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The aim of this work is to gain a better understanding of the diversity of groundwater resource quality in the Auvergne-Rhône-Alpes region (France) using the national Sise-Eaux database. Three matrices were extracted, which included a hollow matrix (approximately 120,000 observations and 21 variables) and two complete matrices (8078 observations with 13 variables each and 150 observations with 20 variables each, respectively). The mapping of these parameters, the chemical profiles of the water, and the characteristics of the variograms make it possible to estimate the importance of the temporal variance compared with the spatial variance. This distinction led to a typology separating 4 groups of chemical parameters and 2 groups of bacteriological parameters, highlighting the information redundancies linking several parameters. A PCA was used to considerably reduce the size of the hyperspace of the data. The study of the factorial axes combined with their distribution over the study area made it possible to discriminate and identify certain mechanisms for acquiring the physico-chemical and bacteriological characteristics of groundwater, the importance of lithology, the components of faecal contamination, and the role of environmental conditions. A typology of the parameters by hierarchical clustering on the major part of the information makes it possible to reduce the information to that carried by a few representative parameters. This work is a new step in understanding the diversity of groundwater resources in general, with a view to more targeted monitoring based on this diversity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R2 were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R2 going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs. [ABSTRACT FROM AUTHOR]

In France, and more generally in Europe, the high number of groundwater bodies (GWB) per administrative region is an obstacle for the management and monitoring of water for human consumption by regional health agencies. Moreover, GWBs show a high spatial, temporal, physico-chemical, and bacteriological variability. The objective is to establish homogeneous groupings of GWB from the point of view of water quality and the processes responsible for this quality. In the Occitanie region in southwestern France, the cross-referencing of two databases, namely the French reference system for groundwater bodies and SISE-EAUX, provided a dataset of 8110 observations and 15 parameters distributed over 106 GWB. The 8-step approach, including data conditioning, dimensional reduction by Principal Component Analysis, and hierarchical clustering, resulted in 20 homogeneous groups of GWB over the whole region. The loss of information caused by this grouping is quantified by the evolution of the explained variance. Splitting the region into two large basins (Adour-Garonne and Rhône Méditerranée) according to the recommendations of the European community does not result in a significant additional loss of information contained in the data. A quick study of a few groups allows to highlight the specificities of each one, thus enabling targeted guidelines or recommendations for water quality management and monitoring. In the future, the method will have to be tested on the scale of large European watersheds, as well as in the context of an increase in the number of parameters. [ABSTRACT FROM AUTHOR]

When studying large multiparametric databases with very heterogeneous parameters (microbiological, chemical, and physicochemical), covering a wide and heterogeneous area, the probability of observing extreme values (Z-score > 2.5) is high. The information carried by these few samples monopolizes a large part of the information conveyed by the entire database. The study of the spatial structure of the data and the identification of the mechanisms responsible for the water quality are then strongly degraded. Data transformation can be proposed to overcome these problems. This study deals with a database of 8110 groundwater analyses (Occitanie region, France), on which the bacteriological load was measured in Escherichia coli and Enterococci, in addition to electrical conductivity, major ions, Mn, Fe, As and pH. Three modes of data conditioning were tested and compared to the treatment with raw data. The results show that log transformation is the best option, revealing a relationship between E. coli content and all the other parameters. By reducing the impact of extreme values without eliminating them, it allowed a concentration of information on the first factorial axes of the PCA, and consequently a better definition of the associated processes. The spatial structure of the principal components and their cartographic representation is improved. The conditioning of the data with the square root function led to an intermediate improvement between the logarithmic transformation and the absence of conditioning. The application of these results should allow a targeted, more efficient, and therefore, less expensive monitoring of water quality by Regional Health Agencies. [ABSTRACT FROM AUTHOR]

Knowledge of the quantity and quality of groundwater is a prerequisite to encourage investment in the development of a region and to consider the sedentarisation of populations. This work synthesises and analyses data concerning the chemical quality of the available water acquired in the Foum el Gueiss catchment area in the Aures massif. Two families of waters are observed, on the one hand, calcium and magnesian chlorated-sulphate waters and on the other hand, calcium and magnesium bicarbonate waters. Multivariate statistical treatments (Principal Component Analysis - PCA and Discriminant Analysis - DA) highlight a gradient of minerality of the waters from upstream to downstream, mainly attributed to the impact of climate, and pollution of agricultural origin rather localised in the lower zones. These differences in chemical composition make it possible to differentiate spring, well and borehole waters. The main confusion is between wells and boreholes, which is understandable because they are adjacent groundwater, rather in the lower part of the catchment area. The confusion matrix on the dataset shows a complete discrimination with a 100% success rate. There is a real difference between spring water and other samples, while the difference between wells and boreholes is smaller. The confusion matrix for the cross-validation (50%). [ABSTRACT FROM AUTHOR]

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In France, the data resulting from monitoring water intended for human consumption are integrated into a national database called SISE-Eaux, a useful and relevant tool for studying the quality of raw and distributed water. A previous study carried out on all the data from the Provence-Alpes-Côte d'Azur (PACA) region in south-eastern France (1061 sampling points, 5295 analyses and 15 parameters) revealed that the dilution of the information in a heterogeneous environment constitutes an obstacle to the analysis of ongoing processes that are sources of variability. In this article, cross-referencing this information with the compartmentalization into groundwater bodies (MESO) provides a hydrogeological constraint on the dataset that can help to better define more homogeneous subsets and improve the interpretation. The approach involves three steps: (1) A principal component analysis conducted on the whole dataset aimed at eliminating information redundancy; (2) an unsupervised grouping of groundwater bodies having similar sources of variability; (3) a principal component analysis carried out within the main groups and sub-groups identified, aiming to define and prioritize the sources of variability and the associated processes. The results supported by discriminant analysis and machine learning show that the grouping of MESO is the best-suited scale to study ongoing processes due to greater homogeneity. One of the eight main groups identified in PACA, corresponding to the accompanying aquifers of the main rivers, is analyzed by way of illustration. Water–rock interactions, redox processes and their effects on the release of metals, arsenic and fecal contamination along different pathways were specifically identified with varying impacts according to the subgroups. We discussed both the significance of the principal components and the mean values of the bacteriological parameters, which provide information on the causes and on the state of contamination, respectively. Based on the results from two different groups of MESO, some guidelines in terms of a strategy for resource quality monitoring are proposed. [ABSTRACT FROM AUTHOR]