In a study published in the journal Aerosol Research, Bashir Olasunkanmi Ayinde and colleagues combined a classic boundary-layer wind tunnel with a cutting-edge deep learning system to watch, frame by frame, exactly when individual tyre wear particles tear loose from a glass surface. The particles they tested were pristine tyre wear fragments generated on a laboratory test stand from car tyres supplied by Continental Reifen Deutschland GmbH, meaning they had never been mixed with road dust or aged by weathering. This deliberately idealized setup allowed the team to isolate the fundamental physics of detachment without the confounding complexity of real-world road environments.

The technical challenge was considerable. Tyre wear particles are not neat spheres; scanning electron microscope images reveal jagged, irregular fragments with sharp edges and wildly varying aspect ratios. Traditional image processing methods, which rely on fluorescence thresholds or binary segmentation, struggle with such shapes and demand laborious manual preprocessing prone to operator bias. The Bayreuth team instead trained a YoloV8nano instance segmentation model, a convolutional neural network architecture released by Ultralytics in 2023, to detect each particle as a separate object and delineate its pixel-level boundary. The model detected more than 90 percent of particles across image frames, and after systematic testing of input resolutions from 416 to 1280 pixels, the researchers settled on the highest resolution, which maximized precision and recall at an acceptable computational cost.

Equally important was getting the particles onto the test surface in the first place. The team compared three seeding methods: cap tipping, sieving, and a custom-built low-cost pressurized apparatus that fires a roughly 20-millisecond pulse of compressed air through a dual-orifice assembly, ejecting particles into an airtight chamber where they settle gravitationally onto glass slides. The pressurized method proved the clear winner, producing a uniform near-monolayer deposit concentrated in the 80 to 180 micrometre range with minimal agglomeration. The tipping method, by contrast, yielded clumpy, heterogeneous distributions that degraded segmentation quality, with most particles scoring Dice similarity coefficients of 0.2 or below, while pressurized seeding pushed large particles consistently above 0.8.

With the experimental stage set, the researchers subjected the particle-laden slides to ten stepwise increases in airflow inside a 730-centimetre open-circuit wind tunnel. Friction velocity, the key variable characterizing the wind stress at the surface, was carefully calibrated using both logarithmic wind profile fitting and independent eddy covariance measurements, which agreed within about 4 percent. A high-resolution camera captured images every 10 seconds over an 18 by 12 millimetre field of view, and a tracking script assigned consistent identifiers to particles across frames, declaring a particle detached only when it moved beyond a 1.8-millimetre matching threshold. Eight replicate experiments were run to ensure statistical robustness.

The headline result: tyre wear particles require a bulk threshold friction velocity of approximately 0.26 metres per second to begin detaching, but that threshold is far from uniform across the population. Particles in the smaller size class, 80 to 150 micrometres, detached at a mean threshold of 0.33 metres per second, while larger particles of 150 to 300 micrometres needed roughly 25 percent more wind stress. Shape mattered just as much. Rounded particles with circularity between 0.8 and 1.0 lifted off at 0.33 metres per second, whereas highly irregular particles with circularity between 0.3 and 0.6 resisted until 0.40 metres per second. Across the full analysed range, the most easily mobilized and most stubborn particles differed by a factor of about 1.3 in their detachment thresholds.

The explanation lies in contact geometry. Irregular tyre fragments tend to lie flatter against the substrate, creating larger effective contact areas and multiple points of adhesion. In the force balance framework the researchers used, irregular particles gain a longer adhesion lever arm while simultaneously presenting a shorter drag lever arm to the airflow, meaning the aerodynamic moment must grow substantially before the particle yields. Even protruding edges that extend into faster-moving regions of the boundary layer, which should enhance aerodynamic forcing, proved secondary to the strengthened adhesion. Wind, in effect, preferentially strips away the finer and rounder particles first, leaving coarser, more angular fragments stubbornly anchored in place.

