In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.

The cross-referencing of two databases, namely the compartmentalization into groundwater bodies (GWB) and the quality monitoring (2830 observations including 15 physico-chemical and bacteriological parameters, on 662 collection points and over a period of 27 years) is applied to better understand the diversity of the waters of the island of Corsica (France) and to facilitate the surveillance and quality monitoring of the groundwater resource. Data conditioning (log-transformation), dimensional reduction (PCA), classification (AHC) and then quantification of the information lost during grouping (ANOVA), highlight the need to sub-divide the groundwater bodies in the crystalline part of the island in order to take better account of lithological diversity and other environmental factors (slope, altitude, soil thickness, etc.). The compartmentalization into 15 units, mainly based on structural geology, provides less information than the grouping into 12 units after subdivision of the crystalline region. The diversity of the waters in terms of chemical and bacteriological composition is discussed, and the results encourage a review of the compartmentalization of the island’s GWBs, with a view to more targeted monitoring based on this diversity.

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection.

The objective of this study is to assess the quality of groundwater around the first industrial mine in Burkina Faso, whose activities have been closed since 1999. The diagnosis of pollution by trace metal elements of groundwater around the old Poura gold mine was carried out through the determination of the contents of Trace Metal Elements (TME) at MP-AES and the measurements of the physicochemical parameters in situ on 38 samples of borehole water. These data made it possible to calculate the pollution risk index, to establish correlations between the different physicochemical parameters on the one hand and with the trace metal elements on the other hand, from the Pearson matrix and to carry out a multivariate statistical analysis, in particular that in principal components (PCA), with a view to determining the origin of the polluting elements. The results obtained made it possible to identify four samples of groundwater with an acidic pH (GWF18 (6.44), GWF19 (6.39), GWF23 (6.1) and GWP01 (5.99)) and 34 samples of groundwater whose Cd, Al, Fe, As, Pb, or Hg contents are higher than the standards in force in Burkina Faso. The Pollution risk Index (PI) indicates that 35 groundwater samples are slightly polluted and 3 groundwater samples are very heavily polluted (GWP01 (PI = 456%), GWF06 (PI = 132.5%), GWF03 (PI = 105.1%)). The Pearson correlation matrix and the principal component analysis (PCA) show that the origin and mobilization of the trace metal elements involved (Al, As, Cd, Cr, Fe, Hg and Pb) are linked to natural mineralization and anthropogenic activities such as mining and agricultural activities, favored by the infiltration of water into the subsoil through geological structures such as fractures and faults.

This study analyses groundwater quality in the Rhône–Mediterranean–Corsica (RMC) basin using 27,741 samples collected from 7,625 sampling points and 224 groundwater bodies, covering a 34-year period (1987–2021). The objective is to quantify, using statistical and machine learning methods, the respective contributions of temporal variability, spatial variability, and lithology to the hydrochemical and microbiological composition of aquifers. The results reveal a marked dichotomy between microbiological and physico-chemical parameters. Faecal contamination (E. coli, enterococci) is dominated by a strong temporal component (33–36% of variance), whereas spatial structuring by lithology remains very limited (< 11%). Conversely, major physico-chemical parameters (calcium, conductivity, sulphates, etc.) exhibit near-zero temporal variability (<7%) and a spatial structuring strongly controlled by lithology, with gains in explained variance of up to 20% through lithological grouping. Linear discriminant analysis achieves an overall classification rate of 56.2%, but with wide disparities: the crystalline basement is perfectly discriminated (76%), while alluvial formations are not discriminated at all (0%), as their chemical signature is more strongly influenced by anthropogenic pressures than by lithology. Machine learning methods, particularly k-nearest neighbours (k-NN), significantly improve performance (76.2% vs. 56.2% for LDA), confirming the existence of nonlinear structures in the data. Unsupervised validation via K-means confirms the reality of the lithological signal. From an operational standpoint, these findings justify a differentiated approach to groundwater management: a geological approach for chemical parameters, and an approach based on land use and agropastoral practices for bacteriological parameters. The proposed methodology – combining PCA, HCA, nested ANOVA, LDA and machine learning – provides a transferable analytical framework applicable to other major European river basins.