Application of Water Quality Index (WQI) and Regression Analysis of Groundwater in Budigumma Village, Anantapur District, Andhra Pradesh

Authors

  • Ambiga K Department of Civil & Strucutral Engineering, SCSVMV, Enathur, Kanchipuram – 631 561, Tamilnadu, India

DOI:

https://doi.org/10.30564/hsme.v1i2.1455

Abstract

The research work is aimed at assessing the subsurface or groundwater suitability for human use or consumption depends upon the calculated water quality index values, correlation coefficient and regression analysis. The water quality index (WQI) is main important tool to calculate the characteristics of drinking water quality in rural, urban and industrial area. Different parameters which is measured and determination of the water quality index for selecting parameters. Further to study the correlation and regression method in this research work. Totally fifteen groundwater samples were collected from the Budigumma Village Anantapur district in the state Andhra Pradesh in India. Nine water quality parameters has been considered for the computation of water quality index such as pH, total dissolved solid (TDS), total hardness (TH), calcium (Ca), magnesium (Mg), nitrates (NO3), chlorides (Cl ), sulphates (SO4), fluorides (F ). The World Health Organization (WHO) has been assessed to the suitability of groundwater for drinking purposes or other uses for public and determining of WQI. This WQI index values ranged from 97.78 to 108.37. The study shows that 87% area comes under the poor category of drinking purposes and the remaining 13% comes under as good water for drinking purposes as per the WQI classification. The correlation and regression analysis gives as an outstanding device for the calculation of different parameter values within realistic degree of precision. The subsistence of strong correlation or relationship between the total hardness and magnesium is determined. The analysis of selected parameters revealed that proper treatment before use or consumption and protected from more contamination.

Keywords:

Groundwater; Physical characteristics; Chemical characteristics; Water classification; Water Quality Index; Correlation and Regression

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Vol. 1 , Iss. 2 (October 2019)

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