Search Results (7,131)

The European Commission’s decision to establish a 10% v/v volumetric ethanol cap in gasoline (E10) under Renewable Energy Directive II (RED II), further reinforced by the stricter sustainability criteria introduced in RED III, marks a critical turning point for the global ethanol industry. By 2023, worldwide ethanol production reached approximately 116–118 bn liters annually, with the United States dominating at 52% and Brazil contributing about 28%. First-generation (1G) ethanol is increasingly constrained in its expansion within the road transport sector, necessitating the identification of alternative markets to absorb surplus volumes. RED III emphasizes the deployment of advanced biofuels and renewable fuels of non-biological origin (RFNBOs), mandating either a 14.5% reduction in greenhouse gas emissions or a 29% renewable energy share by 2030, thereby encouraging sectoral diversification. The maritime sector emerges as a promising outlet to accommodate surplus ethanol. However, regulatory inconsistencies persist: ReFuel EU excludes crop-based biofuels, while Fuel EU Maritime permits certified options. Aviation remains limited to advanced biofuels. Meanwhile, IMO policies, including MEPC 83 and 84, introduce emissions pricing and lifecycle assessment frameworks, promoting low-emission fuels without clearly recognizing ethanol’s competitiveness. This study evaluates ethanol utilization in marine engines under these constraints. Full article

The rapid transition toward electric vehicles (EVs) in Saudi Arabia requires reliable and sustainable charging infrastructure capable of supporting long-distance highway transportation. However, the deployment of emergency charging systems is challenged by sparse charging infrastructure, stochastic emergency charging demand, battery degradation under harsh climatic conditions, and the need for cost-effective integration of renewable energy resources. This study presents a three-stage unified techno-economic planning framework for renewable-assisted emergency EV charging networks that integrates strategically located charging hubs with a coordinated fleet of solar-assisted Mobile Emergency Charging Vehicles (MECVs). The proposed framework jointly optimizes charging hub locations, photovoltaic (PV) generation capacity, battery energy storage system (BESS) sizing, and MECV allocation while explicitly accounting for stochastic emergency charging demand, renewable-energy utilization, and temperature-dependent battery degradation. Emergency charging demand is modeled using Monte Carlo simulation based on EV penetration scenarios, and battery aging is incorporated into the optimization through a temperature-dependent degradation model. The planning problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and comparatively solved using three independent metaheuristic algorithms, namely the Musical Chairs Algorithm (MCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The proposed framework is evaluated using two representative highway corridors in Saudi Arabia. The results indicate that renewable-assisted charging can reduce annual grid-related CO₂ emissions by approximately 25,360 and 57,641 t CO₂/year for the Riyadh–Dammam and Riyadh–Makkah corridors, respectively. Battery degradation contributes approximately 12.7–14.2% of the total annualized system cost, highlighting the importance of incorporating lifecycle degradation into infrastructure planning. Temperature sensitivity analysis further indicates the significant influence of harsh climatic conditions on battery lifetime, renewable-energy utilization, and overall system economics. The proposed framework provides a practical planning methodology for developing reliable, sustainable, and economically viable emergency EV charging infrastructure in regions with similar geographical and climatic characteristics. Full article

Accurate and computationally efficient prediction of radioactive plume dispersion is essential for rapid risk assessment and emergency response in the event of accidental atmospheric releases. This study presents a three-dimensional, mass-conservative numerical framework for simulating mesoscale transport and transformation of radionuclides under varying meteorological conditions. The proposed model is based on the advection–diffusion equation and incorporates key physical processes, including turbulent diffusion, gravitational settling, precipitation washout, radioactive decay, surface absorption, and re-emission. A hybrid numerical scheme is developed that combines a semi-implicit Crank–Nicolson discretization for diffusion terms with a second-order Total Variation Diminishing (TVD) scheme employing a Van Leer limiter for advection. The framework is intended as a computationally tractable engineering model; operational applicability requires further assessment using measured runtime performance and independent physical validation. In addition, a modified parameterization of turbulent-diffusion coefficients is introduced, extending classical Pasquill–Gifford formulations to better represent mesoscale atmospheric behavior with bounded dispersion characteristics. The numerical implementation is verified against an analytical constant-coefficient advection–diffusion solution using concentration-error norms, plume-moment errors, mass-balance analysis, and grid-refinement tests. Additional idealized iodine-131 simulations illustrate the behavior of the framework under moderate-wind and stable atmospheric conditions. The results highlight the dominant role of advective transport and precipitation scavenging under moderate wind conditions, as well as strong localization effects under stable atmospheric stratification. The proposed model provides a practical compromise between the simplicity of Gaussian plume approaches and the high computational cost of full-physics atmospheric models, indicating its potential for rapid engineering calculations, subject to further validation against atmospheric observations and established operational dispersion models. Full article

