Abstract
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.
Highlights
Since groundwater aquifers are less vulnerable to pollution than surface waters, they are prioritized for supplying drinking water networks
Surveying water characteristics is a relevant tool to assess the quality of groundwater intended for human consumption and monitor its compliance with drinking water standards, but the analysis of the ongoing processes responsible for the acquisition of water characteristics is challenging when the observations and parameters involved are numerous and the spatial variability is high
After assigning each sample to a groundwater body according to its location and the depth of the production well, we propose a 3-step approach
Summary
Since groundwater aquifers are less vulnerable to pollution than surface waters, they are prioritized for supplying drinking water networks. Waters percolating through the surface layers record information related to land use and can provide information on an aquifer vulnerability to contamination, generally of anthropogenic origin [1,2]. Protecting this resource, which is globally under severe pressure [3,4], involves assessing the most vulnerable areas and compatible land use, and understanding the mechanisms responsible for water characteristics that vary in space and time [5]. The approach varies, including geochemical and multivariate statistical analysis, end-member mixing, diverse geostatistical procedures for analyzing geochemical structures, and modeling at various scales [6,7,8,9,10,11,12,13,14,15,16]
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