Search Results (853)

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

The Barcelona Metropolitan Area (AMB) has implemented various policies to reduce car use and promote more sustainable mobility. Initiatives such as superblocks, Low Emission Zones (LEZs), and the Bicivia network aim to transform the urban model in response to environmental and congestion challenges. However, the high reliance on private vehicles for intermunicipal travel, uneven infrastructure, and social resistance to certain changes remain significant issues. This study examines the evolution of mobility patterns and assesses the effectiveness of the above policies in fostering real and sustainable change. A mixed-methods approach was adopted, which combined an exploratory factor analysis (EFA) of 2011–2024 data, trend linear regression, and a comparative international analysis. The EFA identified four key structural dimensions: traditional transport infrastructure, active mobility and bus lines, public bicycles and mixed use, and transport efficiency and punctuality. The findings reveal a clear reduction in private car use and an increase in sustainable modes of transport. This indicates that there are prospects for future transformation. Nonetheless, challenges persist in intermunicipal mobility and the public acceptance of the measures. This study provides empirical and comparative evidence and emphasizes the need for integrated metropolitan governance to achieve a resilient and sustainable urban model. Full article

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

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM originates from non-traffic sources. Therefore, vehicle-related PM emissions are typically estimated using simulation models based on average emission factors. This study uses the COPERT (Computer Programme to Calculate Emissions from Road Transport) model to estimate emissions from road vehicles under current conditions and future scenarios. These include the introduction of Euro 7 standards and a shift from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). The analysis considers exhaust and non-exhaust emissions, as well as indirect emissions from electricity generation for BEV charging. The conducted study showed, among other findings, that replacing internal combustion engine vehicles with electric ones could reduce PM2.5 emissions by approximately 6% (2% when including indirect emissions from electricity generation) and PM10 emissions by about 10% (5% with indirect emissions), compared to the Euro 7 scenario. Full article

The increasing levels of ozone pollution have become a significant environmental issue in urban areas worldwide. Previous studies have confirmed that the urban ozone pollution in China is mainly controlled by volatile organic compounds (VOCs) rather than nitrogen oxides. Therefore, a study on the emission characteristics and source analysis of VOCs is important for controlling urban ozone pollution. In this study, hourly concentrations of 57 VOC species in four groups were obtained in April 2022, a period of high ozone pollution in Kunming, China. The ozone formation potential analysis showed that the accumulated reactive VOCs significantly contributed to the subsequent ozone formation, particularly aromatics (44.16%) and alkanes (32.46%). In addition, the ozone production rate in Kunming is mainly controlled by VOCs based on the results of the empirical kinetic modeling approach (K_NOx/K_VOCs = 0.25). The hybrid single-particle Lagrangian integrated trajectory model and polar coordinate diagram showed high VOC and ozone concentrations from the southwest outside the province (50.28%) and the south in local areas (12.78%). Six factors were obtained from the positive matrix factorization model: vehicle exhaust (31.80%), liquefied petroleum gas usage (24.16%), the petrochemical industry (17.81%), fuel evaporation (11.79%), coal burning (7.47%), and solvent usage (6.97%). These findings underscore that reducing anthropogenic VOC emissions and strengthening controls on the related sources could provide a scientifically robust strategy for mitigating ozone pollution in Kunming. Full article

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO₂eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO₂eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

This study analyses aspects related to the electromobility transition. Emerging technologies have enabled the production and commercialisation of electric vehicles to reduce polluting emissions. However, significant obstacles are present in this global transition. The analysis identifies that public policies play a crucial role in the development of electromobility, and emphasises how new business models in electromobility are emerging to satisfy changing customer demands. Concerns related to raw materials extraction, battery disposal, and vehicle-to-grid (V2G) integration are also important to consider. The relationship between technologically advanced countries and raw material-producing nations must balance socioeconomic, historical, labour, and ecological factors. In order to have a standard reference, this study considers for the analysis the political, economic, social, technological, environmental, and legal factors (PESTLE). An analysis of future scenarios considering pessimistic and optimistic trends revealed that, compared with the actual trends, important actions must be taken to develop electromobility not only from the technological aspect. These results provide a comprehensive analysis of electromobility sustainability and its importance for multidisciplinary stakeholders related to the actual challenges towards electromobility, the electric network capabilities, and the importance of creating new jobs and products based on a circular and sustainable economy. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

In this study, a large portion of data on the chemical composition of precipitation falling in the South Baikal region shows the main factors determining their formation in 2017–2024. Taking into account the high variability of meteorological conditions in the region, both in time and in space, a method of observing the chemical composition of atmospheric precipitation has been developed, which makes it possible to determine its composition depending on the conditions of air mass formation. Using statistical analysis, marker substances characterizing the main groups of sources influencing the composition of atmospheric precipitation were identified. Joint analysis of air mass trajectories and data on chemical composition of precipitation allowed for establishing the areas of location of potential sources of precipitation pollution. All precipitation events were categorized based on the similarity of air mass formation conditions and chemical composition. Precipitation composition data collected on the shores of Lake Baikal reflect the influence of different types of pollutants such as industrial emissions, motor vehicles, dust storms, and forest fires. The results of the study are relevant for air quality assessment in the region and demonstrate the potential of using precipitation chemistry data to understand the long-range transport of pollutants, which contributes to sustainable development by increasing the availability of air quality data in ecologically significant regions such as Lake Baikal. Full article

As hydrogen fuel cell vehicles gain momentum as crucial zero-emission transportation solutions, the urgency to address hydrogen permeability through the polymer liner becomes paramount for ensuring the safety, efficiency, and longevity of Type IV hydrogen storage tanks. This paper synthesizes existing research findings, analyzes the influence of different materials and structures on gas permeability, elucidates the dissolution and diffusion mechanisms of hydrogen in plastic liners, and discusses their engineering applications. We focus on measurement methods, influencing factors, and improvement strategies for liner gas permeability. Additionally, we explore the prospects of Type IV hydrogen storage tanks in fields such as automotive, aerospace, and energy storage industries. Through this comprehensive review of liner gas permeability, critical insights are provided to guide the development of efficient and safe hydrogen storage and transportation systems. These insights are vital for advancing the widespread application of hydrogen energy technology and fostering sustainable energy development, significantly contributing to efforts aimed at enhancing the performance and safety of Type IV hydrogen storage tanks. Full article

This study presents the development of a manufacturing process for a double-serpentine (DS) exhaust nozzle for unmanned aerial vehicles (UAVs) based on carbon fiber-reinforced silicon carbide matrix composites (C/SiCs). The DS nozzle is designed to reduce infrared emissions from hot exhaust plumes, a critical factor in enhancing stealth performance during UAV operations. The proposed nozzle structure was fabricated using a multilayer configuration consisting of an inner C/SiC layer for thermal and oxidation resistance, a silica–phenolic insulation layer to suppress heat transfer, and an outer carbon fiber-reinforced polymer matrix composite (CFRPMC) for mechanical reinforcement. The C/SiC layer was produced by liquid silicon infiltration, preceded by pyrolysis and densification of a phenolic-based CFRPMC preform. The final nozzle was assembled through precision machining and bonding of segmented components, followed by lamination of the insulation and outer layers. Mechanical and thermal property tests confirmed the structural integrity and performance under high-temperature conditions. Additionally, oxidation and ablation tests demonstrated the excellent durability of the developed C/SiC. The results indicate that the developed process is suitable for producing large-scale, complex-shaped, high-temperature composite structures for stealth UAV applications. Full article