Statistical Analysis of the Spatio-temporal Water Quality Characteristics of the Nakdong River

Seo, Mijin; Cho, Changdae; Im, Taehyo; Kim, Sang-Hoon; Yoon, Hyunjeong; Kim, Yong-Seok; Kim, Gyeonghoon

doi:10.5322/jesi.2019.28.3.303

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…However, the water quality factors could be described at the upand midstream stations and the mid-and downstream stations below Dalseong weir, depending on dominant trends. Seo et al [31] also spatially classified the Nakdong River as the up-and midstream and mid-and downstream segments at a non-weir station between Dasa (Gangjeong-Goryeong weir) and Nongong (Dalseong weir) stations using cluster analyses. The concentration of total coliforms was especially influenced by decreases in pH and DO and increases in SS at the up-and midstream weir stations and by decreased BOD and increased precipitation at the mid-and downstream weir stations.…”

Section: Results Of Regression Analysismentioning

confidence: 99%

“…The inflow of non-point pollutants, such as fertilizer and livestock feces, by rainfall could lead to the occurrence and increase of coliform bacteria in a suitable aquatic environment. Several studies have reported the impact of non-point pollution by rainfall on coliform bacteria [9,31,41]. Jung et al [30] mentioned that phosphorus could easily be adsorbed onto clay soil and soil organic matter.…”

Section: Results Of Regression Analysismentioning

confidence: 99%

“…SPSS is a popular and powerful statistical analysis tool that can help process and analyze large complex data. It has been a lot utilized for multivariate analyses, such as correlation and regression analyses, in various fields of engineering and science [31][32][33][34]. Correlation analysis determines whether the relationship between two variables is present or absent, and that two variables are associated to a degree.…”

Section: Discussionmentioning

confidence: 99%

“…Lee et al [9] indicated high concentrations of coliform bacteria during summer and fall seasons in the mid-to downstream section of the Nakdong River because of the inflow of pollutants by rainfall, similar to the present study results. Seo et al [31] demonstrated via cluster and factor analyses that influential water quality factors affecting the Nakdong River mainstream from July to October were similar to those over the entire period from January to December, pointing out the need for coliform bacteria management during summer and early fall and further in the mid-to downstream areas. In addition, the impact of water temperature on coliform bacteria was reported by Ramteke et al [40] and Baek [19].…”

Section: Distribution Of Coliform Bacteriamentioning

confidence: 99%

“…Furthermore, to the authors' knowledge, no study has performed regression analyses of the relationship between coliform bacteria and water quality factors at weir stations, and the present study is a first attempt on the topic. Seo et al [31] analyzed water quality characteristics in the Nakdong River after the construction of weirs using multivariate statistical analyses, and identified a need for coliform bacteria management in the mid-to downstream section in summer. However, they used a total of thirty-eight stations, which included eight weir stations.…”

Section: Introductionmentioning

confidence: 99%

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Relationship between Coliform Bacteria and Water Quality Factors at Weir Stations in the Nakdong River, South Korea

Seo

Lee

Kim

2019

Water

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Artificial structures installed in rivers can change the natural physical, physiochemical, and biological characteristics of the rivers. Coliform bacteria are important water quality indicators, related to human health. This study investigated the relationship between coliform bacteria and water quality factors at eight weir stations constructed in the Nakdong River, a major river in South Korea. Fifteen water quality factors were analyzed at these sites from 2012 to 2016 using correlation and multiple regression analyses. The results for all stations confirmed the analytical validity, with high adjusted R 2 values of approximately 0.6 and 0.8 on average for total and fecal coliforms, respectively. The results showed influential water quality factors affecting the concentration of coliform bacteria at weir stations. Specifically, total coliforms were mostly affected by organic matter and fecal coliforms were mostly affected by phosphate phosphorus and suspended solids. Rainfall was the most influential factor affecting both coliforms. Further, both coliforms were negatively affected by organic matter below the Dalseong weir in the mid-to downstream area of the Nakdong River. A positive relationship with phosphate phosphorus was indicated at all weir stations. To the authors' knowledge, this kind of study has never been attempted so far. Thus, the study results can provide important information on influential water quality factors related to coliform bacteria, especially in the Nakdong River, creating a foundation for future water quality management.

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Section: Results Of Regression Analysismentioning

confidence: 99%

Section: Results Of Regression Analysismentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Distribution Of Coliform Bacteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relationship between Coliform Bacteria and Water Quality Factors at Weir Stations in the Nakdong River, South Korea

Seo

Lee

Kim

2019

Water

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Analysis of spatiotemporal variation in river water quality using clustering techniques: a case study in the Yeongsan River, Republic of Korea

Lee

Kang

Ryu

et al. 2020

Environ Sci Pollut Res

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Statistical analysis of water quality change by total maximum daily load policy stage

Jo,

Choi,

Kwon

2024

Appl Water Sci

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This study aimed to derive major pollutants for standard watersheds with relatively high pollution levels and identify their trends. Hence, the water quality index (WQI) and multivariate statistical techniques were used to analyze the water quality evaluation of standard watershed during the total maximum daily load (TMDL) policy period implemented since 2004. The WQI was calculated for 41 standard watersheds, divided into 14 main streams and 27 tributaries. Consequently, in the main stream, WQI decreased as it moved downstream after the MS5 site. In the tributary, WQI values were found to be low at TS11, TS15, TS16, and TS17 sites. However, the Mann–Kendall test results indicated that the overall WQI value was increasing, suggesting that the water quality of the study basin was improving as the TMDL policy phase progressed. Principal component analysis of the group with relatively low WQI among the standard watersheds revealed that water temperature, dissolved oxygen, total phosphorus, chemical oxygen demand, biochemical oxygen demand, and electrical conductivity were the main variables in the main stream cluster. In the tributary stream cluster, total phosphorus, dissolved oxygen, total suspended solids, total nitrogen, and electrical conductivity showed high loading values. This study proposed a series of methods to provide the basic data required for watershed management through detailed water quality assessment methods, statistical techniques for deriving major pollutants, and trend analysis of water quality evaluation.

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Statistical Analysis of the Spatio-temporal Water Quality Characteristics of the Nakdong River

Cited by 7 publications

References 19 publications

Relationship between Coliform Bacteria and Water Quality Factors at Weir Stations in the Nakdong River, South Korea

Relationship between Coliform Bacteria and Water Quality Factors at Weir Stations in the Nakdong River, South Korea

Analysis of spatiotemporal variation in river water quality using clustering techniques: a case study in the Yeongsan River, Republic of Korea

Statistical analysis of water quality change by total maximum daily load policy stage

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