Search Results (251)

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. As future controlling structures tend to become autonomized in building heating layouts, encouraging distributed heating services, the research scope calls for creating lightweight building energy system modeling as well monitoring and controlling methods. Following this notion, the proposed methodology turns a programmable controller into a smart thermostat that utilizes surrogate modeling formed by the ALAMO approach and is applied in a 4-m-by-4-m-by-2.85-m environmental chamber setup heated by a heat pump. The results indicate that the smart thermostat trained on the indoor environmental conditions of the chamber for a one-week period attained a predictive RMSE of 0.082–0.116 °C. Consequently, it preplans the heating hours and applies preheating controlling strategies in real time effectively, using only the computational power of a conventional controller, essentially managing to attain at least 97% thermal comfort on the test days. Finally, the methodology has the potential to meet the requirements of future building energy systems featured in urban-scale RES-based district heating networks. Full article

District heating (DH) systems offer a sustainable solution to local energy needs by improving energy efficiency, reducing emissions, and fostering economic development. Despite their growing technological relevance, DH systems remain underexplored in the economics, business, and management literature. This study addresses this gap by conducting a bibliometric analysis of DH research at the intersection of these fields, using data extracted from the Web of Science. We identify major theoretical foundations, including the resource-based view, stakeholder theory, and institutional economics, and explore key themes such as economic viability, business model innovation, regulatory frameworks, and sustainability strategies. By framing DH systems within broader economic and managerial discourses, our findings highlight the interdisciplinary nature of DH research and suggest critical avenues for future investigation, including the role of emerging technologies, consumer behavior, and policy design, and contribute to low-carbon, sustainable development. Full article

This study focuses on the issue of visual sustainability of colors in commercial historical districts, taking the historical area of Xueshi Street–Wuzoufang Street in Suzhou, China as a case study. It explores how to balance modern commercial development with the protection of historical culture. Due to the impact of commercialization and the introduction of various immature protection policies, historical districts often face the dilemma of coexisting “color conflict” and “color poverty”. Traditional color protection methods are either overly subjective or excessively quantitative, making it difficult to balance scientific rigor and adaptability. Therefore, this study provides a detailed literature review, compares and selects current quantitative color research methods, and proposes a comprehensive color analysis framework based on ViT (Vision Transformer), the CIEDE2000 color difference model, and K-means clustering (V-C-K framework). Using this framework, we conducted an in-depth analysis of the color-harmony situation in the studied area, aiming to accurately identify color issues in the district and provide optimization strategies. The experimental results show that the commercial colors of the Xueshi Street–Wuzoufang Street historical district exhibit a clear phenomenon of polarization: some areas have colors that are overly bright, leading to visual conflict, while others have colors that are too dull, lacking vitality and energy; furthermore, some areas display a mix of both conditions. Based on this situation, we then compared the extracted negative colors to the prohibited colors in the mainstream Munsell color system’s urban-color management guidelines. We found that colors with “high lightness and high saturation”, which are strictly limited by traditional color criteria, are not necessarily disharmonious, while “low lightness and low saturation” colors that are not restricted may not guarantee harmony either and could exacerbate the area’s “dilapidated feeling”. In other words, traditional color-protection standards often emphasize the safety of “low saturation and low lightness” colors unilaterally, ignoring that they can also cause dullness and discordance in certain environments. Under the $\Delta E$ (color difference value) threshold framework, color recognition is relatively more sensitive, balancing the inclusivity of “vibrant” colors and the caution against “dull” colors. Based on the above experimental results, this study proposes the following recommendations: (1) use the $\Delta E00$ threshold to control the commercial colors in the district, ensuring that the colors align with the historical atmosphere while possessing commercial vitality; (2) in protection practices, comprehensively utilize the ViT, CIEDE2000, and K-means quantitative methods (i.e., the V-C-K framework) to reduce subjective errors; (3) based on the above quantitative framework, while referencing the reasonable parts of existing protection guidelines, combine cooperative collaboration, cultural group color preference surveys, policy incentives, and continuous monitoring and feedback to construct an operable plan for the entire “recognition–analysis–control” process. Full article

