In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca²⁺ > Mg²⁺ > Na⁺ > K⁺, and Cl⁻ > SO₄²⁻ > HCO₃⁻ > NO₃⁻, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R² values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from the dam failure event correspond to various breach parameters, such as width, slope, and formation time. This study aimed to simulate the dam breach failure scenario of the Yabous dam (northeast Algeria) and analyze its influence on the related areas (urban and natural environments) downstream of the dam. The simulation was completed using the sensitivity analysis method to assess the impact of breach parameters and flooding on the dam break scenario. The flood wave propagation associated with the dam break was simulated using the one-dimensional HEC-RAS hydraulic model. This study applied a sensitivity analysis of three breach parameters (slope, width, and formation time) on five sites selected downstream of the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m³/s, which gradually attenuated along the river course to reach 1972.7 m³/s at about 8.5 km downstream the dam. This study established the map of flood risk areas that illustrated zones threatened by the flooding wave triggered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height, and width revealed positive and similar changes for the increases in adjustments (±25% and ±50%) of breach width and slope in the five sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width. Meanwhile, the adjustments (±25% and ±50%) of the flood hydrograph did not significantly influence the flood parameters downstream of the dam. In the present study, the HEC-RAS 1-D modeling demonstrated effectiveness in simulating the propagation of flood waves downstream of the dam in the event of dam failure and highlighted the impact of the breach parameters and the flood hydrographical pattern on flood wave parameters.

The presence of dyes in water stream is a major environmental problem that affects aquatic and human life negatively. Therefore, it is essential to remove dye from wastewater before its discharge into the water bodies. In this study, Banyan (Ficus benghalensis, F. benghalensis) tree leaves, a low-cost biosorbent, were used to remove brilliant green (BG), a cationic dye, from an aqueous solution. Batch model experiments were carried out by varying operational parameters, such as initial concentration of dye solution, contact time, adsorbent dose, and pH of the solution, to obtain optimum conditions for removing BG dye. Under optimum conditions, maximum percent removal of 97.3% and adsorption capacity (Qe) value of 19.5 mg/g were achieved (at pH 8, adsorbent dose 0.05 g, dye concentration 50 ppm, and 60 min contact time). The Langmuir and Freundlich adsorption isotherms were applied to the experimental data. The linear fit value, R² of Freundlich adsorption isotherm, was 0.93, indicating its best fit to our experimental data. A kinetic study was also carried out by implementing the pseudo-first-order and pseudo-second-order kinetic models. The adsorption of BG on the selected biosorbent follows pseudo-second-order kinetics (R² = 0.99), indicating that transfer of internal and external mass co-occurs. This study surfaces the excellent adsorption capacity of Banyan tree leaves to remove cationic BG dye from aqueous solutions, including tap water, river water, and filtered river water. Therefore, the selected biosorbent is a cost-effective and easily accessible approach for removing toxic dyes from industrial effluents and wastewater.

This study aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the composition and origin of its chemical elements. Therefore, chemometrics techniques, such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while autocorrelation analysis (AA), supplemented by a simple spectral density analysis (SDA), is used for the TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, total dissolved solids (TDS) and electrical conductivity (EC) controlled three groups. The elements SO₄²⁻, K⁺, and Ca²⁺ are closely related to TDS, the elements Na⁺, Cl⁻, and Mg²⁺ are closely related to CE, while HCO³⁻ and NO³⁻ indicate the dissociation of other chemical elements. AA shows a linear interrelationship of EC, Mg²⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻. However, NO₃⁻ and HCO₃⁻ indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg²⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻ have a similar trend with EC. Nonetheless, pH, Ca²⁺, HCO₃⁻ and NO₃⁻ exhibit multiple peaks related to the presence of several distinct cyclic mechanisms. Using these techniques, the authors were able to draw the following conclusion: the geochemical processes impacting the chemical composition are (i) dissolution of evaporated mineral deposits, (ii) water–rock interaction, and (iii) evaporation process. In addition, the groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca⁺ and another recorded only with negative charges on HCO₃⁻ and NO₃⁻. On the other hand, SO₄²⁻, K⁺, Ca²⁺, and TDS are the major predominant elements in the groundwater’s chemical composition. Chloride presence mainly increases the electrical conductivity of water. The lithological factor is dominant in the overall mineralization of the Plio Quaternary surface aquifer waters. The origins of HCO₃⁻ and NO₃⁻ are as follows: HCO₃⁻ has a carbonate origin, whereas NO₃^– has an anthropogenic origin. The salinity was affected by Mg²⁺, SO₄²⁻, Cl⁻, Na⁺, K⁺, and EC. Ca²⁺, HCO₃⁻, and NO₃⁻ result from human activity such as the usage of fertilizers, the carbonate facies outcrops, and domestic sewage.

Healthcare workers were prioritized in vaccination campaigns globally because they are exposed to the highest risk of contamination by SARS-CoV-2. This study evaluated the self-reported post-vaccination side effects of inactivated (BBIBP-CorV and CoronaVac) and adenoviral vector-based (AZD1222, Gam-COVID-Vac and Ad26.COV2.S) vaccines among Algerian healthcare workers using a validated questionnaire. The final analysis included 721 healthcare workers, with a predominance of females (59.1%) and younger individuals 20–30 years old (39.4%). Less than half (49.1%) of the respondents reported at least one local side effect, while 53.8% reported at least one systemic side effect. These side effects were more prevalent among viral vector vaccinees than inactivated virus vaccinees. The most common local side effects were injection site pain (39%) and arm pain (25.4%), while fatigue (34.4%), fever (28.4%), headache (24.8%) and myalgia (22.7%) were the most prevalent systemic side effects. The side effects appeared earlier among inactivated virus vaccines recipients and generally lasted for 2 to 3 days for the two vaccinated groups. The risk factors associated with a higher prevalence of side effects included female gender, allergic individuals, individuals with regular medication, those who contracted the COVID-19 disease and those who received two doses for both inactivated and viral-based vaccines groups. Despite the higher prevalence of post-vaccination side effects among adenoviral vector vaccines recipients, both vaccines groups were equally effective in preventing symptomatic infections, and no life-threatening side effects were reported in either vaccine group.

In this study, the authors aimed at characterizing 11 Algerian kinds of honey taken from various geographical locations (beehives located at Djelfa (Medjbara and Dzaira), Laghouat, Aflou, Medea, Tiaret, Sidi bel-Abbes, Tiaret, Ain-Safra, Mostaganem, El Bayadh, and Ghardïa). The authors investigated the physicochemical parameters of these honey samples, including density, water content, electrical conductivity, ash content, pH, hydroxymethylfurfural (HMF) content, free acidity, and color. The physicochemical parameters obtained were found to be within acceptable ranges according to the international standards (Codex Alimentarius) for 9 out of 11 analyzed samples: density 1.38–1.50 g/cm³ (the same as kg/L, as commonly used for honey), water content 14.03–18.80%, electrical conductivity 0.38 × 10⁻¹ − 6.41 × 10⁻¹ mS/cm, ash content 0.06–0.48%, pH 3.50–4.50, free acidity 11–47 meq/kg, and color 1.1–9.2 Pfund index. Analysis of HMF content showed that only two honey samples have high values (117.7 and 90.0 meq/kg). Most samples of Algerian honey are of suitable quality according to international standards.