Search Results (117)

Diesel particulate matter—primarily ultrafine particles (UFPs), defined as particles smaller than 0.1 µm—are released by diesel-powered vehicles, especially those used in heavy-duty hauling. While much of the existing research on traffic-related air pollution focuses on urban environments, limited attention has been paid to how complex topography influences the concentration of UFPs, particularly in areas with significant truck traffic. With a focus on Morgantown, West Virginia, an area distinguished by a steep topography, this study investigates how travel over two different terrain conditions affects UFP concentrations close to roadways. Specifically, we sought to determine if the truck count taken from simultaneous video evidence could be used as a surrogate for varying topography in determining the concentration of UFPs. This study shows that “TRUCK COUNT” and “TRUCK SPEED” have a linear relationship and yield a possible surrogate measure of the lung dose of UFP number concentration. Our results demonstrate a statistically significant (p < 0.1) linear relationship between truck count and UFP number concentration (R = 0.77 and 0.40), validating truck count along with truck speed as a medium effect surrogate for estimating near-road UFP exposure. Dose estimation using the Multiple-Path Particle Dosimetry (MPPD) model further revealed that approximately 30% of inhaled UFPs are deposited in the alveolar region, underscoring the public health relevance of this exposure pathway in topographically complex areas. This method ultimately awaits comparison with health effects to determine its true potential as a useful exposure metric. Full article

Decarbonizing Oregon’s heavy-duty trucking sector, which accounts for 24% of the state’s transportation emissions, is essential for meeting carbon reduction targets. Drop-in fuels such as renewable diesel, biodiesel, and synthetic fuels provide an immediate and effective solution, reducing emissions by up to 80% while utilizing the existing diesel infrastructure. In 2023, Oregon’s heavy-duty trucks consumed 450 million gallons of diesel, with drop-in fuels making up 15% of the fuel mix. Renewable diesel, which is growing at a rate of 30% annually, accounted for 10% of this volume, thanks to incentives from Oregon’s Clean Fuels Program. By 2030, drop-in fuels could capture 40% of the market, reducing CO₂ emissions by 3.5 million metric tons annually, assuming continued policy support and advancements in feedstock sourcing. Meeting the projected demand of 200 million gallons annually and securing sustainable feedstock remain critical challenges. Advances in synthetic fuels, like Power-to-Liquids (PtL) from renewable energy, may further contribute to decarbonization, with costs expected to decrease by 20% over the next decade. Oregon aims for a 50% reduction in emissions from heavy-duty trucks by 2050, using a mix of drop-in fuels and emerging technologies. While hydrogen fuel cells and electric trucks face challenges, innovations in infrastructure and vehicle design will be key to the success of Oregon’s long-term decarbonization strategy. Full article

Direct injection of hydrogen at high pressures into an otherwise unmodified heavy-duty diesel engine offers a near-term pathway to near-zero greenhouse gas emissions for commercial vehicles. Hydrogen direct-injection engines maintain diesel-like performance with equal or better thermal efficiency. Supplying the hydrogen for injection pressures of ~30 MPa requires a high-pressure supply. Onboard hydrogen compression enables more complete utilization of the stored compressed hydrogen; however, it introduces a significant parasitic load on the engine. The magnitude of this load depends on factors such as the compressor’s configuration, capacity, pressure ratio, efficiency, and the engine’s operating conditions. This paper presents an exergy analysis of an onboard hydrogen compression system that uses hydraulically driven free-floating pistons, sized for heavy-duty commercial vehicles. Minimizing the parasitic loads from the compressor is essential to retain vehicle performance and maximize system-wide efficiency. The exergy analysis approach provides a comprehensive understanding of the whole compression system by comparably quantifying the losses across all components. A one-dimensional model of the compression system, developed in GT-SUITE^TM and validated with experimental data, is used to quantify the main exergy loss components. Exergy efficiency ranges from 12% to 45% under varying pressure ratios and cycle frequencies, with a pronounced increase in efficiency observed at higher cycle frequencies. Major exergy losses occur in the hydraulic driving system up to 79%, especially during retracting and idle phases for lower pressure ratios and cycle frequencies. Within the compression cylinder, exergy destructions account for less than 10% of the total work input, wherein heat transfer and piston friction are identified as the dominant contributors to exergy destruction, with their effects intensifying at higher pressure ratios. This work highlights the challenges of onboard gas compression and develops a systematic framework that can compare compressor design alternatives for different driving cycles. Full article

