Search Results (9,953)

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

This study offers a detailed environmental, energy, and economic evaluation of thermal modernisation options for an existing single-family home in southern Poland. A total of 24 variants, combining different heat sources (solid fuel, biomass, natural gas, and heat pumps) with various levels of building insulation, were analysed using energy performance certification methods. Results show that, from an energy perspective, the most advantageous scenarios are those utilising brine-to-water or air-to-water heat pumps supported by photovoltaic systems, reaching final energy demands as low as 43.5 kWh/m²year and primary energy demands of 41.1 kWh/m²year. Biomass boilers coupled with solar collectors delivered the highest renewable energy share (up to 99.2%); however, they resulted in less notable reductions in primary energy. Environmentally, all heat pump options removed local particulate emissions, with CO₂ reductions of up to 87.5% compared to the baseline; biomass systems attained 100% CO₂ reduction owing to renewable fuels. Economically, biomass boilers had the lowest unit energy production costs, while PV-assisted heat pumps faced the highest overall costs despite their superior environmental benefits. The findings highlight the trade-offs between ecological advantages, energy efficiency, and investment costs, offering a decision-making framework for the modernisation of sustainable residential heating systems. Full article

Polycyclic aromatic hydrocarbon (PAH) derivatives, specifically azaarenes and nitrated and oxygenated PAHs, are emerging contaminants of concern due to their increased toxicity and persistence compared to the parent PAHs. Despite their toxicity, their simultaneous analysis in complex matrices, such as in fumes emitted from bituminous mixtures, remains challenging due to limitations of conventional analytical techniques. To address this, an advanced methodology was developed using Ultra-High-Performance Liquid Chromatography coupled with High-Resolution Mass Spectrometry (UHPLC-HRMS Orbitrap Eclipse) equipped with an APCI source for the simultaneous identification and quantification of 14 PAH derivatives. Chromatographic and ionization parameters were optimized to ensure maximum sensitivity and selectivity. Following ICH Q2(R2) guidelines, the method was validated, demonstrating excellent linearity (R² > 0.99), high mass accuracy (≤5 ppm), strong precision (<15%), and excellent sensitivity. Limits of detection (LODs) ranged from 0.1 µg L⁻¹ to 0.6 µg L⁻¹ and limits of quantification (LOQs) ranged from 0.26 µg L⁻¹ to 1.87 µg L⁻¹. The validated method was successfully applied to emissions from asphalt pavement materials collected on quartz filters under controlled conditions, enabling the identification and quantification of all 14 targeted compounds. These results confirm the method’s robustness and suitability for trace-level analysis of PAH derivatives in complex environmental matrices. Full article

This study examines the impact of renewable energy, economic growth, and trade openness on CO₂ emissions in the EU-15 countries over the period 1980–2022, employing the ARDL modeling framework. In addition, a panel PMG-ARDL model is employed as a robustness check. The analysis identifies cointegration among the variables in 11 out of the 15 countries studied. Economic growth is found to increase CO₂ emissions, highlighting the ongoing challenge of aligning economic expansion with environmental objectives. The estimated coefficients for economic growth range from 0.43 to 5.70, depending on the country. Renewable energy significantly reduces emissions, highlighting its critical role in achieving sustainability (the corresponding coefficient moves in the range −0.13 to −0.96). Trade openness generally shows a neutral impact on emissions across most cases. Overall, renewable energy contributes to reducing CO₂ emissions, whereas the effects of economic growth and trade openness remain mixed and country-specific. These findings highlight the need to promote cleaner technologies, enhance energy efficiency, and ensure broader access to environmentally friendly energy sources. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Nitrogen dioxide (NO₂) plays a crucial role in environmental processes and public health. In recent years, NO₂ pollution has been monitored using a combination of in situ measurements and satellite remote sensing, supported by the development of advanced retrieval algorithms. With advancements in satellite technology, large-scale NO₂ monitoring is now feasible through instruments such as GOME-2C and TROPOMI. However, the fixed local overpass times of polar-orbiting satellites limit their ability to capture the complete diurnal cycle of NO₂, introducing uncertainties in emission estimation and pollution trend analysis. In this study, we evaluated differences in NO₂ observations between GOME-2C (morning overpass at ~09:30 LT) and TROPOMI (afternoon overpass at ~13:30 LT) across three representative regions—East Asia, Central Africa, and Europe—that exhibit distinct emission sources and atmospheric conditions. By comparing satellite-derived tropospheric NO₂ column densities with ground-based measurements from the Pandora network, we analyzed spatial distribution patterns and seasonal variability in NO₂ concentrations. Our results show that East Asia experiences the highest NO₂ concentrations in densely populated urban and industrial areas. During winter, lower boundary layer heights and weakened photolysis processes lead to stronger accumulation of NO₂ in the morning. In Central Africa, where biomass burning is the dominant emission source, afternoon fire activity is significantly higher, resulting in a substantial difference (1.01 × 10¹⁶ molecules/cm²) between GOME-2C and TROPOMI observations. Over Europe, NO₂ pollution is primarily concentrated in Western Europe and along the Mediterranean coast, with seasonal peaks in winter. In high-latitude regions, weaker solar radiation limits the photochemical removal of NO₂, causing concentrations to continue rising into the afternoon. These findings demonstrate that differences in polar-orbiting satellite overpass times can significantly affect the interpretation of daily NO₂ variability, especially in regions with strong diurnal emissions or meteorological patterns. This study highlights the observational limitations of fixed-time satellites and offers an important reference for the future development of geostationary satellite missions, contributing to improved strategies for NO₂ pollution monitoring and control. Full article

