The experiments were conducted in an open-circuit boundary layer wind tunnel (Fig. ), which is identical to the one used by . The wind tunnel is 730 cm long. The test section is approximately 300 cm long and has a rectangular cross-section measuring 54 cm × 27 cm. The inner walls of the wind tunnel are lined with polystyrene foam, and flow is controlled by a set of 12 fans 26.5 cm in diameter (RAB O TURBO 250, DALAP GmbH; Germany), supported by a stepless transformer (LSS 720-K, Thalheimer Transformatorenwerke GmbH, Germany) controlled and adjusted by an automated robotic system, which enabled smooth, continuous, and reproducible variation in the output voltage up to the maximum level. Ambient laboratory air is drawn into the tunnel by the fan system and passes through a honeycomb flow straightener to maintain uniform and well-conditioned flow conditions entering the contraction zone before entering the test section. The flow is subsequently discharged into the outside air. A HEPA filter (EU2 classification, 10 µm pore size; Erwin Telle GmbH, Germany) is installed at the outlet of the wind tunnel to remove residual particles from the flow before discharge.

The upstream fetch length is approximately 170 cm long and covered with an aerodynamically smooth lining at the bottom, onto which passive roughness elements in the form of LEGO^® bricks were placed in a symmetrically staggered pattern along the entire section of the test section to simulate the desired rough wall-boundary conditions. To maintain a uniform surface consistency, a detachable substrate mount was positioned along the span of these roughness elements such that the mounted substrate was flush with their top. The detachment experiments were conducted with standard laboratory glass slides (Thermo Fisher Scientific), measuring 75 mm × 25 mm, oriented with the long side perpendicular to the streamwise flow direction. A high-resolution camera (Sony Alpha 7RII) equipped with a long-distance microscopy lens (CF-1 lens, K2 DistaMax Infinity, USA) was placed directly above the substrate mount with an adjustable object distance of approximately 35 cm to capture detachment events at 10 s intervals, lit by a 50 W LED strip to illuminate the test section uniformly. Moreover, a three-dimensional hot-wire constant-temperature anemometer (model 55P095, Dantec dynamics), mounted on a traverse system, measured turbulent airflow statistics. The probe was calibrated using a dedicated probe calibrator with temperature correction applied during calibration. Directional sensitivity was accounted for using the default manufacturer's calibration coefficient. A pitot tube was used as the velocity reference during calibration, and the probe was manually aligned with the mean streamwise flow direction. A temperature probe for the constant-temperature anemometer system (model 90P10, Dantec Dynamics) was also installed. In addition, a combined relative humidity sensor and temperature sensor (Model HC2A, Rotronic) was installed to measure the air temperature and relative humidity of the system and compare them across runs.

Pristine tyre wear particles used in this study were supplied by Continental Reifen Deutschland GmbH and were generated on a laboratory test stand. The material consists of tread-based compositions made from natural and synthetic rubber, with mineral fillers and standard additives, including 6PPD and diphenylguanidine. The particles have not undergone road wear or environmental ageing and have not been mixed with road dust; therefore, they are treated as an idealized, well-characterized reference material. For the detachment experiments, TWPs from car tyres were used. Additional particles from a truck tyre composition were used only to increase the diversity of particle morphologies in the image dataset for training and optimizing the deep learning framework. Representative high-resolution imaging of the car TWPs (Fig. ) was obtained for visual inspection of the material using a scanning electron microscope (SEM). These particles exhibit irregular shapes and jagged textures with numerous sharp edges and span a broad range of sizes and aspect ratios.

As a spherical reference for comparison, we used previously obtained threshold data for polyethylene microspheres of similar size cohorts from a preceding study conducted in the same wind tunnel . These microspheres had a density of approximately 1025 kg m⁻³. SEM images of the polyethylene particles (see ) show nearly perfectly spherical microspheres with smooth surfaces, in clear contrast to the irregular TWP aggregate. The density of the car TWPs is about 1300 kg m⁻³, as specified by the manufacturer and consistent with values reported for comparable tyre and tread wear particles .

