From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel

Titelangaben

Ayinde, Bashir Olasunkanmi ; Babel, Wolfgang ; Olesch, Johannes ; Wagner, Daniel ; Agarwal, Seema ; Laforsch, Christian ; Brehm, Julian ; Nölscher, Anke ; Thomas, Christoph:
From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel.
In: Aerosol Research. Bd. 4 (2026) Heft 2 . - S. 345-372.
DOI der Verlagsversion: https://doi.org/10.5194/ar-4-345-2026

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Abstract

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.