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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Policy & Risk Sign in to save

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods

Marine Pollution Bulletin 2025 17 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 68 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Qiannan Duan, Qiannan Duan, Qiannan Duan, Hailong Zhang, Qiannan Duan, Qiannan Duan, Qiannan Duan, Baoxin Zhai, Pengwei Yan, Pengwei Yan, Jianchao Lee, Xiangyi Yang, Xiangyi Yang, Xiangyi Yang, Xiangyi Yang, Xiangyi Yang, Xiangyi Yang, Weidong Wu, Chi Zhou, Chi Zhou Hailong Zhang, Hailong Zhang, Hailong Zhang, Pengwei Yan, Pengwei Yan, Pengwei Yan, Baoxin Zhai, Pengwei Yan, Pengwei Yan, Xiangyi Yang, Xiangyi Yang, Jianchao Lee, Jianchao Lee, Jianchao Lee, Jianchao Lee, Weidong Wu, Weidong Wu, Weidong Wu, Chi Zhou, Chi Zhou Chi Zhou, Chi Zhou Chi Zhou

Summary

This review examines current methods for detecting and measuring microplastics in water, soil, and biological samples, including microscopy and spectroscopy techniques. The authors highlight how artificial intelligence could make detection faster and more accurate. Standardized testing methods and better health risk assessments are needed to understand and manage the dangers microplastics pose to human health.

Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological samples, assessing pre-treatment procedures like digestion and separation. It examines the application and limitations of identification techniques, including microscopic observation, spectroscopic analysis, and thermal analysis. The review highlights the potential of AI technology to enhance detection efficiency and precision. It underscores the necessity of standardized protocols for consistent sampling and detection, and the importance of systematic risk assessment methodologies for managing environmental and health risks associated with MPs. The paper concludes with recommendations for future research, emphasizing the standardization of methods, advancement of detection technologies, integration of AI, and comprehensive health risk assessments. This review will be helpful for researchers to comprehensively understand the current main detection technologies and risk assessment methods of the MP, and to accelerate the establishment of an artificial intelligence regulatory framework for MPs.

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