ABSTRACT Graphical abstract showing two-stage sensor configuration evaluation: economic screening followed by leak localization using DE, GA, and NSGA-II across multiple leak scenarios. Leakage remains a major challenge for water utilities. However, pressure-based leak localization studies are often limited to single-leak assumptions and economic assessments that are disconnected from actual detection performance. This paper presents an integrated hydraulic–economic framework that links pressure-based leak localization outcomes with life-cycle economic evaluation under realistic multi-leak conditions. The framework adopts a two-stage approach: (1) predefined sensor configurations are screened using a volume-based Net Present Value model driven by detection and false-negative rates, (2) the most economically competitive configurations are subjected to detailed hydraulic leak localization analysis. Leak localization is performed using Genetic Algorithm, Differential Evolution, and NSGA-II. These algorithms are evaluated across single-leak, clustered multi-leak, and multi-leak scenarios including dominant and minor leaks. A pre-selection strategy is used to identify candidate leak nodes using distance-weighted pressure residual formulation and a Jacobian sensitivity matrix to enhance spatial discrimination and computational efficiency. Results reveal a hydraulic observability limitation, where certain leaks remain undetectable regardless of sensor configuration or algorithm. While higher sensor density maximizes total economic benefit, marginal returns diminish significantly. NSGA-II proves to be more robust against false negatives, while moderate densities are near-optimal for economic benefits. The findings support decision-oriented sensor deployment in water distribution networks.
ABSTRACT Arsenic in groundwater is still one of the largest chronic exposures in public health, reaching an estimated 140 million people in some 50 countries. Iron-based adsorbents are among the cheapest treatment options available, and a reliable way of predicting how they behave under changing water chemistry would be genuinely useful for design. This paper reports a deliberately limited step towards that goal. It builds a synthetic dataset of 380 observations for five iron phases, zerovalent iron, goethite (FeOOH), haematite (Fe2O3), magnetite (Fe3O4) and ferrihydrite (Fe(OH)3), not by extracting measurements from the literature but by writing down a multiplicative algebraic generator (Gaussian pH term, Langmuir-type dose term, pseudo-second-order time term, mild Arrhenius temperature term and a concentration-saturation term), parameterised from ranges reported in 16 experimental studies and perturbed with 2.5% Gaussian noise. An Adaptive Neuro-Fuzzy Inference System trained by a hybrid least-squares/Adam scheme and refined by Particle Swarm Optimisation was then asked to learn that generator and was benchmarked against XGBoost, Random Forest, Support Vector Regression and an Multilayer Perceptron (MLP) under identical group-aware splits. On the held-out test set, the model reached R2 = 0.9340 [root mean square error (RMSE) 5.99%] for removal efficiency and R2 = 0.9963 (RMSE 0.66 mg/g) for adsorption capacity; a bagged ensemble raised removal to R2 = 0.9369 but pushed capacity down to 0.8924. These numbers should not be read as predictive skill. They measure how well each architecture recovers a known algebraic function, and the capacity figure is further inflated because q = C0 × (η/100)/D is computed from variables that are themselves model inputs, a form of leakage it quantifies rather than hides. Even with that limitation, two findings still hold up, and these, it says, are the real contribution. First, the Takagi-Sugeno-Kang consequent structure recovers a separable multiplicative function far more accurately than tree- or kernel-based learners (ΔR2 for capacity of +0.40 to +0.60), which is a statement about architecture, not about arsenic. Second, the PSO stage bought almost nothing (removal R2 0.9338 → 0.9340), and group k-fold cross-validation collapsed to removal R2 = 0.699 ± 0.12 and capacity R2 = 0.619 ± 0.37, with onefold at −0.06 a textbook instance of the benchmark inflation described by Kapoor & Narayanan 2023. The framework is offered as a proof of concept and a cautionary benchmark; it is not yet a design tool, and validation against real experimental and field data is a precondition for any practical use.