Prior to conducting the detachment experiments, three different particle seeding methods were evaluated for their ability to deposit particles homogeneously and as a monolayer onto the test substrate. These methods were assessed with the aim of minimizing particle agglomeration, which can introduce significant bias in detachment measurements if not properly controlled . Some previous studies used a simple cap tipping method for particle deposition. Among the recent examples is the study by , which employed this technique in MP detachment experiments using fluorescent microspheres. In their method, microspheres were stored in a sealed vial, which was flipped once to allow some particles to adhere to the inside of the cap. The cap was then unscrewed and gently tipped, allowing a small quantity of particles to fall gently onto the glass substrate (Fig. a). This method requires no specialized equipment beyond the vials and substrate, and its simplicity and gentle deposition make it attractive for detachment studies. Despite some limitations, such as uneven particle distribution and low deposition throughput, we evaluated it for our TWP seeding trials. Another easy and widely used approach in MP particle size fractionation and recovery is through sieving, which we also adopted for particle seeding because it is simple, reproducible, and well suited to laboratory-controlled experiments . Dried, stacked stainless sieves with mesh sizes of 200 µm (upper) and 150 µm (lower) were employed to isolate particles that were smaller than 200 µm onto the substrate (Fig. b). During the sieving process, gentle manual tapping was applied to the frame so that particles adhering to the mesh could easily dislodge, thereby reducing particle clusters.

Due to the lumpiness of particles aggravated by their high rubber content, we developed a low-cost pressurized seeding apparatus to reproducibly deposit a monolayer of particles while minimizing the agglomeration on substrates (Fig. c). Particles are loaded onto a porous media filter housed in a custom, adjustable PVC socket and overlaid with a 600 µm sieve; together, these layers form a dual-orifice assembly that promotes uniform particle entrainment. Laboratory compressed air (6 bar) is regulated by a control valve and stored in a 2 L stainless steel pressure buffer (Festo CRVZS-2 HTI GmbH) fitted with a safety valve to cap the internal pressure at 2 bar. A solenoid valve (Bürkert type 0330) with a control switch then releases a short (∼ 20 ms), high-velocity air pulse through the dual orifices into an airtight acrylic chamber secured by a saddle clamp. The substrates are placed inside the chamber so that particles ejected by the pulse of pressurized air gravitationally settle on them. This system also allows simultaneous seeding of multiple substrates in a single operation, improving the throughput, reproducibility, and efficiency. Finally, all three seeding methods were compared by placing the seeded substrates in the wind tunnel and capturing images at various orientations and replicate trials.

To analyse detachment of TWPs frame by frame, we used YoloV8 model, a deep-learning-based instance segmentation model released by Ultralytics in January 2023 . In this study, instance segmentation was referred to as when each individual particle was detected as a separate object, and the pixel-level boundary was delineated. This is important as TWPs vary in size and shape, and accurate particle outlines are required to detect the spatial deposition pattern and subsequently to quantify the detachment dynamics. The YoloV8n model was selected for its favourable trade-off between segmentation accuracy and computational efficiency in frame-to-frame analysis. Although larger YoloV8 models can achieve higher detection accuracy, they require greater computational resources. For this application, the model provided sufficient accuracy with efficient processing. Additional details on the model architecture and comparisons among other model families are provided in Appendix .

The trade-off between the performance and image input resolution for the deep learning model is evident, with detection accuracy improving consistently at higher resolutions (Fig. ). The evaluated metrics precision, recall, and mean average precision at 50 % overlap threshold (mAP@50) and across multiple overlap thresholds (mAP@50-95) improve steadily with higher resolution during fine-tuning. The lowest resolution at 416 pixels produces the weakest performance, the highest number of missed detections, and the lowest confidence value. As the image size increases to 640 and 960 pixels, these metrics improved, and at 1280 pixels, it achieved the maximum performance in precision and recall, indicating that the model detects more objects with a greater confidence value as the image size increases for both outputs. In particular, the stricter metric (mAP@50-95) shows a notable improvement at the highest resolution, especially for mask prediction, reflecting more accurate object localization. This suggests that finer object details and boundaries are better captured at higher input resolutions. However, such behaviour is expected as altering the image size greatly affects the accuracy of any model; hence adapting a higher-resolution image or images similar in resolution to those used during inference helps preserve more details and yield higher segmentation accuracy . Although a higher-resolution image size increased computational cost and training time, the gain in segmentation performance was substantial. While larger YoloV8 models can achieve slightly higher precision than the smallest model , the improvement is modest relative to the more than a 2-fold increase in computational load. Hence, 1280-pixel input resolution was adopted for subsequent analysis based on the comparative assessment of the observed gains in detection accuracy and segmentation performance relative to the associated increase in computational cost during training.

