Microplastic and tyre wear particles at a highway: a case study from Norway

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Using µFTIR and Py-GC/MS, we characterised the polymer composition, particle sizes, and estimated mass across matrices. Polypropylene dominated in road runoff and road dust, while polyester and polyamide were most frequent in air samples. MP concentrations were highest in road dust [567–4250 counts/m 2 or 31–291 µg/m²], followed by road runoff [65–598 counts/L or 0.4–11.7µg/L] and air [5–12 counts/day or 0.16–0.22 µg/day]. TWP concentration was below the detection limit in the air samples, while for road runoff it was in the range 281–1470 µg/L, and for road dust it was 33500–178777 µg/m 2 . Although meteorological parameters such as wind speed and precipitation must influence airborne MP capture, no strong correlations were identified. The results suggest that road runoff and road dust better reflected local traffic-related emissions, while air samples were more affected by atmospheric transport. This highlights the need to consider environmental context and sampling strategy when assessing airborne MP pollution. Our findings emphasize the importance of multi-matrix approaches to understand the distribution and behaviour of traffic-derived MPs in complex environments. microplastics tyre wear particles road runoff µFTIR multi-matrix study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction MP pollution has become a significant environmental issue due to its widespread presence across various ecosystems (Kasmuri et al., 2022 ), including marine ((Muñiz & Rahman, 2025 ; Zúñiga Umaña et al., 2025 ), freshwater (Shen et al., 2025 ; Sunny et al., 2025 ), terrestrial environments (Pan et al., 2024 ; Sahai et al., 2025 ), and even the atmosphere (Sridharan et al., 2021 ; Y. L. Wang et al., 2025 ). MPs, typically defined as plastic fragments smaller than 5 mm, include both primary plastics, intentionally manufactured for products like cosmetics and cleaning agents, and secondary plastics, which result from the breakdown of larger plastic items (Barnes et al., 2009 ). While studies on MPs in marine environments are abundant, research on their presence in other environments, such as road dust, road runoff, and air, remains limited (Horton et al., 2017 ; Morioka et al., 2023 ). Road dust, generated by the abrasion of vehicle components and road surfaces and dust blown in from the surroundings, is a significant source of MPs in urban and peri-urban areas (Premarathna et al., 2025 ). Previous research has indicated that road dust contributes substantially to the total MP load transported into aquatic systems through stormwater runoff (F. Liu et al., 2019 ; Monira et al., 2022 ). TWPs, derived from the abrasion of vehicle tyres, constitute a major portion of the MPs found in road dust, alongside plastics from other sources such as brake wear and road markings. Once released into the environment, these MPs can be transported by air, water, and solid surfaces, posing risks to both ecosystems and human health (Giechaskiel et al., 2024 ; Molazadeh et al., 2023 ). Studies have shown that the highest concentrations of MPs in urban stormwater sediments are associated with road dust, with tyre wear being one of the dominant contributors to the MP load in these areas (Monira et al., 2022 ; Rasmussen et al., 2024 ). Different techniques have been used to quantify MPs in a variety of environmental matrices. Micro-Fourier Transform Infrared Spectroscopy (µFTIR), described e.g. by Vianello et al. ( 2013 ), is a powerful tool to identify polymer types in MP samples, as well as their mass and morphology. However, µFTIR has limitations in identifying certain types of MP, such as particles derived from car tyre rubber. Pyrolysis gas chromatography mass spectrometry (Py-GC/MS) technique can, on the other hand, quantify both TWPs and other types of MPs. However, this technique can solely quantify its mass and not the number and size of individual particles (Giechaskiel et al., 2024 ; Monira et al., 2022 ). There are also polymer types, where this method struggles due to a lack of specific pyrolysis markers and interference with naturally occurring material, for example soot interfering with the quantification of PVC (Goßmann et al., 2022 ). This study investigates the presence of MPs and TWPs in three environmental matrices (road dust, road runoff, and air) at a heavily trafficked highway in Bamble, Telemark County, Norway. By adopting a multi-matrix approach, the study aims to provide a comprehensive understanding of how MPs disperse in areas with high vehicular traffic. The research explores not only the concentrations of MP but also potential correlations between climatic factors, such as wind speed, precipitation, and air temperature, and the presence of MP in the air. While this work is exploratory and limited by the number of samples collected, it provides valuable preliminary data on MP contamination at and near highways. 2. Materials and methods 2.1 Sampling 2.1.1 Sampling location This study was conducted along the E18 highway in Bamble, a municipality in the Telemark county of southern Norway (coordinates: 59.032822, 9.677411). Water samples were collected at a test station established to study pollutants from the E18. This station was developed to collect large volumes of representative surface runoff from the roadway. The system includes a 20-meter-long drainage channel (class E600, load capacity up to 20 tons, Ø110 mm connection) that gathers water from the road surface. Runoff from the gutter was conveyed by gravity through a 110 mm pipe into a concrete collection tank. A flow meter was installed on the pipe to continuously measure the volumetric flow rate entering the tank. The collection tank has a diameter of 650 mm, an inlet positioned 850 mm above the bottom, and an overflow outlet at 700 mm. This provides the tank with a capacity of 928.7 litres. The total width of the road at the sampling site is 10.5 m, consisting of two main lanes and the beginning of an exit lane located approximately 265 m from a nearby roundabout. The Bamble tunnel is situated approximately 950 m from the test station. Traffic data from the Bamble tunnel were used to estimate the traffic volume at the sampling point. Air samples were collected at the test station using passive air samplers (Particle Vision Sigma 2). The sampler was positioned 17 m from the highway and 33 m from a small secondary road located on the opposite side. In addition, the station was equipped with a weather monitoring system that continuously recorded meteorological conditions. Online instruments at the station were connected to a Supervisory Control and Data Acquisition (SCADA) system, enabling real-time data logging and remote storage in the cloud. Road surface samples were collected during a period when the road was temporarily closed for tunnel maintenance, allowing access to the road surface near the Bamble tunnel. A map illustrating the sampling location and plots is provided (Fig. 1 ). 2.1.2 Air Air samples were collected at the test station using Particle Vision-Sigma 2 passive air samplers (Rausch, 2022). The passive air sampler (Fig. 1 ) was installed 17 m from the highway at a height of 3.65 m, equipped with a custom-made Teflon container. To ensure that collected particles were securely sequestered, a zinc chloride solution of 1.7 g/cm³ density, known for its high viscosity and low evaporation rate, was added to the bottom of each sampling container. Four samples were taken from March to June 2023 (Table 1 ). Table 1 Overview of air sampling periods, including start and end dates, times, and duration for each sampler. A Sigma 2 passive sampler was used. Sample Code Start Date Start Time End Date End Time Days Air_1 30/03/2023 14:15 19/04/2023 12:00 20 Air_2 19/04/2023 12:30 23/05/2023 11:50 34 Air_3 23/05/2023 12:00 12/06/2023 16:00 20 Air_4 12/06/2023 16:10 20/06/2023 15:15 8 2.1.3 Road runoff Road runoff samples were taken using the Universal Filtering Unit (UFO) system (Y. Liu et al., 2023 ; Rist et al., 2020 ), which can filter large quantities of road runoff through a 10 µm stainless steel filter (Ø 167 mm) in a few hours. Samples were taken from the station tank overflow as shown in Fig. 1 . The tank was kept thoroughly mixed to minimize sedimentation bias, ensuring a representative distribution of microplastic particles during sampling. The goal was to continue filtering until four filters had become clogged, typically indicating that sufficient material had been collected. Three samples were collected from October 2022 to April 2023 (Table 2 ). Table 2 Overview of water sampling events, including date, start and end times, and sampled volume for each sample Sample Code Date Start Time End Time Volume (L) RRW_1 28/10/2022 21:15 23:16 42.5 RRW_2 18/01/2023 13:39 15:46 34.1 RRW_3 19/04/2023 15:14 15:41 9.3 2.1.4 Road dust Road dust was collected near the Bamble tunnel using a custom-made stainless-steel device called "Dusty" ((Iordachescu et al., 2024 ). This device includes a removable aluminium bucket mounted on a sack truck for easy mobility. The bucket features a detachable lid fitted with a Ø 167 mm 10 µm steel filter (Filtertek, Denmark) and a rigid pipe connected to a floor nozzle for efficient surface sampling. An industrial vacuum cleaner attached to the lid provides suction needed for dust collection. The 'Dusty' device, built entirely from metal to prevent contamination, uses a silicone gasket for a tight seal between the bucket and the lid. Prior to sampling, road particles were loosened from the road surface with a steel bristle broom. One-square-meter sections were sampled for analysis. In total, four sections of road were sampled for an equivalent of four samples (Fig. 1 ). 2.2 Sample preparation 2.2.1 Air The particles trapped in the Teflon containers were flushed and filtered on a 10 µm stainless steel filter. Pre-filtered (0.7 µm) deionised warm water (< 50°C) was passed through the filter to clear out any remaining zinc chloride. The samples were then transferred into 50% EtOH and were gradually evaporated. Once the evaporation was completed, the volume was fixed to three mL of EtOH. 2.2.2 Road runoff Each filter was placed in a crystallization dish, and a sodium polytungstate (SPT) solution of 1.9 g/cm 3 density was added to cover the filters. Subsequently, the filters were sonicated to detach the particles from the filters. The SPT solution containing the sample was transferred to a separatory funnel, agitated with compressed air, and subsequently allowed to settle. After settling overnight, the precipitatewas removed. This settling and removal cycle was repeated over several consecutive days. The SPT was then separated via a 10 µm steel filter, retaining the sample. This sample was immersed in a 5% w/w sodium dodecyl sulphate (SDS) solution, incubated at 55°C for 24 hours, and subsequently filtered. The samples were then combined with 200 mL of 0.7 µm pre-filtered demineralised water and subjected to catalysed oxidation using Fenton's reagent (Simon et al., 2018 ), incorporating 145 mL of 50% H 2 O 2 , 65 mL of 0.1 M NaOH, and 62 mL of 0.1 M FeSO 4 . After 24 hours, the samples underwent another filtration and were reintroduced to the SPT, undergoing a density separation analogous to the initial step. The final concentrated sample was placed into HPLC-grade 50% ethanol in a 150 mL beaker and transferred into a headspace vial, from which ethanol was evaporated at 50°C under a gentle flow of N 2 in a TurboVap® LV bath. The sample was then standardised to a final volume of 10 mL with HPLC-grade 50% ethanol. 