Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areas for urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city. 36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

This study focuses on assessing the hydrogeochemical characteristics and irrigation suitability of groundwater in the Ras El Aioun and Merouana districts, using an integrated approach that combines physicochemical analysis, machine learning (ML), and Geographic Information Systems (GISs). Thirty groundwater samples were collected in June 2023 and subjected to extensive analyses, including major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, SO₄²⁻), pH, TDS, alkalinity, and hardness. Hydrochemical facies analysis revealed that the Ca-HCO₃ type was dominant (93.33%), with some samples exceeding FAO limits, particularly for Na⁺, K⁺, SO₄²⁻, Cl⁻, Mg²⁺, and HCO₃⁻. Assessment of groundwater irrigation suitability revealed generally favorable conditions based on three key parameters: all samples (100%) were classified as excellent based on the Sodium Adsorption Ratio (SAR < 10), 70% showed good-to-permissible status by Sodium Percentage (Na% < 60), and 83.3% were within safe limits for Residual Sodium Carbonate (RSC < 1.25 meq/L). However, the Permeability Index (PI > 75%) categorized 96.7% of samples as unsuitable for long-term irrigation due to potential soil permeability reduction. Additionally, Total Hardness (TH < 75 mg/L) indicated predominantly soft water characteristics (90% of samples), particularly in the central study area, suggesting possible limitations for certain agricultural applications that require mineral-rich water. GIS-based spatial analysis showed that irrigation suitability was higher in the eastern and western regions than in the central zone. Advanced machine learning algorithms provide superior predictive capability for water quality parameters by effectively modeling complex, non-linear feature interactions that conventional statistical approaches frequently fail to capture. Three ML models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were used to predict the Irrigation Water Quality Index (IWQI). XGBoost outperformed the others (RMSE = 2.83, R² = 0.957), followed by RF (RMSE = 3.12, R² = 0.93) and SVR (RMSE = 3.45, R² = 0.92). Integrating ML and GIS improved groundwater quality assessment and provided a robust framework for sustainable irrigation management. These findings provide critical insights for optimizing agricultural water use in water-scarce regions.

Groundwater (GW) quality and contamination by potentially toxic elements (PTEs) are major concerns for environmental sustainability, particularly in arid regions. The aim of this study was to assess the human health risks associated with GW contamination by PTEs in the Terminal Complex (TC) aquifer of the Tolga oasis, located in northeastern Algeria. Seventeen GW samples were analyzed using standard methods to determine contamination levels and associated health risks. Results showed that GW was generally contaminated with lead (Pb), which exceeded the WHO permissible limit of 0.01 mg/L in 76.47 % of the samples. Although some samples were rich in Cr and Mn, their levels were below WHO guidelines. Pollution indices, including Contamination Factor (CF), Heavy Metal Pollution Index (HMI), and Nemerow Pollution Index (NPI), indicated that over 50 % of the samples had medium to high pollution levels. These indices were further estimated using artificial neural network (ANN) and Multiple Linear Regression (MLR) machine learning models, whose performances were validated by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency Coefficient (NSE). The Taylor diagram analysis showed that MLR models were more accurate than ANN models in estimating GW pollution indices. Mapping these indices using support vector machine (SVM) algorithms and applying chemometric statistical techniques, including principal component analysis (PCA), revealed that alteration of geological formations and anthropogenic activities significantly affected GW contamination by PTEs in the study area. The assessment of health risks associated with heavy metals revealed a significant non-carcinogenic risk, particularly for children, with 41.17 % of samples exceeding the hazard index threshold of 1 due to Pb exposure, while carcinogenic risks were low. This study establishes predictive models based on heavy metal pollution indices, providing crucial information on the spatial distribution of GW contamination. The results support the development of targeted mitigation strategies and intervention plans to safeguard GW resources and public health in the region.

The Aptian sedimentary succession exposed in the southern Bellezma Mountains contains a low diversity trace fossil assemblage, with ichnofossils being common in the middle part of the studied section. The recorded trace fossils comprise eight ichnogenera: Archaeonassa, Cochlichnus, Chondrites, Gyrochorte, Palaeophycus, Protovirgularia, Rhizocorallium, and Thalassinoides. Ethologically, these ichnogenera display feeding, crawling, and dwelling activities of epi- and infaunal organisms. The presence of these trace fossils is mainly related to the activity of the deposit feeders, due to the accumulation of nutrients on the seafloor in unconsolidated, poorly sorted, soft substrate after storms under good environmental conditions. Together with sedimentological features, the ichnoassemblage suggests that the studied Aptian sedimentary succession was deposited in the upper offshore zone, corresponding to the Cruziana ichnofacies. These trace fossils are reported herein for the first time in the Bellezma Mountains. Furthermore, this study represents the first investigation of Mesozoic trace fossils in eastern Algeria.

