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Microplastics are tiny plastic particles ranging from 0.1 µm to 5 mm in size, and present in all environments, leading to unavoidable human exposure. Atmospheric microplastics can travel long distances, reaching areas such as mountain ranges, remote islands, and Polar Regions, carried by prevailing wind patterns, atmospheric circulation, and weather processes. The amount of microplastics inhaled through air is significantly higher than those ingested through food and drinking water, making it essential to assess atmospheric microplastic levels. However, assessment of atmospheric microplastics is challenging, involving complex steps such as sampling, extraction, identification, and risk evaluation. Integrating artificial intelligence (AI) with conventional analytical methods enhances atmospheric microplastic research across various stages, from data collection to policy-making. Therefore, this chapter explores the current advantages of AI techniques, including machine learning (ML) and deep learning (DL) in atmospheric microplastic research and discusses the current limitations and future potential of these tools in detail.

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Harnessing Artificial Intelligence for the Detection and Analysis of Microplastics and Associated Chemicals in the Atmosphere

  • 章节

  • 发表日期：

    2026年

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  • S. Veerasingam,
  • Prince Jebedass Isaac Chandran,
  • Hana Ahmed Abdallah Dardeer,
  • M. Ranjani,
  • Hashir Puthkkudi Kuningarath,
  • Saud Ghani,
  • Fadhil Sadooni

摘要

Microplastics are tiny plastic particles ranging from 0.1 µm to 5 mm in size, and present in all environments, leading to unavoidable human exposure. Atmospheric microplastics can travel long distances, reaching areas such as mountain ranges, remote islands, and Polar Regions, carried by prevailing wind patterns, atmospheric circulation, and weather processes. The amount of microplastics inhaled through air is significantly higher than those ingested through food and drinking water, making it essential to assess atmospheric microplastic levels. However, assessment of atmospheric microplastics is challenging, involving complex steps such as sampling, extraction, identification, and risk evaluation. Integrating artificial intelligence (AI) with conventional analytical methods enhances atmospheric microplastic research across various stages, from data collection to policy-making. Therefore, this chapter explores the current advantages of AI techniques, including machine learning (ML) and deep learning (DL) in atmospheric microplastic research and discusses the current limitations and future potential of these tools in detail.