Search Results (6,259)

Superhydrophobic magnetic nanomaterials (SHMNMs) are emerging as multifunctional platforms for efficient oil–water separation due to their combination of extreme water repellency, strong oil affinity, and external magnetic responsiveness. This review presents a comprehensive analysis of recent advances in the design, synthesis, and environmental application of SHMNMs. The theoretical foundations of superhydrophobicity and the physicochemical behavior of magnetic nanoparticles are first outlined, followed by discussion of their synergistic integration. Key fabrication techniques—such as sol–gel synthesis, electrospinning, dip-coating, laser-assisted processing, and the use of biomass-derived precursors—are critically assessed in terms of their ability to tailor surface morphology, chemical functionality, and long-term durability. The review further explores the mechanisms of oil adsorption, magnetic separation, and material reusability under realistic environmental conditions. Special attention is paid to the scalability, mechanical resilience, and environmental compatibility of SHMNMs in the context of water treatment technologies. Current limitations, including reduced efficiency in harsh media, potential environmental risks, and challenges in material regeneration, are discussed. This work provides a structured overview that could support the rational development of next-generation superhydrophobic materials tailored for sustainable and high-performance separation of oil and organic pollutants from water. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

This study examines the adverse environmental impacts of solar photovoltaic parks located in established protected areas, aiming to determine the level of landscape fragmentation through the calculation of relevant landscape metrics. For this purpose, a case study was carried out in a Mediterranean Natura 2000 Special Protection Area (SPA), and landscape metrics were calculated using Geographic Information System spatial analysis tools. The analysis of metrics showed that the installation of renewable energy parks within the designated protected area negatively affect landscape fragmentation and the absence of carefully defined and evidence-based mitigation measures. The land cover categories that are significantly affected are those considered critical habitats of bird species that have been designated as SPAs. The results of this study highlight the need to integrate, in the National Renewable Energy Spatial Plans, specific biodiversity objectives, such as conservation objectives and the suspension of the installation of photovoltaic parks in certain areas that are important for conservation of biodiversity, in order to ensure the overall sustainability of renewable energy production. Full article

Electrocoagulation (EC) processes have emerged as an efficient solution for different inorganic and organic effluents. The main characteristics of this versatile process are its ease of operation and low sludge production. The literature indicates that EC can be successfully used as a single process or a step within a combined treatment system. If used in a combined system, this process could be employed as a pre-, a post-, or middle treatment step. Additionally, the EC process has been used in both continuous and batch modes. In most studies, EC has achieved significant improvements in the treated water quality and relatively low total energy consumption. This review presents a comprehensive evaluation and analysis of standalone and combined continuous EC processes. The influence of key operational parameters on continuous EC performance is thoroughly discussed. Furthermore, recent advancements in reactor design, modeling, and process optimization are addressed. The benefits of integrating other treatment processes with the EC process, such as advanced oxidation, membranes, chemical coagulation, and adsorption, are also evaluated. The performance of most standalone and combined EC processes used for organic pollutant treatment and published in the last 25 years is critically analyzed. This review is expected to give researchers many insights to improve their treatment scenario with recent and efficient environmental experiences, sustainability, and circular economy. The clearly presented information is expected to guide researchers in selecting efficient, cost-effective, and time-saving treatment alternatives. The findings ensure the considerable potential of continuous EC treatment processes for organic pollutants. However, more research is warranted to enhance process design, operational efficiency, scale-up, and economic viability. Full article

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

Continuous progress in the sector of additive manufacturing has drastically aided the design and fabrication of urban furniture, offering high levels of customization and adaptability. This work looks into the potential of 3D printing to transform urban public spaces by allowing for the creation of functional, aesthetically pleasing, and user-centered furniture solutions. Through additive manufacturing processes, urban furniture can be tailored to meet the unique needs of diverse communities, allowing for the extended usage of sustainable materials, modular designs, and smart technologies. The flexibility of 3D printing also promotes the fabrication of complex, intricate designs that would be difficult or cost-prohibitive using traditional methods. Additionally, 3D-printed furniture can be optimized for specific environmental conditions, providing solutions that enhance accessibility, improve comfort, and promote inclusivity. The various advantages of 3D-printed urban furniture are examined, including reduced material waste and the ability to rapidly prototype and iterate designs alongside the potential for on-demand, local production. By embedding sensors and IoT devices, 3D-printed furniture can also contribute to the development of smart cities, providing real-time data for urban management and improving the overall user experience. As cities continue to encourage and adopt sustainable and innovative solutions, 3D printing is believed to play a crucial role in future urban infrastructure planning. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