Cement manufacturing is the second largest anthropogenic source of Hg emissions in the environment. Therefore, the establishment of analytical methodologies that can be utilized in the determination of Hg concentration from cement raw materials and cement is of great importance. The total Hg and Hg thermospecies in cement raw materials and cements were determined by thermal desorption techniques with a Zeeman Hg analyzer. No chemical pre-treatment of samples is required for this technique prior to analysis. An optimum single-stage temperature program was applied to determine total Hg at an optimum heating rate of approximately 5 °C s⁻¹ while Hg thermospecies were determined over four stages at an optimum heating rate of approximately 0.2 °C s⁻¹ per stage from ambient temperature to 720 °C. Total mercury concentrations in cement raw materials ranged between 2.19 ng g⁻¹ and 395 ng g⁻¹, while in cement, concentrations ranged between 1.32 ng g⁻¹ and 31.0 ng g⁻¹. The highest Hg contents were found in dust return (580 ng g⁻¹ and 679 ng g⁻¹). Hg thermospecies determination showed that cement raw materials and cements contain one Hg thermospecies that is released at 20–180 °C while dust return contained one to four Hg thermospecies that could be released at 20–180 °C, 180–360 °C, 360–540 °C, and/or 540–720 °C, thus indicating that new Hg compounds are formed during cement production. Full article

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on human health in Europe (including Denmark) and from source to critical nitrogen load exceedances for NH₃-sensitive nature in Denmark. The Danish Eulerian Hemispheric Model (DEHM) is used for modelling the air pollution concentrations in Europe and nitrogen depositions on land and water surfaces in Denmark arising from NH₃ emissions from mink farming in Denmark. The Economic Valuation of Air (EVA) pollution model system is applied for deriving the health effects and corresponding socio-economic costs in Denmark and Europe arising from the emissions from mink farming. On a local scale in Denmark, the deposition resulting from the NH₃ emissions from mink farming is modelled using the results from the OML-DEP model at a high resolution to derive the critical nitrogen load exceedances for Danish nature areas sensitive to NH₃. From the analysis of the impacts through human exposure to the air pollutants PM_2.5, NO₂, and O₃, it is concluded that in total, ~60 premature deaths annually in Europe, including Denmark, can be attributed to the emissions of NH₃ to the atmosphere from the mink farming sector in Denmark. This corresponds to annual socio-economic costs on the order of EUR 142 million. From the analysis of critical load exceedances, it is concluded that an exceedance of the critical load of nitrogen deposition of ~14,600 hectares (ha) of NH₃-sensitive nature areas in Denmark can be attributed to NH₃ emissions from mink farming. The cost for restoring nature areas of this size, damaged by eutrophication from excess nitrogen deposition, is estimated to be ~EUR 110 million. In 2020, the mink sector in Denmark was shut down in connection with the COVID-19 pandemic. All mink were culled by order of the Danish Government, and now in 2025, the process of determining the level of financial compensation to the farmers is still ongoing. The socio-economic costs following the impacts on human health in Europe and nitrogen-sensitive nature in Denmark of NH₃ emissions from the now non-existing mink sector can therefore be viewed as socio-economic benefits. In this study, these benefits are compared with the expected level of compensation from the Danish Government to the mink farmers, and the conclusion is that the compensation to the mink farmers breaks even with the benefits from reduced NH₃ emissions over a timescale of ~20 years. Full article

