Groundwater vulnerability to pollution mapping of Ranchi district using GIS

Abstract

Groundwater pollution due to anthropogenic activities is one of the major environmental problems in urban and industrial areas. The present study demonstrates the integrated approach with GIS and DRASTIC model to derive a groundwater vulnerability to pollution map. The model considers the seven hydrogeological factors [Depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography or slope (T), impact of vadose zone (I) and hydraulic Conductivity(C)] for generating the groundwater vulnerability to pollution map. The model was applied for assessing the groundwater vulnerability to pollution in Ranchi district, Jharkhand, India. The model was validated by comparing the model output (vulnerability indices) with the observed nitrate concentrations in groundwater in the study area. The reason behind the selection of nitrate is that the major sources of nitrate in groundwater are anthropogenic in nature. Groundwater samples were collected from 30 wells/tube wells distributed in the study area. The samples were analyzed in the laboratory for measuring the nitrate concentrations in groundwater. A sensitivity analysis of the integrated model was performed to evaluate the influence of single parameters on groundwater vulnerability index. New weights were computed for each input parameters to understand the influence of individual hydrogeological factors in vulnerability indices in the study area. Aquifer vulnerability maps generated in this study can be used for environmental planning and groundwater management.

Integration of GIS and F-Hydra Model for Aquifer Vulnerability Monitoring in the Afram Plains, Ghana

“…Sensitivity analysis presents a thorough detail on the influence of weights or ratings assigned to the individual parameters considered for the model (Krishna et al, 2015). There are 2 types of sensitivity analysis namely; single-parameter analysis and map removal sensitivity analysis.…”

“…Besides, this value solely depends on the weight assigned to the individual parameters. Several authors have applied sensitivity analysis to aquifer vulnerability evaluation (Rahman, 2008;Hasiniaina et al, 2010;Samake et al, 2011;Krishna et al, 2015;Tsoy, 2015;Ouedraogo et al, 2016;Balaji et al, 2021;Ghouili et al, 2021;Li et al, 2021;Pelekanos et al, 2021;Rokhshad et al, 2021).…”

Modified Index-Overlay Method to Assess Spatial–Temporal Variations of Groundwater Vulnerability and Groundwater Contamination Risk in Areas with Variable Activities of Agriculture Developments

“…The three different vulnerability maps were generated by overlaying the weightings and ratings from eight parameter maps. The range of vulnerability indices was classified into three classes (low, moderate, and high) based on the classification method of natural breaks (Jenks) that describe the relative probability of contamination of the groundwater resources [42]. Figure 6a-c show the vulnerability indices obtained from the conventional DRASTIC, modified DRASTIC, and modified AHP/DRASTIC models, respectively.…”

Study of SaaS Mode WebGIS based on JavaScript Engine

“…The service assembly layer implements system functions by combining services. Since services are independent of each other, different functions can be implemented in different combinations [3]. Tenants can choose their existing services to assemble or customize their own service assembly to implement their own business logic functions.…”