Search Results (5,137)

To address the lack of spatiotemporal integrative analysis in studies on sustainable water resource utilization in Gansu Province and to provide scientific guidance for local water management and efficient use, this study employs the water resource ecological footprint and ecological carrying capacity models. Taking the period from 2014 to 2023 as the study interval, we conduct a quantitative assessment of sustainable water use in Gansu Province at both temporal sequences and spatial scales across prefecture-level cities, incorporating indicators such as ecological surplus/deficit and ecological pressure index. The results indicate that, temporally, the water ecological footprint of Gansu shows a decreasing-then-increasing trend, with an average ecological carrying capacity of 6.594 million ha, consistently remaining in ecological deficit and experiencing substantial ecological pressure, with agricultural water use identified as the primary pressure source. The water ecological footprint per 10,000 Yuan of GDP initially decreased and then increased, suggesting that the effectiveness of water-saving measures has lacked sustainability. Spatially, significant regional differentiation is observed: the water quantity ecological footprint is “high in Hexi, low in southeastern Gansu,” with expansion in high-value areas; although the water quality ecological footprint has generally declined, southeastern Gansu remains a core pressure zone. The Hexi water resource pressure belt continues to expand, indicating that while water use efficiency is improving across the province, ecological pressure is intensifying. The study highlights issues such as limited water endowment and highly water-intensive industries in Gansu, and proposes differentiated management and industrial optimization strategies to address challenges in sustainable water use, thereby providing a theoretical basis for scientific water resource management in the region. Full article

This article examines the comprehensive assessment of the environmental and economic performance and social significance of wastewater treatment plants (WWTPs), using a company in Western Kazakhstan as an example. During the analysis of the treatment plant’s performance, the potential for using activated sludge as an alternative energy source was explored. Initially, it was found that activated sludge could be used as boiler fuel at the treatment plant. In the future, after drying the sludge, it could be used to replace coal at combined heat and power plants (CHPs) when production is set up. The article analyzes international research and modern approaches to sustainable water use and municipal and industrial wastewater treatment. The paper examines the role of wastewater treatment facilities in minimizing anthropogenic impacts on ecosystems and reducing production costs. It also assesses the prospects for greening protected areas and the social impact of modernizing the region’s infrastructure. The research aims to optimize wastewater treatment facilities through the conservation of resources, water reuse and the use of energy-saving technologies. Practical recommendations have been developed to improve the efficiency and sustainability of wastewater treatment plants, taking into account the climatic conditions of Western Kazakhstan. Water conservation is a goal of sustainable development. Conserving water resources through high-efficiency treatment plants will save natural water, which is also the goal of this study. The article’s materials may be useful for specialists in water management, environmental policy, environmental design, and regional planning. Full article

Precise identification and monitoring of small agricultural water bodies are essential for sustainable water resources management in arid and semi-arid regions, where even limited water losses can significantly affect agricultural productivity and local water security. However, the accurate detection of small ponds remains a major challenge in remote sensing, to address this challenge, this study proposes an integrated three-step framework that combines high-resolution remote sensing imagery, machine and deep learning techniques, and hydrological analysis to identify agricultural ponds and quantify their evaporation losses in Bastam, Iran. In the first step, a dedicated annotated dataset comprising 1061 RGB satellite images, each with a spatial size of 256 × 256 pixels and a ground resolution of 0.5 m, was developed for model training and evaluation. Using this dataset, three deep learning models BiSeNet, UNet3+, and SegNet and four traditional supervised classifiers Maximum Likelihood, Neural Network, Mahalanobis Distance, and Minimum Distance were implemented and compared for pond detection. The results demonstrated that deep learning models consistently outperformed conventional classifiers in delineating small agricultural ponds. Among all evaluated methods, BiSeNet achieved the highest segmentation performance, with an IoU of 82.08%, an F1-score of 90.15%, a precision of 91.86%, and a recall of 88.50%. Among the conventional classifiers, Maximum Likelihood combined with a 5 × 5 spatial kernel produced the best performance, achieving an IoU of 76.90%, an F1-score of 86.93%, a precision of 90.87%, and a recall of 83.32%, whereas simpler classifiers such as Minimum Distance showed only marginal improvements after kernelization. In the final step, the detected ponds were used to estimate evaporation losses through the Meyer method. The hydrological analysis revealed a clear periodic pattern in evaporation and a cumulative water loss of 388,636.7 m³ over a nine-month period, highlighting the considerable impact of evaporation on the efficiency of small agricultural water storage systems in dry environments. Based on these findings, practical mitigation strategies, including evaporation-reducing chemical surface films and floating covers, are discussed as potential options for reducing water loss. Overall, the proposed framework demonstrates the clear advantage of deep learning for the accurate identification of small agricultural ponds and provides an integrated methodological basis for monitoring water bodies and evaluating associated evaporation losses. The study offers a practical and transferable approach for supporting agricultural water management and improving water-use efficiency in arid and semi-arid regions. Full article

