Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Atmospheric Dispersion Unknown Source Parameters Determination Using AERMOD and Bayesian Inference Along Markov Chain Monte Carlo

Document Type : Research Paper

Authors

A Haghighattalab
A.R Zolfaghari
A.H Minouchehr
H.A Kiya
Abstract
Occurance of hazarious accident in nuclear power plants and industrial units usually lead to release of radioactive materials and pollutants in environment. These materials and pollutants can be transported to a far downstream by the wind flow. In this paper, we implemented an atmospheric dispersion code to solve the inverse problem. Having received and detected the pollutants in one region, we may estimate the rate and location of the unknown source. For the modeling, one needs a model with ability of atmospheric dispersion calculation. Furthermore, it is required to implement a mathematical approach to infer the source location and the related rates. In this paper the AERMOD software and Bayesian inference along the Markov Chain Monte Carlo have been applied. Implementing, Bayesian approach and Markov Chain Monte Carlo for the aforementioned subject is not a new approach, but the AERMOD model coupled with the said methods is a new and well known regulatory software, and enhances the reliability of outcomes. To evaluate the method, an example is considered by defining pollutants concentration in a specific region and then obtaining the source location and intensity by a direct calculation. The result of the caluclation estimates the average source location at a distance of 7km with an accuracy of 5m which is good enough to support the ability of the proposed algorithm.

Highlights

  1. 1.    S. Guo, R. Yang, H. Zhang, W. Weng, W. Fan, Source identification for unsteady atmospheric dispersion of hazardous materials using Markov Chain Monte Carlo method, International Journal of Heat and Mass Transfer, 52 (2009) 3955–3962.

 

  1. 2.    B. Kosovic, R. Belles, F.K. Chow, L.D. Monache, K. Dyer, L. Glascoe, W. Hanley, G. Johannesson, S. Larsen, G. Loosmore, J.K. Lvndqvist, A. Mirin, S. Nevman, J. Nitao, R. Serban, G. Sugiyama, R. Aines, Dynamic data-driven event reconstruction for atmospheric releases, UCRL-TR-229417 (2007).

 

  1. 3.    K. Shankar Rao, Source estimation methods for atmospheric dispersion, Atmospheric Environ-ment, 41 (2007) 6964-6973.

 

  1. 4.    Joo Yeon Kim, Han-Ki Jang, Jai Ki Lee, Source reconstruction of uknown model parameters in atmospheric dispersion using dynamic bayesian inference, Progres in Nuclear Science and Technology, 1 (2011) 460-463.

 

  1. 5.    C.T. Allen, S.E. Haupt, G.S. Young, Source characterization with a genetic algorithm coupled dispersion backward model in corporating SCIPUFF, Journal of Applied Meteorology, 41 (2007) 465-479.

 

  1. 6.    C.T. Allen, G.S. Young, S.E. Haupt, Improving pollutant source characterization by better estimating wind direction with a genetic algorithm, Atmospheric Environment, 41 (2007) 2283-2289.

 

  1. 7.    G. Johannesson, B. Hanley, J. Nitao, Dynamic Bayesian models via Monte Carlo-an introduction with examples, Lawrence Livermore National Laboratory, UCRL-TR-207173 (2004).

 

 

  1. F.K. Chow, B. Kosovic, S.T. Chan, Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations, Int. 6th Symposium on the Urban Environment, American Meteorological Society (2006).

 

  1. S. Neumann, L. Glascoe, B. Kosovic, K. Dyer, W. Hanley, J. Nitao, Event reconstruction for atmospheric releases employing urban puff model UDM with stochastic inversion methodology, 6th Symposium on the Urban Environment, American Meteorological Society, GA (2006).

 

  1.  A. Keats, E. Yee, F. Lien, Bayesian inference for source determination with applications to a complex urban environment, Atmospheric Environment, 41 (2007) 465-479.

 

  1.  G. Cervone, P. Franzese, Monte Carlo source detection of atmospheric emissions and error functions analysis, Computers & Geosciences, 36 (2010) 902-909.

 

  1.  A.J. Cimorelli, S.G. Perry1, A. Venkatram, J.C. Weil, R.J.R.B. Wilson, R.F. Lee, Warren D. Peters, W. Brode, J.O. Pauimer, AERMOD: Description of model formulation, EPA 454/R-02-002d, (2002).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  1.  U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina 27711, User's guide for the AERMOD meteorological preprocessor (AERMET), EPA-454/B-03-002 (2004).

     

  2.  J.M. Bernardo, A.F.M. Smith, Bayesian Theory, Wiley (1994).

 

  1.  A. Gelman, J.B. Carlin, H.S. Stern, D.B. Rubin, Bayesian data analysis (second edition), Boca Raton, Florida: Chapman and Hall/CRC (2004).