This article reports cases of growth anatomical anomalies in two echinoid species, Macraster douvillei (Gauthier) and Mecaster pseudofourneli (Péron and Gauthier), from the Cenomanian deposits of the Bellezma-Batna mountains (northeastern Algeria). Such a topic is first reported in Algeria ever. The large collection (400 specimens) made it possible to distinguish several types of these rare pathologies, each one being illustrated by explanatory drawings. Three types of deformation directly concerning the pentamery, are presented. They are most often resulting from an additional growth zone (6 ambulacra), a complete tetramery represented by a missing growth zone (4 ambulacra) and constrictions or strangulation of the ambulacres. Abnormalities can develop in the rudiment as soon as the larva enters metamorphosis, or shortly afterwards in juvenile broods. They may alter the plate arrangement and the general shape of the test or, on the contrary, result in local deformations, influencing the arrangement or shape of the ambulacra, etc. These malformations resulted from either intrinsic (genetic) or extrinsic (ambiental) conditions.

The species Orbirhynchia mantilliana is newly documented in Algeria, identified within the Upper Campanian marly limestone of the Akhdar Member in the Abiod Formation at Djabel Gueroun, Batna Province, Aurès Basin. It is associated with Upper Campanian faunal markers such as the ammonite species Nostoceras (Bostrychoceras) polyplocum, found within the planktic foraminifera Globotruncana calcarata Zone. The O. mantilliana displays a restricted geographic distribution and biogeographic range, confined to Algeria, the United Kingdom, and Germany.

The aim of this work is to characterize clays of Middle Eocene age from the Ghoufi-Atlas Saharan region, Algeria, in order to know the fields of their use and their eventual valuation. To this end, an outcrop sampling campaign was carried out across these formations. The samples collected were analyzed by X-ray diffraction (XRD), X-ray fluorescence (XRF), and Scanning Electron Microscopy (SEM). Potential samples rich in clay fraction were further analyzed using laser granulometry, physicochemical tests, and copper adsorption recovery tests. The results showed that the clay fraction, which varies between 71 and 86%, are entirely represented Palygorskite, accompanied by 06–18% of dolomite, 03–7% of calcite, and traces of quartz as non-clay minerals. Further analysis showed that these clays have a cation exchange capacity (CEC) of 07 and 07.16 meq/100 g and a specific surface area (SS) of 57 and 60 m²/g. Copper adsorption tests have shown that fixation kinetics are very rapid and that these clays have a very high adsorption capacity.

This presentation examines the Cenomanian–Turonian Bahloul Formation in NE Algeria and NW Tunisia through integrated facies, stratigraphic, structural, and geochemical analysis. It highlights the role of synsedimentary tectonics and OAE2-related sea-level rise in controlling organic-matter distribution and source-rock development in the Algero-Tunisian Atlassic Basin.

A hydrogeological study of the thermal source of Ouled Aïcha in the Aurès Mountains showed that the source emerges in a particular natural context mainly represented by the presence of a vertical fault in the NE-SW direction affecting Cretaceous limestone. This supports the increase on the surface of moderately hot water, whose temperature is approximately 30 °C and an exploited flow of 3 L·s-1. The vertical electric sounding in situ showed the in-depth presence of a saliferous conducting level within a calcareous-resistant mass, which probably settled in the fault's favor. The presence of this saliferous level strongly influences the hydrochemistry of this thermal source. Thus, the water from the source is characterized by high salinity due to its temperature, which favors the dissolution of mineral salts in sufficient quantity throughout its journey (12390 µS/cm). The high concentrations of chlorides, sodium and sulfates indicate a significant contribution of salt from evaporitic formations as for the calcium content indicates that this water is influenced by the dissolution of carbonate formations. These physicochemical characteristics provide this water therapeutic virtue, which can be attributed to its chemical composition, high rock salt content, and low nitrate content. Geothermometry has shown that these thermal waters acquire a high temperature in their original tanks coming from a depth through a fault system that affects the basement.

Evaluating hydrological trends is crucial for the sustainable management of water resources with the escalating impacts of climate change. This study assesses the Wadi Bouhamdane Basin in Northeast Algeria, integrating data from the Gravity Recovery and Climate Experiment (GRACE) and the Follow-On mission (GRACE-FO) with hydrological observations and modeling to provide insights into precipitation and runoff dynamics since 1991. Using techniques such as linear regression, the Mann–Kendall trend test, cumulative departure, mutation analysis, and Morlet wavelet transformations, we identified a declining trend in annual rainfall (− 36.85 mm/decade) and an increase in runoff (20.65 mm/decade). Our rainfall analysis projected droughts from 2018 to 2020 and a water-rich phase in 2024, with predicted fluctuations extending into 2025. GRACE/GFO data from 2002 to 2022 revealed consistent reductions in terrestrial water storage (~ 0.35 cm/year), marked declines during projected drought periods, and insights into post-drought recovery and water accumulation trends. These findings are consistent with the projected wet–dry fluctuations from 2021 to 2023 and suggest the onset of a wetter period around 2024. The runoff sequence is projected to maintain its slight upward trend from 2018 to 2019, with fluctuations from 2018 to 2020, a dry period from 2022 to 2024, and a predicted dry year in 2025. Our combined approach of satellite data with ground-based measurements highlights the complex interactions influencing hydrological responses in semi-arid regions. This study underscores the significance of merging conventional hydrological methods with advanced satellite observations to enhance water management precision and resilience, advocating for a multi-source data framework to inform sustainable water resource policies amid evolving climate conditions.

