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Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.

Heavy-metal sources in urban environments include automobile exhaust, fuel combustion, tires, road asphalt, weathering of building materials, and/or industrial activities. The presence of heavy metals in urban stormwaters constitutes a potential risk for water resources and aquatic life. Results from the present study demonstrated the effectiveness of two different lightweight aggregates (LWAs), Arlita and Filtralite, to remove heavy metals (Ni, Cu, Zn, Cd, and Pb) present in aqueous solutions. These materials were selected because they previously showed great results for water treatment and because of their physicochemical properties. The removal efficiency of the studied materials was evaluated with batch tests containing solutions contaminated with heavy metals (with individual and multiple contaminants) at different concentrations mixed with the LWA particles. Filtralite showed a better performance in heavy metal removal than Arlita: higher adsorption capacity for all metals, and lower release of metals from contaminated particles into washing water. The average removal capacities in tests developed with solutions containing individual contaminants for Arlita and Filtralite were 76% and 90%, respectively, although the values varied across the different contaminants. Metal elimination by LWAs was more effective with individual contaminated solutions than with multielemental ones. The analysis of the adsorption curves, the mineral precipitation on the LWA surface, and the geochemical modeling confirmed that two different mechanisms are responsible for the heavy-metal removal. First, the rough surface of the LWA presents sorbing surface sites of the forming minerals, resulting in the ion-exchange reactions of metal ions. Second, the LWA–water interaction causes an increase in solution pH, which triggers the precipitation and coprecipitation of the metals in the form of oxide and hydroxides. The study confirms that the use of Arlita and especially Filtralite present promising potential to remove heavy metals from urban stormwaters.

Minimizing energy expenditure is one of the main purposes of the managers of pressurized irrigation systems. From the energy consumption standpoint, they can reduce energy consumption by supplying a constant flow into the system (a scheme different from urban water pressurized networks in which water demands depend on users). Managers can keep energy demands (opening and closing valves) while meeting pressure restrictions. We developed a computer application in MATLAB containing a genetic algorithm to find the best moment to open and to close valves to minimize an objective function which measures the differences between the objective and the real injected flows. We tested this program in the pressurized irrigation network of the San Vicente Campus, University of Alicante (Southeast Spain) and we calculated the water and energy balance (from the later and present irrigation network) and the carbon credits not emitted to the atmosphere.

The issues of wastewater treatment and the reuse of water are of great importance, especially in areas where the shortage of conventional resources is a structural problem, as it is in the case of Spain. Wastewater reuse is a valid mechanism to avoid problems derived from droughts and water scarcity. It allows access to water resources in areas with water restrictions and to prevent futures scenarios, due to it being expected that water consumption will double by 2050 over the world. Thus, the likelihood that this unconventional, strategic resource would become scarce is unquestionable, particularly in cases where water planning and exploitation systems prioritize the preservation, protection, and improvement of water quality, as well as the sustainable and efficient use of natural resources. This paper shows how wastewater treatment and reuse are linked, as the reuse of wastewater is associated with a previous regeneration, and both of them are essential tools for maximizing environmental outcomes, as called for in the European Union Directives.

Currently, water demands are increasing notoriously, spreading the pressure on available water resources around the world in both quantity and quality. Similarly, the expected reduction of natural water inputs, due to climate change, depicts a new level of uncertainty. Specifically, Southeast Spain presents water scarcity due to its aridity—irregular and scarce precipitation and high evapotranspiration rates—combined with the competition between several water demands: environment, agricultural dynamics, urban-tourist activities, and industry. The study area of this work is the most relevant functional urban area of Alicante province (SE Spain), where the administration of water management is carried out by a range of authorities at different levels as the consequence of a complex historical development of water governance schemes: at the national, regional, and local levels. This study analyzes 21 municipalities and proposes a conceptual model which was developed by including different origins of water inputs—surface resources, groundwater, desalination, wastewater reuse, or interbasin transfers—and water demands with information obtained from 16 different sources. Our main results denote a relevant water deficit of 72.6 hm3/year even when one of the greatest rates of desalinated water and reused wastewater in Europe are identified here. This negative balance entails restrictions in urban development and agricultural growth. Thus, presented results are noteworthy for the water policy makers and planning authorities, by balancing the demand for water among various end users and providing a way for understanding water distribution in a context of scarcity and increasing demand, which will become one of the most challenging tasks in the 21st century.