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A Method for the Detection of Tire Wear Microplastics in Zebrafish Guts by Laterally Resolved LA-ICP-MS-Based Elemental Fingerprinting and Chemometrics
Summary
Researchers developed a tire wear particle detection method using laser ablation–inductively coupled plasma–mass spectrometry combined with a random forest classifier trained on multi-element signatures, achieving pixelwise identification of tire particles in zebrafish gut tissue at 7-µm resolution — overcoming the limitations of FTIR and pyrolysis-GC-MS for carbon-black-containing particles.
Tire wear particles (TWPs) are generated by mechanical abrasion of tires on road surfaces and represent a significant source of microplastic pollution, contributing an estimated 30-50% of total microplastic emissions in Europe. Due to their persistence and limited biodegradability, TWPs accumulate in terrestrial, aquatic, and atmospheric environments. However, their detection and quantification remain challenging: carbon black hampers FTIR analysis, while pyrolysis-GC-MS yields only bulk mass data without information about particle abundance or size distribution. We present a novel approach to address this gap using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) combined with elemental fingerprinting and machine learning. We apply this method to zebrafish gut tissue to differentiate TWPs from biological tissue, paraffin-embedded material, and other naturally occurring particles. A random forest model trained on multielement signatures enables pixelwise classification of imaging data recorded with 7-μm lateral resolution despite the complexity of both TWP and biological matrices. Our results demonstrate the potential of LA-ICP-MS elemental imaging as a sensitive tool for TWP detection in biological tissue, providing new opportunities for monitoring and ecotoxicological studies.