Water Supply (Jun 2022)

A mathematical approach to evaluate the extent of groundwater contamination using polynomial approximation

  • Purushottam Agrawal,
  • Alok Sinha,
  • Srinivas Pasupuleti,
  • Jitendra Sinha,
  • Ayan Chatterjee,
  • Satish Kumar

DOI
https://doi.org/10.2166/ws.2022.219
Journal volume & issue
Vol. 22, no. 6
pp. 6070 – 6082

Abstract

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Groundwater is being contaminated rapidly due to various anthropogenic activities and geogenic sources. In this direction, assessment of water quality analysis is the basic requirement for nurturing the human being and its evolution. The Water Quality Index (WQI) parameter has been widely used in determining water quality globally. The study aims to provide the suitability of groundwater in the specified region using the polynomial approximation method for drinking and irrigation purposes along with the computation of WQI using the conventional method. Weierstrass's polynomial approximation theorem along with longitudinal and latitudinal values has been used to evaluate the polynomial regarding various physicochemical parameters. To validate the obtained results from the present approach, groundwater quality data collected and analyzed from the Pindrawan tank area in Raipur district, Chhattisgarh, India, have been used. The result is obtained, i.e., the intermediate value of the parameters obtained correctly from the mathematical modeling, with an average error of 7%. This polynomial approximation method can also be used as the substitute of inverse modeling to determine the location of the source in the two-dimensional system. The approach output can be beneficial to administrators in making decisions on groundwater quality and gaining insight into the tradeoff between system benefit and environmental requirement. HIGHLIGHTS Approximation of contaminant concentration of groundwater for various physicochemical parameters.; Use of polynomial approximation in any geological scenario to predict contaminant concentration.; Collect data for a specific region and use the data to find the values of the constants.; Cross-verify the result with the observed value and 7% expected error is obtained.;

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