How Are Micronanoplastics (MNPs) Collected and Identified in Aquatic Environments? A Pathway Review from Sampling to Spectral─Artificial Intelligence

Abstract

The extensive use of plastic products has established micronanoplastics (MNPs) as a critical emerging contaminant in the global water environment, posing significant threats to ecological integrity and human health. This review synthesizes current methodologies for sampling, pretreatment, and identification of MNPs in water environments and evaluates their respective advantages and limitations. Spectroscopic techniques, mass spectrometry, chromatography, thermal analysis, and scanning electron microscopy form the core analytical toolkit for MNP identification. Each method exhibits distinct strengths in its sensitivity, spatial resolution, and applicability. Additionally, we analyzed the application of artificial intelligence (AI) to microplastic sampling and identification. Researchers believe that integrating AI can significantly enhance collection efficiency and improve the accuracy of spectral analysis in sample identification. Future studies should further advance the development of AI in MNP research to facilitate accurate classification and risk assessment in aquatic systems.