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다변량 통계분석을 이용한 미호강 유역 수질 특성 분석

Analysis of water quality characteristics of the Miho River basin using multivariate statistical analysis

  • 유나영 ((주)휴먼플래닛) ;
  • 최별 ((주)휴먼플래닛 환경컨설팅부) ;
  • 서동일 (충남대학교 환경공학과)
  • Yu, Nayoung (Humanplanet Co., Ltd.) ;
  • Choi, Byeoul (Environmental Consulting Dept., Humanplanet Co., Ltd.) ;
  • Seo, Dongil (Department of Envionmental Engineering, Chungnam Unuversity)
  • 투고 : 2024.09.13
  • 심사 : 2024.10.10
  • 발행 : 2024.10.31

초록

본 연구에서는 인근 수질측정지점 간의 수질의 기여도를 간접적으로 파악하고 본류와 지류하천 간의 영향력을 해석하기 위해 시계열 분석이 가능한 STL (Seasonal and Trend decomposition using Loess) 기법을 수질자료에 적용한 결과를 보고하고 있다. STL 기법은 수계 시계열 자료의 이상치를 판별하고 제거할 수 있게 하며 해당 자료의 수질 변화 추세와 계절적 특성을 분석하는 방법이다. 또한, 인접하게 위치한 수질측정지점자료간의 수질 상관도 및 유사도를 분석하기 위해 지점간 상관분석을 실시하였으며 요인분석을 통해 본류와 유입 지류 측정 자료 간의 수질 자료에서 공통요인이 영향을 미치는 지점을 분류하였다. 미호강의 경우 상관도가 높다고 판단된 지점 간의 요인분석 결과 지류하천 보다 본류의 영향이 절대적인 것으로 파악됨에 따라 미호강 상류를 포함한 본류지점에 대한 수질관리가 우선시되어야 할 것으로 분석되었다. 본 연구에서 사용된 방법으로 우리나라 주요 하천 본류의 상·하류 지점과 유입지점 간의 수질 영향력 정도를 분석할 수 있음에 따라 향후 하천의 수질관리 대안 개발에 효과적으로 적용할 수 있을 것으로 판단된다.

In this study, the Seasonal and Trend Composition Using Loess (STL) technique, which enables time series analysis, was applied to indirectly identify the contribution of water quality between nearby water quality measurement points and to interpret the influence between the main stream and tributary streams in the Miho River basin. This technique can identify and remove outliers in the time series data of the water system and analyze water quality change trends and seasonal characteristics. In addition, in order to analyze the correlation and similarity between water quality data at adjacent measurement points, a correlation analysis was conducted, and the points where common factors affect the water quality of the mainstream and inflow tributaries were classified. As a result of factor analysis between points judged to have high correlations in Miho River, it was found that the influence of the mainstream was absolute over tributaries, and it was analyzed that water quality management for the mainstream including the upstream of the Miho River should be given priority. As the degree of water quality influence between points can be analyzed by the method used in this study, it is judged that it can be effectively applied to the development of alternatives for water quality management of rivers in the future.

키워드

과제정보

본 논문은 금강수계 환경기초조사사업의 지원으로 수행되었습니다.

