An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise.

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17-40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Strong metal-support interaction (SMSI) in environmental catalysis: Mechanisms, application, regulation strategies, and breakthroughs.

The strong metal-support interaction (SMSI) in supported catalysts plays a dominant role in catalytic degradation, upgrading, and remanufacturing of environmental pollutants. Previous studies have shown that SMSI is crucial in supported catalysts' activity and stability. However, for redox reactions catalyzed in environmental catalysis, the enhancement mechanism of SMSI-induced oxygen vacancy and electron transfer needs to be clarified. Additionally, the precise control of SMSI interface sites remains to be fully understood. Here we provide a systematic review of SMSI's catalytic mechanisms and control strategies in purifying gaseous pollutants, treating organic wastewater, and valorizing biomass solid waste. We explore the adsorption and activation mechanisms of SMSI in redox reactions by examining interfacial electron transfer, interfacial oxygen vacancy, and interfacial acidic sites. Furthermore, we develop a precise regulation strategy of SMSI from systematical perspectives of interface effect, crystal facet effect, size effect, guest ion doping, and modification effect. Importantly, we point out the drawbacks and breakthrough directions for SMSI regulation in environmental catalysis, including partial encapsulation strategy, size optimization strategy, interface oxygen vacancy strategy, and multi-component strategy. This review article provides the potential applications of SMSI and offers guidance for its controlled regulation in environmental catalysis.

Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation.

Sustained deep emission reduction in road transportation is encountering bottleneck. The Intelligent Transportation-Speed Guidance System (ITSGS) is anticipated to overcome this challenge and facilitate the achievement of low-carbon and clean transportation. Here, we compiled vehicle emission datasets collected from real-world road experiments and identified the mapping relationships between four pollutants (CO2, CO, NOx, and THC) and their influencing factors through machine learning. We developed random forest models for each pollutant and achieved strong predictive performance, with an R2 exceeding 0.85 on the test dataset for all models. The environmental benefits of ITSGS at the urban scale were quantified by combining emission models with large-scale real trajectory data from Zibo, Shandong Province. Based on temporal and spatial analyses, we found that ITSGS has varying degrees of emission reduction potential during the morning peak, flat peak, and evening peak hours. Values can range from 5.71 %-8.16 % for CO2 emissions, 13.63 %-16.25 % for NOx emissions, 13.69 %-16.45 % for CO emissions, and 4.84-7.07 % for THC emissions, respectively. Additionally, ITSGS can significantly expand the area of low transient emission zones. The best time for achieving maximum environmental benefits from ITSGS is during the workday flat peak. ITSGS limits high-speed and aggressive driving behavior, thereby smoothing the driving trajectory, reducing the frequency of speed switches, and lowering road traffic emissions. The results of the ITSGS environmental benefits evaluation will provide new insights and solutions for sustainable road traffic emission reduction. SYNOPSIS: Large-scale deployment of Intelligent Transportation - Speed Guidance System is a sustainable solution to help achieve low-carbon and clean transportation.

Emission characteristic, spatial distribution, and health risk of polycyclic aromatic compounds (PAHs, NPAHs, and OPAHs) from light-duty gasoline and diesel vehicles based on on-road measurements.

