تعیین میزان اهمیت ضریب پراکندگی طولی در انتقال آلاینده در رودخانه‌ها با استفاده از شبیه‌سازی مونت کارلو

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه سازه های آبی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

پارامترهای مختلفی وجود دارد که برای تعیین ضریب پراکندگی در رودخانه­ها مهم است، به عنوان مثال پارامترهای هیدرودینامیکی و هندسه رودخانه. بنابراین تعیین دقیق این ضریب کار دشواری است. فرمول­های تجربی مختلفی برای تخمین ضریب پراکندگی در رودخانه­ها وجود دارد. این فرمول­ها عمدتاً در دامنه شرایطی که اعتبار آن‌ها اعلام شده است، دقیق هستند. دانستن شرایطی که تحت آن ضریب پراکندگی در رودخانه­ها تأثیر زیادی دارد، بسیار حائز اهمیت است. بنابراین در این شرایط باید آن را با دقت بیش­تری تعیین کرد. هدف اصلی از این مطالعه ارائه یک روش جدید برای تعیین موقعیت­هایی است که در آن، ضریب پراکندگی تأثیر معنی­داری بر حمل و نقل مواد آلاینده دارد. روش پیشنهادی مبتنی بر روش شبیه­سازی مونت کارلو است. این روش با استفاده از چندین مورد فرضی و همچنین یک مورد واقعی، صحت­سنجی و اعتبارسنجی شده است. نتایج نشان می­دهد که الگوی زمانی منبع آلودگی، عامل اصلی در تأثیر ضریب پراکندگی در حمل و نقل مواد آلاینده است. یافته اصلی تحقیق این است که گاهی اوقات می­توان ضریب پراکندگی را با خطاهای بزرگ در نظر گرفت و هیچ تغییر مهمی در نتایج شبیه‌سازی حمل و نقل مواد آلاینده رخ ندهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Determine of The Importance of Longitude Dispersion Coefficient on Solute Transport in Rivers Using the Monte Carlo Simulation

نویسندگان [English]

  • Elham Karami cheme
  • Mehdi Mazaheri
Department of Water Structures, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
چکیده [English]

There are various parameters which are important in determining the dispersion coefficient in rivers, e.g. hydrodynamic parameters and river geometry. Thus it is a challenging task to determine this coefficient accurately. There are different empirical formulas to estimate the dispersion coefficient in rivers. These formulas are mostly accurate in the range of conditions they validated. It is important to know the conditions in which the dispersion coefficient effect is significant in rivers. Thus, in this conditions, one should determine it with more accuracy. The main purpose of this study is to present a new method for determining the situations in which, dispersion coefficient has significant effect on solute transport mechanism. The proposed method is based on the Monte Carlo simulation method. The method was verified and validated using several hypothetical and also a real test cases. The results show that the time pattern of pollution source is a key factor in the dispersion coefficient effects on solute transport mechanism. The main finding of the study is that, sometimes it is possible to consider the dispersion coefficient with large errors and no significant changes occur in results of the solute transport simulation.

کلیدواژه‌ها [English]

