IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v11y2014i11p11860-11878d42394.html
   My bibliography  Save this article

Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources

Author

Listed:
  • Hong Yao

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
    School of Geography, Nantong University, Nantong 226001, China)

  • Xin Qian

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China)

  • Hailong Gao

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China)

  • Yulei Wang

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China)

  • Bisheng Xia

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China)

Abstract

Ten metals were analyzed in samples collected in three seasons (the dry season, the early rainy season, and the late rainy season) from two rivers in China. No observed toxic effect concentrations were used to estimate the risks. The possible sources of the metals in each season, and the dominant source(s) at each site, were assessed using principal components analysis. The metal concentrations in the area studied were found, using t -tests, to vary both seasonally and spatially ( P = 0.05). The potential risks in different seasons decreased in the order: early rainy season > dry season > late rainy season, and Cd was the dominant contributor to the total risks associated with heavy metal pollution in the two rivers. The high population and industrial site densities in the Taihu basin have had negative influences on the two rivers. The river that is used as a source of drinking water (the Taipu River) had a low average level of risks caused by the metals. Metals accumulated in environmental media were the main possible sources in the dry season, and emissions from mechanical manufacturing enterprises were the main possible sources in the rainy season. The river in the industrial area (the Wusong River) had a moderate level of risk caused by the metals, and the main sources were industrial emissions. The seasonal and spatial distributions of the heavy metals mean that risk prevention and mitigation measures should be targeted taking these variations into account.

Suggested Citation

  • Hong Yao & Xin Qian & Hailong Gao & Yulei Wang & Bisheng Xia, 2014. "Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources," IJERPH, MDPI, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:11:p:11860-11878:d:42394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/11/11/11860/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/11/11/11860/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Xiao Wang & Nikolaos Katopodes & Chunqi Shen & Hua Wang & Yong Pang & Qi Zhou, 2016. "Control of Pollutants in the Trans-Boundary Area of Taihu Basin, Yangtze Delta," IJERPH, MDPI, vol. 13(12), pages 1-12, December.
    2. Xiao Huang & Liping He & Jun Li & Fei Yang & Hongzhuan Tan, 2015. "Different Choices of Drinking Water Source and Different Health Risks in a Rural Population Living Near a Lead/Zinc Mine in Chenzhou City, Southern China," IJERPH, MDPI, vol. 12(11), pages 1-18, November.
    3. 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.
    4. 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.
    5. Baocui Liang & Xiao Qian & Shitao Peng & Xinhui Liu & Lili Bai & Baoshan Cui & Junhong Bai, 2018. "Speciation Variation and Comprehensive Risk Assessment of Metal(loid)s in Surface Sediments of Intertidal Zones," IJERPH, MDPI, vol. 15(10), pages 1-16, September.

    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:11:y:2014:i:11:p:11860-11878:d:42394. 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.