Search Results (6,096)

The convergence of food insecurity, water scarcity, and environmental degradation has intensified the global search for sustainable agricultural models. Integrated Microalgal–Aquaponic Systems (IAMS) have emerged as a novel multi-trophic platform that unites aquaculture, hydroponics, and microalgal cultivation into a closed-loop framework for resource-efficient food production and water recovery. This critical review synthesizes empirical findings and engineering advancements published between 2008 and 2024, evaluating IAMS performance relative to traditional agriculture and recirculating aquaculture systems (RAS). Reported under controlled laboratory and pilot-scale conditions, IAMS have achieved nitrogen and phosphorus recovery efficiencies exceeding 95% while potentially reducing water consumption by up to 90% compared to conventional farming. The integration of microalgal photobioreactors enhances nutrient retention, may contribute to internal carbon capture, and enables the generation of diversified co-products, including biofertilizers and protein-rich aquafeeds. Nevertheless, significant barriers to commercial scalability persist, including the biological complexity of maintaining multi-trophic synchrony, high initial capital expenditure (CAPEX), and regulatory ambiguity regarding the safety of waste-derived algal biomass. Technical challenges such as photobioreactor upscaling, biofouling control, and energy optimization are critically discussed. Finally, the review evaluates the alignment of IAMS with UN Sustainable Development Goals 2, 6, and 13, and outlines future research priorities in techno-economic modeling, automation, and policy development to facilitate the transition of IAMS from pilot-scale innovations to viable industrial solutions. Full article

Subsea Christmas trees serve as key technical equipment for subsea oil and gas development, as they regulate the flow of oil and gas at subsea wellheads. Most deep-water subsea Christmas trees deployed in China depend on imports, resulting in high procurement costs. Post-operation, these systems are typically hoisted and recovered using drill pipes and steel wire ropes. However, the harsh and dynamic deep-sea environment complicates the prediction of the tree movement posture in seawater, making safe retrieval an urgent challenge in marine oil and gas resource exploitation. Focusing on 2000 m water depth subsea Christmas tree installation and retrieval, with a specific sea area in the South China Sea as the case study, this paper applies OrcaFlex software version 11.4 to analyze drill pipe stress during retrieval and investigate movement posture changes of the tree body across different stages. Meanwhile, targeting varied operational sea conditions and integrating orthogonal test analysis, this paper quantifies the influence of parameters (wave height, ocean current velocity, and retrieval speed) on the retrieval process. The findings provide theoretical guidance and technical support for China’s deep-water subsea Christmas tree installation and retrieval operations. Full article

Protecting groundwater against pollution from agricultural sources is a key aspect of sustainable management of soil and water resources. Implementation of sustainable strategies for agricultural production can be supported by modeling tools, which allow us to quantify the effects of different agricultural practices in the context of groundwater vulnerability to contamination. In this study we present a method to assess groundwater vulnerability to nitrate pollution based on a combination of the SWAT agro-hydrological model and the DRASTIC index method. SWAT modeling was applied to assess different scenarios of agricultural practices and identify solutions for sustainable management of soil and groundwater and reduction of nitrate pollution. The developed method was implemented for groundwater resources in a study area (Puck Bay region, southern Baltic coast), which represented a complex multi-aquifer system formed in Quaternary fluvioglacial deposits (sand and gravel) separated by moraine tills. In order to investigate the effects of different agricultural practices, 12 scenarios have been defined, which were grouped into four classes: crop type, fertilizer management, tillage, and grazing. An overlay index structure was applied, and ratings and weights to several factors were assigned. All analyses were processed using GIS tools, and the results are presented in the form of maps, which categorize groundwater vulnerability to nitrate pollution into five classes, ranging from very low to very high. The results reveal significant variability in groundwater vulnerability to nitrate pollution in the study area. Agricultural practices have a very strong influence on groundwater vulnerability by controlling both recharge rates and nitrogen losses from the soil profile. The most pronounced increases in vulnerability were associated with scenarios involving excessive fertilization and intensive grazing. Among crop types, potato cultivation appears to pose the greatest risk to groundwater quality. Full article

Edge detection is a key building block in real-time image-processing applications such as drone-based infrastructure inspection, autonomous navigation, and remote sensing. However, its computational cost remains a challenge for resource-constrained embedded systems. This work presents a hardware-accelerated edge detection architecture based on a homogeneous 2D-mesh Network-on-Chip (NoC) integrating systolic arrays to efficiently perform the convolution operations required by the Sobel filter. The proposed architecture was first developed and validated as a 3 × 3 mesh prototype on FPGA (Xilinx Zynq-7000, Zynq-7010, XC7Z010-CLG400A, Zybo board, utilizing 26,112 LUTs, 24,851 flip-flops, and 162 DSP blocks), achieving a throughput of 8.8 Gb/s with a power consumption of 0.79 W at 100 MHz. Building upon this validated prototype, a reduced 2 × 2 node cluster with 14-bit word width was subsequently synthesized at the physical level as a proof-of-concept using the OpenLane RTL-to-GDSII open-source flow targeting the SkyWater 130 nm PDK (sky130A). Post-layout analysis confirms the manufacturability of the design, with a total power consumption of 378 mW and compliance with timing constraints, demonstrating the feasibility of mapping the proposed architecture to silicon and its suitability for drone-based infrastructure monitoring applications. Full article

