Abstract
The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis (PCA), regression analysis, and cluster analysis, were applied to 26 water quality parameters. PCA identified a reduced number of mean 2 varifactors, indicating that 96% of temporal and spatial changes affect the water quality in this stream. First factor from factor analysis explained 66% of the total variance between velocity, total-P, NO3–N, Ca2+, Na+, TS, TSS, and TDS. Bray-Curtis cluster analysis showed a similarity of 96% between sites IV and V and 94% between sites II and III. The dendrogram of seasonal similarity showed a maximum similarity of 97% between spring and autumn and 82% between winter and summer clusters. For nitrate, nitrite, and chloride, the trend in accumulation factor (AF) showed that the downstream concentrations were about 2.0, 2.0, and 2.9, times respectively, greater than upstream concentrations.
Highlights
River water quality is of great environmental concern since it is one of the major available fresh water resources for human consumption [1, 2]
In this paper we present Journal of Ecosystems a methodology for examining the impact of all the sources of pollution in Sukhnag stream (Kashmir Himalayas) and to identify the parameters responsible for spatiotemporal variability in water quality using cluster analysis (CA) and factor analysis (FA)
This research assessed the linkages between spatio-temporal variability and water quality using multivariate statistics in the Sukhnag stream, Kashmir Himalayas
Summary
River water quality is of great environmental concern since it is one of the major available fresh water resources for human consumption [1, 2]. After the industrial revolution the carrying capacity of the rivers to process wastes reduced tremendously [3, 4] Anthropogenic activities such as urban, industrial, and agricultural as well as natural processes, such as precipitation inputs, erosion, and weathering of crustal materials affect river water quality and determine its use for various purposes [1–5]. The nonlinear nature of environmental data makes spatio-temporal variations of water quality often difficult to interpret and for this reason statistical approaches are used for providing representative and reliable analysis of the water quality [17]. Multivariate statistical techniques such as cluster analysis (CA) and factor analysis (FA) have been widely used as unbiased methods in analysis of water quality data for drawing out meaningful conclusions [18, 19].
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