Direct measurement of brake and tire wear particles based on real-world driving conditions

With implementing vehicle emission control policies, tailpipe particulate emissions have been gradually controlled, and the relative contribution of non-tailpipe particulate emissions, such as brake and tire wear, has further increased. A unified and scientific method for sampling non-tailpipe particulate matter (PM) emissions is essential to improve the accuracy of the emission characteristics and factors. This study proposes a novel sampling method based on real-world driving conditions to obtain information on emissions and extract characteristic conditions for tire and brake pad wear. We extracted 200 representative braking segments for simulation experiments based on road type, initial and final velocities, temperature, and deceleration rate. Two standard test cycles to simulate the tire wear conditions of the front and rear wheels were constructed based on velocity, lateral, and vertical forces. Under the real-world driving condition test cycle, the emission factors of PM2.5 and PM10 for brake wear particles of passenger vehicles were 2.66 mg/km and 11.65 mg/km, respectively. In contrast, the emission factors of PM2.5 and PM10 for tire wear particles were 0.21 mg/km and 1.27 mg/km, respectively. Moreover, this study provides insights and basic data for localizing and improving the emission model, which can enhance its applicability and accuracy.

Chemical characteristics of fine tire wear particles generated on a tire simulator

Tire wear is one of the major sources of traffic-related particle emissions, however, laboratory data on the components of tire wear particles (TWPs) is scarce. In this study, ten brands of tires, including two types and four-speed grades, were chosen for wear tests using a tire simulator in a closed chamber. The chemical components of PM2.5 were characterized in detail, including inorganic elements, water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), and polycyclic aromatic hydrocarbons (PAHs). Inorganic elements, WSIs, OC, and EC accounted for 8.7 ± 2.1%, 3.1 ± 0.7%, 44.0 ± 0.9%, and 9.6 ± 2.3% of the mass of PM2.5, respectively. The OC/EC ratio ranged from 2.8 to 7.6. The inorganic elements were dominated by Si and Zn. The primary ions were SO42- and NO3-, and TWPs were proven to be acidic by applying an ionic balance. The total PAHs content was 113 ± 45.0 μg g-1, with pyrene being dominant. In addition, the relationship between the chemical components and tire parameters was analyzed. Inorganic elements and WSIs in TWPs were more abundant in all-season tires than those in winter tires, whereas the content of PAHs was the opposite. The mass fractions of OC, Si, and Al in the TWPs all showed increasing trends with increasing tire speed grade, but the PAHs levels showed a decreasing trend. Ultimately, to provide more data for further research, a TWPs source profile was constructed considering the tire weighting factor.

Brake wear-derived particles: Single-particle mass spectral signatures and real-world emissions

Brake wear is an important but unregulated vehicle-related source of atmospheric particulate matter (PM). The single-particle spectral fingerprints of brake wear particles (BWPs) provide essential information for understanding their formation mechanism and atmospheric contributions. Herein, we obtained the single-particle mass spectra of BWPs by combining a brake dynamometer with an online single particle aerosol mass spectrometer and quantified real-world BWP emissions through a tunnel observation in Tianjin, China. The pure BWPs mainly include three distinct types of particles, namely, Ba-containing particles, mineral particles, and carbon-containing particles, accounting for 44.2%, 43.4%, and 10.3% of the total BWP number concentration, respectively. The diversified mass spectra indicate complex BWP formation pathways, such as mechanical, phase transition, and chemical processes. Notably, the mass spectra of Ba-containing particles are unique, which allows them to serve as an excellent indicator for estimating ambient BWP concentrations. By evaluating this indicator, we find that approximately 4.0% of the PM in the tunnel could be attributable to brake wear; the real-world fleet-average emission factor of 0.28 mg km^-1 veh^-1 is consistent with the estimation obtained using the receptor model. The results presented herein can be used to inform assessments of the environmental and health impacts of BWPs to formulate effective emissions control policies.

Applying machine learning to construct braking emission model for real-world road driving

Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM2.5 from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R2 and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furthermore, the CatBoost-based model was interpreted and visualized by an individual conditional expectation (ICE) plot to break the machine learning "black box", with BEI and MC showing nonlinear monotonic increasing relationships with braking emissions. ICE plot also provides viable technical solutions for controlling brake emissions in the future. Both avoiding aggressive braking driving behavior (e.g., the application of smart transportation technologies) and using brake pads with less metal content (e.g., using ceramic brake pads) can effectively reduce brake emissions. The construction of a machine learning-based brake emission model and the white-boxing of its model provide excellent insights for the future detailed assessment and control of brake emissions.

Determining factors and parameterization of brake wear particle emission

Brake wear emission contributes to an increasingly significant proportion of vehicle-related particulate matter, but knowledge of its emission features and determining factors is still highly insufficient. Here, brake dynamometer experiments were conducted under controlled variables tests and real-world driving conditions to systematically investigate brake wear particle (BWP) emission. Compared to the decelerating process, the separating of pads and disc releases more BWPs, accounting for 47-76% of the total PM_2.5 mass. Particle number and mass distributions exhibit bimodal (< 0.01 µm and 0.8-1.2 µm) and unimodal (2-5 µm) patterns, respectively. Larger speed reduction exponentially amplifies BWP emission, and the significant enhancement of nanoparticles is proved to be related to the evaporation of organic constituents in the pads with threshold ranging from 170 °C to 270 °C. Emissions from front and rear brake assemblies don't agree with braking torque distribution, mainly attributive to the different braking pressures. A parameterization scheme for BWP emission based on kinetic energy loss is further established and proved to sufficiently predict the variation of BWP under real-world driving conditions. Being corrected by 1.8th power of the initial speed, the scheme improves the prediction.

Vehicular non-exhaust particulate emissions in Chinese megacities: Source profiles, real-world emission factors, and inventories

Vehicular non-exhaust emissions account for a significant share of atmospheric particulate matter (PM) pollution, but few studies have successfully quantified the contribution of non-exhaust emissions via real-world measurements. Here, we conduct a comprehensive study combining tunnel measurements, laboratory dynamometer and resuspension experiments, and chemical mass balance modeling to obtain source profiles, real-world emission factors (EFs), and inventories of vehicular non-exhaust PM emissions in Chinese megacities. The average vehicular PM_2.5 and PM₁₀ EFs measured in the four tunnels in four megacities (i.e., Beijing, Tianjin, Zhengzhou, and Qingdao) range from 8.8 to 16.0 mg km^-1 veh^-1 and from 37.4 to 63.9 mg km^-1 veh^-1, respectively. A two-step source apportionment is performed with the information of key tracers and localized profiles of each exhaust and non-exhaust source. Results show that the reconstructed PM₁₀ emissions embody 51-64% soil and cement dust, 26-40% tailpipe exhaust, 7-9% tire wear, and 1-3% brake wear, while PM_2.5 emissions are mainly composed of 59-80% tailpipe exhaust, 11-31% soil and cement dust, 4-10% tire wear, and 1-5% brake wear. Fleet composition, road gradient, and pavement roughness are essential factors in determining on-road non-exhaust emissions. Based on the EFs and the results of source apportionment, we estimate that the road dust, tire wear, and brake wear emit 8.1, 2.5, and 0.8 Gg year^-1 PM_2.5 in China, respectively. Our study highlights the importance of non-exhaust emissions in China, which is essential to assess their impacts on air quality, human health, and climate and formulating effective controlling measures.