@article{fdi:010091969,
  title = {{T}he multi-parameter mapping of groundwater quality in the {B}ourgogne-{F}ranche-{C}omt{\'e} {R}egion ({F}rance) for spatially based monitoring management},
  author = {{B}ousouis, {A}. and {B}ouabdli, {A}. and {A}yach, {M}. and {R}avung, {L}. and {V}alles, {V}. and {B}arbi{\'e}ro, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{G}roundwater, a vital resource for providing drinking water to populations, must be managed sustainably to ensure its availability and quality. {T}his study aims to assess the groundwater quality in the {B}ourgogne-{F}ranche-{C}omt{\'e} region (similar to 50,000 km(2)) of {F}rance and identify the processes responsible for its variability. {D}ata were extracted from the {S}ise-{E}aux database, resulting in an initial sparse matrix comprising 8723 samples and over 100 bacteriological and physicochemical parameters. {F}rom this, a refined full matrix of 3569 samples and 22 key parameters was selected. {T}he data underwent logarithmic transformation before applying principal component analysis ({PCA}) to reduce the dimensionality of the dataset. {T}he analysis of the spatial structure, using both raw and directional variograms, revealed a categorization of parameters, grouping major ions according to the regional lithology. {B}acteriological criteria ({E}scherichia coli and {E}nterococcus) displayed strong spatial variability over short distances, whereas iron ({F}e) and nitrates showed intermediate spatial characteristics between bacteriology and major ions. {T}he {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. {T}hese synthetic maps highlighted the different processes driving the spatial variations in each quality criterion. {O}n a regional scale, the variations in fecal contamination were found to be multifactorial, with significant influences captured by the first four principal components. {T}he 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.},
  keywords = {bacteriological composition ; {B}ourgogne-{F}ranche-{C}omt{\'e} region ; chemical composition ; cluster analysis ; groundwater ; principal component analysis ; {FRANCE}},
  booktitle = {},
  journal = {{S}ustainability},
  volume = {16},
  numero = {19},
  pages = {8503 [17 p.]},
  year = {2024},
  DOI = {10.3390/su16198503},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091969},
}