Employing Multivariate Statistics as a Tool for Developing Water Quality Index (WQI) for the Assessment of Water Quality of Deepor Beel, Assam, India

Abstract

The rapid growth of population has led to intense urbanisation in the city of Guwahati, which has caused the water bodies in the city to deteriorate (both quality as well as quantity wise) to a substantial extent (Das et al., 2003). Deepor Beel (a Ramsar site) is one such water body that has been continuously degraded owing to a sudden increase in the urban built-up of Guwahati city (Dash et al., 2018). Assessment of the water quality of a particular water body requires continuous monitoring and analyses of several parameters. This, in turn, contributes to the development of large and complex water quality datasets that are difficult to interpret. Traditionally, the water quality of a particular water body was assessed by comparing the observed values of some water quality parameters with their corresponding quality standard values (Pesce and Wunderlin, 2000). This, however, makes the sustainable management of water resources very challenging and sophisticated, as well as time-consuming (Sun et al., 2016; Wang et al., 2015). Quality indices have proved to be of immense help to water quality researchers around the globe for the past few decades. This is owed to their extreme simplicity of dataset interpretation. Water quality index (WQI) is a mathematical representation of the datasets for categorising the water quality in a more straightforward yet informative manner, thus assessing the pollution status of a particular water body. Numerous attempts have been carried out in developing WQIs, depending on various methodologies adopted by several researchers (Akter et al., 2016; Bora and Goswami, 2017; Liou et al., 2004; Ramakrishnaiah et al., 2009; Said et al., 2004; Şener et al., 2017; Vasanthavigar et al., 2010; Wu et al., 2018). It has been proved over the years that the WQI approach is the most practical and effective way of water quality representation of a particular waterbody, both spatially and temporally. It also facilitates in comparing various sampling locations based on their pollution levels and determining their trends (Sun et al., 2016).