Desalination plays a vital role in Oran by converting seawater into freshwater to meet local water demands. This scientific paper provides a comprehensive evaluation of the desalinated water quality and its broader impacts.

In Oran, increasing water demand faces limited freshwater resources, highlighting the necessity of desalination despite its environmental and energy challenges.

The study begins with an analysis of the technologies employed in desalination plants in Oran, emphasizing methods such as reverse osmosis or thermal distillation. It examines the effectiveness of these technologies in removing salts and contaminants from seawater to produce potable water.

Furthermore, the paper delves into the environmental and public health impacts associated with desalination. It addresses concerns such as energy consumption, greenhouse gas emissions, and the discharge of brine and chemicals back into the marine environment. Evaluations of the water's chemical composition and potential health implications for consumers are also explored.

In addition to challenges, such as high operational costs and environmental concerns, the research highlights opportunities for improving desalination efficiency and sustainability. Future directions include advancements in technology to reduce energy consumption, enhance water recovery rates, and mitigate environmental impacts.

Overall, the paper aims to provide insights into the current state of desalination in Oran, offering recommendations for optimizing its performance while addressing environmental and health considerations.

Flooding is a recurring problem in many cities in Brazil, resulting in significant health, material, financial, and environmental losses. The uncertainty regarding extreme rainfall events due to climate change makes this problem even more challenging, especially for the municipality of Catu, in the state of Bahia, which also suffers from recurrent flooding. Critical points include the Santa Rita neighborhood, its surroundings, the supply center, and the city center. This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu–BA and its effects on the local economy at different recurrence intervals. Hydrological simulation software based on computational and geoprocessing technologies, such as HEC RAS, HEC HMS, and QGis 3.16, was used. Two-dimensional modeling was applied to assess the flood-prone areas and linear regression was used at different recurrence intervals. The results revealed that the area becomes impassable during flood events, causing significant economic losses, especially for local market vendors. The research concludes that urgent measures are necessary to mitigate the impacts of flooding, proposing the use of modeling and simulation technologies as essential tools for urban management and planning in flood-prone areas. Additionally, the implementation of adequate infrastructure and raising public awareness are crucial to reducing the damage caused by these extreme events.

Today, there are several sustainable wastewater purification techniques that meet the demand for the reuse of treated water and at the same time allow us to provide a new value to the biomass used in the purification. This technique is the high rate algal pond, which is a very advantageous lagoon method. A model station was recently installed in Tunisia at the Superior agronomic institute (SAI) of Chott-Mariam, and the interest of our research is to study its purification performance, to optimize it by using the modeling tool so that we can provide a reusable water for irrigation and agriculture, as well as to valorize the algal biomass produced in a very large quantities by the pond. This research project aims to increase investments in projects including sustainable purification techniques in countries such as Tunisia where the recurring water deficit and the demand for reuse of treated wastewater are particularly present. Our research aims to optimize the treatment process, by applying the modeling tool, which is used as a decision making and sizing tool for the management of this type of treatment system in Tunisia. We are in the process of calibrating a mathematical model of the algal pond by using a series of experimental measurements which are carried out on the experimental station, while allowing the integrated management of effluents and algae produced. Indeed, this technique is based on the symbiotic interaction between heterotrophic bacteria and algae (Oswald, 1978; Santiago 2013) allowing the elimination of the organic load and nutrients (phosphorus and nitrogen), after the step of purification, the algae thus produced in large quantities will undergo a recovery step by bio-flocculation and will be used as bio-fertilizer and as animal feed, where the added value of the project comes from.

Electrocoagulation has shown remarkable effectiveness in the treatment of industrial effluents, particularly in the removal of metal contaminants. The electrochemical processes occurring in aluminum electrodes have been remarkably effective in this regard. In the present study, electrocoagulation experiments were performed on industrial effluents originating from an electroplating bath located in Casablanca, Morocco. The basic objective was to remove chromium from the effluent and to facilitate the reuse of the treated water for various applications within our facility. To achieve this goal, a systematic optimization of several operational parameters affecting the electrocoagulation efficiency was performed. These parameters included voltage, electrode material, stirring speed, and electrode spacing. Their effects on key response variables, namely pH, conductivity, and chromium concentration, were rigorously evaluated. The experiments were carried out in a well-mixed reactor using a synthetic solution with a high concentration of chromium, namely 1000 mg/L Cr6+(aq). The chromium removal efficiency was evaluated under precisely controlled conditions, using aluminum electrodes with applied voltages of 6 V and 12 V, an optimal stirring speed of 600 rpm, and an electrode spacing of 2 cm. This study demonstrates the critical operating parameters that maximize the effectiveness of electrocoagulation in the treatment of chromium-laden industrial effluents, providing valuable insights for industrial applications and environmental management.

