Search Results (15,595)

The cement industry accounts for approximately 8–9% of global greenhouse gas emissions, mainly due to clinker production. Replacing Portland cement with supplementary cementitious materials (SCMs) is a promising strategy to reduce these environmental impacts. This study evaluates the mechanical performance and environmental profile of sugarcane ash (SCA) and energy cane ash (ECA) as SCMs in Portland cement mortars. To the best of the authors’ knowledge, this is the first study to integrate the mechanical performance and life cycle environmental assessment of energy cane ash as a supplementary cementitious material in Portland cement mortars. Mortars incorporating 5–20% ash were tested for compressive strength and accelerated carbonation. An attributional life cycle assessment (LCA), following ISO 14040/44 and using the ReCiPe 2016 method, was conducted with a cradle-to-gate approach. At 28 days under wet curing, 10% ECA replacement achieved slightly higher compressive strength than the reference mortar (102%), while 10% SCA replacement also achieved compressive strength similar to the reference mortar, although slightly lower than that of ECA. Both ashes significantly reduced carbonation depth, indicating improved durability. From a life cycle assessment perspective, replacing cement with 10–20% ECA reduced environmental impacts in nine of the ten impact categories evaluated, including climate change (up to 18.6%), fossil depletion, and metal depletion. SCA also improved most categories, although to a lesser extent. The superior performance of ECA is associated with its higher biomass yield and environmental credits from potassium recovery during ash washing. These results demonstrate that agro-industrial ashes, particularly energy cane ash, can enhance mortar performance while reducing environmental burdens, mitigating industrial waste accumulation, and supporting circular economy strategies and climate change mitigation, thereby offering a viable pathway toward more sustainable cement production. Full article

This paper evaluates the extent to which European Union member states show alignment between strategic innovation capacity and sustainable development outcomes. To achieve this objective, indicators were collected from Eurostat for two dimensions: strategic capacity for innovation (public expenditure on research and development, human resources in science and technology, and the higher education graduation rate) and sustainable development outcomes (real GDP per capita, employment rate, risk of poverty or social exclusion, and greenhouse gas emissions). Going beyond traditional literature, we develop an analysis based on grey clustering using multiple scenarios to illustrate the complex, non-linear relationships and structural bottlenecks in member states. The stability of the classifications was further examined through threshold sensitivity testing across all scenarios and through 200,000 weight-perturbation simulations for an illustrative boundary case. The results reveal distinct performance typologies: a resilient group of “systemic leaders” (including Denmark, Sweden, and the Netherlands) demonstrating consistent excellence across all applied prioritization scenarios, and a stagnant core facing structural challenges regarding both innovation and sustainability (such as Romania and Hungary). The dynamic analysis covering 2021–2024 suggests that strong innovation-capacity indicators are not necessarily associated with equally strong sustainability-outcome indicators, while certain economies in Central and Eastern Europe show positive convergence trends. Supported by stability simulations conducted across multiple scenarios, the study highlights significant alignment gaps between innovation-capacity indicators and sustainability-outcome indicators across the European Union and offers public policy recommendations to stimulate sustainable cohesion and technology adoption. Full article

Against the backdrop of energy conservation and environmental protection, developing more stable and efficient phosphors has become an urgent challenge. In this study, we have synthesized a series of Ca_2.5Hf_2.5Ga₃O₁₂:Eu³⁺ (CHGO:Eu³⁺) red phosphors via a high-temperature solid-state method, which exhibit strong red emission at 610 nm under 394 nm excitation, corresponding to the ⁵D₀→⁷F₂ transition of Eu³⁺. To improve the lattice vacancies caused by charge imbalance when Eu³⁺ is doped into the CHGO lattice to replace Ca²⁺, we introduce the charge compensator M⁺ (M = Li, Na, K). The results of the emission spectrum show that the introduction of charge compensators can effectively improve the luminescence intensity. Among them, K⁺ has the most significant effect on increasing the emission intensity of Eu³⁺, making the emission intensity of the phosphor more than twice that when there are no charge compensation ions. Additionally, the quantum efficiency and thermal stability of these phosphors are significantly improved compared to the CHGO:0.075Eu³⁺ sample before substitution. At 423 K, the emission intensity of the CHGO:0.075Eu³⁺, 0.075K⁺ sample still remains at 88.7% of that at 298 K. The color rendering index of the prepared white LED is 81.2, and its CIE chromaticity coordinates are (0.3212, 0.3065). This indicates that the prepared CHGO:0.075Eu³⁺, 0.075K⁺ red phosphor has broad application prospects in solid-state lighting. Full article

