Applied Water Science最新文献

Evaluating groundwater quality in irrigated areas is crucial for sustainable agriculture, especially as limited water resources and climate change pose significant threat to groundwater resources. Spatial information on groundwater quality is essential for effective management and utilization of water resources, particularly in intensive cropping areas such as irrigated regions in IBIS, Pakistan. However, recent advancements in machine learning (ML) techniques have highlighted that conventional groundwater quality assessment methods are costly and time-consuming, especially for developing nations. Accurate and efficient ML models can address this challenge in agricultural water management by optimally identifying the categories of water quality. This study is conducted to predict groundwater quality using an innovative ensemble-boosting methodology. The data is collected from Rahim Yar Khan’s irrigation system by the Scarp Monitoring Organization. Four irrigation water quality indicators, including sodium adsorption ratio, total dissolved solids, residual sodium carbonate, and electrical conductivity, are used to predict groundwater quality by applying four ML models. The performance of the ML models is assessed using mean squared error, correlation coefficients (r), and root mean square error measures. The proposed Gradient Boosting (GB) approach combines the advantages of interpretable tree models and boosting approaches. Experimental results validate the utility of the proposed approach with a 99% accuracy in predicting groundwater quality, compared to conventional ML techniques. Based on the proposed GB model and the inverse distance weighting interpolation technique, the groundwater quality distribution in the Hazardous Area is 17.34%, the Marginal Area is 79.36%, and the Safe Area is 3.30%. Enhancement and validation of groundwater quality index predictions are carried out using k-fold validation and hyperparameter tuning. Results indicate that the ML models have the potential to accurately delineate different groundwater quality zones for managing water resources and ensuring sustainable agriculture. Water quality assessment through the proposed approach can help managing the groundwater for the regions susceptible to deterioration of water quality thus contributing to better irrigation governance.

Accurate estimation of daily precipitation is essential for effective water resource management and climate risk assessment. Satellite precipitation products (SPPs) offer valuable spatial coverage but remain limited by uncertainties, particularly in arid and semi-arid regions. To address these challenges, this study develops a robust merging framework that integrates four SPPs with auxiliary topographic and meteorological data using ensemble machine learning models (EMLMs). Within this framework, we introduce for the first time the Multiple Linear Regression–based Sine Cosine Algorithm (MLR-SCA), designed to improve merging performance relative to the widely used Bayesian Model Averaging (BMA). Daily precipitation observations from 80 synoptic stations across Iran (2014–2022) were employed for training and validation. Results demonstrate that the proposed MLR-SCA significantly outperforms BMA, increasing the correlation coefficient (CC) by 132%, reducing RMSE by 34% and MAE by 19%, and achieving substantial improvements in KGE (+ 1142%), POD (+ 40%), and CSI (+ 47%), while reducing FAR (–24%) and BIAS (–7%). Although merging slightly reduced categorical event-detection skill in some cases, the EMLM framework consistently produced more accurate, stable, and reliable precipitation estimates across diverse climatic zones. Compared with existing approaches, the proposed framework offers three main advantages: (1) stronger performance across arid, semi-arid, and semi-humid climates; (2) improved detection of extreme precipitation events, which are often underestimated by raw SPPs; and (3) greater robustness through the simultaneous integration of multiple SPPs and auxiliary datasets. These findings highlight the potential of the EMLM–MLR-SCA framework to support operational hydrology, water resource planning, and climate adaptation in data-scarce regions.

This study presents a comprehensive evaluation of a high-pressure desalination pipeline system designed to evaporate seawater at elevated temperatures and facilitate its transfer to designated locations through pressure differentials. The system utilizes energy derived from waste heat generated by a cement factory. A detailed case study examines a 26 km transmission line from the Khuzestan Cement Factory to Mount Asmari and an 84 km line to Garreh, with estimated daily water production of 12,465 m³ for Mount Asmari and 6,685 m³ for Garreh. To validate the system’s performance, a laboratory model was developed and subjected to rigorous testing under varying temperatures ranging from 110 to 150 degrees Celsius, with water throughput measured between 14 kg/hour and 46 kg/hour. Comprehensive testing ensured alignment of experimental data with theoretical calculations, revealing discrepancies of no more than 10%, thereby reinforcing the operational reliability of the system. Furthermore, a comparative analysis of the proposed desalination technology against existing water treatment and transportation methods underscores its numerous advantages, positioning it as a viable and efficient solution for addressing global water scarcity challenges. This research highlights the potential for sustainable freshwater production and the innovative integration of waste heat utilization in desalination processes.

A modified cellulose phase was synthesized by anchoring aminoguanidine onto the surface of cellulose fibers to produce a novel adsorbent (AK1) that is cost-effective, biodegradable, and easily processable. To enhance its affinity for anionic dyes specifically Eriochrome Cyanine R (ECR), Zincon, and Congo Red (CR) the amino groups were cationized through reaction with acetic acid, forming cationic ammonium salts. This reaction yielded the aminoguanidine-functionalized cationic cellulose derivative (AK2).Comprehensive characterization of the modified cellulose materials (AK1 and AK2) was performed using Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Energy-Dispersive X-ray Spectroscopy (EDX), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), elemental analysis, nitrogen physisorption, and zeta potential measurements. These analyses confirmed the successful incorporation of cationic ammonium salt groups onto the cellulose surface. The maximum adsorption capacities of AK2 for Eriochrome Cyanine R, Zincon, and Congo Red were 252 mg/g, 203.4 mg/g, and 153.2 mg/g, respectively, at pH 3. The adsorption behavior of the modified cellulose toward these anionic dyes was investigated through kinetic and isotherm studies. The results indicated that the dominant adsorption mechanisms were physisorption and monolayer adsorption, with equilibrium data fitting well to the Langmuir isotherm model. Moreover, the adsorption kinetics followed a pseudo-first-order model. The modified cellulose retained more than 92% of its adsorption efficiency after five regeneration cycles, demonstrating excellent reusability.