The comparison with spherical particles is perhaps the most striking finding. Drawing on threshold data for smooth polyethylene microspheres measured in the identical wind tunnel in a previous study, the team tested the widely used semi-empirical threshold model of Shao and Lu, originally formulated for spherical sand grains. The model reproduced the microsphere behaviour with standard parameters, but it systematically underestimated the tyre particle thresholds. Even pushing the cohesion parameter to the upper limit of the range accepted for natural dust and sand failed to close the gap. Allowing both the cohesion term and the aerodynamic scaling parameter to vary freely recovered the observed trend, but only with an aerodynamic efficiency parameter substantially larger than values typically used for mineral particles, a reflection of the morphological irregularity and multiple substrate contact points of tyre fragments.

Density, notably, was not the culprit. The tyre wear particles are about 1300 kilograms per cubic metre, compared with roughly 1025 for the polyethylene microspheres, but within the studied size range of 106 to 125 micrometres this difference would shift predicted thresholds by less than 10 percent, whereas the measured difference reached a factor of two. Morphology, not mass, dominates the aerodynamics of these particles. The researchers also note that the particle Reynolds and Stokes numbers in their experiments indicate inertial effects become relevant for the larger fragments, meaning particles do not respond instantaneously to turbulent gusts and require sustained forcing to overcome adhesion and gravity.

The authors are careful to frame their work as a deliberately idealized first step rather than a field-ready parameterization. Real roads add vehicle-induced vibrations, wake turbulence, surface roughness, environmental ageing, and road dust, all of which likely modify detachment behaviour. Still, the implications are significant. Resuspension models used to predict how microplastics travel through urban air currently rest on spherical-particle assumptions that tyre wear particles violate. By demonstrating that size and shape jointly control preferential mobilization, and by showing that deep learning can quantify these dynamics with high accuracy at modest computational cost, the Bayreuth team has laid a mechanistic foundation for the next generation of microplastic transport models, and for future experiments on the rougher, messier surfaces where tyre particles actually live.

Subject of Research: Aerodynamic detachment and transport of tyre wear microplastic particles under controlled wind tunnel conditions

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

Article References: Ayinde, B. O., Babel, W., Olesch, J., Wagner, D., Agarwal, S., Laforsch, C., Brehm, J., Nölscher, A., & Thomas, C. K. (2026). From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel. Aerosol Research, 4(2), 345-372. https://doi.org/10.5194/ar-4-345-2026

Image Credits: AI Generated

DOI: 10.5194/ar-4-345-2026

Keywords: tyre wear particles, microplastics, wind tunnel, deep learning, aerosol research, particle detachment, threshold friction velocity, resuspension, particle morphology, air pollution, image segmentation, environmental transport

The scale of the problem begins with the chemistry. Since the 1950s, PFAS have been manufactured on an industrial scale through two routes: electrochemical fluorination, which yields mixtures of branched and linear isomers, and telomerization, which produces shorter, linear chains with even numbers of carbon atoms. The OECD expanded the formal definition in 2021 to include any fluorinated substance containing at least one fully fluorinated methyl or methylene carbon atom, and a 2018 OECD/UNEP global inventory identified 4,730 PFAS-related CAS numbers that may have been on the world market. The structural secret of their staying power lies in the carbon-fluorine bond itself. With a bond dissociation energy of roughly 485 to 544 kilojoules per mole, a bond length of just 1.35 angstroms, and fluorine’s unmatched electronegativity of 4.0, the bond is among the strongest polar covalent bonds in organic chemistry. Although each individual bond is highly polarized, the symmetric tetrahedral arrangement of multiple carbon-fluorine bonds cancels the dipoles, leaving the perfluoroalkyl tail nonpolar, hydrophobic, lipophobic and nearly immune to attack by external electric fields.