Methane (CH₄) is the second most significant greenhouse gas after carbon dioxide (CO₂). Due to its high short-term global warming potential and the feasibility of emission abatement, monitoring methane point-source emissions has become a critical strategy for mitigating climate change. To address existing technical bottlenecks associated with spatial coverage limitations and surface-induced signal interference in satellite-based monitoring, this study proposes an integrated methane monitoring framework termed Multi-Band Multi-Constraint (MBMC) using Sentinel-2 MSI imagery. The MBMC framework combines a Low-Reflectance Artifact Detection (LRAD) algorithm, a Multi-Band Multi-Pass (MBMP) differential absorption retrieval model, and Integrated Mass Enhancement (IME)-based emission quantification. The LRAD module effectively suppresses artifacts caused by low-reflectance surfaces and heterogeneous backgrounds, thereby improving the signal-to-noise ratio (SNR) and retrieval accuracy of methane column enhancements. In addition, a semi-automatic plume segmentation workflow integrating morphological operations with a spatial database is developed to improve methane plume extraction and source localization. The framework was validated using data from single-blind controlled methane release experiments conducted in Arizona, USA. Results show that the proposed method achieved a mean absolute percentage error (MAPE) of 21.6% for emission rates ranging from 0.5 to 3.0 t/h, demonstrating promising performance for Sentinel-2-based screening and quantification of methane point sources, particularly for emissions above approximately 0.5 t/h under favorable observation conditions. The framework was further applied to Sentinel-2 observations over a natural gas field in Northwest China, where multiple methane point sources associated with gas gathering stations were successfully identified and quantified. The proposed framework provides a practical approach for high-resolution and high-frequency satellite monitoring of methane point sources and supports the refinement of methane emission inventories and mitigation strategies in the oil and gas sector. Full article

Methane ($C{H}_4$) is the second most important anthropogenic greenhouse gas, accounting for approximately 30% of current global warming. Since 2007, atmospheric methane concentrations have been increasing at an accelerating rate, reaching a record 1945.85 ppb in November 2025. The emergence of high-resolution satellite constellations has transformed our ability to detect, quantify, and attribute methane emissions from space. This review provides a comprehensive analysis of the current state of high-resolution global methane mapping, examining: (1) the evolution of satellite missions from coarse-resolution sounders like TROPOMI (5.5 × 7 km) to very high-resolution imagers including WorldView-3 (3.7 m), GHGSat (50 m), and the recently launched Tanager-1 (30 m); (2) advances in retrieval algorithms, including the transition from physics-based matched filter methods to deep learning approaches such as U-Net architectures achieving F1-scores of 78.4% on Sentinel-2 imagery; (3) integration of satellite observations with atmospheric inverse models for flux estimation; (4) the impact of satellite-derived data on policy frameworks including the Global Methane Pledge and EPA’s Super-Emitter Program; and (5) remaining challenges including cloud contamination, detection limit trade-offs, and the need for sustained validation networks. We synthesize findings from over 200 peer-reviewed studies and analyze 42 years of NOAA global methane observations to demonstrate how the convergence of improved spatial resolution, machine learning, and international coordination is enabling unprecedented transparency in global methane monitoring. The review concludes with recommendations for future satellite missions and data assimilation strategies needed to meet the Global Methane Pledge target of 30% emission reductions by 2030. Full article

