Critical Water Research Articles

Fabrication and characterization of extracted microsized chitosan embedded PVDF membrane for wastewater treatment

Abstract Critical water crisis due to overpopulation, industrialization and urbanization has resulted to the shortfall of providing safe drinking water to one billion people in the world. This work intends to fabricate modified hydrophobic polyvinylidene fluoride (PVDF) membrane by hydrophilic chitosan extracted from waste fish scales for wastewater treatment. Chitosan was blended with PVDF in N-methyl-2-pyrrolidone solution by solution casting method. Three different concentrations of 1, 3 and 5 wt% of chitosan were taken. The morphological, spectral and thermal analysis of the produced films were elaborately studied. The contact angle decreased with the increase of chitosan content from 82.64° to 60.97°. The tensile strength decreases from 1.11 to 0.38 MPa and Young’s modulus decreased from 12.08 to 7.4 MPa with increase in chitosan content in the film from 1 to 3 %. The performance of microparticle embedded membrane was compared with the pristine PVDF membrane, and it was affirmed that the microsized-chitosan embedded PVDF membrane manifests a broad promising application perspective towards wastewater treatment owing to its excellent water flux (76 L m−2 h−1) as well as antibacterial properties.

Influence of environmental conditions on the growth of Pleurotus ostreatus in sand

Pleurotus ostreatus, a saprotrophic fungus, has been proposed for the remediation of organic contaminants in soils and more recently for modifying the hydraulic and mechanical behaviour of granular soils. The in situ performance of fungal-based biotechnologies will be controlled by the fungal growth and associated biochemical activity that can be achieved in soil. In this study, the influence of environmental conditions (temperature, degree of saturation), substrate type (lignocellulose and spent coffee grounds) and concentration on the mycelium growth of P. ostreatus in sand are investigated. Furthermore, the evolution of growth/survival indicators (respiration, ergosterol concentration) and enzymatic activity (laccase, manganese peroxidase) are investigated. Temperature was shown to have a strong influence on the growth of P.ostreatus in sand: growth was observed to be delayed at low temperatures (e.g. 5 °C), whereas growth was prevented at high temperatures (e.g. 35 °C). No growth was observed at very low degrees of saturation (Sr=0% and 1.2%), indicating there is a critical water content required to support P.ostreatus growth. Within the mid-range of water contents tested radially, growth of P.ostreatus was similar. However, growth under saturated soil conditions was restricted to the air-water atmosphere due to the requirement for oxygen availability. Low substrate concentrations (1%–5%) resulted in high radial growth of P.ostreatus, whereas increasing substrate content further acted to reduce radial growth, but visual observations indicated that fungal biomass density increased. These results are important for understanding the feasibility of P.ostreatus growth under specific site conditions and for the design of successful treatment strategies.

Analysis of groundwater and surface water quality using modelled water index and multivariate statistics in Kampala and Mbarara Districts, Uganda

The demand for domestic, public and industrial use of water increases with population growth. However, activities from humans and natural environment have resulted into decline in water quality. The aim of this study was to evaluate the quality of groundwater and surface water collected from Kampala and Mbarara districts of Uganda using water quality index and multivariate statistics. A total of 10 physicochemical parameters (pH, water temperature, chloride, sulphate, nitrate, electrical conductivity, total dissolved solids, total suspended solids, total solids, and phosphate) were measured monthly to evaluate the quality of water in the study districts. Results showed that sulphate, electrical conductivity and total dissolved solids were observed with significantly high values in surface water than the groundwater for both seasons. Among all the parameters measured, electrical conductivity was observed with the highest value (2129.67 ± 1.41 µS/cm) while nitrate concentration (0.57 ± 0.02 mg/L) was observed with the lowest during the wet season in surface water. In addition, the mean values of nitrate, electrical conductivity, total dissolved solids and sulphate detected in the groundwater and surface water were within the recommended limits, while phosphate and chloride were observed with mean concentrations above the regulatory limits in 50% of the samples. The correlation analysis revealed strong and positive association between electrical conductivity and total dissolved solids in groundwater and surface water for both seasons. Also, the chemical make-up of groundwater and surface water within the study districts revealed that the determination of water quality rests on two major indices (conductivity and anthropogenic-pollutants) based on the factor analysis. The two indices accounted for 0.619% in groundwater and over 0.70% in surface water, indicating more pollution in surface water. Moreover, the water quality index (WQI) values obtained ranged from 14.81 to 115.73 in dry season and 7.77 to 108.24 during the wet season. According to WQI classification, 42% of the samples collected fell within the “excellent” and “good” categories while others fell within “poor” and “unfit” categories (58%) during the dry season. Based on these findings, appropriate treatment methods, proper sanitation and waste management should be implemented in locations with critical water conditions. Also, frequent monitoring of groundwater and surface water quality by environmental protection agency is highly recommended.

