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From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel
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Every time your car tires roll on the road, they shed tiny plastic particles—and this study used a wind tunnel to figure out exactly how strong the wind needs to be to lift these particles into the air we breathe. The surprising finding: particle shape matters more than expected, since irregular, jagged tire particles stick to surfaces more stubbornly than smooth, round ones, meaning they may need stronger winds to become airborne pollution. This matters because understanding what makes tire microplastics take flight helps scientists better predict—and eventually reduce—our exposure to this widespread but understudied source of air pollution.
The transport of tyre wear particles (TWPs) remains poorly understood despite its recognized contribution to airborne microplastic pollution. We address this knowledge gap by investigating how idealized tyre wear particles detach from an idealized reference surface under controlled wind tunnel conditions. Our study aimed at simplifying the system and isolating the fundamental mechanisms controlling how particle size and shape influence this important initial transport mode. The experiments were conducted in a boundary-layer wind tunnel, where a near-monolayer of particles was seeded onto glass substrates. Time-resolved visual imaging at 0.1 Hz was combined with automatic image analysis using an open-source, deep learning segmentation model, which allows for detecting individual particles, quantifying their detachment, and tracking their size and shape with high model accuracy. For the detachment experiments, pristine tyre wear particles generated on a laboratory test stand with car test tyres supplied by Continental Reifen Deutschland GmbH, providing a well-characterized and idealized particle source. Among the seeding methods tested, we identified a low-cost pressurized seeding approach to produce the most uniform and reproducible particle distribution for subsequent detachment analysis. Across the analysed size range (80 to 300 µm), larger and more irregularly shaped particles exhibited significantly higher threshold friction velocities for detachment than smaller and more rounded particles. Ensemble fits yield a bulk threshold friction velocity of approximately 0.26 m s−1, with size- and shape-resolved detachment threshold velocity values varying by a factor of approximately 1.3 between the most easily detached and most resistant particles. The application of the Shao and Lu semi-empirical fluid threshold model reproduced the size-dependent threshold friction velocity of smooth polyethylene microspheres investigated in a preceding study using the identical wind tunnel, but it underestimates that of tyre wear particles unless the effective cohesion and aerodynamic scaling parameter are increased beyond values typically used for dust and sand. This behaviour is consistent with tyre wear particles experiencing stronger, more effective adhesion than smooth, rounded grains of similar size due to their irregular morphology and multiple contact points with the substrate. The density differences between the tyre wear particles (∼1300 kg m−3) and microspheres (∼1025 kg m−3) showed negligible influence within the studied size range (106 to 125 µm). We conclude that particle morphology, specified by both size and shape, plays a dominant role in controlling the aerodynamic detachment from the idealized glass substrate. This morphological effect was evident across the investigated TWP size range, whereas density effects were secondary within the compared particle types. Because controlled laboratory studies using well-defined particles and simplified surfaces are a necessary step towards isolating these fundamental mechanisms, our findings provide insights for improving microplastic and tyre wear particle resuspension models and highlight the need for future studies on more realistic environmental surfaces and broader particle size and density ranges.
More Papers Like This
From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel
AI summary Read the abstract
Every time you drive, your tires shed tiny plastic particles that can become airborne and float in the air we breathe. This study used wind tunnel tests and AI-powered image analysis to show that particle shape and size—not just wind speed—determine how easily these particles get picked up by wind, with irregularly shaped, larger particles clinging more stubbornly to surfaces than smooth, rounded ones. Understanding these patterns matters because it helps scientists better predict how tire-wear microplastics travel through our air and environment, which is a key step toward assessing their potential health risks down the road.
From seeding to detachment: leveraging deep learning to quantify the transport of tire wear microplastics in a wind tunnel
AI summary Read the abstract
Wind tunnel experiments using AI-based video analysis quantified how tire wear particles detach from surfaces and become airborne, identifying the wind speed thresholds at which detachment accelerates. Since tire wear particles are one of the largest global sources of microplastics, understanding their atmospheric transport helps explain how these particles spread far from roads.
Quantifying the detachment dynamics of microplastic car tire-wear particles using a deep learning framework in a laboratory wind tunnel.
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Researchers used a deep learning framework to analyze video footage of tire wear particles in a wind tunnel to quantify how these irregularly shaped particles detach from road-like surfaces under varying air flows. The AI-based tracking system revealed that detachment dynamics are strongly influenced by particle orientation and surface-flow interactions, filling a gap in airborne TRWP emission characterization.
Comment on ar-2026-2
AI summary Read the abstract
Researchers used a wind tunnel and machine-learning image analysis to measure precisely how much wind force is needed to dislodge tire wear particles — a major source of airborne microplastic pollution — from a surface, finding that larger and more irregularly shaped particles require significantly more wind energy to become airborne than smaller, rounder ones. Standard models developed for sand and dust underestimated the stickiness of tire particles, likely because their irregular, jagged shapes create more contact points with surfaces. These measurements are needed to build better models of how tire microplastics spread through the air from roads.
Comment on ar-2026-2
AI summary Read the abstract
Tire wear particles blown off roads by wind are a significant airborne source of microplastic pollution, but the physics of how wind detaches these irregularly shaped particles from surfaces is poorly understood. This study used wind tunnel experiments and computer vision tracking to measure exactly how much wind force is needed to dislodge tire particles of different sizes and shapes, finding that larger and more irregularly shaped particles require significantly more force to lift than smaller, rounder ones. These measurements can improve models that predict where tire microplastics travel through the air and how they eventually deposit in soils and waterways.
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