Qijun Zhang

Vehicular non-exhaust emissions have become a dominant source of particulate pollution in urban areas. However, the dynamic physicochemical evolution transforming solid friction materials into aerosols under extreme braking conditions remains elusive, which significantly hinders accurate source apportionment. To bridge this gap, we established a comprehensive macro-to-micro analysis framework, integrating transient emission kinetics, multi-scale chemical fingerprinting, and machine learning techniques to decipher the formation mechanism of brake wear particles (BWPs).By combining laboratory chassis dynamometer experiments (under WLTC and extreme AMS cycles) with high-resolution online monitoring (EEPS/APS), we captured the real-time formation dynamics. Crucially, at friction interface temperatures exceeding 300°C, we observed distinct "banana-shaped" particle size distribution evolution, directly indicative of rapid particle nucleation and subsequent growth events. However, connecting these macro-kinetics to micro-composition revealed a striking physicochemical paradox. Nanoscale single-particle elemental mapping indicated that ultrafine particles were predominantly composed of metallic elements (Fe/Cu) with negligible carbon signals. In sharp contrast, bulk surface spectroscopy (XPS/FTIR) of collected PM2.5 samples revealed a composition overwhelmingly dominated by organic carbon functional groups derived from resin binders.To reconcile this discrepancy, we pioneered a novel hybrid machine learning methodology. This approach uniquely couples Deep Residual Networks (ResNet) for texture extraction with XGBoost for geometric decision-making. This intelligent analysis allowed for high-throughput quantification of single-particle morphology, revealing that these burst-phase nanoparticles exhibit near-perfect sphericity, ruling out mechanical abrasion. Consequently, we propose a mechanism of metal-vaporization induced heterogeneous nucleation, whereby trace metallic components vaporize to form high-density condensation nuclei. These nuclei subsequently trigger the heterogeneous condensation and coating of semi-volatile organic vapors, thereby forming particles with a distinctive metal-core/organic-shell architecture. Our results redefine the braking process as an active high-temperature physicochemical reactor, providing a robust, data-driven foundation for understanding the complex formation mechanisms of non-exhaust emissions.

Vehicular non-exhaust emissions have become a dominant source of particulate pollution in urban areas. In this work, we systematically investigated the emission, transformation, and health effects of vehicular non-exhaust pollutants. Our study focused on three key aspects: (1) real-world emissions and contributions of brake wear particles (BWPs) and tire wear particles (TWPs); (2) heterogeneous aging of atmospheric reactive gases on BWP surfaces; and (3) the evolution of health impacts during atmospheric aging of BWPs and TWPs. By combining laboratory chassis-dynamometer experiments with real-world tunnel observations, we quantitatively constrained non-exhaust emissions from both controlled and actual driving conditions. A new methodology was developed to enable direct and quantitative measurement of BWP emissions on a chassis dynamometer, providing a robust experimental foundation for emission characterization. Tunnel observations under real driving conditions further allowed us to derive BWP and TWP emissions in China using both bulk-composition and single-particle source apportionment approaches, yielding mutually consistent estimates. Complementary flow-tube simulations revealed that BWP surfaces exhibit pronounced heterogeneous reactivity toward atmospheric oxidants and trace gases such as SO₂, NO₂, and O₃. Unexpectedly, these reactions led to substantial sulfate formation from SO₂ oxidation, efficient HONO production from NO₂ uptake, and rapid O₃ decomposition. Moreover, we also found that atmospheric aging by OH and O₃ considerably enhances the oxidative potential of BWPs and TWPs, implying elevated health risks following environmental transformation. Our results reveal that vehicular non-exhaust emissions not only constitute an important primary source of urban particulate matter, but also serve as highly reactive mediators in atmospheric chemistry. We highlight the urgent need to explicitly incorporate non-exhaust sources into air-quality models and account for atmospheric aging when evaluating the health burden associated with traffic-related particles.

In the assisted driving-based Intelligent Transportation System (ADITS) featuring speed-guided functionality, intelligent vehicles are crucial for enhancing transportation sustainability. However, the impact of ADITS on transportation sustainability at different penetration rates remains unclear. This study combines road testing with simulation to address this gap. By integrating road test data, traffic survey data, ADITS algorithms, and Monte Carlo uncertainty analysis, the study validates the simulation results’ reliability. Correction coefficients are provided to refine energy consumption and carbon emissions simulations. The findings indicate that the differences between simulation data and on-road test data are minimal. However, under varying penetration rate environments, the differences in most variables are statistically significant. At 100% penetration, the adjusted energy use in congested traffic stands at 1.05 MJ/km, with carbon emissions of 81.6 g/km. In uncongested scenarios, these values drop to 0.65 MJ/km and 55.4 g/km, respectively. With the increasing penetration rate, energy efficiency and decarbonization efficiency gradually improve, achieving an optimization of 23%−27%. Intelligent vehicles optimized for uncongested conditions exhibit smoother driving patterns, mitigating aggressive maneuvers and contributing to green transport. In congested scenarios, intelligent vehicles face constraints from leading vehicles, limiting speed optimization. At lower penetration rates (0.25), ADITS can worsen traffic conditions. However, with higher penetration rates, speed fluctuations decrease, leading to more uniform road speeds, reduced high-speed accelerations, and lower energy consumption and carbon emissions. These findings provide theoretical support for the implementation and development of intelligent transportation systems.

The non‐exhaust emissions of motor vehicles derived from brake and tire wear have caused great concern, thereby requiring an investigation of their impact on air quality. We develop a high‐resolution vehicle non‐exhaust emissions inventory for Tianjin City based on detailed link‐level traffic profiles and localized emission factors. We conduct atmospheric simulations of ambient particulate matter (PM) concentrations contributed by vehicle non‐exhaust emissions. The results show that brake and tire wear emissions surpassed exhaust emissions for the first time in 2013 (PM_2.5). Areas with high emission intensities are primarily concentrated in central urban areas with high human activity and population and motor vehicle density. Local vehicle non‐exhaust emissions can be responsible for approximately 3% of the urban PM concentrations, and may reach 7.13% in some time periods. In the future, controlling vehicle non‐exhaust emissions will effectively alleviate urban PM pollution during the new energy vehicles era.