The present study experimentally investigates the influence of n-octanol addition to Jet-A fuel on the combustion performance and emission behavior of a micro-turboprop-based hybrid UAV (“Unmanned Aerial Vehicle”) power system. The experiments were conducted on a dedicated hybrid propulsion test bench equipped with a KingTech micro-turboprop engine mechanically coupled to a T-Motor electric generator and supplying a regulated 48 V DC bus. The system is capable of delivering approximately 3 kW of continuous electrical power, with peak values reaching 3.5 kW. Jet-A and three n-octanol/Jet-A blends containing 10%, 20%, and 30% n-octanol by volume, denoted O10, O20, and O30, respectively, were tested under four operating regimes ranging from idle to 2500 W electrical load. Exhaust gas temperature, carbon monoxide, sulfur dioxide, nitrogen oxides, electrical output, and near-field pollutant dispersion were evaluated. The results show that n-octanol addition affects engine behavior in a strongly load-dependent manner. At idle, the O10 blend reduced CO concentration from approximately 2520 ppm for Jet-A to approximately 2270 ppm, corresponding to a reduction of about 9.9%. At the same operating condition, O10 reduced exhaust gas temperature from approximately 498.3 °C to 463.2 °C, while O20 and O30 produced stronger cooling effects. At intermediate regimes, the oxygenated molecular structure of n-octanol contributed to lower CO formation in selected cases, indicating improved combustion-completeness behavior. At high load, however, exhaust gas temperatures converged toward or exceeded those of Jet-A, particularly for O30, showing that higher octanol fractions may introduce additional thermal constraints. Among the tested fuels, O10, corresponding to 10% n-octanol by volume, provided the most balanced behavior across the investigated operating range, from idle to 2500 W electrical load. The dispersion measurements performed at 30 m from the source further showed that ambient pollutant concentrations are strongly influenced by wind speed, wind direction, and plume transport. These findings support moderate n-octanol blending as a promising transitional strategy for small-scale hybrid UAV propulsion systems, while highlighting the need for future repeated testing, direct fuel-flow measurement, and numerical dispersion modeling. Full article

Pavement deterioration poses substantial technical-economic burdens and brings notable social-environmental risks: degraded road surfaces increase vehicle maintenance expenditures, raise traffic accident probabilities, lower transportation operational efficiency, and produce extra carbon emissions caused by aggravated fuel consumption. Automatic pavement defect detection is therefore a vital technical prerequisite for timely road maintenance and mitigating those adverse consequences. However, existing methods suffer from two prominent limitations: missed detection of subtle defects under complex scenarios and high model complexity. To tackle the above limitations, this paper proposes CAR-YOLO, a lightweight pavement defect detection algorithm built upon an improved YOLOv11n. First, a convolution-based context-guided module is incorporated to enhance the exploitation of defect contextual information, thus improving the feature representation quality and detection accuracy of subtle defects. Second, the original neck network is replaced with the attention scale sequence fusion framework (ASF), which fuses multi-scale features to boost detection performance for subtle, complex-texture pavement defects. Finally, the lightweight RepViT network is introduced into the backbone network to streamline the model architecture and reduce model complexity while maintaining detection precision. Experimental results demonstrate that the CAR-YOLO model achieves an mAP₅₀ of 63.8% on the RDD2022 Chinese UAV dataset, approximately 5% higher than the baseline YOLOv11n. The parameter count is reduced from 2.58 M to 1.71 M, corresponding to a compression ratio of 33.6%; the computational load drops from 6.3 G to 4.4 G, a reduction of around 30.2%. While delivering higher detection accuracy, the proposed algorithm effectively reduces the parameter scale and computational overhead. Full article

