بازیابی شدت منابع آلاینده در رودخانه در دامنه دو بعدی تحت شرایط واقعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه سازه های آبی، دانشکده کشاورزی، دانشگاه تربیت مدرس

2 هیات علمی دانشگاه تربیت مدرس

3 هیات علمی دنشگاه تربیت مدرس

چکیده

از سه دهه گذشته تاکنون رویکردها و روش‌های زیادی بر مبنای حل مسئله معکوس جهت بازیابی تابع ً در محیط آب‌های زیرزمینی مورد بررسی قرار گرفته است، اما تعداد پژوهش‌ها شدت منابع آلاینده خصوصا در رودخانه‌ها بسیار محدود است؛ بنابراین ارائه روشی که بتواند هم‌زمان علاوه بر دقت در شناسایی تابع شدت منابع آلاینده در رودخانه، شرایط و حالات پیچیده جریان و بستر را در نظر بگیرد و محدودیت‌ها را نیز کاهش دهد، می‌تواند مؤثر باشد. در پژوهش حاضر حل معکوس معادله جابه‌جایی - پراکندگی جهت بازیابی تابع شدت منابع آلاینده به حل یک دستگاه معادلات خطی فرامعین از نوع مسئله بدخیم منجر می‌شود. در این پژوهش با استفاده از یک مدل عددی مبتنی بر رویکرد ریاضی ماتریس معکوس بر پایه روش تنظیم تیخونف و نتایج حاصل از اصل برهم‌نهی به بازیابی تابع شدت منابع آلاینده و زمان دقیق رهاسازی آلاینده از منبع پرداخته شده است. مدل مذکور به بازیابی توابع شدت زمانی منابع چندگانه آلاینده در حالت پیچیده پرداخته است. همچنین مدل ارائه شده با استفاده از داده‌های واقعی رودخانه اوهایو واقع در إیالات متحده آمریکا در حالت دو بعدی تحت شرایط واقعی جریان صحت‌سنجی شد. در نهایت نیز چارچوب کلی و عملی جهت کاربرد در شرایط واقعی ارائه شد. نتایج محاسبات نشان داد که مدل معکوس مذکور به خوبی قادر است با کمترین اطلاعات پایین‌دست و سطح خطای بالا در برداشت داده‌های میدانی، توابع شدت منابع آلاینده را در هر نقطه از رودخانه بازسازی کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Recovering the Temporal Release Rate of Pollutant Sources in the River in Two dimensional and real condition

نویسندگان [English]

  • siamak amiri 1
  • jamal mohammad vali samani 3
1 water structures engineering department, agriculture faculty, Tarbiat Modares university, Tehran, Iran
3 water structures engineering department, agriculture faculty, Tarbiat Modares university, Tehran, Iran
چکیده [English]

Over the past three decades, many approaches and methods have been investigated based on the inverse problem solving to recover the temporal release rate of pollutant sources, especially in groundwater. But, number of studies is limited about the rivers; therefore, developing a method which can determine temporal release rate of pollutant sources in the river precisely and at the same time be able to consider the conditions of the flow and bed is promising. In the present study, the inverse solution of the advection-dispersion equation for recovering the temporal release rate of pollutant sources leads to the solution of a linear overdetermined system of equations type of ill-posed problem. Therefore, in this research a numerical model based on the inverse matrix approach based on the Tikhonov regularization method and the results of the superposition principle has been applied to the recovery of the temporal release rate of pollutant sources and the exact time of release of the pollutant from the source. The model has been designed to retrieve the complexity time of multiple pollutant sources in a complex state. Also, the model has been verified using real two-dimensional data of Ohio River located in the United States. Finally, a general and practical framework has been introduced to apply in real condition. Eventually, the computational results were showed that, the inverse model can recover the temporal release rate of pollutant sources using the lowest field and downstream data containing high error rate at each point of the river.

