Applied Water Science最新文献

{"title":"Determining the optimal design characteristics of trapezoidal piano key weirs for improved water discharge","authors":"Ye-jin Seo,&nbsp;Sangik Lee,&nbsp;Jong-hyuk Lee,&nbsp;Byung-hun Seo,&nbsp;Dongsu Kim,&nbsp;Dongwoo Kim,&nbsp;Yerim Jo,&nbsp;Won Choi","doi":"10.1007/s13201-026-02955-2","DOIUrl":"10.1007/s13201-026-02955-2","url":null,"abstract":"<div><p>As extreme flood events become more frequent and severe, improving the discharge performance of weirs has emerged as a key hydraulic engineering objective. This study evaluates the influence of geometric design parameters and flow characteristics on the discharge capacity of trapezoidal piano key weirs (TPKWs). To analyze major design effects, nine models were simulated by varying width ratios (1.25, 1.75, and 2.25) and sidewall angles (+ 2°, + 4°, and − 2°). The numerical model was validated using laboratory experiments, achieving an RMSE of 0.0137 and a MAPE of 3.18%, confirming its predictive reliability. As auxiliary parameters, square (Sq) and newly proposed triangular (Tri) parapets were applied to the two highest-performing base geometries to assess additional performance enhancement. Computational fluid dynamics (CFD) simulations were used to analyze three-dimensional flow structures, including streamline redistribution, velocity reduction, and nappe interference. The results showed that modifying the width ratio and sidewall angle increased discharge efficiency by up to approximately 20%. Furthermore, the Tri-parapet configuration yielded up to 72.3% improvement in discharge efficiency relative to the reference geometry at the lowest head ratio (<span>({H}_{t}/P)</span> = 0.07), with the gain decreasing to 4.9% at the highest head (<span>({H}_{t}/P)</span> = 0.73). These findings provide a physically based explanation of how geometric parameters control crest utilization and overflow behavior, and they offer practical insights for optimizing PKW designs for dam rehabilitation and flood-routing enhancement.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02955-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating socio‑environmental and demographic factors in machine learning forecasts of water consumption","authors":"Mohammad Vakili,&nbsp;Reza Behmanesh,&nbsp;Hossein Mousazadeh,&nbsp;Amir Ghorbani,&nbsp;Farahnaz Akbarzadeh Almani,&nbsp;Kai Zhu,&nbsp;Lóránt Dénes Dávid","doi":"10.1007/s13201-026-02934-7","DOIUrl":"10.1007/s13201-026-02934-7","url":null,"abstract":"<div><p>Reliable forecasting of water consumption is essential for water resources management because it enables policymakers and utilities to balance supply and demand effectively. This study examines seasonal (three-month horizon) water consumption in Isfahan Province, Iran, using a modeling table of 528 seasonal observations spanning 24 subscriber categories over the period 2016–2021. The predictor set includes subscriber category, year, season, temperature, rainfall, a COVID-19 indicator, death rate, and birth rate. The analysis compares five machine learning algorithms, namely Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost), under a random 80/20 holdout split with 7-fold cross-validation on the training partition. All five models achieve high predictive accuracy, but their relative performance depends on metric choice, computational cost, and sensitivity to low-consumption subscriber categories.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02934-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of integrated GIS techniques for optimization of urban water management","authors":"Deepa D. Naik,&nbsp;Navendu Chaudhary,&nbsp;Jyoti Jain Tholiya,&nbsp;Vijay Jawale","doi":"10.1007/s13201-026-02944-5","DOIUrl":"10.1007/s13201-026-02944-5","url":null,"abstract":"<div><p>Sustainable management of water resources in urban areas is a critical issue due to rapid urbanization. Building resilient urban water supply and demand systems requires an appropriate understanding of how anthropogenic activities, climate change, and environmental factors affect water availability and distribution. An integrated, holistic approach is essential to achieve effective urban water resource management. This research reviews the challenges of water scarcity driven by unprecedented urban expansion. The study aims to analyse water issues across the globe and understand their water management system. Systematic