Search Results (3,116)

Agriculture uses 80% of global water resources, driving several water management challenges across the world. These challenges require the exchange of effective practices. We found that California’s Tulare Lake Basin (TLB) and Mexico’s Nazas–Aguanaval watershed share key features, leading us to propose the TLB as a model of climate resilience. After contrasting the policies for TLB with those for Nazas–Aguanaval, we found that no constrained pricing policy proposal exists for the Nazas–Aguanaval watershed. We apply a hydro-economic model using Positive Mathematical Programming to support an incentive structure for reducing water use in agriculture while maximizing profits. The optimal crop policy could reduce water demand by 20%, with a trade-off of an 11% reduction in profits. This would save 185.4 hm³/year, which represents 90% of the volume required for an ongoing infrastructure project for urban water supply in the watershed. Additionally, implementing a price of 14 USD/dam³ could increase the irrigation district’s revenue, boosting farmers’ profits by up to 16% and district revenue by up to 134%. Our results demonstrate the benefits of applying Positive Mathematical Programming in a semiarid watershed to support water and agriculture policy. This research is a starting point for increasing the climate resilience of watersheds under water and financial stress. Full article

As global efforts towards green development intensify, eco-friendly materials have become pivotal to achieving sustainability. Wood, a natural, renewable, and environmentally benign biomass, holds great promise for green material applications due to its abundance and ecological benefits. Recent advances in functional modification techniques—such as oxidation, grafting, and nanoparticle incorporation—have significantly enhanced wood’s physical and chemical properties while introducing new environmental functions. These developments have expanded its applications in pollution control, resource recovery, and environmental restoration. In particular, modified wood exhibits outstanding adsorption capacity for heavy metal ions (Pb²⁺, Cd²⁺, Cu²⁺), offering an efficient and sustainable approach to water pollution remediation. This paper reviews the fundamental structure and properties of wood, summarizes recent progress in the development of functionalized wood for heavy metal ion adsorption, and analyzes the influence of various modification methods on adsorption performance. Finally, it outlines future directions for optimizing wood functionalization technologies, providing theoretical foundations and practical guidance for advancing their applications in wastewater treatment and heavy metal pollution control. Full article

Marine spatial planning (MSP) has emerged as a fundamental process for achieving the balanced development of marine ecology, economy, and society. However, increasing conflicts among multiple marine uses, particularly between port development, industrial activities, fisheries, recreation, and ecological protection, highlight the pressing demand for robust and science-based planning tools. In this study, we propose an integrated analytical framework for multi-objective spatial suitability evaluation to optimize MSP. Using the coastal waters of Dalian, China, as a case study, we evaluated the spatial suitability of five key marine activities (ecological protection, mariculture, port construction, wind energy farm development, and coastal tourism) and applied a multi-criteria decision-making approach to inform spatial zoning. The results emphasize the region’s ecological significance as providing critical habitats and migratory corridors for protected and threatened species as well as fishery resources, while also revealing substantial spatial overlaps between conservation priorities and human activities, particularly in nearshore zones. The optimized zoning scheme classifies 22.0% of the coastal waters as Ecological Redline Zones, 32.4% as Ecological Control Zones, and 45.6% as Marine Exploitation Zones. This science-based spatial classification effectively reconciles ecological priorities with development needs, providing a spatially explicit and policy-relevant decision support tool for MSP. Full article