Heavy metal pollution refers to the presence of excessive levels of heavy metal elements in soil beyond their natural background concentrations, posing serious threats to human health and ecological systems. Several factors are involved in the contamination disparity in agriculture soils from various terrains, demanding extra care. An examination of the topographical HM dispersions in farmland soils from the Licheng District was conducted to reveal spatial changes in pollution levels and sources and to establish an empirical framework to develop targeted remediation strategies and promote sustainable land management practices. Cd and As had over-standard rates of more than 50% in the low-lying area, whereas the HMs in the high-lying area had over-standard rates of more than 50%. Also, the rates of HMs in high terrain were higher than in low terrain. Using the single-factor pollution index, only low-lying Cu, Ni, Pb, and Hg contamination levels were clean in low-lying and high-lying areas. The overall decline in HM pollution occurred from high to low terrain, triggered by soil physicochemical properties and human interventions. Meanwhile, strong anthropogenic influence fell in high terrain for pollution. Nevertheless, low levels of HM-integrated contamination prevailed in both topographies. Natural and anthropogenic processes gave rise to environmental pollution, such as soil formation, fertilization, metal smelting, and traffic emissions. Overall, the district held a low risk for HMs. The results highlight that strong anthropogenic interventions resulted in increased HM contamination, in addition to natural processes. It is possible to further reduce HM pollution and risk by promoting scientific agricultural techniques, new energy vehicles, and cleaner production. Full article

The current changing climate requires the development of water–energy–food (WEF) nexus-oriented systems capable of mainstreaming climate-smart innovations into resource management. This study demonstrates the cross-sectoral impacts of climate change on interlinked sectors of water, energy, and food in Narok County, Kenya, and Vhembe District, South Africa. This study used projected hydroclimatic extremes across past, present, and future scenarios to examine potential effects on the availability and accessibility of these essential resources. The projected temperature and rainfall are based on nine dynamically downscaled Coupled Model Intercomparison Project Phase 5 (CMIP 5) of the Global Climate Models (GCMs). The model outputs were derived from two IPCC “Representative Concentration Pathways (RCPs)’’, the RCP 4.5 “moderate scenario”, and RCP 8.5 “business as usual scenario”, also defined as the addition of 4.5 W/m² and 8.5 W/m² radiative forcing in the atmosphere, respectively, by the year 2100. For the climate change projections, outputs from the historical period (1976–2005) and projected time intervals spanning the near future, defined as the period starting from 2036 to 2065, and the far future, spanning from 2066 to 2095, were considered. An ensemble model to increase the skill, reliability, and consistency of output was formulated from the nine models. The statistical bias correction based on quantile mapping using seven ground-based observation data from the South African Weather Services (SAWS) for Limpopo province and nine ground-based observation data acquired from the Trans-African Hydro-Meteorological Observatory (TAHMO) for Narok were used to correct the systematic biases. Results indicate downscaled climate change scenarios and integrate a modelling framework designed to depict the perceptions of future climate change impacts on communities based on questionnaires and first-hand accounts. Furthermore, the analysis points to concerted efforts of multi-stakeholder engagement, the access and use of technology, understanding the changing business environment, integrated government and private sector partnerships, and the co-development of community resilience options, including climate change adaptation and mitigation in the changing climate. The conceptual climate and WEF resource modelling framework confirmed that future climate change will have noticeable interlinked impacts on WEF resources that will impact the livelihoods of vulnerable communities. Building the resilience of communities can be achieved through transformative WEF nexus solutions that are inclusive, sustainable, equitable, and balance adaptation and mitigation goals to ensure a just and sustainable future for all. Full article

Nature-based solutions (NBSs) offer a promising framework for addressing urban environmental challenges while also enhancing social and economic resilience. As cities seek to achieve carbon neutrality, the integration of NBSs with renewable energy sources (RESs) presents both an opportunity and a challenge, requiring an interdisciplinary approach and an innovative planning strategy. This study aims to explore potential ways of achieving synergies between NBSs and RESs to contribute to urban resilience and climate neutrality. Focusing on the railway station district in western Thessaloniki (Greece), this research is situated within the ReGenWest project, part of the EU Cities Mission. This study develops a comprehensive, well-structured framework for integrating NBSs and RESs, drawing on principles of urban planning and energy systems to address the area’s specific spatial and ecological characteristics. Using the diverse typologies of open spaces in the district as a foundation, this research analyzes the potential for combining NBSs with RESs, such as green roofs with photovoltaic panels, solar-powered lighting, and solar parking shaders, while assessing the resulting impacts on ecosystem services. The findings reveal consistent benefits for cultural and regulatory services across all interventions, with provisioning and supporting services varying according to the specific solution applied. In addition, this study identifies larger-scale opportunities for integration, including the incorporation of NBSs and RESs into green and blue corridors and metropolitan mobility infrastructures and the development of virtual power plants to enable smart, decentralized energy management. A critical component of the proposed strategy is the implementation of an environmental monitoring system that combines hardware installation, real-time data collection and visualization, and citizen participation. Aligning NBS–RES integration with Positive Energy Districts is another aspect that is stressed in this paper, as achieving carbon neutrality demands broader systemic transformations. This approach supports iterative, adaptive planning processes that enhance the efficiency and responsiveness of NBS–RES integration in urban regeneration efforts. Full article

