• DocumentCode
    2228663
  • Title

    Assessment of the Water Quality near the Dam Area of Three Gorges Reservoir Based on Bayes

  • Author

    Li Chuanqi ; Wang Wei

  • Author_Institution
    Coll. of Civil Eng., Shandong Univ., Jinan, China
  • fYear
    2009
  • fDate
    26-28 Dec. 2009
  • Firstpage
    145
  • Lastpage
    148
  • Abstract
    Water quality stochastic assessment based on Bayes is proposed in this paper. It is applied to assess monthly water-quality monitoring data at the typical sections in Three Gorges Reservoir from the year 2004 to 2006. The typical sections locate at Huanglingmiao, Badong, Guandukou, Xiangxi and so on. The conclusions obtained are listed as follows: (1) Water quality in the reservoir is favorable, and is between Class I and II of Environmental Quality Standard for Water Quality(GB3838-2002). (2)Water quality at any monitoring section has seasonal changes in one year but annual variation. In general, water quality in rainfall year is worse than that in dry year. (3) Nutrients are the key factor that affects the water quality, which will potentially bring on water pollution near the dam of the reservoir. So, it is important to pay more attention to take effective measures for pollution control.
  • Keywords
    Bayes methods; hydrological techniques; rain; reservoirs; stochastic processes; water pollution; water quality; AD 2004 to 2006; Badong; China; Environmental Quality Standard for Water Quality; GB3838-2002; Guandukou; Huanglingmiao; Three Gorges Reservoir; Xiangxi; dam area; dry year; monthly water-quality monitoring data; nutrients; rainfall year; water pollution; water quality stochastic assessment; Bismuth; Civil engineering; Condition monitoring; Educational institutions; Environmental factors; Quality assessment; Reservoirs; Rivers; Water pollution; Water resources;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Engineering (ICISE), 2009 1st International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-4909-5
  • Type

    conf

  • DOI
    10.1109/ICISE.2009.349
  • Filename
    5455364