Search Results (290)

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

Aquaculture is a crucial food-producing sector that can supply more essential nutrients to nourish the growing human population. However, it faces challenges, including limited water quality and space competition. These constraints have led to the intensification of culture systems for more efficient resource use while maintaining or increasing production levels. However, intensification introduces stress risks to cultured organisms by, for instance, overcrowding, waste accumulation, and water quality deterioration, which can negatively affect the growth, health, and immunity of animals and cause diseases. Additionally, environmental changes due to climate and anthropogenic activities further intensify the environmental stress for aquaculture organisms, including crustaceans. Shrimp are one of the most widely cultured and consumed farmed crustacea. Relative to aquatic vertebrates such as fish, the physiology of crustaceans has simpler physiological structures, as they lack a spinal cord. Consequently, their stress response mechanisms follow a single pathway, resulting in less complex responses to stress exposure compared to those of fish. While stress is considered a primary factor influencing the growth, health, and immunity of shrimp, comprehensive research on crustacean stress responses remains limited. Understanding the stress response at the organismal and cellular levels is essential to identify sensitive and effective stress biomarkers which can inform the development of targeted intervention strategies to mitigate stress. This review provides a comprehensive overview of the physiological changes that occur in crustaceans under stress, including hormonal, metabolic, hematological, hydromineral, and phenotypic alterations. By synthesizing current knowledge, this article aims to bridge existing gaps and provide insights into the stress response mechanisms, paving the way for advancements in crustacean health management. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

The integration of water resources, water environment, and water ecology (hereinafter “three-water”) is essential not only for addressing the current water crisis but also for achieving sustainable development. Chaohu Lake is an important water resource and ecological barrier in the middle and lower reaches of the Yangtze River, undertaking such functions as agricultural irrigation, urban water supply, and flood control and storage. Studying the performance of “three-water” in the Chaohu Lake Basin will help to understand the pollution mechanism and governance dilemma in the lake basin. It also provides practical experience and policy references for the ecological protection and high-quality development of the Yangtze River Basin. We used the DPSIR-TOPSIS model to analyze the performance of the river–lake system in the Chaohu Lake Basin and employed an obstacle model to identify factors influencing “three-water.” The results indicated that overall governance and performance of the “three-water” in the Chaohu Lake Basin exhibited an upward trend from 2011 to 2022. Specifically, the obstacle degree of driving force decreased by 19.6%, suggesting that economic development enhanced governance efforts. Conversely, the obstacle degree of pressure increased by 34.4%, indicating continued environmental stress. The obstacle degree of state fluctuated, showing a decrease of 13.2% followed by an increase of 3.8%, demonstrating variability in the effectiveness of water resource, environmental, and ecological management. Additionally, the obstacle degree of impact declined by 12.8%, implying the reduced efficacy of governmental measures in later stages. Response barriers decreased by 5.8%. Variations in the obstacle degree of response reflected differences in response capacities. Spatially, counties and districts at the origins of major rivers and their lake outlets showed lower performance levels in “three-water” management compared to other regions in the basin. Notably, Wuwei City and Feidong County exhibited better governance performance, while Feixi County and Chaohu City showed lower performance levels. Despite significant progress in water resource management, environmental improvement, and ecological restoration, further policy support and targeted countermeasures remain necessary. Counties and districts should pursue coordinated development, leverage the radiative influence of high-performing areas, deepen regional collaboration, and optimize, governance strategies to promote sustainable development. Full article

Vertical farming systems offer an efficient solution for sustainable food production in urban areas. However, managing nitrate (NO₃⁻) levels remains a significant challenge for improving crop yield, quality, and safety. This study evaluated the effects of nitrate availability on growth performance, nutrient uptake, and water use efficiency in a vertical hydroponic system that intercropped lettuce (Lactuca sativa) with alfalfa (Medicago sativa). The experiment was conducted in a controlled vertical hydroponic system using Nutrient Film Technique (NFT) channels, with nitrogen levels set at 0, 33, 66, 100, and 133% of the standard concentration. The results indicated that the intercropping treatment with 66% nitrate (IC-N_66%) improved water use efficiency by 38% and slightly increased leaf area compared to the other intercropping treatments. However, the control group, which consisted of a monoculture with full nitrate supply, achieved the highest overall biomass. Ion concentrations, including nitrate, calcium, magnesium, and micronutrients, were moderately affected by the intercropping strategy and nitrate levels. These findings suggest that moderate nitrate input, combined with nitrogen-fixing legumes, can enhance resource efficiency in hydroponic systems without significantly compromising yield. These findings offer a promising framework for incorporating legumes into hydroponic systems, minimizing the need for synthetic inputs while maintaining yield. These results support the use of agroecological intensification strategies in highly efficient soilless systems. Full article

