evaluation of seasonal variability in surface water quality of shallow valley lake, kashmir, india, using multivariate statistical techniques

Seasonal variation in water quality of anchar lake has been evaluated, using two multivariate statistical techniques, namely principal component analysis (pca) and cluster analysis (ca). water quality data, collected during four seasons, have been analyzed for 13 parameters and anova has shown that ph (f3= 10.86, p < 0.05), temperature (f3 = 65, p < 0.05), electrical conductivity (f3= 32.72, p < 0.05), calcium (f3= 36.84, p < 0.05), magnesium (f3= 16.52, p < 0.05), nitrate-nitrogen (f3= 48.06, p < 0.05), ammonical nitrogen (f3 =198.75, p < 0.05), and dissolved oxygen (f3= 4.96, p < 0.05) varied by season, whereas the substantial variations of sodium (f2= 7.18, p <0.05), phosphate-phosphorous (f2= 25.31, p < 0.05), biological oxygen demand (f2= 11.02, p < 0.05), and chemical oxygen demand (f2=37.73, p < 0.05) were based on different sites. ca has grouped the three sampling sites throughout the four seasons into three clusters of similar water quality as relatively less-polluted (lp), medium-polluted (mp), and highly-polluted (hp). in addition, pca has been applied on the extract to recognize the factors, responsible for water quality variations in four seasons of the year, resulting in four principal components for winter, summer, and autumn, five ones for spring, accounting for 79.58%, 89.07%, 83.34%, and 93.13% of total variance respectively. thus the factors, responsible for water quality variation, are mainly related to domestic wastewaters, seasonal variation, agricultural runoff, and catchment geology. these results will help decision-makers better understand the influence of various factors on water quality and manage pollution/eutrophication adaptively in anchar lake.