An investigation on water quality variability and identification of ideal monitoring locations by using entropy based disorder indices

Abstract

Optimizing between information needs and information gathered from water quality monitoring networks involve complicated decision making processes and management strategies. The present study investigates upon entropy based variability of water quality using disorder indices. Employing the Shannon's diversity index and the principle of maximum entropy (POME), the study identifies locations which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface run-off, which lead to water quality variability in a waterbody over a monitoring period. A case study on 19 sampling locations over the monitoring period of 2017–2018 has been done on the entire expanse of Deepor Beel (DB), a wetland of international importance designated as a Ramsar site in 2002. Geospatial analysis and isoinformation lines have been employed to generate geospatial maps that clearly depict ideal monitoring locations which encountered highest variability in their water quality over the monitoring period with respect to physico-chemical parameters, BOD, COD and heavy metals. Results indicated 5 sampling locations DB1, DB10, DB11, DB12 and DB18 for physico-chemical parameters and 4 sampling locations DB1, DB4, DB15 and DB17 for heavy metals encountered highest variability and are recommended ideal monitoring locations. The present study introduces an innovative approach to derive maximum useful information by pinpointing sampling stations for regular monitoring purposes. Risk assessment due to heavy metals was also done by using average daily dose and hazard index in the monitoring period. Cr and Pb were critical to human health on consumption.