Multivariate statistical techniques for the evaluation of groundwater quality of Mathura (India)

Vinod Kumar Kushwah1*, Neha Singh2, Kunwar Raghvendra Singh1*

1Civil Engineering Department, GLA University Mathura-281406, India

2 Zoology Department, A. N. College, Patna-800013, India

ARTICLE INFOR: Received: 04 June 2023; Revised: 17 June 2023; Accepted: 17 June 2023

*CORRESPONDING AUTHORS: E-mail: (V.K. Kushwah); (K.R. Singh)

Cite this article: Kushwah, V.K., Singh, N., Singh, K.R., 2023. Multivariate statistical techniques for the evaluation of Groundwater quality of Mathura (India). J. Appl. Sci. Innov. Technol. 2 (1), 47-51.


  • Mathura is a holy city in Uttar Pradesh
  • Groundwater is one of the most important source of water
  • Natural and manmade activities are adversely affecting the quality of groundwater
  • Water monitoring generates large data
  • Multivariate statistical techniques are useful in assessment of observed data sets


The preservation and protection of ground water quality has received worldwide attention due to its adulteration by natural or human activities. In addition to being harmful to aquatic ecosystems, untreated municipal and industrial waste discharges, agricultural runoffs, leachates, and other activities have introduced a variety of trace elements. As a result, numerous programs for monitoring and evaluating water quality have been emerged worldwide to provide accurate information on activities that contribute to the resource degradation. However, utilizing the vast amounts of data, generated by monitoring programs to obtain valuable information about water quality has presented a global challenge. Multivariate statistical techniques (MSTs) can be utilized for treating huge and complex water quality. In the current study, cluster analysis (CA) and principal component analysis (PCA) were used for ground water quality assessment. CA was used to assess the similarities between the sites according to the similarities in their characteristics. Analysis showed that three principal components (PCs) had eigenvalues greater than one. These three PCs explain approximately 92 percent of the variance in the data. Present work will be very useful for policy makers in making policies for control of water pollution and preservation of water resources.

Keywords: Groundwater; Mathura; Cluster analysis; Principal Component Analysis

Scope: Environmental Engineering & Sciences

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