Introduction:

All urban and agricultural water distribution networks (WDNs), irrespectively of their physical and operational characteristics encounter substantial leakages, which result in significant water losses, environmental degradation through increased carbon emissions and noteworthy economic burdens. The current work aims to quantify both the environmental impact, estimated in terms of CO₂ emissions, and the economic implications associated with leakages and evaluate the effect of the most widely used leakage reduction strategies.

Methods:

The effectiveness of the studied approaches is tasted via a real-world application on the WDN of the city of Patras, in western Greece, which exhibits significant leakage rates (more than 40% of the system's input volume). To estimate the total CO₂ emissions and the water production cost, we utilize energy consumption as well as energy billing data associated with pumping and water treatment. Additionally, we use flow time series from pumping stations and individual district metered areas (DMAs) and/or pressure management areas (PMAs) to estimate the water balance of the network. This comprehensive approach allows us to assess both the environmental and economic impacts of leakages.

Results:

The results reveal that the most effective approach for mitigating leakages and their associated environmental and financial costs is by partitioning the network into smaller hydraulically isolated areas, combined with proper pressure management (i.e. design of PMAs). More specifically, the leakage rates and the associated CO₂ emissions and economic costs are reduced up to 40%. These findings highlight the importance of targeted pressure management towards achieving substantial efficiency improvements.

Conclusions:

Mitigating water leakages in WDNs is crucial for achieving environmental sustainability and economic efficiency. By reducing leakages through network partitioning and pressure management, water utilities can significantly reduce both the carbon emissions and the operational costs, contributing to global sustainability goals, as demonstrated by a case study in the city of Patras.

Flood forecasting is critical for effective water resource management, particularly in regions prone to flooding. This study presents an innovative approach to flood-type forecasting at the Saint-Charles station, located in Quebec, Canada, using an Extreme Learning Machine (ELM) methodology. Our objective was to develop a robust classification model to predict flood types, enhancing preparedness and mitigation efforts accurately. The dataset used spans from 2008 to the end of 2023, encompassing various hydrological parameters, meteorological data, and historical flood records. The ELM, known for its rapid learning speed and minimal computational burden, is applied to classify flood types based on the input features extracted from the dataset. The data preprocessing involves the normalization and handling of missing values to ensure model accuracy. Feature selection is performed to identify the most influential variables contributing to flood occurrences, including precipitation, river discharge, and soil moisture levels. The ELM model is trained and validated using a cross-validation technique to avoid overfitting and ensure generalization. The results indicate that the ELM model demonstrates high classification accuracy and efficiency in predicting flood types at the Saint-Charles station. The model's performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. A comparative analysis with other machine learning models highlights the superiority of ELM in terms of speed and predictive capability. This study highlights the potential of ELM as a valuable tool for flood forecasting, providing actionable insights for water resource managers and policymakers. The findings contribute to the body of knowledge in flood management and AI applications in hydrology, paving the way for further research and the implementation of advanced machine learning techniques in environmental monitoring.

A particular threat to surface water and groundwater is the migration of leachate, both from uncontrolled and organised landfills. Leachate is generated by infiltration of rainwater, groundwater and leaching of water from waste. It can contain a wide range of pollutants, including soluble organic compounds, inorganic pollutants, suspended solids, heavy metals and hazardous substances. The use of appropriately selected tools can be helpful in assessing the impact of landfills on environmental elements. It is critical to assess groundwater contamination in the areas around landfills, given that groundwater is the most important source of drinking water and is essential to meet the needs of communities. The aim of this study was to assess the variability in groundwater quality using the Landfill Water Pollution Index (LWPI). The properties of groundwater in the vicinity of two municipal landfills in a Central European country (Poland) were analyzed for a period 11 and 15 years of their operation. The results confirmed that LWPI is an effective method for assessing the quality of groundwater in the vicinity of municipal waste landfills. The obtained results confirm the negative impact of landfills, despite the insulation used. LWPI showed poor quality of groundwater, a visible impact of the landfill (landfill W, average LWPI - 2.34) and moderately polluted waters and a minor impact of the landfill (landfill S, average LWPI - 1.37). In most cases, it was observed that two parameters, EC and TOC, are the main factors contributing to the deterioration in groundwater quality. Analysis conducted on multi-year groundwater data using specific indicators can help managers better understand the impact of municipal solid waste on surrounding areas and help avoid potential operational problems in the future.