EVALUACIÓN DEL IMPACTO DE LOS CAMBIOS EN EL USO Y COBERTURA DE LA TIERRA EN EL ESCURRIMIENTO SUPERFICIAL

The aim of this study was to analyze the behavior of surface runoff as a result of changes in land use and land cover (LULC), using the Rio Novo basin (Brazil) as the analysis site. To estimate surface runoff, the curve number (NRCS-CN) method was used; as input data, the LULC classifications provided by the MapBiomas project and the pedological map of the state of São Paulo (1:250,000) were used. The Rio Novo basin was discretized into ten sub-basins. The results showed major changes in LULC between 1992 and 2022, with a reduction in pasture areas and an increase in annual crops and sugarcane fields, as well as in urban areas. Such alterations are conditioning factors of changes in Curve Number (CN) values, which imply changes in surface runoff values. For a rainfall of 130 mm (roughly 10-year return period), the surface runoff depth values ranged from 3.21 mm (areas with forest cover on soils in hydrological group A) to 101.35 mm (for urban areas on soils in hydrological group C). The urbanization process that occurred in sub-basins 1, 6, and 9 led to an increase in areas with the highest CN values (CN equal to 85 and 90). Locations with increased surface runoff values should be considered as critical areas, where soil and water losses may occur, thereby compromising water security in the basin. Keywords: NRSC-CN method, Geographic Information System, Water Resources Management, MapBiomas.

Caracterización y Análisis de la Calidad del Agua en Entornos Urbanos mediante la implementación de un Sistema Embebido con Tecnología IoT

This study aims to characterize and analyze water quality in urban environments using an IoT embedded system, focusing on San Camilo’s community of Quevedo, Ecuador, where population growth has increased the demand for drinking water. An embedded system was designed to make use of a microcontroller and sensors to monitor critical water quality parameters in real time. The collected data is transmitted to a cloud platform for storage, analysis, and visualization. Spearman's correlation coefficient was preferred for statistical analysis of the data. This allowed for the identification of patterns and relationships between the variables examined. The data revealed significant associations in non-linear data, facilitating a decision regarding the development of predictive models for water management and treatment. The results demonstrate the feasibility and efficiency of using IoT technologies in the continuous monitoring of water quality, offering a scientific basis for the collection and processing of relevant data on the quality of drinking water in residential areas, providing a robust tool to monitor public health and the comfort of the inhabitants.

Co-tracing of groundwater emerging and conventional contaminants in karst area of Southwest China: Distribution, source, and source-specific health risks

Groundwater pollution, especially the co-contamination of emerging contaminants (ECs) and conventional contaminants, is increasingly recognized as a critical global concern. Karst aquifers are found throughout the world, providing critical water sources and sustaining rivers and ecosystems, and are particularly vulnerable to pollution. In this study, the characteristics of the contamination, potential sources, and source-specific health risks related to ECs and trace elements (TEs) in groundwater were investigated in Guiyang (a typical karst city in southwest China). The distribution of TEs followed a descending pattern from urban to suburban to rural areas. Interestingly, the median concentrations (217 ng/L) of Caffeine (CAF) in rural areas surpassed those in urban areas (145 ng/L) and even exceeded these in densely populated areas. Additionally, ECs and TEs in groundwater are significantly influenced by population density and land use types within a 1000-meter buffer zone around the sampling sites. Hierarchical cluster analysis and positive matrix factorization suggested that five principal sources of contamination were identified: domestic wastewater (29.7 %), industrial production (25.2 %), agricultural activities (16.3 %), natural and industrial sources (14.9 %), and pharmaceuticals (13.9 %). Monte Carlo simulations revealed that the non-carcinogenic risks to children in urban areas are significant, with 30.0 % of these risks exceeding the safety threshold. Source-specific health risk assessments indicated that industrial production is the primary source of risk. Furthermore, this study introduces a co-tracing method to trace the sources of groundwater contamination in karst systems. The co-tracing method based on the ECs and TEs allows for a deeper understanding of contaminant transfer and its fate in karst groundwater.

