Multivariate Statistical Analysis: Assessment of Industrial Trace Metal Contamination in Groundwater Resources—A Case Study from Hindupur, Anantapur District

Abstract

In semi-arid regions, particularly those with a high concentration of industrial activities, the contamination of groundwater by trace metals poses a significant risk to both the environment and public health. This is especially true in areas where pollution levels are high. The Geospatial Technology industry offers a diverse selection of tools that can be utilised for a variety of spatial analysis. The multivariate studies, on the other hand, offer a useful insight into comprehending the spatial patterns of the contamination. In the current study, the quality of the groundwater in the Hindupur region, which is situated in the Anantapur district of Andhra Pradesh, is evaluated by employing a multivariate statistical technique that incorporates Principal Component Analysis (PCA), Cluster Analysis (CA), and correlation analysis. This study was carried out in order to evaluate the groundwater in the region. A total of twenty-five groundwater samples which were collected and tested for the presence of trace metals and their significant physicochemical features. These samples were obtained during the pre-monsoon and post-monsoon seasons. In contrast to CA, which was able to categorise sampling locations according to the amount of pollution that was present, PCA was able to identify specific geological and anthropogenic factors that have an impact on water quality. The findings, which suggest that there are elevated concentrations of nitrate, chloride, and organic load in the vicinity of industrial zones, demonstrate a substantial amount of geographical and temporal variability. This is the case because the data indicate that there is a strong correlation between the two. Based on the findings of the study, it is abundantly obvious that there is an immediate need for the region to implement pollution control measures and groundwater management.