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Prediction of Water Quality in Miho River Watershed using Water Quality Models  

Jeong, Sang-Man (Dept. of Civil & Environmental Engineering, Disaster Prevention Research Center, Kongju National University)
Park, Jeong-Kyoo (Dept. of Environmental System, Hyechon College)
Park, Young-Kee (Dept. of Civil & Environmental Engineering, Disaster Prevention Research Center, Kongju National University)
Kim, Lee-Hyung (Dept. of Civil & Environmental Engineering, Disaster Prevention Research Center, Kongju National University)
Publication Information
Journal of Korean Society on Water Environment / v.20, no.3, 2004 , pp. 223-230 More about this Journal
Abstract
The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.
Keywords
QUAL2E model; Box-Jenkins time series model; Pollutant load;
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  • Reference
1 Akaike, H., Information theory and an extension of the maximum likelihood principle, Proc. 2nd International Symposium on Information Theory, Budapest, Akademiai Kiado, pp. 267-281 (1973)
2 Drolc, A., and Koncan, J. Z., Water quality modeling of the river, SAVA, Water Research, 30, pp. 2587-2592 (1996)
3 Mehta, B. M., and Ahlert, R. C., Stochastic variation of water quality of the Passaic River, Water Resour. Res., 11(2), pp. 300-308 (1975)
4 안상진, 연인성, 한양수, 이재경, 신경망모형을 적용한 금강공주지점이 수질예측, 수자원학회지, 34(6), pp. 701-711 (2001)
5 환경부, 환경통계연감 (2001)
6 Yang, M. D., and Merry, C. J., Integration of water quality modeling, remote sensing and GIS, Water Research, 35, pp. 253-263 (1999)
7 금강환경관리청, 99년 금강 중권역 수질오염원현황 (2000)
8 환경부, 표준원단위 조사연구(I) (1999)
9 전경수, 이길성, QUAL2E 모형에 의한 한강수계 수질모델링, 수공학연구발표회논문집, pp. 125-132 (1993)
10 Thomann, R. V., Time-series analysis of water quality data, J. Environ. Eng. Div., ASCE, 105(5), pp. 590-606 (1967)
11 Fisher, H. B., Transverse mixing in a sand bed channel, U.S. Geological survey professional paper, U.S Department of the interior, Washington D.C, pp. 267-272 (1967)
12 김범철, 조규송, 허우명, 김동섭, 소양호 부영향화의 년 변화 추이, 한국육수학회지, 22(3), pp. 151-163 (1989)
13 이흥근, 한강 한류부의 수질변동에 대한 통계학적 특성, 서울대학교 박사학위논문 (1982)
14 윤용운, STATGRAPHICSTM를 이용한 박스 - 젠킨스방법의 시계열 분석론, 경문사 (1995)
15 Box, G. E. P., and Jenkins, G. M., Time series analysis forecasting and control, 2nd, San Francisco, Holden-Day (1976)
16 건설교통부, 금강수계 하천수 사용실태 조사 및 하천유지유량 산정 (1999)
17 건설교통부, 금강수계 유량측정 조사 보고서 (1997)
18 박정규, QUAL2E 모델에 의한 낙동강수계 수질예측에 관한 연구, 원광대학교 공업기술개발연구지, pp. 3-17 (1988)
19 Streeter-Phelps, A Study of the pollution and national purification of the Ohio River, Public Health Bulletin, US Dept. of Health Education and Welfare, pp. 146 (1925)