The Soret-Dufour effects and non-uniform heat generation on gyrotactic and oxytactic microorganisms in stagnation point flow of CNTs-water based hybrid nanofluid flow via a rotating sphere with convective boundary conditions. Through the utilization of gyrotactic and oxytactic microorganisms, the system improves nutrient delivery and fluid mixing, which raises reaction rates and sensor sensitivity. Accurate biosensor performance depends on the hybrid nanofluid improved heat dissipation and stability, which are facilitated by the carbon nanotubes it contains. This approach is useful for applications in environmental monitoring, bioprocess engineering, and medical diagnostics since machine learning also facilitates real-time prediction, optimization, and control of sensor conditions. This research provides a novel numerical solution to this problem using back-propagation intelligent Bayesian regularization in the neural network domain (BIBR-NNs), which has convergent stability. Using a dataset for the proposed (BIBR–NNs) for many MHD-BNF-DSTR scenarios, the Bvp4c numerical technique. To determine the accuracy of the suggested model, the data is processed, appropriately tabulated, and its validity is tested. The BIBR-NNs training, testing, and validation procedures were utilized to assess the estimated solutions for specific occurrences and compare the proposed model for verification.

Industrial regions face a significant problem in groundwater management due to increased water demand and potential contamination risks. To establish an effective groundwater management plan in the industrial region, comprehensive understanding of both groundwater availability and quality is needed. Therefore, the main goal of this study was to determine the groundwater potential zone (GWPZ) of an industrial region while also taking water quality into account. For this purpose, the combination of remote sensing (RS), geographical information systems (GIS), and the fuzzy analytical hierarchy process (FAHP) was used. Geology, drainage density, rainfall, soil, slope, elevation, normalised difference vegetation index (NDVI), land use/land cover (LULC), and topographic wetness index (TWI) layers were integrated for the assessment. The GWPZ map is divided into five classes: very poor (10.01%), poor (21.79%), medium (31.36%), high (27.13%), and very high (9.71%) zones. The water quality parameters that exceeded the desirable limit in the study region was selected for groundwater quality zone (GWQZ) mapping. The GWQZ map was classified into three categories: within desirable limit (26.40%), within permissible limit (49.28%), and above permissible limit (24.32%). The integration of these two maps revealed that certain locations with medium to high GWPZ have low groundwater quality. Some areas of the very poor GWPZ had quite excellent groundwater quality. This study provided valuable insights into the spatial distribution of groundwater resources, highlighting areas where potential conflicts between quantity and quality may arise. This research showed that for sustainable management, both the quality and quantity of groundwater resources should be considered.

The growing urgency to transition toward clean energy systems has heightened interest in marine renewable energy (MRE) as a sustainable solution for coastal regions facing environmental degradation from fossil fuel use. This study applies the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to evaluate and rank MRE options, offshore wind, tidal, and wave energy, based on four key criteria: Efficiency, Cost, Emissions, and Resource Availability. Expert judgment was used to derive weighted preferences, and a structured decision matrix facilitated performance scoring and ranking. The analysis identified Efficiency as the most influential factor, with offshore wind energy emerging as the top alternative due to its strong performance and scalability. The results offer a practical, adaptable framework for supporting energy planning in coastal zones, enabling decision-makers to balance environmental protection and operational feasibility.

The increasing prevalence of refractory pharmaceutical compounds in the environment and their harmful impacts on ecosystems have resulted in severe ecological challenges for living organisms worldwide. A new metal–organic framework (MOF) was designed to address this issue, comprising copper nanoparticles bonded to tryptophan amino acids. This MOF was designed to be an efficient photocatalyst for the degradation of pharmaceuticals such as ciprofloxacin, utilising environmentally sustainable and eco-friendly mater. The physicochemical properties of the MOF catalyst were confirmed through various characterization techniques, including FTIR, DRS, SEM, TEM, XPS, TGA, and BET analyses. The MOF was employed as a photocatalyst for the purification of ciprofloxacin under simulated sunlight irradiation. The degradation experiments demonstrated the complete removal of CIP under optimal conditions with a solution pH of 9, catalyst dosage of 0.5 g/L, and CIP concentration of 20 mg/L within 200 min under simulated sunlight. Furthermore, post-treatment analysis of TOC and COD revealed significant reductions of 63.11% and 76.17%, respectively, indicating extensive mineralization of the antibiotic. Additionally, HO• and h⁺ were identified as the primary oxidizing agents responsible for the photocatalytic degradation of CIP by the MOF catalyst. The material exhibited exceptional photocatalytic performance for CIP even after six consecutive recycling cycles. Mechanistic investigations further revealed a unique heterojunction within the MOF catalyst, where Cu nanoparticles serve as electron acceptors and co-catalysts. These nanoparticles facilitate charge separation and transfer, enhance light absorption, and reduce the recombination of photogenerated electron–hole pairs, thereby significantly improving the photocatalytic activity of the catalyst.