Water Quality of River Beas, India, and Its Correlation with Reflectance Data

Abstract

River Beas originates in the Himalayas in Central Himachal Pradesh, and merges with river Sutlej at Harike in the state of Punjab and this river is a habitat of the endangered freshwater dolphin, Platanista gangetica minor R. The pollution of water bodies is a significant environmental jeopardy as the river is the prime source of water for the consumption by human beings and aquatic species residing in the river. The water quality indices (WQI) of the river Beas using nine standard parameters recommended by National Sanitation Foundation, were determined for pre-monsoon, post-monsoon and winter seasons at three sampling sites between the towns of Beas and Harike over a stretch of 63 km. The results of WQI of river Beas at Harike before confluence with river Sutlej and at Goindwal Sahib is superior to those at the Beas bridge. This is due to self-purification of the water downstream from Beas bridge to Harike. Season-wise, water quality during the pre-monsoon season was somewhat superior at Harike and Beas. This is attributed to fast degradation of organic matter in river during the summer season. Bioassay for appraising the toxicity of river water was also determined by using Girardinus guppii fish and their results indicate that the water of river Beas is not toxic. Toxicity bioassay using Girardinus guppii fish indicates that the water of river Beas is not toxic. Digital number (DN) values of the river water were obtained from Landsat-TM imagery. $${\text{PO}}_{4}^{{3 - }}$$ , TDS and turbidity of the river water were regressed on the reflectance values. Using multiple and β-regression analysis, it was found that $${\text{PO}}_{4}^{{3 - }}$$ , TDS and turbidity contribute positively to band 2(G) and negatively to band 3(R). Multiple linear regression and β-regression analysis showed that $${\text{PO}}_{4}^{{3 - }}$$ and turbidity are regressed on the normalized difference water index negatively and positively respectively. Correlations between the target and output values from artificial neural network for $${\text{PO}}_{4}^{{3 - }}$$ , TDS and turbidity were highly significant. It is suggested that removal strategies like introduction of aquatic phytoremediation plants must be involved in land management policies in order to mitigate the anthropogenic jeopardy. We affirm that the significance of applying water quality management tools using nature dependent solutions and well-organized management policies.