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Updated: Jun 11, 2026

Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System
Published on: May 22, 2020
Data-driven insights into real-world N2O emissions across conventional and hybrid vehicles: Transient impacts and
Huayang Zhao1, Mingshen Ma2, Zhou Yin3
1State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, Shandong 266071, China.
Abstract:
Nitrous oxide (N2O) is a long-lived greenhouse gas with a global warming potential far exceeding that of carbon dioxide and methane. Despite its environmental significance, N2O emissions from modern light-duty vehicles remain poorly characterized under real-world conditions and across emerging powertrain technologies. In this comprehensive study, a diverse fleet of light-duty gasoline vehicles (LDGVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and a light-duty diesel vehicle (LDDV) was systematically investigated using both Worldwide harmonized Light vehicles Test Cycles (WLTC) and Real Driving Emissions (RDE) tests. Detailed analyses were conducted to evaluate operating condition distributions and the coupling relationships among N2O emissions, vehicle driving characteristics, and conventional regulated pollutants. The results demonstrate strong technology-dependent differences. The diesel vehicle exhibited the highest emission factor, primarily driven by non-selective reactions in the aftertreatment system. For gasoline and hybrid vehicles, comparisons between WLTC and RDE revealed that real-world stochastic driving significantly exacerbates transient N2O emissions. Notably, hybrid vehicles exhibited unique N2O profiles highly dependent on their energy management strategies, where frequent engine start-stops and recurring catalyst light-off behaviors dominantly influenced N2O formation. To quantitatively disentangle these complex interactions, a data-driven framework was further implemented. By training models with real-time kinematic parameters (velocity, acceleration, vehicle specific power) and instantaneous exhaust compositions, the feature importance was extracted to precisely identify the primary driving factors for each propulsion system. Ultimately, these multi-dimensional insights provide direct guidance for calibration strategies targeting N2O mitigation in next-generation emission control systems.
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