Assessing Surface Water Quality for Drinking Water Supply using Hybrid GIS-Based Water Quality Index (WQI) in Mahanadi River Basin (MRB), Odisha, India


Abhijeet Das
Department of Civil Engineering, C.V. Raman Global University (C.G.U), Bhubaneswar, Odisha, India


Surface water is an important source for drinking water supply in Mahanadi Basin, Odisha. The research was done to evaluate the water quality, that serves as the source of domestic water supply to many cities. Samples of water were taken from nineteen important sampling areas for a period of 2010-2023 and twenty water quality parameters were examined to determine the WQI, followed by Multi-Criteria Decision-Making (MCDM) evaluation. Employing the Weighted Arithmetic (WA) Water Quality Index (WQI) and Stepwise Weight Assessment Ratio Analysis (SWARA) WQI, this study finds areas where cumulative variables, such as sewage discharge, a falling water table, dilution, and surface runoff, that tends to cause water quality variations in a water body, over a given monitoring period, have had the greatest impact. The WA WQI and SWARA WQI in the study area ranges from 23.78 to 96.09 and 14.6 to 1065.2, respectively. Also, the river water ranged from excellent to very poor, encompassing excellent for approximately 15.8%, good for 68.4%, poor for 10.5% and very poor for 5.3% in case of WA WQI. While the general water quality, as per SWARA-WQI, it varied from excellent to extremely poor, comprising 84.21% excellent, 10.53% poor and 5.26% for extremely poor category. The overall WQI in the study area indicates that the surface water is safe and potable except few localized pockets in SP-(8), (9) and (19) blocks. The cause could be attributed to anthropogenic sources such as domestic sewage and agricultural runoff altered a few parameters– e.g., TKN and TC. Based on geostatistical results, Gaussian model produce a more accurate assessment as per nugget/sill ratio, ASE and RMSE. To delineate the feasible regions for drinking practices, MCDM models such as Compromise Programming (CP), Ordered Weighted Averaging (OWA), and Combined Compromise Solution (CoCoSo), were adopted. Finally, the results demonstrated that WQI generated using both indexing strategies matched the outcomes of MCDM models. To sum up, it is advantageous and gives a clear image of water quality to combine physicochemical properties, WQIs, MCDM, and GIS technologies to evaluate surface water suitability for drinking and their controlling variables.

December 22, 2023
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