Land use composition, water level fluctuations (WLFs), and biogeographical factors are recognized as key drivers of phytoplankton dynamics in reservoir ecosystems. This two-year study presents the first assessment of the combined effects of catchment land use, WLFs, and geographical distance on phytoplankton biomass and community composition across twelve Mediterranean reservoirs in Cyprus, which serve primarily for drinking water supply and irrigation. The results show that higher phytoplankton biomass was recorded in reservoirs whose catchments had >30% coverage by developed land (urban and agricultural), suggesting that increased anthropogenic pressures may lead to nutrient enrichment and elevated productivity. However, despite elevated biomass, no consistent spatial patterns were observed in phytoplankton community composition. The geographical distance between reservoirs had only a minor effect on species distribution, implying that other factors—such as water residence time or hydrological variability—play a more prominent role in shaping community structure. Phytoplankton biomass maxima were most often recorded during periods of elevated water levels and were typically dominated by Chlorophyta, Dinoflagellata, Bacillariophyta, and Charophyta. The pronounced temporal variability in species composition across all reservoirs points to a highly dynamic system, where environmental fluctuations strongly influence community assembly. This study provides the first comprehensive data on phytoplankton in Cyprus reservoirs, highlighting the importance of land use and hydrological regulation for water quality management in similar settings. Importantly, this baseline dataset can support the implementation of the Water Framework Directive (WFD) by contributing to the definition of ecological status classes, establishing reference conditions, and guiding future monitoring and assessment efforts. Expanding such datasets through coordinated, basin-wide monitoring initiatives is essential to improve our understanding of phytoplankton dynamics and their role in ecosystem functioning under the pressures of climate change and intensified land use in this Mediterranean “hot spot”. Full article

The African catfish, Clarias gariepinus, is among the most farmed fish species in Uganda’s rapidly growing aquaculture sub-sector. The enhanced growth performance, increased survival, and resilience to environmental stressors have driven a rising demand among farmers for improved African catfish broodstock and seed. Until recently, most studies of this species have focused on nutrition, physiology, and culture systems, with little known about the genetics, broodstock, and hatchery management of the cultured C. gariepinus populations. This knowledge gap has led to inbreeding depression, resulting in poor seed quality and reduced performance of the broodstock. To enhance catfish aquaculture production, a survey was conducted across multiple catfish hatcheries and farms in Uganda. Using semi-structured questionnaires, the study assessed broodstock management practices, hatchery propagation methods, the suitability of various populations, demographics, challenges, and prospects of hatchery operators. Responses were coded, and descriptive statistics such as frequency, percentages, and means were calculated. Results indicate that there are farmers who continue to source their broodstocks from the natural water bodies in addition to acquiring them from fellow farmers. The estimated effective population size (Ne) for the majority of the respondents was 133.33 and 178.22, with an average breeding coefficient of 0.4% and 0.3%, respectively. There is also a continuous use of shooters (fast-growing cannibalistic fish) by the farmers who hatch and select them to be used as broodstocks later, under the assumption that they have superior genetic traits. The reported hatchability rate was above 70%, with an average survival rate of 60% from larvae to fry. The study identified diseases, inadequate water supply, and electricity as the primary challenges for catfish breeding. While Uganda’s African catfish aquaculture industry is expanding rapidly, certain hatchery practices pose significant risks to its sustainability if not properly addressed. Full article

Managed Aquifer Recharge (MAR) is increasingly being adopted across Europe to enhance water security in semiarid regions, with over 230 operational sites. The Akrotiri MAR system in Limassol, Cyprus, comprises 17 recharge ponds operating since 2016 to counteract saltwater intrusion. This study evaluates MAR effectiveness by analyzing spatial and temporal variations in water quality from 2016 to 2020. Parameters analyzed include nutrients, metals, pesticides, pharmaceuticals, fecal indicators, physicochemical characteristics, recharge and pumping volumes, and groundwater levels. The results show that soil aquifer treatment (SAT) generally improves groundwater quality but certain boreholes exhibited elevated nitrate (range 12.70–31 mg/L), electrical conductivity (range 936–10,420 μs/cm), and chloride concentrations (range 117–1631 mg/L), attributed to recharge water quality, seawater intrusion, and nearby agricultural activities. Tertiary treated wastewater used for recharge occasionally exceeds permissible limits, particularly in E. coli (up to 2420/100 mL), chloride (up to 385 mg/L), and nitrogen (up to 41 mg/L). Supplementing recharge with dam-supplied freshwater improves groundwater quality and raises water levels. These findings underline the importance of continuous monitoring and effective management, adopting sustainable farming practices, and the strict control of recharge water quality. The study offers valuable insights for optimizing MAR systems and supports integrating MAR into circular water management frameworks to mitigate pollution and seawater intrusion, enhancing long-term aquifer sustainability. Full article

