Search Results (6,047)

Emission inventories for high-altitude Andean cities rely on sea-level certification cycles that misrepresent real-world combustion conditions. This study derives altitude-resolved emission factors (EFs) for light-duty gasoline vehicles across 0–4000 m a.s.l. in Ecuador using the purpose-built Andean Ecuador Driving Cycle (aedc), validated against naturalistic data via the Percentile Speed Vector metric (PSV_m10; IGS $=1.89$ vs. IGS $=2.30$ for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording $\mathrm{CO}$, $\mathrm{NO}$, $\mathrm{HC}$, and ${\mathrm{CO}}_2$ at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSV_m10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, $\mathrm{NO}$, $\mathrm{CO}$, and $\mathrm{HC}$ all peaked in the 1000–2000 m band ($\mathrm{NO}$: $0.188\mathrm{g}{\mathrm{km}}^{-1}$, $6.7\times$ the sea-level value; $\mathrm{CO}$: $4.47\mathrm{g}{\mathrm{km}}^{-1}$, $+50\%$; $\mathrm{HC}$: $0.047\mathrm{g}{\mathrm{km}}^{-1}$, $+292\%$), fell in the 2000–3000 m band, and partially rebounded above 3000 m ($\mathrm{NO}$: $0.186\mathrm{g}{\mathrm{km}}^{-1}$); ${\mathrm{CO}}_2$ instead declined monotonically with altitude (182 to $119\mathrm{g}{\mathrm{km}}^{-1}$, $-35\%$), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to $8.11\mathrm{L}/100\mathrm{km}$) consistent with reduced aerodynamic drag at altitude partially offsetting the density penalty. These results show that altitude affects pollutants through distinct, non-monotonic mechanisms rather than a uniform trend, so that single-coefficient altitude corrections introduce systematic bias in Andean emission inventories. Full article

This study investigates the effects of magnesium carbonate (MgCO₃) nanoparticles in addition to cottonseed biodiesel/diesel blends on diesel engine performance and emission characteristics. Experiments were conducted under various engine loads, and Response Surface Methodology (RSM) was employed for modeling and multi-objective optimization of operating parameters. Results showed that biodiesel blends increased brake-specific fuel consumption (BSFC) by up to 16.11% and reduced brake thermal efficiency (BTE) by up to 13.53% compared to diesel fuel, mainly due to lower calorific value and higher viscosity. However, the addition of MgCO₃ nanoparticles improved combustion performance, reducing BSFC by up to 5.25% and increasing BTE by up to 5.87% under optimal conditions. Emission analysis revealed that nitrogen oxide (NO_x) emissions increased by up to 49.06%, while hydrocarbon (HC) and carbon monoxide (CO) emissions decreased by up to 42.44% and 51.93%, respectively, indicating enhanced combustion efficiency. Carbon dioxide (CO₂) emissions increased by up to 17.67% due to improved oxidation reactions. RSM analysis confirmed the statistical significance of the developed models with high coefficients of determination (R² = 0.9178–0.9921). The optimal operating condition was determined to be 52.30 ppm MgCO₃ and 1.51 kW engine load. Validation experiments showed good agreement between predicted and experimental results, with errors ranging from 0.71% to 8.83%, all within acceptable limits. Overall, the study demonstrates that MgCO₃ nanoparticles can partially mitigate the performance drawbacks of biodiesel while improving combustion quality, and RSM is an effective tool for optimizing engine operating conditions. Full article

The use of biochar derived from agricultural wood waste represents a promising long-term carbon storage strategy, contributing to mitigation of climate change effects while offering agronomic benefits. This residue is considered as an appropriate material since it does not compete directly with the food chain. Life Cycle Assessment (LCA) is a widely recognized methodology to evaluate the potential environmental impacts associated with all the stages of the life cycle of a product, process or service. In this study, the potential environmental impact of biochar production and its application on soil have been assessed employing a cradle-to-grave approach. The biochar was produced through the pyrogasification of residual lignocellulosic biomass in a pilot-scale plant. The LCA model has been generated employing the GaBi software (LCA for experts 10.7), in accordance with ISO LCA standards and ILCD Handbook, using the experimental results collected during the test carried out in the pilot plant. Two scenarios have been discussed: a basic scenario, involving the biochar production and application on the soil, and an improved scenario, in which by-products from biochar production are used to replace energy in thermal processes. The Global Warming Potential (GWP) of biochar production resulted in −5.52 kg CO${}_2$ eq./kg of biochar including the sequestered carbon during plant growth and 1.81 kg CO${}_2$ eq./kg of biochar stored in soil and the heat recovery resulted in approximately 20 MJ/kg of biochar of avoided consumption of fossil-based fuels. These findings provide additional support to evaluate biochar potential as an environmentally beneficial solution. Full article