Parkinsonian syndromes comprise overlapping neurodegenerative and non-degenerative disorders, making aetiological diagnosis difficult, while existing procedural guidance does not fully integrate tracer-specific biology with clinical decision-making, multimodal strategies, and emerging quantitative and pathology-directed biomarkers. This review synthesises evidence on the molecular targets, radiopharmaceutical characteristics, biological interpretation, and clinical applications of dopaminergic single-photon emission computed tomography (SPECT) and positron emission tomography (PET), focusing on the dopamine transporter (DAT), aromatic L-amino acid decarboxylase (AADC), vesicular monoamine transporter type 2 (VMAT2), dopamine D₂/D₃ receptors, differential diagnosis, semiquantification, kinetic modelling, and artificial intelligence-assisted interpretation. The evidence confirms that DAT SPECT with [¹²³I]ioflupane ([¹²³I]FP-CIT) remains the most established method for demonstrating or arguing against presynaptic nigrostriatal dysfunction, but an abnormal result cannot establish aetiology, and a normal result can redirect evaluation towards non-degenerative mimics; target-specific PET provides complementary biological information, although availability, standardisation, and prospective validation remain limiting. By additionally integrating genetic and prodromal applications, question-driven multimodal imaging, emerging acquisition approaches, α-synuclein imaging and seed amplification assays, and contemporary biological staging frameworks, this review extends procedural guidance and positions dopaminergic imaging as a targeted functional biomarker selected according to the unresolved clinical question rather than as a stand-alone disease label. Full article

This study evaluates the influence of different crushed gneiss contents on the mechanical, durability, environmental, and economic performance of sand–gravel/gneiss mixtures for sustainable road construction. Natural sand–gravel aggregate was blended with crushed gneiss at three proportions (10%, 30%, and 50% by mass). The experimental program included grain size analysis, compaction characteristics, California Bearing Ratio (CBR), deformation modulus, and freeze–thaw durability tests. Environmental performance was assessed using a cradle-to-site carbon footprint approach based on Life Cycle Assessment principles according to ISO 14040, ISO 14044, and ISO 14067, with the analysis focused on Global Warming Potential (GWP100), combined with a Total Cost of Ownership analysis. Increasing gneiss content improved mechanical performance, with CBR increasing from 45% to 95% and deformation modulus from 110 to 185 MPa. The 70/30 sand–gravel/gneiss mixture provided the most balanced performance among the investigated compositions, combining high bearing capacity, satisfactory freeze–thaw resistance, and favorable environmental and economic characteristics. Its carbon footprint was 3.90 kg CO₂ eq./t, with diesel consumption during aggregate handling identified as the dominant emission source (69%), followed by gneiss transportation (20%) and electricity consumption (12%). Higher gneiss contents and longer transport distances increased environmental impacts and production costs. The results indicate that selecting an appropriate aggregate composition and reducing transport-related emissions can support lower-carbon construction materials while maintaining required engineering performance. Full article

This work presents the design of a novel multi-component surface-coating system incorporating expandable graphite (EG), ammonium polyphosphate (APP), aluminium tri-hydroxide (ATH), alginate, and D-glucosamine hydrochloride (DGH). Importantly, the coated polyurethane (PU) foams demonstrated performance within the corresponding Hazard Level 3 limits for the measured parameters, including Maximum Average Rate of Heat Emission (MARHE), smoke density, and toxicity, providing preliminary indications of their potential for railway fire safety applications. Systematic variations in EG loading revealed substantial improvements in flammability metrics, with the EG-rich formulation achieving a limiting oxygen index (LOI) of 73%, MARHE of 18 kW m⁻², and significantly reduced smoke production. EG transformed the fire behaviour even at high external heat fluxes from flaming to self-extinguishing and only smouldering, promoting rapid formation of a dense, thermally insulating char. Combined interactions between EG, inorganic, and biobased additives reinforced char integrity, suppressed degradation rates, and enhanced condensed-phase protection. Thermogravimetric analysis confirmed increased residue yields (up to 52 weight percentage at 600 °C). Overall, this multi-functional coating offers a cost-effective, low-toxicity strategy for producing flame-resistant PU foams for demanding transportation and construction applications. Full article

