Index-Based Evaluation (IBE) and Geospatial Mapping of Heavy Metal Contamination in Groundwater of an Industrially Influenced Peri-Urban Area of Guwahati, Assam; India
DOI:
https://doi.org/10.46488/Keywords:
Index-Based Evaluation (IBE), Geospatial Mapping, IDW interpolation, Kernel Density Estimation (KDE), Heavy metals, , Groundwater AssamAbstract
This study evaluates the geospatial variability of heavy metal contamination in groundwater within an industrially influenced peri-urbal area spanning parts of Guwahati, Assam and Meghalaya, India. The region is experiencing rapid population and industrial growth, driven by proximity to the state capital and rising urban cost, often with limited consideration for groundwater sustainibility. Groundwater was sampled across the study area in both pre and post monsoon seasons and analysed for heavy metal concentrations using Atomic Abosrption Spectroscopy (AAS). Indexed-Based Evaluation (IBE), comprising Metal Index (MI) and Heavy Metal Pollution Index (HPI) was employed to assess cumulative contamination levels. The results revealed significantly elevated concentrations of Lead, Cadmium, Nickel and Manganese in several locations particularly during the pre- monsoon season. Notably, Lead exceeded permissible limits in over 90% of the samples. MI and HPI values confirmed the widespread contamination, with 21 locations during pre-monsoon and 7 locations during post-monsoon falling into the 'sereiously affected ccategory' (MI>6). Kernel density estimations (KDE) and box plots further supported the temporal patterns of contamination. The geospatial maps generated using GIS and Inverse Distance Weighting (IDW) interpolation tecniques clearly illustrated contaminted hotspots and seasonal dillution effects, with improved water quality observed during post-monsoon due to natural recharge. High contamination is not uniform across the aquifier but concentrated near industrial clusters, indicating point-source or localised anthropogenic inputs rather than widespread geogenic leaching. The methodological framework and outcomes of this study offer a transferable model for industrially stressed aquifers, enabling early detection and spatial prioritization and timely remediation of heavy metal contamination at global scale.