IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i16p8268-d608300.html
   My bibliography  Save this article

Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea

Author

Listed:
  • Md Mamun

    (Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Korea)

  • Kwang-Guk An

    (Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Korea)

Abstract

This study assessed spatial and temporal variations of water quality to identify and quantify possible pollution sources affecting the Yeongsan River using multivariate statistical techniques (MSTs) and water quality index (WQI) values. A 15 year dataset of 11 water quality variables was used, covering 16 monitoring sites. The nutrient regime, organic matter, suspended solids, ionic contents, algal growth, and total coliform bacteria (TCB) were affected by the summer monsoon and the construction of weirs. Regression analysis showed that the algal growth was more highly regulated by total phosphorus (TP; R 2 = 0.37) than total nitrogen (TN, R 2 = 0.25) and TN/TP (R 2 = 0.01) ratios in the river after weir construction and indicated that the river is a P-limited system. After constructing the weirs, the mean TN/TP ratio in the river was about 40, meaning it is a P-limited system. Cluster analysis was used to classify the sampling sites into highly, moderately, and less polluted sites based on water quality features. Stepwise discriminant analysis showed that pH, dissolved oxygen (DO), TN, biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a (CHL-a), and TCB are the spatially discriminating parameters, while pH, water temperature, DO, electrical conductivity, total suspended solids, and COD are the most significant for discriminating among the three seasons. The Pearson network analysis showed that nutrients flow with organic matter in the river, while CHL-a showed the highest correlation with COD (r = 0.85), followed by TP (r = 0.49) and TN (r = 0.49). Average WQI values ranged from 55 to 141, indicating poor to unsuitable water quality in the river. The Mann–Kendall test showed increasing trends in COD and CHL-a but decreasing trends for TP, TN, and BOD due to impoundment effects. The principal component analysis combined with factor analysis and positive matrix factorization (PMF) showed that two sewage treatment plants, agricultural activities, and livestock farming adversely impacted river water quality. The PMF model returned greater R 2 values for BOD (0.92), COD (0.87), TP (0.93), TN (0.91), CHL-a (0.93), and TCB (0.83), indicating reliable apportionment results. Our results suggest that MSTs and WQI can be effectively used for the simple interpretation of large-scale datasets to determine pollution sources and their spatiotemporal variations. The outcomes of our study may aid policymakers in managing the Yeongsan River.

Suggested Citation

  • Md Mamun & Kwang-Guk An, 2021. "Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea," IJERPH, MDPI, vol. 18(16), pages 1-23, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8268-:d:608300
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/16/8268/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/16/8268/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiabo Chen & Jun Lu, 2014. "Effects of Land Use, Topography and Socio-Economic Factors on River Water Quality in a Mountainous Watershed with Intensive Agricultural Production in East China," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    2. Junzhao Liu & Dong Zhang & Qiuju Tang & Hongbin Xu & Shanheng Huang & Dan Shang & Ruxue Liu, 2021. "Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-19, January.
    3. Jiabo Chen & Fayun Li & Zhiping Fan & Yanjie Wang, 2016. "Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China," IJERPH, MDPI, vol. 13(10), pages 1-27, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Quan Wang & Xianhua Wu & Bin Zhao & Jie Qin & Tingchun Peng, 2015. "Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in th," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    2. Xueru Guo & Rui Zuo & Li Meng & Jinsheng Wang & Yanguo Teng & Xin Liu & Minhua Chen, 2018. "Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment," IJERPH, MDPI, vol. 15(2), pages 1-19, February.
    3. Batara Surya & Syafri Syafri & Hernita Sahban & Harry Hardian Sakti, 2020. "Natural Resource Conservation Based on Community Economic Empowerment: Perspectives on Watershed Management and Slum Settlements in Makassar City, South Sulawesi, Indonesia," Land, MDPI, vol. 9(4), pages 1-31, March.
    4. Namsrai Jargal & Ho-Seong Lee & Kwang-Guk An, 2021. "Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches," IJERPH, MDPI, vol. 18(23), pages 1-17, November.
    5. Se-Rin Park & Soon-Jin Hwang & Kyungjin An & Sang-Woo Lee, 2021. "Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    6. Se-Rin Park & Sang-Woo Lee, 2020. "Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality," IJERPH, MDPI, vol. 17(5), pages 1-22, March.
    7. Yichun Xie & Chao Liu & Shujuan Chang & Bin Jiang, 2022. "Urban Sustainability: Integrating Socioeconomic and Environmental Data for Multi-Objective Assessment," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    8. Mehdi Aalipour & Naicheng Wu & Nicola Fohrer & Yusef Kianpoor Kalkhajeh & Bahman Jabbarian Amiri, 2023. "Examining the Influence of Landscape Patch Shapes on River Water Quality," Land, MDPI, vol. 12(5), pages 1-15, May.
    9. Qinghui You & Na Fang & Lingling Liu & Wenjing Yang & Li Zhang & Yeqiao Wang, 2019. "Effects of land use, topography, climate and socio-economic factors on geographical variation pattern of inland surface water quality in China," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-14, June.
    10. Pingyang Liu & Juan M. Moreno & Peiying Song & Elona Hoover & Marie K. Harder, 2016. "The Use of Oral Histories to Identify Criteria for Future Scenarios of Sustainable Farming in the South Yangtze River, China," Sustainability, MDPI, vol. 8(9), pages 1-24, August.
    11. Jiabo Chen & Fayun Li & Zhiping Fan & Yanjie Wang, 2016. "Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China," IJERPH, MDPI, vol. 13(10), pages 1-27, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8268-:d:608300. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.