Biomass and other solid wastes create potential environmental and health hazards in our modern society. Conversion of the wastes into energy presents a promising avenue for sustainable energy generation. However, the feasibility of the approach is limited by the challenges in material handling because of the special properties of the materials. Despite their critical importance, the complexities of material handling often evade scrutiny until operational implementation. This paper highlights the challenges inherent in standard solid material-handling processes, preceded by a concise review of common solid waste typologies and their physical properties, particularly those related to biomass and biowastes. It delves into the complexities of material flow, storage, compaction, agglomeration, separation, transport, and hazard management. Specialised characterisation techniques essential for informed process design are also discussed to mitigate operational risks. In conclusion, this paper emphasises the necessity of a tailored framework before the establishment of any further conversion processes. Given the heterogeneous nature of biomaterials, material-handling equipment must demonstrate adaptability to accommodate the substantial variability in material properties in large-scale production. This approach aims to enhance feasibility and efficacy of any energy conversion initiatives by using biomass or other solid wastes, thereby advancing sustainable resource utilisation and environmental stewardship. Full article

This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO₂ emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO₂ emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO₂ equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R² ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

Excessive nitrogen (N) fertilizer usage in agriculture has prompted the exploration of sustainable strategies to enhance nitrogen use efficiency (NUE) while maintaining crop yield and quality. Processed tomatoes (Solanum lycopersicum L.) were grown for two years (2023 and 2024) following a two-way factorial randomized complete block (RCBD) design, considering three biostimulants and three N regimes as two factors, to assess their morphophysiological, biochemical, anatomical and yield performances. Nitrogen application significantly influenced biomass accumulation, the leaf area index (LAI), nitrogen uptake and yield with notable comparable values between reduced and optimal nitrogen dose, indicating improved nitrogen use efficiency. Biostimulants showed limited effects alone but enhanced plant performance under reduced nitrogen conditions, particularly improving chlorophyll content, crop growth, N uptake, yield and anatomical adaptations. Moreover, compared to 2024, biostimulant application enhanced tomato growth more evidently in 2023 due to environmental variations, likely due to the occurrence of stress conditions. Importantly, biostimulants, together with N regimes, i.e., optimal and reduced doses, showed improved anatomical traits, especially regarding leaf thickness and thickness between the two epidermises, indicating adaptive responses that may support sustained productivity under N-limited conditions. Among the biostimulants used, the processed tomatoes responded better to protein hydrolysate and endophytic N-fixing bacteria than to seaweed extract. These findings suggest that although biostimulants alone were not affected, integrating them with reduced N fertilization provides a viable strategy for optimizing tomato production, conserving resources and minimizing the environmental impact without compromising yield or quality. Full article

This study contributes to scientific theory by analyzing the models proposed within the framework of circular agriculture to determine how the three dimensions of sustainability—environmental, economic, and social—are integrated into their implementation. A systematic review was conducted on articles published between 2016 and 2025, indexed in the Scopus and Web of Science databases, as well as the relevant grey literature. The methodology employed an extensive content analysis designed to minimize bias, applying filters related to specific knowledge areas to delimitate the search scope and enhance the precision of the research. The findings reveal that the research on circular agriculture models is predominantly grounded in the principles of the circular economy and its associated indicators. Moreover, these models tend to focus on environmental metrics, often neglecting a comprehensive exploration of the social and economic dimensions of sustainable development. It can be concluded that a significant gap persists in the literature regarding the circularity of agriculture and its socio-economic impacts and the role of regulatory frameworks, aspects that future research must address in order to achieve sustainability in circular agriculture. Full article

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

This review provides a comprehensive synthesis of phosphorus (P) dynamics relevant to salmonid aquaculture, with a particular focus on Atlantic salmon. It explores the global P cycle, the chemical forms of P in aquatic systems, and the implications of P sourcing, processing, and availability in aquafeeds. The review distinguishes between digestibility and availability of P, summarizes requirement studies, and evaluates the contribution of marine, animal, vegetable, and inorganic sources to dietary P. It also examines how aquaculture system design, particularly recirculating aquaculture systems (RASs), influences P accumulation and emissions. By integrating nutritional, physiological, and environmental perspectives, this review offers a uniquely holistic view of P efficiency and sustainability in salmonid aquaculture. Full article

This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, and spatial practice. The study adopts a conceptual, problem-oriented approach, eschewing the conventional systematic aggregation of existing data. The objective of this study is to identify, interpret and categorise the key issues that are shaping the evolving discourse on timber architecture. The analysis is based on peer-reviewed literature published between 2020 and 2025, sourced from the Scopus and Web of Science Core Collection databases. Fifteen thematic challenges have been identified and classified according to their recognition level in academic and design contexts. The subjects under discussion include well-established topics, such as life cycle assessment and carbon storage, as well as less commonly explored areas, such as symbolic durability, social acceptance, traceability, and the upcycling of low-grade wood. The review under consideration places significant emphasis on the importance of integrating technical, cultural, and perceptual dimensions when evaluating timber architecture. The article proposes an interpretive framework combining design thinking and transdisciplinary insights. This framework aims to bridge disciplinary gaps and provide a coherent structure for understanding the complexity of timber-related challenges. The framework under discussion here encourages a broader understanding of wood as not only a sustainable building material but also a vehicle for systemic transformation in architectural culture and practice. The study’s insights may support designers, educators, and policymakers in identifying strategic priorities for the development of future-proof timber-based design practices. Full article