Abstract: This study aims to draw attention to the problem of Ghoramara Island's ongoing submergence and reshaping in relation to the island's natural ecosystem. Remote sensing and GIS will be the primary tools to examine the situation and provide controls. The morphological alteration of the island between 2010 and 2022 has been studied in great detail. The island's size has decreased to 3.49 square kilometers for several reasons, including anthropogenic and natural processes occurring in this area. An NDVI, or a Normalized Difference Vegetation Index, has also been developed to monitor changes in the vegetation cover between 2016 and 2022. The island has shrunk as a result of storm surges, and pictures clearly show where the tropical cyclones Amphan and Yaas struck the land mass and inflicted significant damage. To halt erosion, earthen embankments were constructed. A bioengineering approach to solving the problem was tried but was ineffective. If the government can offer effective management of the Ghoramara Island coast protection program, which incorporates bioengineering methods and is already in operation, this could greatly help the locals. Ghoramara is eroding due to a variety of circumstances, but if the locals want to halt this, they must take the appropriate measures. For instance, they should crop scientifically, plant trees on the island's side, and have the local government maintain the earthen ridges. Erosion and channel sedimentation will stop if the channel's depth can be maintained.

Highly managed Mediterranean river basins are experiencing severe environmental issues due to the overexploitation and degradation of both groundwater and surface water resources. Water resource managers face mounting concerns over the allocation of these limited resources, environmental quality, and planning amidst current and future climatic uncertainties. Understanding the hydrological components and water balance is crucial to addressing key questions related to water availability under various scenarios and future climatic conditions. In this study, the Soil and Water Assessment Tool (SWAT+) was utilized to develop an eco-hydrological model for the Cervaro river basin in Southern Italy. The objective was to evaluate baseline conditions and assess future climate projections towards the end of the 21st century. The model was driven by high-resolution data from Regional Climate Models (RCMs) under the Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 scenarios. The baseline model was calibrated using observed streamflow data, ensuring a robust assessment of goodness-of-fit objective functions. Future hydrological responses were projected for the mid-century time scale. Our simulations revealed significant spatial variations in water fluxes, with an increase in potential evapotranspiration and a decrease in precipitation and surface runoff. These findings underscore the substantial impact that future scenarios may have on sustainable water resource management and the evaluation of climate change effects in this region. The insights gained from this study are essential for informing adaptive management strategies and policy decisions aimed at mitigating the impacts of climatic changes on water resources in highly managed Mediterranean river basins.

Pakistan, with an estimated population of 241.5 million (2023), ranks as the world’s fifth most populous country. Due to an increase in population, Pakistan is facing a serious water shortage, which is affecting people’s lives. Pakistan has 143 large and small water storage reservoirs, with major ones like Tarbela, Mangla, and Chashma collectively holding a storage capacity of 18.92 Million Acre Feet (MAF). Pakistan’s annual water requirement is approximately 1,000 Billion Cubic Meter (BCM), while the current availability is only around 180 BCM, indicating a substantial water deficit. This deficit is expected to grow with population growth and rising water demand in the future. To address this, authorities need to create strong policies for conserving water. In response to the extending challenges posed by rapid urbanization and population growth in Pakistan, this comprehensive research has delved into the necessary task of transforming urban water management for sustainable cities. The objective was to propose an innovative framework that redefines urban planning and governance, integrating smart technologies to ensure the sustainable use of water resources. The rising demand for fresh water can be linked to the growing population. The internet of things (IoT), a technology that connects devices and systems, can play a crucial role. For the urban water management, we utilized IoT sensors for real-time data collection and AI algorithms for internet decision making. IoT sensors and AI algorithms in urban water management improve real-time data utilization, enhancing decision making for efficient urban water resource management.