Internet of Things-Driven Precision in Fish Farming: A Deep Dive into Automated Temperature, Oxygen, and pH Regulation

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

Suitability Assessment and Optimization of Small Dams and Reservoirs in Northern Ghana

Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation purposes, critical water issues persist during dry periods. Key drivers in this failure are attributed to the lack of studies and/or the number of inadequate studies on suitable dam siting. This study focused on assessing the sites of selected small dams in northern Ghana, employing various methods such as stream order analysis and the Analytic Hierarchy Process within a Geographic Information System framework. Results showed that many existing dams are poorly sited, with over half located far from major stream networks, resulting in drying out during the dry season and failing to meet sustainable water storage standards. This study proposed new dam locations that would allow achieving a significant increase in storage capacities from 30% to 60%. These results highlight the necessity for decision-makers to adopt research-based approaches to address water shortages effectively, balancing agricultural, domestic, economic, and environmental needs. Future research should integrate climate change considerations, long-term monitoring, environmental impact assessments, and advanced decision-making techniques such as machine learning.

Impact of Extreme Drought on Vegetation Greenness in Poyang Lake Wetland

The Poyang Lake Wetland, an internationally significant ecosystem, frequently experiences drought during the flood season. However, the total impact of extreme drought on wetland vegetation remains poorly understood. This study determined the standardised precipitation evapotranspiration index (SPEI) and analysed drought trends within the Poyang Lake Basin. Additionally, spatiotemporal variations in wetland vegetation under drought conditions were examined by analysing the mean normalised difference vegetation index (NDVI) values and categorising NDVI classifications. The key factors affecting wetland vegetation and its respective thresholds were determined. The Poyang Lake Basin has experienced increasing aridity over the past 3 years. In response to this trend, the wetland vegetation area in Poyang Lake expanded, whereas vegetation greenness declined. Notably, in the year following an extreme drought, Poyang Lake’s vegetation greenness was lower than that during the same period in previous years. Regardless, the correlation analysis showed no significant relationship between the SPEI values and the wetland vegetation greenness; however, water level changes significantly impacted the wetland vegetation, with a correlation coefficient of −0.89 (p < 0.001). A critical water level of 14 m was identified as the threshold at which sudden changes in the mean NDVI were observed. This research offers valuable insights into hydrological management strategies to protect Poyang Lake Wetland’s vegetation under drought conditions. Future studies should enhance the differentiation of drought tolerance among different wetland plant species, thereby achieving differentiated hydrological management.

The assessment of groundwater reserves, quality, and balance in Quang Nam region, Vietnam: using MIKE BASIN and MODFLOW models

Abstract This comprehensive study examines the groundwater reserves and quality within the Quang Nam region, Vietnam, utilising the integrated MIKE BASIN and MODFLOW models. The research systematically analyses the coastal and key economic zones, covering 259,039 hectares, including urban and rural districts. The study delineates the region's hydrogeology, revealing a significant volume of groundwater reserves with a static water level, suggesting robust aquifer systems, particularly in the Dien Ban and Thang Binh districts. Furthermore, hydrogeochemical analysis assessed groundwater quality, indicating suitability for agriculture, which accounts for 81% of the region's water demand and other uses. The study's novelty lies in its dual approach of quantifying groundwater reserves and evaluating their quality, addressing a critical regional water resource management gap. The findings present an optimistic scenario for the current and future sustainability of water resources, with the calculated groundwater reserves demonstrating ample capacity to support the varied needs of the region. This research establishes a foundational understanding of Quang Nam's hydrological system. It provides strategic insights for sustainable water management in response to socio-economic development and climate change projections up to 2030. Climate projections for Quảng Nam province under RCP 4.5 and RCP 8.5 scenarios indicate significant increases in rainfall, temperature, sea level rise, and saltwater intrusion, highlighting the critical need for adaptive strategies to protect local ecosystems and communities from these escalating climate threats.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and Neural Network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03-4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Machine‐Learning Based Multi‐Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought

