Conductive nanofiltration: From materials to applications.

Harnessing phytoremediation of heavy metals for sustainable water cleanup: a comprehensive review and future directions.

Semi-automatic silicon rubber based multi-vial microextraction for determination and removal of pesticides from waters and tea.

Global inland waters trophic status from space observation: scientific advances and future challenges.

Regional water and land resource equilibrium optimization allocation under climate change

Externalities of climate adaptation in common-pool groundwater resources

Water is a critical natural resource in Saudi Arabia, a region characterized by its arid and semiarid environments. Recognizing this, Crown Prince Mohammed bin Salman’s Vision 2030 places strong emphasis on conserving and maintaining natural water resources in the region as a cornerstone of Saudi Arabia’s long-term development goals. The vision emphasizes improving water efficiency, enhancing wastewater treatment, reducing reliance on nonrenewable groundwater resources, and investing in innovative desalination technologies. Hence, this survey was conducted to understand the status of water resources in Saudi Arabia, with a focus on climate change, the availability of groundwater and its recharging processes, water consumption for agricultural and irrigation activities, domestic and industrial water demands, and the productivity of wastewater treatment plants. Ultimately, a circular economy model was proposed as an effective means of optimizing water management in the region. This review is expected to facilitate a deeper understanding of Saudi Arabia’s transformation and the potential next-generation strategies that can be implemented.

Multidimensional assessment framework for water resources allocation

This study investigates the application of the Jaya algorithm as a simplified and efficient approach for solving multiobjective optimization problems in irrigation water resource planning. The analysis focuses on a real-world irrigation system in Bangladesh, where it is crucial to balance agricultural productivity with ecological water requirements, especially during periods of water scarcity. A previously established modeling structure was adopted, incorporating the same hydrological, agronomic, and economic input parameters, along with identical system constraints and optimization objectives. Unlike evolutionary algorithms that depend on control parameters or genetic operators, the Jaya algorithm offers a simple approach that does not require calibration parameters, such as mutation rate, crossover probability, or learning factors, to derive optimal water allocation strategies aimed at maximizing net returns and minimizing environmental flow deficits. The optimization results demonstrate that the Jaya algorithm effectively generates a well-spread Pareto front, capturing the trade-offs between conflicting water demands. Compared to conventional evolutionary techniques for similar conditions, Jaya showed equivalent economic outcomes and, in some cases, greater satisfaction with ecological needs. The study also reveals that fluctuations in rainfall patterns have a more pronounced impact on both economic returns and ecological outcomes than do variations in reservoir inflow. Overall, the research confirms the practicality of the Jaya algorithm for integrated water resource planning and emphasizes its simplicity and ease of use, making it a promising solution for sustainable irrigation water resource planning in regions vulnerable to climate variability.

Enhancing water resource recovery: Ozone-ultrafiltration-forward osmosis process for simultaneous tannery wastewater treatment and seawater desalination

Multi-link network modeling of water resource systems: identifying critical linkages driving resilience dynamics

The risk of water resource carrying capacity (WRCC) reveals the complexity and uncertainty within water systems. This understanding helps in identifying issues related to water resource overloading and ecosystem imbalances, which is of paramount importance in sustainable development. Water-rich regions bear significant responsibility for water resource allocation, with fluctuations in carrying capacity risks having widespread consequences. However, current research lacks regional assessments of the water resource carrying capacity risks in water-rich regions. Therefore, this study assessed the water resource carrying capacity risks (WRCCR) of the Three Gorges Ecological Economic Corridor of the Yangtze River, China, a typical water-rich region, by constructing a hazard index and a vulnerability index. The results indicated that (1) the WRCCR exhibited a medium-high risk (WRCCR ranging approximately between 0.6 and 0.8), initially declining and then rising during 2015–2019; (2) the development of urban agglomerations and the industrial structure fundamentally impact the WRCCR; (3) the consumption of advantageous resources coupled with low industrial productivity is a significant cause of the water resource supply-demand imbalance; and (4) in the future, water-rich regions should strengthen ecology-driven economic transformation and industrial ecologization to mitigate the WRCCR.