The global rise in obesity and associated metabolic disorders has fueled the demand for safe, multi-target therapeutics derived from natural sources. In this study, an artificial neural network combined with a genetic algorithm (ANN-GA), was employed to optimize the ultrasound-assisted extraction of loquat leaf extract (LLE). The optimal parameters—0.6% Tween 80, 328 W ultrasonic power, and 49 °C—enhanced extraction efficiency while minimizing energy consumption and surfactant use. In vitro, LLE exhibited potent dose-dependent inhibition of α-glucosidase (IC₅₀ = 3.75 μg/mL, 21.6-fold stronger than acarbose) and pancreatic lipase (IC₅₀ = 78.3 μg/mL), indicating strong inhibitory activity against these digestive enzymes. In high-fat diet-induced obese mice, LLE supplementation dose-dependently reduced body weight gain, serum total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose while elevating high-density lipoprotein cholesterol (HDL-C). In addition, LLE ameliorated liver injury (decreased ALT/AST) and oxidative stress (lowered MDA, increased SOD). These findings highlight LLE as a promising multi-target natural product for managing obesity and related metabolic disorders. Full article

Coupled fire–atmosphere models (CFAMs) are increasingly proposed as an important tool for investigating a range of scientific and management questions, including the design of fuel management strategies. Uncertainty in CFAM outputs arises from environmental and fuel inputs, and a host of user-defined simulation choices such as fire approach angle and ignition timing. In this study, we used the Wildland–Urban Interface Fire Dynamics Simulator (WFDS) to quantify uncertainty in rate of spread and canopy consumption across pre- and post-restoration ponderosa pine stands. Two ensembles were conducted: (1) varying fire approach angles across 12 rotations and (2) varying ignition time in 80 simulations (five per plot, with 0 s delay, 250 s delay, and three random intervals in between). Metrics evaluated were rate of spread and percent canopy consumption. Variability was quantified using coefficients of variation (CVs). Approach-angle variation produced a mean CV of 5.46% for rate of spread, with treated stands exhibiting reduced variability relative to untreated stands. Canopy consumption showed a mean CV of 6.16%, with treatment having no effect. Ignition-time variation produced smaller CVs (rate of spread: 2.47%; canopy consumption: 3.60%) with no differences between management conditions. These results indicate that user-defined configuration choices contribute measurable but modest uncertainty, and that structural modifications from management can reduce sensitivity of fire spread. Incorporating these sources of uncertainty into formal frameworks will improve interpretation of CFAM outputs for operational and research applications. Full article

This study investigated the effects of partial substitution of diesel fuel with hydrogen in dual-fuel operation on the engine operating characteristics, energy performance, and exhaust emissions of a compression-ignition passenger car engine. Hydrogen was supplied into the intake manifold, and the vehicle was tested on a chassis dynamometer at a constant vehicle speed under varying load conditions. To avoid the onset of abnormal combustion, the maximum stable H₂ mass fraction under diesel–hydrogen dual-fuel operation had to be reduced from 19% to 11% as engine load increased over the brake mean effective pressure (BMEP) range of 0.38–0.88 MPa, while the corresponding H₂ volume fraction in the intake air increased from 4.2% to 6.3%. Hydrogen addition reduced diesel fuel consumption and CO₂ emissions, although its effect depended strongly on engine load. At a 10% H₂ mass fraction, diesel fuel consumption decreased by 20–25%, and CO₂ emissions decreased by 17–21%. At low load, hydrogen addition improved brake thermal efficiency (BTE) and reduced NO_x emissions. At higher loads, reaching the hydrogen flammability limit reduced the excess-air ratio, deteriorated diesel combustion quality, and increased NO_x emissions and the exhaust smoke absorption coefficient. Higher H₂ concentrations reduced combined NO_x + HC emissions at low load but increased them at medium and high loads. Overall, hydrogen enrichment was most effective at low and medium loads, below the abnormal combustion onset limit, indicating that load-sensitive hydrogen dosing is required for efficient and environmentally favourable diesel–hydrogen dual-fuel operation. Full article