Under the dual pressures of freshwater scarcity and intensifying soil salinization in arid regions, the efficient use of brackish water has become a critical pathway for alleviating regional water stress. This narrative review seeks to synthesize a new paradigm characterized by deep integration of smart agriculture with brackish water irrigation. Further review shows that smart agriculture can open a new pathway for precision regulation of brackish water irrigation. Through reviewing existing studies on how brackish water irrigation affects soil properties and crop growth, we summarized the issues that emerged and proposed an integrated framework for sustainable brackish water application combined with smart agricultural management. Conventional brackish water irrigation increases the risks of deterioration in soil physicochemical properties, disruption of microbial community structure, and inhibition of crop growth and yield. On this basis, we propose a paradigm framework for smart brackish water irrigation, consisting of paradigm foundations, key technologies, application scenarios, and long-term goals. This framework clarifies the core connotations of intelligent water-salt coordination, dynamic threshold management, and multi-source data-driven decision-making, and promotes the transition of brackish water irrigation toward greater precision, intelligence, and system integration. This review can establish a technical system for smart brackish water utilization and provide both theoretical and practical support for the high-quality, efficient, and sustainable use of brackish water resources in arid regions. Full article

Antibiotic residues pose a serious threat to environmental safety. In this study, nitrogen/boron co-doped biochar (NBLB) was successfully prepared using lignosulfonate, an industrial byproduct, as the precursor via a one-step impregnation–pyrolysis method. NBLB was applied to activate peroxymonosulfate (PMS) to degrade tetracycline (TC) in water. The results showed that NBLB-activated PMS degraded 93.4% of TC within 60 min, which was 42.6%, 20.4%, and 27.8% higher than those of undoped biochar, nitrogen-doped biochar, and boron-doped biochar, respectively. Additionally, the NBLB/PMS system exhibited high tolerance to common aqueous anions such as Cl⁻, $\mathrm{C}{\mathrm{O}}_3^{2-}$, ${\mathrm{PO}}_4^{3-}$, ${\mathrm{NO}}_3^{-}$ and $\mathrm{S}{\mathrm{O}}_4^{2-}$. BET tests indicated that the specific surface areas of undoped, N-doped, B-doped, and NBLB were 133.98, 280.22, 304.13, and 342.35 m² g⁻¹, respectively, exhibiting a significant enhancement in the specific surface area of NBLB. The co-doping of N and B constructed a hierarchical pore structure, providing an efficient dispersion platform for active sites such as C = O, BC₂O, and pyridinic-N. Quenching experiments and EPR detection demonstrated that the degradation of tetracycline (TC) in the NBLB/PMS system followed a synergistic oxidation mechanism dominated by ¹O₂ and ${\mathrm{O}}_2^{\text{•}-}$, supplemented by ^•OH and $\mathrm{S}{\mathrm{O}}_4^{\text{•}-}$. This study prepared high-performance nonmetallic catalysts through a solid waste resource utilization strategy, providing a green and feasible path for the treatment of antibiotic pollution. Full article

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource efficiency. This study proposes AER-SFRDNet, a multisource sensor fusion and deep reinforcement learning framework for revenue-aware greenhouse regulation. Crop images, environmental sensor sequences, management records, resource consumption data, and economic variables from facility tomato production are integrated into a unified agro-economic state representation. A yield–cost–revenue prediction module estimates the yield, resource consumption, production cost, sales revenue, and net revenue. These outputs guide a reinforcement learning module that optimizes continuous actions including irrigation, ventilation, supplemental lighting, heating, and $C{O}_2$ application. A comparison with traditional machine learning, unimodal deep learning, multimodal learning, and reinforcement learning baselines shows lower prediction errors and improved regulation outcomes. AER-SFRDNet achieves a yield MAE of $0.486$, yield RMSE of $0.674$, yield $R^2$ of $0.893$, cost RMSE of $0.613$, and net revenue MAPE of $10.82$. In regulation experiments, the cumulative net revenue and input–output ratio reach $1.276$ and $1.263$, while the normalized energy use, water use, and carbon emissions per unit yield decrease to $0.752$, $0.781$, and $0.741$, respectively. The results suggest that agro-economic state modeling can support greenhouse decisions that consider both revenue and sustainability-related resource efficiency. Full article