Effective water resource management in agriculture is a pivotal challenge for environmental sustainability and the economic viability of crop production. The present study, conducted at the CREA research station (Monterotondo, Italy), analyzed a precision irrigation strategy based on an automated drip irrigation system with soil moisture sensors, applied to a 15-year-old high-density poplar plantation for energy production. Five treatments were compared: a non-irrigated control (T0) and four irrigation levels based on soil moisture thresholds (T1 ≤ 20%, T2 ≤ 30%, T3 ≤ 40%, T4 ≤ 50%). The aim of this study was to assess the economic feasibility of irrigated poplar plantations, considering expected increases in biomass production and related environmental impacts. The economic evaluation used the Life Cycle Costing (LCC) method, while the environmental assessment applied Life Cycle Assessment (LCA) with the AWARE indicator to quantify the water scarcity footprint. Finally, an integrated assessment using the TOPSIS multi-criteria method was performed to identify the most sustainable treatment. Over the 15-year period, T0 (no irrigation) was the preferred option (Preferred Index Pi = 1.000), followed by T3 (Pi = 0.637) and T4 (Pi = 0.586), considering equal weighting of economic and environmental impacts. Conversely, the low irrigation treatment (T1) was the least sustainable (Pi = 0.379), followed by T2 (Pi = 0.486). While irrigation appears unviable if environmental impacts are prioritized, higher biomass value can improve the economic sustainability of treatments with greater water use (T3 and T4) when economic factors dominate. Full article

Accurate precipitation forecasting is paramount for water security and disaster mitigation, yet it remains formidable due to atmospheric stochasticity and the inherent class imbalance in rainfall datasets. This study proposes an integrated “architecture-feature-augmentation” framework to circumvent these limitations. Through a systematic evaluation of CNN-LSTM and Transformer architectures, we delineate distinct performance profiles: The Transformer model, when coupled with feature engineering and physics-informed augmentation, yields a peak F1-score of 0.1429, marking the optimal configuration for harmonizing precision and recall. Conversely, CNN-LSTM demonstrates superior robustness in extreme event detection, consistently maintaining high recall rates (up to 0.90) across diverse scenarios. We identify feature engineering as a critical performance modulator, substantially bolstering CNN-LSTM’s baseline metrics while enabling the Transformer to realize its maximum predictive capacity. Although synthetic oversampling techniques—such as SMOTE and GAN—effectively extend the detection range for heavy precipitation, physics-informed augmentation provides the most consistent performance gains, particularly in multi-class contexts. We conclude that the Transformer, augmented by physical constraints, is the optimal candidate for high-precision requirements, whereas CNN-LSTM, integrated with synthetic augmentation, offers a more sensitive alternative for early warning systems prioritizing recall. These findings provide empirical guidance for advancing extreme weather preparedness and strategic water resource management. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

As the impacts of climate change intensify, water resource conflicts are escalating globally, particularly in regions with uneven water distribution, such as Central Asia. Long-standing disputes over water allocation persist between Kyrgyzstan and Uzbekistan. This paper aims to examine the conflicts and challenges in water allocation between the two countries and explore the potential of Decision Support Systems (DSSs) as a viable solution. The paper begins by reviewing the historical evolution of water allocation in Central Asia, analyzing upstream–downstream disputes and notable cooperation efforts, with a focus on key water agreements. It then outlines the definitions, development, and classifications of DSSs in the context of water allocation and presents two illustrative case studies—the Tarim River Basin in Xinjiang, China, and the Nile River Basin in Africa. These cases demonstrate the applicability of DSSs in water-scarce regions with similar socio-ecological dynamics and complex multi-country, cross-sectoral water demands. Building on these insights, the paper analyzes the key challenges to implementing DSSs for transboundary water allocation in Central Asia, including limited data availability and sharing, insufficient technical capacity, chronic funding shortages, socio-political complexities, climate change impacts, and the inherent difficulty of modeling complex systems. In response, a set of targeted pragmatic recommendations is proposed. While acknowledging its limitations, the paper argues that establishing a structured, system-based decision-making framework—namely DSSs—can help stakeholders enhance climate-informed strategic planning and foster cooperation, ultimately contributing to more equitable and sustainable water resource allocation in the region. Full article