Biochar is no longer considered merely a substitute for conventional fillers in asphalt materials; rather, it represents a multifunctional modifier that aligns with the goals of sustainable road design and urban mobility in smart cities. Its application now extends to the rheological modification of asphalt binders, mitigation of asphalt fume emissions, improvement in aging resistance and interfacial adhesion, and assessment of carbon sequestration potential. Biochar can improve the high-temperature stability, rutting and aging resistance, and asphalt–aggregate adhesion of asphalt materials in a suitable dosage, and at the same time reduce emissions of volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), hydrogen sulfide (H₂S), and other fumes. However, the above effects are highly dependent on the biochar feedstock, production process, physicochemical properties, particle size, dosage and degree of dispersion. An excess amount or uneven distribution will reduce the crack resistance and fatigue life at low temperatures; phase separation may also occur and VOC emissions will increase. Therefore, the main problem in this area has shifted from whether biochar is effective to when it can be applied for particular pavement performance goals, what pollutant control targets are aimed for, and over what life-cycle periods. This review integrates evidence obtained at the binder, mastic, and mixture scales and critically evaluates the influence of biochar on pavement performance, fume emissions, aging, interfacial adhesion, and environmental safety. It also argues that empirical dosage selection should be replaced by coordinated optimization of biochar structure, material performance, emission mitigation, and life-cycle impacts. Verification of the low-carbon benefits and environmental safety of biochar-modified asphalt will ultimately require standardized assessment frameworks and consistently defined system boundaries. Ultimately, this work provides a foundation for integrating biochar-modified asphalt into eco-friendly and resilient road infrastructures, aligning with the goals of smart urban mobility and sustainable transportation. Full article

The building and public works sector is a major source of greenhouse gas emissions in developing countries due to the high material intensity of infrastructure and the dependence on imported materials. In Burkina Faso, as in most African countries, environmental assessments remain limited by the lack of locally representative emission factors, creating uncertainty in carbon accounting. This study estimates emission factors for key construction materials in Burkina Faso using a simplified cradle-to-gate Life Cycle Assessment (LCA) adapted to national technical, energy, and logistical conditions. The materials analyzed include cement, steel, flat glass, aluminium, aggregates, sand, timber, ceramic tiles, paint, bitumen, and asphalt mixes. The results reveal significant variations in the carbon intensity of construction materials in the Burkina Faso context. Aluminium exhibits the highest emission factor (12.804 tCO₂/t), followed by paint (2.50 tCO₂/t), steel (1.970 tCO₂/t), Portland cement (1.060 tCO₂/t), ceramic tiles (0.99 tCO₂/t), flat glass (0.84 tCO₂/t), bitumen (0.628 tCO₂/t), asphalt mixes (0.130 tCO₂/t), timber (0.16 tCO₂/t), aggregates (0.008 tCO₂/t), and sand (0.0013 tCO₂/t). Portland cement exhibits a slightly higher emission factor than the values commonly reported in the literature (0.85–0.95 tCO₂/t), while steel, aluminium, flat glass, ceramic tiles, bitumen, asphalt mixes, timber, and paint remain within internationally reported ranges. In contrast, sand exhibits a slightly lower emission factor than typical literature values, reflecting its limited processing requirements. Aggregates and sand show the lowest emission factors due to minimal industrial processing and greater local availability. These differences are mainly driven by import dependence, long- distance maritime and road transportation, the characteristics of the national electricity mix, and local production conditions. The study supports the development of context-specific carbon assessments and environmental databases for Burkina Faso and other West African countries. Full article

