Search Results (435)

Heavy-duty vehicles cause a significant percentage of the harmful gas emissions from the automotive industry. This article presents the development of a compression system for hydrogen as part of the H2REF-DEMO hydrogen refueling station, joining the European efforts to promote hydrogen (H₂) as a fuel that can play a key role in the energy transition of these types of vehicles. The H2REF-DEMO project, co-funded by the European Union’s “Horizon. Europe” programme under the “Clean Hydrogen Partnership” (grant agreement no. 101101517), involves a partnership between companies and research centers that aims to investigate the possibility of compressing hydrogen through hydraulic power to handle large vehicle refueling applications, such as bus fleet depots, trucks, or trains. The basic principle is the exploitation of hydraulic power to compress hydrogen through hydro-pneumatic bladder accumulators. The hydraulic power units, in fact, pump oil into the accumulators, causing a deformation of the bladder containing H₂ and thus a consequent gas compression. In this article, we focus on the development of the compression system, from the theoretical starting point to the core final layout of the refueling station for large vehicles. We also exploit a lumped parameter numerical model to both support the system design and virtually test its first control logic. The latter, in particular, allows the system to operate in three modes—Bypass, Parallel, and Serial modes—thus leaving room for testing basic and more complex control strategies. The results of numerical simulations demonstrate the effectiveness of this innovative compression technology and its considerable efficiency in terms of refueling time and energy consumption, especially when compared to the standard systems used for this application. These are thus encouraging results that can support the development of an actual H2REF-DEMO hydraulic test rig for hydrogen compression. Full article

Underground mining environments present elevated occupational health risks, primarily due to substantial exposure to diesel exhaust emissions within confined and poorly ventilated spaces. This study assesses the real-world performance of two advanced retrofit emission control systems—Proventia NOxBuster and Purifilter—installed on underground mining trucks operating in a Spanish mine. Emissions of carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO₂) were quantified using a Testo 350 multigas analyser, while ultrafine particle (UFP) concentrations were measured with an Engine Exhaust Particle Sizer (EEPS-3090) equipped with a thermodiluter. Controlled tests under both idling and acceleration conditions revealed substantial reductions in pollutant emissions: CO decreased by 60–98%, NO by 51–92%, and NO₂ by 20–87%, depending on the system and operational phase. UFP concentrations during idling dropped by approximately 90%, from 542,000 particles/cm³ in untreated trucks to below 50,000 particles/cm³ in retrofitted vehicles. Under acceleration, the Proventia NOxBuster achieved reductions exceeding 95%. Conversely, Purifilter-equipped trucks exhibited a counterintuitive increase in UFPs within the 5.6–40 nm range, potentially due to ammonia slip events during selective catalytic reduction (SCR). Despite these discrepancies, both systems demonstrated considerable mitigation potential, albeit highly dependent on exhaust temperature (optimal: 200–450 °C), urea dosing precision, and maintenance protocols. This work underscores the necessity of in situ performance verification, regulatory vigilance, and targeted intervention strategies to protect underground workers effectively. Further investigation is warranted into the long-term health benefits, system durability, and nanoparticle emission dynamics under variable load conditions. Full article

Current research on the performance and emissions of vehicles and internal combustion engines should include analysis of efficiency-enhancing technologies and emission reduction strategies across a variety of vehicle systems. To improve both performance and emission control, it is necessary to examine advanced heavy-duty driveline technologies, considering their real-world impact on fuel economy and emission reduction under various driving conditions. This article will deal with predictive cruise control (PCC) and its influence on the operating characteristics of a truck, specifically a semi-trailer combination. The measurement was carried out using dynamic driving tests of a truck on a selected road. The use of electronic systems for automatically maintaining the vehicle’s motion states (especially speed) based on the specified conditions most often has several benefits for the driver not only from the point of view of vehicle operation but also from the point of view of transport companies (cost reduction). It is generally known that the use of these electronic systems reduces the vehicle’s fuel consumption and therefore also reduces the amount of exhaust gases. Comparing the individual directions of the road tests, the difference in relative maximum power utilization between the driver and the PCC system was 26.42% in the ST-MY direction and 23.81% in the MY-ST direction. The use of PCC also results in fuel savings of up to 17.11%. This study provides new insights into the quantification of the impact of PCC on fuel consumption in real operating conditions and highlights the potential for integrating PCC into driver assistance systems and logistics planning to reduce costs and emissions in freight transport. Further research could focus on applying this system in specific road conditions. Full article

