IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i6p1020-d215720.html
   My bibliography  Save this article

Analyses on the Temporal and Spatial Characteristics of Water Quality in a Seagoing River Using Multivariate Statistical Techniques: A Case Study in the Duliujian River, China

Author

Listed:
  • Xuewei Sun

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Huayong Zhang

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Meifang Zhong

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Zhongyu Wang

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Xiaoqian Liang

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Tousheng Huang

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

  • Hai Huang

    (Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China)

Abstract

In the Duliujian River, 12 water environmental parameters corresponding to 45 sampling sites were analyzed over four seasons. With a statistics test (Spearman correlation coefficient) and multivariate statistical methods, including cluster analysis (CA) and principal components analysis (PCA), the river water quality temporal and spatial patterns were analyzed to evaluate the pollution status and identify the potential pollution sources along the river. CA and PCA results on spatial scale revealed that the upstream was slightly polluted by domestic sewage, while the upper-middle reach was highly polluted due to the sewage from feed mills, furniture and pharmaceutical factories. The middle-lower reach, moderately polluted by sewage from textile, pharmaceutical, petroleum and oil refinery factories as well as fisheries and livestock activities, demonstrated the water purification role of wetland reserves. Seawater intrusion caused serious water pollution in the estuary. Through temporal CA, the four seasons were grouped into three clusters consistent with the hydrological mean, high and low flow periods. The temporal PCA results suggested that nutrient control was the primary task in mean flow period and the monitoring of effluents from feed mills, petrochemical and pharmaceutical factories is more important in the high flow period, while the wastewater from domestic and livestock should be monitored carefully in low flow periods. The results may provide some guidance or inspiration for environmental management.

Suggested Citation

  • Xuewei Sun & Huayong Zhang & Meifang Zhong & Zhongyu Wang & Xiaoqian Liang & Tousheng Huang & Hai Huang, 2019. "Analyses on the Temporal and Spatial Characteristics of Water Quality in a Seagoing River Using Multivariate Statistical Techniques: A Case Study in the Duliujian River, China," IJERPH, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:6:p:1020-:d:215720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/6/1020/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/6/1020/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Soumaya Hajji & Nabila Allouche & Salem Bouri & Awad M. Aljuaid & Wafik Hachicha, 2021. "Assessment of Seawater Intrusion in Coastal Aquifers Using Multivariate Statistical Analyses and Hydrochemical Facies Evolution-Based Model," IJERPH, MDPI, vol. 19(1), pages 1-18, December.
    2. Jiancai Deng & Fang Chen & Weiping Hu & Xin Lu & Bin Xu & David P. Hamilton, 2019. "Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model," IJERPH, MDPI, vol. 16(22), pages 1-16, November.

    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:16:y:2019:i:6:p:1020-:d:215720. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.