Groundwater Quality Assessment using Multivariate Data Analysis in North Tamil Nadu Region, India

IJEP 44(2): 176-184 : Vol. 44 Issue. 2 (February 2024)

Aman Kumar1, Abhishek K.1, Rishab Dabas1, Saravanan Kothandaraman1* and Kumar G.2

1. Vellore Institute of Technology, School of Civil Engineering, Chennai – 600 127, Tamil Nadu, India
2. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Department of Civil Engineering, Chennai – 600 062, Tamil Nadu, India


Groundwater measurement has become very important in today’s world. In recent years, advanced technology has helped simplify challenging tasks by replacing conventional methods. The aim of the study is to map the spatial and temporal difference of pre- and post-monsoon groundwater quality and to identify the priority set of groundwater quality parameters that control the maximum variance using principal component analysis (PCA) method. For this purpose, data of pre- and post-monsoon season of North Tamil Nadu (Chennai, Vellore, Krishnagiri, Thiruvallur, Villupuram, Dharmapuri, Thiruvannamala and Kanchipuram) were collected for five years (2014-2018) from Water Resource Department. The groundwater quality parameters recorded were NO2, NO3, Ca, Mg, Na, K, Cl, SO4, CO3, HCO3, F, pH, EC, HAR, SAR and RSC. By the use of PCA, correlation matrix, total variance of components, rotated component matrix and factor scores were defined. The mappings of pre- and post-monsoon season groundwater quality indicate that the influence of human activity is one of the most important factors controlling chemical composition of groundwater in the study area. The SPSS v22 was used to conduct T-test and PCA. In PCA, Eigen value is important in measuring the significance of the factor. In this study, five extracted factors explain 80% of data set variance. Assessment of groundwater quality is important for sustainable water and land management plans for the future.


Principal component analysis, Mapping of groundwater quality, Groundwater quality assessment, North Tamil Nadu region, Multivariate data analysis


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