Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier

Rausch, Juanita; Jaramillo‐Vogel, David; Perseguers, Sébastien; Schnidrig, Nicolas; Grobéty, Bernard; Yajan, Phattadon

doi:10.1016/j.scitotenv.2021.149832

Cited by 77 publications

(59 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on recent intensive measurements, non-exhaust emissions from road vehicles is an emerging issue in Switzerland's urban areas. Brake wear, tyre wear, road wear, and resuspension of road dust have been shown to be important components of Switzerland's urban PM load (Hüglin and Grange, 2021;Grange et al, 2021;Rausch et al, 2022). Such emissions are generated by abrasive processes and although there is a tendency of such PM to be in the coarse-mode, these emissions can also significantly enhance fine PM concentrations.…”

Section: Pm In Switzerlandmentioning

confidence: 99%

Linking Switzerland's PM10 and PM2.5 oxidative potential (OP) with emission sources

Grange

Uzu

Weber

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Particulate matter (PM) is the air pollutant which causes the greatest deleterious heath effects across the world and PM is routinely monitored within air quality networks where PM mass according to its size, and sometimes number are reported. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric which aims to classify PM in respect to its oxidising ability in lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June, 2018 and May, 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations which involved the quantification of a large number of PM constituents and OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable in time and space with Bern-Bollwerk, an urban-traffic sampling site having the greatest levels of OPv among the Swiss sites (especially when considering ), with more rural locations such as Payerne experiencing lower OPv. However, urban-background and suburban sites did experience significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were: 0.4–4.1, 0.6–3.0, and 0.3–0.7 nmol min−1 m−3 for the , , and respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources which were identified contributed to OPv on average, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass). A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely: copper, zinc, iron, tin, antimony and somewhat manganese and cadmium as well as three specific wood burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium) were the most important PM components to explain and predict OPv. The combination of a metal and a wood burning specific tracer led to the best performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical, the models simply required a variable to be present to represent the emission source or process. The modelling process also showed that may be a more specific metric for OP than the other assays employed in this work. This analysis strongly suggests that the anthropogenic and locally emitted road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv, and presumably biological harm reductions in Switzerland.

show abstract

Section: Pm In Switzerlandmentioning

confidence: 99%

Linking Switzerland's PM10 and PM2.5 oxidative potential (OP) with emission sources

Grange

Uzu

Weber

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…20-125 µm) were dispersed with compressed air using the Morphology G3ID device manufactured by Malvern. The dispersion was performed on a boron substrate (Rausch et al, 2022), which has the advantage of allowing the characterization and quantification of carbonaceous particles such as tire wear, bitumen, road markings, and biogenic/organics. This is possible because boron substrates do not contain the element carbon as ordinary substrates for SEM analysis.…”

Section: Dispersion Of Particles On Boron Substratesmentioning

confidence: 99%

“…Besides the chemical and morphological (size, circularity, aspect ratio, etc.) parameters delivered by the AZtecFeature software, the algorithm originally developed for air samples automatically extracts and calculates 21 complementary morphological and textural (backscatter-signal based) parameters (e.g., fractal dimensions, convexity, gray kurtosis, modified Ripley's K-functions) (Rausch et al, 2022). For the particle classification, all the available chemical and morpho-textural descriptors (in total 67) are combined in a "random forest" model trained with 113,043 particles from 138 WDS samples and six air samples (Sigma-2 samples, from Stockholm, Sweden).…”

Section: Data Treatment and Particle Classification With MLmentioning

confidence: 99%

“…Non-exhaust particles accumulated as road dust on the road surface can also be suspended in the air by traffic and/or wind or transported in splash and spray from vehicles during wet conditions (Gustafsson et al, 2019). Traffic-derived non-exhaust particles contain numerous pollutants such as metals, organic compounds, and microplastics (MP), and are often found in the environment with encrustations of minerals or as mixtures with other particles (Abbasi et al, 2017;Järlskog et al, 2021;Panko et al, 2012;Rausch et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The authors showed promising results and identified TRWP (6-120 µm) based on shape (elongated/round) and chemical composition, but the method required an extensive sample preparation prior to analysis, and the identification of TWP was not automated. Recently, a new approach combining automated SEM/EDX single-particle analysis with a machine learning-based (ML) classification algorithm for differentiation and quantification of the most dominant particles in urban air samples (including TWP and non-exhaust particles) was presented (Rausch et al, 2022). The identification and differentiation of the single-particle subclasses and their proportions were based on elemental composition combined with morphological and textural properties.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Järlskog

Jaramillo‐Vogel²,

Rausch³

et al. 2022

Water Air Soil Pollut

Self Cite

View full text Add to dashboard Cite

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

show abstract

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Sharma,

Barman,

Mishra

et al. 2024

Land Degrad Dev

View full text Add to dashboard Cite

In recent decades, the ubiquity of micro and nanoplastics (MNPs) pollution has emerged as a global concern due to its potential risks to both ecological and human health. The detrimental effects of MNPs have resulted in significant changes in the physicochemical and biological properties of terrestrial soil and benthic sediment. These alterations have led to a disrupted nutrient cycle, potential future climate hazards, and further impacts on ecosystem services. This review aims to discuss the possible origins, composition, abundance, life cycle, and transport processes of MNPs in aquatic and terrestrial ecosystems. It seeks to understand the potential ecological and human health risks associated with MNPs pollution and explores their implications for biodiversity conservation and ecosystem services. Moreover, this review highlights the advanced analytical techniques used to detect and quantify MNPs. It further suggests policy frameworks to combat this pollution in natural environments. The findings of this review are intended to assist environmentalists, microbiologists, hydrologists, and policymakers in identifying scientific gaps and pragmatic solutions to reduce MNPs pollution. Future research should investigate the intake mechanism and impact of MNPs on plants in different ecosystems, as well as their effects on food chains and human health. This will ensure that the ecological and human health risks associated with MNPs pollution can be timely mitigated.

show abstract

Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier

Cited by 77 publications

References 53 publications

Linking Switzerland's PM<sub>10 </sub>and PM<sub>2.5</sub> oxidative potential (OP) with emission sources

Linking Switzerland's PM<sub>10 </sub>and PM<sub>2.5</sub> oxidative potential (OP) with emission sources

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

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Contact Info

Product

Resources

About

Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier

Cited by 77 publications

References 53 publications

Linking Switzerland's PM&lt;sub&gt;10 &lt;/sub&gt;and PM&lt;sub&gt;2.5&lt;/sub&gt; oxidative potential (OP) with emission sources

Linking Switzerland's PM&lt;sub&gt;10 &lt;/sub&gt;and PM&lt;sub&gt;2.5&lt;/sub&gt; oxidative potential (OP) with emission sources

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

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Contact Info

Product

Resources

About

Linking Switzerland's PM<sub>10 </sub>and PM<sub>2.5</sub> oxidative potential (OP) with emission sources

Linking Switzerland's PM<sub>10 </sub>and PM<sub>2.5</sub> oxidative potential (OP) with emission sources