Secondary Organic Aerosol Formation Potential from Vehicular Non-tailpipe Emissions under Real-World Driving Conditions.

Traffic emissions are a dominant source of secondary organic aerosol (SOA) in urban environments. Though tailpipe exhaust has drawn extensive attention, the impact of non-tailpipe emissions on atmospheric SOA has not been well studied. Here, a closure study was performed combining urban tunnel experiments and dynamometer tests using an oxidation flow reactor in situ photo-oxidation. Results show a significant gap between field and laboratory research; the average SOA formation potential from real-world fleet is 639 ± 156 mg kg fuel-1, higher than the reconstructed result (188 mg kg fuel-1) based on dynamometer tests coupled with fleet composition inside the tunnel. Considering the minimal variation of SOA/CO in emission standards, we also reconstruct CO and find the critical role of high-emitting events in the real-world SOA burden. Different profiles of organic gases are detected inside the tunnel than tailpipe exhaust, such as more abundant C6-C9 aromatics, C11-C16 species, and benzothiazoles, denoting contributions from non-tailpipe emissions to SOA formation. Using these surrogate chemical compounds, we roughly estimate that high-emitting, evaporative emission, and asphalt-related and tire sublimation share 14, 20, and 10% of the SOA budget, respectively, partially explaining the gap between field and laboratory research. These experimental results highlight the importance of non-tailpipe emissions to atmospheric SOA.

Transforming waste brake pads from automobiles into Nano-Catalyst: Synergistic Fe-C-Cu triple sites for efficient fenton-like oxidation of organic pollutants.

The arbitrary disposal of used brake pads from motor vehicles has resulted in severe heavy metal pollution and resource wastage, highlighting the urgent need to explore the significant untapped potential of these discarded materials. In this study, The in-situ growth of highly dispersed Fe2O3 nanocrystals was achieved by simple oxidation annealing of brake pad debris(BPD). Interestingly, Cu remained unoxidized and acted as a "valence state transformation bridge of Fe2O3" to construct the "triple Fe-C-Cu sites". The Fenton degradation experiment of pollutants was conducted under constant temperature conditions at 40 °C, a stirring rate of 1300 rpm, a pH value of 3, a catalyst dosage of 0.5 g/L, pollutant dosage ranging from 50 to 400 mg/L, and H2O2 dosage of 0.25 g/L. Experimental results showed that BPD treated at 300 °C for 2 h exhibited optimal Fenton-like oxidation activity, achieving rapid degradation of over 90 % of refractory antibiotics, such as tetracycline and ciprofloxacin, in organic wastewater within 10 min. This remarkable performance was mainly attributed to the synergistic effect of "Fe-C-Cu triple sites", where the electron-donating role of C in the Fe-C and Cu-C interfaces facilitated the conversion of the Fe(III) to Fe(II) and Cu(II) to Cu(I). In addition, the ability of Cu2+ to accept electrons at the Fe-Cu interface promoted the transition from Fe (II) to Fe (III). This "balance of electron gain and loss" accelerated the interfacial electron transfer and the recycle of dual Fenton sites, Fe(II)/Fe(III) and Cu(I)/Cu(II), to generate more ·OH from H2O2. Therefore, this strategy of functionalizing BPD as Fenton-like catalysts without the addition of external Fe provides intriguing prospects for understanding the construction of Fe-based Fenton catalysts and resource utilization of Fe-containing solid waste materials.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China.

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km-1 veh-1 and 0.057 ± 0.057 g (kg-fuel)-1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km-1 veh-1 and 10.5 ± 2.52 mg km-1 veh-1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8-53.6 %, with 0.32 ± 0.45 mg km-1 veh-1 and 0.018 ± 0.020 g (kg-fuel)-1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km-1 veh-1 and 3.06 ± 0.91 mg km-1 veh-1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

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.

Research on the Extraction of Hazard Sources along High-Speed Railways from High-Resolution Remote Sensing Images Based on TE-ResUNet.

