In the next decades, some countries will suffer from water shortage because of the increased population and the increased percentage of economic development. Accordingly, it is necessary to find the alternative way to solve this problem and investigate the development. Nanoscale graphene pieces known as graphene quantum dots (GQDs) exhibit distinctive characteristics that render them intriguing contenders for an array of novel uses. Cellulose acetate (CA) membranes were prepared from CA powder, formamide, and acetone with different concentrations of graphene quantum dots using the technique of phase inversion. The produced membranes were subjected to Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and contact angle studies to characterise their structural, morphological, hydrophilic, and biofouling behaviour. The graphene quantum dots additives proved to enhance the performance as well as antibiofouling characteristics of the CA-RO membranes. The CA membrane showed a flow of water 42.8 L/m2 h and a 99.4% salt rejection rate using a 10,000 ppm NaCl solution after annealing.

Developing a product that serves a dual purpose as an adsorbent/photocatalyst for ecological applications is a compelling study area. This article discusses a facile ultrafast synthesis of BiOCl/Bi2S3/biochar (BiOCl/Bi2S3/BC) by solid-state reaction with coupled roles as photocatalyst during sunlight exposure and adsorbent when there is no light. BiOCl/Bi2S3/BC was characterized by XRD, SEM, EDX, HR-TEM, XPS, FTIR, Raman, and DR/UV–Vis spectroscopy techniques. The adsorption efficiency and its parameters were explored using ciprofloxacin (CPF) as a contaminant model drug in the absence of light irradiation. The coupled role as adsorbent/photocatalyst was considered under direct sunlight irradiation. BiOCl/Bi2S3/BC30 achieved removal of 81.25% in the dark and reached 90% during sunlight exposure within 1 h. BiOCl/Bi2S3/BC30 exhibited a triplet initial adsorption rate, and doubled rate constant of photocatalysis (11.34 mg g−1 min−1, 0.642 min−1) compared to BiOCl/Bi2S3 (3.88 mg g−1 min−1, 0.256 min−1), respectively, indicating the adsorptive, catalytic, and cocatalytic role of BC. Mechanism studies indicated that BiOCl/Bi2S3/BC separated CPF by adsorption via electrostatic interaction, π–π conjunction, and hydrogen bonding while the photocatalysis occurred through the S-scheme mechanism where •O2− and h+ play the predominant role in the photocatalytic degradation. Besides, the BiOCl/Bi2S3/BC30 hybrid was stable and revealed acceptable recyclability after four consecutive cycles for CPF removal from their aqueous solutions. This work provided an ultrafast, simple, economical, and efficient strategy for the application of BiOCl/Bi2S3/BC hybrid structure that has a dual function of adsorption and photocatalysis for the elimination of CPF from water.

Extraction of cellulose nanocrystal (CNC) from agricultural waste is a cost-effective and environmentally beneficial technique. Six nanocomposite membranes were created by combining varying concentrations of CNC and tetraethyl orthosilicate (TEOS) with polyethersulfone (PES) for the higher removal efficiency of phosphoric acid from wastewater. It was found that the M6 (CNCs/PES/TEOS) membrane has a higher separation efficiency of 82.4% and an appropriate water flux of 169.2 (L.mole/h) at a phosphoric acid concentration of 50 ppm. This result indicated that using 1 wt. % CNCs and 1 wt. % TEOS in the polymer mixture enhances the polymeric membrane's mechanical properties and hydrophilic characteristics with a moderate porosity close to 42%. The mechanical test revealed that the M6 membrane, with a strength of 30.8 MPa and an elongation of 45.17%, exhibited good compressible behavior and flexibility. Moreover, it has an optimal antifouling capability, with a flux recovery percentage close to 99%.

Upper Gilgel Abay watershed (UGAW) has important physiographical and hydrological implications for the Blue Nile basin, but research on its morphometry and hydrology is limited. This study tried to delineate the UGAW and its sub-watersheds, compute basic morphometric parameters, and link each value with its drainage characteristics. Ranking and prioritization of the sub-watersheds were also done to suggest future interventions. GIS and remote sensing techniques were utilized to delineate the sub-watersheds and compute 32 selected basic morphometric parameters. A 12.5-m resolution ALOS PALSAR DEM was the input data used in GIS software to analyze and characterize the hydro-geo-morphometric features. The findings indicated that the watershed exhibits a dendritic pattern with six stream orders. A 1314.4 km total flow length for all orders is distributed within a 1657.5 km2 catchment area. The infiltration number is high, causing greater surface runoff, while its low circularity ratio (0.27), elongation ratio (0.52), and bifurcation ratio (2.1) connote the basin’s nearly compact shape. Besides, it has high basin relief (1650 m), slope, and ruggedness number, implying high erosional potential in the area. Generally, the watershed experiences high runoff and minimal infiltration, making it highly prone to soil erosion and land degradation. UGAW was divided into six sub-watersheds (SW1–SW6), each characterized by distinct hydro-geo-morphometric features. The prioritization of these sub-watersheds was based on 20 purposely selected parameters. Consequently, the sub-watersheds were ranked from highest to lowest priority as follows: SW6, SW5, SW4, SW3, SW1, and SW2. This ranking indicates that SW6 and SW5 are more susceptible to various forms of degradation in land, water, and other natural resources, necessitating a high priority for the implementation of integrated land and water resource management strategies.