2.2.3 Road dust Coarser particles from the collection bucket were initially sieved on a 500 µm mesh, removing macro debris. The larger particles (> 500 µm) were stored for later inspection. The < 500 µm fraction was transferred to 100 mL of SPT at a density of 1.9 g/cm (Klöckner et al., 2020 ). Concurrently, filters containing the finer particles were submerged in SPT solution within a crystallising dish and subjected to sonification to dislodge adhered particles. The resultant SPT solution, now laden with these particles, was merged with the previously sieved fraction. Between 3 and 15 g of road dust per sample (particles < 500) µm were analysed. The road dust samples followed the same treatment protocol as road runoff samples but were concentrated to 5 mL after evaporation. 2.3 µFTIR analysis For the polymer analysis, a subsample of the particles was placed on a zinc selenide transmission window (Ø 13×2 mm) set within a compression cell (Pike Technologies) that reduced the viewing area to Ø10 mm. Aliquots were applied in 50 or 100 µL increments using a glass capillary micropipette and dried at 50°C on a heating plate to prevent aggregation. The chemical composition of these particles was analysed using µFTIR with a Focal Plane Array (FPA), using a Cary 620 FTIR microscope and a Cary 670 IR spectrometer (Agilent Technologies, USA). The entire Ø10 mm active area was scanned at 15x magnification, using a Cassegrain objective and an MCT detector with a 128x128 FPA, providing a 5.5 µm pixel resolution. Scans were performed in transmission mode, spanning a spectral range of 3750–850 cm − 1 at 8 cm − 1 resolution with 30 co-added scans per pixel. A background scan with 120 co-added scans was taken before each sample scan. The acquired infrared images for the samples were analysed using the siMPle software (Primpke et al., 2019 ). This approach reduces much of the human bias that would otherwise occur during data analysis. For each air sample, 300 µL were analysed using µFTIR, with a total of four samples processed. Similarly, for road runoff, three samples were prepared, and 50 µL of each were scanned using µFTIR. In the case of road dust, four samples were examined, with 300 µL analysed for each, except for one sample, for which 100 µL were processed. The mass of each MP was estimated following the approach outlined in Simon et al. ( 2018 ). The approach finds the equivalent ellipse assuming the length of the particle as the ellipse's major dimension. It then assumes the particle is an ellipsoid with the third dimension of the ellipse, its thickness, as a fraction of the second dimension of the ellipse. In this case, this fraction was set to 60% of the second dimension of the ellipse. The density of the particle was set to the density of the polymer type to which it was assigned. 2.3 Pyrolysis-GC/MS analysis As µFTIR cannot reliably identify car tyre rubber TWPs were instead quantified by Py-GC/MS. The system comprised an EGA/Py-3030D (Frontier Labs, Japan) micro furnace pyrolyzer with a AS-1020E (Frontier Labs, Japan) auto-shot sampler coupled to a TRACE™ 1310 GC with an ISQ™ single quadrupole GC–MS (Thermo Fisher Scientific, USA). Aliquots (25 to 1000 µL per sample) were thermally decomposed at 600°C with an interface temperature of 280°C, using helium (He) as carrier gas at a flow of 1 mL min − 1 and a 30:1 split ratio. The samples were separated in the GC using a temperature program from 40°C (2 min hold) to 300°C (5 min hold) at a heating rate of 10°C min − 1 and detected in the MS (electronic ionization at 70 eV, transfer line at 250°C, ion source at 200°C) in scan mode in the range of 35–500 m/z . The internal standard was deuterated anthracene. An external calibration curve was prepared from cryo-milled car- and truck-tyre treads (Genan, Denmark). Quantification targeted 4-vinylcyclohexene as the quantification marker compound, as widely applied for tyre-rubber assessment (Lindfors et al., 2025 ; More et al., 2023 ; Öborn et al., 2024 ). Although both Py-GC/S and µFTIR provide mass information, the results are not directly comparable. µFTIR yields estimated masses based on image-derived dimensions and assumed thickness and density, whereas Py-GC/MS quantifies polymer mass from polymer-specific pyrolysis products (Kirstein et al., 2021 ). To clearly distinguish, the term MP is used for MPs detected by µFTIR, while TWP is used for TWPs detected by Py-GC/MS. 3. Results and discussion 3.1 Air 3.1.1 Concentrations Table 3 presents the MP concentrations in the air samples, reported both as particle counts per day and estimated mass (µg/day). The results show noticeable variability between sampling events. Air_1 exhibited the lowest concentration, with 5.0 counts/day and 0.2 µg/day, whereas Air_4 showed the highest values at 26.3 counts/day and 0.95 µg/day. Although samples with higher MP counts generally exhibited higher total MP mass, the relationship was not proportional. This discrepancy is attributed to the heterogeneous composition of the particles, as different polymers possess distinct densities and masses. The estimated MP mass detected by µFTIR remained consistently below 1 µg/day across all samples, indicating generally low airborne MP burdens during the sampling periods. Regarding TWP, all samples reported values below the detection limit (1 µg, defined by signal-to-noise ratio (S/N) > 10(Goßmann et al., 2021 )), which could either reflect their absence or concentrations too low to be quantified by the method applied. Table 3 Estimated MP concentration in the air samples Sample name MP [counts/day] MP mass [µg/day] TWP mass [µg/day] Air_1 5.0 0.16 < 0.050* Air_2 12.4 0.28 < 0.029* Air_3 9.0 0.22 < 0.050* Air_4 26.3 0.95 < 0.125* Note: *The detection limit of the Pyrolysis-GC/MS is 1 µg, but here was recalculated in terms of concentration, considering that the samples have different durations in days. Figure 2 contextualizes the concentrations by displaying key environmental and traffic-related parameters for each sampling period. Given the limited sample size, the purpose of this figure is illustrative rather than inferential. From Panels (a) and (b) of Fig. 2, it is evident that the lowest MP concentration (Air_1) occurred at the lowest temperature and the highest wind speed. While both factors may contribute to particle retention, wind speed appears to have played a more dominant role in this dataset. Higher wind velocities consistently aligned with lower MP concentrations, suggesting that increased air movement may hinder particle deposition by dispersing MPs away from the samplers. Although previous studies have linked increased MP concentrations to temperature (Chen et al., 2025 ), this relationship may be mediated by wind: lower wind speeds not only reduce dispersion but also contribute to warmer ambient conditions. Therefore, considering its direct role in particle transport and deposition, wind speed may be a more influential variable in this context. In Panel (c), no clear relationship emerges between precipitation and MP concentrations across the four samples. However, previous studies (Chen et al., 2025 ; Enyoh et al., 2019 ) have shown that rainfall, especially when intense, can enhance atmospheric MP deposition. This occurs through wet deposition, where particles are removed from the air by raindrops, and through a first-flush effect, where a high load of MPs is rapidly washed out at the onset of rainfall. The absence of this trend in the present data may be due to the low rainfall intensities recorded or the limited number of events, which may not have triggered these mechanisms. Panel (d) shows traffic volume near the sampling station. While traffic intensity varied across the samples, its relation to MP concentrations was not straightforward. For instance, Air_3 and Air_4 had similar traffic intensities, yet MP levels were markedly different. This suggests that beyond the number of vehicles or the intensity of road use, meteorological conditions, such as wind speed and precipitation, may have played a more decisive role in determining how many particles were captured during sampling. In environments with high traffic, weather patterns may significantly influence the retention and deposition of airborne MP, shaping local concentrations more than traffic density alone. The influence of meteorological conditions on MP concentrations has been noted in several studies (Kannankai & Devipriya, 2024 ; Vengatesh et al., 2024 ). In aquatic environments, higher wind intensity has been linked to lower shoreline accumulation of MPs due to increased horizontal transport and water column mixing (Garello et al., 2023 ). A similar effect has been suggested in air, where stronger winds may disperse MPs more widely, limiting their capture by passive samplers positioned near the source (Y. Liu et al., 2025 ). In certain atmospheric conditions, particles may also acquire electric charges, which can further affect their transport and settling behaviour (H. Wang et al., 2025 ; Zhang et al., 2025 ). In high-traffic environments, these dynamics become more complex. Tire and road wear are known sources of airborne MPs, but their detectability near emission points can vary with wind conditions. Recent findings by Y. Liu et al. ( 2025 ) emphasize that meteorological factors such as wind speed, thermal turbulence, and precipitation influence both the atmospheric transport and deposition of MPs. Their review highlights how these conditions affect suspension time, resuspension potential, and settling velocity, factors that directly impact the probability of detection in passive sampling systems. These insights underscore the importance of considering atmospheric variability when interpreting MP concentrations and suggest that further long-term monitoring is needed to disentangle emissions from environmental dynamics. 