technological trends were observed in urban water science, from econometric techniques of the 1980s to geospatial technologies in the 20th century to extensive integration of artificial intelligence in the 21st century. The study aims to highlight the integration of advanced techniques within a Geographic Information System (GIS) framework for urban water management. It provides a comprehensive overview of various spatial techniques and analytical technologies, including Geospatial models, geostatistical models, hydraulic models, and artificial intelligence/machine learning approaches, and discusses how these methodologies are interoperable within a GIS environment to study water. Combining various techniques is a good practice for accurate analysis and prediction-based urban water studies. GIS is a powerful decision-support tool that enables multidisciplinary approaches by integrating diverse spatial and non-spatial datasets on a single digital platform for comprehensive urban water analysis. An integrated GIS framework provides better resource allocation, raises awareness about responsible water use, and promotes water-saving technologies.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02944-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface engineered VN-rGO nanocomposite for rapid and efficient cationic dye adsorption","authors":"Hridya C. Prakash,&nbsp;Alexey A. Mikhaylov,&nbsp;Petr V. Prikhodchenko,&nbsp;Dhamodaran Santhanagopalan,&nbsp;Sudip K. Batabyal","doi":"10.1007/s13201-026-02937-4","DOIUrl":"10.1007/s13201-026-02937-4","url":null,"abstract":"<div><p>Organic dyes are extremely carcinogenic and mutagenic pollutants that pose a serious threat to aquatic ecosystems and human health, necessitating the development of efficient remediation strategies. In this work, Vanadium nitride-reduced graphene oxide (VN-rGO) was synthesised using an ammonium peroxovanadate precursor and investigated as a novel adsorbent for the removal of cationic dyes, including methylene blue (MB) and rhodamine B (RhB). The synergistic effect of the high surface area of rGO and chemically active VN sites promotes multiple binding interactions with dye molecules, resulting in rapid and efficient dye removal. The VN-rGO achieved complete adsorption of MB (1 ppm) and RhB (0.7 ppm) within 30 s under the investigated conditions. The effect of parameters such as contact time, adsorbent dosage, adsorbate concentration, and reusability without regeneration was systematically investigated. To understand the adsorption mechanism, Freundlich, Langmuir, and Temkin isotherm models were employed. The linear and non-linear Freundlich models provide the best fit for both dyes (R² = 0.96-0.98), suggesting multilayer adsorption on a heterogeneous surface. The linear and non-linear pseudo-second-order kinetic model showed the best agreement with the experimental data (0.96-0.99), suggesting a chemisorption-dominated adsorption process. The adsorption behaviour is attributed to the combined effect of electrostatic interactions, surface complexation, hydrogen bonding, and π-π stacking. Furthermore, the VN-rGO demonstrated effective dye removal over multiple cycles without regeneration, highlighting its potential as a rapid and reusable adsorbent for wastewater treatment.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02937-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of groundwater level forecast modelling using deep learning models combined with EMD and EEMD in Cangzhou, China","authors":"Aihua Wei,&nbsp;Yongbing Jiang,&nbsp;Fengxia Wang,&nbsp;Linzhen Chang,&nbsp;Haipeng Guo,&nbsp;Xisheng Zang,&nbsp;Zhiyuan Yao","doi":"10.1007/s13201-026-02954-3","DOIUrl":"10.1007/s13201-026-02954-3","url":null,"abstract":"<div><p>Accurate groundwater level forecasting is crucial for sustainable water resource management and environmental protection. The main objective of this study was to develop hybrid models combining deep learning models with data processing techniques for groundwater level forecasting. Two groundwater level observations in Cangzhou City, Hebei Province, China, were used as case studies. First, the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) were used to pre-process the dataset. Then, two dynamic deep learning techniques, namely long short-term memory (LSTM) and bidirectional LSTM (BiLSTM), were used as the base model. The hybrid models combined two deep learning