Search Results (61,522)

The short half-life of positron emission tomography (PET) radioisotopes makes transport time a critical factor in medical logistics. While drones have demonstrated advantages in short-range medical deliveries, the feasibility and benefits of long-distance drone transport remain largely unexplored. In a comparative simulation-based modelling framework, this study explores whether long-range drone transport (117–376 km) can improve delivery performance of fluorodeoxyglucose-18 ([¹⁸F]FDG) PET isotopes compared with two existing ground-only routes (146 km and 348 km) and two combined car–airplane routes (532 km and 546 km). Simulated transport times, radioactive decay losses, and economic implications were estimated using drone speeds of 150, 200, and 250 km/h. Hourly weather data from 2023–2024 were incorporated to model flight feasibility and weather-related no-fly conditions. Time savings were translated into preserved radioactive activity and analyzed together with break-even transport costs. A drone speed of 150 km/h provided limited benefit, whereas speeds of 200–250 km/h preserved activity corresponding to a reduction from the current total use of 118 GBq to 72 and 65 GBq, respectively. Weather constraints reduced feasible winter flights by up to 30%. Estimated break-even drone costs ranged from EUR 3–18/km and increased to EUR 14–20/km when accounting for preserved isotopes, corresponding to annual economic gains of EUR 1.0–1.7 million. These results suggest that long-range drone transport could reduce isotope losses and improve diagnostic capacity, although feasibility depends on drone costs, weather resilience, and integration into clinical logistics systems. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO₂ trajectories in the EU-27 during 2000–2022 using a harmonized annual panel built primarily from the European Commission’s Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO₂ emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO₂ emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver. Full article

In the context of the global energy transition and a strengthening climate agenda, this study provides a comparative assessment of sustainable development strategies adopted by leading national oil and gas companies in China and Russia, focusing on their contribution to decarbonisation and SDG 7 and SDG 13. The study combines content analysis of corporate ESG reporting with a quantitative assessment of key environmental indicators. The analysis covers Gazprom, Rosneft, Lukoil, Novatek and Tatneft (Russia), and CNPC (PetroChina), Sinopec and CNOOC (China) over 2020–2024. The quantitative assessment includes Scope 1–2 greenhouse gas emissions, emissions intensity per unit of energy produced, operational energy consumption, renewable energy share, water intensity and green capital expenditures. The results reveal differences in national adaptation models: Chinese companies follow a centralised, state-driven approach integrated into strategic planning and five-year programmes, while Russian companies demonstrate a more fragmented, corporate-oriented model focused on technological modernisation. The strongest divergence is observed in governance integration and low-carbon investment structures, while emissions intensity trends remain gradual in both cases. Based on these findings, recommendations are proposed to strengthen sustainability and climate governance in Russian and Chinese oil and gas companies. The findings rely on self-reported ESG data, which involve differences in reporting boundaries and calculation methodologies. Full article

This study aimed to investigate the influence of the synthesis parameters on the corrosion resistance of 3D-printed Inconel 718 components. Samples were fabricated using laser powder bed fusion (PBF-LB/M) with different angles of inclination. Corrosion tests were conducted by immersion for 1000 h in a 3.5% aqueous NaCl solution at 20 °C and 45 °C, and by the potentiodynamic polarization measurements. Detailed analysis of changes in morphology, chemical composition, and roughness of 3D prints was performed using scanning electron microscopy, combined with energy-dispersive X-ray spectroscopy, and optical profilometry. To quantify the dissolution of alloy components during the long-term measurements, the post-corrosion solutions were analyzed using microwave plasma–atomic emission spectroscopy. The obtained results demonstrate that inclination angle significantly affects corrosion rate and electrochemical kinetics, with measurable differences in mass loss, Icorr values, and surface degradation morphology observed between orientations. The findings indicate that build orientation governs microstructural anisotropy and surface characteristics, which in turn influence corrosion susceptibility. The novelty of this work lies in the systematic and multi-method evaluation of inclination angle as an independent structural parameter controlling corrosion kinetics in PBF-LB/M-fabricated Inconel 718, providing new insight into structure–corrosion relationships in additively manufactured nickel-based superalloys. Full article