Hydrogen supplementation in compression-ignition diesel engines is increasingly investigated as a practical retrofit approach for reducing the environmental impact of existing marine and stationary diesel power systems. This study examines the effects of hydrogen enrichment on combustion stability, pressure behavior, harmonic response, fuel consumption, and exhaust emissions in a 1966 Deutz A12L 714 marine auxiliary generator-set. The engine was operated at 900, 1200, and 1500 rpm with hydrogen supplied through the intake-air stream at flow rates up to 130.15 L/min. Results indicate that hydrogen enrichment improved fuel consumption and combustion-related dynamic behavior without increasing peak cylinder pressure or exhaust-gas temperature. Low-order vibration harmonics, particularly the 1X and 3X components associated with torque ripple and cyclic combustion variability, decreased with hydrogen addition. COV(Pmax) remained below 0.27% across all operating conditions, indicating preserved combustion stability, while hydrocarbon emissions and fuel consumption decreased by approximately 30% and 13%, respectively, at the highest hydrogen enrichment conditions. Phase-averaged pressure traces obtained showed virtually unchanged combustion-cycle structure and periodicity under maximum hydrogen enrichment. The findings indicate that hydrogen enrichment improves combustion stability and overall engine performance without increasing combustion severity, supporting its potential application as a retrofit solution for marine auxiliary engines, distributed generators, and other legacy diesel-engine systems. Full article

The dynamics of methane (CH₄), nitrous oxide (N₂O) and carbon dioxide (CO₂) in groundwater have rarely been investigated. As dissolved gases they may be transported to distant sites and, hence, to the atmosphere. Crumps Cave (CC) is located on a perched aquifer in south-central Kentucky. Water was sampled at a waterfall within the cave located 15 m below the surface, at two adjacent surface wells 15 m and 50 m deep, providing samples from the epikarst and regional aquifer, respectively. Dissolved gases and geochemistry parameters were analyzed for seasonal changes across three years of weekly monitoring (2015–2017) using Kruskal–Wallis H tests and Bonferroni-corrected pairwise comparisons. Dissolved CO₂ concentrations are mainly controlled by percolation through the epikarst, influenced by soil respiration, and vary with rainfall and seasonal temperature fluctuations. CH₄ showed a site-dependent pattern: concentrations were significantly elevated in warm seasons at the shallow and deep wells, where anaerobic conditions and agriculturally derived organic matter promote methanogenesis; no seasonal variation was detected at the cave site, where oxic conditions limit CH₄ year-round. N₂O was significantly elevated in cold seasons at all three sites, driven by cold-season denitrification of agriculturally derived nitrates. N₂O did not differ between sites, indicating seasonal temperature-driven denitrification as the primary control rather than site hydrology, with cold-season denitrification of agriculturally derived nitrates from fertilizer application. Indirect gas emissions are characteristic of karst systems and may be transported or stored in aquifers through complex interactions of groundwater recharge, microbial activity, and seasonal land-use variability. Full article

This study conducts a comprehensive investigation into prospective policy alternatives within Pakistan’s power sector using the PAK-TIMES model, targeting the critical challenges of energy scarcity and environmental degradation. Focused on the period from 2022 to 2050, the research evaluates the impact of various policies on energy consumption, supplies, carbon emissions, and expenditures in alignment with Pakistan’s Nationally Determined Contributions (NDC) directed at combatting climate change. The study explores three distinct scenarios: a business-as-usual (BAU) scenario, along with five policy (5% Eff, 10% Eff, 15% REN, 30% REN, 50% REN) scenarios categorized into energy efficiency and renewable integration. The first scenario concentrates on the deployment of energy-efficient devices, while the second scenario delves into diverse levels of renewable energy integration. Key results reveal that energy demand is projected to surge substantially under the BAU scenario, increasing significantly from 3459 PJ in 2022 to 7912 PJ by 2050. In contrast, scenarios prioritizing energy efficiency can potentially curb the total energy supply by 2.3%, while renewable energy integration can expand up to 1.3% compared to business-as-usual by 2050. These alternative scenarios also exhibit the potential to slash greenhouse gas (GHG) emissions from the power sector by up to 15%. Notably, the PAK-TIMES model emerges as a valuable decision support tool for the Pakistani government to facilitate the execution of energy efficiency and renewable energy policies aimed at fulfilling its NDCs, while also contributing to the fulfillment of Sustainable Development Goals (SDGs) 7 (affordable and clean energy) and 13 (climate action). The study underscores the pivotal role of policy interventions in simultaneously mitigating energy challenges and combatting climate change for sustainable development. Full article