That tail is only half of the story. Every PFAS molecule pairs its water-repellent fluorinated backbone with a polar, hydrophilic head group, typically a carboxylate or sulfonate, giving the compounds an amphiphilic, surfactant-like character that lowers the surface tension of water. This dual nature is precisely what makes PFAS so mobile and so troublesome: the head group drags the molecule into aqueous phases, while the tail refuses to let go of interfaces. In living systems, the consequences are severe. PFAS accumulate in serum proteins and hepatic tissue, disrupt the hypothalamic-pituitary-thyroid axis, interfere with androgen and estrogen signaling, activate the PPAR-alpha nuclear receptor and perturb lipid metabolism, and generate reactive oxygen species that oxidize lipids and damage DNA. Human half-lives stretch over years, and the compounds have been detected in blood, lung and kidney tissue. The United States Environmental Protection Agency responded on April 10, 2024 by finalizing drinking water standards for six PFAS compounds, while long-chain variants were phased out under REACH between 2011 and 2016.

The phase-out, however, created an unexpected paradox. The short-chain PFAS introduced as replacements bioaccumulate less in humans and animals, but they are dramatically more mobile in water, harder to remove in treatment plants, prone to uptake by plants and capable of climbing the food chain. They have already been flagged as substances of very high concern under REACH. The review emphasizes that this structural transition has been addressed only superficially in the literature, even though chain length and functional group chemistry fundamentally control how PFAS partition between air, water, soil and living tissue. Understanding those partitioning behaviors is now the central challenge of PFAS science.

At the air-water interface, the governing parameter is the dimensionless Henry’s law constant, the ratio of a compound’s gas-phase concentration to its aqueous concentration. Neutral, volatile precursors such as fluorotelomer alcohols exhibit constants between 0.31 and 2.82 and can travel thousands of kilometers through the atmosphere before degrading into persistent perfluorinated carboxylic acids. Ionic terminal compounds like PFBS show negligible volatility and remain locked in water. One of the most striking findings compiled in the review concerns sea spray aerosols. As waves burst bubbles at the ocean surface, perfluoroalkyl acids become enormously enriched in the ejected droplets relative to bulk seawater, and field studies show a significant positive correlation between PFAA concentrations and sodium ions, with PFOA correlating at r = 0.77. Aerosol particles a few micrometers in radius can carry PFAS more than 300 kilometers, with atmospheric residence times ranging from days to weeks, effectively allowing the ocean to re-emit these chemicals into the air alongside sea salt.

Beneath the surface, the vadose zone, the unsaturated soil layer between ground level and the water table, acts as a delayed-release reservoir. Two retention mechanisms operate simultaneously: sorption to soil solids and accumulation at air-water interfaces within soil pores. Because adsorption at these interfaces is nonlinear, lower concentrations paradoxically produce higher retardation factors; when PFOA concentrations dropped from 1.0 to 0.01 milligrams per liter, the retardation factor rose from 2.0 to 2.8, and PFOS retardation climbed from 1.35 to 3.69 under comparable dilution. In saturated soil, the soil-water distribution coefficient Kd governs mobility, and it is exquisitely sensitive to pH. At high pH, negatively charged organic matter and minerals electrostatically repel anionic PFAS head groups, weakening sorption; at low pH, protonated clay and organic carbon bind the compounds more tightly. Long-chain compounds such as PFOS show Kd values falling from 42 to 17 liters per kilogram across the pH 3 to 9 range, while short-chain PFBA barely responds, remaining stubbornly mobile throughout.

In living organisms, partitioning follows a different logic. Linear isomers accumulate more readily than branched ones, which are eliminated faster due to their lower octanol-water partition coefficients. Protein binding, quantified through the protein-water partition coefficient, dominates bioaccumulation: PFOS binds human serum albumin with a dissociation constant of just 0.62, stronger than PFOA, while short-chain PFBA interacts weakly. Fish sampled across global studies carry approximately 62 micrograms of PFAS per kilogram, exceeding regulatory advisory limits, and more than 90 percent of human exposure occurs through food and drinking water. Molecular dynamics simulations reveal why chain length matters at the membrane: short-chain PFAS are too stubby to pack against the hydrocarbon tails of lipid bilayers and interact only as a monolayer, whereas long-chain compounds form tightly cohesive van der Waals-packed layers that lodge deep in the membrane core and resist removal.

