Lipid Dysregulation Induced by Gasoline and Diesel Exhaust Exposure and the Interaction with Age.

Limited knowledge exists regarding gasoline and diesel exhaust effects on lipid metabolism. This study collected gasoline and diesel exhaust under actual driving conditions and conducted inhalation exposure on male young and middle-aged C57BL/6J mice for 4 h/day for 5 days to simulate commuting exposure intensity. Additionally, PM2.5 from actual roadways, representing gasoline and diesel vehicles, was generated for exposure to human umbilical vein endothelial cells (HUVECs) and normal liver cells (LO2) for 24, 48, and 72 h to further investigate exhaust particle toxicity. Results showed that diesel exhaust reduced total cholesterol and low-density lipoprotein cholesterol levels in young mice, indicating disrupted lipid metabolism. Aspartate aminotransferase and alanine aminotransferase levels increased by 53.7% and 21.7%, respectively, suggesting potential liver injury. Diesel exhaust exposure decreased superoxide dismutase and increased glutathione peroxidase levels. Cell viability decreased, and reactive oxygen species levels increased in HUVECs and LO2 following exposure to exhaust particles, with dose- and time-dependent effects. Diesel exhaust particles exhibited more severe inhibition of cell proliferation and oxidative damage compared to gasoline exhaust particles. These findings provide novel evidence of the risk of disrupted lipid metabolism due to gasoline and diesel exhaust, emphasizing the toxicity of diesel exhaust.

Optical properties of vehicular brown carbon emissions: Road tunnel and chassis dynamometer tests.

Brown carbon (BrC), as an important light-absorbing aerosol, significantly impacts regional and global climate. Vehicle emission is a nonnegligible source of BrC, but the optical properties of BrC emitted from vehicles remain poorly understood. This study evaluates the absorption Ångström exponent (AAE) of traffic-related light-absorbing aerosols (i.e., AAETr) and the absorption emission factor (EFabs) of vehicular BrC via chassis dynamometer tests and a road tunnel measurement in Tianjin, China. AAETr are estimated as 0.98-1.33 and 1.11 ± 0.001 for tested vehicles and on-road vehicle fleet, respectively. The AAE of vehicular BrC (AAEBrC) is 3.83 ± 0.092 for on-road vehicle fleet. The vehicle technology updates effectively reduce the EFabs of vehicular BrC. Among the four tested China 5 and China 6 gasoline vehicles in the chassis dynamometer tests, BrC EFabs of China 5 gasoline direct injection vehicle is the highest, while China 6 mixing fuel injection vehicle exhibits the lowest EFabs. The BrC EFabs of on-road vehicle fleet at 370 nm wavelength are 0.081 ± 0.0058 m2 kg-1 for mixed fleet, 0.074 ± 0.018 m2 kg-1 for gasoline vehicles (GVs), and 1.66 ± 0.71 m2 kg-1 for diesel vehicles (DVs) in the tunnel measurement. EFabs of GV fleet in the road tunnel is higher than China 5 and China 6 vehicles, as China 1-4 vehicles accounted for 26.8% of the total vehicle fleet in the tunnel. EFabs of vehicular BrC are lower than those from biomass burning and coal combustion emissions. The light absorption of BrC from GVs and DVs accounts for 7.2 ± 2.1% and 1.5 ± 0.77% of total traffic-related absorption at 370 nm, respectively. Our study provides optical features of BrC from vehicle source and could contribute to estimating the impacts of vehicular aerosol emissions on global and regional climate change.

Comparison of CO2, NOx, and VOCs emissions between CNG and E10 fueled light-duty vehicles.

In China, natural gas (NG) is the main vehicle fuel after gasoline and diesel, and the number of NG vehicles ranks first in the world. At present, there are many studies on the conventional gaseous pollutants and particulate matter of NG vehicles, but very few studies on their VOCs. In this study, the chassis dynamometer is used to test CNG/E10 bi-fuel light-duty vehicles, analyze the advantages of CNG in CO2, fuel thermal efficiency, and cost, and discuss its disadvantages in NOx emission. Most importantly, the emission characteristics and ozone formation potential of VOCs in the exhaust of CNG vehicles were analyzed in the study. Compared with E10, CNG fuel can reduce CO2 emission by about 20 %, improve thermal efficiency by about 13 %, and save fuel costs by about 50 %. However, it will increase NOx and NO2 emissions by about 10 % and 13 % respectively. As for VOCs, the emission factor of VOCs from CNG fuel is about 54 % of E10 fuel. The VOCs group with the highest proportion in the exhaust of CNG-fueled vehicles is alkanes, >80 %. while the alkanes and alkenes with the highest proportion in E10 fuel are 30 % and 23 % respectively. C2 VOCs emitted by CNG account for >70 %, while C2 VOCs emitted by E10 are <60 %, followed by C4 VOCs, about 10 % - 30 %. The OFPs of VOCs in CNG exhaust is about 13.7 % of E10. Alkenes contribute the most to ozone, and the OFPs of alkenes in CNG and E10 vehicle exhaust accounts for about 55.3 % and 78.8 % of TVOCs respectively. The results of this study are helpful to improve people's understanding of the environmental value of using NG vehicles.

