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Real-time water quality monitoring of River Ganga (India) using internet of things.
- Source :
- Ecological Informatics; Nov2022, Vol. 71, pN.PAG-N.PAG, 1p
- Publication Year :
- 2022
-
Abstract
- Achieving and maintaining suitable water quality is one of the important parameters to ensure health and well-being of the human as well as ecosystems. Among the various aquatic ecosystems, riverine ecosystems are more prone to pollution and therefore needs to be monitored frequently and on regular time intervals. In this context, real-time water quality monitoring system offers excellent opportunity to keep track of the water quality on a continuous basis; which not only helps to identify the affected location and pollution source, but also creates alert enabling the authorities to take immediate action. One such real-time water quality monitoring system was installed in the River Ganga (India), considering the fragility and significance of the Gangetic ecosystem. In this paper, we have presented the details of the real-time water quality monitoring system installed in River Ganga and results obtained through it for various parameters. The results have also been compared with the standard values. Additionally, based on this preliminary investigation, limitations and recommendations have also been presented to further enhance the utility of the system. [Display omitted] • RTWQMS based on IoT was developed and installed in River Ganga, India. • Up to 17 number of water quality parameters could be monitored in real time. • Values measured through RTWQMS were in good approximation with laboratory values. • Robustness of RTWQMS may be improved to enhance the accuracy and applicability. [ABSTRACT FROM AUTHOR]
- Subjects :
- WATER quality monitoring
INTERNET of things
FECAL contamination
WATER quality
Subjects
Details
- Language :
- English
- ISSN :
- 15749541
- Volume :
- 71
- Database :
- Supplemental Index
- Journal :
- Ecological Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 159795695
- Full Text :
- https://doi.org/10.1016/j.ecoinf.2022.101770