Motor vehicles emit a large amount of air pollutants. Inspection and Maintenance (I/M) systems serve as a pivotal strategy for mitigating emissions from operational diesel trucks. However, the prevalent issue of blind repairs persists due to insufficient diagnostic capabilities at maintenance stations (M stations). To address this challenge, a multi-source information fusion methodology is proposed, integrating load deceleration testing from inspection stations (I stations), on-board diagnostics (OBD) data, and manual measurements at M stations. Critical diagnostic parameters—including nitrogen oxides (NOx) and particulate matter (PM) emissions, the ratio of measured wheel-side power to rated power, intake volume, common rail pressure, and exhaust back pressure—are systematically selected through statistical analysis and expert evaluations. An adaptive membership function is developed to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework. Validation using 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data) demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%. By minimizing unnecessary repairs and optimizing maintenance efficiency, this approach significantly reduces resource waste and the lifecycle environmental footprints of diesel fleets. The proposed fuzzy-logic-based model effectively detects latent faults during routine maintenance, directly contributing to sustainable transportation through reductions in NOx and PM emissions—critical for improving air quality and advancing global climate objectives. This establishes a scalable technical framework for the effective implementation of I/M systems in alignment with sustainable urban mobility policies. 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

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (${CO}_2$ and ${NO}_x$). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of ${CO}_2$ emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities. Full article

Diesel-engine Heavy-Duty Vehicle (HDV) exhaust gas mixture contains pollutants including unburned hydrocarbons, carbon monoxide, nitrogen oxides, and particulate matter. A catalyst-based emission control system is commonly used to eliminate the above pollutants. However, the excess of oxygen that exists in the exhaust gasses of diesel engines hinders the efficient and selective reduction of nitrogen oxides over conventional catalytic converters. The AdBlue^® solution, which is currently used to eliminate nitrogen oxides, is based on ammonia. The latter is toxic in high concentrations. The aim of this work is to develop an Oxygen Reduction System (ORS) to remove oxygen from the exhaust gas of diesel engines, allowing the successful catalytic reduction of nitrogen oxides on a reduction catalyst without the need for ammonia. The ORS device consists of dense composite dual-phase membranes that allow the permeation of oxygen and carbon dioxide. Even though the oxygen concentration gradient across the membranes favors oxygen spontaneous diffusion from the atmosphere to the exhaust gas, the carbonate ion-based technology proposed herein utilizes the big difference in the concentration of carbon dioxide across the membrane to remove oxygen without any power consumption requirement. The results of this study are promising for the application of O₂ reduction in diesel HDVs. Full article

Decarbonizing long-haul, heavy-duty transport in Europe focuses on battery-electric trucks with high-power chargers or electric road systems and fuel-cell-electric vehicles with hydrogen refueling stations. We present a comparative life cycle assessment and total cost of ownership analysis of these technologies for 20% of Germany’s heavy-duty, long-haul transport alongside internal combustion engine vehicles. The results show that fuel cell vehicles with on-site hydrogen have the highest life cycle emissions (65 Mt CO₂e), followed by internal combustion engine vehicles (55 Mt CO₂e). Battery-electric vehicles using electric road systems achieve the lowest emissions (21 Mt CO₂e) and the lowest costs (EUR 45 billion). In contrast, fuel cell vehicles with on-site hydrogen have the highest costs (EUR 69 billion). Operational costs dominate total expenses, making them a compelling target for subsidies. The choice between battery and fuel cell technologies depends on the ratio of vehicles to infrastructure, transport performance, and range. Fuel cell trucks are better suited for remote areas due to their longer range, while integrating electric road systems with high-power charging could offer synergies. Recent advancements in battery and fuel cell durability further highlight the potential of both technologies in heavy-duty transport. This study provides insights for policymakers and industry stakeholders in the shift towards sustainable transport. The greenhouse gas emission savings from adopting battery-electric trucks are 54% in our high-power charging scenario and 62% in the electric road system scenario in comparison to the reference scenario with diesel trucks. Full article