The three seeding methods employed to deposit TWPs onto the substrates yielded different particle distribution characteristics that influenced segmentation performance (Fig. ). The tipping method produced the most heterogeneous distribution with pronounced agglomeration, resulting in a lower-quality mask with most data points clustered at Dice coefficient ≤0.2 across most circularity and larger particles (≥200 µm). Sieving demonstrated improved performance with reduced agglomeration, shifting the distribution of the metric upward, particularly for particles with C<0.6. The pressurized methods yielded the optimal dispersion, producing the clearest masks with large particles (≥200 µm) consistently exceeding Dice coefficients of 0.8, and even the smallest size category exhibited moderate segmentation accuracy. This seeding method disperses particles more uniformly across the substrate, reducing agglomerates, as seen from the dense contour in Fig. c, with smaller density exceeding the largest particle size category. Across all the methods, particle size and shape modulate segmentation accuracy. The larger the particle size, the more distinctive the edges and pixels are, so the Dice coefficient rises steadily with equivalent diameter. Conversely, smaller particles and those closer to a perfect sphere tend to give little textural details for the network to learn from, explaining why in Fig. c some small points remain near the lowest Dice coefficient. The particle size distributions across the three seeding methods also reveal distinct uniformity and agglomeration behaviours, with all methods exhibiting unimodal right-skewed patterns (Fig. ). The pressurized method produces particles concentrated within the 80–180 µm range, with higher relative frequency and density at the peak, indicating a greater population which is evenly spread with optimized particle sizes. This distribution exhibits reduced right-skewness, suggesting fewer oversized clusters. In contrast, the tipping method yielded a broader particle size distribution, characterized by higher right-skewness and more frequent large clusters, reflecting increased agglomeration and spatial heterogeneity. Benchmarking against commercial equipment (Microtrac Retsch GmbH, Fig. , Appendix B), which provides the laboratory reference size distribution predominantly in the 125–200 µm range, confirms that the pressurized method distribution aligns with the expectation from the laboratory analysis, establishing an optimal baseline for future detachment studies.

The experimental setup captured TWP detachment events across an 18 mm-by-12 mm field of view on the glass surfaces, showing particles of different sizes and shapes (Fig. a). The deep learning framework detected over 90 % of TWPs across image frames (Fig. b), demonstrating robust individual particle tracking with unique particle identifiers. A Dice coefficient threshold of 0.5 validated segmentation masks, ensuring that the same particle was correctly tracked until it either detached from the glass surface or exited the camera frame. Particle detachment may occur through rolling, sliding, or lift-off . However, our optical tracking approach focused explicitly on identifying detachment events rather than distinguishing among these motion modes. Some particles underwent small positional migrations before complete frame exit, requiring careful tracking to distinguish between temporary movement and actual detachment. In light of this observation, we employed a distance-based particle-matching technique using a threshold of 200 pixels, equivalent to 1.8 mm, to associate particles across frames. This defined distance ensured that particles maintaining their identification while migrating within the field of view were not prematurely classified as detached. A particle was only considered to be detached when it had moved beyond this matching threshold and could no longer be tracked across consecutive frames.

The time evolution of the detachment fraction for all eight replicate experiments is shown as a function of time (Fig. , Appendix D). Notably, the detachment behaviour varied among the replicates. The remaining fraction on the substrate after the 30 min exposure time ranged from 57 % to 86 %. Such incomplete removal has been reported in detachment studies and depends on different factors, including exposure time and velocity increment during stage change . In general, the TWPs were not detached at a single value of u*, but over a range of incremented u* values, with the cumulative detached fraction differing across replicates. In some cases, a sharp initial jump in particle release was observed immediately after the fan started, which is attributed to tunnel vibrations dislodging the most weakly bound particles first. In addition, the transient spin-up of the flow during stage changes may have contributed, as the friction velocity increases sharply at some stage change before reaching a quasi-steady phase. This interpretation is consistent with previous studies showing that weakly adhered particles are removed earlier, whereas more strongly adhered particles require larger or more intermittent aerodynamic forcing for detachment due to the stochastic nature of the turbulent flow . Furthermore, the observed detachment behaviour may have been influenced by local micro-surface roughness and humidity conditions. Although humidity can influence particle detachment , the experiments were conducted at a moderate relative humidity below 45 %, and this effect was not examined explicitly in the present study. Since the glass substrate used here was relatively smooth, sheltering effects were likely reduced compared with rougher surfaces , and adhesion was therefore expected to depend primarily on close-contact forces such as van der Waals and electrostatic interactions .

