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Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km2), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset's total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed. [ABSTRACT FROM AUTHOR]

Groundwater, a vital resource for providing drinking water to populations, must be managed sustainably to ensure its availability and quality. This study aims to assess the groundwater quality in the Bourgogne-Franche-Comté region (~50,000 km2) of France and identify the processes responsible for its variability. Data were extracted from the Sise-Eaux database, resulting in an initial sparse matrix comprising 8723 samples and over 100 bacteriological and physicochemical parameters. From this, a refined full matrix of 3569 samples and 22 key parameters was selected. The data underwent logarithmic transformation before applying principal component analysis (PCA) to reduce the dimensionality of the dataset. The analysis of the spatial structure, using both raw and directional variograms, revealed a categorization of parameters, grouping major ions according to the regional lithology. Bacteriological criteria (Escherichia coli and Enterococcus) displayed strong spatial variability over short distances, whereas iron (Fe) and nitrates showed intermediate spatial characteristics between bacteriology and major ions. The PCA allowed the creation of synthetic maps, with the first seven capturing 80% of the information contained in the database, effectively replacing the individual parameter maps. These synthetic maps highlighted the different processes driving the spatial variations in each quality criterion. On a regional scale, the variations in fecal contamination were found to be multifactorial, with significant influences captured by the first four principal components. The 22 parameters can be grouped into six categories based on their spatial and temporal variations, allowing for the redefinition of a resource management and monitoring strategy that is adapted to the identified spatial patterns and processes at the regional scale, while also reducing analytical costs. [ABSTRACT FROM AUTHOR]

The presence of fermentative hotspots in municipal waste dumps has been reported for several decades, but no study has focused on their size and shape. The uncontrolled landfill of Soub Sekt, covering an area of about 8 hectares in the Tadla plain in Morocco, is the source of a permanent pollution plume in the groundwater, detected by self-potential (SP) measurements. The study aims to detect and characterize these hotspots as well as the leachates that form within them. These hotspots are typically circular and smaller than 3 m in size, and they are concentrated within recent waste deposits. Intense electron transfer activities, particularly during redox reactions leading to metal solubilization, result in very low SP values (down to −60 mV), facilitating their detection. Several successive field campaigns suggest that they are active for 2–3 weeks. Due to the low permeability of the soils, highly mineralized leachates (average Electrical Conductivity 45 mS cm−1) rich in organic ions accumulate on the soil surface at the base of the waste windrows. There, they evolve by concentration due to evaporation and oxidation due to slow diffusion of atmospheric O2. Despite the small size of the hotspots generating the leachates, the accumulation of leachates in ponds and the low soil permeability limits the percolation rate, resulting in moderate but permanent groundwater pollution. [ABSTRACT FROM AUTHOR]

In Sahelian Africa, the characteristics of boreholes are often lost and, when several aquifers are present on the same site, it is difficult to know which one is being tapped or is likely to be contaminated, which hinders good management of the resource. In this study conducted on 153 wells distributed in the four major aquifers of Burkina Faso, the variation in chemical composition within the aquifers is high compared to that between the aquifers. In spite of this, treatment by linear statistical analysis and/or machine learning allows the discrimination of the aquifers with a success rate of about 80%. The introduction of water isotopes as an additional parameter and a dimensional reduction by principal component analysis allowed a discrimination rate of 87.6% to be achieved. The pathway of water from sedimentary to basement aquifers explains some of the confusion. [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]

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]