Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha
Abhijeet Das
Abstract
In this Baitarani Watershed, Odisha, this study emphasizes on analysing the seasonal variation (post-monsoon) of the water quality rating of the river in terms of the Water Quality Index (WQI). Study assessed the hydro-chemical variables, collected from thirteen sampling sites, during 2021–2024; and the whole river was investigated for 15 physicochemical parameters. Again, environ-metrics techniques, such as principal component analysis (PCA), and hierarchical (H) cluster analysis (CA), were used to assess the hydro-chemical variables. In all sites, the indicator Turbidity did not meet the drinking water quality limits (< 5NTU). During the post-monsoon season, the obtained WA-WQI value scored as 21.7 to 191, signifying excellent to unsuitable water quality. In this context, the WAWQI (Weighed Arithmetic Water Quality Index) values show that almost 61.54 % sampling sites have poor to unsuitable quality of water. On the contrary, the computed CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) value of the present research, varied between 23 and 97. These values indicate that water quality ranges from excellent to very poor water quality. Spanning a spectrum, the values of Integrated Weight (I)-WQI oscillated between 14 and 97. About 23.08 % remained within the excellent-good category, suggesting low pollution. These values also indicate 76.92 % of samples renders poor water and thus, significant contamination of the research zone by elements like turbidity, EC, and TDS indicates that the water quality in these areas is below drinkable limits and requires purification before use. The method, CA grouped four zones into three clusters, i.e., relatively low-polluted, medium-polluted, and high polluted. During post-monsoon season, most of the water quality characteristics were lower owing to dilution by monsoon rainfall, while pollutants were relatively higher in at some places, which might be due to reduced river flow and concentrated pollutants. The PCA resulted into 4 components namely PC-1 (51.31 %), PC-2 (16.044 %), PC-3 (11.799 %) and PC-4 (9.04 %) and indicated that particularly PC-1 contributes parameters such as turbidity, EC, TDS, Na + , K + , Ca 2+ , and Mg 2+ , were mostly influenced by mineralization, ions dissolution, and rock weathering. Ultimately, this innovative study from both indexing techniques, concludes that out of the 13 sampling sites, around 61.54 % (WA), 76.92 % (IWQI) and 53.85 % (CCME) is observed to be the most polluted site. CA and PCA identified that natural phenomena, along with agricultural, municipal, and industrial discharges, are the major polluting sources in the river basin. • Multidisciplinary approach: Integrating WA, CCME WQI, and IWQI models. • The CA and PCA methods leads to more reasonable results. • The key parameters affecting water quality are turbidity, TDS, and EC. • Informing policy makers for proactive water management.