https://www.jwwse.ir/ Journal of Water and Wastewater Science and Engineering en daily 1 Mon, 22 Jun 2026 00:00:00 +0330 Mon, 22 Jun 2026 00:00:00 +0330 https://www.jwwse.ir/article_244378.html Leakage in water distribution networks is a major concern due to the considerable losses of water and energy it entails. Leakage is typically divided into three categories: reported, unreported, and background leakage. Background leakage originates from very fine cracks that cannot be detected through conventional leak detection or visual inspection. Because these leaks often remain undetected for extended periods, they can cause significant cumulative water losses. This study aims to provide a comprehensive review of background leakage rate models with respect to pressure and time in water distribution systems. A review of over 50 relevant studies revealed that more than half focused on pressure management as the most effective strategy to reduce background leakage, whereas less than one-third investigated the time-dependent growth of leakage rates. Despite its importance, temporal variation in background leakage has received limited attention in modeling studies, mainly due to challenges in estimating leakage growth rates and the scarcity of reliable data. Accordingly, this research reviews both pressure-based and time-dependent leakage models and discusses how these models can be applied to the analysis and management of water distribution networks. https://www.jwwse.ir/article_247703.html Predicting pipe failures in water distribution networks is crucial for transitioning from reactive maintenance to proactive management. However, developing accurate predictive models faces challenges posed by imbalanced data (failures vs. non-failures) and the recurring nature of these events, which are often overlooked. This research addresses these challenges by developing a weighted logistic regression model, a machine learning technique. The study utilized 12 years of real-world pipe failure data from the water distribution network of District 2 in Tehran. The data were randomly split into two subsets: 70% for training and 30% for validation, to verify the model’s performance on unseen data. The model’s performance was evaluated using the Receiver Operating Characteristic (ROC) curve. Results demonstrated high predictive power with an Area Under the Curve (AUC) of 0.88 on the validation set. Pipe length was identified as the most significant predictor, with an odds ratio of 1.02. A negative coefficient for pipe age (-0.104) was a key finding, attributed to survivor bias stemming from premature failures due to improper installation. This model can serve as a reliable tool for water utilities to prioritize pipe inspections and replacements, emphasizing the critical role of installation quality alongside the pipe’s physical characteristics. https://www.jwwse.ir/article_252121.html The aim of this research is to remove total arsenic from arsenic-contaminated water. In this research, the effect of different amounts coagulant and coagulant aids (chloroferric, polyaluminum chloride, bentonite, hydrated lime, and anionic polyelectrolyte) are studied on increasing the efficiency of arsenic removal from arsenic-contaminated water. Jartest is used to predict coagulation conditions, determine the appropriate pH, and the amount of chemicals required for flocculation. Atomic absorption spectrometry equipped with a graphite furnace and tube is used to measure arsenic. This study is conducted on surface water samples with a concentration of 86 mg/l. By examining different conditions of coagulants, coagulant aid, pH change, and perchlorine, the results were succeeded in removing arsenic by about 95% from arsenic-contaminated water and obtained the most optimal conditions (pH = 7.5, perchlorine = 7.5 ppm, ferric chloride = 21 ppm, bentonite = 2 ppm, hydrated lime = 2 mg/l, and polyelectrolyte = 0.03 ppm). https://www.jwwse.ir/article_252685.html Water, as a fundamental pillar of sustainable development, faces increasing challenges in Iran, including population growth, imbalanced urban development, unsustainable exploitation of groundwater resources, and climate change. Within this context, water and wastewater companies, as primary providers and managers of essential infrastructure services, play a decisive role in achieving organizational sustainable development. This paper adopts a review-analytical approach, examining the experience of the Mashhad Water and Wastewater Company as a case study. Relying on existing literature, it presents a framework for evaluating and localizing sustainable development strategies. Findings indicate that employing analytical approaches tailored to institutional and organizational conditions can lead to a more precise identification of capacities, improved managerial decision-making, and enhanced organizational sustainability. Furthermore, the results suggest that this framework, with appropriate adaptation and localization, is generalizable to other water and wastewater companies across the country and can serve as a basis for developing practical strategies at the national level. Ultimately, this research emphasizes the necessity of transitioning from general approaches to models suitable for local contexts. https://www.jwwse.ir/article_252137.html Water demand management requires not only technical and economic interventions but also communication that makes scarcity meaningful and specifies feasible actions. Using quantitative content analysis, this study examined 160 written water-management messages disseminated in Tehran from 2017 to 2026. Messages were coded by year, length, platform, content type and tone, dominant persuasive technique, framing, and actionability. Descriptive statistics, chi-square tests, Cramer’s V, and binary logistic regression were used. 20% of the corpus was independently recoded, yielding 85% simple agreement. Results showed that 34.4% of messages belonged to 2026, 66.2% contained no more than ten words, and 70.6% were disseminated through outdoor media. Moreover, 29.4% lacked a clear technique and 56.9% lacked identifiable framing. Numerical evidence was the most frequent technique, followed by warning; direct practical guidance was dominant in only 4.4% of messages. The adjusted model was significant: messages from 2026 had higher odds of containing a clear technique (OR = 6.29), while an identifiable content formulation or tone was associated with lower odds (OR = 0.44); length and platform were not significant. The findings reveal a gap between message visibility and actionable behavior and concern textual persuasive capacity rather than demonstrated behavioral effects. https://www.jwwse.ir/article_250868.html Organic