AbstractSoil moisture (SM) is an essential climate variable, governing land‐atmosphere interactions, runoff generation, and vegetation growth and productivity. Timely forecasts of SM spatial distribution and vertical profiles are needed for early detection and prediction of potential droughts. However, previous studies have primarily concentrated on historical or near real‐time soil moisture mapping, with less effort devoted to the development and integration of soil moisture forecast components within drought assessment systems. A satellite‐driven machine‐learning approach was developed in this study to build complex relationships between diversified predictor data sets and in situ multi‐layer SM measurements from the Montana Mesonet, a regionally dense environmental station network in the US upper Missouri and Columbia basins. The resulting 30‐m daily SM predictions showed strong performance against in situ SM measurements from 4‐, 8‐ and 20‐inch soil layers, and with 1‐ to 2‐week forecast lead times (R > 0.91; RMSE ≤ 0.047 cm3/cm3). The machine‐learning model was subsequently applied to the entire Montana region, and the SM deficit forecasts with both 1‐ and 2‐week lead times successfully depicted onset, progression, and termination phases of the 2017 Montana flash drought, which was not effectively identified from prevailing operational systems. The resulting system is capable of delineating local scale SM heterogeneity, and could be extended to predict other critical water cycle variables, potentially enhancing future drought forecasts through multivariate assessments and benefiting water resource management, agricultural practices, and the provision of ecosystem services.

A sustainable approach for efficient ammonium recovery, and simultaneous TOC removal from aqueous solutions by vesicle-like iron phosphate-based carbon nanomaterials

NH4+ is an ion with versatile potential; however, the release of wastewater containing this component, regardless of its high or low concentration, causes severe eutrophication in aquatic systems and contaminates numerous manufacturing processes. Thus, this study developed a sustainable method that can simultaneously remove, recover NH4+, polish water, oxidize organic matter, and yet release a material that can still be used as fertilizer. Regarding NH4+ removal, FeP400 rapidly exhibited an exceptional NH4+ uptake capacity (343.5 mg g-1) within 8 min, even in dairy processing wastewater with high NH4+ concentrations and diverse co-existing components. FeP400 could oxidize organic compounds spontaneously to remove TOC, indirectly enhancing its NH4+ uptake up to 33.5 % through charge balance mechanisms. The adsorption process involved both chemical (i.e., double-salt precipitation) and physical mechanisms (i.e., H-bonding and electrostatic interaction), as confirmed by thermodynamics, FT-IR, and XPS analyses. Regarding recovery, FeP400 can be reused for over 10 cycles with high removal (81 %) and NH4+ recovery (88 %), a significant improvement over conventional options. FeP400 also performed efficiently under flowing conditions using low-range NH4+ and TOC samples over 10 cycles, polishing not only 34.1 L of water with undetected NH4+, neutral pH, and extremely low TOC but also effectively recovering the NH4+ uptake at an economical cost. Lastly, its environmentally friendly nature, which contains essential nutrients for plant growth, further enhances its recyclability after release. Thus, FeP400 is believed to offer a transformative, sustainable, and highly efficacious solution to the NH4+contamination and critical ultrapure water issues that industries urgently address.