Detachment rate spans a velocity range because adhesion is heterogeneous. Since detachment occurs across a broad range of friction velocity, it is appropriate to define a representative threshold friction velocity (u*th) at which a significant fraction (50 %) of particles have detached . The detachment curve shows a monotonic increase in the ndet with friction velocity, as described by the two models (Fig. ). Applying both models to the ensemble-binned data yielded very similar characteristic thresholds. The global logistic and linear-regression fit yielded fluid thresholds of 0.25 and 0.26 m s⁻¹, respectively, and the mean threshold of the eight replicates was 0.26±0.06 m s⁻¹. The spread across the binned points could be argued to indicate that TWPs experience broadly similar effective adhesion on the uniformly smooth glass substrate, suggesting that variability may be driven by differences in local contact geometry and microscale adhesion.

Particle morphology is another factor that influences detachment. Across all experiments, TWPs spanned a broad range of sizes and shapes (Fig., Appendix D), and the comparison of the initial and final population indicates that detachment does not occur uniformly across the distribution, but preferentially from a particular sub-range (Figs. c and d). Equivalent diameter ranges from 80–300 µm, with most particles between 100–250 µm and a peak between 120–160 µm. Circularity varies from 0.3 (highly irregular) to 1.0 (nearly rounded), with most particles clustering mainly between 0.6–0.8 and only a few fractions being nearly rounded.

The threshold friction velocity estimated from the detachment curves varied systematically with particle size (Fig. a–c). For the smaller-sized bin (80–150 µm), the mean threshold across samples was 0.33±0.05 m s⁻¹, whereas the larger-sized bin (150–300 µm) had a mean threshold value of 0.41±0.03 m s⁻¹. Thus, particles in the upper size bin detached at friction velocities roughly 25 % higher than those from the lower size bin, even though both belong to the same bulk population. This size dependence is consistent with findings from , who reported lower thresholds for smaller (81–89 µm) than for larger (110–210 µm) irregular volcanic ash particles for both laboratory experiments and field studies. This also agrees with the semi-empirical force balance model of , from which it can be inferred that threshold friction velocity reaches a minimum around 75–100 µm, and tends to increase with particle size for larger particles as the effect of gravity becomes increasingly important relative to cohesive forces. The TWP sizes investigated in this study fall largely within this gravity-dominated regime, so a monotonic increase in threshold with equivalent diameter is expected and is reflected in the estimated fluid threshold values.

Particle shape quantified by circularity also had a pronounced effect on detachment rate (Fig. a–d). More rounded TWPs (C=0.8–1.0) detached at lower threshold values (0.33±0.02 m s⁻¹), the intermediate class (C=0.6–0.8) at 0.37±0.03 m s⁻¹, and the highly irregular TWPs (C=0.3–0.6) at higher threshold values (0.40±0.02 m s⁻¹). This monotonic increase in these threshold friction velocities with decreasing circularity suggests that irregular TWPs are more resistant to aerodynamic removal than rounded ones. A possible explanation is that irregularly shaped particles can form stronger adhesive contacts with the surface because of their larger effective contact area and multiple contact points. A highly irregular TWP is likely to lie flatter on the substrate, and its deformable nature can further increase its contact area. By contrast, more rounded particles can have a smaller contact footprint and therefore a lower degree of adhesive interaction. Although irregular particles can experience similar or even slightly enhanced aerodynamic forces due to protruding parts extending into higher-velocity regions of the boundary layer, this effect appears to be secondary to their stronger adhesion. In terms of the conceptual two-dimensional force balance schematic shown in Fig. , we explain this behaviour as a consequence of the larger adhesion lever arm (la) of irregular particles, caused by their extended footprint, while simultaneously exhibiting a smaller drag lever arm (ld) and a reduced area exposed to the airflow. Depending on the particle orientation and contact geometry, the aerodynamic moment (Fd⋅ld), with a potential contribution from lift, can be insufficient to overcome the adhesion moment (Fa⋅la), which would require a higher threshold friction velocity for detachment.

These findings also align with other studies discussing particle shape effects. reported that irregularly shaped particles showed significantly lower detachment rates compared to glass microspheres of the same cohort under similar conditions. Furthermore, Fig.  extends our comparison to polyethylene microspheres of the same size cohort (115 µm). In other words, under identical conditions, a polyethylene microsphere requires approximately half the shear force needed to lift a similarly sized TWP. This comparison highlights how variability in particle morphology and particle roughness can alter the balance of forces. Thus, the elevated threshold observed for TWPs arises due to the effect of a broad range of morphological differences.