Membrane technologies play a role in water treatment, energy conversion, and industrial separation processes; however, their performance is limited by fouling, polarization phenomena, transport inefficiencies, and energy consumption. This study presents a review of the integration of computational fluid dynamics and machine learning in membrane technologies, complemented by an environmental and engineering assessment of representative membrane materials. A systematic literature screening based on PRISMA guidelines was conducted using the Scopus (Elsevier B.V., Amsterdam, The Netherlands) and Web of Science (Clarivate, Philadelphia, PA, USA) databases, yielding 1421 records, of which 54 studies met the predefined relevance criteria. The analysis revealed a transition from conventional physics-based approaches toward hybrid simulation–machine learning frameworks, with artificial neural networks, surrogate models, and optimization emerging as the dominant methodologies. Energy consumption was identified as the most frequently investigated variable, particularly in desalination, fuel cell, electrodialysis, and hydrogen production systems. A complementary material-level assessment showed that conventional polymeric membranes, especially polyamide-based systems, remain dominant due to their performance and economic feasibility, whereas advanced materials such as graphene, carbon nanotubes, and perovskites offer promising functional properties but face challenges. The findings highlight the potential of integrated simulation–machine learning–material assessment frameworks to accelerate the development of intelligent and sustainable membrane technologies for future applications. Full article

Heavy-duty methanol engines are regarded as a promising low-carbon solution for commercial vehicle decarbonization, yet the comprehensive coupled characteristics of fuel consumption, multi-dimensional exhaust emissions, and the corresponding aftertreatment adaptability between stoichiometric and lean-burn technical routes remain insufficiently quantified, restricting the optimized application of methanol powertrains for China-VI emission compliance. To address this research gap, this study systematically investigates two China-VI compliant heavy-duty methanol engines with stoichiometric and lean-burn combustion strategies under cold-start and hot-start Worldwide Harmonized Transient Cycle. And a comparative analysis is conducted to clarify the differences in the fuel consumption, raw exhaust emission (including regulated pollutants, particulate matters, greenhouse gases, and unregulated pollutants), and the catalytic performance of aftertreatment systems between two engines with stoichiometric and lean-burn strategy. Results demonstrate that the lean-burn strategy achieves a 6% reduction in methanol fuel consumption compared with stoichiometric combustion, delivering superior fuel economy. In terms of regulated gaseous pollutants, both combustion strategies satisfy China-VI emission limits for CO and NO, while lean-burn combustion effectively lowers raw CO and NO emissions and reduces the purification pressure of aftertreatment systems. Non-methane Hydrocarbon emission under cold-start condition is identified as the primary compliance challenge, requiring a minimum aftertreatment conversion efficiency of 95%. Although lean-burn increases raw exhaust NMHC emission under hot-start condition, the post-catalyst emission could still meet the regulation limits. For particulate pollutants, lean-burn strategy realizes substantial reductions in both PM and PN emissions, which can meet emission standards without the corresponding aftertreatment system. In contrast, the stoichiometric combustion faces a risk of PN emission exceeding the regulation limit under cold-start conditions even with aftertreatment system. Additionally, lean-burn strategy optimizes greenhouse gas emission performance by cutting CO and CH₄ emissions. Regarding unregulated pollutants, lean-burn strategy increases raw exhaust unburned methanol and formaldehyde emissions, particularly under cold-start condition, but significantly inhibits NH₃ emission. This study quantitatively clarifies the performance trade-offs and adaptation advantages of lean-burn and stoichiometric strategy for heavy-duty methanol engines, providing fundamental data support and technical guidance for the low-carbon and low-pollution optimization of heavy-duty methanol vehicles. Full article

