Subsidence is an important environmental and safety issue in the mining sector, yet there remain voids in knowledge in terms of management and prediction. This study aims to improve knowledge on the impact of mining operations on the surface, reducing their effect on the environment, increasing the safety of mining operations, monitoring stress behavior and predicting rock mass. Therefore, an analysis was carried out to process and analyze the measured subsidence data and, subsequently, create a numerical model to predict the surface subsidence of a case study mine. The model was developed based on a finite element method (FEM). It was achieved by considering the geological characteristics of the area, the design features of the mine, the surface subsidence measured over twelve years and the time-dependent behavior of the geological layers. The correlation obtained between the measured subsidence and the modelling results was very satisfactory, with a 90% confidence level, over the years analyzed. Hence, the efficiency of the system was confirmed, enabling the evaluation and the prediction of potential surface effects, and therefore improving the safety and environmental levels of the mining area.

There are significant tin reserves in the dumps and tailings from Llallagua. Currently, this waste is being processed using gravity concentration or a combination of gravity concentration with a final stage of froth flotation. A process mineralogy study of the tailings and their products after processing in Llallagua was carried out to determine the failings of the processing system in order to contribute to designing an improved new processing scheme. The mineralogy of the feed tailings, concentrate, and final tailings was determined by X-ray diffraction, scanning electron microscopy, and mineral liberation analysis. The tailings were composed of quartz, tourmaline, illite, K-feldspar, plagioclase, cassiterite, rutile, zircon, and monazite. The concentrate essentially contains cassiterite (57.4 wt.%), tourmaline, quartz, hematite, rutile and rare earth minerals, mainly monazite and minor amounts of xenotime and florencite. The concentrate contained 52–60 wt.% of SnO₂ and 0.9–1.3 wt.% REE. The final tailings contained 0.23–0.37 wt.% SnO₂ and 0.02 wt.% of Rare Earth Elements (REE). Only 57.6 wt.% of cassiterite from the concentrate was liberated. The non-liberated cassiterite was mainly associated with quartz, tourmaline, and rutile. The average grain size of monazite was 45 µm and 57.5 wt.% of this was liberated. In other cases, it occurs in mixed particles associated with tourmaline, quartz, cassiterite, and muscovite. To improve the sustainability of this mining activity, the concentrate grade and the metal recovery must be improved. Reducing the particle size reduction of the processed tailings would increase the beneficiation process rates. In addition, the recovery of the REE present in the concentrate as a by-product should be investigated.

A mineralogical and mineral chemistry study was carried out in the San Luis Alta telluride-rich gold deposit, mid-south Peru, to contribute towards determining its formation and improving the ore processing. The San Luis mineralization is considered an intrusion-related gold deposit located in the Arequipa segment of the Coastal Batholith. The mineralization occurs in quartz veins hosted in diorites and granodiorites from the Tiabaya Super-Unit. These veins are sulfide-rich in the deep areas and contain abundant iron oxides. Sulfides are mainly pyrite with minor chalcopyrite and galena. Native gold and telluride minerals are abundant. Mineral chemistry was determined using an electron microprobe. The mineralogy of veins was classified into four stages. Gold occurs in the three last stages either in large grains, visible to the naked eye, or, more frequently, in grains of less than 10 µm. Gold appears as grains encapsulated in pyrite, Fe oxides, quartz and filling fractures. The first stage is characterized by the deposition of quartz and massive pyrite, which does not contain gold. During the second stage, hessite, calaverite, petzite and altaite are formed. Additionally, Bi-tellurides, mainly volynskite, rucklidgeite, kochkarite and tellurobusmuthine, are formed. Some of these minerals occur as blebs encapsulated in pyrite, suggesting that a Bi-Te-rich melt was formed from the ore-forming hydrothermal fluid and transported the Au and Ag elements. This stage was followed by a fracturing event and tellurobismuthite, tetradymite and montbrayite precipitated. In the last stage, a supergene replacement formed covellite, bornite and goethite. Te-Bi minerals do not appear in this stage, but selenium minerals occur in minor amounts. Chlorargyrite and iodargyrite occur and are associated with gold.

The aim of this research is to investigate the effect of corporate social responsibility (CSR) on total factor productivity (TFP) in the European mining industry, considering micro- and macroeconomic indicators of the relationship between CSR and TFP. Employing data from 40 European mining companies from content analysis, CSR Hub, and the World Bank between 2018 and 2021, this paper utilizes a combination of Data Envelopment Analysis (DEA) and panel regression techniques to test the research hypotheses. The findings suggest that the TFP of European mining firms is positively affected by CSR initiatives implemented by the companies. Also, the empirical results depict that the CSR-TFP relationship is mainly established on institutional criteria. The results also indicate that CSR-related factors, namely, transparency and reporting, training, health and safety, and resource management, are the impacting indicators. The study broadens the horizons of this line of research and can be beneficial to CEOs, managers, experts, policymakers, decision-makers, and economists in the field of mining who are willing to promote responsible and sustainable mining.

Subsidence is one of the main environmental impacts of underground mining worldwide. Besides, the increasing complexity of underground mining due to greater depths and interaction with inhabited and environmentally sensitive areas can lead to challenges that may threaten the viability of mining due to phenomena such as subsidence. This research aims to increase the knowledge about surface subsidence due to underground mining, characterising the main factors that rule mining subsidence utilising an actual mine that extracts potash. The calculation methodology was based on 74 sections of the subsidence basin, using GPS measurements and the InSAR technique, with data collected over twelve nonconsecutive years from 1995 to 2021. Thus, three different active areas and one residual area were determined. Average boundary angles and their average distances of influence for the active regions have also been determined. Furthermore, using the least squares method, the subsidence basin curve was defined using a Gaussian function. The algorithm that governs the subsidence process has been successfully calculated, allowing the approximation of the deformation of any point within an area of interest. The novelty of this paper is twofold: the results obtained provide a detailed subsidence behaviour and a prediction model of the case study. Furthermore, the methodology implemented can be applied to other subsidence basins with mines in their area of influence. Hence improving the surface mining area’s safety levels and managing the environmental impacts.