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 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 classify quantify the relative number following subclasses contained in environmental dust: tire (TWP), bitumen (BiWP), markings, reflecting glass beads, metallics, minerals, biogenic/organics. The method is non-destructive, rapid, repeatable, enables information about size, shape, elemental composition 2–125 µm. results showed that enabled differentiation between TWP BiWP for > 20 µm with satisfying results. Furthermore, concentration was similar both analyzed size fractions (2–20 20–125 µm), minerals as most dominant subclass x̄ = 78%, 74%) followed by particles, TBiWP, 19%, 22%). Road marking wear, metal contributed 1%, 0.1%, 1% 2–20-µm fraction 0.5%, 0.2%, 0.4% 20–125-µm fraction. present show dust appreciably consists within coarse fine study delivers quantitative evidence importance tires, marking, beads besides metals MP pollution traffic environments based on (real-world) samples