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 10⁵ 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NO_x) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NO_x emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

This research proposed the method of using cumulative positive and negative elevation increment indicators based on road segment to identify the slope characteristics of mountain city roads. Furthermore, it proposed the adoption of these indicators, combined with driving dynamics and emission theory, to analyze the correlation mechanism between the road slope and the actual driving fuel consumption and emissions. Three routes with different slope characteristics were selected in the mountain city of Chongqing, and six road driving tests were conducted using a Class N2 heavy-duty diesel vehicle. Finally, a comprehensive and in-depth study on fuel consumption and emission characteristics was carried out. The results show that the cumulative positive and negative elevation increment indicators based on road segment can correctly identify the complex slope characteristics of mountain city roads. Moreover, using the above indicators, the research method based on the theory of driving dynamics and emission successfully revealed the correlation mechanism between the slope of mountain city roads and the fuel consumption and emissions. Overall, the changes in fuel consumption factor and pollutants CO, NO_X, and PN are positively correlated with the change in slope. The increase in slope leads to a rise in load, thereby increasing the required power, fuel consumption, and rich combustion conditions, ultimately leading to an increase in pollutants. It should be noted that driving dynamics also affect fuel consumption and emissions, leading to the specific rate of change between slope and fuel consumption not being consistent and a significant increase in the PN (Particulate Number) on some road sections. In addition, exhaust gas temperature may have a certain impact on emissions. Full article

Agriculture may hold the key to a sustainable future. By efficiently capturing atmospheric CO₂, we can simultaneously produce food, feed, biomass, and biofuels. For more eco-friendly soil processing practices, biofuels can replace diesel in agricultural machinery, significantly reducing the carbon footprint of crop production. Thus, biofuel production can be a sustainable solution for a future with a decreasing carbon footprint. This paper examines the possibility of replacing petroleum-based fuels with 100% biofuels to continue powering heavy-duty vehicles, where the use of electric vehicles is not the optimal solution. This study particularly focused on the operating scenario of heavy-duty engines under medium to high loads, typical of transport or soil processing in agriculture. Diesel was used as a benchmark, and each alternative, such as vegetable oil, methyl ester (B100), and methyl ester–ethanol blends (90B10E, 80B20E, and 70B30E), was tested individually. To find a sustainable fuel substitute, the goal was to identify a biofuel with a kinematic viscosity similar to that of diesel for a comparable spray process. Experimental results showed that an 80% methyl ester and 20% ethanol blend had a kinematic viscosity close to that of diesel. In addition to diesel, this blend resulted in a 48.6% reduction in exhaust gas opacity and a 6.54% lower specific fuel consumption (BSEC). The main aim of the tests was to find a 100% biofuel substitute without modifying the fuel injection systems of existing engines. Full article