Vehicle exhaust is the primary source of polycyclic aromatic compounds (PACs). Real road tests using a portable vehicle measurement system on light-duty gasoline vehicles and light-duty diesel trucks were conducted to investigate gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and oxy-PAHs (OPAHs) in vehicle exhaust with different emission standards, fuel types, and altitudes. The results showed that with the tightening of emission standards, the overall emission factors (EFs) of PACs decreased. Compared with China V diesel vehicles, the emissions of PAHs, OPAHs, and NPAHs from China VI diesel vehicles were 75.1 %, 84.4 %, and 61.2 % lower, respectively. With a ∼100 m increase in altitude, the EFs of PAHs, OPAHs, and NPAHs of diesel vehicles increased 1.88, 1.92, and 1.59 times due to incomplete combustion. In addition, the EFs of PAHs and OPAHs in gasoline vehicles were lower than those in diesel vehicles. In contrast, the proportion of PAHs with highly toxic components, such as dibenzo[a,h]anthracene (DahA) and benzo[a]pyrene (BaP), and the EFs of gas-phase NPAHs in gasoline vehicles were higher than those in diesel vehicles. Furthermore, the emissions of 1,8-DNP from diesel vehicles cannot be disregarded. 1,8-DNP was the main gas-phase NPAHs emitted by China VI and China V diesel vehicles, accounting for 49.3 % and 26.0 %, respectively. Moreover, gas-phase PACs contributed more to the EFs than particle-phase PACs, whereas particle-phase PACs have greater toxic effects. Although the EFs of PAHs are more than 100 times those of NPAHs, the toxic equivalent concentrations (TEQBaP) of PAHs in diesel and gasoline vehicles were approximately 6.5 times and 35 times those of NPAHs. The spatial distribution characteristics revealed that PACs emissions were mainly concentrated in urban areas and highways, and the differences in the toxicity of PACs emissions between different cities depended on the proportion of diesel vehicles. The average TEQBaP of PAHs and NPAHs in Haidong, Haibei, Huangnan, Hainan, Guoluo, and Yushu was 8.42 µg/m3 and 0.36 µg/m3, respectively, while those of Xining and Haixi were 0.24-0.29 µg/m3 and 0.09-0.108 µg/m3 higher, respectively. This study provides a comprehensive understanding of the emission characteristics, health risks, and spatial distribution of PACs from diesel and gasoline vehicle PACs in urban areas.

Exhaust and evaporative volatile organic compounds emissions from vehicles fueled with ethanol-blended-gasoline.

By 2020, China has implemented the use of 10% ethanol-blended-gasoline (E10), which is expected to notably impact vehicular volatile organic compounds (VOCs) emissions. The adoption of E10 reduced certain emissions but raised concerns with about more reactive oxygenated volatile organic compounds (OVOCs). This study aimed to evaluate the impact of E10 on the total VOCs emissions from both exhaust and evaporative emissions by conducting tests on the CHINA V (or CHINA VI) light-duty gasoline vehicles (LDGVs) using 0% ethanol blended gasoline (E0) and E10. E10 reduces VOCs emissions in the exhaust, and reduces the ozone and secondary organic aerosol generation potential of VOCs in the exhaust, as evidenced by the lower emission factors (EFs), ozone formation potentials (OFPs) and secondary organic aerosol formation potential (SOAFPs) in the CHINA V LDGVs. Evaporative emissions showed differences in emitted VOCs, with lower EFs, OFPs and SOAFPs for the CHINA V LDGVs fueled with E10. The CHINA VI LDGVs also exhibited reduced EFs, OFPs and SOAFPs. These findings highlight the environmental benefits of E10 in the CHINA VI-compliant LDGVs; however, the effectiveness of the earlier CHINA V standard vehicles requires further evaluation.

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.

Effects of heat waves and cold spells on blood parameters: a cohort study of blood donors in Tianjin, China.

BACKGROUND: With the increasing occurrence of extreme temperature events due to climate change, the attention has been predominantly focused on the effects of heat waves and cold spells on morbidity and mortality. However, the influence of these temperature extremes on blood parameters has been overlooked. METHODS: We conducted a cohort study involving 2,752 adult blood donors in Tianjin, China, between January 18, 2013, and June 25, 2021. The generalized additive mixed model was used to investigate the effects and lagged effects of heat waves and cold spells on six blood parameters of blood donors, including alanine aminotransferase (ALT), white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), and platelet count (PLT). Subgroup analyses were stratified by sex, age, and BMI. RESULTS: Heat waves and cold spells are associated with changes in blood parameters, particularly HB and PLT. Heat waves increased HB and PLT, while cold spells increased HB and decreased PLT. The effect of heat waves is greater than that of cold spells. The largest effect of heat waves on HB and PLT occurred at lag1 with 2.6 g/L (95% CI: 1.76 to 3.45) and lag7 with 9.71 × 10^9/L (95% CI: 6.26 to 13.17), respectively, while the largest effect of cold spells on HB and PLT occurred at lag0 with 1.02 g/L (95% CI: 0.71 to 1.33) and lag2 with -3.85 × 10^9/L (95% CI: -5.00 to -2.70), respectively. In subgroup analysis, the effect of cold spells on ALT was greater in the 40-49 age group. CONCLUSION: We indicated that heat waves and cold spells can impact hemoglobin and platelet counts in the human body. These findings provide evidence linking heat waves or cold spells to diseases and may reduce health risks caused by extreme temperature events.