Rotary tillage–seeding combined operations require stable power take-off (PTO) speed during rotary tillage and accurate tracking of the prescribed travel speed for seeding. Existing energy management strategies for hybrid electric tractors mainly focus on fuel economy and commonly use fixed objective weights, limiting their ability to adjust control priorities under changing operating conditions. To address this issue, an operation-quality-oriented energy management strategy based on model predictive control, termed OQ-EMS/MPC, is proposed. An equivalent combined-operation condition was constructed using the PTO-side rotary-tillage load, drive-side equivalent traction load, segmented travel-speed reference, and equivalent seeding-quality risk. A condition-severity index integrating the PTO-load coefficient of variation, PTO-load impact intensity, and equivalent seeding-quality risk was developed to distinguish steady, fluctuating, and impact-dominated conditions. Based on the identified condition, the weights assigned to PTO-speed regulation, equivalent seed synchronization, and energy economy were adjusted online. These weights were used in the MPC to optimize torque allocation among the engine, motor-generator 1 (MG1), and motor-generator 2 (MG2). The proposed strategy was validated on a dual-side loading bench and compared with a rule-based energy management strategy and a fixed-weight MPC strategy. The overall PTO-speed root-mean-square error (RMSE) was reduced to 1.76 r/min, representing reductions of 58.40% and 45.66% relative to the two comparative strategies, respectively. The equivalent seed-synchronization RMSE was reduced by 69.15% and 52.66%, respectively. Under the impact-dominated condition, the PTO-speed RMSE decreased to 1.65 r/min. The normalized composite cost decreased by 13.53% and 6.26%, while the equivalent fuel consumption increased by 3.40% and 3.76%, respectively. The results demonstrate that the proposed strategy improves PTO-speed stability and equivalent seed-synchronization performance as operating severity increases while accounting for energy economy. Full article

Hydrogen-powered cargo bikes are gaining increasing attention in the framework of urban logistics, thanks to their enhanced agility to travel and park in congested areas, low carbon footprint, and extended traveling range. Nevertheless, deploying a fleet of hydrogen-powered cargo bikes can be challenging, necessitating the parallel assessment of the operation of the fleet, the H₂ refueling strategy, and eventually the design of the respective refueling infrastructure. The objective of this article is to demonstrate a systematic methodological framework for deriving strategic insights into the deployment of a fleet of cargo bikes equipped with a hydrogen fuel cell unit. Employing advanced longitudinal-based vehicle mathematical modeling, coupled with statistical techniques, the energy consumption of the vehicle was analyzed under a broad spectrum of operating conditions. Moreover, the refueling strategy was analyzed for different ranges of vehicle fleets, and enhanced options for designing the respective hydrogen stations were charted. Ultimately, this work provides strategic insights that could be used by cargo bike fleet operators and H₂ refueling infrastructure designers. Full article

This study provides the main performance estimates for new concepts, using flexible gasification operation modes, adaptable to prevailing market conditions, for the production of bio-synthetic natural gas (bio-SNG) and biochar from biogenic residues and waste, such as bark, straw, and Solid Recovered Fuel (SRF). Dedicated integrated process models were developed in Aspen Plus based on and validated against data from experimental campaigns in a gasification and gas cleaning pilot plant. Simulation runs show that the proposed concepts convert biomass to bio-SNG 10% more efficiently than the reference case, mainly due to the considerably reduced oxygen demand at the Autothermal Reformer (ATR) enabled by the improved catalyst. The co-production mode schemes showed promising results in terms of overall plant efficiency, at 76.5–78.2%, and total carbon utilisation, at 41–55.3%. The hybrid cases require an electrolyser with a power capacity almost 70% of the biomass thermal input to the gasifier, resulting in a total electricity consumption of up to 0.769 kWhe/kWh of biofuel. In return, they achieve over 50% utilisation of the carbon contained in the feedstock for biofuel production and a 70.1–76.5% total plant energy efficiency. Efficient biofuel and biochar production unlock negative emission potential, further strengthening the value of these flexible concepts. Full article

Uncoupling protein 2 (UCP2) is expressed in various tissues throughout the body, but its expression in the spleen exceeds that of other organs. However, the precise function of UCP2 for spleen physiology is unclear. The spleen acts as a hub connecting the nervous system and immune system to cardiovascular and metabolic diseases. Here, we analyzed the impact of hypertension on the spleen and the role of UCP2 in this process. Experiments were performed with UCP2-knockout rats and their wild-type littermates. Hypertension was induced by administering the nitric oxide inhibitor L-NAME via tap water. Genetic depletion of UCP2 increased spleen size (splenomegaly) and strongly impaired the expression of genes coding for mitochondrial proteins. Among them, genes coding for proteins involved in oxidative metabolism, such as pyruvate dehydrogenase alpha 1, and the detoxification of reactive oxygen species were down-regulated. Collectively, these alterations in metabolism favor glycolysis and proliferation. Moreover, NOS3 was among the strongest down-regulated genes in UCP2^−/− rats, and the inhibition of nitric oxide synthase by L-NAME mimicked large parts of the expression profile. Neither the depletion of UCP2 nor L-NAME-induced hypertension or combinations thereof affected chronic inflammation. In summary, UCP2 controls fuel consumption in splenic cells in a nitric-oxide-dependent way. Full article