ABSTRACT Water contamination caused by organic pollutants leads to severe deterioration of water quality and major environmental concern worldwide. The implementation of effective monitoring and remediation measures is necessary for sustainable water treatment. A batch reactor using titanium oxide (TiO2) as a catalyst to remove total organic carbon (TOC) and chemical oxygen demand (COD) in seawater by solar photocatalysis was used. Response surface methodology was carried out with input parameters such as TiO2 dosage 1–4 g/L, pH value 6–9 and reaction time 60–300 min and percentage reduction of TOC and COD as output variables. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Bayesian Ridge Regression (BRR) and Long Short-Term Memory networks (LSTM) were used for prediction. The accuracy of models is determined by metric analysis. GB model recorded the highest R2=0.993 (COD) and 0.980 (TOC) with minimal error MSE of 3.43 COD and 0.009 TOC. Whereas RSM showed R2=0.953, 0.950 for COD and TOC. AdaBoost demonstrated R2 of 0.973 of COD, in addition BRR and LSTM showed lower prediction accuracy. The findings shed light on the possibility of machine learning methods in improving prediction of water quality parameters and optimizing of water treatment processes.
ABSTRACT Graphical showing groundwater salinization in the Termal Complex aquifer (Cheggua SE Algeria); the results show severe miniralizattion with high electrical conductivity,total dissolved solids and salinity . This study evaluates groundwater quality and salinization risk in the Terminal Complex aquifer of Cheggua, southeastern Algeria, using physicochemical data from 27 groundwater wells. The analyzed parameters included pH, electrical conductivity (EC), total dissolved solids (TDS), salinity, and turbidity. Results indicate a highly mineralized aquifer, with EC values ranging from 4,054 to 13,980 μS/cm, TDS from 2,027 to 6,994 mg/L, and salinity between 2.15 and 8.09 psu. Mean EC (5,980 μS/cm), TDS (2,990 mg/L), and salinity (3.25 psu) values confirm widespread saline groundwater conditions. Groundwater pH was neutral to slightly alkaline (mean 7.75), while turbidity remained generally low (median 1.2 FNU). Multivariate statistical analyses revealed strong positive correlations among EC, TDS, and salinity (r > 0.95), whereas turbidity exhibited a moderate negative correlation with mineralization (r = −0.49). Multiple linear regression accounted for 85% of salinity variability (R2 = 0.85). Hierarchical cluster analysis identified four distinct hydrochemical groups reflecting increasing mineralization levels and marked spatial heterogeneity. One-way analysis of variance demonstrated significant differences in EC, TDS, and salinity across depth categories (p < 0.05). Geographic Information Systems (GIS)-based spatial analysis delineated vulnerable zones characterized by TDS values exceeding 4,000 mg/L, highlighting the combined effects of saline intrusion, evaporite dissolution, and groundwater overexploitation. These findings provide valuable insights into groundwater salinization processes and support sustainable groundwater management in arid and hyper-arid regions.
ABSTRACT Due to population growth, agricultural development, and industrialization worldwide, the quality of groundwater resources is declining. This study assessed heavy metal contamination using quality indicators in rural drinking water in Babol, Iran (2022): pollution index (Cd), heavy metal pollution index (HPI), heavy metal evaluation index, and heavy metal index (MI), as well as risk assessment using deterministic and probabilistic methods for populations at risk. In this descriptive cross-sectional study, 60 groundwater samples were collected from 30 stations in the dry and wet seasons. The results showed that the concentration of iron (Fe) shows the highest average concentration, followed by zinc (Zn) and copper (Cu). During the wet season, the HPI showed high and moderate contamination in 6.6 and 93.4% of the areas, respectively. The results of the MI values showed that 23.3 and 40% of the areas were undrinkable in the dry and wet seasons. Hazard quotient index was calculated in the order Cr > Cu > Pb > Fe > Zn > Mn > Cd in three age groups (men, women, and children). The highest and lowest carcinogenic risk values were for Pb (3.03 × 10−2) in the children group and Cd (8.7 × 10−7) in the women group, respectively. Analyzing the results, we see that the excess lifetime cancer risk (ELCR) values for total heavy metals (Pb, Cr, and Cd) and all three age groups are generally very low. The results of the Monte Carlo simulation are presented. The highest ELCR of heavy metals in water in the women's group is 0.0309.