Accurate and detailed mapping of coastal wetlands is essential for effective wetland resource management. However, due to periodic tidal inundation, frequent cloud cover, and spectral similarity of land cover types, reliable coastal wetland classification methods remain limited. To address these issues, we developed an integrated pixel- and object-based hierarchical classification strategy based on multi-source remote sensing data to achieve fine-grained coastal wetland classification on Google Earth Engine. With the random forest classifier, pixel-level classification was performed to classify rough wetland and non-wetland types, followed by object-based classification to differentiate artificial and natural attributes of water bodies. In this process, multi-dimensional features including water level, phenology, variation, topography, geography, and geometry were extracted from Sentinel-1/2 time-series images, topographic data and shoreline data, which can fully capture the variability and dynamics of coastal wetlands. Feature combinations were then optimized through Recursive Feature Elimination and Jeffries–Matusita analysis to ensure the model’s ability to distinguish complex wetland types while improving efficiency. The classification strategy was applied to typical coastal wetlands in central Jiangsu in 2020 and finally generated a 10 m wetland map including 7 wetland types and 3 non-wetland types, with an overall accuracy of 92.50% and a Kappa coefficient of 0.915. Comparative analysis with existing datasets confirmed the reliability of this strategy, particularly in extracting intertidal mudflats, salt marshes, and artificial wetlands. This study can provide a robust framework for fine-grained wetland mapping and support the inventory and conservation of coastal wetland resources. Full article

Rural tropical regions face escalating threats from zoonotic AIV and dengue virus but lack sewered infrastructure for conventional wastewater surveillance. We implemented surface water-based surveillance (SWBS) in peri-urban Dhaka (Bangladesh) and Ruili (China) from July to November 2023 and coupled it with machine learning-enhanced digital epidemiology. Reverse transcription quantitative PCR (RT-qPCR) was employed to detect the M gene of AIV and to subtype H1, H5, H7, H9, and H10 in surface water. Wild bird feces (n = 40) were collected within 3 km of positive sites to source-track AIV. For the dengue virus, a serogroup-specific RT-qPCR assay targeting the CprM gene was used. Genomic sequencing of AIV and dengue virus was performed to elucidate phylogenetic relationships with local clinical strains. Clinical data related to dengue fever were also collected for correlation analysis. Meanwhile, 13 dengue-related keyword search volumes were harvested daily from Google, Bing and Baidu for four cities to reveal the relationship between dengue epidemics and the web search index. AIV H5 was detected in Dhaka city from week 38, peaking at week 39, while dengue virus was persistently detected from week 29 to week 45, aligning with clinical trends. Time-series cross-correlation analysis revealed that variations in surface water viral load led clinical case reports by approximately two weeks (max CCF = 0.572 at lag −2). In Ruili city, dengue virus was detected from week 32 to week 44. To sharpen sensitivity, 383 weekly web search series for 13 dengue keywords from four countries were screened; random-forest and XGBoost models retained five symptom queries that generated a composite index explaining 79% of variance in dengue RNA levels in an independent Ruili test set (n = 24) and reduced superfluous sampling by 35%. Phylogenetic analysis verified identity between water-derived and patient-derived DENV-2, confirming local transmission. The study demonstrates that AIV SWBS is optimally integrated with wild bird sampling for source attribution, whereas dengue SWBS achieves maximal efficiency when combined with real-time web search monitoring, providing tailored, low-cost early-warning modules for resource-constrained tropical settings. Full article

The large-scale adoption of renewable energy sources, while environmentally beneficial, introduces significant frequency fluctuations due to the inherent variability of wind and solar output. Electric vehicle (EV) integration with substantial battery storage and bidirectional charging capabilities offers potential mitigation for these fluctuations. This study addresses load frequency regulation in multi-area interconnected power systems incorporating diverse generation resources: renewables (solar/wind), conventional plants (thermal/gas/hydro), and EV units. A hybrid controller combining the proportional–integral–derivative with second derivative (PIDD²) and tilted derivative (TD) structures is proposed, with parameters tuned using an innovative optimization method called the Tianji’s Horse Racing Optimization (THRO) technique. The THRO-optimized PIDD²-TD controller is evaluated under realistic conditions including system nonlinearities (generation rate constraints and governor deadband). Performance is benchmarked against various combination structures discussed in earlier research, such as PID-TID and PIDD²-PD. THRO’s superiority in optimization has also been proven against several recently published optimization approaches, such as the Dhole Optimization Algorithm (DOA) and Water Uptake and Transport in Plants (WUTPs). The simulation results show that the proposed controller delivers markedly better dynamic performance across load disturbances, system uncertainties, operational constraints, and high-renewable-penetration scenarios. The THRO-based PIDD²-TD controller achieves optimal overshoot, undershoot, and settling time metrics, reducing overshoot by 76%, undershoot by 34%, and settling time by 26% relative to other controllers, highlighting its robustness and effectiveness for modern hybrid grids. Full article

