Evaluation of river water quality monitoring stations by principal component analysis

Abstract

The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.