Environmental Fluid Mechanics

, Volume 2, Issue 1–2, pp 143–169 | Cite as

Filament-Based Atmospheric Dispersion Model to Achieve Short Time-Scale Structure of Odor Plumes

  • Jay A. Farrell
  • John Murlis
  • Xuezhu Long
  • Wei Li
  • Ring T. Cardé


This article presents the theoretical motivation, implementation approach, and example validation results for a computationally efficient plume simulation model, designed to replicate both the short-term time signature and long-term exposure statistics of a chemical plume evolving in a turbulent flow. Within the resulting plume, the odor concentration is intermittent with rapidly changing spatial gradient. The model includes a wind field defined over the region of interest that is continuous, but which varies with location and time in both magnitude and direction. The plume shape takes a time varying sinuous form that is determined by the integrated effect of the wind field. Simulated and field data are compared. The motivation for the development of such a simulation model was the desire to evaluate various strategies for tracing odor plumes to their source, under identical conditions. The performance of such strategies depends in part on the instantaneous response of target receptors; therefore, the sequence of events is of considerable consequence and individual exemplar plume realizations are required. Due to the high number of required simulations, computational efficiency was critically important.

chemical plume tracing odor dispersion model pheromone dispersion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bossert, W.H. and Wilson, E.O.: 1963, The analysis of olfactory communication among animals, J. Theor. Biol. 48, 443–469.Google Scholar
  2. 2.
    Dusenbery, D.B.: 1992, Sensory Ecology: How Organisms Acquire and Respond to Information, W.H. Freeman, New York.Google Scholar
  3. 3.
    Vickers, N.J.: 2000, Mechanisms of animal navigation in odor plumes, Biol. Bull. 198, 203–212.Google Scholar
  4. 4.
    Zimmer, R.K. and Butman, C.A.: 2000, Chemical signaling processes in the marine environment, Biol. Bull. 198, 168–187.Google Scholar
  5. 5.
    Hassler, A.D. and Scholz, A.T.: 1983, Olfactory Imprinting and Homing in Salmon, Springer-Verlag, New York.Google Scholar
  6. 6.
    Lohmann, K.J.: 1992, How sea turtles navigate, Sci. Amer. 266, 82–88.Google Scholar
  7. 7.
    Nevitt, G.A.: 2000, Olfactory foraging by Antarctic procellariiform seabirds: Life at high Reynolds numbers, Biol. Bull. 198, 245–253.Google Scholar
  8. 8.
    Devine, D.V. and Atema, J.: 1982, Function of chemoreceptor organs in spatial orientation of the lobster, Homarus americanus: differences and overlap, Biol. Bull. 163, 144–153.Google Scholar
  9. 9.
    Basil, J. and Atema, J.: 1994, Lobster orientation in turbulent odor plumes: simultaneuos measurements of tracking behavior and temporal odor patterns, Biol. Bull. 187, 272–273.Google Scholar
  10. 10.
    Atema, J.: 1995, Chemical signals in the marine environment: dispersal, detection, and temporal signal analysis, Proc. Natl. Acad. Sci. USA 92, 62–66.Google Scholar
  11. 11.
    Weissburg, M.J. and Zimmer-Faust, R.K.: 1994, Odor plumes and how blue crabs use them in finding prey, J. Exp. Biol. 197, 349–375.Google Scholar
  12. 12.
    Mafra-Neto, A. and Cardé, R.T.: 1994, Fine-scale structure of pheromone plumes modulates upwind orientation of flying moths, Nature 369, 142–144.Google Scholar
  13. 13.
    Cardé, R.T.: 1996, Odour plumes and odour-mediated flight in insects. In: Olfaction in Mosquito-Host Interactions, pp. 54–70, CIBA Found. Symp. 200, John Wiley & Sons.Google Scholar
  14. 14.
    Cardé, R.T. and Mafra-Neto. A.: 1996, Mechanisms of flight of male moths to pheromone. In: R.T. Cardé and A.K. Minks (eds.), Insect Pheromone Research. New Directions, pp. 275–290, Chapman and Hall, New York.Google Scholar
  15. 15.
    Grasso, F.W.: 2001, Invertebrate-inspired sensory-motor systems and autonomous, olfactoryguided exploration, Biol. Bull. 200, 160–168.Google Scholar