 

  1.  N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys, 21 (1953) 1087-1092.

 

  1.  C. Andrieu, N. De Freitas, A. Doucent, M.I. Jordan, An introduction to MCMC for machine learning, Machine Learning, 50 (2003) 5-43.

 

  1.  W.R. Gilks, S. Richardson, D.G. Spiegelhalter, Markov chain Monte Carlo in practice, Chapman and Hall, London, UK (1996).

 

 W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57(1) (1970) 97-109.

Keywords

AERMOD Software
Bayesian Inference
Morkov Chain Monte Carlo
Atmospheric Dispersion
Unknown Source
  1. 1.    S. Guo, R. Yang, H. Zhang, W. Weng, W. Fan, Source identification for unsteady atmospheric dispersion of hazardous materials using Markov Chain Monte Carlo method, International Journal of Heat and Mass Transfer, 52 (2009) 3955–3962.

 

  1. 2.    B. Kosovic, R. Belles, F.K. Chow, L.D. Monache, K. Dyer, L. Glascoe, W. Hanley, G. Johannesson, S. Larsen, G. Loosmore, J.K. Lvndqvist, A. Mirin, S. Nevman, J. Nitao, R. Serban, G. Sugiyama, R. Aines, Dynamic data-driven event reconstruction for atmospheric releases, UCRL-TR-229417 (2007).

 

  1. 3.    K. Shankar Rao, Source estimation methods for atmospheric dispersion, Atmospheric Environ-ment, 41 (2007) 6964-6973.

 

  1. 4.    Joo Yeon Kim, Han-Ki Jang, Jai Ki Lee, Source reconstruction of uknown model parameters in atmospheric dispersion using dynamic bayesian inference, Progres in Nuclear Science and Technology, 1 (2011) 460-463.

 

  1. 5.    C.T. Allen, S.E. Haupt, G.S. Young, Source characterization with a genetic algorithm coupled dispersion backward model in corporating SCIPUFF, Journal of Applied Meteorology, 41 (2007) 465-479.

 

  1. 6.    C.T. Allen, G.S. Young, S.E. Haupt, Improving pollutant source characterization by better estimating wind direction with a genetic algorithm, Atmospheric Environment, 41 (2007) 2283-2289.

 

  1. 7.    G. Johannesson, B. Hanley, J. Nitao, Dynamic Bayesian models via Monte Carlo-an introduction with examples, Lawrence Livermore National Laboratory, UCRL-TR-207173 (2004).

 

 

  1. F.K. Chow, B. Kosovic, S.T. Chan, Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations, Int. 6th Symposium on the Urban Environment, American Meteorological Society (2006).

 

  1. S. Neumann, L. Glascoe, B. Kosovic, K. Dyer, W. Hanley, J. Nitao, Event reconstruction for atmospheric releases employing urban puff model UDM with stochastic inversion methodology, 6th Symposium on the Urban Environment, American Meteorological Society, GA (2006).

 

  1.  A. Keats, E. Yee, F. Lien, Bayesian inference for source determination with applications to a complex urban environment, Atmospheric Environment, 41 (2007) 465-479.

 

  1.  G. Cervone, P. Franzese, Monte Carlo source detection of atmospheric emissions and error functions analysis, Computers & Geosciences, 36 (2010) 902-909.

 

  1.  A.J. Cimorelli, S.G. Perry1, A. Venkatram, J.C. Weil, R.J.R.B. Wilson, R.F. Lee, Warren D. Peters, W. Brode, J.O. Pauimer, AERMOD: Description of model formulation, EPA 454/R-02-002d, (2002).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  1.  U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina 27711, User's guide for the AERMOD meteorological preprocessor (AERMET), EPA-454/B-03-002 (2004).

     

  2.  J.M. Bernardo, A.F.M. Smith, Bayesian Theory, Wiley (1994).

 

  1.  A. Gelman, J.B. Carlin, H.S. Stern, D.B. Rubin, Bayesian data analysis (second edition), Boca Raton, Florida: Chapman and Hall/CRC (2004).

 

  1.  N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys, 21 (1953) 1087-1092.

 

  1.  C. Andrieu, N. De Freitas, A. Doucent, M.I. Jordan, An introduction to MCMC for machine learning, Machine Learning, 50 (2003) 5-43.

 

  1.  W.R. Gilks, S. Richardson, D.G. Spiegelhalter, Markov chain Monte Carlo in practice, Chapman and Hall, London, UK (1996).

 

 W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57(1) (1970) 97-109.

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