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.

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.