Saltwater intrusion has significant and diverse impacts on agriculture, freshwater resources, and the well-being of coastal communities. To effectively address this issue, precise models for predicting saltwater intrusion must be developed, as well as timely information for reaction planning. In this study, a spectrum of machine learning (ML) methodologies, specifically Random Forest Regression (RFR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Ridge Regression (RR), was systematically employed to predict salinity levels within the coastal environs of the Mekong Delta, Vietnam. The input dataset comprised hourly salinity measurements from Tran De, Long Phu, Dai Ngai, and Soc Trang stations and hourly water-level data from Tran De station and hourly discharge data from the Can Tho hydrological station. The dataset was partitioned into two distinct sets for the purpose of model development and evaluation, employing a division ratio of 75% for training (constituting 8469 observations) and 25% for testing (comprising 2822 observations). The results indicate that ML models are suitable for short-term salinity prediction, with a forecasting time of up to 16 h in this area. These research findings highlight the potential of machine learning in addressing saltwater intrusion and provide valuable insights for developing appropriate response policies. By leveraging the strengths of these models and considering the optimal forecasting time, policymakers can make informed decisions and implement effective measures to mitigate the impacts of saltwater intrusion in the Mekong Delta.

This study presents a comprehensive analysis of flood hazard mapping in Ankara, the capital of Türkiye, highlighting the critical vulnerability of this major urban center to climate-related disasters. By applying advanced boosting algorithms—specifically, XGBoost, GradientBoost, and CatBoost—along with hyperparameter optimization through the Fick’s law algorithm (FLA), this research introduces an innovative methodology aimed at improving the reliability and accuracy of flood hazard assessments in Ankara’s urban landscape. The analysis utilizes an extensive dataset that integrates topographic, meteorological, hydrological, and anthropogenic variables to provide critical insights into the dynamics of urban flooding with a focus on Ankara’s vulnerability. This approach is novel in that it incorporates FLA for hyperparameter optimization, marking a significant advancement in flood hazard modeling and achieving higher model accuracy and generalizability. Notably, among the various determinants of flood hazard identified, elevation emerges as the most influential factor affecting flood risk in Ankara. This finding underscores the complex relationship between urban geography and flood hazards, and highlights the need for targeted urban planning and infrastructure development strategies to effectively mitigate flood risk. The implications of this research extend beyond the local setting, contributing valuable insights to the global discourse on climate change adaptation and urban resilience. By combining cutting-edge machine learning techniques with in-depth geographic analysis, this study offers a scalable and innovative model for flood hazard assessment and management, providing a critical tool for cities around the world facing similar challenges.

Titanium dioxide (TiO2) is applicable in photocatalysis and light-induced processes for activation of substrates, to be superiorly applicable for converting toxic containments into harmless fragments. Carbon nanostructures (CNs) are extensively attracted the attention of researchers, attributing to their diversity and exclusive physicochemical characters. Herein, the affinity of CNs either under acidic or basic conditions is studied for enhancing the catalytic potency of TiO2 in order to be applicable without light, that is considered to be more economic, energy and cost-saving purposes. Currently, carboxymethyl starch was exploited as an origin for CNs under the infrared-assisted conditions. Afterward, CNs were successfully uploaded within TiO2 under both acidic (CNs@TiO2-formic) and basic (CNs@TiO2-NaOH), to be applicable for catalytic degradation of methylene blue. CNs-formic were successfully prepared with slight smaller average size (6.6 ± 1.9 nm) rather than the base-CNs-NaOH (9.8 ± 3.7 nm). CNs@TiO2-formic were exhibited with higher catalytic performance rather than CNs@TiO2-NaOH. MB degradation percent reaches 98% after only 30 min by exploiting CNs@TiO2-formic as a catalyst in the absence of light. Moreover, without irradiation, t1/2 was superiorly shortened by nearly ten times under the catalytic performance of CNs@TiO2-formic in the absence of light.