3.1.2 Polymer distribution Figure 3 presents the polymer distribution in the air samples, separated by counts (Panel a) and estimated mass (Panel b). The distribution is expressed as the percentage of each polymer type relative to the total number of MPs or the total mass in each sample. The most common polymers in the air samples were polyester (PES) and polyamide (PA), observed in nearly all samples. In terms of MP counts, the presence of PES in Air_1, Air_2, and Air_4 accounted for approximately 30%, 86%, and 52%, respectively. When comparing estimated mass of MPs for the same samples, PES was predominantly present in Air_2 and Air_4, with contributions of 80% and 46%, respectively. However, in Air_1, the percentage of PES by mass dropped to 22%, as the sample contained a high number of smaller particles whose individual masses were too low to substantially affect the total mass. Conversely, the proportion of PA increased from 20% in MP counts to 45% in MP mass for Air_1, indicating that the PA particles in this sample were larger and contributed more substantially to the total mass. A similar pattern was observed in Air_3, where PA was the most representative polymer both in terms of counts and estimated mass, comprising 56% of the particle count and 54% of the mass. This further supports the finding that PA was present as larger particles, influencing both the counts and estimated mass distributions. Other studies have reported PA, polyethylene (PE), and polypropylene (PP) as the most frequently detected polymers in air samples. Zheng et al. ( 2024 ) found that PA, which was also among the predominant polymers in our dataset, accounted for more than 50% of all identified airborne MPs across indoor and outdoor environments in China. Nandi et al. ( 2024 ) highlighted the dominance of PP and PE in outdoor air in India. In our case, PE was present in all samples, albeit in low abundance, whereas PP was detected in only two samples. These compositional contrasts may reflect regional differences in local emission sources, sampling methodologies, and variations in chemical identification (e.g., µFTIR vs. LDIR), underscoring the need for harmonization of airborne MP characterization. 3.2 Road runoff 3.2.1 Concentrations Table 4 presents the concentrations of MP in the road runoff samples, measured in counts per litre (MP counts/L), MP estimated mass (µg/L) and TWP mass (µg/L). The data reveals significant variation in both particle counts and mass across the three samples. Table 4 Estimated MP concentration in the road runoff samples. Sample name AAU MP [counts/Liter] MP mass [µg/litre] TWP mass [µg/litre] RRW_1 80 0.4 281.15 RRW_2 598.2 11.7 269.81 RRW_3 64.5 0.4 1470.13 RRW_3 had the lowest MP concentration, with 64.5 MP counts/L and 0.4 µg/L. RRW_1 exhibited a slightly higher concentration, with 80 MP counts/L and 0.4 µg/L. In contrast, RRW_2 had the highest concentration, with 598.2 MP counts/L and 11.7 µg/L of MP, representing nearly eight times the particle count and a much higher estimated mass compared to RRW_1 and RRW_3. These results demonstrate considerable variation in MP concentrations across the road runoff samples, reflecting differences in the presence of MPs in the runoff water. The higher concentration observed in RRW_2 suggests a more concentrated sample with a significantly greater estimated mass of MPs. In contrast, the estimated mass of MPs in RRW_1 and RRW_3 was similar. However, the MP counts differed, with RRW_1 exhibiting higher particle counts than RRW_3. This difference was due to RRW_1 containing a greater number of smaller particles, while RRW_3 had fewer particles that were larger or less dense. Interestingly, the TWP mass in RRW_3 was markedly higher (1470.13 µg/L) than in RRW_1 and RRW_2 (281.15 and 269.81 µg/L, respectively), despite RRW_3 having the lowest MP counts. This highlights that RRW_3 was likely dominated by larger and/or denser TWPs, possibly from recent or localised runoff events with high rubber content. The elevated TWP mass suggests that specific sources or flow dynamics in this sample may have contributed disproportionately to the total mass. 3.2.2 Polymer distribution Figure 4 shows the polymer distribution in the road runoff samples, presented both by MP counts (Panel a) and estimated MP mass (Panel b). The distribution is shown as the percentage of each polymer type relative to the total MP content for each sample. The high proportion of PP observed in RRW_1 and RRW_3 is consistent with findings of Lindfors et al. ( 2025 ), who reported that PP, PE, and PES accounted for over 90% of estimated MP mass and counts in road runoff. Moreover, the elevated presence of PA in RRW_2, particularly in counts, may be attributed to emissions from textiles and tyre reinforcement materials (Hirschberg & Rodrigue, 2023). Notably, the dominance of PP in RRW_3, which also coincided with the highest recorded concentration of TWP mass (1470 µg/L), might reflect a localised accumulation driven by pavement roughness. Smyth et al. ( 2025 ) emphasised that road surface composition significantly influences TWP release, and their study demonstrated that certain pavement types can be particularly prone to shedding PP and PE particles under mechanical stress. These results highlight the importance of considering both the distribution of count and the distribution of mass in the analysis of MP pollution in runoff. As shown by Lindfors et al. ( 2025 ), different land uses and particle size distributions strongly influence polymer prevalence, and the combined use of µFTIR and Py-GC/MS is essential for revealing both the diversity and abundance of MPs across urban catchments. 3.3 Road dust 3.3.1 Concentrations Table 5 presents MP and TWP concentration found in road dust samples. The data are expressed in terms of MP counts per square meter (counts/m²), estimated MP mass (µg/m²), and TWP mass (µg/m²). Substantial differences were observed among the sampling sites, with MP concentrations ranging from 567 to 4250 counts/m² and estimated MP mass ranged from 30.6 to 291.4 µg/m². For road dust from Swedish parking lots, Iordachescu et al. (2024) reported ranges of 5.78-4951 counts/g and 0.06-95.3 µg/g, using a similar μFTIR-based methodology. The combination of estimated MP mass and quantified TWP mass allows for a more comprehensive understanding of traffic-related MP pollution. Table 5 Estimated MP concentration in the road dust samples. Sample name AAU MP [counts/m 2 ] MP mass [µg/m 2 ] TWP mass [µg/m 2 ] RD_1 1400 108.0 33500 RD_2 1300 111.8 169427 RD_3 566.7 30.6 85578 RD_4 4250 291.4 178777 3.3.2 Polymer distribution In the analysed road dust samples, PP was the predominant polymer in both particle counts and estimated mass, accounting for over 90% of the total particles and contributed substantially to overall mass (Fig. 5 ). However, when considering the estimated mass distribution, a relative increase in polymers such as PES, polyurethane (PU), PE, and polyvinyl chloride (PVC) was observed. This indicates that although these polymers had lower counts, they contributed more to the overall mass due to their higher density or larger size. This contrast underscores the importance of considering both particle count and mass to gain a more comprehensive understanding of MP contributions in complex matrices, such as road dust. Our findings are consistent with those of Iordachescu et al. ( 2024 ), who noted that MP mass can be dominated by fewer but heavier particles, particularly in urban contexts with significant influence from industrial materials and traffic-related debris. 3.4 All samples Figure 6 shows how the types of polymers varied depending on the matrices (air, road dust, and road runoff) and how these distributions differed when measured by count (Panel a) versus estimated mass (Panel b). In road dust, PP overwhelmingly dominated, making up more than 90% of both the number of particles and the overall mass. In contrast, air samples revealed a broader mix of polymers, with PES and PA appearing more frequently, suggesting diverse sources such as textiles and atmospheric fallout. Road runoff showed a blend of PP and PA, pointing to mixed origins such as urban debris and tyre or fabric-related inputs. These differences underline how each environmental compartment had its own fingerprint when it came to MP pollution. Figure 7 illustrates the size distribution of MP across all samples, categorised by their major dimension. Most particles in all matrices were below 100 µm, with the highest frecuencies between 40 and 80 µm. Road dust samples showed the highest particle counts across all size ranges, particularly dominating the 60 µm bin. In contrast, particles in road runoff and air samples were less abundant and more evenly distributed, with air showing a modest peak around 50 µm and road runoff exhibiting lower counts overall. The results confirm the predominance of small-sized particles (< 100 µm), aligning with expectations for environmental samples and emphasising the relevance of fine particles in MP pollution assessments. A Principal Component Analysis (PCA) was conducted based on the polymeric composition of MPs across all samples (Fig. 8 ). The first two principal components explained 36.08% and 20.79% of the total variance, respectively. The air samples were clearly separated from both road dust and road runoff samples along PC1, indicating a distinct polymer profile likely influenced by atmospheric sources. In contrast, road runoff and road dust samples exhibited considerable overlap, suggesting shared sources or similar input pathways, possibly linked to vehicular emissions or urban surface runoff. Notably, Road runoff_2 appeared isolated along PC1, driven by higher contributions of PA and PE, which were previously identified as significant components in this sample. The direction and length of the vectors indicate that PP, PA, PE, and PES were the main contributors to variability in the dataset. These results emphasize the importance of site-specific factors in shaping the MP signature of each matrix and support the interpretation that different environmental compartments may retain unique polymer fingerprints, while others share overlapping pollution profiles. 