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Polymer–Shape Coupling and Machine-Learning-Derived Microplastic Assemblages in Surface Seawater of the Southern South China Sea
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Scientists analyzed nearly 900 microplastic particles from South China Sea waters and found that certain plastic types tend to show up in specific shapes, like PET plastic mostly forming tiny fibers. This matters because these waters feed seafood we eat, and understanding common microplastic patterns helps researchers better track and predict human exposure risks.
Microplastics (MPs) in offshore surface waters are often characterized using individual particle attributes, whereas the joint organization of size, shape, color, and polymer composition remains poorly resolved. Here, surface seawater samples were collected from 34 stations in the South China Sea. A total of 1007 recovered particles were individually analyzed using micro-Fourier transform infrared spectroscopy (μ-FTIR). Of these, 874 were confirmed as MPs, whereas 133 were identified as non-plastic materials and treated as excluded particles. MP abundance varied substantially among stations, with an overall mean of 1.70 ± 0.65 items/L. Small particles; fibers; transparent, blue, white, and black particles; polyethylene terephthalate (PET); polypropylene (PP); and polyethylene (PE) predominated. Contingency analysis revealed a significant association between polymer composition and shape, whereas associations with size class and color were weaker. A mixed-type machine-learning workflow identified five interpretable assemblages: small PET fibers, medium-to-large PET fibers, PP-rich fibers, PE-rich fragments, and large mixed-polymer fibers. Random Forest interpretation and Gower distance-based partitioning around medoids sensitivity analysis indicated that these assemblages represent recurrent combinations of particle features rather than discrete natural or source-specific classes. Overall, polymer–shape coupling was a major organizing feature of MPs in the investigated offshore waters, highlighting the value of integrated particle identification and interpretable multivariate analysis for resolving MP heterogeneity.
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