GRIP

Tire and road wear particles (TRWP) constitute a threat to the environment. TRWP can easily reach soils in the proximity of road networks by airborne and discharge transport, posing the risk of TRWP accumulation in roadside soils. However, despite single records of TRWP mass concentrations the number of analysed samples is too low and the site selection is to biased to really assess TRWP concentrations at streets. Furthermore, even less is known about the numbers, sizes, shapes and spatial distribution of TRWP in roadside soils. The proposed G R I P project aims to overcome this significant knowledge gab by combining a larger field sampling campaign, a novel particle-based analytical approach and cross-validated mass-estimation modelling to exploit the generated TRWP data in a geospatial modelling approach. This approach will allow to predict the spatial distribution of TRWP accumulation in soils along road networks. Sites with automated traffic counters will be selected to combine the traffic data to TRWP concentrations and properties. TRWP extraction from the soil matrix and a microscope-based machine learning approach will enable proper direct measurement of TRWP and reveal information on TRWP properties. Using 3D microscopy, mass-estimation models will provide additional TRWP mass concentrations, cross-validated by common mass-based TRWP analytical methods. The data will be used for geostatistical analysis and random forest machine learning, to generate a geospatial model of TRWP concentration levels. The G R I P project will provide reliable TRWP data at a total of 80 locations and 240 roadside soil samples and a prediction of all soils from national road networks in Switzerland and Germany. The final map of predicted spatial TRWP occurrence, will allow for the first time to know TRWP concentrations and properties on a representative scale and deliver a basis for ecotoxicological investigations and possible mitigation measures.

Project start at 01.01.2026