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.

[Railway Emission Trends in China Based on Fuel Life Cycle Analysis].

Railway transportation is one of the main modes of modern transportation. Under the dual constraints of air quality improvement and carbon neutrality achievement, clarifying the emission trend of CO2 and pollutants in railway transportation is of great significance for pollution and carbon reduction in the transport sector. In this study, the CO2 and pollutant emission characteristics of Chinese railways from 2001 to 2018 were analyzed based on the fuel life cycle method. Then, railway emission trends from 2019-2030 were assessed combined with scenario analysis. The results showed that with the advancement of railway electrification, the use of new diesel locomotives, and the continuous upgrading of fuel standards, the total CO2 and pollutant emissions in the fuel life cycle of railway transportation showed an upward and downward trend, respectively. In 2018, the total emissions of CO2, NOx, CO, BC, and SOx from railway transportation were 3780.29×104t, 11.98×104t, 3.94×104t, 0.20×104t, and 3.08×104t, respectively. Accelerating the improvement of power structure and reducing unit energy consumption were the best single control strategies to reduce railway emissions of CO2, SOx, NOx, BC, and CO, respectively. Under the comprehensive scenario of actively responding to railway pollution and carbon reduction, the emission reduction rates of CO2, NOx, CO, BC, and SOx could reach 35%, 37%, 39%, 32%, and 45%, respectively. The stagnation of power structure reform or the railway electrification process will lead to a significant increase in total emissions of railway transportation. Therefore, the pollution and carbon reduction of railway transportation requires continuous attention.

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.

[Chemical Component of Particulate Matters and VOCs Characteristics During Vehicle Brake Processes].

Six sets of brake systems were tested using a brake dynamometer, and the brake wear particles (BWPs) and volatile organic compounds (VOCs) were collected during the braking process. In total, 39 elements, 12 water-soluble ions, 7 carbon components, and 18 polycyclic aromatic hydrocarbons (PAHs) in BWPs were extracted and detected, and 74 VOCs in gas samples were analyzed. The average mass fractions of 12 inorganic elements (i.e., Sb, Mg, Cu, Zn, Ti, Ca, Si, Zr, K, Ba, Al, and Fe) with higher contents in PM2.5 and PM10 were 43.4% and 40.3%, respectively, and the average mass fraction of Fe was the highest, accounting for 16.6% and 13.1% of PM2.5 and PM10, respectively. The average mass fractions of the 12 water-soluble ions in PM2.5 and PM10 were 16.5% and 12.6%, respectively, and NO3-, SO42-, and Ca2+ were the ions with high contents. The average mass fraction of total carbon (TC) in PM2.5 and PM10 were 21.9% and 18.1%, respectively, and the average mass fraction of organic carbon (OC) was approximately five times that of elemental carbon (EC). There were six types of PAHs with a detection rate greater than 50%, among which naphthalene (Nap) was the most abundant. The average mass concentration of 74 VOCs was 316.04 µg·m-3, of which the aromatic hydrocarbon had the highest mass concentration. The compositions of BWPs and VOCs emitted by the six sets of brake systems were quite different, which was mainly determined by the brand and raw materials of the brake pads.

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 PM2.5 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.

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.

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 PM2.5 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 PM2.5 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 PM2.5 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.

Occurrence of benzothiazole and its derivates in tire wear, road dust, and roadside soil.

Benzothiazole (BT) and its derivates are commonly used as vulcanization accelerators in rubber production. Information on the occurrence of BTs in road dust (RD) and on human exposure to these compounds is very limited. BT and its six derivates in tire wear particles (TWPs) and RD were determined in this study. Samples were extracted using solid-liquid extraction, purified by a HLB SPE column, and determined by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). All seven BTs were found in 17 TWPs samples from different tire brands. The mass fractions of all seven BTs (∑BTs) in TWPs ranged from 46.93 to 215 µg/g with an average concentration of 99.32 µg/g. Benzothiazole and 2-hydroxybenzothiazole (2-OH-BT) were the two major compounds, accounting for 56%-89% of the total. The seven BTs were also found in all 36 sets of RD samples (each set included one sample of TSP (particles < 75 µm in diameter), PM10 (particles < 10 µm in diameter) and PM2.5 (particles < 2.5 µm in diameter)) fractions of RD. The median ∑BTs concentration was highest in PM2.5 (26.62 µg/g), followed by PM10 (22.03 µg/g), and TSP (0.68 µg/g). Of the seven BTs, BT, 2-aminobenzothiazole (2-NH2-BT), 2-mercaptobenzothiazole (MBT), and 2-(methylthio)benzothiazole (MTBT) were distributed in PM2.5 and 2-OH-BT was distributed in PM2.5-10 of RD. Based on the mass fractions of BTs in the TSP, PM10, and PM2.5 fractions of RD, human exposure via ingestion, inhalation and dermal absorption were evaluated. Ingestion was found to be the main exposure pathway in humans, and daily intake of BTs in PM2.5 was highest, followed by PM10 and TSP, respectively. Children may suffer more health risks than adults when exposed to RD.