algorithms (LSTM and BiLSTM) with EMD and EEMD, respectively, yielding 4 models: EMD-LSTM, EEMD-LSTM, EMD-BiLSTM, and EEMD-BiLSTM. Five evaluation metrics, including the root mean squared error (RMSE), the mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), the Kling-Gupta efficiency (KGE), and the coefficient of determination (R²), were used to evaluate the proposed models. All the proposed models could achieve satisfactory results, with R², NSE, and KGE values greater than 0.87. The hybrid models performed better than LSTM and BiLSTM, with reductions of up to 75% in RMSE and 71% in MAE. Furthermore, the EEMD-LSTM and EEMD-BiLST models achieved relatively lower RMSE and MAE values and higher NSE and KGE values for both wells, compared with the EMD-LSTM and EMD-BiLSTM models. The two decomposition algorithms significantly improved the performance of the LSTM and BiLSTM models, especially the EEMD. This study demonstrated that the EMD and EEMD models, combined with LSTM and BiLSTM models, can be a powerful tool for addressing non-stationary, complex groundwater level time series data.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02954-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the performance of anammox-coupled technologies for sustainable treatment of nitrogenous wastewater","authors":"Adegoke Isiaka Adetunji,&nbsp;S’fiso Thuthukani Gumbi","doi":"10.1007/s13201-026-02943-6","DOIUrl":"10.1007/s13201-026-02943-6","url":null,"abstract":"<div><p>Wastewater from industries, municipalities, or the agricultural sector contains huge amounts of nitrogenous substances, causing serious risks to the immediate locality and public health. Anammox technology is recognized as a preferred option for treating nitrogenous wastewater due to its sustainability, cost-effectiveness, and eco-friendliness. In addition, this technique is characterized by the absence of organic carbon, lower sludge production, reduced energy consumption and greenhouse gas emissions, and high nitrogen removal efficiency. However, the efficacy of the wastewater treatment process is deterred by the presence of inhibitory pollutants, including microplastics, heavy metals, and antibiotics. Consequently, the coupling of anammox with other biological wastewater treatment processes, including partial nitrification (PN), partial denitrification (PD), simultaneous partial nitrification anammox and denitrification (SNAD), constructed wetlands (CWs), microbial electrolysis cells (MECs), and microbial fuel cells (MFCs), offers an effective approach to sustainable and improved nitrogen removal from wastewater. The addition of biochar, zeolite, graphene, metal ions, and hydrazine in the culture medium promotes anammox bacterial growth and catalytic activity, as well as pollutant removal efficiency. Therefore, this review provides insights into various anammox-coupled biological technologies for the efficient treatment of nitrogenous wastewater and elucidates strategies to optimize anammox performance. Some challenges that impede anammox activity, along with their respective antidotes, are also highlighted.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02943-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic modification of pine needle-derived activated carbon for dye removal with facilitated recovery from wastewater","authors":"Anjas Asrani,&nbsp;Brijesh Prasad,&nbsp;Win-Jet Luo,&nbsp;Mohit Bajaj,&nbsp;Viktoriia Bereznychenko","doi":"10.1007/s13201-026-02947-2","DOIUrl":"10.1007/s13201-026-02947-2","url":null,"abstract":"<div><p>Biomass based activated carbon as an adsorbent attained significant attention in today’s world for the industrial waste water treatment due to its sustainable nature, high surface area and adsorption ability. In the present study, activated carbon (AC) was developed from of Pinus roxburghii (Chir Pine) dry needle biomass and was magnetically modified through physical activation. The iron oxide nanoparticles were integrated by in-situ technique to impart improved magnetic separability. The prepared samples of magnetic modified activated carbon (MMAC) were systematically characterized for evaluating the surface morphology, presence of surface functional groups, crystalline structure and magnetic properties. The obtained results confirmed regular tubular vacant spaces, which were later on magnetically modified by dispersing Fe₃O₄ nanoparticles in the hollow spaces