The construction industry is a major source of environmental degradation as it is responsible for a significant share of global CO₂ emissions, especially from cement and aggregate consumption. This study fills the need for sustainable construction materials by developing and evaluating a low-carbon fiber-reinforced concrete (FRC) made of steel slag powder (SSP), processed recycled concrete aggregates (PRCAs), and waste steel rivet fibers (WSRFs) derived from industrial waste. The research seeks to reduce dependency on virgin materials while maintaining high values of mechanical performance and durability in structural applications. Sixteen concrete mixes were used in the experimental investigations with control, SSP, SSP+RCA, and RCA, reinforced with various fiber dosages (0%, 0.2%, 0.8%, 1.4%) by concrete volume. Workability, density, compressive strength, tensile strength, and water absorption were measured according to the appropriate standards. Compressive and tensile strength increased in all mixes and the 1.4% WSRF mix had the best performance. However, it was found that a fiber content of 0.8% was optimal, which balanced the improvement in strength, durability, and workability by sustainable reuse of recycled materials and demolition waste. It was found by failure mode analysis that the transition was from brittle to ductile behavior as the fiber content increased. The relationship between compressive, tensile strength, and fiber content was visualized as a 3D response surface in order to support these mechanical trends. It is concluded in this study that 15% SSP, 40% PRCA, and 0.8% WSRF are feasible, specific solutions to improve concrete performance and advance the circular economy. Full article

As a major source of carbon emissions, the carbon-based power generation industry requires a scientifically robust environmental performance evaluation system to facilitate its green transition and sustainable development. Focusing on unique transition dynamics across four carbon-based power generation formats, this study compares environmental dimension indicators across typical ESG evaluation frameworks and proposes an innovative evaluation index model of environmental performance based on common metrics, with a particular emphasis on their contribution potential to the “Dual Carbon Goals”. The framework’s core innovation lies in its Dual Carbon-focused indicator system, which evaluates three critical indicators overlooked by mainstream ESG methodologies. It extends to include upstream/downstream processes, addressing gaps in current evaluation systems. The findings reveal that core environmental issues, such as climate change, pollution emissions, and resource utilization, exhibit broad commonality in ESG evaluations. Among the assessed indicators, carbon emission intensity carries the highest weight, underscoring its centrality in each power generation sector’s efforts to align with the Dual Carbon Goals. Furthermore, the analysis demonstrates that underground coal gasification combined cycle power generation has a relatively favorable environmental performance, ranking slightly below natural gas combined cycle but above shale gas combined cycle power generation. In contrast, traditional coal-fired power generation exhibits significantly poorer environmental outcomes, highlighting both the efficacy of technological upgrades in reducing emissions and the urgent need for transitioning away from conventional coal-based power. Full article

Ultra-high-energy cosmic rays (UHECRs) with energies exceeding ${10}^{19}$ eV are believed to originate from extragalactic environments, potentially associated with relativistic jets in active galactic nuclei (AGN). Among AGNs, blazars, particularly those detected in very-high-energy (VHE) gamma rays, are promising candidates for UHECR acceleration and high-energy neutrino production. In this work, we investigate three blazar sources, AP Librae, 1H 1914–194, and PKS 0735+178, using multiwavelength spectral energy distribution (SED) modeling. These sources span a range of synchrotron peak classes and redshifts, providing a diverse context to explore the physical conditions in relativistic jets. We employ both leptonic and lepto-hadronic models to describe their broadband emission from radio to TeV energies, aiming to constrain key jet parameters such as magnetic field strength, emission region size, and particle energy distributions. Particular attention is given to evaluating their potential as sources of UHECRs and high-energy neutrinos. Our results shed light on the complex interplay between particle acceleration mechanisms, radiative processes, and multi-messenger signatures in extreme astrophysical environments. Full article

Driven by the “dual carbon” goal, virtual power plants (VPPs) are the core vehicle for integrating distributed energy resources, but the multiple uncertainties in wind power, electricity/heat load, and electricity price, coupled with the impact of carbon-trading cost, make it difficult for traditional scheduling methods to balance the robustness and economy of VPPs. Therefore, this paper proposes an oxy-fuel combustion capture (OCC)-VPP architecture, integrating an OCC unit to improve the energy efficiency of the system through the “electricity-oxygen-carbon” cycle. Ten typical scenarios are generated by Latin hypercube sampling and K-means clustering to describe the uncertainties of source and load probability distribution, combined with the polyhedral uncertainty set to delineate the boundary of source and load fluctuations, and the stepped carbon-trading mechanism is introduced to quantify the cost of carbon emission. Then, a three-stage stochastic–robust scheduling model is constructed. The simulation based on the arithmetic example of OCC-VPP in North China shows that (1) OCC-VPP significantly improves the economy through the synergy of electric–hydrogen production and methanation (52% of hydrogen is supplied with heat and 41% is methanated), and the cost of carbon sequestration increases with the prediction error, but the carbon benefit of stepped carbon trading is stabilized at the base price of 320 DKK/ton; (2) when the uncertainty is increased from 0 to 18, the total cost rises by 45%, and the cost of purchased gas increases by the largest amount, and the cost of energy abandonment increases only by 299.6 DKK, which highlights the smoothing effect of energy storage; (3) the proposed model improves the solution speed by 70% compared with stochastic optimization, and reduces cost by 4.0% compared with robust optimization, which balances economy and robustness efficiently. Full article

