Surface Water Quality Modelling Using Water Quality Index (WQI) and Geographic Information System (GIS) on the Mahanadi Basin, Odisha: Fid-Bau Portal

Surface Water Quality Modelling Using Water Quality Index (WQI) and Geographic Information System (GIS) on the Mahanadi Basin, Odisha

Das, Abhijeet

Abstract

The Mahanadi River basin in Odisha relies heavily on surface water for its drinking water supply. Surface water quality due to geogenic factors, aggravated by anthropogenic activities, is a significant threat to human wellbeing and agricultural practices. Consequently, surface water quality profiling has received remarkable academic attention in recent decades that produces an ample amount of research results. This work makes an effort to comprehend the acceptability of water for human consumption by utilising the effectiveness of Canadian council of ministers of the environment water quality index (CCME WQI) and multi-criteria decision-making (MCDM) approach like Weighted aggregated sum-product assessment (WASPAS). The investigation was conducted in two stages. Surveying the study area and collecting and interpreting water sample were part of the first phase. The observed water quality data sets were subjected to the second phase's application of CCME WQI and WASPAS. 19 sampling sites were used in the research region during the 2022–2023 sampling campaign, which was based on physico-chemical criteria. Spatial maps has been prepared using Arc GIS, on the basis of the selective chosen quality parameters to decipher the various quality classes at each hydro-station for drinking purpose. However, this study conducts a comprehensive systematic literature review to summarize and structure the existing literature and to identify current research trends and hotspots. Reported results revealed that while comparing with WHO guidelines, total coliform (TC) and total Kjeldahl nitrogen (TKN) found to be higher than the permissible, in most of the locations. The CCME WQI in the study ranges from 34.61 to 85.58. It indicates good to poor water. According to percentage distribution, it shows that 73.68% indicates ‘good water’, 15.79% of samples is in ‘fair category’ and 10.53% falls under ‘poor’ water category. The interpolated map displays that ‘ST-8 and ST-9’ are located in a poor region and require treatment before consumption. Poor zone delivers about anthropogenic inputs such as domestic sewage and agricultural runoff. Employing the WASPAS approach, it indicates areas where cumulative variables, such as sewage discharge, a falling water table, dilution and surface runoff, which cause water quality variability in a water body throughout a monitoring period, have had the greatest impact. Three sampling sites (ST-8 [Cuttack D/s], 9 [Paradeep] and 19 [Choudwar D/s]) were identified by the results as having had the most fluctuation and being the most polluted monitoring sites. In order to identify sampling stations for routine monitoring reasons, this study introduces a novel methodology by combining both WQI and WASPAS. According to the current research using both approaches, the majority of the region's surface water is of acceptable quality, with a few exceptions at a few locations, but it still needs to be treated before consumption in order to avoid the dangers of geogenic and anthropogenic contamination. The findings of this innovative work provides insights and understanding on surface water pollution regimes and minimize uncertain causes because of the high spatial distribution of coliform and nitrogen occurrence, and will also be helpful to policy makers for better planning, investments, and management to supply potable water in the area.

Graphical Abstract

A flow diagram titled graphical abstract. The study area is identified and surface water samples are collected. Multiple parameters like p H, D O, B O D, T C, T S S, alkalinity, and C O D are checked. The data generated is computed using W A S P A S method and final rank assessment is done.