The decarbonization of essential water supply infrastructure is a critical challenge for water-stressed and geographically constrained regions, particularly islands where both water and electricity systems are highly dependent on external or fossil-based resources. In Northern Cyprus, approximately 70% of domestic water demand is met through imported water via pipeline, while electricity generation relies predominantly on fuel oil, resulting in high greenhouse gas emissions and environmental burden. This study evaluates an integrated renewable energy-based supply system using a medium-scale concentrating solar power (CSP) plant with parabolic trough collectors coupled to thermal desalination. The proposed configuration is assessed as an alternative energy-driven infrastructure option for reducing dependence on imported water and fossil-based electricity. System performance was evaluated by estimating electricity and freshwater production under local climatic conditions, demonstrating that the proposed configuration can meet both the associated electrical energy requirements and domestic water demand in the selected region. A cradle-to-gate life cycle assessment (LCA) was conducted to quantify the environmental impacts of the integrated system and support sustainability-oriented decision-making. The LCA results identify residual fossil-based electricity, phosphoric acid consumption, and brine discharge as the main environmental hotspots. Overall, the findings show that CSP-driven desalination can provide a viable and more sustainable option for integrated energy and water supply in water-scarce coastal regions with high solar potential, highlighting its relevance for renewable energy integration, water-energy nexus planning, and resource-efficient infrastructure development. Full article

China hosts abundant coal-rock gas (CRG) resources, which have become a critical unconventional natural gas contributor to national reserve expansion and production increment. Supercritical carbon dioxide (ScCO₂) fracturing is recognized as a green and efficient stimulation technology, exhibiting great potential for high-efficiency CRG exploitation. To clarify the effects and intrinsic mechanisms of ScCO₂ treatment on coal fracture initiation, propagation, and CRG recovery enhancement, true triaxial fracturing and CO₂-CH₄ displacement experiments were performed in combination with multiple microscopic characterization methods, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Scanning electron microscopy (SEM). The multi-scale experimental investigation systematically revealed the fracture development mechanism, permeability variation characteristics, and microstructural evolution of coal reservoirs under ScCO₂ interactions. The results indicate that ScCO₂ fracturing significantly lowers the coal fracture initiation threshold compared with conventional hydraulic fracturing, with the breakdown pressure reduced by 26.2% and the initiation time shortened by 37.5%. Such advantages facilitate coal fracture activation and the development of complex fracture networks. Long-term ScCO₂ soaking induces the dissolution of inorganic minerals (e.g., calcite, plagioclase, and clay minerals) and the extraction of inherent organic matter within coal matrices. The coupled hydro-chemical reactions reconstruct the coal pore structure, enlarge pore throats, and improve reservoir permeability, achieving a maximum permeability enhancement of approximately 1.6 times. Meanwhile, ScCO₂ displacement yields a prominent CRG recovery performance, with an ultimate gas recovery factor up to 93.85%. The CRG enhancement mechanism of ScCO₂ fracturing is comprehensively attributed to three core coupled effects. First, ScCO₂ dynamic fracturing generates intricate fracture networks, which greatly optimize reservoir seepage channels and flow space. Second, the ScCO₂–formation water–coal interaction modifies coal physical properties via mineral dissolution and organic matter extraction, thereby improving reservoir permeability. Third, the preferential adsorption of CO₂ over CH₄ triggers effective competitive adsorption and gas displacement, further promoting adsorbed methane desorption and elevating CRG recovery efficiency. This study provides a solid theoretical foundation for the field application of ScCO₂ fracturing technology and offers valuable insights into the green, efficient, and sustainable development of deep coal-rock gas resources. Full article

Phosphate concentrations in agricultural drainage water frequently exceed those observed in municipal wastewater and, when discharged directly into river systems, pose a significant risk to their ecological status and potential. Developing cost-effective and scalable solutions for phosphate interception at the source is therefore essential for achieving the water quality goals outlined in environmental sustainability frameworks. This study evaluated the phosphate removal capacity of calcium hydroxide-modified metakaolin-based reactive materials as a sustainable approach to limiting agricultural nutrient loads entering river ecosystems. Two synthesis methods were compared: a fusion method and a low-temperature method. The resulting materials—metakaolin (M), low-temperature synthesized metakaolin (MLT), and Ca(OH)₂-modified low-temperature metakaolin (CaMLT)—were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), textural property analysis, and scanning electron microscopy (SEM). Phosphate removal performance was evaluated in batch experiments under varying reactive material dosages (2.5–10.0 g·L⁻¹), initial phosphate concentrations (1.0–7.0 mg·L⁻¹), and contact times (1–144 h). The CaMLT material achieved the highest phosphate removal efficiency of 56% at a phosphate concentration of 1.0 mg·L⁻¹, a dose of 10.0 g·L⁻¹, and a contact time of 144 h. At higher phosphate concentrations (3.0 and 7.0 mg·L⁻¹), removal efficiencies decreased to 23% and 21%, respectively, under the same experimental conditions. The results were benchmarked against data reported in the literature. The findings indicate that low-temperature synthesized metakaolin modified with calcium hydroxide demonstrates potential as a reactive material for phosphate capture in agricultural drainage systems, offering a promising pathway toward more sustainable water resource management and freshwater ecosystem protection. Full article