Similar to other municipal facilities, landfills are a substantial source of emissions of various biological pollutants. Numerous sustainability challenges result from the extremely high variability of emissions of harmful biological agents, which necessitates precise detection of microbiological emissions from these municipal facilities. This study aimed to assess whether a municipal waste landfill impacts indicator microorganisms and bacterial endotoxins occurring in soils within the landfill’s zone of influence. The research was conducted directly at the landfill site and in the surrounding area. Soil samples were collected monthly from eight sites over three years. Microbiological analyses included determination of total Salmonella counts and bacteria of the coliform group, Clostridium spp., Clostridium perfringens, and bacterial endotoxin concentrations. Results revealed a significant effect of the landfill on soil sanitary quality, indicating that adverse impacts depended mainly on the distance from the active waste sector of the landfill. The results also confirmed the usefulness of bacterial endotoxins as indicators of soil contamination with microorganisms within the municipal landfill and surroundings. Parametric statistical analyses effectively characterised contamination levels, and the Newman–Keuls multiple comparison test proved to be a rapid and reliable tool for assessing exceedances of established sanitary standards. Findings indicate that fresh waste is a critical source of microbiological contamination in soils, and they emphasise the value of combined microbial and endotoxin monitoring for sustainable landfill environmental assessment and management. While the current study focuses on soil contamination, future research should evaluate the impact of landfill on indicator microorganisms and bacterial endotoxins in air and water. Full article

China’s power sector is undergoing a complicated transformation characterized by intricate dependence on the dominant coal infrastructure and abundant renewable energy resources. This study assesses China’s carbon-neutral transition pathways for the period of 2024–2060 by using the “Establish Before Breaking” principle within a policy-informed, high-resolution energy system modeling framework. To examine the technological, economic, and environmental trade-offs of various carbon-neutral strategies, four scenarios (Reference (REF), Carbon Capture and Storage (CCS), Renewable-Based (REB), and Integrated (ING)) were developed, and their impacts were assessed through the application of the Low Emission Analysis Platform and the Next Energy Modeling (LEAP–NEMO) model. The results reveal that the ING scenario represents the most feasible and policy-consistent pathway, achieving an 88% renewable electricity share and a total installed capacity of approximately 8000 gigawatts (GW) by 2060. This pathway relies on a dual-track strategy that combines accelerated renewable deployment—supported by geographical complementarity and multi-regional Power-to-X (PtX) systems—with the strategic stabilization of conventional generation assets. The findings further demonstrate that retaining a small but critical share of flexible coal-CCS (0.2–0.5%) and nuclear capacity is necessary to address sub-daily variability, mitigate duck-curve effects, and ensure power system reliability under high renewable penetration. This integrated approach offers a systematic pathway for deep decarbonization within China’s unique energy context, ensuring a just, equitable, and sustainable transition. Full article

Urban heat islands (UHIs) pose escalating threats to public health and thermal comfort in dense urban environments. However, physics-based evaluations of material-specific cooling interventions and their integration into operational digital twin platforms remain limited. This study develops an integrated framework connecting computational fluid dynamics (CFD) modeling with digital twin visualization to evaluate UHI mitigation strategies. The objectives are to quantify the thermal mitigation effects of surface emissivity optimization on land surface temperature (LST) and pedestrian-level air temperature ($T_{air}$) to establish a data preprocessing pipeline converting CFD outputs into platform-independent visualization datasets, and to comparatively evaluate 2D GIS-based and 3D voxelization visualization approaches. Four emissivity scenarios were simulated using STAR-CCM+ for a 4 km² residential area in Gwacheon City, Republic of Korea. Comprehensive optimization (Case D) reduced the mean LST from 46.6 °C to 42.0 °C and T_air from 35.7 °C to 35.3 °C. Concrete-only optimization achieved 90.5% of the total thermal reduction while decreasing spatial variability (σ) from 7.1 to 5.8 during peak hours. The voxel-based 3D visualization provided a superior representation of vertical thermal stratification compared to 2D mapping. These findings establish a scalable foundation for climate-responsive urban management. Full article

The growing greenhouse gas emissions from agricultural production are increasing the pressure on poultry producers to use practices that reduce the environmental impact of farms, mainly by reducing ammonia emissions. The study evaluated the effect of adding effective microorganisms (EMs) and zeolite to litter on productivity, meat quality, selected physiological parameters, and air and litter quality in broiler chickens. In the experimental group, zeolite was applied at 3 kg/10 kg to the litter, and it was sprayed with a solution of EMs and water in a 1:4 ratio. Spraying was repeated weekly until the end of the production cycle. The litter additives reduced the moisture content and pH of the litter (p < 0.05) in the experimental group. This group also showed lower air humidity (p < 0.01) and reduced levels of ammonia and carbon dioxide compared to the control group (p < 0.05). A positive effect of litter additives on production results and the health of broiler chickens was noted. In summary, the addition of 3 kg/10 kg of zeolite to the litter, combined with spraying the litter with a 1:4 solution of EMs and water, can contribute to reducing the emission of harmful gas admixtures generated on broiler farms without negatively affecting production efficiency. Full article