Background/Objectives: Dementia with Lewy bodies (DLB) is a clinically heterogeneous neurodegenerative disorder characterized by cognitive, neuropsychiatric, motor, and autonomic manifestations. Reduced striatal dopamine transporter availability on ¹²³I-ioflupane single-photon emission computed tomography (SPECT) is an established indicative biomarker of DLB. However, the associations among expert visual interpretation, regional semiquantitative dopamine transporter measurements, autonomic manifestations, and dementia severity remain incompletely characterized. This study aimed to evaluate the relationship between visual ¹²³I-ioflupane SPECT classification and regional DaTQUANT z-scores in patients with clinically probable DLB and to explore their associations with autonomic manifestations and dementia severity. Methods: This retrospective, cross-sectional study included 30 patients who received a clinical diagnosis of probable DLB according to the 2017 DLB Consortium criteria before the ¹²³I-ioflupane SPECT results became available. SPECT images were assessed visually by experienced nuclear medicine physicians and semiquantitatively using DaTQUANT software. Bilateral striatal, putaminal, caudate, and putamen-to-caudate ratio z-scores were analyzed in relation to visual scan classification, Clinical Dementia Rating (CDR) scores, and retrospectively ascertained autonomic manifestations, including orthostatic hypotension, delayed orthostatic hypotension, supine hypertension, postprandial hypotension, constipation, and urinary incontinence. Results: Scans were visually classified as supportive of nigrostriatal dopaminergic degeneration in 23 of 30 patients (76.7%) and as non-supportive in seven (23.3%). Bilateral striatal, putaminal, and caudate z-scores were significantly lower in visually supportive scans than in non-supportive scans (all p < 0.001), whereas putamen-to-caudate ratio z-scores did not differ significantly between the groups. None of the evaluated autonomic manifestations was significantly associated with either visual scan classification or regional DaTQUANT measurements. No regional DaTQUANT measurement was significantly associated with dementia severity. A modest positive correlation was observed between the left putamen-to-caudate ratio z-score and CDR (Spearman’s ρ = 0.373, nominal p = 0.050); however, given the small sample size and multiple regional comparisons, this borderline finding was considered exploratory. Conclusions: Regional DaTQUANT measurements were consistent with expert visual interpretation of ¹²³I-ioflupane SPECT in patients with clinically probable DLB. Associations of dopaminergic imaging measurements with autonomic manifestations and dementia severity were limited. Semiquantitative analysis may complement visual interpretation, but its findings should be interpreted within the broader clinical and biomarker context. Larger prospective studies incorporating standardized autonomic testing, appropriate control groups, longitudinal follow-up, and complementary biomarkers are warranted. Full article

During rapidly changing emission conditions, including climate overshoot pathways, the conventional globally well-mixed representation of CO₂ may not fully resolve its short-term spatial heterogeneity and associated radiative effects. This study aims to diagnose how source-specific three-dimensional transport and the meteorological and radiative environments sampled by emitted CO₂ are associated with differences in source-normalized radiative effect (RE) during the active emission phase, using China and India as two contrasting source-region cases. Fossil-fuel CO₂ concentration increments from China and India in 2010 were isolated using IAP-AACM with online source tagging, driven by WRF meteorology and CarbonTracker flux and initial fields. Top-of-atmosphere longwave radiative perturbations relative to the realistic baseline atmosphere were then calculated using libRadtran to quantify the RE, while CO₂-mass-weighted meteorological fields were analyzed to characterize the environments sampled along the transport pathways. In the 2010 case, the global annual mean RE was 3.46 mW·m⁻²·GtC⁻¹ for Indian emissions and 3.15 mW·m⁻²·GtC⁻¹ for Chinese emissions, corresponding to a 9.8% difference. The maximum local annual mean RE reached 10.0 mW·m⁻²·GtC⁻¹ for India and 5.5 mW·m⁻²·GtC⁻¹ for China, indicating a substantially stronger source-dependent spatial contrast than suggested by the global mean alone. CO₂-mass-weighted diagnostics showed that the Indian source sampled higher surface skin temperatures (T_sk), lower atmospheric temperatures (T_k), and lower cloud water paths (CWP) than the Chinese source, conditions that are physically consistent with its larger RE. In contrast, the higher precipitable water vapor (PW) sampled along the Indian transport pathways would tend to enhance spectral masking and partially offset this contrast. These results indicate that, before global mixing is approached, the realized radiative effect per unit CO₂ emission reflects the combined influence of source features, three-dimensional atmospheric transport, and the meteorological and radiative environments sampled by the transported CO₂. Full article

