Search Results (472)

Understanding the differential impacts of emission sources of volatile organic compounds (VOCs) on formaldehyde (HCHO) levels is pivotal to effectively mitigating key photochemical radical precursors, thereby enhancing the regulation of atmospheric oxidation capacity (AOC) and ozone formation. This investigation systematically selected and analyzed year-long VOC measurements across three urban zones in Shenzhen, China. Photochemical age correction methods were implemented to develop the initial concentrations of VOCs before source apportionment; then Positive Matrix Factorization (PMF) modeling resolved six primary sources: solvent usage (28.6–47.9%), vehicle exhaust (24.2–31.2%), biogenic emission (13.8–18.1%), natural gas (8.5–16.3%), gasoline evaporation (3.2–8.9%), and biomass burning (0.3–2.4%). A machine learning (ML) framework incorporating Shapley Additive Explanations (SHAP) was subsequently applied to evaluate the influence of six emission sources on HCHO concentrations while accounting for reaction time adjustments. This machine learning-driven nonlinear analysis demonstrated that vehicle exhaust nearly always emerged as the primary anthropogenic contributor in diverse functional zones and different seasons, with gasoline evaporation as another key contributor, while the traditional reactivity metric method, ozone formation potential (OFP), tended to underestimate the role of the two sources. This study highlights the primacy of strengthening emission reduction of transportation sectors to mitigate HCHO pollution in megacities. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

This article presents experimental results related to the influence of bioethanol content in fuel blends on the performance and emissions of a spark-ignition engine. Tests were conducted for six ethanol–gasoline mixtures (ranging from 0% to 100% ethanol) under three engine control strategies: factory settings, a fuel dose increased by 10%, and a fuel dose increased by 20%—both with an ignition timing adjustment of +3°. Measurements included engine power and torque, as well as emissions of CO, CO₂, HC, O₂, and particulate matter, all performed under a full engine load. The results revealed the strong dependence of engine behavior on ethanol content. Increasing the ethanol concentration significantly reduced CO and HC emissions, as well as markedly lowering particulate emissions—particularly at 30% ethanol. Conversely, pure ethanol led to substantial reductions in power (up to 28%) and torque (up to 32%) compared to conventional gasoline. Adjustments to the fuel dose and ignition timing partially mitigated these losses. Emissions of CO₂ and oxygen content in exhaust gases varied depending on the blend, highlighting the complex nature of the combustion process. The findings contribute to the understanding of renewable fuel behavior in SI engines and underscore the influence of both fuel composition and control strategies on performance and emission characteristics. Full article

This study introduces an original sustainability-oriented methodology for calculating pollutant emissions (g/km) based on the ECE-15 driving cycle, aimed at evaluating passenger car compliance with various Euro emission standards. Four vehicles—two diesel and two gasoline-powered—representing Euro 4 to Euro 6 categories, respectively, were tested under controlled laboratory conditions. CO, HC, NO_x, and CO₂ emissions were measured and analyzed using the developed method. The Euro 4 Nissan Qashqai+2 exceeded the CO limit by 2.07 times, while NO_x and HC emissions were below the threshold by 1.46 and 50%, respectively. CO₂ exceeded the limit by only 6.2%. The Euro 5 Nissan Qashqai showed extremely low CO and HC levels—33 and 333 times below the limit—but exceeded NO_x by 1.32 times, with CO₂ emissions 62.8% above the target. Both Euro 6 vehicles (VW Passat) exhibited undetectable CO emissions, HC levels under 2% of the limit, and NO_x reduced by 3.81 to 15 times. However, their CO₂ emissions remained elevated, at 2.9% and 51.4% above the standard, respectively. The results confirm the effectiveness of modern emission control technologies, while also highlighting that CO₂ remains a major challenge, particularly for powerful gasoline vehicles. Full article

The aim of this article is to assess the sale structure impact of selected, popular brands of passenger vehicles on total CO₂ emissions in the context of the energy transition in the transport sector. A detailed analysis was conducted of the projected sales of gasoline-, diesel-, hybrid-, as well as electric-powered vehicles over the years 2021–2028. Based on the available empirical data, a mathematical model was developed to estimate emissions over the entire life cycle of vehicles, taking into account the unit carbon footprint of each type of drivetrain and the expected number of vehicles sold. The results indicate a gradual decline in total CO₂ emissions during the analyzed period, mainly due to the increasing share of alternative drivetrains. Despite the growth in electric vehicle sales, their impact on emission reductions remains limited due to the long lifespan of conventional vehicle fleets. The article concludes with a proposal to expand the LCA model to include regional, energy, and recycling components, which could help in formulating more effective climate policies. Full article

