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Dimension Reduction and Analysis of a 10-Year Physicochemical and Biological Water Database Applied to Water Resources Intended for Human Consumption in the Provence-Alpes-Côte d’Azur Region, France

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Geoscience, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, 10100 Rabat, Morocco
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Earth Sciences Department, Université de Ouagadougou (Pr J. Ki-Zerbo), B.P. 7021 Ouagadougou 03, Burkina Faso
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UMR 1114 INRAE EMMAH Avignon Université, 84916 Avignon, France
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IRD, CNRS, Université de Toulouse, UMR 5563, Géoscience Environnement Toulouse, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, 31400 Toulouse, France
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ARS Provence-Alpes-Côte d'Azur, 132, Boulevard de Paris, 13002 Marseille, France
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BRGM, Direction régionale Provence-Alpes-Côte d’Azur, 117 avenue de Luminy, 13009 Marseille, France
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Laboratoire en Géosciences appliqués et marines, Géotechnique et Géo risques (LR3G), Essadi-Faculté des Sciences, Université Abdelmalek, 93000 Tétouan, Morocco
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Author to whom correspondence should be addressed.
Water 2020, 12(2), 525; https://doi.org/10.3390/w12020525 (registering DOI)
Received: 16 January 2020 / Revised: 9 February 2020 / Accepted: 11 February 2020 / Published: 13 February 2020
(This article belongs to the Section Water Resources Management and Governance)
The SISE-Eaux database of water intended for human consumption, archived by the French Regional Health Agency (ARS) since 1990, is a rich source of information. However, more or less regular monitoring over almost 30 years and the multiplication of parameters lead to a sparse matrix (observations × parameters) and a large dimension of the hyperspace of data. These characteristics make it difficult to exploit this database for a synthetic mapping of water quality, and to identify of the processes responsible for its diversity in a complex geological context and anthropized environment. A 10-year period (2006–2016) was selected from the Provence-Alpes- Côte d’Azur region database (PACA, southeastern France). We extracted 5,295 water samples, each with 15 parameters. A treatment by principal component analysis (PCA) followed with orthomax rotation allows for identifying and ranking six principal components (PCs) totaling 75% of the initial information. The association of the parameters with the principal components, and the regional distribution of the PCs make it possible to identify water-rock interactions, bacteriological contamination, redox processes and arsenic occurrence as the main sources of variability. However, the results also highlight a decrease of useful information, a constraint linked to the vast size and diversity of the study area. The development of a relevant tool for the protecting and managing of water resources will require identifying of subsets based on functional landscape units or the grouping of groundwater bodies.
Keywords: hydrochemistry, water resource, hydrogeology, multivariate statistics, France. hydrochemistry, water resource, hydrogeology, multivariate statistics, France.
MDPI and ACS Style

Tiouiouine, A.; Yameogo, S.; Valles, V.; Barbiero, L.; Dassonville, F.; Moulin, M.; Bouramtane, T.; Bahaj, T.; Morarech, M.; Kacimi, I. Dimension Reduction and Analysis of a 10-Year Physicochemical and Biological Water Database Applied to Water Resources Intended for Human Consumption in the Provence-Alpes-Côte d’Azur Region, France. Water 2020, 12, 525.

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