The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data (pH, turbidity, electrical conductivity, temperature, and chlorine), perform anomaly detection across parameters, and conduct a comprehensive investigation of water quality incidents that correlate with detected anomalies in water supply systems. This study can contribute to the development of an early detection and response system related to water quality incidents in water supply systems. Future work will focus on enhancing the application of systems for early detection of water quality incidents by expanding the data, developing anomaly detection methods by applying machine learning techniques, and figuring out the correlations between anomalies and water quality incidents. Full article

This article is devoted to the determination of the average daily electric load and the average electric load during the hours of maximum load, taking into account the generalized coefficient A_i, using data on electricity consumption for apartment buildings and individual residential buildings in Chelyabinsk and the cities of Dushanbe and Khorog in the Republic of Tajikistan. The results of modeling the average daily electric load, taking into account the developed generalized coefficient A_i, showed that the specific power values for apartments in apartment buildings and in individual residential buildings in the city of Chelyabinsk and the cities of Dushanbe and Khorog of the Republic of Tajikistan were overestimated, taking into account the applicability in the Republic of Tajikistan of the same standard values of specific electric loads (SELs) for apartments in apartment buildings (ABs) as in the Russian Federation. According to the results of modeling using data on the average monthly electricity consumption for 226 apartments in ABs and for individual residential buildings in Chelyabinsk, and according to the proposed approach, the average daily electric load on days during the month varied in the range of 2–3.5 kW/sq and below, while that for the cities of Dushanbe and Khorog of the Republic of Tajikistan varied in the range of 2–5 kW/sq and below, which did not exceed the SEL given by RB 256.1325800.2016. However, because of the lack of other energy sources (gas supply and hot water supply) in the conditions of the Republic of Tajikistan, on the basis of the obtained maximum load time factor and the generalized coefficient ${\mathrm{A}}_{\mathrm{i}\left(\mathrm{E}\right)}$, the obtained values of actual capacity exceeded the maximum during peak hours by 1.2–2.5 times the SEL given by RB 256.1325800.2016. To increase the durability and serviceability of power supplies and enhance the effectiveness of forecasting, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its own regression model. The decrease in mean absolute error due to clustering was 0.52 MW (57%). The use of meteorological conditions allowed the forecast error to be reduced by 0.22 MW (27%). High accuracy in electrical consumption forecasting leads to increased quality of power system management in general, including under such key indicators as reliability and serviceability. Full article

Groundwater resources in the Kou sub-basin of southwestern Burkina Faso play a critical role in supporting domestic water supply, agriculture, and industry in and around Bobo-Dioulasso, the second-largest city in Burkina Faso. This study synthesizes over three decades of research on groundwater vulnerability, recharge mechanisms, hydrochemistry, and residence time across the region’s sedimentary aquifers. The Kou basin hosts a complex stratified system of confined and unconfined aquifers, where hydrochemical analyses reveal predominantly Ca–Mg–HCO₃ facies, alongside local nitrate (0–860 mg/L), iron (0–2 mg/L) and potassium (<6.5 mg/L–190 mg/L) contamination. Vulnerability assessments—using parametric (DRASTIC, GOD, APSU) and numerical (MODFLOW/MT3D) models—consistently indicate moderate to high vulnerability, especially in alluvial and urban/peri-urban areas. Isotopic results show a deep recharge for a residence time greater than 50 years with deep groundwater dating from 25,000 to 42,000 years. Isotopic data confirm a vertically stratified system, with deep aquifers holding fossil water and shallow units showing recent recharge. Recharge estimates vary significantly (0–354 mm/year) depending on methodology, reflecting uncertainties in climatic, geological, and anthropogenic parameters. This review highlights major methodological limitations, including inconsistent data quality, limited spatial coverage, and insufficient integration of socio-economic drivers. To ensure long-term sustainability, future work must prioritize high-resolution hydrogeological mapping, multi-method recharge modeling, dynamic vulnerability assessments, and strengthened groundwater governance. This synthesis provides a critical foundation for improving water resource management in one of Burkina Faso’s most strategic aquifer systems. Full article