4. Conclusion This exploratory study investigated the presence, composition, and distribution of MPs and TWPs in road dust, road runoff, and air along a heavily trafficked highway in Bamble, Norway. PP was the predominant polymer across matrices, especially in road dust and road runoff, while PES and PA were more common in air samples. Most particles were below 100 µm, with road dust showing the highest concentrations. The combined use of µFTIR and Py-GC/MS enabled a comprehensive characterization of polymer types, estimated MP mass, and TWP mass contributions. While road dust and road runoff effectively captured MPs and TWPs originating directly from the studied road segment, air samples posed more challenges. Due to atmospheric transport processes, airborne particles generated near the road may travel varying distances before deposition, making proximity-based sampling less representative under certain weather conditions. Although meteorological parameters did not show strong correlations with MP and TWP concentrations in this study, their role in particle dispersion and deposition remains critical. Future studies should further explore these interactions to better understand the influence of wind, rainfall, and temperature on traffic-related MP pollution. Declarations Availability of data and materials: t he data presented in this study are available on request from the corresponding author. Competing interests: th e authors declare no conflict of interest. Funding: t his research was funded by Norges Forskningsråd, 303712– Treat RW project. Authors' contributions: S.C. did part of the analysis and wrote the main manuscript text. L. I. planned the sampling campaign, conducted part of the sampling and did part of the analysis. S. S. R. contributed to planning the sampling campaign and conducted part of it. J. L. contributed to part of the analysis. L. M. contributed to planning the sampling campaign. 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Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2025 Read the published version in Microplastics and Nanoplastics → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 13 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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06:40:04","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130515,"visible":true,"origin":"","legend":"","description":"","filename":"0c281f3f98fa46cf9dc0dd4032581e761structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/45de9038be19ec963576a3d7.xml"},{"id":92144215,"identity":"e918bf14-de30-4136-b81c-ee4d94c1c719","added_by":"auto","created_at":"2025-09-25 06:40:04","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139641,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/d57a4c69ecc70b8b6e1c13da.html"},{"id":92144186,"identity":"d3c85871-75cf-45f9-825f-9dd8b02d8ca2","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":496903,"visible":true,"origin":"","legend":"\u003cp\u003eSampling points in the study area. Right: road dust samples were taken at the entrance to the Bamble tunnel. Left: schematic diagram of the station where air and road runoff samples were taken.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/6e9ef3a4bb038fff5580738e.png"},{"id":92144190,"identity":"b5f3ca84-e7b2-4b78-910e-5bbbd5085745","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200085,"visible":true,"origin":"","legend":"\u003cp\u003eMP and weather parameters. a) MP vs. Temperature (T). b) MP vs. Wind. c) MP vs. Accumulated precipitation (AP). d) MP vs. Traffic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/443bdc48684ad2b8ad95abfd.png"},{"id":92144187,"identity":"a10aa91c-f3e6-4383-9136-0ca0a950f920","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72666,"visible":true,"origin":"","legend":"\u003cp\u003ePolymer distribution in the air samples. a) by counts, b) by mass.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/48c8b5c6e14229df0f1cd690.png"},{"id":92144542,"identity":"2db172d4-f763-4eac-b084-7aa89f44cf59","added_by":"auto","created_at":"2025-09-25 06:48:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77084,"visible":true,"origin":"","legend":"\u003cp\u003ePolymer distribution in the road runoff samples. a) - by counts, b) - by mass.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/c5d2285b1ce39e0f94700559.png"},{"id":92144192,"identity":"d49f34a8-c4d9-456a-88f1-624500320b35","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94032,"visible":true,"origin":"","legend":"\u003cp\u003ePolymer distribution in the road dust samples. a) - by counts, b) - by mass.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/dc2d6b7b41a5ad9415840ec4.png"},{"id":92144200,"identity":"dbb95cfc-c3dc-459b-be20-17019ca1997a","added_by":"auto","created_at":"2025-09-25 06:40:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81789,"visible":true,"origin":"","legend":"\u003cp\u003ePolymer distribution for all samples in the three studied matrices.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/cfd6d23b7a3bdfa6b24342ad.png"},{"id":92144198,"identity":"9a4e35de-3337-4d91-b7f0-75f306feccff","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46572,"visible":true,"origin":"","legend":"\u003cp\u003eSize distribution based on the major dimension of the particles found in all samples\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/50eea0867c1a0471536b1065.png"},{"id":92144191,"identity":"71f614b8-4e89-4a61-9a3b-0973eef90d94","added_by":"auto","created_at":"2025-09-25 06:40:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":68265,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/19eee27512f1d416e410f7b9.png"},{"id":99545164,"identity":"159272a3-a080-4a1f-a229-79f27b54ad21","added_by":"auto","created_at":"2026-01-05 15:59:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7607783/v1/7ade6969-cfd1-44c7-a898-e7291d345084.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microplastic and tyre wear particles at a highway: a case study from Norway","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMP pollution has become a significant environmental issue due to its widespread presence across various ecosystems (Kasmuri et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including marine ((Mu\u0026ntilde;iz \u0026amp; Rahman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Z\u0026uacute;\u0026ntilde;iga Uma\u0026ntilde;a et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), freshwater (Shen et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sunny et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), terrestrial environments (Pan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sahai et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and even the atmosphere (Sridharan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Y. L. Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). MPs, typically defined as plastic fragments smaller than 5 mm, include both primary plastics, intentionally manufactured for products like cosmetics and cleaning agents, and secondary plastics, which result from the breakdown of larger plastic items (Barnes et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While studies on MPs in marine environments are abundant, research on their presence in other environments, such as road dust, road runoff, and air, remains limited (Horton et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morioka et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRoad dust, generated by the abrasion of vehicle components and road surfaces and dust blown in from the surroundings, is a significant source of MPs in urban and peri-urban areas (Premarathna et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous research has indicated that road dust contributes substantially to the total MP load transported into aquatic systems through stormwater runoff (F. Liu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Monira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TWPs, derived from the abrasion of vehicle tyres, constitute a major portion of the MPs found in road dust, alongside plastics from other sources such as brake wear and road markings. Once released into the environment, these MPs can be transported by air, water, and solid surfaces, posing risks to both ecosystems and human health (Giechaskiel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Molazadeh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies have shown that the highest concentrations of MPs in urban stormwater sediments are associated with road dust, with tyre wear being one of the dominant contributors to the MP load in these areas (Monira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rasmussen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDifferent techniques have been used to quantify MPs in a variety of environmental matrices. Micro-Fourier Transform Infrared Spectroscopy (\u0026micro;FTIR), described e.g. by Vianello et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), is a powerful tool to identify polymer types in MP samples, as well as their mass and morphology. However, \u0026micro;FTIR has limitations in identifying certain types of MP, such as particles derived from car tyre rubber. Pyrolysis gas chromatography mass spectrometry (Py-GC/MS) technique can, on the other hand, quantify both TWPs and other types of MPs. However, this technique can solely quantify its mass and not the number and size of individual particles (Giechaskiel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Monira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are also polymer types, where this method struggles due to a lack of specific pyrolysis markers and interference with naturally occurring material, for example soot interfering with the quantification of PVC (Go\u0026szlig;mann et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study investigates the presence of MPs and TWPs in three environmental matrices (road dust, road runoff, and air) at a heavily trafficked highway in Bamble, Telemark County, Norway. By adopting a multi-matrix approach, the study aims to provide a comprehensive understanding of how MPs disperse in areas with high vehicular traffic. The research explores not only the concentrations of MP but also potential correlations between climatic factors, such as wind speed, precipitation, and air temperature, and the presence of MP in the air. While this work is exploratory and limited by the number of samples collected, it provides valuable preliminary data on MP contamination at and near highways.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sampling\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Sampling location\u003c/h2\u003e\u003cp\u003eThis study was conducted along the E18 highway in Bamble, a municipality in the Telemark county of southern Norway (coordinates: 59.032822, 9.677411). Water samples were collected at a test station established to study pollutants from the E18.\u003c/p\u003e\u003cp\u003eThis station was developed to collect large volumes of representative surface runoff from the roadway. The system includes a 20-meter-long drainage channel (class E600, load capacity up to 20 tons, \u0026Oslash;110 mm connection) that gathers water from the road surface. Runoff from the gutter was conveyed by gravity through a 110 mm pipe into a concrete collection tank. A flow meter was installed on the pipe to continuously measure the volumetric flow rate entering the tank. The collection tank has a diameter of 650 mm, an inlet positioned 850 mm above the bottom, and an overflow outlet at 700 mm. This provides the tank with a capacity of 928.7 litres.