ArticleEnvironmental science & technology letters2025
Qualitative and Quantitative Analysis of Tire Wear Particles (TWPs) in Road Dust Using a Novel Mode of Operation of TGA-GC/MS.
Article in Environmental science & technology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- A review on recent advances in the environmental occurrence of benzothiazoles: analytical methods and their role as non-exhaust traffic tracers.Environmental science and pollution research international · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting and quantifying tire wear particles (TWPs) in the environment pose a unique environmental challenge due to their chemical complexity. There are emerging concerns around TWPs due to their potential high numbers of particles released, outnumbering microplastics, as well as the leaching of toxic additives such as 6-PPD which has been linked to the death of salmon even when present at very low levels (<0.1 μg/L). Analytical techniques such as pyrolysis gas chromatography mass spectrometry (Py-GC/MS) and thermal extraction-desorption gas chromatography mass spectrometry (TED-GC/MS) have been used but also demonstrate limitations including low sample mass, low sample throughput, and complex characterization and quantification procedures. This work aims to overcome these challenges by developing a new approach which utilizes a coupling between thermogravimetric analysis (TGA) and gas chromatography-mass spectrometry (GC/MS). This work is the first to harness conventional TGA-GC/MS for the analysis of tire rubber, with the detection of additives such as 6-PPD, while also pioneering a novel mode of operation, PyroTGA-GC/MS, using fast heating to enable robust quantitative analysis of TWPs in road dust. The limits of detection and quantification of 0.08/0.16 μg and 0.20/0.40 μg for SBR and PI, respectively, are lower than those achieved using Py-GC/MS and TED-GC/MS for SBR and align with those achieved for PI. This study reveals a clear link between the ratio of PI to SBR and the proportion of heavy goods vehicles. This work solves key issues in tire particle analysis related to sample size and throughput. By overcoming these limitations, we introduce a technique that provides an economically viable solution for large-scale commercial analysis of tire rubber and particles.
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Registered trials
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