Groundwater from coastal aquifers plays a significant role in agriculture, but its diminishing of quality often impacts crop production and soil sustainability by leading to soil salinization and the deterioration of irrigation water standards. This study addresses the pressing issue at Mornang Plain in Tunisia utilizing an integrated approach that combines statistical analysis (principal component analysis (PCA) and cluster analysis (CA), geographic information system (GIS), and machine learning (ML) techniques to assess and predict irrigation water quality. Key parameters such as irrigation water quality index (IWQI), potential salinity (PS), sodium percentage (Na%), and sodium adsorption ratio (SAR) were evaluated to assess water quality for agricultural use. The study identified three main groundwater facies (Na-Cl, Ca-Mg-SO₄, Ca-Mg-Cl/SO₄), that displaying distinct chemical signatures shaped by geological, hydrological, and human processes. The analysis showed that over 65% of the groundwater samples fall within the “unsuitable” category for irrigation, with high to severe constraints for soil and crop sustainability. A novel decision tree (DT) based ML model was optimized to predict these irrigation indices, achieving high performance with fewer input parameters. With low RMSE values and R² values ranging from 0.706 to 0.996 across several indices, the DT models showed remarkable predictive accuracy. The models’ efficiency in producing accurate water quality forecasts at lower analytical costs is demonstrated by their R² = 0.992 (RMSE = 1.693) for IWQI and 0.996 (RMSE = 0.822) for PS. This approach provides a cost-effective alternative to traditional methods by reducing the number of chemical parameters required for analysis. The results of this study offer significant insights for water resource management in arid and semi-arid regions, highlighting the potential of ML techniques in predicting irrigation water quality. The findings are valuable not only for Tunisia but also for similar regions worldwide, offering a tool for decision-makers to develop sustainable water management strategies and improve agricultural practices globally.

Introduction purpose: As population growth and industrial expansion continue, surface freshwater reservoirs such as dams have become increasingly vital due to their accessibility and ease of treatment. However, the quality of these water sources has significantly deteriorated, primarily due to the discharge of domestic and industrial wastewater. The proliferation of extensive algal blooms has led to significant challenges in maintaining drinking water quality and raised concerns about public health. This study investigates the impact of various water sources on the physicochemical quality of an Algerian dam over four seasons (December 2020 - October 2021) and explores the factors influencing the occurrence of cyanobacterial blooms to better understand and manage this excessive growth.

Methods: Physicochemical properties and algal composition of the dam water were analyzed monthly to determine nutrient sources and environmental factors affecting cyanobacterial proliferation.

Results: The analysis revealed that the Timgad stream and Reboua valley are notable sources of nutrient enrichment. Elevated temperatures and high nutrient loads, particularly total phosphorus (TP), in Timgad dam water facilitate the proliferation of blue-green algae. Additionally, limited nitrogen content favors the dominance of nitrogen-fixing cyanobacteria such as Aphanizomenon and Oscillatoria. The study also highlights that the low flow rate and high nutrient load of the Timgad stream create favorable conditions for cyanobacterial growth. Conclusions: Nutrient inputs, temperature, and hydrological conditions significantly influence cyanobacterial blooms. Understanding these factors is crucial for implementing effective water management strategies to reduce algal proliferation and protect freshwater quality.

Algerian natural zeolite (denoted NZ) was modified by Hydrochloric Acid (denoted as HZ) and Sodium Hydroxide solution (denoted as NaZ). XRF results indicate that SiO2 is the predominant mineral for the three zeolites. XRD analysis revealed that NZ is primarly  composed of mordenite, followed by chabazite and smaller amounts of quartz.  MEB-EDX results showed slight variations in the Si and Al content for HZ and NaZ, without significant changes to the core structure of the zeolite.  This study aimed to assess the impact of acid and alkalin modification on the removal of two cationic textile dyes (BR₄₆ and BY₁₃) from aqueous solutions. Initial dye concentration, contact time and pH were studied in a batch system. The adsorption capacities of NZ, NaZ and HZ increased with longer contact times, higher initial concentrations, and elevated temperatures. The equilibrium state was rapidly reached and could be described using pseudo-second-order kinetic model. The Freundlish isotherm model provided a good fit for the adsorption process. Pourcentage removal was the highest for NaZ, achieving 97.62% for BR₄₆ and 98.97% for BY₁₃. The lowest removal percentages were noted at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption process was spontaneous and endothermic.