Utility of Multivariate Statistical Analysis to Identify Factors Contributing River Water Quality in Two Different Seasons in Cold-Arid High-Altitude Region of Leh-Ladakh, India

Monitoring water quality of surface water resources is the key concern in determining the potable water quality in high-altitude region. Therefore, there is a need to evaluate different parameters affecting water quality of river and identify the most important variables and factors significantly affecting water quality. In the present study, multivariate statistical methods including cluster analysis and principal component analysis/factor analysis were applied to analyze the Indus River water quality in the Trans-Himalayan region of India. For this total 25 no. of physicochemical parameters were analyzed in water samples taken from seven different monitoring sites in summer and winter season. All the physical, microbial, chemical, and mineral parameters were analyzed by using the standard methods of American Public Health Association, whereas minerals were determined with the inductively coupled plasma optical emission of spectroscopy method. Thereafter, experimental two-season (28 samples × 25 parameters) matrices of both the seasons were run through the multivariate statistical data analysis. The varifactors obtained from the FA of both the seasons and results indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic sanitary waste), and nutrients (non-point sources: agriculture) in the summer season. However, in the winter seasons, results showed that the river water was less affected by anthropogenic activities and natural weathering process. Therefore, it is concluded that quality of Indus River water is affected by agricultural, domestic, and hydrogeochemical sources in the summer season. These findings corroborate suitability of multivariate statistical techniques in the elucidation of various parameters for water quality monitoring and determination of different contamination sources.