## 3 Results and Discussion
### Statistical Analysis  
The objective was to identify contamination signatures, assess geochemical influences, and explore relationships among parameters using statistical and machine learning methods. Such temporal evaluations are vital for understanding anthropogenic influences, particularly those stemming from agricultural practices. The analysis draws upon multivariate statistical techniques, including correlation matrices, principal component analysis (PCA), hierarchical clustering (HCA), and visual exploration through PCA projections and dendrograms (Figure 6). Figure 5: Piper trilinear diagram for groundwater samples for both periods. In Figure 6a, the left panel displays the Pearson correlation heatmap showing strong linear relationships, particularly between EC-TDS and Na\({}^{+}\)-Cl\({}^{-}\). The center panel illustrates a 3D PCA plot revealing the clustering of wells based on similar hydrochemical signatures. The right panel shows a dendrogram based on hierarchical clustering (Ward linkage) of water quality parameters, highlighting close associations such as EC-TDS and  
Figure 6: Multivariate statistical comparison of groundwater quality parameters pre- (**a**) and post- (b) irrigation periods. Ca\({}^{2+}\)-HCO\({}_{3}\)\({}^{-}\). As seen in Figure 6b, the correlation heatmap on the left shows noticeable shifts in inter-parameter relationships, especially increased correlation between NO\({}_{3}\)\({}^{-}\) and Cl\({}^{-}\), likely due to agricultural return flows. The 3D PCA scatter plot in the middle captures altered well groupings, suggesting seasonal shifts in groundwater chemistry. The rightmost dendrogram reflects changes in parameter clustering patterns, with anthropogenic indicators such as NO\({}_{3}\)\({}^{-}\) and K\({}^{+}\) becoming more dominant in cluster formation. Correlation heatmaps provide an effective exploratory tool for visualizing inter-dependencies among hydrochemical parameters. In this study, both pre- and post-irrigation datasets revealed strong linear associations, most notably between EC and TDS (r \(\approx\) 1.0), which is expected due to their mutual reliance on dissolved ion concentrations. This relationship is well-documented in groundwater studies by [PERSON] et al. [41] and [PERSON] et al. [42], where EC served as a composite indicator of ionic pollution and mineral dissolution. Moreover, the correlation between Na\({}^{+}\) and Cl\({}^{-}\) consistently remained high across both sampling periods, suggesting common salinization sources, including the infiltration of irrigation return flows and possible seawater intrusion in coastal aquifers [43]. Similarly, the Ca\({}^{2+}\)-HCO\({}_{3}\)\({}^{-}\) pair exhibited strong correlation, indicating carbonate weathering, a natural geogenic process that remained stable across both periods, as also observed by [PERSON] et al. [44] Of particular interest was the evolving correlation pattern between NO\({}_{3}\)\({}^{-}\) and Cl\({}^{-}\). Pre-irrigation data showed only moderate correlation, whereas post-irrigation patterns revealed a stronger association. This shift may suggest increased agricultural influence during the irrigation season, with nitrate and chloride acting as co-indicators of fertilizer application and septic leaching, as reported in studies by [PERSON] et al. [45] and [PERSON] et al. [46]. Principal component analysis, widely regarded as a dimensionality reduction technique, allowed for effective extraction of dominant geochemical signatures from multivariate data. In both datasets, the first principal component (PC1) accounted for the largest proportion of total variance and was primarily governed by salinity-related parameters--EC, TDS, and Na\({}^{+}\). This finding echo results by [PERSON] et al. [47] and [PERSON] et al. [19], where PC1 reflected overall ionic strength and anthropogenic impact. A key strength of PCA is its ability to expose latent structures.