The magnitude of aerodynamic drag and lift can also be influenced by the flow pattern around the particles. In our study, we also estimated the particle Reynolds number (Rep) as 2≤Rep≤80 and the Stokes number (St) as 7≤St≤100, indicating that Stokes drag alone is not strictly applicable across the full parameter range and that Reynolds-number-dependent drag corrections become relevant . The fluid motion around the vicinity of these particles is influenced mainly by inertia, especially towards the larger particle size class analysed. This feature implies that particle responses to rapid near-wall flow fluctuations are not expected to be instantaneous; rather, stronger and more sustained near-wall forcings are required to overcome adhesion and gravity. This is also consistent with the higher fluid threshold observed for the larger particle class in our findings. It is also noteworthy that detachment motion can occur via rolling, sliding, or lift-off, and many studies and models developed for spherical particles emphasize rolling as an efficient incipient-motion pathway . However, for irregular particles, geometric variation and contact configuration can shift the preferred mode towards sliding, as reported by in their recent studies on the simulations of irregular micro-particles . For particles with non-negligible inertia, direct lift-off under saltation conditions cannot be ruled out. In principle, micro-impacts with surface asperities and particle-to-particle collisions can impart a vertical impulse, and lift-off may occur when the associated kinetic energy exceeds the adhesive potential well . Owing to the substantial variability in TWP morphology, the dominant detachment motion is expected to be complicated. In this study, our tracking approach resolved detachment events but was not designed to unambiguously classify the specific motion mode.

Across the investigated size range, the Shao and Lu fits reproduce the observed trend that TWPs require a higher friction velocity threshold to initiate detachment compared to the polyethylene microsphere (Fig. ). For the microsphere reference data, the model with the standard empirical functions (see Eq. ) returns γ to 3.5×10-4 N m⁻¹, which lies comfortably within the range reported for dry, loose dust and sand particles . This confirms that the reference parameterization is appropriate for the polyethylene microspheres. In contrast, applying the same parameter constraints to the TWPs clearly underestimates the measure thresholds. Even when γ is pushed to the upper limit of the empirical range, 5.0×10-4 N m⁻¹, the predicted fluid threshold curve remains below the observations, indicating that the unmodified Shao and Lu formulation does not fully capture the detachment behaviour of TWPs. Allowing γ to vary freely improves the fit, but the resulting value (1.6×10-3 N m⁻¹) is about 3 times the upper limit proposed for the natural dust and sand particles (γ=1.65×10-4 to 5×10-4 N m⁻¹; ). Such a magnitude is difficult to reconcile with independent estimates of cohesive interparticle forces, including van der Waals forces for dry particles and typical polymeric surfaces, and would imply a strong cohesive contribution even in the size range where cohesion already dominates (De<75 µm). Meanwhile, when both AN and γ are treated as free parameters, the fitted curve recovers the expected increase in the fluid threshold with particle size in the gravity-dominated regime. The best fit values (AN = 0.25 and γ = 1.0×10-4 N m⁻¹) remain within the physically plausible bounds but require a substantially larger aerodynamic efficiency parameter. Similar variations in dimensionless threshold coefficients were reported in aeolian studies, where analogous parameters (AB, Aft) vary with particle threshold Reynolds number (Re*t), bed roughness, interparticle forces, and moisture effects . In this context, the higher AN for TWPs can be interpreted as an effective parameter that reflects morphological irregularity, multiple particle contact points with substrate, and micro-roughness.

Regardless of the calibration strategy, the TWP curve consistently lies above the polyethylene microsphere curve. The density difference between TWPs and polyethylene microspheres is too small to account for this offset alone. Within the Shao and Lu threshold framework, such a density contrast would influence the predicted threshold friction velocity by less than 10 %, whereas the measured thresholds differ by up to a factor of 2. This observation aligns with previous studies which emphasize that particle geometry and roughness enhance the effective adhesion strength of irregularly shaped particles compared to smooth, rounded grains of similar size. In contrast, for particles of comparable size, reported only a 29 % increase in threshold friction velocity between polyethylene and borosilicate microspheres, whereas the corresponding difference between these polyethylene microspheres and TWPs in the present study is 57 %. The approximately 2-fold-larger offset observed suggests that density alone cannot account for the higher thresholds observed for TWPs. Similarly, a minor role of density within a restricted parameter range was reported for particle resuspension under different forcing conditions . Given the limited number of size bins available for the TWPs, the present calibrated parameters should be regarded as a first step toward evaluating the Shao and Lu framework for TWPs. However, no robust TWP dataset was available at equivalent diameters around 10–30 µm, which limits direct evaluation of the small-particle regime, where cohesive effects are expected to be increasingly important. Nevertheless, the present results reveal two robust findings from our idealized experiment: TWPs on smooth glass require considerably higher fluid thresholds than polyethylene microspheres of comparable size, and the magnitude of this offset is best explained by enhanced effective cohesion and reduced aerodynamic leverage arising from TWP morphology rather than from density differences.