Concepts such as reuse, repurposing, upcycling, remanufacturing, and re-powering can be applied to the reuse of combustion engines from passenger cars and trucks in stationary or mobile machines, such as power generators. Technical, economic, environmental, and research analyses indicate that such solutions may be justified; however, their implementation is limited by homologation and emission regulations. In most countries, there are no specific rules governing emissions from power generator engines, while in the European Union, such engines are categorized as mobile generators (portable or trailer-mounted) subject to Stage V (Reg. 2016/1628/EU), stationary generators (permanently installed) subject to the MCP Directive (2015/2193/EU), and emergency generators (limited operation) partially exempt from MCP but requiring registration. Consequently, engines recovered from road vehicles do not meet formal or technical emission compliance requirements for power generators and can only be used under conditional approval for research, experimental, or temporary purposes. This reveals a paradox of modern environmental policy: although reusing functional engines from dismantled vehicles could embody the principles of a circular economy, restrictive emission standards (Stage V, MCP, NSPS) effectively prevent such technological recycling. Addressing this issue requires legislative action and the development of simplified testing methods for used engines in new applications. This article is the first to systematically demonstrate that current Stage V, MCP and NSPS emission frameworks create a regulatory paradox that prevents the circular-economy reuse of functional automotive engines, and it proposes a dedicated secondary type-approval pathway enabling their legal and environmentally controlled application in power generators. Full article

Reducing greenhouse gas (GHG) emissions and fuel consumption remains a critical objective in courier fleet management. Dynamic routing, which continuously updates delivery routes in response to real-time conditions, offers a promising solution. However, its implementation is hindered by challenges in real-time data analytics and intelligent decision-making. This study addresses two underexplored, yet impactful, variables in dynamic fleet routing: (1) the changing weight of delivery trucks due to unloading at each stop and (2) traffic conditions on local roads, where most deliveries occur. We propose a machine learning-driven smart rerouting system that integrates real-time data analytics into a dynamic routing optimization framework focused on minimizing fuel consumption. Our approach consists of two key components. First, trucks are equipped to collect continuous real-time data on vehicle weight, which are analyzed using frequency domain techniques, and traffic conditions, which are interpreted via neural networks. Second, these data inform an optimization model that explicitly captures the relationship between fuel consumption, emissions, vehicle weight, and traffic dynamics. This model surpasses conventional capacitated vehicle routing approaches by embedding real-time reasoning into route planning. Extensive simulation studies demonstrate that the proposed system significantly reduces both GHG emissions and fuel consumption compared to traditional routing models, highlighting its potential for sustainable and cost-effective fleet operations. Full article

Liquefied natural gas (LNG) container trucks are regarded as clean energy vehicles with the potential to reduce air pollution. However, their CO₂ emissions remain relatively high and are not yet well understood. In this study, the actual CO₂ emissions of LNG container trucks in Shanghai were measured using a portable emissions measurement system (PEMS). This study quantitatively analyzed the relationship between traffic congestion levels and CO₂ emissions on elevated roadways, providing new insights into the impact of urban traffic conditions. In addition, distinct emission patterns were revealed under different uphill, downhill, and level road conditions, highlighting the substantial effects of roadway geometry on vehicle carbon emissions. Based on these findings, engine-related factors were identified as the dominant contributors, explaining 74% of the emission variance, while road slope analysis showed that uphill driving increased emissions by 13.41% compared with flat roads, whereas downhill driving reduced them by 76.22%. Finally, an efficient carbon emission prediction model for LNG container trucks was developed using machine learning methods. This study enriches the understanding of carbon emissions from LNG container trucks and provides theoretical support for their future applications in sustainable freight transportation. Full article