Interest in non-centrosymmetric crystalline materials exhibiting second harmonic generation (SHG) has increased due to their potential applications in optical sensing and biosensing. Saccharide-based metal complexes are particularly attractive systems, as chiral sugars can promote non-centrosymmetric crystal packing. In this work, a new lanthanum–β-d-fructose compound, [La(C₆H₁₂O₆)(H₂O)₅]Cl₃ (LaFRUCl), was synthesized using a simple and low-cost method and characterized by single-crystal X-ray diffraction. The compound crystallizes in the orthorhombic space group P2₁2₁2₁ and consists of infinite (La³⁺–fructose)_n chains extending along the [001] direction, forming a one-dimensional Metal–Organic Framework. The nonlinear optical response was evaluated using the Kurtz–Perry powder technique with a Nd:YAG laser (1064 nm) and compared to a sucrose reference. The measured SHG efficiency is comparable to that of previously reported alkaline earth metal–sugar analogs. While the compound’s SHG emission is significant, evaluation of its structural stability under aqueous or physiological conditions is be required before considering biological applications. Full article

Nowadays, synthetic textiles, widely used on the market and largely composed of polyester (polyethylene terephthalate, PET), release microplastic fiber fragments (MPFFs) into the environment, inducing repercussions on ecosystems and health. Reducing these emissions by understanding manufacturing’s influence on MPFF release represents an important challenge for sustainable textile manufacturing and eco-design. This study aims to identify key weaving process factors influencing MPFF release during the first wash, which ends up in wastewater. Employing a Taguchi design of experiments, 18 fabrics were produced on industrial machines from polyester filaments, with different warp and weft densities, weaving patterns, and production speeds. Following identical black dyeing and finishing treatments, the range of the average quantity of MPFF released per fabric varies from 221 mg/kg to 753 mg/kg with an overall mean value of 451 mg/kg across all trials. Among the investigated parameters, warp yarn density and weaving pattern emerged as the most influential factors, accounting for the largest variations in MPFF release. Increasing warp density from 40 to 60 yarns/cm resulted in a substantial increase in MPFF emission, while the 3/1 sateen weave exhibited significantly lower MPFF release compared to plain and ottoman weaves. In contrast, weft density and weft insertion speed showed limited influence relative to experimental variability. No clear correlation was observed between the number of filaments in the weft yarn and MPFF release. These results show that the higher the surface mass, the cover factor, and the drape coefficient, the higher the release of MPFFs. This study shows that it is possible to limit the amount of microfibers generated by textiles by controlling the design and production of fabrics. The results support the integration of microplastic mitigation criteria into sustainable textile engineering and industrial eco-design frameworks. Nevertheless, the complexity of the release mechanisms and potential interactions between factors highlights the importance of conducting further research to determine the specific fabric characteristics that influence MPFF release. Full article

The current research undertook a comprehensive examination of global research related to the use of measurement, reporting, and verification (MRV) techniques for quantifying and tracking greenhouse gas (GHG) emissions from agriculture and livestock farming. Data were collected using a bibliometric analysis of 5340 studies published in the period (1990–2025) and a systematic literature review of 100 studies published in the period (2020–2025). The insights from the findings showed that four MRV techniques were broadly adopted across different regions: (1) inventory techniques (IPCC Tiers, national systems), (2) accounting at the project/product level (LCA, carbon footprint protocols), (3) MRV based on measurement and models (chambers, remote sensing, farm models, AI/ML), and (4) frameworks for governance and standardization (UNFCCC, Paris ETF, PAS 2050, etc.). The findings further revealed the impact of the MRV techniques on agriculture and livestock farming, showing that they facilitated the uptake of low-carbon practices. In agriculture, the MRV techniques showed that lower emissions emerged from mixed cropping, while in livestock farming, the emissions varied based on the feeding stage and type of diet used. However, various challenges arose in the adoption of MRV techniques where there was limited data related to GHG emissions, thereby reducing generalizability. In future work, there is a need for scholars to consider integrating the different MRV techniques to develop an understanding of the problem area. Full article

