Assessment of potability of surface water and its health implication in Mahanadi Basin, Odisha

Abstract

Surface water resource plays a crucial role in economic development, human health, and ecological diversity. Population growth, climate change, and water contamination that result from rising residential, agricultural, and industrial needs have put a great deal of strain on the Mahanadi River in Odisha. The study was conducted to analyse surface water quality fluctuations using monitoring data of 20 water quality indicators at 19 locations during 2012–2022. The proposed work includes Weighted Arithmetic (WA) Water Quality Index (WQI), Canadian Council of Ministers of the Environment (CCME) WQI, Synthetic Pollution Index (SPI), Numerow's Pollution Index (NPI), Overall Index of Pollution (OIP), Comprehensive Pollution Index (CPI), Eutrophication Index (EI), Organic Pollution Index (OPI) and multivariate techniques like Cluster analysis (CA) and Principal Component Analysis (PCA) to analyse surface water quality. The results showed that the surface water was contaminated with high TC and TKN. The WQIs results demonstrated that the categories of excellent, good, poor, extremely poor, and unsuitable water quality that fluctuated at the chosen sampling points. Most of the locations fell into the excellent to good zone, as determined by the WQIs and Pollution indexes (PIs). According to several indexing methods, the results show that WA WQI (15.79%), CCME WQI (10.53%), SPI (15.79%), NPI (15.79%), OIP (15.79%), and CPI (15.79%) of the samples had poor, extremely poor, or unsuitable drinking water quality. The watercourse was highly susceptible to eutrophication at few locations as EI was above zero. Three locations (sites 8, 9 and 19) at the downstream of the river are polluted with organic substances with OPI > 1. To examine the link between the water quality measures, the correlation matrix has been developed. To find out the commonalities in the features of the water quality, hierarchical CA divided the 19 monitoring sites into three groups. Five principal components (PCs), which together account for 93.91% of the total variance, were identified via PCA. The first PC was devoted to mineralization, whereas the remaining PCs were constructed using characteristics that suggested contamination. To investigate the spatial distribution, the spatial analyst tools (IDW method) were used. Combining results with GIS also allows for a deeper analysis of drinking water quality. Ultimately, the combined use of various water quality indices along with GIS, CA, and PCA results in the conclusion that samples from the locations ST-8, ST-9 and ST-19 are the most prevalent and fall into the poor to extremely poor group. Considering the entire study, it was determined that the polluting factors were the fertilizers, land development, storm water runoff, and domestic waste water discharge. It is recommended that the sources of contaminations can be further explored to reduce the pollution load, that might be helpful in the promotion of sustainable ecotourism.