The transition to sustainable transport poses significant challenges for urban mobility, requiring shifts in fuel consumption, emissions reductions, and economic adjustments. This study conducts a scenario-based analysis of Budapest’s transport energy consumption, emissions, and monetary implications for 2020, 2030, and 2050 using the Budapest Transport Model (EFM), which integrates COPERT and HBEFA within PTV VISUM. This research examines the evolution of diesel, gasoline, and electric vehicle (EV) energy use alongside forecasted fuel prices, using the ARIMA model to assess the economic impact of transport decarbonisation. The findings reveal a 32.8% decline in diesel consumption and a 64.7% drop in gasoline usage by 2050, despite increasing vehicle kilometres travelled (VKT). Electricity consumption surged 97-fold, highlighting fleet electrification trends, while CO₂ emissions decreased by 48%, demonstrating the effectiveness of policies, improved vehicle efficiency, and alternative energy adoption. However, fuel price forecasts indicate significant cost escalations, with diesel and gasoline prices doubling and CO₂ pricing increasing sevenfold by 2050, presenting financial challenges in the transition. This study highlights the need for EV incentives, electricity price regulation, public transport investments, and carbon pricing adjustments. Future research should explore energy grid resilience, mobility trends, and alternative fuel adoption to support Budapest’s sustainable transport goals. Full article

Air pollution poses a serious threat to human health and the environment. Emissions from motor vehicles, especially in large cities, contribute significantly to this problem. This study analyzes the results of emission inspections in the Slovak Republic to identify factors influencing emissions and their impact on air quality. The research analyzed data from emission inspections and their relationship to vehicle age, fuel type, and type of failure. The results show that older vehicles, especially those aged 10 to 20 years, have a higher probability of failing to meet emission standards. Specifically, up to 42.75% of diesel vehicles aged 15 to 20 years were rated as unfit, compared to 33.07% of gasoline vehicles in the same age category. An increased proportion of unfit vehicles was recorded for diesel engines, which indicates their negative impact on air quality. The most common failures were related to direct emission measurements. These findings have implications for environmental policy and the regulation of vehicle imports to improve air quality and reduce pollution. Data on emission inspections were drawn from the national system and show knowledge about the observation of emission inspections carried out during one calendar year. The study recommends the introduction of stricter control mechanisms for older vehicles, supporting the renewal of the vehicle fleet, and the implementation of modern technologies to reduce emissions. Rigorous emission inspections are essential for the protection of public health. Regular inspections and modern technologies reduce emissions of harmful substances, thus contributing to the improvement of air quality and public health. Full article

Decarbonizing the transportation sector is critical for sustainable development, particularly in rapidly urbanizing countries like Turkiye. This study analyzes fuel demand elasticities for diesel, gasoline, and LPG across 12 NUTS-1 regions of Turkiye in 2022, using a panel random effects SUR approach. The model accounts for regional variation and fuel interactions, producing robust estimates that uncover significant spatial and temporal differences in consumption patterns. Uniquely, diesel demand displays a significantly positive price elasticity, challenging the conventional assumption of inelasticity. Gasoline demand is moderately price-sensitive, while LPG appears relatively unresponsive. Strong cross-price elasticities—especially between diesel and gasoline—point to substitution effects that can inform more adaptive policy frameworks. Seasonal fluctuations and Istanbul’s outsized impact also shape national trends. These findings underscore the need for differentiated region- and fuel-specific strategies. While higher gasoline taxes may effectively reduce demand, lowering diesel and LPG use will require complementary measures such as infrastructure upgrades, behavioral incentives, and accelerated adoption of alternative fuels. The study advocates for regionally adjusted carbon pricing, removal of implicit subsidies, and targeted support for electric and hybrid vehicles. Aligning fiscal tools with actual demand behavior can enhance both the efficiency and equity of the transition to a low-carbon transportation system. Full article

This study conducts a comparative life cycle assessment (LCA) and life cycle cost (LCC) analysis of battery electric vehicles (BEVs) and gasoline vehicles (GVs) in Heilongjiang Province, China, under cold climate conditions and a coal dominated electricity grid. Environmental impacts were assessed using SimaPro with the ReCiPe 2016 Midpoint (H) method, while cost performance was evaluated over 5-, 10-, and 15-year ownership periods. Results show that BEVs offer lower total ownership costs than GVs, even without subsidies, primarily due to reduced energy and maintenance expenses. In terms of global warming potential, BEVs show a 4.52% reduction compared to GVs. However, BEVs demonstrate higher impacts in several non-climate categories—including ionizing radiation, particulate matter formation, eutrophication, toxicity, and water use—largely due to emissions from coal-based electricity. The derived grid emission factor of 1.498 kg CO₂/kWh underscores the critical role of regional energy structure. These findings suggest that while BEVs provide economic and climate benefits, their overall environmental performance is highly dependent on local grid carbon intensity and seasonal energy demand. Policy recommendations include accelerating grid decarbonization, improving cold weather efficiency, and incorporating multidimensional environmental indicators into transport planning. Full article

