IJEP 42(12): 1472-1479 : Vol. 42 Issue. 12 (December 2022)
1. Chaibasa Engineering College, Chaibasa – 833 201, Jharkhand, India
2. National Institute of Technology, Patna – 800 005, Bihar, India
Abstract
Water quality observation data were collected from 10 representative monitoring sites located in the mainstream of the Harmu river and its tributaries between 2017 and 2021. Based on these data, the water quality and characteristics of harmu river were evaluated by conducting multivariate statistical analysis for 8 pollution indicators. Monitoring site M1 is starting point of the river harmu in city Ranchi and monitoring site M10 is located downstream of the Harmu river, exhibited high-concentration tendencies. The monitoring sites located near the city and midstream and downstream of the Harmu river exhibited high pollution levels in the investigation. To analyse the spatial and temporal variations in the water quality at 10 major monitoring sites in the harmu river, a tributary of Subarnarekha basin, principal component and factor analyses were conducted by separating the average water quality data based upon (a) monitoring site and (b) season. As a result, three factors were obtained for (a) and (b), respectively. In the Harmu river, the first factor was shown to be organic pollutants (total organic carbon and chemical oxygen demand) and as a result of cluster analysis, two statistically significant groups were classified. The results of multivariate statistical analysis indicated that the monitoring sites with high levels of pollution were mostly those sites going through the heart of the city or the sites affected by residential sewage directly, as well as the sites located midstream and downstream of the Harmu river. The water quality pollution level was calculated based on the above study results and priorities for water quality improvement items required in future watershed management were determined in order to facilitate efficient water quality management.
Keywords
Harmu river, Water quality, Water quality pollution level, Factor analysis, Principal component analysis
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