Advertisement

Pollution Episodes in Situations of Weak Winds: An Application of the K-Model

  • P. Melli
  • A. Spirito
  • G. Fronza
Part of the Nato — Challenges of Modern Society book series (NATS, volume 7)

Abstract

The theories of atmospheric diffusion (statistical or Gaussian model, k-theory and Lagrangian Monte Carlo model) are reviewed in order to show that they are not separate approaches, but are all amenable to the same basic physical principles and the same mathematical treatment based on the theory of stochastic differential equations (SDE). In particular it is shown how the k-theory can include either rigorously or heuristically some of the features of the statistical theory and of the Lagrangian Monte Carlo models. An application of the k-theory is then developed to describe summer pollution episodes caused by the emission of a power plant situated in the Po Valley. Different shapes for the diffusion coefficients are chosen on the basis of the previous discussion and values of parameters involved are estimated by least square fitting of the experimental concentration data.

Keywords

Stochastic Differential Equation Planetary Boundary Layer Weak Wind Pollution Episode Stochastic Differential Equation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bacci, P., Bolzern, P., and Fronza, G., 1981, A stochastic predictor of air pollution based on short-term meteorological forecast, J. Appl. Meteor., 20, 121 – 129.ADSCrossRefGoogle Scholar
  2. Finzi, G., Fronza, G., Rinaldi, S., and Spirito, A., 1978: Prediction and real time control of SO2 pollution from a power plant, Proceedings of APCA Annual Meeting, Houston (USA).Google Scholar
  3. Hanna, S.R., 1981, Effects of release height on σz and σx in daytime condition. Air Pollution Modelling and its Application, C. de Wispelaere, ed., Plenum Press, New York.Google Scholar
  4. Janicke, L., 1981, Particle simulation in inhomogeneous turbulent diffusion, Proc. of 12th NATO/CCMS Techn. Meeting, Palo Alto.Google Scholar
  5. Jazwinski, A.H., 1970, “Stochastic processes and filtering theory”, Academic Press.Google Scholar
  6. Lamb, R.G., 1980, Mathematical principles of turbulent diffusion modelling, in: “Atmosperic planetary boundary layer physics”, Proc. of 4thCourse of the International School of Atmospheric Physics, A. Longhetto, ed.Google Scholar
  7. Lamb, R.G., and Durran, D.R., 1978: Eddy diffusivities derived from a numerical model of the convective planetary boundary layer, II Nuovo Cimento, vol. lC, n. l.Google Scholar
  8. Lamb, R.G., Hogo, H., and Reid, L.E., 1979: A Lagrangian Monte-Carlo model of air pollutant transport, diffusion, and removal processes. 4th AMS Symposium of Turbulence, Diffusion and Air Pollution, ( Reno,Nevada )Google Scholar
  9. Lamb, R.G., Hogo, H., and Reid, L.E., 1979, A Lagrangian approach to modeling air pollutant dispersion, EPA Report, EPA-600/4-79–023.Google Scholar
  10. Legg, B.J., and Raupach, M.R., 1982, Markov-chain simulation of particle dispersion in inhomogeneous flows: the mean. drift velocity induced by a gradient in eulerian velocity variance, Boundary Layer Meteorology, 24, 3 – 13ADSCrossRefGoogle Scholar
  11. Melli, P., 1982, Lagrangian modelling of dispersion in the planetary boundary layer of particulate released by a line source. Proceedings of 13th Nato/CCMS ITM, He des Embiez, France.Google Scholar
  12. Melli, P., and Fronza, G., 1981, An Application of Pollution, Episodes Predictor Derived from a K-theory Model, in: “Air Quality Modelling and its Applications”, C. de Wispelaere, ed., Melli, P., and Fronza, G.. 1, Plenum Press.Google Scholar
  13. Pasquill, F., 1974, “Atmospheric Diffusion”, 2nd Ed. Horwood, Chichester.Google Scholar
  14. Reid, J.D., 1979, Markov-chain simulations of vertical dispersion in the neutral surface layer for surface and elevated releases, Boundary-Layer Met., 16, 3 – 22.ADSGoogle Scholar
  15. Robins, A.G., 1978, Plume dispersion from ground level sources in simulated atmospheric boundary layers. Atmos. Environ., 12, 1021 – 1032.CrossRefGoogle Scholar
  16. Runca, E., Melli, P., and Spirito, A., 1979, Real time forecasting of air pollution episodes in the Venetian region; Part I: The advection-diffusion model, Appl. Math. Modelling, 3, 402 – 408.CrossRefGoogle Scholar
  17. Seinfeld, J.H., 1975, “Air Pollution - Physical and Chemical Fundamentals”, McGraw-HillGoogle Scholar
  18. Shir, C.C., and Shieh, L.J., 1973, A preliminary numeric study of atmospheric turbulent flows in idealized planetary boundary layer, J. Atmos. Sci., 30, 1327 - 1339.ADSCrossRefGoogle Scholar
  19. Shir, C.C., and Shieh, L.J., 1974, A generalized urban air pollution model and its application to the study of S02distributionsin the St. Louis Metropolitan Area. J. Appl. Meteor., 13, 185-204.Google Scholar
  20. Slade, D.H., 1968, “Meteorology and atomic energy”, USAEC Div. of Technical Information Extension Oak Ridge, Tenn.CrossRefGoogle Scholar
  21. Wyngaard, J.C., Cote1, O.R., and Rao, K.S., 1974, Modeling the atmospheric boundary layer. Adv. Geoph., 18B.Google Scholar

Copyright information

© Plenum Press, New York 1985

Authors and Affiliations

  • P. Melli
    • 1
  • A. Spirito
    • 1
  • G. Fronza
    • 2
  1. 1.Centro Scientifico IBM via GiorgioneRomeItaly
  2. 2.Dipartimento di ElettronicaCentro Teoria dei Sistemi via PonzioMilan, RomeItaly

Personalised recommendations