With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth uncertainties and fails to fully utilize the flexibility of demand-side resources during the planning phase. This paper proposes a robust planning method for PIESs considering dynamic demand response and multi-timescale uncertainties. First, an energy flow framework encompassing cooling, heating, electricity, gas, and hydrogen is constructed. To overcome the limitations of traditional fixed-boundary DR, a dynamic elastic DR mechanism featuring transferable, substitutable, and curtailable types is established. Transferable demand boundaries are defined by a price–demand elasticity matrix, and actual responses are dynamically adjusted in synergy with system power balance conditions for optimal configuration. Second, multivariate dynamic time warping and hierarchical clustering algorithms derive typical daily scenarios accounting for short-term uncertainties. Finally, information gap decision theory characterizes long-term load growth uncertainty, constructing a robust planning model addressing both timescales. Case studies show that flexible resources and demand response reduce lifecycle cost by 55.24% and carbon emissions by 47.75%. The proposed demand response method further cuts costs by 153,800 yuan and emissions by 11.36%. The robust planning method synergistically addresses multi-timescale uncertainties, ensuring economy while maximizing resilience to uncertain fluctuations. Full article

For soil processes that are known to be temporally dynamic, such as soil respiration, methanogenesis, and nitrification–denitrification, it is challenging to capture temporal variations with field-portable greenhouse gas (GHG) analyzers to provide the most accurate estimates of season-long GHG emissions and global warming potentials (GWPs). The objective of this field study was to evaluate the effects of measurement frequency (i.e., weekly, every other week, and every third week), replication (i.e., three, four, or five), and their interaction on the consistency of season-long carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) emissions and GWP estimates across multiple ecosystems. Results are based on direct, in-field measurements with a field-portable gas analyzer. Field research was conducted throughout the 2024 growing season in a minimally grazed pasture, tallgrass prairie, soybean under conventional and conservation management practices, and cotton under conservation management in Arkansas, USA. Season-long CO₂ emissions and GWP from the tallgrass prairie were 1.1 times (12%) greater from the weekly and every-other-week (16.9 and 17.0 Mg ha⁻¹, respectively), which did not differ, than the every-third-week (14.2 and 14.2 Mg ha⁻¹, respectively) measurement frequencies. Season-long CH₄ emissions from the minimally grazed pasture and conservation-tilled soybean system were ≥7.5 times greater with four and five replications, which did not differ, than with three replications. Global warming potential in the conservation-tilled soybean (13.9 Mg ha⁻¹) and conservation-tilled cotton (21.1 Mg ha⁻¹) systems were ≥1.1 times (13%) greater with the every-third-week than with the weekly data set. Though this study was somewhat limited due the data sub-setting approach used, even using current, state-of-the-art, field-portable GHG analyzers, an appropriate in-field measurement frequency and number of spatial replications should be considered to reliably quantify whole-field, season-long GHG emissions and GWP estimates. Full article

Micro gas turbines are small-scale systems based on the Brayton cycle and represent a viable solution for distributed generation. However, the main limitation to extending their application range is the cycle efficiency. Numerical and experimental analyses of power plants are important for evaluating the energy performance of different cycle configurations. Another issue is the formation of pollutants, including nitrogen oxide emissions. The humidified gas turbine cycle is one alternative to address these problems. Among wet cycles, the regenerative evaporative cycle offers a means to improve gas turbine efficiency. However, this configuration is less commonly discussed in the literature, which focuses more on steam injection cycles. Therefore, this paper presents an energy, exergetic, and nitrogen oxide formation analysis of an evaporative regenerative cycle for a 30 kW micro gas turbine across the gas turbine load range to define the most suitable operational system regime. The novelty of this study lies in an integrated assessment that simultaneously covers the operation of the micro gas turbine at full and part load under different conditions of water injection into the evaporator. The analyses conducted show that, for the micro turbine operating at full load, the benefits in terms of energy and pollutant formation are positive for all fractions of injected water. However, decreases in cycle performance are reported at power outputs below 19 kW compared with the dry cycle. Although nitrogen oxide formation decreases with increasing water injection, the reduction is less pronounced at lower microturbine power levels. Full article