ABSTRACT Schematic diagram of a cartridge-type humidification–dehumidification (HDH) desalination system consisting of a vertical cylindrical tower divided into humidification and dehumidification sections. The column contains multiple sieve trays and chimney trays arranged in a removable cartridge assembly. Heated saltwater flows downward across lower trays while air flows upward through perforations, forming bubbles. The humid air rises to the upper section, where cooler freshwater flows across trays and condensation occurs. Arrows indicate air, saltwater, and freshwater circulation loops between the tower, heater, pumps, and cooling unit. The system achieves up to 15.56 L/h freshwater production with a gain output ratio (GOR) of 3.17. In this study, a new and unique structure is used for the humidification and dehumidification chamber. A tray cartridge tower consisting of four sieves and two chimney trays has been utilized. The performance of this humidification–dehumidification system has been investigated experimentally. In addition to the humidifier and dehumidifier tray tower, this device includes saltwater heating and freshwater cooling systems, along with fresh and saltwater pumps and tanks. The tray tower offers various advantages for gas and liquid two-phase contact, including reasonable pricing, simplicity, and ease of maintenance and cleaning. These trays also address the problems caused by sedimentation in packing. The tower has a diameter of 25 cm and a height of 1.5 m. The effects of air flow rate, saltwater flow rate, freshwater flow rate, saltwater temperature, and freshwater temperature on the exit temperature of the humidification tower, dehumidification, freshwater production, and gain output ratio (GOR) will be investigated. Maximum freshwater production occurs when the inlet freshwater temperature is at its minimum, while the saltwater temperature, air flow rate, and freshwater flow rate are at their highest values. With this system, freshwater production and GOR can reach up to 15.56 L/h and 3.17, respectively.
ABSTRACT In many low- and middle-income countries, unreliable formal water services have prompted households to rely on informal providers to meet their daily needs. While this phenomenon has been extensively studied in urban contexts, little is known about its dynamics in rural areas where formal network coverage already exists. This study examines the persistence of informal distributing vendors in rural Tunisia. It addresses two questions: (1) whether the underperformance of formal water providers drives the emergence of distributing vendors, and (2) whether these vendors engage in any form of injustice. A mixed-method approach combining household surveys, interviews, and quantitative analyses (t-tests and regression models) was used. Results show that rural Tunisia is characterized by the coexistence of formal and informal supply systems. Frequent and prolonged interruptions, as well as poor water quality from formal providers, were identified as key factors motivating vendors to serve specific areas; however, only poor water quality significantly influenced households' decisions to purchase water from informal vendors. The study finds high levels of user satisfaction and perceived fairness in informal water provision.
ABSTRACT This graphical abstract summarizes the study of long-term rainfall variability and trends across Brazilian state capitals from 1958 to 2024 using TerraClimate data. It illustrates the methodological workflow, including the application of the Mann-Kendall test, Sen's slope estimator, and Trend-Free Pre-Whitening (TFPW). The figure highlights spatial differences in rainfall trends, with increasing patterns in the North and South regions and decreasing trends in the Northeast. It also emphasizes that seasonal trends are more pronounced than annual trends and shows the implications of these findings for urban water supply management under climate variability. Understanding long-term rainfall variability is essential for urban water supply planning and climate adaptation in Brazil. This study analyzes annual and seasonal rainfall variability and trends in the 26 Brazilian state capitals and the Federal District using monthly TerraClimate precipitation data from 1958 to 2024. The novelty of this research lies in its integrated nati onwide assessment over a 66-year period, including both annual and seasonal trend analyses and their implications for urban wa ter supply. Descriptive statistics were used to characterize rainfall variability, while the Mann–Kendall test and Sen’s slope est imator were applied to detect long-term trends. Serial autocorrelation was evaluated, and the Trend-Free Pre-Whitening proced ure was applied when necessary. Results reveal strong spatial heterogeneity in rainfall patterns across Brazil. Significant incre asing annual rainfall trends were identified in Belém, Florianópolis, and Porto Alegre, whereas decreasing trends were observe d in Salvador, Aracaju, and Teresina. Seasonal analyses showed increasing precipitation during austral summer (DJF) in northe rn capitals and decreasing winter (JJA) rainfall in parts of Central-West and Southeast Brazil. These findings emphasize the im portance of incorporating long-term rainfall variability and seasonal trends into urban water management and climate adaptation strategies.