Synergistic pollution–carbon mitigation is critical for China’s dual carbon targets. Taking coal-resource Shanxi Province as the case, this study developed an integrated Long-range Energy Alternatives Planning (LEAP)–Community Multiscale Air Quality (CMAQ) coupled framework combined with a three-dimensional vector model to simulate energy consumption, CO₂, and major air pollutant emissions (CO₂, CO, SO₂, NO₂, PM_2.5, and PM₁₀) under Baseline and Policy scenarios (2026–2050). The core novelty of this study lies in methodological innovation: the multi-model linkage realizes full-chain energy-emission-atmosphere simulation, remedying the isolation flaw of single models in prior research. The results indicated that low-carbon levels would rise steadily in both scenarios from 2026 to 2050. The Policy scenario achieved superior long-term low-carbon performance compared with the Baseline scenario and narrowed gaps in underdeveloped social subsystems, despite short-term transition costs. This scenario optimized the overall energy structure yet failed to fully reduce emission loads from residential and transport sectors. It drastically cut carbon and pollutant emissions, optimized spatial emission patterns, and decoupled most air pollutants from carbon emissions. However, this scenario still had prominent limitations: phased delays in emission abatement, strong coupling of CO, NO₂ and carbon emissions, and rising residential carbon emissions. Further pollution–carbon synergy assessment revealed worsening multi-dimensional imbalances under the Baseline scenario. While the Policy scenario experienced temporary systemic imbalance, its long-term coordination level improved steadily. This finding verified that systematic, long-term low-carbon governance constituted the core driver of Shanxi’s green transition. Targeted phased, classified collaborative governance strategies were proposed to resolve structural transformation risks for resource-based regions. Full article

Background: Transport remains one of the hardest sectors to decarbonize in the European Union, and the effectiveness of environmental taxation as a “polluter pays” instrument remains contested for the institutionally distinct Visegrad Four (V4) countries. Methods: Using a balanced panel of four V4 countries over 2000–2024 (n = 100 observations) from Eurostat, we estimate fixed-effects models with Driscoll–Kraay standard errors, complemented by two-way fixed effects, lag, interaction, and robustness specifications, including a sensitivity test for mechanical endogeneity. Results: GDP per capita and freight transport volume are positively associated with emissions (elasticities 0.244 and 0.230, both p < 0.001), while renewable energy share is negatively associated (−2.93%, p < 0.001). Environmental taxes show no significant association at any lag, and their positive association is strongest where highway infrastructure is least developed, disappearing where it is most developed. Freight transport remains the only robust driver once year effects are controlled for; a tax-to-freight intensity indicator reveals a significant three-year lag effect. Conclusions: Environmental taxation alone is not robustly associated with lower transport emissions in the V4; its association is conditional on infrastructure development and best complemented by renewable-energy investment. Findings should be read as descriptive associations rather than causal effects. Full article

Wind power is a cornerstone of China’s renewable energy development, supporting the green and low-carbon transformation and “dual-carbon” goals. With rapid capacity growth and an approaching wave of decommissioning for early onshore wind, assessing the carbon reduction potential from resource recovery is critical yet challenging. This study establishes a net carbon emission assessment framework covering operational and recycling stages, evaluating the carbon reduction potential of decommissioned materials including steel, copper, and resin. Results show that total carbon reduction from resource utilization grows steadily, under the assumed end-of-life resource-utilization pathways; the cumulative carbon reduction potential during 2025–2045 is estimated at approximately 90.85 million t CO₂-eq., with steel contributing roughly 60%. Between 2025 and 2045, carbon reduction from decommissioned wind power materials rises from 1.93 to 5.28 million tons of CO₂-eq., stabilizing the industry’s net emissions at 21 million tons from 2040. Four regional evolution patterns were identified: continuous growth in major wind power bases such as Inner Mongolia and Xinjiang; peak–fallback in early-developed provinces such as Henan and Ningxia; late-stage rise in eastern and central provinces such as Jiangsu and Guangdong; and platform fluctuation in northeastern provinces such as Heilongjiang, Jilin, and Liaoning. These estimates are based on static life-cycle emission factors, exclude transportation between wind farms and recycling facilities, and do not incorporate formal sensitivity or probabilistic uncertainty analysis; therefore, the absolute mitigation values should be interpreted as scenario-based estimates rather than precise forecasts. The findings suggest that authorities should implement differentiated regional decommissioning strategies and plan forward-looking wind power industrial chains to maximize resource recovery and support national dual-carbon objectives. Full article