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The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

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Several rare earth element (REE) mine projects around the world are currently at the feasibility stage. Unfortunately, few studies have evaluated the contamination potential of REE and their effects on the environment. In this project, the waste rocks from the carbonatites within the Montviel proterozoic alkaline intrusion (near Lebel-sur-Quévillon, Quebec, Canada) are assessed in this research. The mineralization is mainly constituted by light REE (LREE) fluorocarbonates (qaqarssukite-Ce, kukharenkoite-Ce), LREE carbonates (burbankite, Sr-Ba-Ca-REE, barytocalcite, strontianite, Ba-REE-carbonates), and phosphates (apatite, monazite). The gangue minerals are biotites, chlorite, albite, ankerite, siderite, and calcite. The SEM-EDS analyses show that (i) the majority of REE are associated with the fine fraction (< 106 μm), (ii) REE are mainly associated with carbonates, (iii) all analyzed minerals preferably contain LREE (La, Ce, Pr, Nd, Sm, Eu), (iv) the sum of LREE in each analyzed mineral varies between ~ 3 and 10 wt%, (v) the heavy REE (HREE) identified are Gd and Yb at < 0.4 wt%, and (vi) three groups of carbonate minerals were observed containing variable concentrations of Ca, Na, and F. Furthermore, the mineralogical composition of REE-bearing minerals, REE mobility, and REE speciation was investigated. The leachability and geochemical behavior of these REE-bearing mine wastes were tested using normalized kinetic testing (humidity cells). Leachate results displayed higher LREE concentrations, with decreasing shale-normalized patterns. Thermodynamical equilibrium calculations suggest that the precipitation of secondary REE minerals may control the REE mobility.

Geochemical characterization including mineralogical measurements and kinetic testing was completed on samples from the Montviel carbonatite deposit, located in Quebec (Canada). Three main lithological units representing both waste and ore grades were sampled from drill core. A rare earth element (REE) concentrate was produced through a combination of gravity and magnetic separation. All samples were characterized using different mineralogical techniques (i.e., quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN), X-ray diffraction (XRD), and scanning electron microscopy with X-ray microanalysis (SEM-EDS)) in order to quantify modal mineralogy, liberation, REE deportment and composition of REE-bearing phases. The REE concentrate was then submitted for kinetic testing (weathering cell) in order to investigate the REE leaching potential. The mineralogical results indicate that: (i) the main REE-bearing minerals in all samples are burbankite, kukharenkoite-Ce, monazite, and apatite; (ii) the samples are dominated by REE-free carbonates (i.e., calcite, ankerite, and siderite); and (iii) LREE is more abundant than HREE. Grades of REE minerals, sulfides and oxides are richer in the concentrate than in the host lithologies. The geochemical test results show that low concentrations of light REE are leached under kinetic testing conditions (8.8-139.6 µg/L total light REE). These results are explained by a low reactivity of the REE-bearing carbonates in the kinetic testing conditions, low amounts of REE in solids, and by precipitation of secondary REE minerals.

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