of AC to obtain strong magnetic responsiveness. The adsorption studies were performed using Malachite Green (MG) industrial dye. The batch adsorption of MG dye from the aqueous solution demonstrated AC’s maximum Langmuir adsorption capacity of Q_max of 416.93 mg/g while the optimized magnetic composite (MMAC3) retained a highly competitive capacity of 369.47 mg/g. AC required complex filtration for recovery and the MMAC3 exhibited high magnetic saturation of 1.22 emu/g using the external magnet from the aqueous solution. Non-linear kinetic analysis and multi-model equilibrium isotherm evaluations (including Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich) were conducted to rigorously define the physical and mass-transfer parameters driving the adsorption capacity. Thermodynamic investigations confirmed that Malachite Green sequestration by both AC and the MMAC 3 composite is a spontaneous, endothermic and entropy-driven physisorption process. The most important finding of the regeneration experiments is the impressive cyclic stability of both the matrices used, with each matrix maintaining more than 80% of the initial adsorption ability after five cycles. Although AC was slightly superior in the amount adsorbed, MMAC 3 stands out because of the capability of fast magnetic separation, making it ideal for large-scale water treatments. As seen from the experimental results, magnetic separation can be performed very efficiently without any additional use of energy-consuming methods such as centrifugation or filtration. This makes the proposed product environmentally friendly.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02947-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148769033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic adsorption–photocatalysis in TiO₂/activated carbon composites: optimization of p-nitrophenol degradation and application to real textile wastewater treatment","authors":"Lalit Kumar,&nbsp;Pushpendra Kumar,&nbsp;Anand Gupta Chakinala","doi":"10.1007/s13201-026-02911-0","DOIUrl":"10.1007/s13201-026-02911-0","url":null,"abstract":"<div><p>The photocatalytic degradation of persistent nitro-aromatic pollutants is often limited by poor light utilization and fast electron-hole recombination in conventional TiO₂ systems. In this work, TiO₂/carbon composites were prepared using activated carbon (AC) and biomass-derived biochars as supports and evaluated for the degradation of <i>p</i>-Nitrophenol (<i>p</i>-NP). Among the different compositions studied, 20 wt% TiO₂ supported on AC showed the best performance, achieving ~ 65% degradation of 20 ppm <i>p</i>-NP within 60 min under UV irradiation, with a pseudo-first-order rate constant of 0.0195 min⁻¹. Characterization results indicate that the improved activity is mainly due to band-gap narrowing (Eg ≈ 2.27 eV), suitable surface charge (-20.7 mV), and effective charge transfer at the TiO₂-carbon interface, which together reduce charge recombination. In comparison, TiO₂ supported on biochars exhibited lower activity, attributed to higher ash content, lower conductivity, and weaker interfacial interaction. The optimized TiO₂/AC composite was further tested with real textile wastewater and achieved ~ 42% degradation, confirming its applicability under complex conditions. These results show that proper selection of carbon support and TiO₂ loading is crucial for improving photocatalytic performance in wastewater treatment.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02911-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating experimental and molecular dynamics insights into fluorescein dye adsorption on natural bentonite","authors":"Talaat Habeeb,&nbsp;Ali H. Bashal","doi":"10.1007/s13201-026-02942-7","DOIUrl":"10.1007/s13201-026-02942-7","url":null,"abstract":"<div><p>This study explores the adsorption of fluorescein dye (FD) onto bentonite (BT) to enhance its potential in environmental remediation and water treatment. X-ray diffraction (XRD) analysis confirmed montmorillonite as the dominant phase in BT, with significant SiO₂ and quartz peaks. SEM and EDX characterization revealed a regular bulk morphology and the presence of silicon, aluminium, magnesium, iron, and trace elements. Adsorption experiments were conducted to evaluate the effects of adsorbent dosage, solution pH, and temperature. Increasing BT dosage from 20 mg to 400 mg enhanced dye removal efficiency from 21% to 78.6%, attributed to the availability of more adsorption sites. However, higher dosages led to reduced efficiency, likely due to limited surface contact between dye molecules and active sites. Optimal adsorption was observed at pH 6 and 30 °C. Adsorption mechanisms were analysed using kinetic and isotherm models, while molecular dynamics simulations confirmed stable fluorescein–bentonite interactions. This study provides new insights into the adsorption behaviour of fluorescein on BT, demonstrating its efficiency as an eco-friendly adsorbent for organic dye removal.