The urgency of climate action has intensified the use of machine learning (ML) to predict vehicular CO₂ emissions; however, the training of machine learning models also generates computational emissions that are seldom reported. This study addresses a paradox central to Green AI: can carbon-intensive algorithms be justified for predicting carbon emissions? Using a public dataset of 7385 light-duty vehicles, we trained nine widely used regression models spanning simple linear baselines, polynomial and regularised linear methods, tree-based learners, ensembles, and a neural network. All experiments were instrumented with CodeCarbon to quantify real-time training footprints under a grid carbon intensity of 450 g CO₂/kWh. Across models, test performance ranged from R² = 0.72 to 0.99, yet training emissions varied by four orders of magnitude, from 0.001 g CO₂ (simple linear regression) to 2.3 g CO₂ (XGBoost). Although XGBoost achieved the highest accuracy (R² = 0.9947), it emitted approximately 2300× more CO₂ than regularised polynomial linear models for only a 0.39-point gain in R². Pareto analysis identifies Lasso and Ridge regression with degree-4 polynomial features as sustainability-optimal, reaching R² = 0.9908 at ~0.004 g CO₂. To unify predictive and environmental efficiency, we introduce Accuracy-per-Gram (APG = R²/CO₂) and Marginal Emissions Cost (MEC = ΔCO₂/ΔR²), demonstrating a steep efficiency cliff beyond regularised linear models. At the fleet scale (100 million vehicles with daily retraining), algorithm choice implies ~84 t CO₂/year for XGBoost versus ~0.15 t for Lasso, highlighting the potential climate cost of marginal accuracy gains. We provide a reproducible carbon-tracking pipeline, Green-AI evaluation metrics, and deployment guidance, arguing that computational sustainability must co-determine model selection for emissions-related ML systems. Most critically, we identify a clear accuracy–carbon emission Pareto frontier, demonstrating that regularised polynomial linear models lie on the sustainability-optimal boundary, while widely used ensemble methods such as XGBoost sit beyond an “efficiency cliff,” where marginal accuracy improvements incur disproportionately high carbon costs. Full article

We present a modular building block strategy for synthesizing phosphonated polyaromatic systems as an alternative to the conventional late-stage phosphonation of prefabricated aromatic scaffolds, which often requires harsh conditions and has limited tolerance for functional groups. A monophosphonated biphenyl building block was obtained via nickel-catalyzed phosphonation of dibromobiphenyl at 170 °C for three hours. This synthesis is more economical and milder than typical high-temperature palladium systems. In parallel, a borated pyrene derivative was prepared by Suzuki–Miyaura borylation. The final palladium-catalyzed Suzuki cross-coupling reaction produced the target compound, octaethyl(pyrene-tetrakis(biphenyl))tetrakis(phosphonate), Et₈-PyTPPE. Single-crystal X-ray diffraction reveals a centrosymmetric molecule that crystallizes in the triclinic space group P–1, with the inversion center located at the central C–C bond of the pyrene core. The pyrene unit is essentially planar, while the biphenylphosphonate arms are highly twisted relative to the core and to each other. The crystal packing is dominated by weak intermolecular interactions, and no significant π–π stacking is observed. Hirshfeld surface analysis shows that H···H (60.5%) and C···H (22.5%) contacts predominate, while O···H interactions (14.4%) with phosphoryl oxygen atoms represent the most relevant directed contacts. From photophysical investigations, Et₈-PyTPPE exhibits blue fluorescence (λ_em. = 452 nm) in solution and aggregation-induced red-shifted emission with nanosecond lifetimes in the solid state, confirming purely fluorescent behavior. Full article

Urban air pollution during winter is a major challenge in many cities, where emissions from residential heating lead to elevated particulate matter levels. Atmospheric dispersion modelling supports the understanding of spatial and temporal pollution behavior and enables the assessment of source contributions relevant for targeted mitigation. In this study, the ADMS-Urban dispersion model was applied to simulate hourly PM_2.5 and PM₁₀ concentrations across the city of Skopje, North Macedonia. Residential heating was the focus of the analysis, while emissions from road traffic and industrial activities were also included to ensure a realistic representation of the urban emission environment. A representative winter day was analyzed to examine the influence of wind patterns and diurnal boundary-layer height variability on particulate matter dispersion. Modelled concentrations were evaluated against measurements from urban air quality monitoring stations and showed good agreement in reproducing both night-time accumulation and daytime dispersion. The results indicate that household heating using biomass is the dominant contributor to wintertime particulate matter emissions, with PM₁₀ prevailing over PM_2.5. These findings underline the need for targeted emission reduction measures in the residential heating sector and demonstrate the usefulness of short-term dispersion modelling for supporting air quality management strategies in Skopje. Full article