Air quality is one of the highest risks to human health worldwide, with vehicles and road transportation being major contributors, especially in urban areas. While EURO standards have successfully targeted exhaust emissions from diesel and gasoline engines over the past decades, non-exhaust emissions remain as a significant and unregulated problem, especially for road transportation.

These non-exhaust emissions, including particles from tires, brakes, and road abrasion, as well as resuspension due to passing traffic, are now more relevant than exhaust emissions for PM10 and PM2.5 and are to be regulated in the upcoming EURO standard. This study presents a fine dust filtration system developed to compensate vehicles emissions and proposes a method for characterizing the collected particles.

The filtration system was tested in the field over one year focusing on particle size distribution, composition, and the quantification of potential sources such as tire, brake, and road wear particles. The effectiveness of the filtration system for exhaust and non-exhaust emission particle collection was assessed using various analysis methods, based primarily on SEM-EDX combined with a machine learning algorithm, to identify and quantify the source apportionment of PM10 and PM2.5 size fractions .

The findings were compared with existing literature to validate the proposed method. Finally, the system's compensation potential was evaluated under different operating scenarios and ambient air pollution levels, demonstrating its suitability for widespread use in supporting vehicle manufacturers to meet future emission regulations such as EURO standards.