The dynamics and prevalence of microplastics in marine water and their absorption by fish in these habitats is a major threat to marine ecosystems. This study focuses on investigating microplastic dynamics in marine waters and their uptake in fish. Water and fish samples were collected from Kumbhabhishekam Port, Kakinada, which is a coastal region with significant industrial and maritime activities. Sampling density was ensured by collecting samples at five strategic points, including areas near industrial discharges, commercial ports, and relatively cleaner zones, with sampling intervals of 1.5 km along the coast . Water quality was assessed using the Water Quality Index (WQI), which integrates parameters such as pH, dissolved oxygen, turbidity, nitrates, and phosphates. The samples are collected and comprehensively analyzed for microplastics. Fourier transform infrared (FTIR) spectroscopy and Gravimetric analysis were employed to identify and quantify microplastics. We found significant accumulation of microplastics in the sea water, as well as within the gills, tissues, and gut of fishes. These particles have the propensity to adsorb harmful pollutants, thereby exacerbating the overall water pollution levels. The presence of microplastics in the water body and their accumulation in fish raises concerns about the transfer of contaminants to higher trophic levels of the food chain and their associated food webs, including humans. This study highlights the impact of waterborne contaminants to marine water. These alarming results underscore the importance of proactive measures to protect our water resources and emphasizes the urgent need for comprehensive mitigation strategies to combat the hazardous accumulation of microplastic dynamics, which, along with their absorption, poses a significant threat to biodiversity and ecological balance and their potential implications for our marine ecosystem.

The Lower Chenab Canal (LCC) region in Punjab, Pakistan, serves as an essential agricultural area that significantly contributes to the country's food production. However, the intensive use of agrochemicals and water resources has raised considerable concerns regarding the sustainability of groundwater quality in this region. This study aims to comprehensively analyze the impact of prevalent agricultural practices on groundwater quality in the LCC region. To achieve this, we conducted extensive field sampling across various agricultural zones within the region, collecting groundwater samples during different cropping seasons. These samples were analyzed for a range of chemical parameters, including nitrates, phosphates, pesticides, heavy metals, and other pollutants. The results were then correlated with agricultural practices, such as fertilizer and pesticide application rates, irrigation methods, and crop rotation patterns. Our findings reveal a strong association between intensive agricultural activities and elevated levels of nitrates and pesticides in groundwater, posing significant health risks to local communities. Notably, regions practicing monoculture and heavy agrochemical use showed higher contamination levels compared to areas with diversified cropping and organic farming practices. Additionally, geospatial analysis using Geographic Information System (GIS) and remote sensing data provided insights into the spatial distribution of groundwater contaminants and identified critical hotspots requiring immediate intervention. This research underscores the necessity for adopting sustainable agricultural practices, such as integrated pest management, precision agriculture, and organic farming, to mitigate groundwater pollution. Furthermore, this study advocates for the implementation of stringent regulatory frameworks and continuous monitoring programs to ensure the protection of groundwater resources. By integrating advanced geospatial tools and data analytics, this research contributes to developing effective strategies for sustainable groundwater management in the LCC region and similar agricultural landscapes globally.

Oxo-anions and cations of heavy metals often exist together in the environment. As they have opposite charges, it is a major challenge to remove them simultaneously and efficiently. In this work, a novel 2D mesoporous guanidinium-based ionic covalent organic framework (iCOF), exhibiting excellent reductivity and stability for the removal of oxo-anions (Cr(VI), V(V), Se(IV)), and cations (Pb(II)) in the pH range of 3–6, was used. In a single medium, rapid removal occurred within one hour with maximum removal capacities of 607.2, 589.6, and 221.6 mg g^-1 for Cr(VI), V(V), and Se(IV), respectively, at 30 ˚C. In addition, the coexistence of Pb(II) with Cr(VI)/V(V)/Se(IV) in the binary medium significantly increased the removal efficiency of both cations and oxo-anions using the iCOF composite. Based on the kinetic and isothermal mathematical models that reveal the chemical nature of adsorption throughout the process. Inspiringly, the system was tested in four types of wastewaters with different pH values to show its practical applicability. The results show that 1 kg of iCOF can decontaminate more than 2000 litres of ⁓10 mg L^-1 Cr(VI), V(V), and Se(IV), at a cost of USD 579 to the EPA’s effluent discharge limit. In addition, the comprehensive work carried out for this study has broader applications that can be used to treat toxic cationic and oxo-anionic heavy metal contaminants from a wide variety of wastewaters.

