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Evaluating the ecological health risks created by major ions, metalloids and trace elements concentrations in groundwater and pollution sources were essential to effectively protect groundwater resources. For this study, A total of 93 samples were collected from multiple aquifers in the Sunan mining area, eastern China. The Positive matrix factorization (PMF) model results revealed the following sources, in percentages. The Quaternary loose aquifer (QLA) water includes CaMg mineral dissolution (30.3 %), salinity (28.2 %), metal industrial wastewater (26.3 %), iron and manganese minerals (8.0 %) and coal gangue (7.2 %). The Permian fractured sandstone aquifer (PFA) water includes CaMg mineral dissolution sources (29.8 %), mine wastewater (28.6 %), aluminosilicate (21.6 %) and pyrite source (20.0 %). The Carbonifer fractured limestone aquifer (CFA) water includes and mine wastewater (34.2 %), CaMg mineral dissolution (25.4 %), pyrite (22.6 %) and aluminosilicate (17.7 %). The Ordovician fractured limestone aquifer (OFA) water includes manganese and aluminum metal minerals (27.9 %), halite dissolution materials (24.9 %), industrial and agricultural waste water (24.0 %) and calcium‑magnesium minerals (23.2 %). A PMF-based assessment of ecological health risk indicates that the concentrations of elements As and Co are the dominant elements impacting non-carcinogenic and carcinogenic risks; and As, Cr, and Cu are the dominant elements impacting potential ecological risks. These mainly originate from geological sources, coal gangue sources, mine drainage sources and agricultural sewage discharge sources. The study showed the sources of groundwater pollution in multiple aquifers and their priority treatment areas, providing a basis for groundwater management and protection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world., (Copyright © 2019. Published by Elsevier B.V.)