Water hyacinth (Eichhornia crassipes) is an invasive aquatic plant with high lignocellulosic content, offering potential as a natural fiber resource for craft-based industries. However, its extremely high initial moisture content (≈95%) presents a major challenge in fiber processing, particularly for small-scale industries that rely on traditional sun-drying methods. These methods are highly dependent on weather conditions, prone to contamination, and produce inconsistent fiber quality. This study adopts a research and development (R&D) approach to design and evaluate an innovative dryer machine specifically for water hyacinth fiber processing. The proposed system utilizes LPG-based heating and controlled airflow to achieve stable drying conditions. Experimental results show that the dryer machine can process 10 kg of wet water hyacinth within 280 min, significantly shorter than the approximately four days required for manual drying. The system reduces the moisture content to below 10%, resulting in improved fiber cleanliness, uniformity, and usability. Although the dried mass produced by the machine is slightly lower compared to manual drying, this is attributed to more effective moisture removal, leading to lower residual water content in the final product. Productivity analysis indicates improved operational consistency and higher processing capacity over extended periods (30–180 days), particularly under varying weather conditions. These findings demonstrate that controlled drying technology provides a reliable and efficient solution for lignocellulosic fiber processing in small-scale industries, contributing to improved material utilization and sustainable biomass management. Full article

The skin serves as the largest protective barrier organ of the human body and is easily impaired by trauma, infection and chronic diseases. Efficient wound dressings are indispensable for repairing infected wounds. Isochlorogenic acid A (IAA), the core active ingredient of Shanyinhua, has superior anti-inflammatory and antibacterial effects. However, low water solubility and weak structural stability restrict its direct application in wound treatment. In this work, IAA@Fe(III) nanoparticles (IAA@Fe(III) NPs) were synthesized through self-assembly and loaded into cross-linked amylopectin (Amy)/carboxymethyl chitosan (CMCS) (AC hydrogel) to construct Amy/CMCS@NPs composite dressings. Characterizations demonstrated that nanoparticles displayed a uniform spherical shape with a size of 114.20 ± 2.29 nm and stable coordination. The hydrogel featured a dense porous structure and outstanding mechanical performance, self-healing ability, adhesion, and swelling properties. In vitro tests proved that 50 mg/mL composite hydrogel exerted nearly 100% bacteriostatic activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), with good biocompatibility, and enhanced cell migration capacity. In vivo assays indicated an 86.5% wound healing rate at day 7. This dressing could downregulate Tumor Necrosis Factor-α (TNF-α) and Interleukin-1β (IL-1β), upregulate Cluster of Differentiation 31 (CD31) and Vascular Endothelial Growth Factor (VEGF), and accelerate wound repair. This study provides a theoretical and experimental basis for the exploitation of IAA-based wound dressings and high-value utilization of Shanyinhua resources. Full article

Accurate prediction and uncertainty quantification of tunnel water inflow are critical for construction safety, risk mitigation, and groundwater-control planning. However, conventional analytical and numerical methods are often limited by simplified assumptions and high computational cost, while many machine learning models lack reliable uncertainty quantification. To address these limitations, this study proposes a framework by coupling vine copula dependence modeling with sparse polynomial chaos expansion (SPCE). The framework utilizes a vine copula to characterize the asymmetric dependence among input parameters. Two distinct approaches are used to construct the SPCE models: the arbitrary polynomial chaos expansion (aPCE) method, assuming independence in the original space, and the Rosenblatt transform-based polynomial chaos expansion (Rt-PCE) method, which maps correlated inputs into an independent space via the Rosenblatt transform to establish rigorous orthogonal polynomials. Validation using a database of 600 cases shows that SPCE models achieve point accuracy comparable to artificial neural network (ANN) and Gaussian process regression (GPR) with superior numerical stability. Notably, Rt-PCE yields the best predictive robustness and outperforms both benchmarks in probability density fitting, particularly in capturing extreme tail behavior. Furthermore, the study confirms that neglecting input dependence biases probability estimations, whereas vine copula-based modeling effectively captures both the central tendency and tail features of inflow distributions. The proposed framework provides decision support for resource-efficient intervention planning under uncertain hydrogeological conditions. Full article