Heavy vehicles present high aerodynamic drag. This results in significant fuel consumption and, consequently, high emissions of harmful substances. This study examines the variation in aerodynamic drag in a European-type truck with different trailer configurations. Passive flow control by geometry modifications of the rear part of the trailer and active flow control using the Coanda effect were tested, with the aim of improving the aerodynamic efficiency of the vehicle. To achieve this, a modular structure of a 1:30 scaled truck was designed to enable different trailer configurations. Drag measurements were performed with a two-component external balance, and PIV tests were conducted to correlate the drag reduction with the aerodynamic changes behind the trailer. Passive control reduced drag by up to 5.7%, and active flow control reduced it by up to 12.6% compared to the unmodified base trailer. PIV flow visualization confirms that blowing effectively reduces the recirculation zone behind the trailer. Full article

This study addresses the operational and environmental pressures of last-mile delivery in dense cities under limited urban logistics hubs. We propose a resource-efficient framework that repurposes existing convenience stores as robotic delivery hubs and formalize its operation via a two-stage optimization coupling truck and robot routing. In controlled simulations, and in a Seoul street network scenario, the approach reduces total completion time relative to a truck-only benchmark and lowers truck activity (truck-kilometers and curb idling), leading to lower estimated CO₂e under standard emission factors. We also observe a nonlinear relationship between the number of hubs and efficiency, suggesting a coverage “sweet spot”. These results indicate that with minimal new infrastructure, reusing commercial assets can improve operational performance and environmental proxies; social and labor outcomes are not measured here and are left for future field evaluation. Full article

A life cycle assessment was conducted to compare the impacts of peas and lentils produced in Canada, France, and Russia, transported to market in Western Europe, to assess the systems-level sustainability implications of changing production and consumption profiles of internationally traded commodity pulse crops. For all but 1–2 impact categories, imported Canadian peas and lentils outperformed those imported from Russia, due to the lower yields, higher levels of tillage, higher field-level emissions, and higher distances of truck transportation for Russian pulses. French peas had higher impacts of production than Canadian peas, for all categories but land use, due to higher levels of fertilizer inputs, irrigation, field activities, and field-level emissions. However, for 7 out of 12 impact categories, the impacts of the transportation of Canadian peas to Western Europe outweighed the higher impacts of the production of French peas. This demonstrates potential sustainability benefits of Canadian pulses, with some trade-offs from the additional impacts of transportation to market, adding nuance to the discussion around the importance of “food miles” in agricultural sustainability. Compared to previous studies, this demonstrates the importance of multi-criteria and regionalized assessments. Full article

To reduce emissions in the existing transportation system and lower carbon dioxide (CO₂) output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system can help save energy and improve passenger comfort. This study investigates an advanced thermal management system for an electric truck cabin with heating panels and added insulation. A one-dimensional (1D) cabin thermal model was also developed and validated with experimental data. The model integrates insulation, heating panels, and a 1D comfort simulation. It is functional mock-up unit (FMU) compatible and connects to larger system simulations and real-time applications. The results show that energy consumption can be reduced by up to 50% with these thermal measures. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions. Full article

The transition to sustainable transport in the logistics sector requires innovative strategies, yet companies still face uncertainty regarding the operational, economic, and environmental feasibility of replacing diesel trucks with electric ones. Electric trucks represent a sustainable alternative, contributing to the reduction in pollutant gas emissions, noise reduction in traffic, and lower operational costs, in addition to building sustainable logistics through recharges from renewable energy sources. Although electric trucks offer sustainability benefits, existing research often lacks analyses based on real-world delivery conditions. In this context, the objective of this paper is to analyze the logistical impact of introducing electric trucks for beverage transportation. This study includes assessments of planned route profiles, economic evaluation during operation, emission mitigation costs, and charging analyses under different pricing models in consumer units. These elements were selected to reflect the actual challenges companies face. The results demonstrate that electric trucks can reduce fuel costs by 83.90% and significantly lower carbon emissions, confirming their viability for last-mile freight transport operations. Therefore, the results demonstrate that the process of replacing diesel trucks with electric ones is a viable alternative for companies due to the savings generated during operation and the reduction in pollutant emissions. Full article