Accurate river runoff prediction plays a vital role in water resource management, agricultural scheduling, disaster prevention, and climate adaptation. To address three long-standing challenges in multi-basin hydrological modeling—the insufficient nonlinear expressiveness of recurrent structures, underestimation of extreme high-flow events caused by sample imbalance, and weak cross-basin generalization—this study proposes a hybrid forecasting framework, KAN–WLSTM, that integrates physical priors with deep learning. Specifically, (i) the KAN replaces linear layers to achieve nonlinear mapping consistent with hydrological mechanisms; (ii) a WMSE loss is adopted to emphasize high-flow samples; (iii) Granger causality analysis is applied for causality-driven input selection; and (iv) Optuna is used to perform Bayesian-based adaptive hyperparameter optimization. Multi-scale experiments based on the CAMELS-GB dataset show that a 14-day lag window yields the best performance, with an average MSE = 1.77 ${({\mathrm{m}}^3/\mathrm{s})}^2$ and NSE of 0.81 across nine representative catchments. Comparative results indicate that the proposed model achieves the best or near-best scores in most metrics, outperforming traditional LSTM by 6.8% in MSE and 2.7% in NSE, while reducing peak discharge errors by up to 18%. In large-sample evaluations across 161 catchments, the KAN–WLSTM model attains an average and median NSE of 0.770 and 0.827, respectively, with the smallest variance and ranked first among all models, demonstrating outstanding robustness and generalization under diverse hydro-climatic conditions. Full article

Nowadays, because of the severe contradiction between water supply and demand, a large amount of ecological water resources are occupied by other water-using sectors, resulting in the rapid degradation of the ecological environment, especially in arid and semi-arid areas of northwestern China. Therefore, in order to deal with the above problems, a multi-objective water resources optimization allocation model based on ecological water demand is established, which not only focuses on ecological water demand, but also can effectively deal with the conflict among multiple objectives. A case study was applied in Wuwei City, Gansu Province, China, which had maximum economic benefit and ecological benefit as objectives. A series of optimal water resources distribution schemes were obtained under different representative hydrological years. From the result, as representative hydrological years changed from wet to dry, economic benefit and ecological water deficit would vary from CNY [52.82, 36.32] × 10⁸ and [2.69, 5.51] × 10⁸ m³, respectively. It indicated that water resources have become one of the factors restricting the sustainable development of Wuwei City. Even when p = 25%, it still cannot meet the water demand. This indicates that Wuwei city needs to aggressively develop water-saving measures and new water resources in the future to deal with the acute water scarcity scenario. In addition, no matter what representative hydrological years are used, the results of the established multi-objective programming model are always in the middle of the results of the individual objective, indicating that the established multi-objective programming model has the advantage of dealing with water competing conflict among different objectives. Full article

The increasing demand for industrial resource optimization has driven the creation of integrated methodologies for the technical assessment of complex operations such as gas oil hydrocracking. This study examines the technical performance of a mass and energy-integrated gas oil hydrocracking process using the Extended Water–Energy–Product (E-WEP) methodology, which enables the quantification of 12 key indicators related to water, energy, and raw material usage. The research addresses the challenge of high demineralized water consumption in conventional hydrocracking processes. The findings show a production yield of 95.77% and a recycled hydrogen reuse rate of 67.99%, expressed as the Index of Reused Unconverted Material (IRUM). In terms of water use, fresh water demand decreased to 26.99 m³/h and wastewater discharge to 21 m³/h, although 77.79% of the total water processed is released as effluent, corresponding to the Wastewater Production Ratio (WPR). From the energy standpoint, total energy consumption increased to 2966.57 MMBTU/h, primarily due to the use of additional electrical equipment for mass integration. The Total Cost of Energy (TCE) reached 3,563,840.10 USD/day, with electricity (1630.82 kWh/t) as the dominant source, negatively influencing the process’s economic efficiency. Despite this energy drawback, the evaluated configuration achieves the most sustainable water use compared to conventional and integrated PVC production schemes, underscoring the importance of adopting holistic evaluations that jointly address technical efficiency, environmental impact, and economic feasibility. Full article

