Long-Term Water Quality Trend Analysis with NTrend 1.0 Program in Nakdong River

NTrend 1.0에 의한 낙동강 수질 장기변동 추세분석

  • Yu, Jae Jeong (Nakdong River Water Environmental Research Center, National Institute of Environmenl Research) ;
  • Shin, Suk Ho (Nakdong River Water Environmental Research Center, National Institute of Environmenl Research) ;
  • Yoon, Young Sam (Nakdong River Water Environmental Research Center, National Institute of Environmenl Research) ;
  • Song, Jae Kee (Department of Statistics, Kyungpook National University)
  • 유재정 (국립환경과학원 낙동강물환경연구소) ;
  • 신석호 (국립환경과학원 낙동강물환경연구소) ;
  • 윤영삼 (국립환경과학원 낙동강물환경연구소) ;
  • 송재기 (경북대학교 통계학과)
  • Received : 2010.05.19
  • Accepted : 2010.09.07
  • Published : 2010.11.30

Abstract

The effect of seasonality on water quality variation is very significant. Generally, it reduce the power of the trend extraction. A parametric time-series model was used for detecting trends in historic constituent concentration data. The effect of seasonality is able to remove from time series decomposition technique. According to such statistic methode, long-term water quality trend analysis system (NTrend 1.0) was developed by Nakdong River Water Environmental Research Center. The trend analysis of BOD variation was conducted with NTrend 1.0 at Goreong and Moolkum site in Nakdong river to show the effect of water quality management action plan. Power test of trend extraction was tried each case of 'deseasonalized and deannulized' data and 'deseasonalized' data. Analysis period was from 1989 to 2006, and it's period was divided again three times, 1989~1993, 1994~1999 and 2000~2006 according to action plan period. The BOD trend was downward in Goreong site during three times and it's trend slope was very steep, and upward in Moolkum during 1989~1993, but it was turned downward during 1994~1999 and 2000~2006. It was revealed that it's very effective to reduce the concentration of BOD by water quality management action plan in that watershed. The result of power test was shown that it is high for trend extraction power in case of 'deseasonalized' data.

Keywords

References

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