The increasing volume of produced water (PW) generated by oil extraction activities has intensified the need for environmentally sustainable strategies that enable its reuse and valorization. Biotechnological approaches, particularly those involving the microbial production of value-added compounds, offer a promising route for transforming PW from an industrial waste into a useful resource. In this context, bacterial exopolysaccharides (EPS) have gained attention due to their diverse functional properties and applicability in bioremediation, bioprocessing and petroleum-related operations. This study evaluated the potential of Lelliottia amnigena to synthesize EPS using oilfield PW as a component of the culture medium in stirred-tank bioreactors. Three conditions were assessed: a control using distilled water (dW), PW diluted to 25% (PW25%) and dialyzed PW (DPW). Batch experiments were conducted for 24 h, during which biomass growth, EPS accumulation and dissolved oxygen dynamics were monitored. Post-cultivation analyses included elemental and monosaccharide composition, scanning electron microscopy and rheological characterization of purified EPS solutions. EPS production varied among treatments, with dW and DPW yielding approximately 9.6 g L⁻¹, while PW25% achieved the highest productivity (17.55 g L⁻¹). The EPS samples contained fucose, glucose and mannose, with compositional differences reflecting the influence of PW-derived minerals. Despite reduced apparent viscosity under PW25% and DPW conditions, the EPS exhibited physicochemical properties suitable for biotechnological applications, including potential use in fucose recovery, drilling fluids and lubrication systems in the petroleum sector. The EPS also demonstrated substantial adsorption capacity, incorporating salts from PW and contributing to contaminant removal. This study demonstrates that PW can serve both as a substrate and as a source of functional inorganic constituents for microbial EPS synthesis, supporting an integrated approach to PW valorization. These findings reinforce the potential of EPS-based bioprocesses as sustainable green technologies that simultaneously promote waste mitigation and the production of high-value industrial bioproducts. Full article

Livestock production in the high Andes is vital for rural livelihoods and food security but is limited by poor pasture quality, environmental variability, and restricted resources. Pasture improvement, achieved through management practices and particularly through fertilization, may enhance productivity and sustainability in high-Andean livestock systems. This study aimed to characterize mixed herds composed of domestic sheep (Ovis aries), alpacas (Vicugna pacos), llamas (Lama glama), and domestic cattle (Bos taurus) and to evaluate the role of pasture fertilization on herd composition and livestock size. Primary data were collected through structured questionnaires administered to 88 randomly selected livestock producers, complemented by direct field observations of grazing areas, corrals, shelters, and water sources. The survey documented herd structure, grazing management, pasture conservation, fertilization practices, and farm infrastructure. Data from multiple farms were analyzed using a clustering approach to group production units with similar characteristics, and statistical models were applied to assess the effects of fertilization, pasture area, and water sources. Three distinct clusters were identified: one dominated by alpacas, another by sheep, and a third by llamas with the most uniform stocking density. Pasture fertilization was most common in the sheep-dominated cluster and was significantly associated with higher sheep numbers, while no significant effects were detected for alpacas, llamas, or cattle. Farms without fertilization showed slightly higher overall livestock size; however, a strong negative interaction between pasture area and lack of fertilization indicated that expanding grazing land alone could not offset low forage quality. These findings suggest that targeted fertilization, when combined with sustainable grazing practices, may contribute to improved herd performance and long-term resilience in heterogeneous Andean livestock systems. Full article

The Dal Lake ecosystem is a vital freshwater body situated in the heart of Srinagar, Kashmir, India. It is not only a natural asset but also a cornerstone of environmental health, economic vitality, cultural heritage, and urban sustainability. In the last few decades, the condition of the lake ecosystem and water quality has deteriorated significantly owing to the intensification of the eutrophication process. Effective integrated management of the lake is crucial for the long-term sustainable development of the region and the communities that rely on it for their livelihoods. The main reasons for eutrophication are the substantial quantity of anthropogenic pollution, especially nutrients, discharged from the catchment area of the lake and the overexploitation of the lake space and its biological resources. The research presented in this paper aimed to diagnose the state of the lake by analysing trends in eutrophication development and its long-term changes related to the catchment area and lake ecosystem relationships. The research period was 25 years, from 1997 to 2023. Land use and land cover data and water quality monitoring data, which are the basis for trophic state assessment, allowed us to analyze the long-term dynamics of eutrophication in the reservoir. For these purposes, GIS-generated thematic maps were created by using QGIS software version 3.44.1, and an appropriate methodology for quantifying eutrophication was chosen and adapted to the specifics of Dal Lake. The obtained results provide a foundation for a eutrophication management strategy that considers the specificity of the Dal Lake ecosystem and the impact of the catchment area. The outcomes highlighted the varied trophic conditions in different lake basins and the dominance of eutrophic conditions during the study period. The research highlights the complexity of the problem and underscores the need for a comprehensive lake management system. Full article