참고문헌

  1. Alharbawee, N.A., and Mohammed, A.J. (2024). "Water quality assessment of tigris river using multivariate statistical techniques." Iraqi Journal of Science, Vol. 65, pp. 1266-1275.
  2. Cho, Y.C., Lee, S.W., Ryu, I.G., and Yu, S.J. (2017). "Assessment of spatiotemporal water quality variation using multivariate statistical techniques: A case study of the Imjin River Basin, Korea." Journal of Korean Society of Environmental Engineers, Vol. 39, No. 11, pp. 641-649.
  3. Cleveland, R.B., Cleveland, W.S., McRae, J.E., and Terpenning, I. (1990). "STL: A seasonal-trend decomposition procedure based on loess." Journal of Official Statics, Vol. 6, No. 1, pp. 3-73.
  4. Giri, A., Bharti, V.K., Kalia, S., Kumar, K., Raj, T., and Chaurasia, O.P. (2019). "Utility of multivariate statistical analysis to identify factors contributing river water quality in two different seasons in cold-arid high-altitude region of Leh-Ladakh, India." Applied Water Science, Vol. 9, 26.
  5. Gwak, B.R., and Kim, I.K. (2016). "Characterization of water quality in Changnyeong-Haman weir section using statistical analyses." Journal of Korean Society of Environmental Engineers, Vol. 38, No. 2, pp. 71-78.
  6. Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2010). Multivariate data analysis. 7th Edition, Pearson Education International, NJ, U.S.
  7. Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and practice, 2nd edition, OTexts, Melbourne, Australia.
  8. Kilic, E., and Yucel, N. (2018). "Determination of spatial and temporal changes in water quality at Asi River using multivariate statistical techniques." Turkish Journal of Fisheries and Aquatic Sciences, Vol. 19, No. 9, pp. 727-737.
  9. Kim, J.I., Choi, J.W., and An, K.G. (2014). "Spatial and temporal variations of water quality in an urban Miho Stream and some influences of the tributaries on the water quality." Journal of Environmental Science International, Vol. 23, No. 3, pp. 433-445.
  10. Lafare, A.E., Peach, D.W., and Hughes, A.G. (2016). "Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK." Hydrogeology Journal, Vol. 24, No. 1, pp. 141-158.
  11. Lee, D.H., Kang, E.T., Joo, J.C., Go, H.W., Ahn, C.M., Bae, Y.H., and Song, K.D. (2022). "Water quality analysis and Chl-a prediction of 15 large-scale freshwater lakes in Korea by multivariate statistical analysis." Journal of Korean Society of Environmental Engineers, Vol. 44, No. 12, pp. 589-602.
  12. Lee, G.H. (2021). "Seasonal adjustment of Korean time series using STL." Journal of the Korean Official Statistics, Vol. 26, No. 2, pp. 31-51.
  13. Legesse, N.S., Kim, J.Y., and Seo, D.I. (2022). "Evaluation of significant pollutant sources affecting water quality of the Geum River using principal component analysis." Journal of Korea Water Resources Association, Vol. 55, No. 8, pp. 577-588.
  14. Ministry of Environment (ME) (2021). Basic plan for installation and operation of water environment measurement network.
  15. Oh, S.M., Shin, H.S., Shin, Y.S., and Jeong, H.C. (2017). "Forecasting the particulate matter in Seoul using a univariate time series approach." Journal of The Korean Data Analysis Society, Vol. 19, No. 5, pp. 2457-2468.
  16. Park, J.B., Kal, B.S., and Kim, S.M. (2019). "Application of multivariate statistical techniques to analyze the pollution characteristics of major tributaries of the Nakdong River," Journal of Wetlands Research, Vol. 21, No. 3, pp. 215-223.
  17. Rim, C.S. (1999). "Multivariate analysis of water quality data at 14 sattions in the Geum-River watershed." Journal of The Korean Environmental Sciences society, Vol. 8, No. 3, pp. 331-336.
  18. Rojo, J., Rivero, R., Romero-Morte, J., Fernandez-Gonzalez, F., and Perez-Badia, R. (2017). "Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing." International Journal of Biometeorology, Vol. 61, No. 2, pp. 335-348.
  19. Rotiroti, M., Zanotti, C., Fumagalli, L., Taviani, S., Stefania, G.A., Patelli, M.., and Leoni, B. (2019). "Multivariate statistical analysis supporting the hydrochemical characterization of groundwater and surface water: A case study in northern Italy." Rendiconti Online Della Societa Geologica Italiana, Vol. 47, pp. 90-96.
  20. Seo, M.J., Cho, C.D., Im, T.H., Kim, S.H., Yoon, H.J., Kim, Y.S., and Kim, G.H. (2019). "Statistical analysis of the spatiotemporal water quality characteristics of the Nakdong River." Journal of Environmental Science International, Vol. 28, No. 11, pp. 303-320.
  21. Seo, Y.M., Kwon, K.H., Choi, Y.Y., and Lee, B.J. (2021). "Assessment of water quality characteristics in the middle and upper watershed of the Geumho River using multivariate statistical analysis and watershed environmental model." Journal of Korean Society on Water Environment, Vol. 37, No. 6, pp. 520-530.
  22. Song, Y.H, Lee, Y.H., Lee, J.G., Park, Y.K., and Kim, K.E. (2021). Policy analysis and response measures for improving water quality in Mihocheon, Report 2021-47, Daejeon Sejong Reserch Institute.
  23. Vieira, J.S., Pires, J.C., Martins, F.G., Vilar, V.J., Boaventura, R.A., and Botelho, C.M. (2012). "Surface water quality assessment of Lis River using multivariate statistical methods." Water, Air & Soil Pollution, Vol. 223, pp. 5549-5561.
  24. Water Environment Information System (WEIS) (2024). accessed 13 July 2024, <https://water.nier.go.kr/web/>.
  25. Yoon, H.Y., Kim, J.H., Chae, M.H., Cho, Y.H., and Cheon, S.U. (2019). "Assessment of water quality in the Miho Stream using multivariate statistics." Journal of Environmental Impact Assessment, Vol. 28, No. 4, pp. 373-386.