Abstract
Knowledge on water quality and its assessment, is necessary for both human health and environmental benefit. To account for spatial distribution, surface water quality parameters were analysed using integrated interpolation, geographical information systems (GIS) and multivariate analysis. A total of 19 locations and 13 water quality indicators were analysed, for a duration of six years (2018-2024). The study’s main objective was to assess the seasonal and regional variations in the water quality index (WQI) of Mahanadi River in Odisha using (N) pi, (S) pi, (O) pi, (C) pi, (E)y-WQI, Int w-WQI and Multivariate Statistical tools namely Factor Analysis (Fa). However, in the current investigation, pH, HCO3-, Na+, K+ and Mg2+ were within the permissible limits as per WHO standards. According to this study, the order of prevalence of ion concentrations is signified as follows: Mg2+ > Ca2+ > K+ > Na+ for cations and HCO3- > Cl- > SO42- for anions. The analysis of (N) pi indicated that about 15.79% of the sampled area, is affected by turbidity content, which is highly unsuitable for consumption. However, the remaining area (84.21%) is within the safe category of water. Classification of water based on (S) pi represents most of water samples falls between good water quality. Three unsuitable samples is noted as a result of excessive TDS and EC. In case of (O)pi, over 84.21% of the samples fell into categories of excellent, indicating the suitability for human activities. Using surface water quality results from (C) pi model, that reflects that out of 19 samples, 16 were suitable for drinking. Whereas 2 were polluted and 1 is seriously polluted, thus promotes unsuitability. Although there are several established techniques for calculating the WQI, the current study uses the quality index to consider a variety of water quality concerns in a cohesive manner. Meanwhile, in case of (E)y-WQI, 84.30% were excellent whereas 10% and 5% were poor and high polluted category. Over 42.11% of the samples fell into the categories of poor/very poor/not suitable, using the Int w-WQI diagram. Therefore, using these six approaches resembles a precise and comprehensive method to comprehend water quality in relation to pollution for human usage. In later stage, a factor analysis (Fa) can be applied to lessen the subjectivity and dimension of water quality characteristics. It reveals that the first five principal components explain almost 95.61% of dataset variation. This method removes the aggregation problems, weighting, opacity, and biases seen in traditional water quality evaluation techniques. The results of Fa suggested that turbidity, TKN, Ca2+ and Cl-, were the primary determinants of the water’s quality. The amount of organic pollution that was released into the river was influenced by anthropogenic activity in the vicinity of the river. In addition, the traditional dense habitation next to the river and the manufacturing waste that is transported from upstream to downstream are the sources of the high amount of TKN in urine and faeces. Therefore, given the high spatial distribution of geogenic turbidity and TKN occurrence, the study’s findings minimize uncertain causes and offer insights into surface water pollution regimes. They will also be useful to policy makers in helping to better plan, allocate resources, and manage the area’s potable water supply.