The Status of Surface Water in West Tripura District, India: An Approach by Using Water Quality Index and Multivariate Statistical Technique

Abstract

The present investigation provides a better interpretation of surface water (rivers, ponds, bills, lakes, etc.) quality utilising entropy weighted water quality index (EWWQI) and different multivariate statistical techniques. Eleven physicochemical parameters including alkalinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca), turbidity, magnesium (Mg), total hardness (TH), chloride (Cl-), and iron (Fe) were analysed and monitored at 23 sampling sites (in December 2018) of West Tripura district. Experimental outcomes of turbidity followed by Fe contamination exceeded recommended WHO standard limit. The maximum values of Fe and turbidity were estimated as 8.745 mg/L and 797.7 NTU, respectively. WQI values confirmed that most of the monitoring locations had poor water quality except three reported areas (S7, S14, and S15) but without Fe and turbidity, estimated WQI confirmed drinkable water condition for entire samples. Multivariate statistical approaches like correlation analysis, principal component analysis (PCA) and cluster analysis (CA) were applied to explore water quality. PCA outcomes recognised three principal factors explaining almost 85% of the total variance. CA investigated three major clusters of 23 sampling sites namely less polluted, highly polluted and moderately polluted zone. Confirming all above, the surface water at the monitoring locations is a major concern which may lead to serious health issues in local people.

Assessing and modelling drinking water quality at the railway stations of Tripura, India, with a possible strategic solution

“…One of the most popular and widely used methods to determine water quality is WAWQI (Zait et al 2022 ; Ahmed et al 2021 ; Pradhan et al 2021 ; Roy and Manna 2021 ). Railway stations have been classified based on estimated WAWQI.…”

“…Horton ( 1965 ) developed the weighted arithmetic water quality index (WAWQI) model and Brown et al ( 1970 ) modified it. Several researchers (Mahammad and Islam 2021 ; Menberu et al 2021 ; Ayogu et al 2020 ; Roy and Manna 2021 ; Adimalla and Qian 2019 ) used this method to calculate WAWQI, which has been computed using the following steps: …”

“…Recently, a number of research work has been done on water quality in Tripura (e.g. Barman et al 2022 ; Karmakar and Singh 2021 ; Roy et al 2021 ; Roy and Manna 2021 ; Paul et al 2019 ; Singh and Kumar 2015 ; Lodh et al 2014 ; Sarkar and Mishra 2014 ; Banerjee et al 2011 ). Based on the bibliometric analysis, maximum research follows water quality parameters based on micro-spatial scale.…”

Application of Multivariable Statistical and Geo-Spatial Techniques for Evaluation of Water Quality of Rudrasagar Wetland, the Ramsar Site of India

“…These studies showed a transition to the use of different scientific methods, including the water quality index, pollution load index, and statistical techniques. A contemporary study, in Tripura, focused on covering various water bodies and using modern methods and advanced statistical tools [44][45][46][47]. In this study area, the authors applied different scientific methods: water quality index (WQI), and pollution load index (PLI), along with statistical analysis.…”

Principal Component Analysis and the Water Quality Index—A Powerful Tool for Surface Water Quality Assessment: A Case Study on Struma River Catchment, Bulgaria

“…The Principal Component Analysis (PCA) is used to reveal the "hidden" factors controlling surface water quality [28][29][30][31][32]. Numerous groups of studies use both CA and PCA as additions to the WQI to introduce the specific outputs of both techniques to water quality assessment [11,[33][34][35][36][37][38][39]. To investigate the temporal behavior of water quality indicators at regions with pronounced seasonality, Discriminant Analysis (DA) is used [13,[40][41][42][43][44].…”