\u003c/p\u003e\u003cp\u003eThe total width of the road at the sampling site is 10.5 m, consisting of two main lanes and the beginning of an exit lane located approximately 265 m from a nearby roundabout. The Bamble tunnel is situated approximately 950 m from the test station. Traffic data from the Bamble tunnel were used to estimate the traffic volume at the sampling point.\u003c/p\u003e\u003cp\u003eAir samples were collected at the test station using passive air samplers (Particle Vision Sigma 2). The sampler was positioned 17 m from the highway and 33 m from a small secondary road located on the opposite side.\u003c/p\u003e\u003cp\u003eIn addition, the station was equipped with a weather monitoring system that continuously recorded meteorological conditions. Online instruments at the station were connected to a Supervisory Control and Data Acquisition (SCADA) system, enabling real-time data logging and remote storage in the cloud.\u003c/p\u003e\u003cp\u003eRoad surface samples were collected during a period when the road was temporarily closed for tunnel maintenance, allowing access to the road surface near the Bamble tunnel. A map illustrating the sampling location and plots is provided (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Air\u003c/h2\u003e\u003cp\u003eAir samples were collected at the test station using \u003cem\u003eParticle Vision-Sigma 2\u003c/em\u003e passive air samplers (Rausch, 2022). The passive air sampler (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was installed 17 m from the highway at a height of 3.65 m, equipped with a custom-made Teflon container. To ensure that collected particles were securely sequestered, a zinc chloride solution of 1.7 g/cm\u0026sup3; density, known for its high viscosity and low evaporation rate, was added to the bottom of each sampling container. Four samples were taken from March to June 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of air sampling periods, including start and end dates, times, and duration for each sampler. A Sigma 2 passive sampler was used.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStart Date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStart Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnd Date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnd Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDays\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30/03/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14:15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19/04/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19/04/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12:30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23/05/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11:50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23/05/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12/06/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir_4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12/06/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16:10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20/06/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15:15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.1.3 Road runoff\u003c/h2\u003e\u003cp\u003eRoad runoff samples were taken using the Universal Filtering Unit (UFO) system (Y. Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rist et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which can filter large quantities of road runoff through a 10 \u0026micro;m stainless steel filter (\u0026Oslash; 167 mm) in a few hours. Samples were taken from the station tank overflow as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The tank was kept thoroughly mixed to minimize sedimentation bias, ensuring a representative distribution of microplastic particles during sampling. The goal was to continue filtering until four filters had become clogged, typically indicating that sufficient material had been collected. Three samples were collected from October 2022 to April 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of water sampling events, including date, start and end times, and sampled volume for each sample\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStart Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnd Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVolume (L)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRRW_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28/10/2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21:15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23:16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRRW_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18/01/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13:39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15:46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRRW_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19/04/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15:14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15:41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.1.4 Road dust\u003c/h2\u003e\u003cp\u003eRoad dust was collected near the Bamble tunnel using a custom-made stainless-steel device called \"Dusty\" ((Iordachescu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This device includes a removable aluminium bucket mounted on a sack truck for easy mobility. The bucket features a detachable lid fitted with a \u0026Oslash; 167 mm 10 \u0026micro;m steel filter (Filtertek, Denmark) and a rigid pipe connected to a floor nozzle for efficient surface sampling. An industrial vacuum cleaner attached to the lid provides suction needed for dust collection. The 'Dusty' device, built entirely from metal to prevent contamination, uses a silicone gasket for a tight seal between the bucket and the lid. Prior to sampling, road particles were loosened from the road surface with a steel bristle broom. One-square-meter sections were sampled for analysis. In total, four sections of road were sampled for an equivalent of four samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sample preparation\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Air\u003c/h2\u003e\u003cp\u003eThe particles trapped in the Teflon containers were flushed and filtered on a 10 \u0026micro;m stainless steel filter. Pre-filtered (0.7 \u0026micro;m) deionised warm water (\u0026lt;\u0026thinsp;50\u0026deg;C) was passed through the filter to clear out any remaining zinc chloride. The samples were then transferred into 50% EtOH and were gradually evaporated. Once the evaporation was completed, the volume was fixed to three mL of EtOH.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Road runoff\u003c/h2\u003e\u003cp\u003eEach filter was placed in a crystallization dish, and a sodium polytungstate (SPT) solution of 1.9 g/cm\u003csup\u003e3\u003c/sup\u003e density was added to cover the filters. Subsequently, the filters were sonicated to detach the particles from the filters. The SPT solution containing the sample was transferred to a separatory funnel, agitated with compressed air, and subsequently allowed to settle. After settling overnight, the precipitatewas removed. This settling and removal cycle was repeated over several consecutive days. The SPT was then separated via a 10 \u0026micro;m steel filter, retaining the sample. This sample was immersed in a 5% w/w sodium dodecyl sulphate (SDS) solution, incubated at 55\u0026deg;C for 24 hours, and subsequently filtered. The samples were then combined with 200 mL of 0.7 \u0026micro;m pre-filtered demineralised water and subjected to catalysed oxidation using Fenton's reagent (Simon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), incorporating 145 mL of 50% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 65 mL of 0.1 M NaOH, and 62 mL of 0.1 M FeSO\u003csub\u003e4\u003c/sub\u003e. After 24 hours, the samples underwent another filtration and were reintroduced to the SPT, undergoing a density separation analogous to the initial step. The final concentrated sample was placed into HPLC-grade 50% ethanol in a 150 mL beaker and transferred into a headspace vial, from which ethanol was evaporated at 50\u0026deg;C under a gentle flow of N\u003csub\u003e2\u003c/sub\u003e in a TurboVap\u0026reg; LV bath. The sample was then standardised to a final volume of 10 mL with HPLC-grade 50% ethanol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Road dust\u003c/h2\u003e\u003cp\u003eCoarser particles from the collection bucket were initially sieved on a 500 \u0026micro;m mesh, removing macro debris. The larger particles (\u0026gt;\u0026thinsp;500 \u0026micro;m) were stored for later inspection. The \u0026lt;\u0026thinsp;500 \u0026micro;m fraction was transferred to 100 mL of SPT at a density of 1.9 g/cm (Kl\u0026ouml;ckner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Concurrently, filters containing the finer particles were submerged in SPT solution within a crystallising dish and subjected to sonification to dislodge adhered particles. The resultant SPT solution, now laden with these particles, was merged with the previously sieved fraction. Between 3 and 15 g of road dust per sample (particles\u0026thinsp;\u0026lt;\u0026thinsp;500) \u0026micro;m were analysed. The road dust samples followed the same treatment protocol as road runoff samples but were concentrated to 5 mL after evaporation.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.3 \u0026micro;FTIR analysis\u003c/h2\u003e\u003cp\u003eFor the polymer analysis, a subsample of the particles was placed on a zinc selenide transmission window (\u0026Oslash; 13\u0026times;2 mm) set within a compression cell (Pike Technologies) that reduced the viewing area to \u0026Oslash;10 mm. Aliquots were applied in 50 or 100 \u0026micro;L increments using a glass capillary micropipette and dried at 50\u0026deg;C on a heating plate to prevent aggregation. The chemical composition of these particles was analysed using \u0026micro;FTIR with a Focal Plane Array (FPA), using a Cary 620 FTIR microscope and a Cary 670 IR spectrometer (Agilent Technologies, USA). The entire \u0026Oslash;10 mm active area was scanned at 15x magnification, using a Cassegrain objective and an MCT detector with a 128x128 FPA, providing a 5.5 \u0026micro;m pixel resolution. Scans were performed in transmission mode, spanning a spectral range of 3750\u0026ndash;850 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 8 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e resolution with 30 co-added scans per pixel. A background scan with 120 co-added scans was taken before each sample scan. The acquired infrared images for the samples were analysed using the siMPle software (Primpke et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This approach reduces much of the human bias that would otherwise occur during data analysis.