Towards the Green Analytics: Design and Development of Sustainable Drinking Water Quality Monitoring System for Shekhawati Region in Rajasthan

In rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the development of a sustainable water quality monitoring system is proposed. The green analy...

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Veröffentlicht in:MĀPAN : journal of Metrology Society of India Jg. 36; H. 4; S. 843 - 857
Hauptverfasser: Khatri, Punit, Gupta, Karunesh Kumar, Gupta, Raj Kumar, Panchariya, P. C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.12.2021
Springer Nature B.V
Schlagworte:
Dissolved oxygen
Drinking water
Electrical resistivity
Least squares method
Mathematical and Computational Physics
Mathematical Methods in Physics
Measurement Science and Instrumentation
Monitoring
Monitoring systems
Numerical and Computational Physics
Original Paper
Oxidation
Parameter uncertainty
Physics
Physics and Astronomy
Regression models
Root-mean-square errors
Rural areas
Simulation
Sustainable development
Theoretical
Water quality
Traceability
Multi-sensor array
Traditional water quality monitoring
Partial least squares regression
Water quality index
Green analytics
ISSN:0970-3950, 0974-9853
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Zusammenfassung:In rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the development of a sustainable water quality monitoring system is proposed. The green analytics principles were considered for developing the proposed system to reduce the measurement’s time consumption and labor cost. Five water quality parameters [pH, oxidation reduction potential (ORP), dissolved oxygen (DO), electrical conductivity (EC), and temperature] have been measured using the developed system. The overall drinking water quality is measured by the proposed partial least squares regression (PLSR) model. The developed system’s performance is determined by mean average percentage error (MAPE), root-mean-square error (RMSE), and R 2 . The traceability of water quality sensors is defined with required uncertainty in water quality parameters. The measured uncertainty is 0.002, 0.892, 0.015, 0.029, and 0.017 for pH, EC, DO, ORP, and temperature, respectively. The relation between estimated and predicted water quality parameters ( R 2  > 0.93) shows that the developed system can be a suitable replacement for traditional water quality monitoring techniques.
Bibliographie:ObjectType-Article-1
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ISSN:0970-3950
0974-9853
DOI:10.1007/s12647-021-00465-x