Exploring vehicle-centric strategies for sustainable urban mobility: A theoretical framework for saving energy and reducing noise in transportation.

Adopting energy-saving and noise-reducing technologies in vehicle transportation has the potential to mitigate urban traffic pollution and promote sustainable urban mobility. However, a universal analytical framework for obtaining the combined energy savings and noise reduction patterns in vehicles is still lacking. This study addresses this gap by integrating a fundamental traffic noise model with a vehicle energy conservation equation. A theoretical framework was constructed that establishes the relationship between vehicle noise and energy consumption, with the theoretical origins of this framework explained. By summarizing a substantial body of classical literature, the typical model's properties are analyzed through the principle of optimality, and the noise interval for combined vehicle energy-saving and noise-reducing is determined. Subsequently, a rigorous vehicle experiment was conducted to validate the proposed framework's effectiveness, utilizing synchronized data on energy consumption and noise. The findings indicate that vehicles can achieve unconstrained combined energy-saving and noise-reducing in four driving states and conditional combined energy-saving and noise-reducing in five driving states. The Recall index demonstrates a verification rate exceeding 0.62 for the combined energy-saving and noise-reducing rules. This research provides valuable insights to support energy-saving and noise-reducing measures in urban traffic.

[Environmental Benefits of Pollution and Carbon Reduction by Bus Fleet Electrification in Zhengzhou].

Electrification of bus fleets is an effective approach to reducing transportation-related pollution and carbon emissions. Evaluating the impact of electrification on existing bus fleets can provide valuable insights for promoting full electrification of public transportation in large cities. Utilizing the fuel life cycle method, we analyzed the CO2 and pollutant emissions of Zhengzhou's bus fleet before and after electrification and evaluated emissions under different electrification scenarios. Our results indicated that after electrification, the fuel life cycle CO2 and PM2.5 emissions increased by 32.6% and 42.6%, respectively, whereas CO, NOx, and VOC emissions decreased by 28%, 34%, and 25%, respectively. Optimizing the power generation structure is a critical factor in reducing CO2 and PM2.5 emissions during the electrification process. The best scenario for comprehensive electrification and power generation structure optimization could result in a 38.7% reduction in CO2, as well as reductions of 80.1% in CO, 84.4% in NOx, 92.2% in VOC, and 30.2% in PM2.5. Prioritizing electrification on long-distance routes is recommended during the replacement process. Additionally, replacing plug-in hybrid natural gas vehicles with pure electric vehicles has both advantages and disadvantages in terms of emission reduction. Achieving pollution reduction and carbon synergies requires advancing fleet replacement and power structure adjustments simultaneously.

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.

Atmospheric environment characteristic of severe dust storms and its impact on sulfate formation in downstream city.

This study comprehensively investigated the impact of dust storms (DSs) on downstream cities, by selecting representative DS events. In this paper, we discussed the characteristics of meteorological conditions, air pollutants, PM2.5 components, and their influence on sulfate formation mechanisms. During DSs, strong winds, reaching speeds of up to 10 m/s, led to significant increases in PM10 and PM2.5, with maximum concentrations of 2684.5 and 429 µg/m3, respectively. Primary gaseous pollutants experienced substantial reductions, with decline rates of 48.1, 34.9, 36.8, and 9.0 % for SO2, NO2, NH3, and CO, respectively. Despite a notable increase in PM2.5 concentrations, only 7.6 % of the total mass of PM2.5 was attributed to ionic and carbonaceous components, a much lower value than observed before the DSs (77.3 %). Concentrations of Fe, Ti, and Mn exhibited increases by factors of 6.5-14.1, 10.4-17.0, and 1.6-4.7, respectively. In contrast to the significant decrease of >76.2 % in nitrogen oxidation ratio (NOR), sulfur oxidation ratio (SOR) remained at a relatively high level, displaying a strong positive correlation with high concentrations of Fe, Mn, and Ti. Quantitative analysis revealed an average increase of 0.187 and 0.045 µg/m3 in sulfate from natural sources and heterogeneous generation, respectively. The heterogeneous reaction on mineral dust was closely linked to atmospheric humidity, radiation intensity, the form of metal existence, and concentrations of it. High concentrations of titanium dioxide and iron­manganese oxides in mineral dust promoted heterogeneous oxidation of SO2 through photocatalysis during the daytime and metal ion catalysis during the nighttime. This study establishes that the metal components in mineral dust promote heterogeneous sulfate formation, quantifies the yield of sulfate generated as a result, and provides possible mechanisms for heterogeneous sulfate formation.

