Tire and road wear particles (TRWP) pose a significant threat to the environment. TRWP easily reach soils near road networks through airborne transport and runoff, leading to accumulation in roadside soils. Despite some existing records of TRWP mass concentrations, the number of analyzed samples has been insufficient and site selection biased, limiting reliable assessment of TRWP concentrations along streets. Moreover, information on the quantity, size, shape, and spatial distribution of TRWP in roadside soils remains scarce.
The "GRIP" project addresses this critical knowledge gap by implementing a large-scale field sampling campaign combined with a novel particle-based analytical approach and cross-validated mass-estimation modelling to fully exploit TRWP data within a geospatial modelling framework. This approach enables prediction of the spatial distribution of TRWP accumulation in soils along road networks. Sites equipped with automated traffic counters have been selected to link traffic data with TRWP concentrations and properties. TRWP are extracted from the soil matrix and analysed using a microscope-based machine learning approach, allowing accurate direct measurement and detailed characterization of TRWP. Additionally, 3D microscopy and mass-estimation models provide complementary TRWP mass concentration data, cross-validated against established mass-based analytical methods. The collected data undergo geostatistical analysis and random forest machine learning to generate a geospatial model of TRWP concentration levels.
The GRIP project will deliver reliable TRWP data across various locations and provide predictions for soils along national road networks in Switzerland and Germany. The final spatial map of predicted TRWP occurrence will provide representative information on TRWP concentrations and properties, laying a foundation for ecotoxicological investigations and informing potential mitigation measures.
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