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers. Full article

The real driving emissions of gasoline and diesel vehicles are significantly influenced by altitude, temperature, and starting conditions. In this study, the real driving emissions (RDEs) of gasoline and diesel vehicles compliant with China V standards were investigated under various conditions. The adaptability of RDE testing in China was evaluated by analyzing vehicle emissions at different altitudes, ambient temperatures, and starting conditions. The results show that, with increasing altitude, CO, NOx, and PN emissions generally exhibit a downward trend, particularly for gasoline vehicles, whose conformity factors remain well below the China VI limit. However, for China V diesel vehicles relying solely on EGR technology, NOx emissions significantly exceed China VI standards, indicating that EGR alone is insufficient to meet regulatory requirements. Temperature variations have little effect on the emissions of China V PFI gasoline vehicles, while diesel vehicles continue to exhibit excessive NOx emissions under varying temperatures. Although the cold-start phase generates substantial pollutant emissions, the EMROAD evaluation method excludes this phase, resulting in limited differences between cold- and hot-start emission results. Nevertheless, the inclusion of cold-start emissions should be considered in future RDE assessments. Full article

To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO₂ emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO₂ emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO₂ emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control. Full article

Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article

As of 2023, China’s transportation energy carbon emissions account for over 10%, which has a significant impact on achieving “dual carbon” goals. China has successively issued various policies to address pollution emissions in the transportation industry. This study mainly analyzes the synergistic effects of pollution reduction and carbon reduction measures implemented in this industry. We selected 2023 as the base year, focused on promoting new energy vehicles (NEVs), advocating bus transit (ABT), and advancing rail transit (ART) as the three major emission reduction policies, and analyzed their synergistic effects on air pollutant control and greenhouse gas emission reduction. Based on national scale data on driving conditions, energy consumption, and emission factors, the synergistic emission reductions in greenhouse gases and air pollutants brought about by the three policies were first calculated. Then, using the coordinate system of synergistic control effects, cross elasticity analysis of pollutants, and normalization evaluation methods, the multi pollutant synergistic control capabilities of each policy were quantified. Quantitative results revealed that the NEV substitution policy achieved a CO₂ reduction of 100.966 million tons in 2023, alongside reductions of 1.0196 million tons (CO), 59,506 tons (NO_x), 103,500 tons (NMHC), 6266 tons (PM₁₀), and 3071 tons (SO₂). Based on the AP_eq ranking, its comprehensive benefits (AP_eq = 166,734.52) significantly outperform ART (AP_eq = 97,414.89) and ABT (AP_eq = 19,796.80). The main research conclusion shows that replacing private gasoline cars with new energy vehicles can have a synergistic emission reduction effect on all five types of air pollutants and greenhouse gases involved in this study, with a positive synergistic effect. Moreover, the policy development priority is relatively better based on the synergistic emission reduction equivalent. Both buses and rail transit have not brought about SO₂ emission reduction, nor have they had a positive synergistic effect on SO₂ and CO₂ emission reduction. On this basis, buses also contribute to NO_x emissions. For other air pollutants, both rail transit and buses can have a synergistic effect of reducing pollution and carbon emissions. Full article

There is an urgent need for a method of evaluating the real energy performance of vehicles that eliminates the effects of external conditions (topography, altitude, and road conditions) and human factors (driving styles), especially in the case of heavy-duty vehicles. Governmental authorities require results on the energy performance of vehicles to develop strategies that result in reductions in greenhouse gas emissions, while fleet managers require results regarding the energy efficiency of existing vehicle technologies to select the technologies that minimize energy consumption and, therefore, operational costs. Aiming to address this need, we propose a method for evaluating the global energy efficiency of road vehicles by monitoring at 1 Hz the operational variables of a vehicle under normal conditions of use for a long time. The variables monitored are engine RPM and vehicle location, speed, payload, and energy consumption. This method was verified using 49 vehicles, representing 23 vehicle technologies. These vehicles varied in size (light duty and heavy duty), application (cars, buses, and freight), energy sources (gasoline, diesel, and electric), and operational conditions (Chile, Ecuador, Colombia, and México). Testing was conducted across various altitudes (0–3600 masl) and topographies (flat and mountainous regions). The results showed that the energy efficiencies for gasoline-fueled light-duty vehicles ranged from 17 to 30%, those for diesel-fueled heavy-duty vehicles ranged from 25 to 42%, and those for electric heavy-duty vehicles (HDVs) ranged from 70 to 80%. Full article