Abstract
Water quality is a critical environmental issue because all forms of life depend on the water. The present study primarily focused on the spatiotemporal trends of water quality in a section of the Bhagirathi-Hugli River, West Bengal, using geospatial technology and integrated statistical methods. For this purpose, 83 samples of 7 water parameters were analysed and compared them with Indian Standards (IS 2004), Environmental Protection Agency (EPA 2001) and World Health Organization (WHO 1993) for the protection of aquatic life and human consumption. Correlation, box and whisker plots, paired sample t test, water quality index (WQI), cluster analysis (CA) and principal component analysis (PCA) were applied as an integrated multivariate statistical approach to understanding the nature of water quality. Pollution sources were identified by PCA indicating different origins both naturally and anthropogenic sources. The box and whisker plots displayed the significantly spatiotemporal variations and concentration of the variables. The paired sample t test identified that the surface water quality varied significantly between the seasons with significant value p < 0.05. Cluster analysis grouped 83 monitoring sites into 4 clusters to identify the pollution status such as low, moderate, high and very high pollution sites. Principal component analysis confirmed that the first three PCs with eigenvalues are higher than 1 contributing 90.83% of total variability for various parameters. The conductivity, total dissolved solids (TDS), salt and pH were expressively influenced by the anthropogenic effect while the temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were affected by seasonal factors. Results of WQI ranged from 45.04 to 83.79, and an average value was 69.55 with 69% samples representing poor water quality for drinking and domestic purposes. It also indicates that the water quality of rural sites was better than industrial and urban sites in both seasons and also shows that it was better for the duration of the post-monsoon than pre-monsoon.
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