Groundwater vulnerability assessment of Hoshangabad and Budni industrial area, Madhya Pradesh, India, using geospatial techniques

• Main Authors: Rakesh Ahirwar, Mohammad Subzar Malik, Jai Prakash Shukla
• Format: Article
• Language: English
• Published: SpringerOpen 2020-03-01
• Series: Applied Water Science
• Subjects: DRASTIC model; Groundwater contamination vulnerability index; Geological setting; ArcGIS; Hoshangabad and Budni industrial area
• Online Access: http://link.springer.com/article/10.1007/s13201-020-1172-9
• ISSN: 2190-5487; 2190-5495

Abstract The quality of groundwater can be determined by hydrogeological formations which vary geographically. Subsurface geology has significant role in governing the movement and quality of groundwater. The present study aims assessment of groundwater contamination vulnerability in Hoshangabad and Budni industrial area using the DRASTIC model approach. The model is hybridization of the seven parameters that provides input to the model. In the model, rating and weightage to each parameter were assigned as per the relative significance of the parameter in groundwater contamination. Groundwater contamination vulnerability index (GWVI) has been computed by integrating of these data layers in ArcGIS environment. The obtained GWVI in the area varies from 66 to 170, which was further divided into five zones, i.e. (1) very low GW contamination zone, (2) low GW contamination zone, (3) moderate GW contamination zone, (4) high GW contamination zone and (5) very high GW contamination zone. Further, the model has been validated by analysing the sulphide concentration in groundwater of the delineated GW contamination vulnerable zones. The model has been found effective for the prevailing hydrogeological settings of the area. The model can serve as an effective tool for the concerned authority, social workers and government/non-organizations for the management of groundwater resources in the area. Further, application of the GIS technique has been found useful in preparing the database of each variable of the model.