There are many potential hazard sources along high-speed railways that threaten the safety of railway operation. Traditional ground search methods are failing to meet the needs of safe and efficient investigation. In order to accurately and efficiently locate hazard sources along the high-speed railway, this paper proposes a texture-enhanced ResUNet (TE-ResUNet) model for railway hazard sources extraction from high-resolution remote sensing images. According to the characteristics of hazard sources in remote sensing images, TE-ResUNet adopts texture enhancement modules to enhance the texture details of low-level features, and thus improve the extraction accuracy of boundaries and small targets. In addition, a multi-scale Lovász loss function is proposed to deal with the class imbalance problem and force the texture enhancement modules to learn better parameters. The proposed method is compared with the existing methods, namely, FCN8s, PSPNet, DeepLabv3, and AEUNet. The experimental results on the GF-2 railway hazard source dataset show that the TE-ResUNet is superior in terms of overall accuracy, F1-score, and recall. This indicates that the proposed TE-ResUNet can achieve accurate and effective hazard sources extraction, while ensuring high recall for small-area targets.

[Characteristics and Source Apportionment of Carbon Components in Road Dust in Anshan].

In order to study the characteristics and sources of carbon fractions in PM2.5 in road dust in Anshan, road dust samples were collected from nine roads in Anshan in October 2014 and re-suspended on filters using a NK-ZXF sampler to obtain PM2.5 samples. A thermal optical carbon analyzer (IMPROVE-TOR) was employed to measure the mass fraction of organic carbon (OC) and elemental carbon (EC) in PM2.5. The results showed that ω(TC) in PM2.5 in road dust was 9.78% (outer loop)-14.00% (Qianshan West Road), ω(OC) was 8.15% (outer loop)-10.84% (Qianshan West Road), and ω(EC) was 1.63% (outer loop)-2.85% (Qianshan West Road). ω(OC) was much higher than ω(EC), indicating that road dust contained a large amount of organic carbon. All OC/EC values were greater than 2.0 during the sampling period, suggesting that there was secondary pollution. Spearman correlation analysis and linear fitting indicated that the sources of OC and EC were basically the same. Cluster analysis results showed that carbon components in PM2.5 in road dust in Anshan mainly originated from vehicle exhaust, biomass burning, and coal combustion emissions.

[Characteristics and Source Apportionment of Carbon Components in Road Dust PM2.5 and PM10 During Spring in Tianjin Derived by Using the Quadrat Sampling Method].

In order to study the characteristics and sources of carbon fractions in PM2.5 and PM10 of road dust in Tianjin, samples of road dust were collected by the quadrat sampling method in April 2015 in Tianjin, and samples were re-suspended on filters by using a NK-ZXF sampler. A Thermal Optical Carbon Analyzer (IMPROVE-TOR) was employed to measure the concentrations of organic carbon (OC) and elemental carbon (EC), and the pollution characteristics and sources were investigated by non-parametric tests and OC/EC ratio, correlation, and cluster analyses. The results showed that ω(total carbon, TC) in PM2.5 of road dust amounted to 4.89% (secondary road) -18.83% (expressway), ω(OC) amounted to 3.57% (secondary road) -15.39% (expressway), and ω(EC) amounted to 1.32% (secondary road) -3.44% (expressway); meanwhile, ω(TC) in PM10 of road dust was 8.14% (secondary road) -19.71% (expressway), ω(OC) was 5.91% (secondary road) -16.28% (expressway), and ω(EC) was 1.96% (main road) -3.43% (expressway). The mass fraction of each carbon component for the expressway was relatively high, and that for the secondary trunk road was relatively low, which may have been due to the large traffic volume on the expressway and corresponding large amounts of exhaust emissions from motor vehicles, whereas there were fewer vehicles on the secondary trunk road. Additionally, ω(OC) was significantly larger than ω(EC) for all types of roads, and ω(EC) did not vary much among the different road types. The non-parametric tests of two related samples showed that there was no significant difference in the mass fraction of each carbon component between PM2.5 and PM10. The correlation analysis showed that the sources of OC and EC in road dust were roughly the same. The OC/EC ratio analysis and cluster analysis showed that the main sources of the carbon components in the dust of roads in Tianjin in spring were coal combustion, motor vehicle exhaust, and biomass burning.