Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database
Abstract
:1. Introduction
- Provide an overview of water quality at the appropriate scale (catchment, aquifer or administrative boundaries, on a regional or national scale);
- Identify trends in quality as early as possible;
- Link observed pollution patterns to land use in order to identify likely sources;
- Guide and support the design and implementation of programs of measures by providing appropriate data.
2. Materials and Methods
2.1. Auvergne-Rhône-Alpes Region
2.2. The Sise-Eaux Database
2.3. Data Conditioning
2.4. Principal Component Analysis
2.5. Parameters Hierarchical Clustering
2.6. Variograms and Kriging
3. Results
3.1. Parameters and Chemical Profiles Distribution
3.2. Diversity of Variograms
3.3. PCA Performed on Matrix M1
3.4. Subsection
- Major elements of lithological origin and electrical conductivity (EC, Ca, HCO3, SO4);
- K and Mg, which are related to this first group but showed a certain dissimilarity;
- Elements involved in redox processes (Fe, Mn, NO2, As);
- Na, Cl, NO3, and H+, which may be of mixed origin, both natural and anthropogenic.
4. Discussion
4.1. Parameters, Lithology and Major Structural Units
4.2. Mechanisms for Acquiring Characteristics
5. Conclusions
- Synthesise the essential information contained in the nearly 80% of information conveyed by 13 parameters. This represents a major dimensional reduction and a significant simplification of the water quality maps.
- Separate, in particular, the different components of spatio-temporal variations in water faecal contamination. This revealed at least 3 sources of variability in groundwater bacteriological quality:
- •
- The main component is linked to the intrusion of surface water by run-off during heavy rainfall events. This is a temporal variability, as it occurs mainly following storms at the end of the summer, but also a spatial variability, as only certain poorly protected catchments are vulnerable to faecal contamination.
- •
- The second component is more related to spatial variability: water with a calcium carbonate profile is less likely to generate faecal contamination, which can be attributed to the flocculent nature of the calcium ion on clay colloids, reducing the risk of solid transport and germs. The distribution of this mechanism, linked to the water chemical profile, is essentially due to lithological variability.
- •
- The third component also concerns the calcium carbonate environment, but anoxic waters that are fairly rich in iron and subject to contamination. It is also linked to spatial variability, concerning shallow areas where water stagnation favours reduced conditions and where the iron (ferric cements in the matrix) is solubilised.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
---|---|---|---|---|---|---|---|
Eigenvalue | 4.71 | 2.78 | 1.86 | 0.91 | 0.71 | 0.62 | 0.41 |
Variability (%) | 36.23 | 21.41 | 14.33 | 6.96 | 5.44 | 4.73 | 3.16 |
Cumulative % | 36.23 | 57.64 | 71.97 | 78.93 | 84.37 | 89.1 | 92.26 |
PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | |
---|---|---|---|---|---|---|
Ent. | −0.334 | 0.657 | −0.579 | −0.084 | −0.254 | −0.104 |
E. coli | −0.324 | 0.655 | −0.594 | −0.092 | −0.241 | −0.099 |
EC | 0.956 | −0.085 | −0.132 | −0.074 | −0.023 | 0.060 |
H+ | −0.602 | 0.080 | 0.598 | −0.166 | 0.049 | −0.023 |
K | 0.060 | 0.657 | 0.334 | 0.479 | −0.005 | 0.200 |
Na | 0.314 | 0.676 | 0.390 | 0.112 | 0.133 | −0.377 |
Ca | 0.929 | −0.181 | −0.189 | −0.109 | −0.019 | 0.070 |
Mg | 0.646 | 0.168 | 0.006 | 0.580 | −0.176 | −0.004 |
Cl | 0.547 | 0.614 | 0.315 | −0.297 | 0.062 | −0.141 |
SO4 | 0.784 | 0.184 | −0.090 | −0.225 | 0.266 | −0.197 |
HCO3 | 0.878 | −0.215 | −0.213 | 0.049 | −0.059 | 0.044 |
Fe | −0.084 | 0.620 | −0.322 | −0.049 | 0.504 | 0.458 |
NO3 | 0.379 | 0.343 | 0.494 | −0.350 | −0.448 | 0.364 |
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Ayach, M.; Lazar, H.; Bousouis, A.; Touiouine, A.; Kacimi, I.; Valles, V.; Barbiero, L. Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database. Resources 2023, 12, 143. https://doi.org/10.3390/resources12120143
Ayach M, Lazar H, Bousouis A, Touiouine A, Kacimi I, Valles V, Barbiero L. Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database. Resources. 2023; 12(12):143. https://doi.org/10.3390/resources12120143
Chicago/Turabian StyleAyach, Meryem, Hajar Lazar, Abderrahim Bousouis, Abdessamad Touiouine, Ilias Kacimi, Vincent Valles, and Laurent Barbiero. 2023. "Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database" Resources 12, no. 12: 143. https://doi.org/10.3390/resources12120143