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

Abstract

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.

Water and Soil Quality of Coffee Plantations in the Western Ghats Region, Chikkamagaluru District, Karnataka, India

“…22,30,31 PCA explains the information on all the critical parameters and outcomes of whole laboratory data by diagonalization of the correlation matrix. 32 Table 4 shows the water samples' PCA results, including the loadings of each principal component (PC). In PCA, eigenvalues are used to decide the number of PC.…”

Determining the Relevant Scale to Analyze the Quality of Regional Groundwater Resources While Combining Groundwater Bodies, Physicochemical and Biological Databases in Southeastern France

“…The PACA region, which covers an area of 31,400 km 2 in the far southeastern region of France, includes 6 administrative departments, namely Alpes-de-Haute-Provence, Hautes-Alpes, Alpes-Maritimes, Bouches-du-Rhône, Var, and Vaucluse. The main characteristics of this region are reviewed in Tiouiouine et al [21], which addresses the significant altitudinal gradient (0 to 4000 m), the geological and lithological diversity and its distribution, the main types of aquifers and their flow type, and the hydrographic network dominated by the Rhône River in the West, the Durance basin crossing the PACA region and some coastal rivers (Argens, Var, etc. ).…”

“…The pH was transformed into H + ion concentration to avoid mixing parameters with linear or logarithmic scale and thus to provide a homogeneous dataset. All 1061 sampling points (Figure 1) were georeferenced [21] and then linked to a single MESO. Some aquifers were superimposed, and it was possible to link samples with these groundwater bodies based on the depth of the production wells.…”

“…A principal component analysis was carried out at the PACA regional scale and recently presented [21] to identify, quantify, and rank the different sources of variability of water quality and explore the underlying processes responsible for this variability. Here, a similar principal component analysis was conducted within each group of MESO and compared to the results obtained at the regional scale.…”

“…(a) (b) Our research team is currently summarizing and reviewing the water quality intended for human consumption in France. Preliminary research on the Provence-Alpes-Côte d'Azur region, which has a dry and changing climate [18], highlighted the main mechanisms responsible for groundwater chemical and bacteriological characteristics but was hampered by the great diversity of environments [21]. The processing of all the data indeed combines very different environments and mixes different sources of variability specific to each landscape unit or each groundwater body, inducing weak correlations among the parameters.…”