\u003c/p\u003e\u003cp\u003eFor each air sample, 300 \u0026micro;L were analysed using \u0026micro;FTIR, with a total of four samples processed. Similarly, for road runoff, three samples were prepared, and 50 \u0026micro;L of each were scanned using \u0026micro;FTIR. In the case of road dust, four samples were examined, with 300 \u0026micro;L analysed for each, except for one sample, for which 100 \u0026micro;L were processed.\u003c/p\u003e\u003cp\u003eThe mass of each MP was estimated following the approach outlined in Simon et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The approach finds the equivalent ellipse assuming the length of the particle as the ellipse's major dimension. It then assumes the particle is an ellipsoid with the third dimension of the ellipse, its thickness, as a fraction of the second dimension of the ellipse. In this case, this fraction was set to 60% of the second dimension of the ellipse. The density of the particle was set to the density of the polymer type to which it was assigned.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Pyrolysis-GC/MS analysis\u003c/h2\u003e\u003cp\u003eAs \u0026micro;FTIR cannot reliably identify car tyre rubber TWPs were instead quantified by Py-GC/MS. The system comprised an EGA/Py-3030D (Frontier Labs, Japan) micro furnace pyrolyzer with a AS-1020E (Frontier Labs, Japan) auto-shot sampler coupled to a TRACE\u0026trade; 1310 GC with an ISQ\u0026trade; single quadrupole GC\u0026ndash;MS (Thermo Fisher Scientific, USA). Aliquots (25 to 1000 \u0026micro;L per sample) were thermally decomposed at 600\u0026deg;C with an interface temperature of 280\u0026deg;C, using helium (He) as carrier gas at a flow of 1 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a 30:1 split ratio. The samples were separated in the GC using a temperature program from 40\u0026deg;C (2 min hold) to 300\u0026deg;C (5 min hold) at a heating rate of 10\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and detected in the MS (electronic ionization at 70 eV, transfer line at 250\u0026deg;C, ion source at 200\u0026deg;C) in scan mode in the range of 35\u0026ndash;500 \u003cem\u003em/z\u003c/em\u003e. The internal standard was deuterated anthracene.\u003c/p\u003e\u003cp\u003eAn external calibration curve was prepared from cryo-milled car- and truck-tyre treads (Genan, Denmark). Quantification targeted 4-vinylcyclohexene as the quantification marker compound, as widely applied for tyre-rubber assessment (Lindfors et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; More et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u0026Ouml;born et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough both Py-GC/S and \u0026micro;FTIR provide mass information, the results are not directly comparable. \u0026micro;FTIR yields estimated masses based on image-derived dimensions and assumed thickness and density, whereas Py-GC/MS quantifies polymer mass from polymer-specific pyrolysis products (Kirstein et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To clearly distinguish, the term MP is used for MPs detected by \u0026micro;FTIR, while TWP is used for TWPs detected by Py-GC/MS.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Air\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Concentrations\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the MP concentrations in the air samples, reported both as particle counts per day and estimated mass (\u0026micro;g/day). The results show noticeable variability between sampling events. Air_1 exhibited the lowest concentration, with 5.0 counts/day and 0.2 \u0026micro;g/day, whereas Air_4 showed the highest values at 26.3 counts/day and 0.95 \u0026micro;g/day. Although samples with higher MP counts generally exhibited higher total MP mass, the relationship was not proportional. This discrepancy is attributed to the heterogeneous composition of the particles, as different polymers possess distinct densities and masses. The estimated MP mass detected by \u0026micro;FTIR remained consistently below 1 \u0026micro;g/day across all samples, indicating generally low airborne MP burdens during the sampling periods. Regarding TWP, all samples reported values below the detection limit (1 \u0026micro;g, defined by signal-to-noise ratio (S/N)\u0026thinsp;\u0026gt;\u0026thinsp;10(Go\u0026szlig;mann et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)), which could either reflect their absence or concentrations too low to be quantified by the method applied.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimated MP concentration in the air samples\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMP [counts/day]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMP mass [\u0026micro;g/day]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTWP mass [\u0026micro;g/day]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.125*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: *The detection limit of the Pyrolysis-GC/MS is 1 \u0026micro;g, but here was recalculated in terms of concentration, considering that the samples have different durations in days.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure 2 contextualizes the concentrations by displaying key environmental and traffic-related parameters for each sampling period. Given the limited sample size, the purpose of this figure is illustrative rather than inferential. From Panels (a) and (b) of Fig. 2, it is evident that the lowest MP concentration (Air_1) occurred at the lowest temperature and the highest wind speed. While both factors may contribute to particle retention, wind speed appears to have played a more dominant role in this dataset. Higher wind velocities consistently aligned with lower MP concentrations, suggesting that increased air movement may hinder particle deposition by dispersing MPs away from the samplers. Although previous studies have linked increased MP concentrations to temperature (Chen et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), this relationship may be mediated by wind: lower wind speeds not only reduce dispersion but also contribute to warmer ambient conditions. Therefore, considering its direct role in particle transport and deposition, wind speed may be a more influential variable in this context.\u003c/p\u003e\n \u003cp\u003eIn Panel (c), no clear relationship emerges between precipitation and MP concentrations across the four samples. However, previous studies (Chen et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Enyoh et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) have shown that rainfall, especially when intense, can enhance atmospheric MP deposition. This occurs through wet deposition, where particles are removed from the air by raindrops, and through a first-flush effect, where a high load of MPs is rapidly washed out at the onset of rainfall. The absence of this trend in the present data may be due to the low rainfall intensities recorded or the limited number of events, which may not have triggered these mechanisms.\u003c/p\u003e\n \u003cp\u003ePanel (d) shows traffic volume near the sampling station. While traffic intensity varied across the samples, its relation to MP concentrations was not straightforward. For instance, Air_3 and Air_4 had similar traffic intensities, yet MP levels were markedly different. This suggests that beyond the number of vehicles or the intensity of road use, meteorological conditions, such as wind speed and precipitation, may have played a more decisive role in determining how many particles were captured during sampling. In environments with high traffic, weather patterns may significantly influence the retention and deposition of airborne MP, shaping local concentrations more than traffic density alone.\u003c/p\u003e\n \u003cp\u003eThe influence of meteorological conditions on MP concentrations has been noted in several studies (Kannankai \u0026amp; Devipriya, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vengatesh et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In aquatic environments, higher wind intensity has been linked to lower shoreline accumulation of MPs due to increased horizontal transport and water column mixing (Garello et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). A similar effect has been suggested in air, where stronger winds may disperse MPs more widely, limiting their capture by passive samplers positioned near the source (Y. Liu et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In certain atmospheric conditions, particles may also acquire electric charges, which can further affect their transport and settling behaviour (H. Wang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In high-traffic environments, these dynamics become more complex. Tire and road wear are known sources of airborne MPs, but their detectability near emission points can vary with wind conditions.\u003c/p\u003e\n \u003cp\u003eRecent findings by Y. Liu et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasize that meteorological factors such as wind speed, thermal turbulence, and precipitation influence both the atmospheric transport and deposition of MPs. Their review highlights how these conditions affect suspension time, resuspension potential, and settling velocity, factors that directly impact the probability of detection in passive sampling systems. These insights underscore the importance of considering atmospheric variability when interpreting MP concentrations and suggest that further long-term monitoring is needed to disentangle emissions from environmental dynamics.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Polymer distribution\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the polymer distribution in the air samples, separated by counts (Panel a) and estimated mass (Panel b). The distribution is expressed as the percentage of each polymer type relative to the total number of MPs or the total mass in each sample. The most common polymers in the air samples were polyester (PES) and polyamide (PA), observed in nearly all samples. In terms of MP counts, the presence of PES in Air_1, Air_2, and Air_4 accounted for approximately 30%, 86%, and 52%, respectively. When comparing estimated mass of MPs for the same samples, PES was predominantly present in Air_2 and Air_4, with contributions of 80% and 46%, respectively. However, in Air_1, the percentage of PES by mass dropped to 22%, as the sample contained a high number of smaller particles whose individual masses were too low to substantially affect the total mass. Conversely, the proportion of PA increased from 20% in MP counts to 45% in MP mass for Air_1, indicating that the PA particles in this sample were larger and contributed more substantially to the total mass. A similar pattern was observed in Air_3, where PA was the most representative polymer both in terms of counts and estimated mass, comprising 56% of the particle count and 54% of the mass. This further supports the finding that PA was present as larger particles, influencing both the counts and estimated mass distributions.