Differentiating and Quantifying Carbonaceous (Tire, Bitumen, and Road Marking Wear) and Non-carbonaceous (Metals, Minerals, and Glass Beads) Non-exhaust Particles in Road Dust Samples from a Traffic Environment

Abstract

Tires, bitumen, and road markings are important sources of traffic-derived carbonaceous wear particles and microplastic (MP) pollution. In this study, we further developed a machine-learning algorithm coupled to an automated scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX) analytical approach to classify and quantify the relative number of the following subclasses contained in environmental road dust: tire wear particles (TWP), bitumen wear particles (BiWP), road markings, reflecting glass beads, metallics, minerals, and biogenic/organics. The method is non-destructive, rapid, repeatable, and enables information about the size, shape, and elemental composition of particles 2–125 µm. The results showed that the method enabled differentiation between TWP and BiWP for particles > 20 µm with satisfying results. Furthermore, the relative number concentration of the subclasses was similar in both analyzed size fractions (2–20 µm and 20–125 µm), with minerals as the most dominant subclass (2–20 µm x̄ = 78%, 20–125 µm x̄ = 74%) followed by tire and bitumen wear particles, TBiWP, (2–20 µm x̄ = 19%, 20–125 µm x̄ = 22%). Road marking wear, glass beads, and metal wear contributed to x̄ = 1%, x̄ = 0.1%, and x̄ = 1% in the 2–20-µm fraction and to x̄ = 0.5%, x̄ = 0.2%, and x̄ = 0.4% in the 20–125-µm fraction. The present results show that road dust appreciably consists of TWP and BiWP within both the coarse and the fine size fraction. The study delivers quantitative evidence of the importance of tires, bitumen, road marking, and glass beads besides minerals and metals to wear particles and MP pollution in traffic environments based on environmental (real-world) samples

Analysis of Tyre Tread for Metal Tracers with Applications in Environmental Monitoring

“…Elemental tracers have been selected, despite organic alternatives being available (Goßmann et al, 2021), as it allows for the use of x-ray fluorescence detectors, that are already used in the air quality monitoring network (Hicks et al, 2021). Scanning electron microscopy with energy dispersive X-ray spectroscopy methods have been developed and validated with machine learning to identify tyre wear particles based on elemental composition, however these too are not able to integrate with already existing metal infrastructure (Järlskog et al, 2022a(Järlskog et al, , 2022b.…”

Contribution of Road Vehicle Tyre Wear to Microplastics and Ambient Air Pollution

“…The annual average mass fraction of tyres in PM 10 was 1.8% at an urban background site in Switzerland and 10.5% at an urban kerbside site [102]. Road dust samples from a highway in Sweden contained more than 10% tyre particles [103]. A study with a single-particle aerosol mass spectrometer roadside of a port highway in China found a 6.6% contribution of tyres to the total PM [104].…”

Research on the Anti-Fouling Properties of Double-Coated Road Markings

“…The surface of markings constructed with this coating is rough, and it has a high porosity, making it easy for pollutants to adhere to the gaps [23]. In some cities with severe pollution and little rainfall, the color of white thermoplastic markings is almost indistinguishable from the asphalt pavement, resulting in severe marking pollution [40].…”