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.

Real-world emission characteristics of an ocean-going vessel through long sailing measurement.

To quantify the emission characteristics of large ocean-going ships, onboard measurements were carried out for a large ocean-going vessel using portable emission measurement system (PEMS). The emission factors (EFs) of conventional pollutants and volatile organic compounds (VOCs) were greatly influenced by real-world operating conditions and engine loads. The sulfur dioxide (SO2) and particulate matter (PM) emissions were mainly influenced by fuel type. The particle size distribution basically showed a single peak pattern, with nucleation mode particles as the main particles and the peak particle sizes ranging between 30 nm and 50 nm. The EFs for particle number (PN) ranged from 2.82 × 1016 to 4.49 × 1016 #/kwh. Carbonaceous components accounted for approximately 31.8% to 41.6% of the PM. SO42-, NH4+, Ca2+, Na+, and NO3- were dominant in water-soluble ions, while V and Ni were high-concentration metal elements, with the ratio of V: Ni ranging from 0.17 to 0.33. Increase in driving speed can lead to the increase in VOCs emissions. Our study presented a comprehensive test method with PEMS, which provides a reference for acquiring future real-world EFs. However, only one representative ship in China using a specific fuel was selected for the test, so it is important to characterize a broader range of ships and fuels.

An AIS-based emission inventory and the impact on air quality in Tianjin port based on localized emission factors.

Ship pollution has become a hot global issue. This study established a basic information database of Tianjin Port ship emissions and used it to screen representative ship types and perform real-world ship measurements by a portable emission measurement system (PEMS), which generated localized emission factors. The results show that the localized emission factors are significantly higher than those recommended in recommended in Chinese guidelines, which will lead to lower calculation results of the previous inventory. A high temporal-spatial ship emission inventory for Tianjin Port was developed using a "bottom-up" method based on automatic identification system (AIS) data by combining localized emission factors. The total estimated ship emissions for SO2, NOX, PM10, PM2.5, THC and CO in 2018 were 1.453 × 104 t, 2.861 × 104 t, 2.04 × 103 t, 1.82 × 103 t, 1.13 × 103 t, and 2.21 × 103 t, respectively. NOX was the primary pollutant, accounting for 56.9%, followed by SO2 (28.9%). The use of low-sulfur fuel in the port area has significantly reduced the discharge of SO2 and primary particles. The main channel and anchorage are the areas with the highest emission intensity. The intermonth ship emissions varied according to the ship activity, lowest in February and highest in May. The contribution of cargo transportation vessels to various pollutant emissions is more than 60%. Main engines (MEs) were the largest source of emissions, followed by auxiliary engines (AEs). NOX and SOX from ships have the greatest impact on the air quality in the surrounding area, especially in summer and autumn, as analyzed by the atmospheric dispersion modeling system (ADMS) model. Our research will update localized emission factors and inventories and evaluate the impact of ship emissions on air quality.

The Effect of Digested Manure on Biogas Productivity and Microstructure Evolution of Corn Stalks in Anaerobic Cofermentation.

The anaerobic fermentation of crop straw and animal wastes is increasingly used for the biogas and green energy generation, as well as reduction of the environmental pollution. The anaerobic cofermentation of corn stalks inoculated by cow dung was found to achieve higher biogas production and cellulose biodegradation. In this study, the effect of mixing corn stalks with cow dung at five different fermentation stages (0, 7, 15, 23, and 31 days of the total fermentation cycle of 60 days) on the further cofermentation process was explored, in order to optimize the corn straw utilization rate and biogas production capacity. In addition, the straw microstructure evolution was investigated by the SEM and XRD methods to identify the optimal conditions for the straw biodegradation process enhancement. The five test groups exhibited nearly identical total biogas productivity values but strongly differed by daily biogas yields (the maximal biogas generation rate being 524.3 ml/d). Based on the degradation characteristics of total solids (TS), volatile solids (VS), and lignocellulose, groups #1 and #3 (0 and 15 days) had the most significant degradation rates of VS (43.73%) and TS (42.07%), respectively, while the largest degradation rates of cellulose (62.70%) and hemicellulose (50.49%) were observed in group #4 (23 days) and group #1 (0 days), respectively. The SEM analysis revealed strong microstructural changes in corn stalks after fermentation manifested by multiple cracks and striations, while the XRD results proved the decrease in peak intensity of cellulose 〈002〉 crystal surface and the reduced crystallinity after cofermentation. The results of this study are assumed to be quite instrumental to the further optimization of the corn stalk anaerobic digestion by inoculation with digested manure for lignocellulose degradation enhancement and biogas productivity improvement.