Vincent Cloutier

TL;DR: In this article, two multivariate statistical methods, hierarchical cluster analysis (HCA) and principal components analysis (PCA), were applied to a subgroup of the dataset to evaluate their usefulness to classify the groundwater samples, and to identify geochemical processes controlling groundwater geochemistry.

TL;DR: In this article, the authors focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment and provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe.

TL;DR: A comprehensive hydrogeochemical study was carried out in the Paleozoic Basses-Laurentides sedimentary rock aquifer system in Quebec over a 1500 km2 study area as discussed by the authors.

TL;DR: It is proposed that dissolved arsenic is mainly derived from the contribution to the well discharge of reducing and alkaline geochemically evolved groundwater that contains arsenic as As(III).

TL;DR: In this paper, a combination of hierarchical cluster analysis (HCA) results, principal component analysis (PCA), binary plots investigations and Piper diagram investigations was applied on 321 samples to specifically enable the identification of two very distinct salinization paths that produce the brackish groundwater in the study area.