Assessing groundwater contamination risk in industrial zone of Ranipet district, Southern India: A modified DRASTIC and Fuzzy-AHP approach

Groundwater plays a substantial function in sustaining the water requirements of Ranipet district, Tamil Nadu, India, across a variety of domains, including drinking water, agriculture, and industry. However, rising demand and industrial activities have caused concern about groundwater pollution and susceptibility in the region. So, this study employed a modified DRASTIC model to find the regions that are most vulnerable to groundwater contamination. The model includes a novel parameter, "proximity to industry,” in addition to the seven traditional factors (depth to water level, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity) to account for the industrial nature of the study region. The fuzzy-analytical hierarchy process (FAHP) was used to compute parameter weights and ratings. All the parameters are overlayed in ArcGIS software to create the groundwater vulnerability map (GWVM). The map revealed that the central area along the Palar River is highly vulnerable to contamination. The southern and northern parts have medium to extremely low groundwater contamination risks. The depth of the water table was the most significant component impacting the vulnerability, as per the sensitivity analysis. The model is further validated by chromium concentrations, which were shown to be high in highly sensitive zones and low in low-risk zones. This study also suggests the need for extensive and periodic groundwater quality assessments in highly vulnerable zones for sustainable water resource management. • The groundwater vulnerability of industrial areas was assessed using the DRASTIC-P parameter. • A novel parameter, "proximity to industry,” was added to the model to account for industrial area. • Weights and ratings of the parameters were calculated using the fuzzy-AHP process. • The results showed that the model can be applied to comparable industrial areas to find groundwater vulnerability.