Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

Author:

Wagh Vasant1,Mukate Shrikant2,Muley Aniket3,Kadam Ajaykumar4,Panaskar Dipak5,Varade Abhay6

Affiliation:

1. School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

2. Centre for Natural Resource Management, National Institute of Rural Development and Panchyat Raj, Hyderabad, T.S 500030, India

3. School of Mathematical Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra 431606, India

4. Department of Environmental Science, Savitribai Phule Pune University, Pune, Maharashtra 411007, India

5. School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra 431606, India

6. Department of Geology, RTM University, Nagpur, Maharashtra 440001, India

Abstract

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

Publisher

IWA Publishing

Subject

Health, Toxicology and Mutagenesis,Water Science and Technology,Environmental Engineering

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