\u003c/p\u003e\n \u003cp\u003eOther studies have reported PA, polyethylene (PE), and polypropylene (PP) as the most frequently detected polymers in air samples. Zheng et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that PA, which was also among the predominant polymers in our dataset, accounted for more than 50% of all identified airborne MPs across indoor and outdoor environments in China. Nandi et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlighted the dominance of PP and PE in outdoor air in India. In our case, PE was present in all samples, albeit in low abundance, whereas PP was detected in only two samples. These compositional contrasts may reflect regional differences in local emission sources, sampling methodologies, and variations in chemical identification (e.g., \u0026micro;FTIR vs. LDIR), underscoring the need for harmonization of airborne MP characterization.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Road runoff\u003c/h2\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Concentrations\u003c/h2\u003e\n \u003cp\u003eTable 4 presents the concentrations of MP in the road runoff samples, measured in counts per litre (MP counts/L), MP estimated mass (\u0026micro;g/L) and TWP mass (\u0026micro;g/L). The data reveals significant variation in both particle counts and mass across the three samples.\u003c/p\u003e\n \u003cp\u003eTable 4 Estimated MP concentration in the road runoff samples.\u003c/p\u003e\n \u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample name AAU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMP [counts/Liter]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMP mass [\u0026micro;g/litre]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWP mass [\u0026micro;g/litre]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRRW_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e281.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRRW_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e598.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e269.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRRW_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1470.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRRW_3 had the lowest MP concentration, with 64.5 MP counts/L and 0.4 \u0026micro;g/L. RRW_1 exhibited a slightly higher concentration, with 80 MP counts/L and 0.4 \u0026micro;g/L. In contrast, RRW_2 had the highest concentration, with 598.2 MP counts/L and 11.7 \u0026micro;g/L of MP, representing nearly eight times the particle count and a much higher estimated mass compared to RRW_1 and RRW_3. These results demonstrate considerable variation in MP concentrations across the road runoff samples, reflecting differences in the presence of MPs in the runoff water. The higher concentration observed in RRW_2 suggests a more concentrated sample with a significantly greater estimated mass of MPs.\u003c/p\u003e\n \u003cp\u003eIn contrast, the estimated mass of MPs in RRW_1 and RRW_3 was similar. However, the MP counts differed, with RRW_1 exhibiting higher particle counts than RRW_3. This difference was due to RRW_1 containing a greater number of smaller particles, while RRW_3 had fewer particles that were larger or less dense.\u003c/p\u003e\n \u003cp\u003eInterestingly, the TWP mass in RRW_3 was markedly higher (1470.13 \u0026micro;g/L) than in RRW_1 and RRW_2 (281.15 and 269.81 \u0026micro;g/L, respectively), despite RRW_3 having the lowest MP counts. This highlights that RRW_3 was likely dominated by larger and/or denser TWPs, possibly from recent or localised runoff events with high rubber content. The elevated TWP mass suggests that specific sources or flow dynamics in this sample may have contributed disproportionately to the total mass.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Polymer distribution\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the polymer distribution in the road runoff samples, presented both by MP counts (Panel a) and estimated MP mass (Panel b). The distribution is shown as the percentage of each polymer type relative to the total MP content for each sample.\u003c/p\u003e\n \u003cp\u003eThe high proportion of PP observed in RRW_1 and RRW_3 is consistent with findings of Lindfors et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), who reported that PP, PE, and PES accounted for over 90% of estimated MP mass and counts in road runoff. Moreover, the elevated presence of PA in RRW_2, particularly in counts, may be attributed to emissions from textiles and tyre reinforcement materials (Hirschberg \u0026amp; Rodrigue, 2023). Notably, the dominance of PP in RRW_3, which also coincided with the highest recorded concentration of TWP mass (1470 \u0026micro;g/L), might reflect a localised accumulation driven by pavement roughness. Smyth et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasised that road surface composition significantly influences TWP release, and their study demonstrated that certain pavement types can be particularly prone to shedding PP and PE particles under mechanical stress. These results highlight the importance of considering both the distribution of count and the distribution of mass in the analysis of MP pollution in runoff. As shown by Lindfors et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), different land uses and particle size distributions strongly influence polymer prevalence, and the combined use of \u0026micro;FTIR and Py-GC/MS is essential for revealing both the diversity and abundance of MPs across urban catchments.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Road dust\u003c/h2\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Concentrations\u003c/h2\u003e\n \u003cp\u003eTable 5 presents MP and TWP concentration found in road dust samples. The data are expressed in terms of MP counts per square meter (counts/m\u0026sup2;), estimated MP mass (\u0026micro;g/m\u0026sup2;), and TWP mass (\u0026micro;g/m\u0026sup2;). Substantial differences were observed among the sampling sites, with MP concentrations ranging from 567 to 4250 counts/m\u0026sup2; and estimated MP mass ranged from 30.6 to 291.4 \u0026micro;g/m\u0026sup2;. For road dust from Swedish parking lots, Iordachescu et al. (2024) reported ranges of 5.78-4951 counts/g and 0.06-95.3 \u0026micro;g/g, using a similar \u0026mu;FTIR-based methodology. The combination of estimated MP mass and quantified TWP mass allows for a more comprehensive understanding of traffic-related MP pollution.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable 5 Estimated MP concentration in the road dust samples.\u003c/div\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample name AAU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMP [counts/m\u003csup\u003e2\u003c/sup\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMP mass [\u0026micro;g/m\u003csup\u003e2\u003c/sup\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWP mass [\u0026micro;g/m\u003csup\u003e2\u003c/sup\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRD_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e108.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e33500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRD_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e111.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e169427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRD_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e566.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e85578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRD_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e291.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e178777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Polymer distribution\u003c/h2\u003e\n \u003cp\u003eIn the analysed road dust samples, PP was the predominant polymer in both particle counts and estimated mass, accounting for over 90% of the total particles and contributed substantially to overall mass (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). However, when considering the estimated mass distribution, a relative increase in polymers such as PES, polyurethane (PU), PE, and polyvinyl chloride (PVC) was observed. This indicates that although these polymers had lower counts, they contributed more to the overall mass due to their higher density or larger size. This contrast underscores the importance of considering both particle count and mass to gain a more comprehensive understanding of MP contributions in complex matrices, such as road dust. Our findings are consistent with those of Iordachescu et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), who noted that MP mass can be dominated by fewer but heavier particles, particularly in urban contexts with significant influence from industrial materials and traffic-related debris.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 All samples\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows how the types of polymers varied depending on the matrices (air, road dust, and road runoff) and how these distributions differed when measured by count (Panel a) versus estimated mass (Panel b). In road dust, PP overwhelmingly dominated, making up more than 90% of both the number of particles and the overall mass. In contrast, air samples revealed a broader mix of polymers, with PES and PA appearing more frequently, suggesting diverse sources such as textiles and atmospheric fallout. Road runoff showed a blend of PP and PA, pointing to mixed origins such as urban debris and tyre or fabric-related inputs. These differences underline how each environmental compartment had its own fingerprint when it came to MP pollution.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the size distribution of MP across all samples, categorised by their major dimension. Most particles in all matrices were below 100 \u0026micro;m, with the highest frecuencies between 40 and 80 \u0026micro;m. Road dust samples showed the highest particle counts across all size ranges, particularly dominating the 60 \u0026micro;m bin. In contrast, particles in road runoff and air samples were less abundant and more evenly distributed, with air showing a modest peak around 50 \u0026micro;m and road runoff exhibiting lower counts overall. The results confirm the predominance of small-sized particles (\u0026lt;\u0026thinsp;100 \u0026micro;m), aligning with expectations for environmental samples and emphasising the relevance of fine particles in MP pollution assessments.