A Review of Research Progress on Sampling, Extraction and Analysis of Micro(nano)plastics in Environmental Samples

With the widespread use of plastic products, micro(nano)plastics (MNPs) have become a new pollutant of global concern, posing a potential threat to ecosystems and human health. However, the current detection technology system of MNPs has not yet established standardized protocols. The detection of nanoplastics is extremely difficult, which seriously hinders comprehensive assessment of environmental and biological effects. The latest research progress of sampling techniques, pretreatment methods and quantitative analysis strategies of MNPs in different environmental media are reviewed, and the application potential of artificial intelligence technology in solving the existing technical bottlenecks is explored. In terms of sampling, the collection of MNPs in the atmosphere requires combining passive and active sampling to fully capture particle dynamics; water sampling is significantly affected by volume and mesh size, with volume sampling being more advantageous for capturing smaller-sized particles; soil and sediment sampling needs to consider heterogeneity and it is recommended to use core samplers to minimize disturbance, while standardizing sampling depth and volume to enhance data comparability. The selection of pretreatment methods directly impacts MNPs recovery rates and detection accuracy. Density separation and Fenton oxidation demonstrate superior performance in organic matter removal efficiency and plastic structure preservation, while emerging techniques such as elutriation and oil extraction offer new approaches for separating MNPs in complex matrices. In detection and analysis, microscopy and spectroscopy are used to determine the shape, size, and composition of MNPs. Quantitative analysis primarily relies on microscopy and spectroscopy to determine their concentration, while mass spectrometry and total organic carbon analysis are used to determine their mass concentration. Each technique has clear application boundaries. The introduction of artificial intelligence technology has significantly improved the efficiency and accuracy of MNPs automatic classification and quantification. Future research urgently needs to establish standardized analytical protocols for MNPs in different environmental media, develop multi-technique integration schemes, promote the deep integration of artificial intelligence and instrumental detection, and construct open-source shared datasets to provide scientific support for precise governance and control of MNPs environmental pollution.