  • Sensitivity analysis
  • Uncertainty analysis
  • Advection-Dispersion Equation
Abderrezzak, K. E. K., Ata, R., and Zaoui, F. (2015). One-dimensional numerical modelling of solute transport in streams: The role of longitudinal dispersion coefficient. Journal of Hydrology, 527, 978-989.
 Alizadeh, M. J., Ahmadyar, D., and Afghantoloee, A. (2017). Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient. Journal of Water Resources Management, 31(6), 1777-1794.
Atkinson, T. and P. Davis. (2000). Longitudinal dispersion in natural channels: l. Experimental results from the River Severn, UK. Hydrology and Earth System Sciences Discussions. 4(3): p. 345-353.
Balf, M. R., Noori, R., Berndtsson, R., Ghaemi, A., and Ghiasi, B. (2018). Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers. Journal of Water Supply: Research and Technology-Aqua, jws2018021.
Banejad, H., Mohebzadeh, H., and Olyaie, E. (2013). Numerical Simulation of the Flow and Contaminant Transport in Groundwater, Case Study: Nahavand Plain Aquifer. Journal of Water and Soil Science. 23(2): p. 43-57.
Benedini, M., and Tsakiris, G. (2013). Water quality modelling for rivers and streams. Journal of Springer Science and Business Media.
Chapra, S.C. (1997). Surface Water Quality Modeling. McGraw-Hill, New York.
Chatila, G. J. (1997). Modeling of pollutant transfer in compound open channels. PhD Dissertation, University of Ottawa, Ontario, Canada.
 Deng, Z.Q., Singh, V.P., Bengtsson, L. (2002). Longitu dinal dispersion coefficient in single channel streams. Journal of Hydraulic Engineering. 128 (10), 901–916.
 Disley, T., Gharabaghi, B., Mahboubi, A., McBean, A. (2015). Predictive equation for longitudinal dispersion coefficient. Hydrol. Process. 29, 161–172. http:// dx.doi.org/10.1002/hyp.10139.
Dobbins, W. E. (1964). BOD and oxygen relationships in stream, Journal of the Sanitary Engineering Division, 90, 53-78
Dou, C., Woldt, W., Bogardi, I., Dahab, M. (1997). Numerical solute transport simulation using fuzzy sets approach. Journal of Contaminant Hydrology, 27 (1e2), 107.
Fischer HB. (1979).Mixing in inland and coastal waters: Academic press.
 Fischer, B.H. (1975). Discussion of ‘‘Simple method for predicting dispersion in streams’’. Journal of nviron. Engineering. Div. 101 (3), 453–455.
Gandolfi, C., Facchi, A., and Whelan, M. J. (2001). On the relative role of hydrodynamic dispersion for river water quality. Water Resources Research, 37(9), 2365-2375.
Guyoanet, D., Come, B., Perrochet, P., Parriaux, A. (1999). Comparing two methods for addressing uncertainty in risk assessments. Journal of EnvironmentalEngineering, 125 (7), 660.
 Iwasa, Y., Aya, S. (1991). Predicting longitudinal dispersion coefficient in open channel flows. n: Proceedings of International Symposium on Environmental Hydraulics, Hong Kong, pp. 505– 10.
Kim, D., (2012). Assessment of longitudinal dispersion coefficients using Acoustic Doppler Current Profilers in large river. Journal of  Hydro-environment Res. 6 (1), 29–39.
 Koussis, A.D., Rodriguez-Mirasol, J. (1998). Hydraulic estimation of dispersion coefficient for streams. Journal of Hydraulic Engineering. 124 (3), 317–320.
Leibundgut, C., Maloszewski, P., and Külls, C. (2011). Tracers in hydrology. John Wiley and Sons.
Li, W. (1972). Effects of dispersion on DO-sag in uniform flow, Journal of the Sanitary Engineering Division., 98, 169-182.
 Li, X., Liu, H., Yin, M. (2013). Differential evolution for prediction of longitudinal dispersion coefficients in natural streams. Journal of Water Resour Manage, 27, 5245– 5260.
 Liu, H. (1977). Predicting dispersion coefficient of stream. J. Environ. Eng. Div. 103 (1), 59–69.
Noori, R., Deng, Z., Kiaghadi, A., and Kachoosangi, F. T. (2015). How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers?. Journal of Hydraulic Engineering142(1), 04015039.
Noori, R., Ghiasi, B., Sheikhian, H., and Adamowski, J. F. (2017). Estimation of the dispersion coefficient in natural rivers using a granular computing model. Journal of Hydraulic Engineering143(5), 04017001.
Noori, R., Karbassi, A., Farokhnia, A., and Dehghani, M. (2009). Predicting the longitudinal dispersion coefficient using support vector machine and adaptive neuro-fuzzy inference system techniques. Environmental Engineering Science26(10), 1503-1510.
Rajeev, R.S., Dutta, S. (2009). Prediction of longitudinal dispersion coefficients in natural rivers using genetic algorithm. Journal of Hydraulic Engineering. 40 (6), 544–552.
Rutherford, J. (1994). River Mixing. Wiley, Chichester, UK.
Ruthven, D. M. (1971). The dispersion of a decaying effluent discharged continuously into a uniformly flowing stream, Journal of Water Resources Management., 5,343-352.
Sarmin, E. N., and Chudov, L. A. (1963). On the stability of the numerical integration of systems of ordinary differential equations arising in the use of the straight line method. USSR Computational Mathematics and Mathematical Physics3(6), 1537-1543.
Seo, I.W., Cheong, T.S. (1998). Predicting longitudinal dispersion coefficient in natural Stream. Journal of Hydraulic Engineering. 124 (1), 25–32.
Shen, C., Niu, J., Anderson, E.J., Phanikumar, M.S., (2010). Estimating longitudinal dispersion in rivers using Acoustic Doppler Current Profilers. Adv. Journal of Water Resources Management. 33 (6), 615–623.
Soncini-Sessa, R, A. Nardini, and A. Kraszewski. (1994). Data gathering campaigns for the calibration of river quality models: [1] Considerations on design criteria, Internal Rep. 94.081, Dip. di Elettron., Politec. di Milano, Milan, Italy.
Tayfur, G., and Singh, V. P.(2005). Predicting longitudinal dispersion coefficient in natural streams by artificial neural network. Journal of Hydraulic Engineering, 131 (11), 991-1000.
Thomann, R. V. (1973). Effects of longitudinal dispersion on dynamic water quality response of streams and rivers, Journal of Water Resources., 9(2), 355-366.
Toprak, Z.F., Sen, Z., Savci, S.M., (2004). Comment on Longitudinal dispersion coefficients in natural channels. Journal of Water Resources. 38 (13), 3139–3143.
Tung and Yen. (2005). Hydrosystem engineering uncertainty analysis, McGraw-Hill, New York, 285p.
Zeng, Y., Huai, W. (2014). Estimation of longitudinal dispersion coefficient in rivers. Journal of Hydro-environment. Res. 8, 2–8.