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

Herein, cluster analysis was applied to evaluate the spatiotemporal variations in water quality variables of a river. The analysis was performed using the data obtained from 15 monitoring stations during 2007–2018 in the Yeongsan River, Republic of Korea. The spatiotemporal analysis successfully clu...

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Bibliographic Details
Published inEnvironmental science and pollution research international Vol. 27; no. 23; pp. 29327 - 29340
Main Authors Lee, Kyoung-Hee, Kang, Tae-Woo, Ryu, Hui-Seong, Hwang, Soon-Hong, Kim, Kyunghyun
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020
Springer Nature B.V
Subjects
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Bioavailability
Chromium
Cluster analysis
Copper
Dispersion
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Historical metallurgy
Lead
Metals
Pore water
Research Article
River engineering
Rivers
Sediments
Spatial analysis
Speciation
Tailings
Waste Water Technology
Water column
Water Management
Water pollution
Water Pollution Control
Water quality
Norway
South Korea
Arctic region
Long-term monitoring
Spatiotemporal variation
Clustering technique
Multivariate analysis
Hierarchical cluster analysis
Water quality
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Summary:Herein, cluster analysis was applied to evaluate the spatiotemporal variations in water quality variables of a river. The analysis was performed using the data obtained from 15 monitoring stations during 2007–2018 in the Yeongsan River, Republic of Korea. The spatiotemporal analysis successfully clustered the annual water quality variables temporally into years of poor water quality (2007–2012) and good water quality (2013–2018), and spatially into stations observing bad water quality (midstream) and good water quality (upstream and downstream). For the spatial cluster analysis results before and after a large river engineering project, the water quality was grouped into four clusters according to regional effects and water pollutant sources. The clustering analysis results clearly reflected changes in the water quality along the river due to the project. Overall, this study demonstrates that cluster analysis can be effectively used for evaluating spatiotemporal variations in river water quality.
Bibliography:ObjectType-Article-1
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ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-020-09276-0