Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea

Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to i...

Full description

Saved in:
Bibliographic Details
Published in:Water (Basel) 2022-03, Vol.14 (5), p.793
Main Authors: Cho, Yong-Chul, Choi, Hyeonmi, Lee, Myung-Gu, Kim, Sang-Hun, Im, Jong-Kwon
Format: Article
Language:English
Subjects:
Analysis
Anthropogenic factors
Aquatic resources
China
Datasets
Drinking water
Dry season
Economic development
Environmental monitoring
Iran
Management
Mathematical models
Methods
Multivariate analysis
Nonpoint source pollution
Pollutants
Pollution
Pollution control
Pollution monitoring
Pollution sources
Quality assessment
Rainy season
Regression analysis
Resource management
River networks
Rivers
South Korea
Statistical analysis
Statistical methods
Surface water
Topography
Water
Water management
Water pollution
Water quality
Water quality control
Water quality management
Water resources
Water resources management
Watershed management
Watersheds
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to identify spatiotemporal changes in surface water in the Imjin River Watershed (IRW), South Korea, distinguish potential pollution sources, and quantify the source apportionment from 2018–2020. An analysis was performed based on multivariate statistical techniques (MST) and the absolute principal component score-multiple linear regression (APCS-MLR) model. Statistically significant groups were created based on spatiotemporally similar physicochemical water quality characteristics and anthropogenic activities: low-pollution (LP) and high-pollution (HP) regions, and dry season (DS) and wet season (WS). There were statistically significant mean differences in water quality parameters between spatial clusters, rather than between temporal clusters. We identified four and three potential factors that could explain 80.75% and 71.99% in the LP and HP regions, respectively. Identification and quantitative evaluation of potential pollution sources using MST and the APCS-MLR model for the IRW may be useful for policymakers to improve the water quality of target watersheds and establish future management policies.
ISSN:2073-4441
2073-4441
DOI:10.3390/w14050793