\u003c/p\u003e\n \u003cp\u003eA Principal Component Analysis (PCA) was conducted based on the polymeric composition of MPs across all samples (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The first two principal components explained 36.08% and 20.79% of the total variance, respectively. The air samples were clearly separated from both road dust and road runoff samples along PC1, indicating a distinct polymer profile likely influenced by atmospheric sources. In contrast, road runoff and road dust samples exhibited considerable overlap, suggesting shared sources or similar input pathways, possibly linked to vehicular emissions or urban surface runoff. Notably, Road runoff_2 appeared isolated along PC1, driven by higher contributions of PA and PE, which were previously identified as significant components in this sample. The direction and length of the vectors indicate that PP, PA, PE, and PES were the main contributors to variability in the dataset. These results emphasize the importance of site-specific factors in shaping the MP signature of each matrix and support the interpretation that different environmental compartments may retain unique polymer fingerprints, while others share overlapping pollution profiles.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis exploratory study investigated the presence, composition, and distribution of MPs and TWPs in road dust, road runoff, and air along a heavily trafficked highway in Bamble, Norway. PP was the predominant polymer across matrices, especially in road dust and road runoff, while PES and PA were more common in air samples. Most particles were below 100 \u0026micro;m, with road dust showing the highest concentrations. The combined use of \u0026micro;FTIR and Py-GC/MS enabled a comprehensive characterization of polymer types, estimated MP mass, and TWP mass contributions.\u003c/p\u003e\u003cp\u003eWhile road dust and road runoff effectively captured MPs and TWPs originating directly from the studied road segment, air samples posed more challenges. Due to atmospheric transport processes, airborne particles generated near the road may travel varying distances before deposition, making proximity-based sampling less representative under certain weather conditions. Although meteorological parameters did not show strong correlations with MP and TWP concentrations in this study, their role in particle dispersion and deposition remains critical. Future studies should further explore these interactions to better understand the influence of wind, rainfall, and temperature on traffic-related MP pollution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: t\u003c/strong\u003ehe data presented in this study are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: th\u003c/strong\u003ee authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: t\u003c/strong\u003ehis research was funded by Norges Forskningsråd, 303712– Treat RW project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eS.C. did part of the analysis and wrote the main manuscript text. L. I. planned the sampling campaign, conducted part of the sampling and did part of the analysis. S. S. R. contributed to planning the sampling campaign and conducted part of it. J. L. contributed to part of the analysis. L. M. contributed to planning the sampling campaign. E. A. V. contributed to planning the sampling campaign. L. C. contributed to structuring the manuscript. J. V. contributed to planning the sampling campaign and structuring the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement: t\u003c/strong\u003ehe authors gratefully acknowledge the Norwegian Research Council for funding (project no. 303712 – Treat RW project). Additional funding and support were provided by Nye Veier AS and the COWI Foundation. The authors also extend their sincere thanks to Espen Hoel and the dedicated team at Nye Veier AS, RISA AS, as well as to Eilen A. Vik and Ocelie Kjønnø at Aquateam COWI, for their valuable technical assistance throughout the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBarnes, D. K., Galgani, F., Thompson, R. C., \u0026amp; Barlaz, M. (2009). Accumulation and fragmentation of plastic debris in global environments. \u003cem\u003ePhilosophical Transactions of the Royal Society of London. Series B, Biological Sciences\u003c/em\u003e, \u003cem\u003e364\u003c/em\u003e(1526), 1985\u0026ndash;1998.\u003c/li\u003e\n \u003cli\u003eChen, C., Long, X., Guo, Z., Li, J., Li, H., Tian, M., Wang, R., Li, Q., Zou, X., Yang, J., Chen, Y., Chang, C., \u0026amp; Guo, Z. (2025). Electrification of wind-blown microplastics and its implication for transport of floating microplastics in air. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, \u003cem\u003e495\u003c/em\u003e. https://doi.org/10.1016/j.jhazmat.2025.138992\u003c/li\u003e\n \u003cli\u003eEnyoh, C. E., Verla, A. 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Quantification of plankton-sized microplastics in a productive coastal Arctic marine ecosystem.\u0026nbsp;\u003cem\u003eEnvironmental Pollution\u003c/em\u003e, \u003cem\u003e266\u003c/em\u003e. https://doi.org/10.1016/j.envpol.2020.115248\u003c/li\u003e\n \u003cli\u003eSahai, H., Aguilera del Real, A. M., Alcayde, A., Bueno, M. J. M., Wang, C., Hernando, M. D., \u0026amp; Fern\u0026aacute;ndez- Alba, A. R. (2025). Key insights into microplastic pollution in agricultural soils: A comprehensive review of worldwide trends, sources, distribution, characteristics and analytical approaches. In \u003cem\u003eTrAC - Trends in Analytical Chemistry\u003c/em\u003e (Vol. 185). Elsevier B.V. https://doi.org/10.1016/j.trac.2025.118176\u003c/li\u003e\n \u003cli\u003eShen, M., Li, Y., Qin, L., Chen, X., Ao, T., Liang, X., Jin, K., Dou, Y., Li, J., \u0026amp; Duan, X. (2025). 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Microplastics in indoor and outdoor environments in China: Characteristic and human exposure risk assessment. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e, \u003cem\u003e287\u003c/em\u003e. https://doi.org/10.1016/j.ecoenv.2024.117328\u003c/li\u003e\n \u003cli\u003eZ\u0026uacute;\u0026ntilde;iga Uma\u0026ntilde;a, J. M., Paniagua, S. A., Brenes, R. C., \u0026amp; Vega Baudrit, J. R. (2025). Microplastic pollution in Costa Rican marine ecosystems: Origins, ecotoxicological impacts, and mitigation strategies. \u003cem\u003eMarine Policy\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e. https://doi.org/10.1016/j.marpol.2025.106772\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microplastics-and-nanoplastics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mina","sideBox":"Learn more about [Microplastics and Nanoplastics](http://microplastics.springeropen.com)","snPcode":"43591","submissionUrl":"https://submission.nature.com/new-submission/43591/3","title":"Microplastics and Nanoplastics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microplastics, tyre wear particles, road runoff, µFTIR, multi-matrix study","lastPublishedDoi":"10.21203/rs.3.rs-7607783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7607783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the presence of microplastics (MPs) including tyre wear particles (TWPs) in three environmental compartments-air, road runoff, and road dust-collected near a heavily trafficked highway in southern Norway. Using \u0026micro;FTIR and Py-GC/MS, we characterised the polymer composition, particle sizes, and estimated mass across matrices. Polypropylene dominated in road runoff and road dust, while polyester and polyamide were most frequent in air samples. MP concentrations were highest in road dust [567\u0026ndash;4250 counts/m\u003csup\u003e2\u003c/sup\u003e or 31\u0026ndash;291 \u0026micro;g/m\u0026sup2;], followed by road runoff [65\u0026ndash;598 counts/L or 0.4\u0026ndash;11.7\u0026micro;g/L] and air [5\u0026ndash;12 counts/day or 0.16\u0026ndash;0.22 \u0026micro;g/day]. TWP concentration was below the detection limit in the air samples, while for road runoff it was in the range 281\u0026ndash;1470 \u0026micro;g/L, and for road dust it was 33500\u0026ndash;178777 \u0026micro;g/m\u003csup\u003e2\u003c/sup\u003e. Although meteorological parameters such as wind speed and precipitation must influence airborne MP capture, no strong correlations were identified. The results suggest that road runoff and road dust better reflected local traffic-related emissions, while air samples were more affected by atmospheric transport. This highlights the need to consider environmental context and sampling strategy when assessing airborne MP pollution. Our findings emphasize the importance of multi-matrix approaches to understand the distribution and behaviour of traffic-derived MPs in complex environments.\u003c/p\u003e","manuscriptTitle":"Microplastic and tyre wear particles at a highway: a case study from Norway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 06:39:59","doi":"10.21203/rs.3.rs-7607783/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T07:04:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T01:36:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T14:56:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138529392265334534210114585807509608016","date":"2025-10-01T07:02:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252707901890232315833818923847739355775","date":"2025-09-29T05:37:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64760547286852165581044790576218519352","date":"2025-09-29T05:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T05:15:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T03:13:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T03:13:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microplastics and Nanoplastics","date":"2025-09-13T13:40:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microplastics-and-nanoplastics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mina","sideBox":"Learn more about [Microplastics and Nanoplastics](http://microplastics.springeropen.com)","snPcode":"43591","submissionUrl":"https://submission.nature.com/new-submission/43591/3","title":"Microplastics and Nanoplastics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f442970-0c62-45f3-864b-2daff4a19c57","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T15:58:43+00:00","versionOfRecord":{"articleIdentity":"rs-7607783","link":"https://doi.org/10.1186/s43591-025-00169-y","journal":{"identity":"microplastics-and-nanoplastics","isVorOnly":false,"title":"Microplastics and Nanoplastics"},"publishedOn":"2025-12-30 15:56:52","publishedOnDateReadable":"December 30th, 2025"},"versionCreatedAt":"2025-09-25 06:39:59","video":"","vorDoi":"10.1186/s43591-025-00169-y","vorDoiUrl":"https://doi.org/10.1186/s43591-025-00169-y","workflowStages":[]},"version":"v1","identity":"rs-7607783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7607783","identity":"rs-7607783","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}