Ecosystem service value and ecological compensation in Qilian Mountain National Park: Implications for ecological conservation strategies

Qilian Mountain National Park (QMNP) is a critical ecological barrier and water conservation area in northwestern China. Its diverse ecosystems provide significant ecosystem services, yet the valuation of these services and their dynamic responses to land use/cover (LULC) changes and ecological protection measures require detailed investigation. This study employs an improved equivalent factor method, tailored to the specific ecosystem types and LULC patterns of QMNP, to evaluate the ecosystem service value (ESV) across different LULC types. Using an elasticity analysis model, the sensitivity of ESV to LULC changes from 1990 to 2022 was quantitatively assessed. The results indicate that the ESV of QMNP increased from 46.13 billion RMB to 50.97 billion RMB between 1990 and 2022, mostly due to the growth of grassland and forest areas. The primary route for increasing the region’s ESV is the conversion of desert to grassland, and this should be a major area of emphasis for next ecological conservation initiatives. Regulating services form the core of the ESV in QMNP, with climate and hydrological regulation contributing significantly to the overall ESV. Maintaining the stability of forest, wetland, and water body areas is crucial for sustaining the total ESV of QMNP. Through the application of carbon emission coefficients and ESV methods, the average range for long-term ecological compensation standards in QMNP is expected to be between 3.78 billion RMB and 28.28 billion RMB annually. Additionally, we explore the impacts of ongoing ecological protection mechanisms on enhancing ESV. There is a pressing need to formulate integrated policy frameworks that balance ecological conservation with sustainable economic development. The study also highlights the necessity of strengthening the protection and foundational research of grasslands and water bodies to enhance the ESV of QMNP. These insights serve as a foundation for potential planning and management strategies in QMNP, aiming to maintain and enhance its ecosystem functions and services, thereby contributing to broader regional and global ecological stability.

Experimental investigations of the shear strength and deformation behavior of a frozen unsaturated silt

There are limited studies in literature with respect to the deformation and shear strength behavior of frozen unsaturated soils that consider sensitive changes in temperatures below 0 °C. For this reason, the focus of the present study is directed towards investigating the shear strength and deformation behavior of a frozen unsaturated silt. As a part of the study, one-dimensional freezing tests were performed on compacted silt samples with various initial water contents and dry densities. The pulsed nuclear magnetic resonance (P-NMR) method was used to obtain distribution of unfrozen water content of silt samples; this information was used to explain the deformation behavior during freezing. The volume change behavior in unsaturated soils due to freezing can be associated to three primary factors: expansion caused by the ice-water phase transition and shrinkage induced by ice-cementation and dehydration. A critical water saturation was also observed at which the frost heave is minimum, or no volume change occurs. In addition, conventional direct shear tests were performed on compacted unsaturated silt samples both in frozen and unfrozen conditions to determine and quantify the shear strength contribution arising from the different cohesion components. The investigations in this study provide valuable information that can be used in rational explanation of the strength and deformation behavior of unsaturated frozen soils.

Determination of Critical Crop Water Stress Index of Tea under Drought Stress Based on the Intercellular CO2 Concentration

Climatic changes have caused seasonal drought to occur frequently in tea fields of low-mountain and hill regions over the past decades. This leads to huge losses in the quality and yields of famous tea, which restricts the economic development of the tea industry. It is crucial to implement suitable irrigation scheduling. The crop water stress index (CWSI) is the main index to assess the water status of the crop. When the crop suffers irreversible drought stress, its critical water status cannot be easily evaluated using the CWSI. The change from stomatal limitations (SLs) to non-stomatal limitations (NSLs) of photosynthesis is vital for accurately recognizing crop drought stress. Thus, the objective of this research is to determine the critical crop water stress index of tea based on intercellular CO2 concentration (Ci) dynamic responses to drought stress. During two sensitive periods of water stress (famous tea harvest season and summer drought season, which are from March to April and July to August, respectively), the dynamic changes in the CWSI in tea were calculated and analyzed based on the CWSI theoretical model. The upper and lower baselines were determined on a daily basis and during a certain period. A critical value of the CWSI represents irreversible drought damage. This was determined by the characteristic response of the Ci of tea leaves during extreme drought stress. The results showed the following: (1) during the famous tea harvest season and summer drought season, the daily variation in CWSI was similar. During a certain period, the former maintained a stable fluctuation, while the latter increased in fluctuation. (2) The Ci showed a trend of fluctuating downward to a low point and then upward during extreme drought stress. After reaching the low point, it quickly increased in the former and stabilized for a day in the latter. When the Ci reached the low point, the upper benchmark of this critical point was 13.9 μmol·mol−1, the lower benchmark was 3.4, and the CWSI was 0.27. This critical CWSI could be used as an irrigation threshold point to ensure normal production for tea fields.

