Abstract
The present study aims at assessing the water quality at Sanjay Lake, Delhi, India using multivariate statistical techniques. A total of 16 physiochemical variables such as pH, conductivity, TDS, COD, DO, chloride, total hardness, magnesium hardness, phosphate, suspended solids, sulphate, nitrate, fluoride, sodium, potassium and calcium were analysed in water samples collected on January, 2020 from 10 sampling sites. The data were evaluated using IBM Statistics software SPSS 25 for principal component analysis, which limits the multiple data dimensions without the loss of vital information for better understanding. Five principal components were defined to be responsible for the data collection, indicating 93% of the total variance of data collection; of which 36.38% by PC1, 24.84% by PC2, 15.13% by PC3, 11.26% by PC4, and 6.36% by PC5 indicated that the domestic sewage, the municipal sewage waste and the untreated industrial effluent discharges may be affecting the water quality. The present study shows that PCA techniques are useful resources for the identification of important surface water quality parameters.
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