Acoustic Emission tomography (AET) has the potential to visualize damage in existing structures, contributing to structural health monitoring. Further, AET requires only the arrival times of elastic waves at sensors to identify velocity distributions, as source localization based on ray-tracing is integrated into its algorithm. Thus, AET offers the advantage of easy acquisition of measurement data. However, accurate source localization requires a large number of elastic wave source candidate points, and increasing these candidates significantly raises the computational resource demand. Lagrange Interpolation has the potential to reduce the number of candidate points, optimizing computational resources, and this potential has been validated numerically. In this study, AET incorporating Lagrange Interpolation is applied to identify the velocity distribution in a defective concrete plate, validating its effectiveness using measured wave data. The validation results show that the defect location in the concrete plate is successfully identified using only 36 source candidates, compared to the 121 candidates required in conventional AET. Furthermore, when using 36 source candidates, the percentage error in applying Lagrange Interpolation is 8.4%, which is significantly more accurate than the 25% error observed in conventional AET. Therefore, it is confirmed that AET with Lagrange Interpolation has the potential to identify velocity distributions in existing structures using optimized resources, thereby contributing to the structural health monitoring of concrete infrastructure. Full article

This study advances the understanding of atmospheric microplastic (MPs) exposure across urban (US), suburban (SS), and rural (RS) areas of Madrid, Spain, for the first time. Air pollution from MPs remains an understudied issue with broad implications for environmental and human health. Recent evidence highlights the need for multipoint studies to accurately establish atmospheric exposure to MPs, especially during winter seasons in the city. To address this issue, this work conducted active sampling of ≤10 μm aerosol particles, following EN 12341:2014 standards, during the 2024–2025 winter season. A quantitative innovative method using UV-assisted optical microscopy was applied to assess daily MPs exposure. To trace the potential sources and transport pathways, air mass back trajectories were modelled using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) software. The results showed an average exposure (n = 4) of 80 ± 20; 55 ± 9 and 46 ± 20 MPs·m⁻³·day⁻¹ during the sampling period in US, SS, and RS, respectively; and an average exposure (n = 4) of 61 ± 11 MPs·m⁻³·day⁻¹ throughout the winter period between November and December 2024 and January and February 2025. The polymers detected as constituents of MPs were polystyrene, polyethylene, polymethyl methacrylate, and polyethylene terephthalate, achieving a correct identification ratio of 100% for the detected microplastic particles. The HYSPLIT results showed diffuse sources of MPs, especially local, regional, and oceanic sources, in the US. In contrast, microplastic contributions in SS and RS areas originated from local or regional sources, highlighting the need for advanced studies to identify the sources of emissions and transport routes that converge in the occurrence of microplastics in the areas studied. These results demonstrate the atmospheric exposure to microplastics in the city, justifying the need for specialized studies to define the health impacts associated with the inhalation of these emerging pollutants. The findings of this research provide clear evidence of exposure to atmospheric microplastics in urban, suburban, and rural environments in Madrid, suggesting the need for further specialized research to rigorously assess the potential risks to human health associated with microplastic inhalation by the city’s population. Full article

Particulate matter (PM), a complex mixture of solid particles and liquid droplets, originates from both natural sources, such as sand, pollen, and marine salts, and anthropogenic activities, including vehicle emissions and industrial processes. While PM itself is not inherently toxic in all its forms, it often acts as a carrier of xenobiotic toxicants, such as heavy metals and organic pollutants, which adhere to its surface. This combination can result in synergistic toxic effects, significantly enhancing the potential harm to biological systems. Due to its small size and composition, PM can penetrate deep into the respiratory tract, acting as a physical “shuttle” that facilitates the distribution and bioavailability of toxic substances to distant organs. The omnipresence of PM in the environment leads to unavoidable and constant exposure, contributing to increased morbidity and mortality rates, particularly among vulnerable populations like the elderly, children, and individuals with pre-existing health conditions. This exposure also imposes a substantial financial burden on healthcare systems, as treating PM-related illnesses requires significant medical resources and leads to higher healthcare costs. Addressing these challenges necessitates effective mitigation strategies, including reducing PM exposure, improving air quality, and exploring novel approaches such as AI-based exposure prediction and nutritional interventions to protect public health and minimize the adverse effects of PM pollution. Full article