Groundwater, an invaluable resource crucial for irrigation and drinking purposes, significantly impacts human health and societal advancement. This study aims to evaluate the groundwater quality in the Mnasra region of the Gharb Plain, employing a comprehensive analysis of thirty samples collected from various locations, based on thirty-three physicochemical parameters. Utilizing tools like the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), as well as Multivariate Statistical Approaches (MSA), and the Geographic Information System (GIS), this research identifies the sources of groundwater pollution. The results revealed Ca²⁺ dominance among cations and Cl⁻ as the primary anion. The Piper and Gibbs diagrams illustrated the prevalent Ca²⁺-Cl⁻ water type and the significance of water–rock interactions, respectively. The PIG values indicated that 86.66% of samples exhibited “Insignificant pollution”. NPI showed notable nitrate pollution (1.48 to 7.06), with 83.33% of samples rated “Good” for drinking based on the WQI. The IWQI revealed that 80% of samples were classified as “Excellent” and 16.66% as “Good”. Spatial analysis identified the eastern and southern sections as highly contaminated due to agricultural activities. These findings provide valuable insights for decision-makers to manage groundwater resources and promote sustainable water management in the Gharb region.

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities’ impact on surface water quality in the Oued Laou watershed, Northern Morocco. Thirteen surface water samples were characterized for 26 physicochemical and biological parameters. In this aspect, emerging techniques such as multivariate statistical approaches (MSA), water quality indices (WQI), irrigation water quality (IWQI), and Geographic Information System (GIS) were employed to identify the sources of surface water pollution, their suitability for consumption, and the distribution of surface water quality. The results showed that the major ion concentrations were reported in the following order: Ca²⁺, Na⁺, Mg²⁺, and K⁺; and HCO₃⁻ > CO₃²⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻ > F⁻ > PO₄³⁻ > NO₂⁻. It was also demonstrated that almost all parameters had concentrations lower than World Health Organization (WHO) limits, except for bicarbonate ions (HCO₃⁻) and the biochemical oxygen demand for five days (BOD₅), which exceeded the WHO limits at 120 mg/L and 3 mg/L, respectively. Furthermore, the types of Ca²⁺-HCO₃⁻ (Calcium-Bicarbonate) and Ca²⁺-Mg²⁺-HCO₃⁻ (Calcium-Magnesium-Bicarbonate) predominated in surface water. The Principal Component Analysis (PCA) indicates that the Oued Laou river was exposed to two forms of contamination, the first being attributed to anthropogenic activities such as agriculture, while the second reflects the water-sediment interaction. The Hierarchical Cluster Analysis (HCA), reflecting the mineralization in the study area, has classified the samples into four clusters. The Inverse Distance Weighting (IDW) of the WQI indicates that 7.69% and 38.46% of the surface water represent, respectively, excellent and good quality for drinking. At the same time, the IWQI revealed that 92.30% of the water surface is good for irrigation. As a result, the combination of WQIs, PCA, IWQI, and GIS techniques is effective in providing clear information for assessing the suitability of surface water for drinking and its controlling factors and can also support decision-making in susceptible locations such as the Oued Laou river in northern Morocco.

Assessing groundwater quality is essential for ensuring the sustainability of agriculture and ecosystems. This study evaluates groundwater contamination by heavy metals (HMs) using GIS approaches, multivariate statistical analysis (MSA), pollution indices (heavy metal pollution index (HPI), metal index (MI), degree of contamination (C_d), ecological risk index (ERI), and pollution index (PI)), and human health risk assessment (HHRA). The results revealed significant variations in heavy metal concentrations across the study area, with the highest concentrations found in the southern and southeastern parts, characterized by intense agricultural activities and uncontrolled landfills. Statistical analyses indicated both natural and anthropogenic sources of contamination. Pollution indices showed medium to high water pollution levels, with HPI values ranging from 20.23 to 128.60, MI values from 3.34 to 12.17, and Cd values from 2.90 to 11.73, indicating varying degrees of contamination. ERI values suggested a low ecological risk across all samples. However, health risk assessments highlighted significant non-carcinogenic and carcinogenic risks, particularly for children, with TCR values for some heavy metals like Ni and Cr exceeding safe limits, indicating potential health hazards. The findings provide a valuable framework for policymakers to develop targeted strategies for mitigating groundwater contamination and ensuring sustainable water quality management.

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

In Morocco, saline irrigation significantly affects soil quality and reduces crop yields. This study evaluates the effect of salt stress on soil properties and the overall performance of the durum wheat variety “Faraj”, aiming to optimize production under saline conditions. A greenhouse experiment was conducted during the 2023–2024 season, using a completely randomized design (CRD) to assess soil properties, plant growth, and yield. Five salinity levels (0.2, 4, 8, 12, and 16 dS m⁻¹) were applied to two soil types: silty-clay (S1) and sandy (S2). Results showed significant changes in soil properties, including increased pH, electrical conductivity, and accumulation of potassium, calcium, and magnesium in soil. Grain yield decreased significantly with increasing salinity, from 1.12 t ha⁻¹ in freshwater to 0.12 t ha⁻¹ at 16 dS m⁻¹ in S1, and from 0.56 t ha⁻¹ in freshwater to 0.12 t ha⁻¹ at 16 dS m⁻¹ in S2. Straw yield was less affected, with values of 1.24 and 1.16 t ha⁻¹ for S1 and S2 at 12 dS m⁻¹, decreasing to 0.80 and 0.55 t ha⁻¹ at 16 dS m⁻¹. The “Faraj” variety shows good tolerance to salinity up to 8 dS m⁻¹ for grain yield and 12 dS m⁻¹ for straw yield, making it particularly suitable for moderately saline environments.

Groundwater is a valuable shared resource in the Moulouya Basin, but it has been in decline due to recent years of low rainfall and rapid population growth. To support socio-economic development, there is increased demand for this precious resource. This paper presents a standard methodology for delimiting potential groundwater zones using geographic information systems (GIS), an integrated analytical hierarchy process (AHP), and remote sensing techniques. Seven parameters that monitor the presence and mobility of groundwater, including drainage density, lithology, slope, precipitation, land use/land cover, distance to river, and lineament density, were incorporated into a raster data model using ArcGIS software. AHP-based expert knowledge was used to prepare a groundwater potential index and assign weights to the thematic layers. The study classified the area into five zones of varying groundwater potential: very high (26%), high (51%), moderate (13%), poor (9%), and very poor (1%). The accuracy of the model was validated by comparing the Groundwater Potential Zones map with data from 96 wells and boreholes across the basin. The validity of the results was confirmed by comparing them with the specific yield of the aquifer in the study area, yielding a high correlation coefficient (R²) of 0.79. The analysis revealed that 89.5% of the boreholes were situated in the high and very high potential zones, demonstrating the reliability and robustness of the employed approach. These findings can aid decision-making and planning for sustainable groundwater use in the water-stressed region.