Evaluation indicators for road traffic energy consumption: a review and prospect.

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

Investigating the impact of high-altitude on vehicle carbon emissions: A comprehensive on-road driving study.

This study addresses the literature gap concerning accurately identifying vehicle carbon emission characteristics in high-altitude areas. Utilizing a portable emission measurement system (PEMS) for real-world testing, we quantified the influence of altitude on carbon emissions from light-duty gasoline (LDGV) and diesel vehicles (LDDV). The Random Forest (RF) algorithm was employed to analyze the complex nonlinear relationships between altitude, meteorological conditions, driving patterns, and carbon dioxide (CO2) emissions, enabling predictions across different altitudes. The results showed that CO2 emissions progressively increase with elevation. Furthermore, as altitude increases, combustion efficiency declines, and the overall impact of driving conditions on emission rates diminishes. Altitude and meteorological factors significantly contributed to CO2 emissions, whereas driving conditions and road grades contributed less. Compared with the COPERT model, the RF model demonstrates strong accuracy in predicting carbon emissions at different altitudes. Specifically, the CO2 emission rate nearly triples as altitude increases from 2.0 km to 4.5 km. This research bridges a critical gap in the understanding carbon emissions from high-altitude vehicles, offering insights into policy development for emission reduction strategies in such regions. Future studies should integrate diverse testing methodologies and comprehensive surveys to validate and extend the findings.

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.

Regenerative braking system effectively reduces the formation of brake wear particles.

Brake wear particles (BWPs) are considered one of the most significant non-exhaust particle emission sources from motor vehicles. Previous studies have primarily focused on BWPs from conventional fuel vehicles (CFVs), with limited research available on BWPs from new energy vehicles (NEVs). We developed an independent BWP emission testing system applicable to NEVs and conducted BWP emission tests on representative NEVs and CFVs under various testing cycles via a chassis dynamometer. The BWP emission characteristics of the NEVs equipped with regenerative braking system significantly differed from those of gasoline vehicles. For transient emission characteristics, gasoline vehicles exhibited higher peak concentrations during brake events than brake drag events, while those with regenerative braking exhibited the opposite feature. Under continuous braking, the concentration of ultrafine particles emitted by NEVs was reduced by more than 3 orders of magnitude compared to gasoline vehicles. In terms of single-particle morphology, BWPs could be mainly divided into three categories: carbonaceous particles, iron-rich particles, and mixed metal particles. We obtained realistic emission characteristics of BWPs from NEVs, which could provide data support and a scientific basis for the formulation of relevant emission standards and control measures in the future.

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.

Effects of Air Pollution Exposure during Preconception and Pregnancy on Gestational Diabetes Mellitus.

This study aimed to investigate the association between air pollution and gestational diabetes mellitus (GDM) in small- and medium-sized cities, identify sensitive periods and major pollutants, and explore the effects of air pollution on different populations. A total of 9820 women who delivered in Handan Maternal and Child Health Hospital in the Hebei Province from February 2018 to July 2020 were included in the study. Logistic regression and principal component logistic regression models were used to assess the effects of air pollution exposure during preconception and pregnancy on GDM risk and the differences in the effects across populations. The results suggested that each 20 µg/m3 increase in PM2.5 and PM10 exposure during preconception and pregnancy significantly increased the risk of GDM, and a 10 µg/m3 increase in NO2 exposure during pregnancy was also associated with the risk of GDM. In a subgroup analysis, pregnant women aged 30-35 years, nulliparous women, and those with less than a bachelor's education were the most sensitive groups. This study provides evidence for an association between air pollution and the prevalence of GDM, with PM2.5, PM10, and NO2 as risk factors for GDM.

Emissions from international airport and its impact on air quality: A case study of beijing daxing international airport (PKX), China.

