GIS-Based Spatial Autocorrelation and Multivariate Statistics for Understanding Groundwater Uranium Contamination and Associated Health Risk in Semiarid Region of Punjab, India

To provide safe drinking water in contaminated hydrogeological environments, it is essential to have precise geochemical information on contamination hotspots. In this study, Geographic Information System (GIS) and multivariate statistics were utilized to analyze the spatial patterns, occurrence, and major factors controlling uranium (U) concentrations in groundwater. The global and local Moran’s I indices were utilized to detect hotspots and cool spots of U distribution. The substantial positive global Moran’s I index (at a p-value of 0.05) revealed a geographical pattern in U occurrences. The spatial clusters displayed patterns of drinking water source with U concentrations below and above the WHO limit, categorized as “regional U cool spots” and “regional U hotspots”, respectively. Spatial autocorrelation plots revealed that the high–high potential spatial patterns for U were situated in the northeastern region of the study area. As the order of queen’s contiguity increased, prospective low–high spatial patterns transitioned from the Faridkot district to the Muktsar district for U. Further, the multivariate statistical analysis methods such as correlation and principal component analysis (PCA) plots revealed substantial positive associations (p-value < 0.05) between U and total dissolved solids (TDS), salinity (SL), bicarbonate (HCO3−), and sodium (Na) in groundwater from both shallow and deeper depth, indicating that these water quality parameters can significantly influence the occurrence of U in the groundwater. The output of the random forest model shows that among the groundwater parameters, TDS is the most influential variable for enrichment of U in groundwater, followed by HCO3−, Na, F−, SO42−, Mg, Cl−, pH, NO3−, and K concentrations. Additionally, the results of health risk assessment indicate that 47.86% and 41.3% of samples pose risks to children and adults, respectively, due to F−−contamination. About 93.49% and 89.14% of samples pose a risk to children and adults, respectively, due to U contamination, whereas 51.08% and 39.13% of samples pose a risk to children and adults, respectively, from NO3− contamination. The current data indicates an urgent need to create cost-effective and efficient remediation techniques for groundwater contamination in this region.