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Theoretical and Applied Climatology

, Volume 107, Issue 3–4, pp 375–387 | Cite as

Estimation of the dispersion of an accidental release of radionuclides and toxic materials based on weather type classification

  • Róbert Mészáros
  • Ádám Leelőssy
  • Csilla Vincze
  • Mihály Szűcs
  • Tibor Kovács
  • István Lagzi
Original Paper

Abstract

We investigate the influence of the regional-scale weather types on the atmospheric dispersion processes of the air pollutants originated from point sources. Hypothetical accidents were simulated with two different dispersion models. During a year’s test period, the 6-h emission of a radionuclide from the Paks Nuclear Power Plant (Paks NPP, Hungary) was assumed every day and the transport and deposition of the radionuclide was simulated by the Eulerian TREX dispersion model over the Central European region. In addition, the ALOHA Gaussian air dispersion model was also used for the local environment of the Paks NPP to simulate hypothetical hourly releases of ammonia during a 10-year period. During both types of model simulations, the dispersion of the plume for each time was analysed and tested with consideration of 13 circulation types corresponding to daily weather patterns over the Carpathian Basin. There are significant correlations between circulation types and plume directions and structures both in local and regional scales. The daily circulation pattern can be easily obtained from weather analyses; the expected size and direction of polluted area after an accidental release can be quickly estimated even before an accident occurs. However, this fast method cannot replace or neglect dispersion model simulations. It gives a ‘first guess’ and a fast estimation on the direction of the plume and can provide sufficient information for decision-making strategies.

Keywords

Dispersion Model Weather Type Circulation Type Accidental Release Carpathian Basin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank András Horányi and László Kullmann (Hungarian Meteorological Service) for the ALADIN data and Csaba Károssy (University of West Hungary, Szombathely) for the preparation of the series of Péczely’s weather type. Weather data were collected and processed by the Hungarian Meteorological Service. This research is supported by Hungarian Research Found (OTKA K68253 and K81933). The Project is supported by the European Union and co-financed by the European Social Fund (grant agreement no. TAMOP 4.2.1./B-09/1/KMR-2010–0003).

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Róbert Mészáros
    • 1
  • Ádám Leelőssy
    • 1
  • Csilla Vincze
    • 1
  • Mihály Szűcs
    • 2
  • Tibor Kovács
    • 3
  • István Lagzi
    • 1
  1. 1.Department of MeteorologyEötvös Loránd UniversityBudapestHungary
  2. 2.Hungarian Meteorological ServiceBudapestHungary
  3. 3.Institute of Radiochemistry and RadioecologyUniversity of PannoniaVeszprémHungary

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