Degrading these molecules remains one of environmental engineering’s hardest problems. Microbial transformation offers little comfort: the carbon-fluorine bond’s dissociation energy of roughly 480 to 560 kilojoules per mole exceeds what most enzymes can attack, PFAS provide essentially no metabolic energy to bacteria, and degradation typically stalls at persistent shorter-chain intermediates rather than achieving mineralization. A notable 2020 study by Yu and colleagues did document reductive defluorination of two C6 compounds by an organohalide-respiring community, with fluoride release occurring only when the fluorinated compounds served as exclusive electron acceptors, and intriguingly, the activity was driven by minor phylogenetic groups rather than the dominant Dehalococcoides. On the abiotic side, photocatalysis begins with decarboxylation, peeling CF2 units from PFOA one at a time until only carbon dioxide, water and fluoride remain, and vacuum ultraviolet systems boosted PFOA degradation from 10 to 95 percent and defluorination from 17 to 94 percent. But most destructive technologies demand extreme conditions, with complete mineralization requiring temperatures near 850 degrees Celsius, making in situ application impractical.

Conventional wastewater treatment fares little better. Primary clarification merely shuttles PFAS between phases, activated sludge leaves most compounds untouched, coagulation-flocculation fails for short-chain variants, and adsorption and ion exchange simply concentrate the chemicals onto spent media requiring disposal. Reverse osmosis and nanofiltration achieve high removal but at substantial cost, energy demand and membrane fouling risk, and they too transfer rather than destroy the contamination. This is where computational modeling is transforming the field. Machine learning tools such as PFAsorptionML, trained on 1,274 measured soil-liquid distribution coefficients covering 51 PFAS, now predict sorption from molecular weight, hydrophobicity and soil organic carbon with robust validation performance, while quantitative structure-property relationships, symbolic regression and graph-based neural networks promise to extend predictions to the thousands of PFAS never experimentally characterized. The review’s authors caution that artificial intelligence should complement, not replace, mechanistic understanding, since extrapolation to poorly represented compounds and matrices remains unreliable.

The overall picture that emerges is sobering but actionable. Long-chain PFAS retard in soils and sediments, binding to iron oxides and organic matter and acting as slow secondary sources to groundwater; short-chain PFAS race through aquifers, dominate surface waters and slip past treatment barriers. Precursor transformation can even increase apparent concentrations of terminal acids over time, meaning that fate assessments must track chemistry, not just movement. The authors argue that future research must prioritize hybrid degradation systems combining chemical and biological processes, cost-effective capture technologies tailored to short-chain compounds, and AI-guided structure-property models capable of screening the more than 15,000 PFAS variants that no laboratory budget could ever test individually. Until then, the forever chemicals will keep traveling, on sea spray, through aquifers and into the food web, exactly as their molecular architecture dictates.

Subject of Research: Environmental fate, transport and degradation of per- and polyfluoroalkyl substances (PFAS)

Article Title: Fate and transport of per -and polyfluoroalkyl substances (PFAS) in environment

Article References: Seethalakshmi, B., Govindasamy, G., Ganesan, S., & Sivagami, K. (2026). Fate and transport of per-and polyfluoroalkyl substances (PFAS) in environment. Results in Chemistry, 31, Article 103846. https://doi.org/10.1016/j.rechem.2026.103846

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103846

Keywords: PFAS, forever chemicals, carbon-fluorine bond, environmental transport, short-chain PFAS, bioaccumulation, air-water partitioning, vadose zone, photocatalysis, machine learning, wastewater treatment, sea spray aerosols