroplastic monitoring

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Microplastic (MP) pollution is a global environmental challenge requiring advanced detection and management strategies.
  • Significant MP pollution hotspots identified in India (Netravathi River), China (Wuhan lakes), and the USA (San Francisco Bay).

Purpose of the Study:

  • To review and emphasize the integration of Artificial Intelligence (AI) and Machine Learning (ML) with analytical techniques for microplastic research.
  • To explore foundational concepts, data resources, preprocessing methods, and limitations of ML algorithms in MP identification, detection, distribution, and management.

Main Methods:

  • Combining AI with analytical techniques such as Raman spectroscopy (RS), Fourier transform infrared spectroscopy (FTIR), image processing (IP), and hyperspectral imaging (HSI).
  • Application of Machine Learning (ML) algorithms with FTIR, Raman, and HSI for enhanced MP detection and characterization.

Main Results:

  • AI-driven analytical techniques significantly improve the efficiency, accuracy, and scalability of microplastic detection.
  • ML algorithms integrated with FTIR, Raman, and HSI achieved high detection efficacy (99%, 99.1%, and 97% respectively).

Conclusions:

  • AI and environmental science offer revolutionary tools for real-time monitoring and mitigation of microplastic pollution.
  • Further research into combining ML technologies is crucial for advancing microplastic research and preserving ecosystem health.