کلیدواژه‌ها [English]

  • nverse Model
  • Tikhonov regularization method
  • Superposition principle
  • Advection-dispersion equation
  • Ill-posed overdetermined linear
  • system of equations

مراجع

[1]  M. Mazaheri, J. Mohammad Vali Samani, H.M.V. Samani, Mathematical model for pollution source identification in rivers, Environmental Forensics, 16(4) (2015) 310-321.
[2]  G. Mahinthakumar, M. Sayeed, Hybrid genetic algorithm—local search methods for solving groundwater source identification inverse problems, Journal of water resources planning and management, 131(1) (2005) 45-57.
[3]  R.M. Singh, B. Datta, Identification of groundwater pollution sources using GA-based linked simulation optimization model, Journal of hydrologic engineering, 11(2) (2006) 101-109.
[4]  A. Di Nardo, G. Santonastaso, R. Battaglia, D. Musmarra, F. Tuccinardi, F. Castaldo, B. Della Ventura, M. Iervolino, R. Velotta, Smart identification system of surface water contamination by an innovative biosensor network, in:  Proceedings of Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE) and to the SECOTOX Conference, 2015.
[5]  S.-p. Zhang, X.-k. Xin, Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm, Applied Water Science, 7(4) (2017) 1955-1963.
[6]  R.M. Neupauer, J.L. Wilson, Adjoint‐derived location and travel time probabilities for a multidimensional groundwater system, Water Resources Research, 37(6) (2001) 1657-1668.
[7]  R. Neupauer, J. Wilson, Backward probabilistic model of groundwater contamination in non-uniform and transient flow, Advances in Water Resources, 25(7) (2002) 733-746.
[8]  R. Neupauer, J. Wilson, Backward probability model using multiple observations of contamination to identify groundwater contamination sources at the Massachusetts Military Reservation, Water Resources Research, 41(2) (2005).
[9]  F. Cupola, M.G. Tanda, A. Zanini, Laboratory sandbox validation of pollutant source location methods, Stochastic environmental research and risk assessment, 29(1) (2015) 169-182.
[10] A. Ghane, M. Mazaheri, J.M.V. Samani, Location and release time identification of pollution point source in river networks based on the Backward Probability Method, Journal of environmental management, 180 (2016) 164-171.
[11] J. Wang, J. Zhao, X. Lei, H. Wang, New approach for point pollution source identification in rivers based on the backward probability method, Environmental Pollution, 241 (2018) 759-774.
[12] A. El Badia, T. Ha-Duong, A. Hamdi, Identification of a point source in a linear advection–dispersion– reaction equation: application to a pollution source problem, Inverse Problems, 21(3) (2005) 1121.
[13]  T. Zhang, Q. Chen, Identification of contaminant sources in enclosed spaces by a single sensor, Indoor air, 17(6) (2007) 439-449.
[14]  Z. Wang, J. Liu, Identification of the pollution source from one-dimensional parabolic equation models, Applied Mathematics and Computation, 219(8) (2012) 3403-3413.
[15] T. Zhang, H. Zhou, S. Wang, Inverse identification of the release location, temporal rates, and sensor alarming time of an airborne pollutant source, Indoor air, 25(4) (2015) 415-427.
[16]  X. Zhang, M. Huang, Ensemble-based release estimation for accidental river pollution with known source position, Journal of hazardous materials, 333 (2017) 99-108.
[17] L. Lei, Y. Xue, W. Zheng, J. Yang, An inverse method based on CFD to determine the temporal release rate of a heat source in indoor environments, Applied Thermal Engineering, 134 (2018) 12-19.
[18] M.H. Chaudry, Open Channel Flow, Springer, NewYork, 2008.
[19] S.C. Chapra, Surface water-quality modeling, McGraw Hill Companies, Inc, New York, 1997.
[20]  R.C. Aster, B. Borchers, and Thurber, C. H., Parameter Estima- tion and Inverse Problems, CA: Elsevier Academic Press, San Diego, 2005.
[21] J. Hadamard, Lectures on Cauchy’s Problem in Linear Partial Differential Equations, Yale University Press, New Haven, 1923.
[22] Y. Wei, T.T. Zhang, S. Wang, INVERSE IDENTIFICATION OF MULTIPLE POLLUTANT SOURCES. 14th Conference of International Building Performance Simulation Association, Hyderabad, India, Dec. 7-9, 2015.
[23] T.T. Zhang, S. Yin, S. Wang, An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source, Atmospheric environment, 77 (2013) 62-77.
[24] G. Koltun, C.J. Ostheimer, M.S. Griffin, Velocity, bathymetry, and transverse mixing characteristics of the Ohio River upstream from Cincinnati, Ohio, October 2004-March 2006, 2331-1258, 2006.
[25] Y. Zeng, W. Huai, Estimation of longitudinal dispersion coefficient in rivers, Journal of Hydroenvironment Research, 8(1) (2014) 2-8.
نشریه مهندسی عمران امیرکبیر
دوره 51، شماره 6
بهمن و اسفند 1398
صفحه 1221-1240

فایل ها

هم رسانی

ارجاع به این مقاله

آمار