Use of Principal Component Analysis for parameter selection for development of a novel Water Quality Index: A case study of river Ganga India

Water Quality Index (WQI) is one of the most widely used concepts for representation of the quality of a water resource. This concept has wide acceptance among policy makers and other stakeholders as this gives a clear and comprehensive picture of the status of the pollution of a water body. The standard step of development of a WQI are parameter selection, assignment of weights, development of sub-index functions and final aggregation of weighted sub- index values. Out of these, the current study focusses on the first step, i.e. parameter selection. The results of this study shall play a crucial role in the development of Ganga Water Quality Index in the future. For the current study, the initially available data has been subjected to Principal Component Analysis (PCA) and this led to reduction of number of parameters from 28 to 9. This has been done to make the process more feasible and economic as this would drastically reduce the time, effort and cost required to monitor samples for a large number of parameters. The finally shortlisted 9 parameters were- Dissolved Oxygen (DO), pH, Conductivity, Biological Oxygen Demand (BOD), Total Coliform (TC), Chlorides, Magnesium, Sulphate, Total Dissolved Solids (TDS). PCA utilizes the variance in the entire data set and projects it in new dimensions, thereby reducing the number of parameters but retaining maximum variance. The use of statistical techniques in WQI development makes it less biased and more objective in nature and forms the basis of development of a Ganga Water Quality Index (GWQI) in future.