The pollution in Dhaka's navigable waterways, including the Buriganga, Balu, Tongi Khal, and Turag rivers, is a significant concern due to rapid industrial and urban expansion. Industrial discharges, domestic sewage and inadequate waste management are the primary sources of this pollution, degrading water quality and threatening aquatic ecosystems. This study aimed to predict the Water Quality Index (WQI) of these rivers using fourteen machine learning (ML) models: Decision Tree Regression, Linear Regression, Ridge Regression, Stochastic Gradient Descent (SGD) Regressor, Extreme Gradient Boosting (XGB) Regressor, Light Gradient Boosting Machine (GBM) Regressor, Elastic Net Regressor, Support Vector Regression (SVM), Random Forest Regression, Bayesian Ridge Regressor, Artificial Neural Network (ANN), AdaBoost Regressor, CatBoost Regressor and Extra Trees Regressor. The objective was to evaluate and compare these models to identify the most effective predictive method for WQI, enabling efficient environmental monitoring and management of urban waterways. Among the evaluated ML models, ANN and Random Forest Regressor performed the best. The ANN model demonstrated superior predictive capability, achieving a Root Mean Squared Error (RMSE) of 2.34, a Mean Absolute Error (MAE) of 1.24, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a Coefficient of Determination (R2) of 0.97. Furthermore, an Adjusted R2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. These findings emphasize the importance of using AI modeling techniques, specifically ANN and Random Forest Regression, to improve the accuracy of WQI forecasts for the waterways. This study contributes to the field of environmental science by offering a novel integration of feature selection techniques with ML models to enhance efficiency and cost-effectiveness of water quality monitoring. Unlike previous studies, this research specifically addresses the challenges of urban waterways in Dhaka, Bangladesh, a region significantly impacted by industrial and urban pollution. To our knowledge, this is the first study to apply such a comprehensive range of ML models to predict the WQI of Dhaka’s four major rivers. By providing a reliable methodology for WQI estimation, this study supports informed decision-making and proactive measures to protect vital water resources.

Extensive construction augmenting the infrastructure and real estate projects underpin Saudi Arabia’s Vision 2030 of sustainable cities. A part of this struggle involves the transformation of the existing infrastructure together with new construction, which generates a large amount of construction and demolition waste (CDW). In the absence of a structured life cycle assessment (LCA) framework, the waste management companies are planning future scenarios (phased expansions of material recovery facilities to improve the recycling rate) primarily on economic grounds. This study assesses the environmental impacts of the existing and planned CDW management practices of the Saudi Investment Recycling Company in Riyadh City by dint of LCA. Impact 2002+ performs life cycle impact assessment of the base case (45% recycling), four treatments (61, 76, 88, and 100% recycling), and zero waste scenarios. The study demonstrates the benefits of current CDW (mixed soil, concrete blocks, clay bricks, glazed tiles, and asphalt) recycling in terms of avoided impacts of non-renewable energy, global warming, carcinogens, non-carcinogens, and respiratory inorganics potentially generated by landfilling. For the treatment scenario of 100% recycling, CDW conversion into a wide range of aggregates (0–50 mm) can replace 10–100% virgin aggregates in backfilling, precast concrete manufacturing, encasements and beddings of water mains and sewers, manholes construction, non-load bearing walls, and farm-to-market roads. To achieve long-term economic and environmental sustainability, municipalities need to improve source segregation, handling, and storage practices to enhance the existing (45%) recycling rate to 100% in the next five years and approach the zero-waste scenario by 2030. The findings of the present study motivate the generators for source reduction as well as encourage the recycling companies and concerned organizations in the continuous performance improvement of the CDW management systems across Saudi Arabia on environmental grounds, as an addition to the perceived economic benefits.

Limited knowledge on the water–energy–carbon nexus of water supply systems (WSSs) with brackish groundwater sources in arid regions exists to date. In addition, the large amount of fossil-fuel energy utilized by treatment processes generating a significant amount of carbon emissions remains a challenge for the municipalities in Saudi Arabia to meet long-term sustainability goals. To achieve Saudi Arabia Vision 2030’s target of sustainable cities with reduced CO₂ emissions, the present study aimed to analyse the water–energy–carbon nexus for WSSs and propose mitigation measures for reducing energy and carbon footprints from both the water management and treatment technologies perspectives. The detailed energy consumption data for three main components (source extraction, water treatment, and conveyance and distribution) of the main WSS, serving the 600,000 population of Buraydah City (Qassim, Saudi Arabia), was obtained from the concerned municipality. The city water treatment plant removes naturally occurring iron, TDS, and radionuclides in the source water with the help of ion detention, oxidation, sand filtration, ultrafiltration, reverse osmosis, chlorination, and backwash water management. The study found that the treatment facility consumes around half of the total system’s energy (131,122 kWh/day); while, with deep confined aquifer (>600 m) and an average water loss of 8%, conveyance and distribution (34%) and source extraction (18%) are consistent with the reported literature. With oil-driven energy, carbon emissions were found to be 10.26, 27.18, and 19.72 million tons CO₂ eq/year for source extraction, water treatment, and conveyance and distribution, respectively. The reverse osmosis process, with higher energy consumption—1.1 kWh/m³ of treated water—than the global average, consumes most (88%) of the treatment plant’s energy and thus needs effective energy management practices. Moving to renewable (solar and wind-driven) sources, subject to a detailed life cycle analysis, can achieve significant energy and associated carbon emission reductions. To sustainably meet the water demand of the growing population in arid regions, the study also suggests raising the awareness of the public about how water conservation can control CO₂ emissions, proactive maintenance of aging infrastructure, and increasing rainwater and treated wastewater reuse, to enhance the operational life of existing treatment facilities.

