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Water quality evaluation of the upper stretch of the river Jhelum using multivariate statistical techniques

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Abstract

Water quality assessment is considered essential for the evaluation of river pollution and its variation, resulting due to either natural or point/non-point anthropogenic sources. This study describes the water quality of the upper stretch of the Jhelum River located in Kashmir Himalaya using multivariate statistical techniques. The descriptive statistics and correlation matrix were applied to explore the correlations between the determined water quality variables whereas; principal component/or factor analysis (PCA/FA) was employed to take out the important factors that control the seasonal water quality variations. The hierarchical cluster analysis (HCA) was employed for the extraction of monitoring stations that can be developed for continuous water quality assessment in this area. Based on the PCA/FA, 95.7% of the total variance in the summer season was described by six factors while 85.8% of the total variance, in the winter season, was described by only four factors. HCA based on two seasons and different levels of pollution grouped the sampling stations into 3 classes described as low-, medium-, and high-pollution regions. Overall, the results illustrated that river water quality has deteriorated progressively from upstream to downstream areas and the methodology adopted can be used for the subsequent water quality surveys to highlight any temporal evolution of river water quality.

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Acknowledgments

The authors are sincerely thankful to the Kashmir University and State Pollution Control Board Srinagar for their support in carrying out the work. The author is grateful for the reviews by anonymous reviewers, which greatly helped improve the content and structure of the manuscript.

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Correspondence to Riyaz Ahmad Mir.

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Editorial handling: Antonio Pulido-Bosch

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Mir, R.A., Gani, K.M. Water quality evaluation of the upper stretch of the river Jhelum using multivariate statistical techniques. Arab J Geosci 12, 445 (2019). https://doi.org/10.1007/s12517-019-4578-7

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