Modeling urban brake wear particle emissions: A ride-hailing case in Chengdu, China

Brake wear particle (BWP) emissions, a major non-exhaust source of urban air pollution, will be regulated under Euro 7 standards. However, current knowledge on quantifying urban BWP emissions and their spatiotemporal variations is insufficient. This study incorporates an operating-mode-based modeling framework with large-scale ride-hailing trajectories and local survey data from Chengdu, China. The local PM10 emission factor was estimated to be 27 ± 4 mg/km/veh, higher than the literature due to frequent braking. By applying interpretable machine learning for trip-level analysis, strong correlations were identified between BWP emissions and driving characteristics like braking frequency, intensity, speed, and road grade, highlighting the need for reducing on-road braking through better driving and traffic management. Spatiotemporal analysis indicated emissions spike during congested hours, which are also highly correlated with sensitive spots like healthcare facilities. The results shed light on targeted strategies to mitigate the environmental and health impacts of BWP emissions.