The Beijing Daxing International Airport is a newly opened airport, and a comprehensive emission inventory of air pollution sources has not yet been established. The lack of basic inventory data will cause difficulties in controlling the air quality (AQ) in and around the airport. Based on actual flight data, we established a comprehensive emission inventory (carbon monoxide (CO), nitrogen oxides (NOX), hydrocarbons (HC), sulfur dioxide (SO2), particulate matter (PM), and carbon dioxide (CO2)) at Beijing Daxing International Airport. Furthermore, we evaluated the impact of airport emissions on the AQ of the surrounding areas using the ADMS-Airport model. The results showed that Beijing Daxing International Airport emitted 1.15 E+03, 1.76 E+03, 1.38 E+02, 1.16 E+02, 3.53 E+01, and 3.75 E+05 t of CO, NOX, HC, SO2, PM, and CO2, respectively, from July 1, 2020, to June 30, 2021. Engine exhaust emissions (landing and takeoff [LTO] cycles) dominated all airport pollutant emissions except for PM from the power plant. Among all aircraft types, B738 emitted the most CO2, as it accounted for almost half of all the flights. The AQ simulations showed that the air pollutant diffusion range was concentrated within 15 km of the airport and the surrounding areas. The contribution of airport emissions to NOX concentrations was most apparent under the most unfavorable meteorological conditions. Based on the average pollutant concentration during the study period, the Gu'an Li Hu Primary School station was the most affected. In particular, NOX concentrations at this station were approximately 50% higher in winter than in summer. Currently, the airport's contribution to pollution in the surrounding areas is insignificant. However, with the continuous increase in the number of flights at the airport, its impact on the AQ in the surrounding areas must be addressed in the future.

Evaluating vehicular exhaust and evaporative emissions via VOC measurement in an underground parking garage.

Vehicular emissions, including both tailpipe exhaust and evaporative emissions, are major anthropogenic sources of volatile organic compounds (VOCs) in urban cities. Current knowledge on vehicle tailpipe and evaporative emissions was mainly obtained via laboratory tests on very few vehicles under experimental conditions. Information on fleet gasoline vehicles emission features under real-world conditions is lacking. Here, VOC measurement was conducted in a large residential underground parking garage in Tianjin, China, to reveal the feature of the exhaust and evaporative emissions from real-world gasoline vehicle fleets. The VOC concentration in the parking garage was on average 362.7 ± 87.7 µg m-3, significantly higher than that in the ambient atmosphere at the same period (63.2 µg m-3). Aromatics and alkanes were the mainly contributors on both weekdays and weekends. A positive correlation between VOCs and traffic flow was observed, especially in the daytime. Source apportionment through the positive matrix factorization model (PMF) revealed that the tailpipe and evaporative emissions accounted for 43.2% and 33.7% of VOCs, respectively. Evaporative emission contributed 69.3% to the VOCs at night due to diurnal breathing loss from numerous parked cars. In contrast, tailpipe emission was most remarkable during morning rush hours. Based on the PMF results, we reconstructed a vehicle-related VOCs profile representing the combination of the tailpipe exhaust and evaporative emission from fleet-average gasoline vehicles, which could benefit future source apportionment studies.

Heavy vehicles' non-exhaust exhibits competitive contribution to PM2.5 compared with exhaust in port and nearby areas.

Heavy port transportation networks are increasingly considered as significant contributors of PM2.5 pollution compared to vessels in recent decades. In addition, evidence points to the non-exhaust emission of port traffic as the real driver. This study linked PM2.5 concentrations to varied locations and traffic fleet characteristics in port area through filter sampling. The coupled emission ratio-positive matrix factorisation (ER-PMF) method resolves source factors by avoiding direct overlap from collinear sources. In the port central and entrance areas, freight delivery activity emissions including vehicle exhaust and non-exhaust particles, as well as induced road dust resuspension, accounted for nearly half of the total contribution (42.5%-49.9%). In particular, the contribution of non-exhaust from denser traffic with high proportion of trucks was competitive and equivalent to 52.3% of that from exhaust. Backward trajectory statistical models further interpreted the notably larger-scale coverage of non-exhaust emissions in the port's central area. The distribution of PM2.5 were interpolated within the scope of the port and nearby urban areas, displaying the potential contribution of non-exhaust within 1.15 µg/m3-4.68 µg/m3, slightly higher than the urban detections reported nearby. This study may provide useful insights into the increasing percentage of non-exhaust from trucks in ports and nearby urban areas and facilitate supplementary data collection on Euro-VII type-approval limit settings.