Mango (Mangifera indica L.) is a popular tropical fruit enjoyed worldwide, with Europe being a significant importer of this fruit. Its climacteric nature and short shelf-life pose challenges for maintaining quality, while emissions from transportation threaten the sustainability of the supply chain. This highlights the importance of low-impact logistics in maintaining fruit quality. This study aimed to evaluate the quality of fresh mangoes in Italy by comparing the different shipping systems (air, sea, and road) for seven cultivars sourced from seven countries. Quality assessment included pomological analysis, PTR-ToF-MS for volatile profiling (n = 11 cultivars × 2 years × 3 replicates), and consumer sensory analysis (n = 65 for untrained panellists in 1 year, n = 8 for trained panellists over 2 years). Results indicated that air and truck transport better preserved fruit quality compared to sea freight, primarily due to shorter transit times, which allowed for harvesting at more advanced ripeness stages. The combination of PTR-ToF-MS and PLS-DA effectively differentiated samples based on the method of transport, showcasing its potential as a quick quality monitoring tool. Mangoes transported by air showed significantly higher levels of volatile organic compounds (VOCs), a 29% greater total soluble solids (TSSs) content, and a 44% lower acidity (TA). Sensorial tests indicated that consumers preferred these mangoes. However, air transport resulted in 30 times higher CO₂ emissions per kg of fruit compared to sea freight (~642,117 CO₂e (kg) vs. ~19,132 CO₂e (kg)), highlighting a critical dilemma between sustainability and quality. These findings provide a framework for developing hybrid logistics strategies that strike a balance between preserving quality and environmental responsibility. Additionally, they support the development of European mango cultivation, which can optimise harvest timing, reduce emissions, and enhance fruit quality. Full article

Motor vehicle emissions are a major air quality concern in Chinese cities. However, traditional population-based emission inventory methods fail to capture the spatial and temporal variations in emissions for effective policy design. This study proposes a high-resolution approach for traffic flow prediction and vehicle emission inventory estimation, using Jinan City, China, as a case study. We leverage multi-source geospatial data and employ a two-fold random forest model to predict hourly traffic flow at a road-segment level. Speed-aligned emission factors were then combined with these data to calculate hourly and road-level vehicle emission estimates. Compared to traditional methods, our approach offers substantial improvements: (1) improved spatiotemporal resolution; (2) enhanced accuracy of traffic flow prediction; and (3) support for more effective vehicle emission control strategies. Results show that heavy-duty vehicles, particularly freight trucks operating on inter-regional corridors through Jinan, contribute 78% more to NO_X emissions than local light-duty vehicles. These transient emissions are typically overlooked in static inventories but constitute a significant source of urban pollution. This study offers valuable insights for combining geospatial data and machine learning to improve the accuracy and resolution of vehicle emission inventories, supporting urban air quality policy and planning. Full article

The rapid growth of e-commerce and the pandemic-driven surge in deliveries have intensified the challenges last-mile logistics poses to urban areas. Road transport, the predominant delivery mode, is a major contributor to greenhouse gas emissions. Despite a downward trend since 2008, emissions rose in 2022, reflecting an increased mobility demand. Light commercial vehicles and trucks impact air and noise pollution due to their high emissions and noise levels. Innovative solutions, such as cargo bikes (CBs), have emerged as sustainable alternatives to mitigate these effects. This paper reports a brief literature review on CBs and evaluates their environmental, economic, and social benefits by comparing real-life data from a shipping company operating with CBs in central Rome to simulated data for motorized delivery vehicles. By analyzing their potential to reduce emissions, improve urban livability, and lower operational costs, this study seeks to raise awareness on CBs’ sustainability as a viable alternative for last-mile logistics. Highlighting these advantages can support policymakers, businesses, and urban planners in fostering a transition to more sustainable urban mobility solutions. Full article

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article