Mine waste rock and drainage pose lasting environmental, social, and economic threats to the mining industry, regulatory agencies, and society as a whole. Mine drainage can be alkaline, neutral, moderately, or extremely acidic and contains significant levels of sulfate, dissolved iron, and, frequently, a variety of heavy metals and metalloids, such as cadmium, lead, arsenic, and selenium. In acid neutralization by carbonate and silicate minerals, a range of secondary minerals can form and possibly scavenge these potentially harmful elements. Apart from the extensively studied microbial-facilitated sulfide oxidation, the diverse microbial communities present in mine rock and drainage may also participate in the formation, dissolution, and transformation of secondary minerals, influencing the mobilization of these metals and metalloids. This article reviews major microbial-mediated geochemical processes occurring in mine rock piles that affect drainage chemistry, with a focus on the role of microorganisms in the formation, dissolution, and transformation of secondary minerals. Understanding this is crucial for developing biologically-based measures to deal with contaminant release at the source, i.e., source control.

Prolonged high-intensity grinding can modify the crystal structure of solid substances and/or induce chemical reaction, which is referred to as mechanochemical reaction. Such reactions can exert positive influences on hydrometallurgical processes, therefore, many researchers have applied mechanochemical reactions for metals dissolution from minerals. The mechanism of mechanochemical reaction has been investigated using solid analyses and simulations. Structural changes caused by mechanochemical reactions are not yet sufficiently clarified because the ground samples are amorphous. The objective of this study was to analyze structural changes of cerianite in weathered residual rare earth ore by mechanochemical reduction. The ore was ground by planetary ball milling for 10, 60 and 720 min. Structural change was analyzed by the X-ray absorption near-edge structure and extended x-ray absorption fine structure analysis at the cerium L_III- and K-edges. These analyses revealed that the structural change of cerianite in this ore induced by mechanochemical reduction involved oxygen vacancy production. The process of the oxygen vacancy formation was closely coupled with the quantum effect of localization–delocalization of the 4f electron of cerium.

A new machine learning approach was developed to predict the quantity of mine waste rock drainage using weather data as the inputs. The novelty of the approach is that it includes spring freshet (melting of snow/ice in spring) as an input to the drainage flow rate model. Specifically, the machine learning approach integrates the decision tree algorithm to classify the occurrence or absence of spring freshet and a long short-term memory (LSTM) algorithm to predict the flow rate of mine waste rock drainage. The two algorithms are integrated by using the classification result of spring freshet as an input to the flow rate model. The machine learning approach developed was applied to predict the drainage flow rate at a case study mine in Canada. The model developed was trained with the local weather data as the inputs and the historical monitoring data of drainage flow rate as the target (output). The results show that the decision tree algorithm is able to classify the occurrence or absence of spring freshet with an accuracy of 91%. The inclusion of spring freshet as an input to the flow rate model significantly improves the performance of the flow rate model. The sensitivity tests show that changes in temperature and atmospheric precipitation influence the drainage flow rate.

Climate change is reshaping hydrologic regimes in snow-dominated watersheds, with important implications for mine rock drainage quantity and contaminant mobilization. This study quantifies potential long-term changes in drainage quantity by coupling a previously published physics-informed machine learning model with a Monte Carlo framework driven by downscaled monthly climate projections from ClimateNA. The proposed methodology was applied to three drainage monitoring stations at a mine site in Western Canada to assess projected drainage responses over the 2011–2100 period. An ensemble of daily weather sequences was generated by sampling historical within-month variability and scaling the resulting series to match projected monthly climate statistics, which were then used as inputs for the drainage model. Trends were assessed using the Mann–Kendall test modified for serial correlation, and their magnitudes were summarized using the Theil–Sen slopes. The trend analysis results indicate scenario-dependent changes in annual drainage across stations, alongside consistent seasonal shifts toward higher spring (April–May) and lower early-summer (June–July) drainage. These patterns are consistent with earlier snowmelt and earlier snowpack depletion. Corresponding shifts in intra-annual flow timing suggest that a larger fraction of annual drainage occurs earlier in the year. Overall, these findings provide a physics-informed basis for changes in drainage quantity and for guiding monitoring, design, and mitigation strategies under a warming climate.