Multivariate Analysis Applied to Aquifer Hydrogeochemical Evaluation: A Case Study in the Coastal Significant Subterranean Water Body between “Cecina River and San Vincenzo”, Tuscany (Italy)

Abstract

The hydrogeochemical characteristics of the significant subterranean water body between “Cecina River and San Vincenzo” (Italy) was evaluated using multivariate statistical analysis methods, like principal component analysis and self-organizing maps (SOMs), with the objective to study the spatiotemporal relationships of the aquifer. The dataset used consisted of the chemical composition of groundwater samples collected between 2010 and 2018 at 16 wells distributed across the whole aquifer. For these wells, all major ions were determined. A self-organizing map of 4 × 8 was constructed to evaluate spatiotemporal changes in the water body. After SOM clustering, we obtained three clusters that successfully grouped all data with similar chemical characteristics. These clusters can be viewed to reflect the presence of three water types: (i) Cluster 1: low salinity/mixed waters; (ii) Cluster 2: high salinity waters; and (iii) Cluster 3: low salinity/fresh waters. Results showed that the major ions had the greater influence over the groundwater chemistry, and the difference in their concentrations allowed the definition of three clusters among the obtained SOM. Temporal changes in cluster assignment were only observed in two wells, located in areas more susceptible to changes in the water table levels, and therefore, hydrodynamic conditions. The result of the SOM clustering was also displayed using the classical hydrochemical approach of the Piper plot. It was observed that these changes were not as easily identified when the raw data were used. The spatial display of the clustering results, allowed the evaluation in a hydrogeological context in a quick and cost-effective way. Thus, our approach can be used to quickly analyze large datasets, suggest recharge areas, and recognize spatiotemporal patterns.

QSAR, ADME-Tox, molecular docking and molecular dynamics simulations of novel selective glycine transporter type 1 inhibitors with memory enhancing properties

“…To minimize the dimension of molecular descriptors in a reduced number of principal components, we have focused on the correlation matrix given by the principal component analysis (PCA) technique, as one of the most applied multivariate analyses [ 42 ]. We have started this task with a database of 40 molecules and 40 different descriptors as illustrated in Table S2 .…”

QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia

“…Principal component analysis (PCA) is one of the most widely applied multivariate techniques. It is used to reduce the size of the variables into a limited number of principal components (linear combinations of the original variables) [20]. In this paper, we calculated 40 different descriptors, which were later reduced to 27 descriptors based on the correlation matrix, since descriptors that are strongly correlated with each other (r pearson > 0.9) were removed.…”

A Study on Hydrochemical Characteristics and Evolution Processes of Groundwater in the Coastal Area of the Dagujia River Basin, China

“…This technique is used to reduce the dimensionality of independent variables by compressing and arranging them into a reduced number of principal components (PCs) [7,24]. Therefore, the main advantage of PCA is its capability to reduce various original variables into a small number of independent new variables to summarize and represent the original dataset with low information loss [10,11]. Based on the standardized groundwater dataset, PCs can be extracted based on the symmetric correlation matrix [25].…”

“…Numerous methods, including the influencing factor method, Pearson's correlation, graphical method, multivariate statistical analysis and geochemical modeling, have been selected to research the hydrochemical properties of groundwater [4,[7][8][9][10][11]. Meanwhile, the geographic information system (GIS) has become a useful and convenient tool to collect, classify and visualize associated non-spatial data and spatial features of groundwater.…”