Analysis of Ground Water Quality Using Statistical Techniques: A Case Study of Madurai City

Introduction

Groundwater makes up to 23% of fresh water in the world. Also, it is easily accessible to all. More than 85% of groundwater is used in rural areas. A total of 50% of groundwater is used for urban drinking needs. Increasing industrialization and population growth lead to groundwater pollution, depletion, contamination, and saltwater intrusion. Level of groundwater table also keep on reducing. Improper solid waste also influences the quality of groundwater. Statistical tools are very helpful to pre-process the data and analyze the relationship between parameters. Patil studied the quality of groundwater using Pearson correlation (Urxqgzdwhu, n.d.). Kim J., Kim, R., Lee, Cheong, Yum, & Chang (2005) investigated the quality of groundwater and identified the factors which influence it using multivariate statistical analysis. Anwar (2014) studied the quality of groundwater using a correlation matrix to analyze the correlation between quality parameters. Jamuna (2018) explored groundwater quality parameters of erosion using multivariate statistical methods. Shahid and Amba (2018) assessed the quality of groundwater of Bangalore city using principal component analysis, cluster analysis, factor analysis, and correlation analysis (Shahid, & Amba, 2018). Sadat-Noori (2013) investigated the quality of the Saveh-Noobaran aquifer to identify the places with the best water quality for drinking using a geographic information system and a water quality index. Thivya (2013) studied the quality of groundwater in different rock-formed aquifers, which resulted suitable for drinking, and for domestic and agriculture usage. Elango and Subramani (2005) studied the spatial distribution of hydrogeochemical constituents of groundwater related to its suitability for agriculture and domestic use.

The objective of this study is to identify the most affected areas in relation to groundwater quality and to determine the water quality parameters which influence water quality for drinking, by using multivariate statistical analysis. Multivariate statistical analysis involves correlation analysis and factor analysis. The authors used Statistical Package for the Social Sciences (SPSS) software for multivariate statistical analysis, and the tool Grapher to plot the piper diagram which shows the cation and anion proportion in groundwater.