Towards cooperation on transboundary rivers: achieving a win–win balance between upstream hydropower generation and downstream water demand under climate change

The páramo is a high-altitude ecosystem characterized by its herbaceous vegetation and distribution in tropical and subtropical regions. This ecosystem is highly sensitive to environmental disturbances, making it a priority area for conservation and research due to its biodiversity and strategic ecosystem functions. Consequently, it is essential to conduct ecological and conservation studies of páramo areas within interdisciplinary frameworks that address the various environmental, geopolitical, economic, and sociocultural challenges. The objectives of this study were: (1) to identify the richness of páramos and the evolution of knowledge in research during the period from 2014 to 2023; and (2) to determine the scientific output, keyword cooccurrence in articles, and the most influential researchers in the field during the period from 2014 to 2023. The methodology employed was descriptive bibliometric analysis, involving a comprehensive search for scientific articles in the Scopus database. For parameter visualization, VOSviewer and the Bibliometrix package in R Studio were used to apply Lotka’s Law. The results show that Colombia is the leading country in scientific production in this field, playing a central role in advancing knowledge about the páramo. Furthermore, the findings indicate that the impact of climate change and intensive human activities (such as agriculture, grazing, pine plantations, and tourism) have increased the risk of páramo degradation, altering hydrological cycles and reducing its regulatory capacity. This bibliometric study provides a robust foundation for the planning of public policies aimed at conservation, sustainable water resource management, and biodiversity protection in páramo ecosystems. Therefore, it is crucial to promote research that considers the páramo as a socio-ecological system, analyzing the interactions between human actors and the natural environment, which will enable the design of more equitable and effective management policies.

ABSTRACT Florida’s growing population and water demands necessitate effective conservation strategies such as irrigation restrictions. This study employed audience segmentation, leveraging the theory of planned behaviour and value-belief-norm theory to assess irrigation restriction compliance. Four distinct segments, the steady supporters, moderate self-interested compliers, encouraged eco-adherents, and reserved resisters, showed diverse profiles regarding values, attitudes and compliance abilities. By identifying theory‑driven compliance subgroups, this study fills a gap in irrigation policy research and extends behavioural theory to a mandatory water‑use context, offering targeted guidance for improving policy communication and conservation outcomes, serving as a model for future challenges in other regions globally.

ABSTRACT Colombia is recognized worldwide for its biodiversity and abundant water resources. However, water quality is deteriorating and availability is declining in several regions, driven by disruptions in the hydrological cycle caused by climate change, intensive agriculture, mining, and ineffective management policies. This paper reviews the main challenges facing Colombia’s water resources by analysing the factors affecting both quality and quantity. Our findings highlight climate change and human pressures as the principal drivers. From a hydrological perspective, we propose solutions including expanding data acquisition and management systems, advancing understanding of hydrological processes, improving climate forecasting, and promoting sustainable practices for water conservation. Although centred on Colombia, these insights reflect broader water resource issues in South America and other developing countries, suggesting that the proposed solutions may be applied in similar contexts to strengthen water governance, mitigate risks, and ensure resilience in the face of growing environmental pressures.

Water scarcity is a dynamic condition influenced by a variety of factors, including environmental variables but also political, economic, technological, and social variables. This research reflects the intersection of natural resources, governance, and human systems. Redefining water scarcity is a crucial factor for greater sustainable management in the face of increasing climate variability and geopolitical stress. The traditional water scarcity indices overlook the cumulative impact of climate change, socio-economic patterns, governance, and policies. To bridge this gap, we propose the Integrated Water Strategic Resilience Index (IWSRI), a novel, multidisciplinary index that quantifies water scarcity on the basis of water availability, quality, climate resilience, and socio-political considerations. By integrating hydrological, environmental, and socio-political factors, IWSRI can potentially serve policymakers, researchers, and stakeholders with an interdisciplinary tool for strategic water resource planning. This study outlines the theoretical and mathematical foundations of IWSRI, highlighting its ability to enhance decision-making in transboundary water management, disaster preparedness, and sustainable development. The application of IWSRI is particularly relevant for regions facing severe water stress and political instability, where water availability is both an environmental and security challenge. MENA countries, Israel, Turkey, Qatar, and the UAE possess high water resilience due to solid infrastructure and good governance, while Yemen, Syria, and Libya possess low resilience, driven by conflict and poor management. Egypt, Iran, and Algeria demonstrate moderate resilience due to potential in water management policy. In this respect, while emphasizing its broader applicability as a global tool for assessing water scarcity resilience, this research applies the IWSRI to the MENA region, as its climate, socio-political instability, and regional water stress make it a relevant case study to test its overall efficacy.