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02942-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation and prediction of seasonal variation in surface water chemistry for beneficial use of drinking, irrigation, and industrial in Mahanadi River of Paradip area, Odisha, India, using an GIS: integrated water quality indices, and machine learning approaches","authors":"Abhijeet Das","doi":"10.1007/s13201-026-02933-8","DOIUrl":"10.1007/s13201-026-02933-8","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Surface water is the primary source of drinking water in the Mahanadi River and its distributaries, and it is used for agricultural, industrial as well as domestic purposes. However, increasing urbanization, agricultural activities, and seawater intrusion have raised concerns about groundwater quality. This study evaluates the surface water quality (WQ) in nine selected locations by analysing twelve physicochemical parameters&lt;b&gt;,&lt;/b&gt; computing the Weighted Arithmetic (WA) Water Quality Index (WQI), Numerow’s Pollution Index (NPI), Overall Index of Pollution (OIP), Synthetic Pollution Index (SPI), and applying Multivariate techniques namely, Corelation, Cluster Analysis (CA), and Principal Component Analysis (PCA), in order to identify key pollutants. Again, the current work is intended to create and review four machine learning (ML) models, namely, Support Vector Machine (SVM), Random Forest Model (RFM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGB), for surface water quality prediction and creating spatial water quality maps to direct conservation initiatives in the heavily urbanized and polluted region being monitored. On the basis of physicochemical results, BOD, PO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;3−&lt;/sup&gt;, EC, TDS, and F&lt;sup&gt;−&lt;/sup&gt; were major contributors to surface water pollution, particularly in urban and agricultural areas. Findings revealed that WA WQI values ranged from 40.36 to 176.13, indicating that water quality varied from good to unsuitable category&lt;b&gt;.&lt;/b&gt; The NPI indicated deteriorated water quality, accounting 77.78%. Considering OIP, acceptable water quality was detected around 33.33% and 66.66% of water samples renders poor class, indicating a significant risk of microbial contamination and the necessity for appropriate treatment prior to human consumption. SPI results indicate that 55.56% of the samples were classified as “good” (SPI &lt; 0.5), could be consumed for drinking, and remaining 44.44% were classed as polluted. The surface water in the area is appropriate for use for irrigation at a variety of locations, depending on the calculated indices of SAR (5.88–35.66), MH (41.22–76.55), PI (45–85), % Na (15.22–92), and KI (0.43–2.5). For the majority of samples, the predominant water species in the region, as determined by Piper categorization, is in the category indicated as Mg&lt;sup&gt;2+&lt;/sup&gt;–Cl&lt;sup&gt;−&lt;/sup&gt;–SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt; water type, resulted from the dissolution of magnetite, SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2−&lt;/sup&gt;, and Cl&lt;sup&gt;−&lt;/sup&gt; minerals in the aquifer area. Gibb’s plot proven that rock-water interaction dominated the sources of surface water's chemical components. Correlation matrix describes the relationship between water quality parameters, allowing for the detection of natural or anthropogenic influences on subsurface water. Three cluster groups representing varying contamination levels were generated from nine sampling sites using twelve water quality metrics. According to their respective Eigen","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 9","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02933-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}