Organic dyes are widely employed globally by the chemical and textile industries. The need to handle water effluent from the dyeing process, to mitigate environmental pollution, cannot be overemphasized. For instance, reactive blue dye is extensively utilized in the textile sector, and controlling its disposal and degradation is crucial in safeguarding the environment. Titanium (IV) tetraisopropoxide was used to synthesize titanium dioxide TiO₂, which was then doped with ferric oxide Fe₂O₃ to create a co-doped TiO₂-Fe₂O₃ photocatalyst. This photocatalyst was employed to degrade blue dye in water under sunlight irradiation. The characterization of the photocatalysts was performed using various techniques: Fourier Transform Infrared (FTIR) Spectroscopy to determine the chemical composition, Scanning Electron Microscopy (SEM) to examine the morphology, X-ray Diffraction (XRD) to assess the crystallinity, and UV–vis Diffuse Reflectance Spectroscopy (DRS) to analyze the light absorption properties. Doping TiO₂ with Fe₂O₃ reduced the photocatalyst's bandgap from 3.76 eV (in pure TiO₂) to 2.83 eV, enhancing its absorption properties in the visible light spectrum, as confirmed by UV-DRS. This improvement in light absorption is consistent with the degradation results of the blue dye, achieving a complete 100% removal efficiency after 120 minutes of sunlight irradiation. This study demonstrates that the photocatalyst is chemically stable and can be reused multiple times. After four cycles, the removal efficiency decreased by less than 15%, indicating its durability and reusability. The doped photocatalyst exhibited increased efficiency in harnessing solar energy to generate electron–hole pairs, resulting in superior photocatalytic activity. This method eliminates the need for electricity, making it both eco-friendly and cost-effective.

The determination of rainfall patterns in urban areas influences the availability of water resources, weather systems, and urban planning. In this study, the 24-hour rainfall pattern in 15 rain gauges over the Tehran province was determined and statistically analyzed. In total, 137 storm events with a duration of 20 to 28 hrs each, recorded in a time step of 1 min over a 30 year period, were used. To extract the 24-hour rainfall patterns, Pilgrim and Cordery’s methods were adopted. The Chi-square test was used to assess the accuracy and meaningfulness of the obtained patterns. Then, one-way analysis of variance (ANOVA) was adopted to test the difference between the patterns in the stations and also between the four standard patters in the Soil Conservation Service (SCS) of USA. The results of this study reveal that the higher percentages of the 24-hour rainfall depth occurred in the middle quartiles of rainfall time. ANOVA tests reveal that a significant difference (with a 95% confidence level) was detected between the 15 patterns obtained in the stations in Tehran, and also the between the patterns from Tehran’s and the SCS. The topograpical and local geographical atmospheric conditions (e.g., heat islands) influence the rainfall pattern in Tehran. Continued research in this field is essential for developing effective strategies to mitigate the impacts of changes in rainfall patterns to ensure the resilience of urban communities and ecosystems.

Büyükçekmece District is situated on the European side of Istanbul, with a coastline along the Marmara Sea. However, factors such as climate change, increasing population leading to uncontrolled urbanization, and improperly implemented stream rehabilitation projects expose the district to flood risks. The main objective of this research is to produce a flood risk map of Büyükçekmece District and contribute to the development of flood management strategies by examining its correlation with socioeconomic factors. In this study, fundamental factors affecting flood risk such as slope, aspect, elevation, land use, geological structure, proximity to river, soil type, and precipitation were identified. Using the Analytic Hierarchy Process (AHP), the weights of these criteria were calculated, and flood risk maps were generated based on these weights. Maps based on neighborhood boundaries considering socioeconomic indicators were produced using open data to address social vulnerability in high-risk areas. According to the analysis results, neighborhoods that should be prioritized in disaster management preparedness and damage reduction stages were identified. In conclusion, this research aims to shed light on the development of more effective and comprehensive strategies against flood risks in Büyükçekmece District. It emphasizes the critical role of local socioeconomic dynamics and information in determining flood management strategies.

This study investigated the impact of climate change on water resources and the propagation of climate model variations in the Almyros basin, Greece, with a particular focus on the intensively utilized groundwater reserves essential for irrigation. Facing significant degradation in both their quantity and quality, understanding the future trajectory of groundwater resources is imperative. Climate change effects are evaluated through the employment of bias-corrected Med-CORDEX climate model projections, specifically considering the RCP8.5 emission scenario. Utilizing an Integrated Modelling System (IMS) comprising surface hydrology (UTHBAL) and groundwater hydrology (MODFLOW) modules, the future status of coastal water resources was simulated. The methodology involved a multi-step process: First, we acquired climate model data for various future climate model simulations. Subsequently, these data were used as an input for an Integrated Modelling System (IMS) simulating variables like recharge rates, evapotranspiration, groundwater levels, and others. The findings provide crucial insights for sustainable water resource management in the Almyros basin amidst changing climatic conditions. Through this approach, this study aimed to elucidate the propagation of climate model variability in the hydrological variables and processes, and it highlights the necessity of integrating advanced climate model projections with comprehensive hydrological modelling to project and estimate the variability of climate change impacts on coastal groundwater systems.