This study investigates improving district heating (DH) systems by analyzing the effects of low-temperature operation on network efficiency, heat losses, and indoor temperature stability. A mathematical model is developed to simulate building heat performance under different supply temperatures, substation connection types, and envelope materials. The methodology involves detailed hourly heat load simulations and optimization techniques to assess the impact of temperature flexibility and heat accumulation within buildings. The results reveal that a 10 °C reduction in supply temperature leads to a heat loss decrease of up to 20%, significantly improving system efficiency. Moreover, buildings with higher thermal inertia and indirect substation connections exhibit better resilience to short-term temperature fluctuations, ensuring more stable indoor conditions. The analysis also demonstrates that optimizing temperature control can reduce operational costs by 19%, primarily by minimizing excessive heat supply and utilizing stored thermal energy effectively. Despite slight temperature fluctuations in extreme conditions, the system maintains indoor comfort levels within acceptable limits. This study concludes that transitioning to a lower-temperature DH system is feasible without compromising reliability, provided heat accumulation effects and supply flexibility are carefully managed. These findings offer a replicable approach for improving DH efficiency in networks with diverse building configurations. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

With the challenge of increasing global carbon emissions and climate change, the importance of solar energy as a clean energy source is becoming more pronounced. Accurate solar radiation prediction is crucial for planning and operating solar energy systems. However, the accurate measurement of solar radiation faces challenges due to the high cost of instruments, strict maintenance, and technical complexity. Therefore, this paper proposes a deep learning approach that integrates the Sparrow Search Algorithm (SSA), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks for solar radiation forecasting. The study utilizes solar radiation data from Songjiang District, Shanghai, China, from 2019 to 2020 for empirical analysis. Initially, a correlation analysis was conducted to identify the main factors affecting the intensity of solar radiation, including temperature, clear-sky GHI, solar zenith angle, and relative humidity. Subsequently, the forecasting effectiveness of the model was compared on datasets of 10 min, 30 min, and 60 min, revealing that the model performed best on the 60 min dataset, with a determination coefficient (R²) of 0.96221, root mean square error (RMSE) of 65.9691, and mean absolute error (MAE) of 37.9306. Moreover, comparative experimental results show that the SSA-CNN-LSTM model outperforms traditional LSTM, BiLSTM, and CNN-LSTM models in forecasting accuracy, confirming the effectiveness of SSA in parameter optimization. Thus, the SSA-CNN-LSTM model provides a new and efficient tool for solar radiation forecasting, which is of significant importance for the design and management of solar energy systems. Full article

Detailed hydraulic modeling of a water distribution network (WDN) in an urban area is implemented therein, based on data from geoinformatic tools (GIS), to investigate and analyze the network’s hydraulic response to different scenarios of operation. A detailed mapping of the water meters of the consumers in the urban district is therefore conducted in the frame of a District Metered Area (DMA) zoning. Different consumptions according to water meters and patterns of daily water demand, resulting from both theoretical and measured data from a limited number of smart meters, are used in the hydraulic simulations. The analysis conducted assists common engineering practice to identify critical locations for constructing new hydraulic infrastructure, resulting in the restructuring and reorganization of the DMA, assisting to face existing and common problems of WDNs within the general framework of DMA design and efficient water management. A case study on the WDN of Efkarpia, located in the city of Thessaloniki, Greece, satisfying the principal design criteria of DMAs, is presented in this work, under both normal and emergency conditions. Hydraulic analysis is performed based on different scenarios, mainly consisting of different consumptions according to water meters and different demand patterns, all resulting in high pressures in the southern part of the DMA. Hydraulic simulations are then performed considering two basic operating scenarios, namely the operation of the old DMA of Efkarpia and a new DMA, which is reduced in size. The two scenarios are compared in terms of estimated pressures in the studied area, as well as in terms of energy consumption in the upstream pumping station. The comparisons reveal that the new DMA outperforms the old one, with a large increase in the pressure at nodes where low pressures were assessed in the old DMA, a reduction in daily pressure variation up to 45%, and quite significant energy savings assessed around 21.6%. Full article

