We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Development of a parametrized and regionalized life cycle inventory model for tire and road wear particles
Summary
Researchers developed a detailed model for estimating tire and road wear particle emissions, a major but often overlooked source of microplastics from vehicle traffic. The model accounts for nine key factors including road texture, driving behavior, temperature, and tire type, and can generate estimates at both individual vehicle and national scales. The study found that road surface roughness, aggressive driving, and wet conditions are the biggest drivers of large particle emissions, while temperature and vehicle load most affect fine particle release.
Tire and Road Wear Particles (TRWPs), composed of degraded tire rubber and road particles, are non-exhaust traffic emissions and a significant global source of microplastics. With concerns regarding environmental and health effects, accurate emission estimates are needed to better understand their occurrence and potential impacts. Existing methodologies for quantifying TRWP emissions and fail to account for key parameters related to vehicle operation, environmental, and driving conditions influencing emissions, while overlooking geographical variations. This research article introduces a novel, parametrized, and regionalized inventory model for quantifying TRWP emissions, providing context-specific estimates at vehicle and national scales. It aims to complement Life Cycle Inventory databases allowing a comprehensive Life Cycle Impact Assessment of TRWPs in future works. A vehicle-specific methodology, incorporating nine key parameters - road texture, carried load, driving behavior, speed, road wetness, paved or non-paved roads, temperature, humidity, and tire type (summer, friction, studded) - to estimate both large (10-500 µm) and fine (<10 µm) tire wear particle emissions. Results indicate that road texture, driving behavior and road wetness mainly influence large particle emissions while increasing temperature and carried load predominantly affect fine particle emissions. The driving environment significantly influences both emission types. Despite limited data accuracy and availability, the design of the model allows for continuous updates to refine its applicability across diverse contexts.