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

Abstract

A B S T R A C TThe 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.

Spatial and Seasonal Surface Water Quality Assessment in a Tropical Urban Catchment: Burío River, Costa Rica

“…Although there are practical guidelines for the implementation of these assessments, local conditions such as land use, national regulations, geography, and geomorphology, for example, should also be considered [9]. The assessments usually involve the collection and analyses of: biological (e.g., Escherichia coli) [10]; chemical (e.g., dissolved oxygen) [11]; physical (e.g., water temperature);…”

Analysis of water quality using multivariate statistical methods in Duliujian River, China

“…PCA was employed to identify the main indexes that spatially affect water quality. Before performing the PCA, suitability of the variables was tested using the Kaiser-Meyer-Olkin (>0.5) and Barlett's sphericity tests(p<0.05) [10]. In the study, the Barlett's sphericity test result was p=0.00 with KMO 0.570, which meant these parameters were satisfactory for PCA.…”

Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

“…RCs further simplify the data structure coming from PCA [1,22]. The varimax rotation technique prevents multiple variables from being loaded to a single component, allowing for easy interpretation of significant variables [24]. Because these rotations are performed in a subspace, the new rotated components explain less variance than the original principal components, but the total variance remains the same after rotation [19].…”

“…Principal component analysis (PCA) has been a particularly popular method to extract important information from the complicated datasets, with studies dating back to the 1930s [19]. In the field of water quality research, PCA and factor analysis (FA) were usually applied together to identify critical water quality parameters that are responsible for temporal and spatial variations of river water quality [20][21][22][23][24][25][26][27]. However, the application of PCA to identify principal water quality monitoring stations was rarely reported in the literature.…”