Characterisation of tire and tire wear with Py-GC/MS – Identification and quantitative application to complex environmental samples

, Goßmann Isabel, Halbach Maurits, Scholz-Böttcher Barbara.

Tire wear particles (TWP) are assumed to be the most dominant source of microplastics (MP) ending up in the marine environment. The constantly released TWP in admixture with road particles are considered be a threat to ecosystems [1]. The identification potential of TWP pyrolysis products and their respective characteristic fragment ions were proven based on earlier publications [2]. The indicator fragments were included to the Py-GC/MS method for simultaneous mass quantitative analysis for various types of MP established by Fischer & Scholz-Böttcher [3]. Different types of truck and car tire treads were characterized, accordingly. An average truck and car tire was defined each and used for further quantification. The approach was applied to different complex environmental samples (road dust, sea salts, blue mussels, and sediments). The resulting TWP mass loads were compared, differentiated into car and truck impact, and related to thermoplastic MP share. Resulting data show an expected variability in rubber composition of tires regarding to their designated use. Truck tires consist almost exclusively of natural rubber (NR). Car tires show greater quantities of synthetic rubber such as styrene-butadiene rubber (SBR). TWP were present in all analysed compartments except the blue mussels. Generally, car tire wear concentrations exceeded truck tire wear by far, proving the estimated ratio of passenger car tires to truck tires of 10 to 1 based on production and usage data [4] experimentally. Although TWP mass loads surpassed the thermoplastic MP share close to the TWP source, thermoplastic MP clearly dominated in the aquatic samples indicating a poor long-distance transport potential of TWP. 1. Wagner, S. et al., 2018. Water Research; 139; 83-100. 2. Eisentraut, P. et al., 2018. Environmental Science & Technology Letters; 5; 608-613. 3. Fischer, M., Scholz-Böttcher, B.M., 2019. Analytical Methods; 11; 2489-2497. 4. Bertling et al., 2018. Frauenhofer UMSICHT.

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