Utility of Multivariate Statistical Analysis to Identify  Factors Contributing Groundwater Quality in High  Altitude Region of Leh-Ladakh, India

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Abstract

Many factors have been found responsible for the groundwater quality like interaction of different water sources, types of soils, other several natural factors, and anthropogenic factors. Therefore, the objectives of the present work were to identify different factors affecting the ground water quality based on multivariate analysis. This study also aimed to determine the usefulness of multivariate statistical techniques to improve our understanding of factors affecting groundwater properties and their interactions. The original matrix consisted of 25 physico-chemical parameters analyzed in 70 number of ground water samples collected from different sites of sampling stations. All the physical and chemical parameters were analyzed by the standard methods of APHA, whereas minerals were determined by ICP-OES method. Thereafter, experimental 70 × 25 matrix was run through the multivariate statistical data analysis which consists of Principal Component Analysis (PCA), R and Q mode Factor Analysis (FA), and Cluster Analysis (CA).

Results showed that physico-chemical factors are important source of variation in the groundwater quality. Interestingly, multivariate analysis revealed that other factors such as dissolution of salts present in the underlying rocks, presence of nutrient load, non-mixing/partial mixing of different types of ground water and moderate type of exchange between river water with adjacent ground water has been found to affect groundwater quality. The study also showed the significant role of multivariate statistical analysis in evaluation and interpretation of the groundwater quality data. The outcome of this study can be used as baseline data to inhospitable and critical areas for future sustainable development and proper management of groundwater system which will ultimately produce good water quality.

Keywords

Groundwater

high altitude

multivariate statistical analysis

water quality

Conflict of interest

The authors declare they have no competing interests.

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