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35--50
Tytuł artykułu

Locating the Source of Atmospheric Contamination Based on Data From the Kori Field Tracer Experiment

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accidental releases of hazardous material into the atmosphere pose high risks to human health and the environment. Thus it would be valuable to develop an emergency reaction system which can recognize the probable location of the source based only on concentrations of the released substance as reported by a network of sensors. We apply a methodology combining Bayesian inference with Sequential Monte Carlo (SMC) methods to the problem of locating the source of an atmospheric contaminant. The input data for this algorithm are the concentrations of a given substance gathered continuously in time. We employ this algorithm to locating a contamination source using data from a field tracer experiment covering the Kori nuclear site and conducted in May 2001. We use the second-order Closure Integrated PUFF Model (SCIPUFF) of atmospheric dispersion as the forward model to predict concentrations at the sensors' locations. We demonstrate that the source of continuous contamination may be successfully located even in the very complicated, hilly terrain surrounding the Kori nuclear site. (original abstract)
Czasopismo
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Strony
35--50
Opis fizyczny
Twórcy
autor
  • National Centre for Nuclear Research, Poland; Polish Academy of Sciences, Warsaw
  • National Centre for Nuclear Research, Poland; Siedlce University
  • National Centre for Nuclear Research, Poland
Bibliografia
  • [1] BORYSIEWICZ M., WAWRZYNCZAK A., KOPKA P., Stochastic algorithm for estimation of the model's unknown parameters via Bayesian inference, Proc. Federated Conference on Computer Science and Information Systems, Wrocław, IEEE Press, 2012, 501-508.
  • [2] BORYSIEWICZ M., WAWRZYNCZAK A., KOPKA P., Bayesian-based methods for the estimation of the unknown model's parameters in the case of the localization of the atmospheric contamination source, Foundations of Computing and Decision Sciences, 2012, 37 (4), 253-270.
  • [3] CHOW F.K., KOSOVIC B., CHAN S.T., Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations, [in:] Sixth Symposium on the Urban Environment, American Meteorological Society, Atlanta 2006.
  • [4] DOUCET A., DE FREITAS J.F.G., GORDON N.J., Sequential Monte Carlo Methods in Practice, Springer Series in Statistics for Engineering and Information Science, New York 2001, 3-14.
  • [5] GELMAN A., CARLIN J., STERN H., RUBIN D., Bayesian Data Analysis, Chapman and Hall/CRC, 2003.
  • [6] ERMAK D., NASSTROM J., A Lagrangian stochastic diffusion method for inhomogeneous turbulence, Atmospheric Environment, 2000, 34, 1059-1068.
  • [7] HAN M.H., KIM E.H., SUH K.S., HWANG W.T., Field tracer experiments over nuclear sites for the validation of a Korean real-time atmospheric dispersion and dose assessment system (FADAS), International Journal of Environment and Pollution, 2001, 16 (1-6), 227-236.
  • [8] HAN M.H., KIM E.H., SUH K.S., HWANG W.T., JEONG H.J.S., Simulation of the dispersion of radioactive effluents over the Kori site using field tracer experiment, Journal of Nuclear Science and Technology, 2004, 41, Supplement 4, 423-426.
  • [9] JOHANNESSON G., HANLEY B., NITAO J., Dynamic Bayesian models via Monte Carlo. An introduction with examples, Lawrence Livermore National Laboratory, UCRL-TR-207173, 2004.
  • [10] JOHANNESSON G., DYER K.M., HANLEY W.G., KOSOVIC B., LARSEN S.C., LOOSMORE G.A., MIRIN A., Sequential Monte-Carlo based framework for dynamic data-driven event reconstruction for atmospheric release, Proc. Joint Statistical Meeting, Minneapolis, MN, American Statistical Association and Cosponsors, 2005, 73-80.
  • [11] KEATS A., YEE E., LIEN F.S., Bayesian inference for source determination with applications to a complex urban environment, Atmospheric Environmental, 2007, 41, 465-479.
  • [12] SUGIYAMA G., KOSOVIC B., HANLEY W., JOHANNESSON G., LARSEN S., LOOSMORE G., DYER K., Bellesynamic data-driven event reconstruction for atmospheric releases, Lawrence Livermore National Laboratory, UCRL-TR-229417, 2007.
  • [13] LIU J.S., CHEN R., Sequential Monte Carlo Methods for dynamical system, Journal of the American Statistical Association, 1998, 93, 1032-1044.
  • [14] MONACHE D.L., LUNDQUIST J.K., KOSOVIC B., JOHANNESSON G., DYER K.M., AINES R.D., VOGT P.J., Bayesian inference and Markov chain Monte Carlo sampling to reconstruct a contaminant source on a continental scale, Journal of Applied Meteorology and Climatology, 2008, 47 (10), 2600-2613.
  • [15] PUDYKIEWICZ J.A., Application of adjoint tracer transport equations for evaluating source parameters, Atmospheric Environment, 1998, 32, 3039-3050.
  • [16] SENOCAK I., HENGARTNER N.W., SHORT M.B., BRENT W., Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference, Atmospheric Environment, 2008, 42, 7718-7727.
  • [17] SUH K.S., KIM E.H., HWANG W.H., JEONG H.J., HAN M.H., Atmospheric dispersion modeling over the Kori nuclear site, 11th International Congress of the International Radiation Protection Association, Madrid, Spain, 2004.
  • [18] SYKES R.I., PARKER S.F., HENN D.S., CERASOLI C.P., SANTOS L.P., PC-SCIPUFF Version 1.2PD, Technical Documentation, ARAP Report No. 718, Titan Corporation, 1998.
  • [19] TURNER D.B., Workbook of atmospheric dispersion estimates, Lewis Publishers, USA, 1994.
  • [20] WAWRZYNCZAK A., BORYSIEWICZ M., KOPKA P., Sequential Monte Carlo in Bayesian assessment of contaminant source localization based on the distributed sensors measurements, Parallel Processes in Applied Mathematics, Lecture Notes in Computer Sciences 8385, Part II, Chapt. 38, 2013.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171387759

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