Even though water is a renewable resource, the majority of the available water on the planet is unfit for human use. Moreover, the drinkable water demand is ever-increasing as a result of rising population, urbanization, and life standards, which makes the needs for sustainable, economic, and environment-friendly treatment alternatives of utmost importance. Seawater desalination using solar stills has been proposed as a promising alternative that may help to solve drinkable water scarcity issues. In the past decades, many studies have been conducted to assess the performance of different types of solar stills aiming to enhance their productivity. Computational fluid dynamic (CFD) numerical simulation is one of the approaches that have been used recently to assess the performance of solar stills. The present study performed a systematic review and bibliometric analysis to provide a comprehensive overview of CFD numerical simulation uses as a tool to assess solar stills performance. A total of 486 publications were collected initially from different databases for the period between 2012 and 2022. The collected publications were filtered through several stages reaching 43 publications of highest significance. The collected data were analyzed descriptively, and the bibliometric mapping was presented. Furthermore, the basics and principles of CFD numerical simulation of solar stills efficiency were described and discussed. Later, the previous studies were analyzed to understand the algorithms, methods, and still types used. Finally, future research scopes and conclusions were stated. The presented knowledge in this study can help to provide a deep overview of using CFD in studying the efficiency of solar stills and inspire researchers to identify future research ways and gaps.

Small communities and most rural settlements in the Kingdom of Saudi Arabia (KSA) store domestic wastewater in residential septic tanks and transport it to the nearest centralized wastewater treatment plant. Without a sanitary sewerage system, the residents encounter various socioeconomic and environmental challenges related to sewage collection vehicles, the production of objectionable gases, and leaking septic tanks. The present study developed a resident perception-based methodology to appraise the sustainability of a low-cost ceramic filter bioreactor-type decentralized wastewater treatment system (DWWTS) for a small community of 1300 residents (160 households) in Qassim (KSA). In addition to six demographic factors, nine indicators assessed residents’ perceptions about existing and proposed wastewater management systems. A hierarchical-based system of sub-indices evaluated the three dimensions of sustainability using four environmental, nine social, and three economic indicators. The indicators translated into dichotomous questions posed to 34 respondents in the study area. The statistical analysis assessed the association of responses with the willingness to accept (WTA) the proposed DWWTS. A subjective rating scheme translated the responses into performance scores, and a fuzzy-based method aggregated the scores into sub- and top-level indices. The top of the hierarchy showed a close agreement between the resident’s perception and DWWTS’ sustainability. The study found that residents’ knowledge about environment and resource conservation resulted in a moderately high willingness to reuse treated effluent and WTA the decentralized system. The study also showed that the economic viability of a DWWTS remained at a moderate performance level due to a low monthly waste disposal cost. The study’s findings present a high potential for sustainable community-maintained DWWTS initially supported by the government. The proposed approach facilitates decision-makers working in ministries concerning water resources, environmental protection, and agricultural production in evaluating the sustainability of DWWTS for small communities in arid regions.

In addition to a wide range of socio-economic impacts, traffic congestion during the era of the COVID-19 pandemic has been identified as a critical issue to be addressed. In urban neighborhoods, the timespan of traffic congestion hazard (H_TC) after the curfew lift is subjected to the commuters’ decisions about home-to-shopping center departures. The decision for departing early or late for shopping depends on both the internal (commuter related) and external (shopping center related) factors. The present study developed a practical methodology to assess the H_TC period after the curfew timings. An online questionnaire survey was conducted to appraise the commuters’ perception about departure time and to assess the impact of eight internal (family size, involvement in other activities, nature of job, education level, age, number of vehicles, number of children, and availability of personal driver) and three external (availability of shopping center of choice in near vicinity, distance to shopping center, and size of the city) factors on their decision. With an acceptable 20% response rate, Chi-square and Cramer’s V tests ascertained family size and involvement in other activities as the most significant internal factors and availability of shopping center of choice as the primary external factor. Age, number of children, and size of the city influenced to some extent the commuters’ decisions about early or delayed departure. Large associations were found for most of the factors, except education level and availability of drivers in a household. Fuzzy synthetic evaluation (FSE) first segregated the commuters’ responses over a four level-rating system: no delay (0), short delay (1), moderate delay (3), and long delay (5). Subsequently, the hierarchical bottom-up aggregation effectively determined the period of highest traffic congestion. Logical study findings revealed that most (about 65%) of the commuters depart for shopping within 15 min after the curfew lift, so H_TC in the early part (the first one hour) of the no curfew period needs attention. The traffic regulatory agencies can use the proposed approach with basic socio-demographic data of an urban neighborhood’s residents to identify the H_TC period and implement effective traffic management strategies accordingly.