Characterization of particulate matter from non-exhaust traffic emissions
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Characterization of particulate matter from non-exhaust traffic emissions


Ambient air pollution has been one of the leading risk factors in the global burden of disease, whose contribution to mortality as well as various adverse health outcomes has been well-documented. Among the various sources of air pollution, urban traffic emissions have been reported to be among the largest contributors to various criteria pollutants, including particulate matter (PM). A large body of research has linked traffic-related PM with various health outcomes in metropolitan areas. Therefore, the governments have used their legislative power to reduce vehicle exhaust emissions for the benefit of public health. On the other hand, vehicular non-exhaust emissions from abrasion of auto parts (e.g., brake and tire) have been less investigated, and their impact on public health has not been clearly identified. The current dissertation aimed to provide insight on vehicular non-exhaust emissions, including factors affecting them, their spatial variation, health effects, and contributing sources. The dissertation is divided into the following chapters: Chapter 1 discusses the background and significance of vehicular non-exhaust emissions, chapter 2 presents the literature review, chapters 3 and 4 summarize the original research, and chapter 5 presents the conclusions and future directions.Chapter 2 summarizes the conducted literature review on vehicular non-exhaust emissions. The majority of the literature review was focused on the brake and tire wear particles, which are the most significant vehicular non-exhaust emission sources. Based on the literature review, brake and tire wear particles were both found to have a unimodal mass-based particle size distribution, while their reported particle number distributions were reported to be more variable with mode diameters in different size ranges. The reported brake and tire wear PM2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) emission factors were found to be largely variable, indicating the need for updating the non-exhaust emission inventories in future. The inorganic elements associated with brake (Ba, Cr, Cu, Fe, Mn, Sb, Sn, Zr) and tire (Zn), as well as their organic content (e.g., Polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds) have been reported to be associated with oxidative stress and various health effects. Further, the effects of transportation electrification on non-exhaust emissions and the potential contribution of electric vehicle (EV) Li-ion batteries to non-exhaust emissions were investigated. In Chapter 3, we evaluated the impact of braking deceleration rate and vehicle’s mass on the generated brake and tire wear particles during real-world driving cycles for three test vehicles with different masses (2800 – 5800 lbs.). Regardless of the braking intensity, the brake and tire wear PM2.5 and PM10 concentrations of the heaviest vehicle were significantly higher than the lightest vehicle. Moreover, a positive association was observed between the braking deceleration rate and the peak values in brake and tire wear PM2.5 and PM10 concentrations of all test vehicles, underscoring the important role of driving style and braking intensity on the generated brake and tire wear particles. In Chapter 4, we analyzed the spatial variation of ambient PM2.5 and PM2.5-10 (particulate matter with 2.5 < aerodynamic diameter ≤ 10 μm) elemental composition across the greater Los Angeles area. Traffic-related elements associated with brake wear (e.g., Ba, Cu, Mo, Sb, Zr), tire wear (Zn), and exhaust (Pd) particles were shown to be 3-5 times higher at the locations with high traffic intensity than the background sites. With respect to the previous findings in Los Angeles area, the PM2.5 elemental concentrations have generally increased, despite the reduction in PM2.5 concentration. The significant increase in Li concentration in this region could be linked to the EV Li-ion battery degradation, while the reduction in Ni and V concentrations was potentially due to the regulations on marine fuel composition. Using principal component analysis, four and three source factors were extracted for PM2.5 and PM2.5-10, respectively, whose relative contributions were further quantified based on multiple linear regression analysis. For PM2.5, traffic was the dominant source with 27% contribution, while mineral dust had the largest contribution (45%) to PM2.5-10.

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