The European Water Framework Directive (2000/60/EC) promotes an integrated approach to water management, recognizing water as a shared resource and defining quality objectives. Within this framework, Sustainable Drainage Systems (SuDS) provide effective solutions to improve water quality, control runoff, mitigate hydrogeological risk, and enhance urban resilience. This study investigates the application of SuDS for quantitative stormwater management in a 290-ha industrial district within the Metropolitan City of Milan. Using a synthetic design storm as a benchmark, the study provides event-scale evidence of the performance of SuDS under observed rainfall events, a topic often underrepresented in the literature. Two hydrologic–hydraulic models were developed using SWMM ver. 5.2: a baseline model representing current conditions and a design model integrating SuDS across 24 hectares. Simulations were performed for four rainfall events representative of typical conditions and for a synthetic 10-year return period design event. Results show that, under observed events, SuDS reduce total CSO volumes by 44% and peak flows by 47%, while decreasing overflow activation by around 11%, with the highest effectiveness during ordinary rainfall conditions. Compared with the synthetic 10-year design event, SuDS exhibit similar volume reductions but lower peak-flow attenuation and overflow frequency reduction, highlighting different system responses under real and design rainfalls. Full article

Aquatic environments have been a critical source of nutrition for millennia, with wild fisheries supplying protein and nutrients to populations worldwide. A notable shift has occurred in recent decades with the expansion of aquaculture, now representing a fast-growing sector in food production. Aquaculture plays a key role in mitigating the depletion of wild fish stocks and addressing issues related to overfishing. Despite its potential benefits, the sustainability of both wild and farmed aquatic food systems is challenged by anthropogenic pollution. Contaminants from agricultural runoff, industrial discharges, and domestic effluents enter freshwater systems and eventually reach marine environments, where they may be transported globally through ocean currents. Maintaining water quality is paramount to food safety, environmental integrity, and long-term food security. In addition to conventional seafood products such as fish and shellfish, foods such as those derived from microalgae are gaining attention in Western markets for their high nutritional value and potential functional properties. These organisms have been consumed in Asia for generations and are now being explored as sustainable foods and ingredients as an alternative source of protein. Contaminants in aquatic food products include residues of agrochemicals, persistent organic pollutants (POPs) such as dioxins, polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFASs), as well as brominated flame retardants and heavy metals. Public and scientific attention has intensified around plastic pollution, particularly microplastics and nanoplastics, which are increasingly detected in aquatic organisms and are the subject of ongoing toxicological and ecological risk assessments. While the presence of these hazards necessitates robust risk assessment and regulatory oversight, it is important to balance these concerns against the health benefits of aquatic foods, which are rich in omega-3 fatty acids, high-quality proteins, vitamins, and trace elements. Furthermore, beyond direct human health implications, the environmental impact of pollutant sources must be addressed through integrated management approaches to ensure the long-term sustainability of aquatic ecosystems and the food systems they support. This review covers regulatory frameworks, risk assessments, and management issues relating to aquatic environments, including the impact of climate change. It aims to serve as a comprehensive resource for researchers, policymakers, food businesses who harvest food from aquatic systems and other stakeholders. Full article

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins. Full article

The growing demand for sustainable and renewable materials has intensified interest in agro-industrial waste as an alternative source of cellulose. This review critically examines current approaches to cellulose production from major agro-industrial residues, including cereal straw, corn residues, rice waste, sugarcane bagasse, and oilseed by-products. Emphasis is placed on the relationship between feedstock composition and extraction efficiency, highlighting how lignin distribution, hemicellulose content, and mineral impurities influence pretreatment severity, cellulose yield, and process sustainability. The review systematically analyzes chemical, enzymatic, and mechanical processing routes, with particular attention being paid to pretreatment strategies, fibrillation intensity, and yield variability. Beyond cellulose recovery, key sustainability indicators—such as energy demand, water and chemical consumption, waste generation, and chemical recovery—are evaluated to provide a system-level perspective on process efficiency. The analysis demonstrates that cellulose yield alone is an insufficient criterion for sustainable process design and must be considered alongside environmental and techno-economic metrics. Advanced applications of agro-waste-derived cellulose are discussed using a feedstock-driven approach, showing that high functional performance can often be achieved with moderately processed cellulose tailored to specific end uses. Finally, the review addresses challenges related to feedstock heterogeneity, mineral management, standardization, and industrial scale-up, underscoring the importance of biorefinery integration, closed-loop resource management, and harmonized quality descriptors. These insights provide a foundation for the development of scalable and sustainable cellulose production pathways based on agro-industrial waste. Full article