Groundwater quality assessment is crucial for sustainable water resource management in Maharashtra, India, where groundwater helps for main water sources for irrigation, domestic, and industrial sectors. Despite numerous studies on regional groundwater quality, there remains a lack of integrated research combining hydrogeochemical analyses with advanced spatial and statistical techniques. This study addresses this gap by developing a comprehensive groundwater quality assessment framework that uniquely integrates hydrogeochemical analyses, geographic information system (GIS) techniques, water quality index (WQI), and multivariate statistical approaches in the Morna River Basin. A total of 82 water samples were analyzed for physicochemical parameters in the pre-monsoon (PRMS) and post-monsoon (POMS) seasons. The WQI analysis revealed that 46.15% of samples exhibited excellent water quality, while 48.72% showed good quality during both seasons, though a notable quality decrease was observed during the POMS. Correlation analysis identified significant positive associations (p < 0.05) between key parameters, including Mg-TH, EC-pH, and Ca2+-TH. Principal component analysis identified six components explaining 75.534% of total variance in PRMS, with the first component contributing 17.437%. In POMS, five components explained 70.963% of variance, with the first component contributing 20.653%. Factor analysis revealed that mineral dissolution, agricultural activities, and anthropogenic inputs were the primary factors influencing the water chemistry. The spatial distribution maps generated through GIS analysis identified hotspots of contamination. This integrated approach provided a robust framework for understanding the complex interactions between natural and anthropogenic factors impact on the groundwater quality. The results suggest regural monitoring of water quality and an identified hotspots and implementation of rules and regulations on the agricultural practices and waste disposal. This research contributes to support of groundwater management strategies and provides a methodological framework appropriate to similar hydrogeological settings in other area or worldwide.

The problem of access to clean water has been highlighted by the United Nation’s Sustainable Development Goals, and in areas such as Islamabad, Pakistan, water pollution is more of an immediate concern. The impact of excessive use of fertilizers coupled with improper waste management has harmed aquifers. This necessitates the need for tools to map out regions of concern and assist with clean-up strategies. This paper uses an amalgamation of the DRASTIC model and GIS capabilities to evaluate the contamination threat to aquifers in Islamabad. The model involves seven components: depth to water, recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity, and formulates an index of susceptibility within the range of 275–900. The areas were classified into five categories according to their level of susceptibility: very low (275–400; 22 km2, 2%), low (400–525; 306 km2, 28%), moderate (525–650; 500 km2, 47%), high (650–775; 221 km2, 21%), and very high (775–900; 26 km2, 2%). Twenty-eight of the samples had nitrate concentrations ranging from − 0.72 ppm to 2.8 ppm which helped calibrate the model and did not show a high correlation with the DRASTIC index. This suggests that the contamination was limited and did not originate from widespread sources. The results highlight the importance of focusing measures on high-risk areas, such as Rawal Lake and the National Agricultural Research Center, where risks of contamination are severe. The baseline that the present study has developed is useful in terms of safe groundwater extraction and also offers a workable methodology for urban groundwater management practices in the world. Its usefulness is enhancing policies aimed at protecting clean water resources and reducing the risk of environmental degradation in sensitive areas worldwide.

Cavitation is one of the primary causes of breakdown and failure on chute spillways, causing surface damage and structural destruction. In this research, a three-dimensional two-phase flow over an ogee spillway was modeled using the FLOW-3D model for the Gelevard-Neka spillway and validated with the available field data. After analyzing the hydrodynamic parameters of flow, a method was presented to predict the intensity and location of cavitation damage on the spillway surface based on the support vector machine (SVM) model. The hydraulic parameters, including flow velocity, pressure, and cavitation index, were introduced to the SVM model, and the cavitation damage level, from no damage to major damage, was predicted along the spillway structure. The validation flow results agreed well with the field data, and the normalized root-mean-square error value of 0.0196 was obtained. In the prediction of cavitation damage using the SVM model, the MAE, R, and RMSE for the training stage were, respectively, 0.32, 0.882, and 0.127, and for the testing stage were 0.024, 0.857, and 0.133. The results show reasonable performance of the SVM model in the prediction of cavitation damage. According to the results, the spillway is susceptible to cavitation damage with the most significant damage anticipated to occur in the distance range of 70–190 m from the spillway origin. Based on the importance of the aerators in protecting the spillway from cavitation damage, it is recommended to investigate the various effects of aerators on mitigating cavitation damage.