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The current study consists of evaluating surface water pollution using an integrated approach based on pollution assessment indices and statistical approaches (principal component analysis (PCA), logistic regression (LR) analysis, and cluster analysis (CA)). Thirty-four water samples from the High Ziz basin (semi-arid area), occupied by abandoned mine sites in SE Morocco, were collected and analysed for physicochemical parameters and heavy metals (Cu, Zn, Fe, Pb, Ni, and Cd). The mean concentrations of Pb, Zn, Cu, and Fe in the water samples were 41.9 mg/L, 14.8 mg/L, 20.1 mg/L, and 8.1 mg/L, respectively. The large part of the samples is in a near neutral high metal zone based on the plot of heavy metals (Fe+Cu+Pb+Zn+Ni) versus pH. The HPI, HEI, and CI pollution indices were found to average around 586.2, 321.8, and 315.8 respectively, these show strong correlations with heavy metals. The HPI, CI, and HEI values were above 100 as critical pollution index value. These indices indicate that the surface water samples are critically polluted with respect to studied heavy metals. Therefore, the high metal content of the studied samples should be of great concern for the irrigation activity. Moreover, the water quality index (WQI) shows that 76% of the water samples are unsuitable for drinking. The CA analysis grouped 34 samples into four clusters according to the similarity proprieties observed in water quality. Furthermore, the total variance is explained by 75.7% of the water quality with four components according to PCA analysis. Based on LR analysis, the hazard area is strongly correlated with the river system near abandoned mine sites, agricultural and urban areas. The water quality in the study area is controlled by natural/geogenic processes according to the interpretation of pollution indices and statistical approaches (PCA, CA, and LR). The distribution intensity of Pb, Zn, Cu, and Fe metals in the study area's surface water constitutes an environmental concern and needs a monitoring network for sustainable water resources management.

Establishing a circular economy in mining begins with a dedicated sampling strategy as its fundamental phase. This specific approach is crucial for enhancing resource retrieval and isolating essential minerals from mining residues. By carefully examining and defining the makeup of waste materials, mining activities can discover overlooked possibilities, promoting sustainability. A thoughtfully planned sampling strategy not only reduces environmental harm but also sets the stage for the effective use of resources. In doing so, the mining industry can shift towards a circular model, adhering to the principles of waste reduction, material reuse, and ultimately promoting a more environmentally conscious and economically viable industry. In the phosphate industry and during the pre-concentration process of phosphate ore through screening, significant amounts of mining waste, consisting of various lithologies including indurated and fine phosphate, coarse-grained silicified phosphate, limestone, and marls, are deposited in waste rock stockpiles. Collecting representative samples from these heterogeneous materials presents challenges in accurately characterizing the entire stockpile. To overcome this issue, circular mining wells were implemented as a novel sampling method in waste rock stockpiles, enabling the collection of intact representative samples. This paper shares a successful experience in constructing three concrete-lined wells within a phosphate mine waste rock stockpile measuring 662 m in length, 240 m in width, and ranging in height from 0 to 65 m. The wells were dug at various depths, ranging from 20 m to 55 m, with a circular section and a diameter of 1.5 m. An integrated method utilizing analytical techniques in conjunction with numerical modeling via Robot Structural Analysis software (version of 2020) was utilized to assess the stress on the well supports and confirm their stability. This methodology serves as a valuable tool for evaluating the stability of similar wells, ensuring the safety of operators. The structural model yielded a stress level of 1 MPa, which aligned with the values obtained from the analytical model. Sensitivity analysis was performed on various parameters (friction angle, Poisson Ratio, and gravity), and the safety factor consistently remained above 1.5 for all scenarios investigated up to a depth of 60 m. Consequently, this study demonstrates that concrete-lined wells can be utilized safely for intact sampling in waste rock stockpiles. This sampling operation will allow the pursuit of optimizing resource utilization and enhancing environmental sustainability, by studying phosphate distribution in the Phosphate Mine Waste Rock (PMWR) for better recovery. [ABSTRACT FROM AUTHOR]

This article examines the use of multisensor data fusion for land classification in three Moroccan cities. The method employs a Random Forest classification algorithm based on multispectral, synthetic aperture radar (SAR), and derived land surface temperature (LST) data. The study compares the proposed approach to existing datasets on impervious surfaces (Global Artificial Impervious Area—GAIA, Global Human Settlement Layer—GHSL, and Global 30 m Impervious Surfaces Dynamic Dataset—GIS30D) using traditional evaluation metrics and a common training and validation dataset. The results indicate that the proposed approach has a higher precision (as measured by the F-score) than the existing datasets. The results of this study could be used to improve current databases and establish an urban data hub for impervious surfaces in Africa. The dynamic information of impervious surfaces is useful in urban planning as an indication of the intensity of human activities and economic development. [ABSTRACT FROM AUTHOR]