ABSTRACT This study evaluates groundwater quality in the Eloor Industrial Belt, Kerala, India, using CCME WQI and multi-season hydrochemical analysis. Groundwater samples from 10 open wells were collected during pre-monsoon, monsoon, and post-monsoon seasons in 2019 and 2020 and analysed for 19 physicochemical parameters, including major ions, nutrients, and heavy metals. Statistical techniques (such as correlation analysis) were applied to examine interrelationships among water quality parameters and to assess the influence of groundwater-level variability on overall water quality. The results indicate that most wells located near industrial zones exhibit marginal to poor groundwater quality, whereas wells situated in residential areas show good to excellent quality. Strong positive correlations were observed among total dissolved solids, electrical conductivity, total hardness, magnesium, calcium, and chloride, reflecting dominant ionic controls linked to industrial contamination. Seasonal groundwater-level fluctuations associated with drought conditions had no statistically significant effect on CCME WQI values, suggesting that industrial pressures exert a stronger influence on groundwater quality than short-term hydrogeological variability. By integrating long-term groundwater quality data, CCME WQI assessment, and groundwater-level analysis in a climatically sensitive industrial region, this study provides novel insights into groundwater quality degradation mechanisms and offers scientifically robust evidence to support groundwater monitoring, management, and environmental policy development.
ABSTRACT Peak daily water demand (Kd) is a critical parameter in water supply planning and distribution system design, ensuring that system capacity meets customer water consumption and enhancing investment efficiency. Identifying appropriate indicators requires consideration of local usage patterns and the impacts of climate change. This paper introduces a novel hybrid model that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) with Artificial Neural Networks (ANN) to forecast Kd using data collected from DMA01 in District 5, Ho Chi Minh City, Vietnam, covering the years 2017–2022, including the COVID-19 period. SARIMA provided initial predictions, with ANN refining residuals to achieve an MAPE of 9%. When applied to DMA01, with 1,730 customers over six years, showed that the Kd value of 2.87 for the next planning period exceeds the Vietnamese standard (1.1–1.4) and is consistent with U.S. and Australian benchmarks. The results of the study highlight its potential to enhance water demand forecasting and to offer a scalable solution for developing regions facing climate challenges.
ABSTRACT Workflow diagram showing data preprocessing, machine learning models, hybrid ensemble models, SHAP explainability, and BOD prediction framework for water quality analysis. This study investigates the prediction of biochemical oxygen demand (BOD) levels across India's groundwater, lakes, and rivers using hybrid machine learning (ML) and explainable AI techniques. Traditional water-quality assessment approaches are often time-consuming and computationally expensive, motivating the need for efficient predictive frameworks. A comprehensive dataset collected from multiple Indian water bodies between 2017 and 2021 was analysed using ML algorithms including Random Forest, Support Vector Regressor, Gradient Boosting, XGBoost, and Multi-Layer Perceptron. Hybrid ensemble approaches incorporating stacking and feature-engineering techniques were further developed to improve predictive performance. Results demonstrated that hybrid models achieved higher predictive accuracy and stability than standalone ML models. Statistical significance testing confirmed the superiority of the ensemble approaches. To enhance transparency and interpretability, SHAP (Shapley Additive Explanations) analysis was applied to identify influential water-quality parameters, including fecal coliform, total coliform, and conductivity. The proposed framework provides an interpretable and computationally efficient approach for water-quality prediction and supports evidence-based decision-making for sustainable water-resource management.
