Integrated spatial modeling of transportation corridors and land use impacts on groundwater metal pollution using QGIS

Groundwater contamination by heavy metals in rapidly urbanizing industrial corridors poses serious environmental and public health risks, yet the role of transportation networks in aggravating this problem remains underexplored. This study develops an integrated geospatial framework using QGIS to assess the impact of road density and traffic corridors on groundwater metal pollution across 30 sampling sites in the Peenya Industrial Area, Bangalore. Metal Index (MI) values ranged from 0.03 to 53.18, with significantly higher contamination observed at sites within 100 m of major roads. Spatial interpolation and buffer analysis revealed strong correlations between MI and road density, confirming transport corridors as critical drivers of localized pollution. Upon road density analysis, the locations with greater road length, around 11,476 m in a 100-m radius, was associated with increased levels of MI, which implies the impact of an infrastructure component on the contamination. The findings demonstrate that vehicular emissions, road runoff, and industrial traffic substantially elevate groundwater contamination, and the GIS-based approach provides a scalable tool for urban infrastructure planning and selective groundwater quality management. While the study establishes robust spatial associations, further research is needed to validate the framework across diverse urban contexts and incorporate temporal monitoring. This work highlights the methodological novelty of integrating buffer analysis, IDW interpolation, and zonal statistics into groundwater risk assessment, offering policy-relevant insights for sustainable land-use planning.