This study focuses on understanding the health impacts of hazardous air pollutant (HAP) emissions from the Kaohsiung Coastal Industrial Park and port areas in southern Taiwan on neighboring communities. Six important HAPs (formaldehyde, benzene, arsenic, vinyl chloride, 1,3-butadiene, and diesel particulate matter (DPM)) were identified in this area. By considering the impact of emissions from stationary sources, mobile sources, and port activities, the relative importance of each emission source was assessed. In addition, the AERMOD (AMS (American Meteorological Society)/EPA (U.S. Environmental Protection Agency)) diffusion model was employed to simulate the increases in target pollutant concentrations and to analyze the influence and spatial distribution of various emission sources on atmospheric HAP concentrations in nearby communities. This study further evaluated the exposure risks of composite HAP sources, to understand their impacts and to determine their control priorities. The findings revealed that emissions and carcinogenic weighting from composite sources, particularly DPM emissions from port activities, including from ocean-going vessels and heavy-duty vehicles, had a significant impact. The maximum incremental concentration for DPM in the study area occurred around the port area, whereas the maxima for formaldehyde, benzene, arsenic, vinyl chloride, and 1,3-butadiene were all observed within the industrial complex. DPM emissions from port activities, 1,3-butadiene emissions from mobile sources, and benzene emissions from stationary sources were the composite sources with the greatest potential impacts. Over 90% of health risks were due to DPM, and the remaining health risks were due to 1,3-butadiene (6%), benzene (2%), arsenic (1%), and other species (less than 1%). DPM emissions were primarily influenced by port activities (77%), 1,3-butadiene emissions by mobile sources (45%), and benzene emissions by stationary sources (41%). A total of 25% of the area had risk values greater than 10⁻³, and 75% of the area had risk values between 10⁻³ and 10⁻⁴. The risk values in the densely populated areas were all greater than 10⁻⁴. The potential risk hotspots with risk values greater than 10⁻³ were located on the northwest side of the port and downwind of the industrial park. The key pollutants contributing to these hotspots were, in order, DPM (up to 80% cancer risk), formaldehyde, and 1,3-butadiene, all of which were significantly influenced by port activities. This indicates that the control of, and reduction in, HAP emissions from port activities should be prioritized. Full article

The increasing number of heavy-duty vehicles (HDVs) on roads has become a major contributor to harmful emissions, posing critical environmental challenges and exacerbating global warming. This study aims to establish correlations between road types and the emissions they generate, offering actionable insights for logistics planning and strategies to mitigate diesel vehicle emissions. The analysis is based on input data from a selected transport company, covering parameters such as vehicle type, average mileage, speed, and driving style, as well as environmental conditions like ambient temperature and humidity. Emissions and energy consumption levels are estimated using the COPERT model. A key research challenge involves accurately predicting and managing air pollution caused by HDVs under varying vehicular, technological, and fuel conditions, as well as fluctuating atmospheric and operational factors. The findings indicate that highway driving produces the highest emissions of pollutants such as Se and Zn, while urban peak hours record the highest levels of NO_x, NO, and NO₂. These results emphasise the critical role of strategic route selection in reducing total emissions and managing levels of individual harmful substances. This research highlights the importance of integrating sustainable practices into transport planning to reduce environmental impacts, align with global climate objectives, and advance sustainable development in the transport sector. Full article

Motor vehicles emit a large amount of air pollutants. NOx and particulate matter (PM) account for 53.2% and 74.7%, respectively, of vehicle emissions in China. Using the technical guidelines for compiling road vehicle emission inventories, the emission factors based on the onboard diagnostics (OBD) system of heavy-duty diesel vehicles are obtained. The trajectory of heavy-duty diesel vehicles is corrected using big data interpolation, and the correction coefficients for different vehicle speeds are fitted to calculate the corresponding correction factors. Simultaneously, the Weather Research and Forecasting model is used for the meteorological correction of emissions, a heavy-duty diesel vehicle emission inventory under the community multiscale air quality model is established, and the distribution characteristics of pollution emissions from heavy-duty diesel vehicles in Chengdu are analyzed at the time and space levels. Overall, the pollutant gasses emitted by heavy-duty diesel vehicles in Chengdu are largely concentrated at the city center. In 2023, the total annual emissions of the pollutants NOx, CO, fine PM, and volatile organic compounds from heavy-duty diesel vehicles in Chengdu were 10,590.60, 28,852.90, 686.18, and 657.60 tons, respectively. NOx and CO have the highest proportions among the major pollutants, accounting for 70.7% and 26%, respectively. Full article