ABSTRACT This work designed a new wastewater management system based on control parameters and optimization techniques. The developed framework makes significant contributions by presenting a hybrid position of sun flower and fire hawk (HP-SFFH) for optimizing wastewater treatment processes. The steady-state analysis is performed by adding wastewater to the pure water line. Here, the control parameters like starting time, addition period of external carbon, hydraulic retention time (HRT), and internal rate of return (IRR) are optimized using an HP-SFFH algorithm. Moreover, parameters like aeration efficiency (AE), pumping efficiency (PE), and effluent quality (EQ) are improved using HP-SFFH model. Finally, the suggested approach’s performance is evaluated over various prior frameworks. The proposed model’s pumping energy is 0.016 kWh/d, which offers a significant improvement over classical optimization techniques. The outcome confirmed that the designed model, which encompass various treatment processes and management strategies, help to protect public health, conserve water resources, and minimize environmental impact.

Groundwater resource utilization in many developing countries, including Ethiopia, is significantly constrained by limited information on its quality and quantity, often due to challenges associated with geophysical and hydrogeological assessments. In recent years, geographic information system (GIS) and remote sensing (RS) technologies have emerged as valuable tools for understanding the spatial distribution of groundwater resources, aiding in their planning, exploration, monitoring, and management. Thus, this study aims to delineate potential groundwater availability zones in the Angereb Watershed, located in northwestern Ethiopia, using a geospatial approach integrated with multi-criteria evaluation (MCE) and the analytical hierarchical process (AHP) model. Multiple thematic layers were prepared from various data sources, including Landsat 8 OLI, Shuttle Radar Topography Mission (SRTM), geological maps, soil, and rainfall data. Key factors influencing groundwater availability namely drainage density, lineament density, lithology, slope, soil type, mean annual rainfall, and Normalized Difference Vegetation Index (NDVI) were selected and weighted using the AHP model within ArcGIS 10.3. The analysis identified lithology, lineament density, slope, and drainage density as dominant factors, collectively accounting for approximately 85.3% of the influence on groundwater potential. The final groundwater potential index (GWPI) map categorized the study area into four zones: very high (0.1%; 546ha), high (12.5%; 93,404ha), medium (79.4%; 592,302ha), and low (8%; 59,818ha) potential. The high-potential zones were primarily located in the northwestern and southern regions, influenced largely by favorable geological and physiographic conditions. The predictive performance of the GWPI map was validated using the receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis, yielding an AUC value of 0.83, indicating strong model reliability. This study demonstrates that the integration of MCE with GIS and remote sensing techniques, supported by AHP, offers a cost-effective and reliable method for delineating groundwater potential zones and can serve as a valuable tool for groundwater management and planning in similar data-scarce environments.

Compound drought and heat events (CDHEs) have received increasing attention due to their significant impacts on water resource management, agricultural productivity, and ecosystem stability. Despite notable progress in identifying CDHEs and constructing related indices, most forecasting approaches still rely on conventional statistical frameworks. These are often inadequate for capturing the complex nonlinear interactions and spatiotemporal dependencies among meteorological variables. To address this limitation, we employed four deep learning models (i.e., CNN-LSTM, ConvLSTM, SA-ConvLSTM, and SwinLSTM) to predict the spatiotemporal evolution of the standardized compound drought and heat index (SCDHI). Among them, SA-ConvLSTM and SwinLSTM integrate attention mechanisms, enhancing the models' ability to capture spatial variability and identify relevant features. Using meteorological variables derived from ERA5 reanalysis data, we applied these models to forecast the 10-day (climatological dekad) spatiotemporal evolution of the SCDHI across the Yangtze River Delta (YRD) region in China from 2004 to 2023. Experimental results demonstrate strong predictive performance across all models, with SwinLSTM achieving the highest coefficient of determination (R2 = 0.892). Compared to the baseline ConvLSTM, SwinLSTM improved the R2 by 3.24% and reduced the RMSE and MAE to 0.422 and 0.276, respectively, showing superior accuracy in capturing complex spatiotemporal patterns of compound events. This study provides an effective and generalizable framework for the short-term spatiotemporal prediction of CDHEs, supporting early warning and regional climate resilience efforts.