The Yangtze River Basin serves as the socioeconomic core of China, and rapid development in recent years has intensified the conflict in the area between economic growth and ecological conservation. This study evaluated the spatiotemporal evolution of the land ecological security (LES) across 11 provinces and municipalities in the Yangtze River Basin from 2008 to 2023 by using the framework of the drivers, pressures, state, impact, and response model of intervention. We forecasted the trends of LES (2024–2033) by using a grey prediction model and identified the key obstacles to it through an obstacle degree model. The findings revealed the following: (1) Economic density (D3) and per capita water resources (S4) had significantly high weights, disproportionately impacting LES. Shanghai scored highest for Drivers, Impact, and Response subsystems, while Tibet led in Pressures and State. (2) Basin-wide LES scores improved from “less safe” to “critical safe” but saw no fundamental breakthrough. LES exhibited a three-tier spatial pattern: higher in the middle-lower reaches (e.g., Shanghai, Jiangsu) and lower in the upper reaches (e.g., Qinghai). Tibet remained “critical safe” with minor fluctuations; other regions improved gradually yet mostly remained “less safe” or “critical safe”. (3) Forecasts (2024–2033) indicate continued overall LES improvement. Shanghai and Jiangsu are projected to reach “safe” status, Qinghai will remain “unsafe”, while most others persist as “critical safe”. Basin LES remains fragile, requiring intervention. (4) The Drivers (D) and State (S) subsystems were the primary constraints on LES. Critical obstacle indicators included economic pressure (per capita GDP (D2), D3), resource availability (S4, ratio of effectively irrigated area (I1)), land productivity (agricultural/forestry output per unit area (I3)), and forest coverage rate (R6). Enhancing LES necessitates implementing regionally tailored policies addressing spatial variations, prioritizing urban economic optimization, strengthening water resource management, and ensuring effective cross-regional governance. Full article

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management. Full article

Mycosporine-like amino acids (MAAs) are multifunctional, UV-absorbing and antioxidant metabolites produced by marine algae, offering promising applications in biotechnology and dermocosmetic sciences. In this study, MAAs were sustainably extracted from nori seaweed (Porphyra spp.) using an ultrasound-assisted aqueous method, an eco-friendly approach that ensures efficiency and industrial scalability. Chromatographic enrichment followed by MALDI-TOF mass spectrometry confirmed the presence of bioactive compounds, including porphyra-334, palythine, and myc-ornithine. The enriched fraction exhibited potent antioxidant activity (low IC₅₀ in DPPH and ABTS assays) and significant anti-elastase effects, highlighting its potential as a natural anti-aging agent. To optimize delivery, MAAs were incorporated into a stable water-in-oil nanoemulsion, which maintained droplet sizes below 400 nm and a low polydispersity index (PDI < 0.2) for up to four months. A randomized, double-blind clinical study in 20 volunteers further demonstrated that the MAA-based nanoemulsion significantly improved skin hydration (+53.6%) and reduced transepidermal water loss (TEWL), confirming its humectant and barrier-strengthening efficacy. These findings position Porphyra spp. as a sustainable marine resource for producing MAAs, and demonstrate their practical potential as natural, multifunctional ingredients in eco-conscious cosmetic and pharmaceutical formulations. Full article

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article