dyes released into industrial effluents are considered among the most hazardous pollutants in aquatic environments due to their chemical stability, toxicity, and resistance to biodegradation. In this study, a chitosan-supported mesoporous SBA-15 nanocomposite (CTS/SBA-15) was successfully synthesized and evaluated for the removal of methyl orange from aqueous solutions. The structural and physicochemical properties of the synthesized adsorbent were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and, Brunauer–Emmett–Teller (BET) surface area analysis, confirming the successful formation of the nanocomposite with desirable surface characteristics. The effects of operational parameters including adsorbent dosage, initial dye concentration, pH, contact time, and temperature on the adsorption performance were systematically investigated. The results revealed that the adsorption efficiency was strongly influenced by the operating conditions, where decreasing pH, increasing adsorbent dosage, and prolonging contact time enhanced the removal efficiency, while increasing initial dye concentration and temperature reduced the adsorption performance. The maximum methyl orange removal efficiency (approximately 94%) was achieved at pH 5, adsorbent dosage of 0.16 g, contact time of 30 min, temperature of 25 °C, and initial dye concentration of 1 mg L−1. Kinetic investigations demonstrated that the pseudo-second-order model provided the best agreement with the experimental data, indicating the significant role of surface interactions in the adsorption process. In addition, the equilibrium data were better fitted by the Langmuir isotherm model, with a maximum adsorption capacity of 2.39 mg g−1, suggesting monolayer adsorption on the adsorbent surface. Thermodynamic analysis further confirmed the spontaneous and exothermic nature of the adsorption process. Moreover, the CTS/SBA-15 nanocomposite exhibited satisfactory stability and reusability after several adsorption–desorption cycles. Overall, the synthesized CTS/SBA-15 nanocomposite can be considered an effective and reusable adsorbent for the removal of anionic dyes from aqueous environments, offering a sustainable and environmentally friendly approach for dye-contaminated wastewater treatment. https://www.jwwse.ir/article_247700.html Machine learning approaches have gained increasing attention in recent years for predicting pipe failures; however, most existing studies have relied on one or two specific models, lacking a comprehensive comparative assessment of diverse algorithms and their practical applicability.This study develops and systematically evaluates six machine learning models, logistic regression, decision tree, random forest, support vector machine, neural network, and gradient boosted tree, for pipe failure prediction. The models were constructed using the dataset from the water distribution network of District 2 in Tehran, which includes detailed physical and operational pipe attributes. A 70–30 training–validation split was employed to ensure robust model development and assessment. The results reveal that, contrary to initial expectations, more sophisticated models such as random forest and gradient boosted tree exhibited substantial overfitting, achieving near perfect accuracy on training data but demonstrating poor generalizability to validation data, thereby limiting their suitability for operational deployment. The Decision Tree model was identified as the superior option, achieving an Area Under the Curve of 0.92, a sensitivity of 93%, and an accuracy of 92%. Given its high interpretability, this model can be effectively utilized in the operational management systems of water distribution networks for predicting pipe failures. https://www.jwwse.ir/article_252688.html In this study, a novel nanocomposite, AC@CuFe2O4@MCM-41@GQD, was synthesized using sesame waste. To evaluate its performance in the photocatalytic removal of the anionic dye Congo red from aqueous media, the effects of key operational parameters including pH, nanocomposite dosage, initial dye concentration, contact time, and temperature were investigated in a solar micro-photoreactor system. The results showed that this nanocomposite, owing to its mesoporous structure and numerous active sites, achieved high removal efficiency; under optimal conditions (pH 5, dosage 0.5 g L–1, concentration 20 mg L–1, time 12 min, temperature 25 °C), removal efficiency exceeded 96%. Comparative studies confirmed the synergistic effect of adsorption and photocatalytic degradation in the presence of sunlight. Kinetic data followed the pseudo-second-order model (R2=0.9970), indicating a chemisorption mechanism. The adsorption isotherm was best fitted by the Langmuir model with a maximum capacity of 62.50 mg g–1, representing monolayer adsorption on uniform active sites. Thermodynamic studies with positive enthalpy (132.40 kJ mol–1) and negative Gibbs free energy changes confirmed the endothermic and spontaneous nature of the process. The nanocomposite maintained significant efficiency over 6 reuse cycles. This structure is a promising, economical, and efficient option for the advanced treatment of industrial wastewater containing organic pollutants. https://www.jwwse.ir/article_252941.html Good corporate governance is a fundamental requirement for improving productivity, transparency and accountability in companies, particularly in Iran's water and wastewater sector, which faces water stress. Yet no context-specific model exists at the corporate level of this industry. This study aimed to design a model of citizen participation in good corporate governance in Iran's water and wastewater companies. A qualitative approach was adopted, using thematic analysis based on Braun and Clarke's six phases and Attride-Stirling's thematic network technique. Data came from semi-structured interviews with 12 academic experts and senior managers, selected by purposive sampling, until theoretical saturation; trustworthiness was assessed using Lincoln and Guba's criteria. The analysis yielded 135 basic themes, 18 organizing themes and four global themes: political-legal, cultural-social, economic and scientific-educational. Transparency, accountability, distributive justice, social trust, innovation and digital transformation, participation in decision-making, citizen oversight and social responsibility constitute the main categories of the model. The thematic network shows that the political-legal dimension acts as an enabler, the scientific-educational as a capacity builder, the cultural-social as a mediator, and the economic as an incentive. The model can inform participatory policies and the inclusion of a participation index in managerial performance appraisal.