The transition toward renewable energy has accelerated globally in response to climate commitments and the need for sustainable energy systems, raising important questions about its fiscal implications. Focusing on India, this study investigates the relationship between renewable energy consumption and public debt while controlling for key macroeconomic factors, including economic growth, inflation, and non-renewable energy consumption. Using annual data from 1992 to 2022, the analysis employs the Autoregressive Distributed Lag (ARDL) model and Dynamic ARDL simulations to examine both short-run and long-run dynamics. In addition, Kernel-based Regularized Least Squares (KRLS) is applied to explore heterogeneous marginal effects and potential nonlinearities in the relationship between renewable energy expansion and government debt. The results reveal a time-dependent relationship between renewable energy consumption and public debt. In the short run, renewable energy expansion contributes to a reduction in public debt through efficiency gains and reduced dependence on fossil fuels. However, in the long run, renewable energy consumption exerts a positive and statistically significant impact on government debt, reflecting the substantial investment requirements associated with renewable energy infrastructure development. Economic growth consistently reduces public debt, while inflation provides only temporary relief in the short term. Robustness checks using FMOLS and DOLS confirm the stability of the long-run estimates. These findings highlight the importance of integrating renewable energy policies with prudent fiscal planning and expanding private investment mechanisms to support sustainable energy transitions. Full article

This study investigates the performance and hydrogen consumption of a 120 kW PEM fuel cell stack in a SANY heavy-duty truck under high-load operating conditions. A stack model was developed based on first-generation Toyota Mirai single-cell data and verified for applicability to the target vehicle. The effects of operating parameters—including temperature, hydrogen and air partial pressures, and membrane water activity—on stack ECOP, power, efficiency, and vehicle hydrogen consumption under the C-WTVC driving cycle were analyzed. Multi-objective optimization using NSGA-II identified parameter combinations that improved overall stack performance. Results show that key operational parameters significantly influence both stack and vehicle-level performance. After optimization, the stack exhibited enhanced power and efficiency, and the truck’s equivalent hydrogen consumption decreased to 2.227 kg, which is lower than both the unoptimized and reference conditions. Full article

Last-mile delivery systems are under greater pressure to deliver cost-efficient, reliable, environmentally friendly, and resilient services amid operational challenges. Distance/cost is the usual optimization criterion for traditional vehicle routing methods, and factors related to disruptions, such as the delay frequency, delay severity, and delivery failure risk, are often treated separately or neglected. This study proposes a green-resilient last-mile delivery optimization framework that integrates operational costs, delivery delays, carbon emissions, and operational risk within a single multi-objective decision model. The proposed framework models the capacitated vehicle routing problem with time windows, accounting for vehicle capacity, service time commitments, fuel consumption, emission-level estimates, working hour limits, and lateness penalties and incorporating a disruption-based operational risk score. The risk score is based on the delay frequency, delay severity, and failure probability and can inform routing decisions based on efficiency and resilience. The framework is tested with a case study of urban last-mile delivery and compared with several benchmark scenarios: the current operational plan, a distance-based vehicle routing problem (VRP), a cost-based VRP, a green VRP, and a delay-aware vehicle routing problem with time windows (VRPTW). The results reveal balanced improvements in key performance indicators, in line with the proposed framework. It reduces the total distance by 35.11%, total operational cost by 34.01%, fuel consumption by 10.46%, CO₂ emissions by 9.34%, estimated late orders by 93.45%, and total delay minutes by 80.10%, and there are no working hour violations compared to the current case. Other sensitivity, weight, and ablation analyses illustrate the trade-offs among cost/service reliability/environmental goals and risk exposures. The results show that operational risk can be incorporated into the green last-mile routing problem to facilitate more comprehensive—and thus more robust and sustainable—delivery planning in the context of disruptions in urban environments. Full article