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Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea

Han River Environment Research Center, National Institute of Environmental Research, 42, Dumulmeori-gil 68beon-gil, Yangseo-myeon 12585, Korea
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Academic Editor: Bommanna Krishnappan
Water 2021, 13(21), 2976; https://doi.org/10.3390/w13212976
Received: 19 September 2021 / Revised: 15 October 2021 / Accepted: 18 October 2021 / Published: 21 October 2021
(This article belongs to the Section Water Quality and Contamination)
To investigate the effects of rapid urbanization on water pollution, the water quality, daily unit area pollutant load, water quality score, and real-time water quality index for the Jinwi River watershed were assessed. The contribution of known pollution sources was identified using multivariate statistical analysis and absolute principal component score-multiple linear regression. The water quality data were collected during the dry and wet seasons to compare the pollution characteristics with varying precipitation levels and flow rates. The highest level of urbanization is present in the upstream areas of the Hwangguji and Osan Streams. Most of the water quality parameter values were the highest in the downstream areas after the polluted rivers merged. The results showed a dilution effect with a lower pollution level in the wet season. Conversely, the daily unit area pollutant load was higher in the rainy season, indicating that the pollutants increased as the flow rate increased. A cluster analysis identified that the downstream water quality parameters are quite different from the upstream values. Upstream is an urban area with relatively high organic matter and nutrient loads. The upstream sewage treatment facilities were the main pollution sources. This study provides basic data for policymakers in urban water quality management. View Full-Text
Keywords: dry and wet seasons; spatiotemporal variations; cluster analysis; real-time water quality index (RTWQI); absolute principal component score-multiple linear regression (APCS-MLR) dry and wet seasons; spatiotemporal variations; cluster analysis; real-time water quality index (RTWQI); absolute principal component score-multiple linear regression (APCS-MLR)
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MDPI and ACS Style

Choi, H.; Cho, Y.-C.; Kim, S.-H.; Yu, S.-J.; Kim, Y.-S.; Im, J.-K. Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea. Water 2021, 13, 2976. https://doi.org/10.3390/w13212976

AMA Style

Choi H, Cho Y-C, Kim S-H, Yu S-J, Kim Y-S, Im J-K. Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea. Water. 2021; 13(21):2976. https://doi.org/10.3390/w13212976

Chicago/Turabian Style

Choi, Hyeonmi, Yong-Chul Cho, Sang-Hun Kim, Soon-Ju Yu, Young-Seuk Kim, and Jong-Kwon Im. 2021. "Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea" Water 13, no. 21: 2976. https://doi.org/10.3390/w13212976

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