With the rapid process of urbanization, water conflicts between different water use industries and areas are increasing. Therefore, China has implemented the three-cordons system of water resources management since 2012, when how to make more reasonable regulation of water resources became an urgent problem in most areas of China. In this study, taking the Yuanhe River Basin as an example, an integrated model for the simulation and regulation of water resources considering water quantity and quality from a river basin perspective was proposed, where the water supply was constrained by requirements of water resources management. First, the water resources system was conceptualized into a topologically hydraulic network in the form of point, line, and area elements, including 80 water use units and 79 water supply units. Then, taking the water quantity and quality as constraint conditions in the water supply for corresponding water use sectors, a management-oriented integrated model was established, which highlights the cordon control of the total water use and the pollution load limits of a basin. Finally, through a model simulation, the total water supply was controlled by regulating the water resources, while the pollutant loads into rivers depended on the discharge of water users. Based on the model, strategies for the utilization of water resources and achieving emission reductions of pollution loads were provided. The results of the proposed model in the Yuanhe River Basin showed that benchmarked against the total water demand of 1.705 billion m³, the water shortage was 212 million m³ with a rate of 13.5%, and the loads of COD (Chemical Oxygen Demand) and NH3-N (Ammonia Nitrogen) were 29,096.7 and 2587.3 tons, respectively. The model can provide support for integrated water resources regulation in other basins or regions through a simulation of the natural–social water resources systems, and help stakeholders and decision-makers establish and implement advantageous strategies for regional efficient utilization of water resources. Full article

Agrivoltaic offers a promising solution to integrate photovoltaic energy production with ongoing agricultural activities. This research investigates the impact of agrivoltaic on food security, using a transdisciplinary approach to study the responses of crop production in terms of biomass and food quality produced. Mainly chicory plants were grown in full sunlight (control plot) and shade plots generated by potential photovoltaic panels. Two water regimes (high and low water supply) were used to analyze variations in food security in both plots. The results showed that agrivoltaic systems effectively mitigate crop water stress caused by high temperatures and heat waves, improving food security by increasing biomass production and preserving food quality. While previous research has attributed the benefits of agrivoltaics primarily to improved soil moisture, this study demonstrates that the positive effects are primarily driven by differences in light intensity and air temperature between the shaded and control plots. The results have strong implications for water resource management, showing that agrivoltaics can reduce water use by approximately 50% compared to traditional agroecosystems without compromising food security. Agrivoltaics can address the challenges of water scarcity due to declining rainfall and reduce production costs associated with water use. Properly designed agrivoltaic systems offer a cleaner, more sustainable alternative to traditional agricultural practices, helping to adapt agriculture to climate change. Full article

The Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consuming and labor-intensive, making real-time and efficient water quality monitoring challenging. To address this issue, this study proposes a Raspberry Pi-based multi-sensor system for rapid water quality detection and improved monitoring efficiency. This system integrates multiple sensors to measure key water quality parameters, such as pH, total dissolved solids (TDSs), temperature, and turbidity, while recording data in real-time. The data were continuously collected over a period of five months (July to November 2024). The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. Among these models, Random Forest exhibited the best overall performance, achieving an accuracy of 98.10% and an F1 score of 98.99%. These results show that the dataset demonstrates high reliability in anomaly detection and classification tasks, accurately identifying deviations in water quality. This approach not only enhances the efficiency of water quality monitoring but also provides technological support for urban drinking water safety management. Full article

The water supply pipeline system is responsible for providing clean drinking water to residents, but pipeline leaks can lead to water resource wastage, increased operational costs, and safety hazards. To effectively detect the leakage level in the water supply pipelines and address the difficulty of accurately distinguishing fine-grained leakage levels using traditional methods, this paper proposes a fine-grained leakage identification method based on Convolutional Neural Networks (CNN) and the Selective State Space Model (Mamba). An experimental platform was built to simulate different leakage conditions, and multi-axis sensors were used to collect data, resulting in the creation of a high-quality dataset. The signals were converted into frequency-domain images using Short-Time Fourier Transform (STFT), and CNN was employed to extract image features. Mamba was integrated to capture the one-dimensional time dynamic characteristics of the leakage signal, and the CosFace loss function was introduced to increase the inter-class distance, thereby improving the fine-grained classification ability. Experimental results show that the proposed method achieves optimal performance across various evaluation metrics. Compared to SVM, BP neural networks, and CNN methods, the accuracy was improved by 17.9%, 15.9%, and 3.0%, respectively. Compared to Support Vector Machine (SVM), Backpropagation neural network (BP), attention mechanism with the LSTM network (LSTM-AM), CNN, and inverted transformers network (iTransformer) methods, the accuracy improved by 17.9%, 15.9%, 7.8%, 3.0%, and 2.3%, respectively. Additionally, the method enhanced intra-class consistency and increased inter-class differences, showing outstanding performance at different leakage levels, which could contribute to improved intelligent management for water pipeline leakage detection. Full article