IJEP 44(2): 176-184 : Vol. 44 Issue. 2 (February 2024)
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
Abstract
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.
Keywords
Principal component analysis, Mapping of groundwater quality, Groundwater quality assessment, North Tamil Nadu region, Multivariate data analysis
References
- Pandi, D., S. Kothandaraman and M. Kuppusamy. 2017. Identifying runoff harvesting sites over the Pennar basin, andhra pradesh using SCS-CN method. Int. J. Civil Eng. Tech., 8 (3):65–73.
- Pandi, D., S. Kothandaraman and M. Kuppusamy. 2021. Hydrological models: a review. Int. J. Hydrol. Sci. Tech., 12(3): 223-242.
- Pandi, D., S. Kothandaraman and M. Kuppusamy. 2020a. Delineation of potential groundwater zones based on multicriteria decision making technique. J. Groundwater Sci. Eng., 8 (2): 80-194. DOI: 10.1 9637/j.cnki.2305-7068.2020.02.009.
- Pandi, D., S. Kothandaraman and M. Kuppusamy. 2020b. Factor analysis based prioritization of morphometric parameters at sub-watershed level. Water Energy Int. J., 63(9):52-59.
- Mangan, P., M.A. Haq and P. Baral. 2019. Morphometric analysis of watershed using remote sensing and GIS— A case study of Nanganji river basin in Tamil Nadu, India. Arabian J. Geosci., 12(6): 202.
- Ramesh, D., D.K. Raju and R. Jaganathan. 2009. Morphometric characteristics of Nandiyar river basin, Tamil Nadu, India. Nature Env. Poll. Tech., 8(3): 545-550.
- Purandara, B.K. and N. Varadarajan. 2003. Impacts on groundwater quality by urbanization. J. Indian Water Resour. Soc.,23:107–115.
- Balamurugan, P., et al. 2023. Quality and health risk assessment of groundwater for drinking and irrigation purpose in semi-arid region of India using entropy water quality and statistical techniques. Water. 15(3): 601. doi: 10.3390/w15030601.
- Lee, J.Y., et al. 2001. Statistical evaluation of geochemical parameter distribution in a groundwater system contaminated with petroleum hydrocarbons. J. Env. Qual., 30(5): 1548–1563.
- Asadi, S.S., P. Vuppala and M.A. Reddy. 2007. Remote sensing and GIS techniques for evaluation of groundwater quality in municipal corporation of Hyderabad (zone-V), India. Int. J. Env. Res. Public Health. 4(1): 45–52.
- Sadat-Noori, S.M., K. Ebrahimi and A.M. Liaghat. 2014. Groundwater quality assessment using the water quality index and GIS in Saveh-Nobaran aquifer. Iranian Env. Earth Sci., 71(9): 3827–3843.
- Tobias, S. and J.E. Carlson. 1969. Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate behavioral res., 4(3): 375-377.
