The vulnerability analysis of groundwater contamination and Bayesian-based spatial modelling

The groundwater vulnerability assessment is an effective measure to analyse potential quality of available water in increasingly populated and industrial areas in plain topography. The objective of this research was to evaluate contamination susceptibility and its spatial modelling in district Sheikhupura, using fuzzy logic and Bayesian interpolation method for adjusted DRASTIC model and HVF (hazard vulnerability factor) model. The present research espoused a specific factor-based approach to model the existing contamination rate of heavy metals with underplaying DRASTIC parameters for estimated pollution rate and related vulnerability. The modifications were also applied in ranks and weights by analysing the association of parameters with respect to heavy metals (Cr, Cd, Pb). The heavy metals concentrations in groundwater were evaluated at the 54 different random sites of water pumps. The heavy metals concentration was used to identify risk of pollution associated with aquifer and soil type. The adjusted DRASTIC model was analysed for groundwater vulnerability by using different parameters including, aquifer media, net recharge, soil type, hydraulic conductivity, topography and depth of water table. It was assessed by using EC, TDS, hardness, heavy metals and COD, BOD parameters. The results showed the vulnerability areas and modelled efficiently at spatial scale than conventional DRASTIC model. The very high vulnerability zone is 10.75% with > 200 drastic values; however, main study area is covered with moderate vulnerability class (42.49%). Most of the effluent-irrigated and neighbouring agricultural sites were located around the high and relatively high-risk areas 11.56% to 20.29% areas, which may raise risk of chemicals and pesticides seepage in the groundwater.

Automated summary

Groundwater vulnerability assessment is crucial for analyzing water quality potential in populated and industrial areas with plain topography. This research used fuzzy logic and Bayesian interpolation to evaluate contamination susceptibility and spatial modeling in Sheikhupura district, Pakistan. Modifications were made to the DRASTIC model and HVF model, considering heavy metal pollution rates and vulnerability factors. The results showed efficient modeling of vulnerability areas, with moderate vulnerability being dominant in the study area but high-risk areas were found near effluent-irrigated and agricultural sites, implying a risk of chemical and pesticide seepage into groundwater.