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A multimodal Raman – XRF approach for identifying marine coating microplastics in environmental samples
Summary
Ships continuously shed microplastics from their hull coatings into the ocean, but these coating-derived particles are difficult to distinguish from other plastics using standard methods. This study developed a combined approach using both Raman spectroscopy and X-ray fluorescence (XRF) to identify marine coating fragments by their chemical fingerprints, achieving much better discrimination between paint types than either method alone. The technique can be deployed using portable instruments in the field, offering a practical tool for tracking and attributing paint-derived microplastic pollution in coastal and open-ocean monitoring.
Marine coatings represent an emerging but poorly characterized source of ocean microplastics. These particles are difficult to distinguish from conventional plastics due to overlapping physical appearances and spectral features. In this study, we developed and tested a combined approach using Raman spectroscopy and X-ray fluorescence (XRF) to identify and classify marine coating fragments and distinguish them from common ocean microplastics. Raman PCA explained 63 % of variance (PC1 = 34 %, PC2 = 29 %) but showed substantial spectral overlap among several antifouling and non-antifouling coatings, limiting discrimination based solely on organic vibrational signatures. In contrast, integration of XRF elemental features with Raman data increased the explanatory power of PC1-42.5 %, producing more compact and well-separated clusters among epoxy, antifouling, anticorrosive, and topcoat coatings. The integration of Raman and XRF substantially improved classification accuracy, clearly separating copper-rich, zinc-based, epoxy, and topcoat coatings, and overcoming ambiguities present in single-method analyses. Field application of this combined approach is feasible using portable Raman and XRF instruments, offering a powerful tool for high-throughput environmental monitoring. This study highlights the importance of multimodal spectral libraries and complementary techniques for reducing misclassification and improving source attribution of marine particles, thereby advancing strategies to assess and mitigate the impacts of coating-derived microplastics in ocean environments.