This review paper provides a comprehensive examination of current strategies and technical considerations for reducing return temperatures in district heating (DH) systems, aiming to enhance the utilization of available thermal energy. Return temperature, a parameter indirectly influenced by various system-level factors, cannot be adjusted directly but requires careful management throughout the design, commissioning, operation, and control phases. This paper explores several key factors affecting return temperature, including DH network, heat storage, and control strategies as well as the return temperature effect on the heat source. This paper also considers the influence of non-technical aspects, such as pricing strategies and maintenance practices, on system performance. The discussion extends to the complex interplay between low return temperatures and temperature differences, and between operational temperature schemes and economic considerations. Concluding remarks emphasize the importance of adopting a holistic approach that integrates technical, operational, and economic factors to improve DH system efficiency. This review highlights the need for comprehensive system-level optimization, effective management of system components, and consideration of unique heat production characteristics. By addressing these aspects, this study provides a framework for advancing DH system performance through optimized return temperature management. Full article

Gold production poses significant environmental challenges, including resource depletion, CO₂ emissions, and toxic chemical usage. Similarly, improper WEEE management harms the environment. However, WEEE contains valuable metals such as gold, making it central to circular economy (CE) strategies and an alternative to mining. This study assesses the climate impact of pyrometallurgical gold recovery from WEEE using life cycle assessment (LCA). The study found that the carbon footprint of producing gold pyrometallurgically from WEEE is 2000 kg CO₂eq/kg. These emissions are largely tied to the carbon content of waste, meaning that low-carbon energy sources have a limited impact. This creates a conflict between CE goals and CO₂ reduction. Scenario analysis shows that utilizing waste heat for district heating significantly lowers emissions. The other strategies used to improve the environmental performance include separating the plastic fraction before smelting, using biogenic plastic in WEEE, and carbon capture and storage (CCS). Transport accounts for just 10% of the total carbon footprint. Future regulations must address multiple factors—EEE production, waste management, smelter infrastructure, global socioeconomic dynamics, and consumer behavior—as higher recycling rates alone will not solve WEEE challenges. Full article

The Haidian District was, historically, rich in water resources. However, with urban development, the groundwater levels have declined, and most rivers have lost their ecological baseflows. To restore the aquatic ecosystems, the district has implemented a cyclic water network and advanced water replenishment projects. Nonetheless, the existing replenishment strategies face challenges, such as an insufficient scientific basis, lack of data, and high energy consumption. There is an urgent need to develop a scientifically robust ecological water replenishment system and optimize pump station scheduling to enhance water resource management efficiency. This study addresses the ecological water replenishment needs of seasonal rivers by integrating the Literature method, Rainfall-Runoff method, and R2cross method to develop a comprehensive approach for calculating the ecological flow and water depth. The proposed method simultaneously meets the ecological functionality and landscape requirements of seasonal rivers. Additionally, the SWMM model is employed to design intelligent pump station scheduling rules, optimizing the replenishment efficiency and energy consumption. Through field measurements and data collection, the ecological water demands of the river channels in different areas are assessed. Using a hydrodynamic model, the dynamic variations in the ecological flow and water depth are simulated. For the Cuihu, Daoxianghu, and Yongfeng areas, this study reveals that the current replenishment volume is insufficient to meet the landscape and ecological needs of the rivers. Most rivers require a 20–30% increase in water levels, with the Dazhai qu needing a substantial rise from 0.17 m to 0.3 m, representing an increase of 76%. Additionally, the results demonstrate that intelligent pump station scheduling can significantly reduce operating costs and energy consumption by dynamically adjusting the replenishment timing and flow rates. This approach optimizes the intervals between equipment activation and deactivation, thereby balancing ecological and energy-saving goals. This research not only provides technical support for the precise calculation of ecological replenishment volumes and the intelligent management of pump stations, but also offers scientific references for water resource management in similar regions. The findings will enhance the ecological functions and landscape quality of the rivers in the Haidian District while promoting refined and intelligent regional water resource management. Moreover, this study presents innovative solutions and theoretical foundations for water resource regulation under the backdrop of climate change. Full article

Transitioning to Positive Energy Districts (PEDs) is essential for achieving carbon neutrality in urban areas by 2050. This study presents a multi-objective optimization framework that balances energy, environmental, and economic performance, addressing the diverse priorities of multiple stakeholders. The framework enhances PED design by systematically evaluating technical solutions, including renewable-based electrification, demand-side management (DSM), energy storage, and retrofitting. The framework is applied to the Usquare district in Brussels, Belgium, as a case study. The results indicate that expanding photovoltaic (PV) capacity is crucial for achieving PED targets, with renewable-based electrification potentially reducing carbon emissions by up to 79%. The incorporation of demand-side management (DSM) and battery storage improves system flexibility, reduces grid dependency, and enhances cost-effectiveness. Although slightly more costly, retrofitting existing buildings provides the most balanced approach, offering the lowest CO2 emissions and the highest self-consumption ratio. This study presents a comprehensive decision-making support framework for optimizing PED design and operation, offering practical guidance for urban energy planning and contributing to global efforts toward carbon neutrality. Full article