Groundwater is an essential natural resource for human societies and ecosystems. Traditional groundwater monitoring relies on in situ wells, which are susceptible to discontinuity, influencing water resource management. To overcome this deficiency, this study proposes a remote sensing-based groundwater level (GWL) monitoring system that uses machine learning (ML) algorithms and remotely sensed hydrological parameters to reconstruct well-specific GWL time series. Four machine learning algorithms, including K-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a weight-mean Ensemble strategy, were adopted to construct the models at each well individually for monitoring GWL. Specifically, the GWL data for ~770 wells across the conterminous United States (CONUS) were modeled using remotely sensed precipitation (P), evapotranspiration (ET), terrestrial water storage anomaly (TWSA), and soil moisture (SM) datasets during the period from 2004 to 2019. Afterwards, the performances of models were evaluated during an independent period from 2020 to 2023. The results show that the Ensemble model outperforms the individual baseline models evaluated in this study (i.e., KNN, RF, and XGBoost), achieving a mean coefficient of determination (R²) of 0.81, root mean square error (RMSE) of 0.34 m, normalized RMSE (NRMSE) of 11.8%, and Nash–Sutcliffe efficiency (NSE) of 0.78. The results demonstrate that the proposed system can effectively reconstruct GWL dynamics for most wells. This can be a compensation for missing records for hydrologically significant wells, which are those with historical groundwater observations. Full article

Emulsified oily wastewater from industrial activities remains challenging to treat because kinetically stable oil droplets hinder efficient separation, threatening water resources and ecological environments. To address this issue, this work develops an inclined solar-driven evaporator based on a polypyrrole (PPy)-coated bacterial cellulose (BC) gel (PPy-BC gel), which has enlarged effective evaporation areas and an environmental heat effect. Under one sun (1 kW m⁻² under standard solar illumination), the PPy-BC gel achieves an evaporation rate of 2.14 kg m⁻² h⁻¹, which is 494% higher than that of the uncoated BC gel. In diesel-in-water emulsions with oil concentrations ranging from 0 to 15 vol%, the gel maintains stable evaporation performance, achieving an oil removal efficiency exceeding 99% across all tested concentrations. After 15 consecutive cycles of treating actual oily wastewater, no significant performance degradation is observed. The collected condensate exhibits excellent water quality, with removal efficiencies for total organic carbon (TOC), chemical oxygen demand (COD), and total dissolved solids (TDS), and ionic conductivity (IC) exceeding 94%. This work presents a solar-powered evaporation platform, which demonstrates potential in the stable treatment of complex oily wastewater, and offers a sustainable reference solution for decentralized industrial wastewater management. Full article

The efficient utilization of natural resources and agricultural wastes aligns well with the UN Sustainable Development Goals. Sunflower seed husks are an affordable and renewable source of cellulose that can be used as an alternative to wood-based resources. However, the yield and quality of cellulose are affected by the presence of components such as phytomelanin, hemicellulose, and lignin. In this study, cellulose pulp (CP) was extracted from untreated, water-treated, and water and alkali-treated SFH. The optimal peroxyacetic acid (PAA) to biomass ratio was established to assess the influence of pre-treatment on CP properties. Water and alkali pre-treatments significantly increased CP yield and reduced residual lignin, hemicellulose, and ash compared to untreated samples. The optimal yield of CP for SFH-NaOH was 55.73%. All microcrystalline cellulose (MCC) types exhibited comparable α-cellulose content, confirmed by the IR band at 1430 cm⁻¹. XRD showed lower crystallinity in untreated CP-SFH relative to pre-treated samples. SEM revealed porous fibrous structures across all MCCs. Pre-treatment also improved the thermal stability and ζ-potential of cellulose nanocrystals (CNCs) obtained from MCC, without altering morphology. CNC yields were determined for all three CP variants. The CP-SFH-NaOH sample had the maximum CNC yield of 52.12%. Phytomelanin with admixture of lignin was recovered from alkaline extracts (8.56%) and fully characterized. Overall, the findings demonstrate the potential of integrated SFH utilization to produce high-quality cellulose derivatives and phytomelanin with admixture of lignin. Full article