Integrating deep learning techniques for effective river water quality monitoring and management

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.

Hydraulic fracturing in cement mortar subjected to seepage-sulfate coupled attack under varying pH levels

Cement-based structures are prone to hydraulic fracturing in acidic sulfate and seepage environments, posing a significant safety threat to engineering projects. To investigate the impact of pH levels on hydraulic fracturing in cement-based materials under seepage-sulfate coupled attack, hydraulic fracturing test and splitting tensile strength test have been conducted on cement mortar specimens permeated with Na2SO4 solution. A mathematical model is developed to establish the relationship between the critical water pressure of hydraulic fracturing and the splitting tensile strength at varying pH levels. The results show that the critical water pressure and splitting tensile strength initially increase and then decrease with the increase in erosion duration under pH levels of 7 and 3, while they continuously decrease with the increase in erosion duration under pH level of 1. Furthermore, the peak values of the critical water pressure and splitting tensile strength gradually diminish, which indicates an increased deterioration in hydraulic fracturing resistance with the decrease in pH value. The relationship between critical water pressure damage coefficient and erosion duration can be accurately described by a quadratic polynomial. Overall, the findings of this study can serve as a useful reference for enhancing the structural performance of cement mortar subjected to seepage-sulfate coupled attack at varying pH levels.

Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization Using Concentration-Discharge Hysteresis.

Suspended sediment is a critical water quality parameter and an indicator of geomorphic processes, but suspended sediment dynamics in urban streams may not conform to the first-flush model widely used for other pollutants. We analyzed discharge and turbidity data for 367 events from three urban watersheds (impervious cover 16-45%) in Cleveland, Ohio (USA). Less intensely urbanized watersheds exhibit higher turbidity compared to that of the most highly urbanized watershed. Proportionally, more counterclockwise hysteresis is observed in the two less urbanized watersheds, and more clockwise hysteresis occurs in the highly urbanized watershed. However, hysteresis patterns are driven by different mechanisms in each watershed, and geomorphic analysis was critical to identifying the underlying mechanisms. In the least urbanized watershed, spatial rainfall variability controls sediment hysteresis. In the intermediate watershed, the erosion of upstream weathered shale banks during dry periods plays a significant role in the sediment supply and shaping hysteresis. In the most urbanized watershed, high eroding banks in downstream reaches lead to more frequent clockwise hysteresis. Overall, we suggest that as the impervious surfaces increase, the availability of instream sediments (bed and banks) plays an increased role in suspended sediment dynamics, and geomorphology remains essential for guiding management decisions.

Climate change impact on water scarcity in the Hub River Basin, Pakistan

The Hub River Basin (HRB), a critical transboundary water source for Sindh and Baluchistan provinces in Pakistan, may face worsening water scarcity due to climate change and population growth. This study aims to assess the current state of water scarcity in the HRB and assesses its vulnerability to these pressures in future. To evaluate the baseline water scarcity in the HRB, a calibrated and validated Soil and Water Assessment Tool (SWAT) was established. Five General Circulation Models (GCMs) were employed to project the future climate under Representative Concentration Pathways (RCP 4.5 and 8.5) for the HRB. Sector-specific indicators were also used to assess the temporal and altitudinal sensitivity of the basin to climate change. These climate projections were incorporated in the SWAT model to simulate flows for three different periods: Early Future (EF; 2010–2039), Mid Future (MF; 2040–2069), and Far Future (FF; 2070–2099). The SWAT model results indicate significant increase in mean flows simulated by SWAT, ranging from 15.27 to 52.78 m3/s under RCP 4.5 and RCP 8.5 compared to baseline flows at HRB. Additionally, the study examines the temporal variation in basin stress and scarcity levels using Falkenmark and Water scarcity indicators. The findings indicate a general decrease in the basin's stress and scarcity levels, potentially benefiting water users of the HRB, especially under RCP8.5. This study offers crucial insights for shaping policies and strategies to adapt to climate change and population growth, ultimately aiming to minimize their impacts on HRB's water resources. By informing water managers and promoting sustainable water management practices, this research can help prevent future conflicts over water allocation and infrastructure development linked with the HRB.