Assessment of Spatiotemporal Variations in the Water Quality of the Han River Basin, South Korea, Using Multivariate Statistical and APCS-MLR Modeling Techniques

Hyeonmi Choi,Sang-Hun Kim,Soon-Ju Yu,Jong-Kwon Im,Yong-Chul Cho

doi:10.3390/agronomy11122469

Abstract

This study evaluated the spatiotemporal variability of water quality in the Han River Basin (HRB) as well as the contributions of potential pollution sources using multivariate statistical and absolute principal component score-multiple linear regression (APCS-MLR) modeling techniques. From 2011 to 2020, data on water quality parameters were collected from 14 sites in the Ministry of Environment’s water quality monitoring network. Using spatiotemporal cluster analysis, these sites were classified into two periods over the year (dry and wet seasons) and into three regions: low pollution region (LPR), moderate pollution region (MPR), and high pollution region (HPR). Through principal component analysis, we identified four potential factors accounting for 80.1% and 74.1% of the total variance in the LPR and MPR, respectively, and three that accounted for 72.7% of the total variance in the HPR. APCS-MLR results indicated domestic sewage and phytoplankton growth (25%), domestic sewage and seasonal influence (29%), and point pollution sources caused by domestic sewage and industrial wastewater discharge (31%) as potential factors for the LPR, MPR, and HPR. These results demonstrate that the multivariate statistical techniques and the APCS-MLR model can be effectively used to monitor network design, quantitatively evaluate potential pollution sources, and establish efficient water quality management policies.

Highlights

Water is a fundamental element for all living organisms and is the most basic resource for industrial production activities [1]
Ammonia nitrogen exists in aqueous solution as either ammonium ion or ammonia, depending on the pH of the solution, and it has been reported that NH3-N predominates at pH levels above 7 and NH4-N predominates at pH levels below 7 by equilibrium reaction [51]
Multivariate statistical analysis approaches and absolute principal component score (APCS)-multiple linear regression (MLR) were used in this study to assess the spatiotemporal variability of water quality and contributions of various probable pollution sources in the Han River Basin (HRB), South Korea

Summary

Introduction

Water is a fundamental element for all living organisms and is the most basic resource for industrial production activities [1]. Anthropogenic factors constitute specific pollution sources, whereas natural factors that include seasonal phenomena are significantly affected by the climate of river basins [3,6,7] Seasonal changes, such as precipitation, surface runoff, and groundwater flow, significantly influence river pollutant concentrations [8,9]. Preventing the pollution of rivers and monitoring their water quality by acquiring reliable data is important because rivers are the main sources of water for agricultural, domestic, industrial, and recreational purposes [3,11]. Acquiring this information is difficult because water quality varies spatiotemporally [2,12]. Huge and complex data matrices composed of physicochemical parameters can be generated through such monitoring programs, deriving and interpreting different variables that affect water quality is difficult [16]

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Methods

Results

Conclusion