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.

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.

Groundwater is one of the important determinants of human health in many regions of Morocco. Indeed, it is one of the government’s top concerns. However, slow and wrong decisions are hindering the advancement of the quality of groundwater in Morocco. The traditional monitoring methods are costly, time-consuming, and human-resource-intensive, especially in such a developing country. An exploratory study was conducted in the Jouamaa Hakama region in Morocco to test and compare groundwater quality using in situ measurements, water quality indices (WQIs), and a fuzzy logic (FL) method. The methodology followed in this study involves using and comparing four WQIs and FL based on in situ measurements at nine different wells along the Ouljat Echatt River downstream of the Chrafate wastewater treatment station. Twelve physical–chemical and bacteriological parameters: potential of hydrogen (pH), temperature (T°), turbidity (TURB), chemical oxygen demand (COD), biochemical oxygen demand in five days (BOD₅), suspended matter (SM), phosphate (PO₄³⁻), nitrite $(N{O}_2^{-}$), nitrate $(N{O}_3^{-}$), ammonium $(N{H}_4^{+}$), dissolved oxygen (DO), electrical conductivity (EC), and fecal coliforms (FC) were measured in 2016 and 2017. The results show that all wells are of poor quality according to FL and WQIs; the Weighted Geometric WQI and Oregon WQI indicate that the groundwater is below the acceptable standard for human consumption, while the Weighted Arithmetic WQI and Logarithmic WQI indicate that the majority of wells are of good quality. These tested indices represent an excellent tool to support decision making and can be highly helpful in monitoring groundwater quality in vulnerable areas such as the Jouamaa Hakama region in the north of Morocco.

For many years, rare earth elements (REEs) have been part of a wide range of applications (from cell phones and batteries to electric vehicles and wind turbines) needed for daily life all over the world. Moreover, they are often declared to be part of “green technology”. Therefore, the data obtained from the United States Geological Survey (USGS) on the reserve and production of rare earth elements underwent treatment using the multivariate imputation by chained equations (MICE) algorithm to recover missing data. Initially, a simple linear regression model was chosen, which only considered fixed effects (β) and ignored random effects (U_i). However, recognizing the importance of accounting for random effects, the study subsequently employed the multilevel Linear Mixed-Effects (LME) model. This model allows for the simultaneous estimation of both fixed effects and random effects, followed by the estimation of variance parameters (γ, ρ, and σ²). The study demonstrated that the adjusted values closely align with the actual values, as indicated by the p-values being less than 0.05. Moreover, this model effectively captures the sample’s error, fixed, and random components. Also, in this range, the findings indicated two standard deviation measurements for fixed and random effects, along with a variance measurement, which exhibits significant predictive capabilities. Furthermore, within this timeframe, the study provided predictions for world reserves of rare earth elements in various countries until 2053, as well as world production forecasts through 2051. Notably, China is expected to maintain its dominant position in both reserve and production, with an estimated production volume of 101,985.246 tons, followed by the USA with a production volume of 15,850.642 tons. This study also highlights the periodic nature of production, with a specific scale, as well as periodicity in reserve. These insights can be utilized to define and quantify sustainability and to mitigate environmental hazards associated with the use of rare earth materials in the energy industry. Additionally, they can aid in making informed decisions regarding at-risk rare earth reserves, considering potential future trends in electric vehicle (EV) production up to the year 2050.

The processed discharges from Tangier Automotive City’s (TAC) Chrafate Wastewater Treatment Plant (WWTP) contaminate the Jouamaa Hakama groundwater and the Ouljat Echatt river. We aimed to study the unknown interactions between surface water (SW) and groundwater (GW). A total of nine Jouamaa Hakama GW samples and eleven Ouljat Echatt SW samples were taken and analyzed in 2021 and 2022 to determine 16 physical and chemical parameters (pH, temperature (T), electrical conductivity (EC), dissolved oxygen (DO), total hardness (TH), turbidity (TURB), and total dissolved solids (TDS), cations: Na⁺, K⁺, Mg²⁺ and Ca²⁺, anions: Cl⁻, CO₃²⁻, HCO₃⁻, NO₃⁻, and SO₄²⁻). For exploitation of the data, we used a methodology based on hydrochemical modeling (HM), principal component analysis (PCA), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), inverse distance weighted interpolation (IDW) using Geographic Information Systems (GIS), and regression analysis (RA). We studied the interaction of the surface water of the river (contaminated by discharges from the WWTP) with the shallow groundwater on a strip of 100 m on either side of the river to understand the transverse and longitudinal dispersion of this pollution The investigations indicated that the major ions found in GW and SW were characterized in a different order in the anion list order Cl⁻ > CO₃²⁻ > NO₃⁻ > HCO₃⁻ > SO₄²⁻ and Cl⁻ > SO₄²⁻ > CO₃²⁻ > NO₃⁻ > HCO₃⁻, respectively, while the concentrations of cations showed the same order for both: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. As a result, GW showed in the Piper diagram the type of sodium chloride to magnesium carbonate, while SW belongs to the sodium chloride to magnesium sulfate type. The WQI showed that the river waters are all unsuitable for use (WQI > 100), while the GW is of poor quality (WQI > 76). Moreover, the results of the GW–SW interaction along the river revealed a significant relationship (R² = 0.85), which means that strong circulation and the infiltration of contaminated SW into shallow GW occur in this area. The approaches followed have been proven effective in evaluating water quality for human and animal uses. These results can help decision-makers in the region take suitable management measures to mitigate this environmental problem.