Applied Water Science最新文献

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.

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, t_1/2 was superiorly shortened by nearly ten times under the catalytic performance of CNs@TiO₂-formic in the absence of light.

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.

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.

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.

In the East Nile Delta Region, Egypt, water scarcity is a major concern, as the shallow Quaternary aquifer has been observed to experience a persistent decline in groundwater levels alongside a deterioration in groundwater quality. The present study was carried out to identify the processes that control water quality mainly in relation to salinity sources and the suitability for drinking and irrigation purposes in the study area. To achieve this aim, 19 surface water and 110 groundwater samples were analyzed for physical parameters (TDS, pH, temperature, and EC), major ions (Na, Ca, K, Mg, Cl, SO₄, and HCO₃) along with spatial and multivariate statistical analysis. The geospatial distribution of the total dissolved solids (TDS) and majority of the major chemical ions show an evident increase toward the north and northeast parts of the study area. Graphical methods using Gibbs plot and Piper diagram showed that water chemistry was mainly affected by weathering and water rock interaction while the geochemical evolution results revealed the dissolution and precipitation of carbonates and silicates, ion exchange processes, dissolved evaporite minerals, and anthropogenic activities. Still, the geochemical processes of surface water and groundwater were different. Additionally, the chemical data were analyzed using factor analysis to identify the most important factors influencing the variation in water quality. In the present study, three main factors explaining 92.47 and 80.95% of the total variance were identified as responsible for the surface water and groundwater chemistry variations from which the first factor (53.79% and 54.08% for surface water and groundwater, respectively), represented a natural weathering and salt accumulations, the second factor (28.60 and 13.52% for both waters) constituted agricultural activities, and the third factor (10.9 and 13.89% for the two types of water) is a contribution from dissolution processes. The results also indicated that 66.3% of the samples fell into the excellent category, 16.4% were considered good, 11.8% were doubtful, and 5.5% were unsuitable. In terms of surface water, 89.5% were classified as excellent, with 10.5% rated as good for irrigation, because of their high sodium levels and salinity. The study results provide a basis for the sustainable utilization of regional water resources.

Urban underground space (UUS) provides innovative solutions to urban challenges such as overpopulation, resource scarcity, and environmental issues. However, UUS's lack of comprehensive evaluation standards hinders its sustainable development. This study systematically introduces a novel framework for evaluating green ecological UUS, integrating methodologies such as factor analysis, the analytic hierarchy process (AHP), and the Delphi method. The framework assesses UUS projects across their lifecycle, focusing on energy efficiency, resource conservation, and environmental protection. Validation through real-world projects, including Chengdu's Tianfu Square and East Railway Station, demonstrated its practical utility, classifying both projects as two-star ecological grades. Real-world projects, such as Chengdu's Tianfu Square and East Railway Station, validated the framework, classifying them into two-star ecological grades based on the proposed standards. This research provides a valuable theoretical and practical tool for establishing a robust evaluation standard for sustainable UUS development, contributing to global urban sustainability efforts.

Heavy metals can be extracted from aqueous solutions by using the Zn-Co-Fe/LDH adsorbent (2:2:1 M), which was made by coprecipitation. Many of analytical methods, inclusive the scanning electron microscope (SEM), X-ray diffraction (XRD), and infrared spectroscopy (FTIR), were applied to evaluate the produced adsorbent. The impact of adsorption factors consisting adsorbent dosage, time, initially adsorbate concentration, and solution pH was estimated. The Zn-Co-Fe/LDH material’s crystal structure is verified by X-ray diffraction investigation of the sample. As³⁺, Pb²⁺, and Hg²⁺ ions were successfully removed from the watery solution using the Zn-Co-Fe/LDH adsorbent. At pH range from 3 to 9, the maximal removal of As³⁺ ions reached 74%, whereas that of Pb²⁺ and Hg²⁺ ions reached 100%. The maximum adsorption capacity of LDH was determined by utilizing five model isotherms, the best model was Langmuir–Freundlich model and the maximum adsorption capacity was 529.63 mg/g, 2741.5 mg/g, and 1852.9 mg/g, respectively. The temperature experiments were conducted at 25, 35, 45, and 55 °C to investigate the thermodynamic parameters (Delta) H^o, (Delta) S^o, and (Delta) G^o. The calculated values show exothermic and non-spontaneous adsorption processes. The results revealed that heavy metals removal mechanisms involved physical and chemical adsorption. The reuse of adsorbent study was performed and discussed. In regard to this study, Zn-Co-Fe/LDH is a material that shows promise for treating industrial wastewater by effectively removing heavy metals from aqueous solutions.

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.

This study presents a rigorous investigation into determining the optimal placement of a drainage well and cutoff wall to effectively mitigate the uplift force and seepage discharge in small concrete dams. A sophisticated numerical model based on the two-dimensional Laplace equation was developed for this purpose. The Laplace equation was discretized using the finite difference method with a second-order central schema, and the resulting system of equations was efficiently solved using the Gauss–Seidel method with an over-relaxation factor of 1.95. The Neumann boundary conditions were applied to the dam body and cutoff wall, while Dirichlet boundary conditions were imposed on the drainage well, as well as the upstream and downstream sections of the dam. The results exhibited an excellent agreement between the numerical simulations and the observed data, with a mean absolute percentage error of 3.54%. The findings from the numerical simulations revealed that the optimal location for the drainage well is at a distance of 0.2L from the upstream face of the dam, where L represents the dam length. This location resulted in a notable reduction of approximately 38% in the uplift force. Additionally, utilizing a cutoff wall at the upstream portion of the dam led to a reduction of about 15% in the uplift force. Remarkably, when both a cutoff wall and a drainage well were employed at their respective optimal locations, the uplift force decreased by an impressive 53%.