Particle number emissions on mountainous roads: machine learning insights from on-road testing

Zhiwen Jiang, Yujie Wu, Lin Wu, Haomiao Niu, Wei Feng, Wentian Xu, Jiawei Yin, Qijun Zhang, Yanjie Zhang, Hongjun Mao,
Particle number emissions on mountainous roads: machine learning insights from on-road testing,
Environmental Research,
Volume 287,
2025,
123048,
ISSN 0013-9351,
https://doi.org/10.1016/j.envres.2025.123048.
(https://www.sciencedirect.com/science/article/pii/S0013935125023011)
Abstract: Mountainous roads pose unique challenges for controlling vehicular fine particulate number (PN) emissions, a critical pollutant impacting air quality and public health. This study integrates on-road testing with interpretable machine learning to analyze PN emission characteristics of light-duty gasoline and diesel vehicles in China's Qinling Mountains, assessing terrain, driving, and environmental influences. On-road testing results indicate that steep gradients reduce vehicle speeds by 10–50 % and elevate vehicle-specific power (VSP), increasing PN emissions by up to 132 % during uphill driving. High altitudes (>1.6 km) exacerbate PN emissions due to reduced air density, with diesel vehicles showing greater sensitivity. Ensemble learning models (R2 > 0. 91) and SHAP analysis uncover nonlinear terrain-driver interactions, identifying a 25–70 km/h speed range for minimizing PN, a synergistic altitude–gradient effect that elevated emissions by 1.2–3.6 times. Terrain-integrated prediction models and speed optimization strategies are proposed to mitigate PN emissions, providing a scientific basis for managing traffic emissions in mountainous regions.
Keywords: Vehicle emission; Portable emission measurement system; Ensemble machine learning; Mountainous roads