Enhanced monitoring of water quality variation in Nakdong River downstream using multivariate statistical techniques

Abstract

The variation in downstream river water quality was investigated using three multivariate statistical techniques: factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). Four main factors (FA1, FA2, FA3, and FA4) were defined as changes of “organic matter and nitrogen,” “suspended solid and climate conditions,” “phosphorous and electrical conductivity,” and “discharge,” respectively. The states of each factor were clustered into Low, Normal (Normal_low and Normal_high), and High groups using CA. These groups used to summarize water quality data measured as a series of numbers of contaminants for fast evaluation of water quality and enhanced monitoring capability. To set up a procedure for enhanced monitoring of water quality, Fisher’s linear discriminant functions were deduced to determine the groups in which newly obtained water quality data should be included. To investigate the effectiveness of the proposed tool for enhanced monitoring of river water quality, a case study was conducted of the data analysis procedures applied to Nakdong River downstream and the monitoring results were examined.

Keywords:

• River water quality
• Multivariate statistical techniques
• Factor analysis
• Cluster analysis
• Discriminant analysis

Acknowledgments

This research was supported by the Korea Ministry of Environment as “The Eco-innovation project.” This work was also financially supported by the second stage of the Brain Korea 21 Project in 2013.