Water resource management is critical for sustainable development and is essential for the smooth functioning of ecosystems and human activities. Yet, water scarcity and drought can severely impact water supplies and agricultural productivity. Consequently, there is an urgent need for accurate predictions of water scarcity and drought conditions. Despite this, few studies have used machine learning to predict water scarcity and drought accurately. Therefore, this study collected daily hydrological data from January 1, 2016 to March 24, 2023, totaling 21,120 observations. In light of this, we developed a deep learning integrated model (CNN-BILSTM-AM) that combined an attention mechanism (AM), a convolutional neural network (CNN), and a bi-directional long-term and short-term memory network (BILSTM) to predict water scarcity and drought. The outcomes demonstrate that the integrated CNN-BILSTM-AM model effectively captures the nonlinear and time-varying characteristics of water scarcity and drought. With excellent adaptability to random sample selection, data frequency, and sample structure breaks, its prediction accuracy (with a value of 95.09%) significantly outperforms that of the traditional and single models. This study expands the knowledge of machine learning in water resource management and extends the research into the prediction of water scarcity and drought. It provides decision support and risk management tools for water resource managers, agricultural stakeholders, and government decision-makers.

The increase in pharmaceutical contaminants in the aquatic environment, originating from manufacturing processes and human excretion from incomplete metabolism, poses an alarming risk to the environment and for health. This results in the necessitating of the development of methods for effective water treatment. Traditional water treatment processes have been proven to be inadequate in the elimination of these pollutants. As a result, attention is being paid to the field of photocatalytic degradation, particularly on the utilization of silver-based catalysts. This systematic review delves intp silver-based materials used as a catalyst for the photocatalytic degradation of pharmaceutical compounds present in bodies of water. Silver-based catalysts, such as silver nanoparticles, silver-based semiconductors, and silver-based nanocomposites, are analyzed for their physicochemical properties that help enhance its catalytic performance. This review article explores the factors that influence the efficacy of silver-based catalysts, such as their crystal structure, morphology, surface area, and particle size. Furthermore, this review highlights silver-based photocatalysts’ recent advancements, challenges, and some comparative analyses on other catalysts. By synthesizing the latest studies, this review article presents the potential and limitations of silver-based photocatalysts to help in guiding future research directions and applications in environmental remediation, specifically in solving the problems ofpollutants in water.

The vegetation–soil moisture relationship is complex and nonlinear. Vegetation activity determines the space–time distribution and availability of soil moisture. In this study, we have used the 3rd generation Global Inventory Monitoring and Modeling Systems (GIMMS3g.v1) Normalized Difference Vegetation Index (NDVI) dataset to represent the vegetation activity in the Upper Blue Nile River Basin (UBNRB). We also used the distributed Water and Energy Processes (WEP) hydrological model to generate the soil moisture data. We used the Global Land Cover (GLC2000) data to identify specific land cover types. To examine the trends in the NDVI and soil moisture information, we employed the non-parametric Mann–Kendal trend test along with the Theil–Sen slope estimation technique. We applied another non-parametric correlation analysis named Spearman correlation to investigate the degree of relationship between soil moisture changes and vegetation responses. We limited our study to growing season (April–October) NDVI and soil moisture values to reflect the vegetation's growth status better. The results show that shrublands in northwestern lowlands along the Ethio-Sudan border and southern Ethiopian highlands exhibit a significantly increasing trend. Overall, 32.3% of the NDVI pixels and 66.6% of the soil moisture pixels show a significant trend. The sparse grassland of the southwestern lowlands shows a decreasing vegetation activity trend. In total, 28.8% of the valid pixels (excluding pixels of no data, cities, and waterbodies) show a higher correlation (ρ > 0.7), whereas 49.7% indicate a correlation between 0.5 and 0.7, mainly in the shrubland, grassland, and forested areas. The correlation between same month (lag0) NDVI and soil moisture is substantially higher than the subsequent previous month's (lag1-lag5) soil moisture values. This quick response relates to croplands, shrublands, and grasslands, confirming their sensitivity to short-term soil moisture changes. Forested areas of the basin did not show an appreciable correlation with soil moisture fluctuations.