Allergic diseases are increasing globally, particularly among children, who are highly vulnerable due to critical windows of immune development. This review examines climate change as a key environmental determinant driving the rising burden of pediatric allergic diseases, including asthma, allergic rhinitis (AR), atopic dermatitis (AD), and food allergy (FA). Climate change influences disease risk through interconnected pathways, such as increased air pollution, altered aeroallergen patterns, and more frequent extreme weather events. Elevated carbon dioxide (CO₂) levels and rising temperatures prolong pollen seasons and enhance allergenicity, while pollutants such as ozone (O₃) and particulate matter (PM) exacerbate airway inflammation and immune dysregulation. Emerging evidence emphasizes the role of early-life exposure, particularly during prenatal and early postnatal periods, when environmental insults can induce long-term effects via epigenetic modifications and immune reprogramming. These mechanisms may increase susceptibility to allergic sensitization and subsequent disease development. Epidemiological studies consistently link exposure to air pollution, including PM_2.5 (PM with aerodynamic diameter < 2.5 μm) and nitrogen dioxide (NO₂), with increased risk of allergic diseases in children. Additionally, climate change-related events such as wildfires, sand and dust storms, and thunderstorms further elevate exposure to allergens and pollutants, contributing to acute exacerbations and disease progression. Climate change may also contribute to allergic diseases through microbiome dysbiosis, as altered environmental microbial exposures, biodiversity loss, air pollution, and antibiotic-associated microbial disruption may impair immune tolerance and promote allergic sensitization in children. Addressing this growing public health challenge requires integrated mitigation strategies to reduce greenhouse gas (GHG) emissions and improve air quality, alongside adaptive interventions to enhance resilience and reduce exposure. Understanding these mechanisms is essential for developing targeted prevention strategies and protecting child health in a changing climate. Full article

The increasing interdependence between environmental degradation, geopolitical instability, and public-health pressures requires structured information-system architectures capable of integrating heterogeneous data and transforming them into decision-support knowledge. In this context, this study develops a data-driven information system architecture for the exploratory analysis of environmental, geopolitical, and health-related risks in the EU-27 during 2013–2023. The proposed system is structured as a multi-layered analytical pipeline designed to process country-year panel data and generate interpretable outputs. The methodological framework integrates Principal Component Analysis (PCA) for exploratory dimensionality reduction, K-Means clustering for structural pattern identification, a RandomTree classification model for translating cluster membership into decision rules, and a Two-Part Fixed Effects Model. Experimental results indicate an optimal and interpretable clustering configuration at k = 3, revealing three broad structural profiles among EU Member States. A moderate positive relationship is identified between greenhouse gas emissions per capita (GHGE) and health expenditure (SHA) (r = 0.34), while geopolitical risk (GPR) exhibits weak and statistically insignificant associations. This association is interpreted cautiously, as it may reflect the combined effect of industrial activity, environmental exposure, economic development, and the higher financial capacity of some Member States to allocate resources to healthcare systems. The results indicate the dominant contribution of GHGE and SHA in differentiating the identified profiles, while GPR shows limited explanatory power within the analyzed context. The RandomTree model achieved an accuracy of 93.58% in reproducing the cluster labels; however, it is used as an interpretability layer rather than as an independent validation of clustering. The system supports the identification of vulnerability-related structural patterns and provides an exploratory basis for future data-driven monitoring and early-warning applications. Full article

In order to reduce aviation’s negative environmental effects and support international efforts to battle climate change, the International Civil Aviation Organization (ICAO) seeks to cut greenhouse gas (GHG) emissions. About 2–3% of the world’s CO₂ emissions come from aviation, and at high altitudes, the fraction of other GHGs that significantly alter the atmosphere is considerably greater. In this study, hourly wind speed data at 100 m height from ECMWF’s fifth-generation reanalysis (ERA-5) were used over a period of 40 years (1985–2025). Hourly assessments of wind speeds at 40 m and 80 m heights are conducted in ERA-5, with biases at specific ground locations rectified via the Global Wind Atlas (GWA). This research estimates and analyzes many factors, including Weibull statistical parameters, daily and monthly wind speed variations, cumulative distribution function (CDF), and atmospheric turbulence intensity. The energy generation from several wind turbine types at different elevations was assessed. The findings indicate that the examined location revealed fair potential for the construction of large-capacity wind energy units at heights equal to or above 80 m. Turbines that are less than 50 m tall are spread out at least 10 km around the airport runway. While turbines that are less than 150 m tall are spread out at least 15 km away from the airport runway. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article