  16. 16.
    Arbas, E.A., Willis, M.A. and Kanzaki, R.: 1993, Organization of goal-oriented locomotion: pheromone-modulated flight behavior of moths. In: Beer, R.D., Ritzmann, R.E. and McKenna, T., (eds.), Biological Neural Networks in Invertebrate Neuroethology and Robotics, pp. 159–198, Academic Press, San Diego.Google Scholar
  17. 17.
    Baker, T.C. and Vickers, N.J.: 1996, Pheromone-mediated flight in moths. In: R.T. Cardé and A.K. Minks (eds.), Insect Pheromone Research. New Directions, pp. 248–264, Chapman and Hall, New York.Google Scholar
  18. 18.
    Willis, M.A. and Arbas, E.A.: 1996, Active behavior and reflexive responses: Another perspective on odor-modulated locomotion. In: R.T. Cardé and A.K. Minks (eds.), Insect Pheromone Research. New Directions, pp. 304–319, Chapman and Hall, New York.Google Scholar
  19. 19.
    Witzgall, P.: 1996, Modulation of pheromone-mediated flight in male moths. In: R.T. Cardé and A.K. Minks (eds.), Insect Pheromone Research. New Directions, pp. 265–274, Chapman and Hall, New York.Google Scholar
  20. 20.
    Kaissling, K.-E.: 1997, Pheromone-controlled anemotaxis in moths. In: Lehrer, M. (ed.), Orientation and Communication in Arthropods, pp. 343–374, Birkhuser Verlag, Basel.Google Scholar
  21. 21.
    Belanger, J.H. and Willis, M.A.: 1996, Adaptive Control of Odor-Guided Locomotion: Behavioral Flexibility as an Antidote to Environmental Unpredictability, Adaptive Behav. 4, 217–253.Google Scholar
  22. 22.
    Belanger J.H. and Arbas, E.A.: 1998, Behavioral strategies underlying pheromone-modulated flight in moths: Lessons from simulation studies, J. Comp. Physiol. A 183, 345–360.Google Scholar
  23. 23.
    Sutton, O.G.: 1947, The problem of diffusion in the lower atmosphere, Quart. J. Roy. Meteorol. Soc. 73, 257-281.Google Scholar
  24. 24.
    Sutton, O.G.: 1953, Micrometeorology, McGraw-Hill, New York.Google Scholar
  25. 25.
    Gifford, F.A.: 1960, Peak to average concentration ratios according to a fluctuating plume dispersion model, Int. J. Air Poll. 3, 253–260.Google Scholar
  26. 26.
    Gifford, F.A.: 1968, An outline of theories of diffusion in the lower layers of the atmosphere. In: D. H. Slade (ed.), Meteorology and Atomic Energy, pp. 65–116, US Atomic Energy Comm.Google Scholar
  27. 27.
    Briggs, G.A.: 1973, Diffusion estimation for small emissions, ATDL Contributions File No. (Draft) 79, Air Resources Atmospheric Turbulence and Diffusion Laboratory, NOAA, Oak Ridge, Tennessee.Google Scholar
  28. 28.
    Griffiths, R.F.: 1994, Errors in the use of the Briggs parameterization for atmospheric dispersion coefficients, Atmos. Environ. 28, 2861–2865.Google Scholar
  29. 29.
    Jones, C.D.: 1983, On the structure of instantaneous plumes in the atmosphere, J. Hazard. Mater. 7, 87–112.Google Scholar
  30. 30.
    Murlis, J., Elkinton, J.S. and Cardé, R.T.: 1992, Odor plumes and how insects use them, Annu. Rev. Entom. 37, 505–532.Google Scholar
  31. 31.
    Thomas, M.D.: 1961, Effects of air pollution on plants. In: Air Pollution, p. 442, Columbia University Press, New York.Google Scholar
  32. 32.
    Elkinton, J.S., Cardé, R.T., and Mason, C.J.: 1984, Evaluation of time-average dispersion models for estimating pheromone concentration in a deciduous forest, J. Chem. Ecol. 10, 1081–1108.Google Scholar
  33. 33.
    Metais, O.: 1997, Numerical simulation of geophysical turbulence and eddies. In: R.L. Dewar and R.W. Griffiths (eds.), Two-Dimensional Turbulence in Plasmas and Fluids, pp. 37–63, American Institute of Physics.Google Scholar
  34. 34.
    Terracol, M., Sagaut, P. and Basdevant, C.: 2001, A multilevel algorithm for large-eddy simulation of turbulent compressible flows, J. Comp. Phys. 167, 439–474.Google Scholar
  35. 35.
    Byers, J.A.: 1996, Temporal clumping of bark beetle arrival at phermone traps: Modeling anemotaxis in chaotic plumes, J. Chem. Ecol. 22, 2133–2155.Google Scholar
  36. 36.
    Byers, J.A.: 1999, Effects of attraction radius and flight paths on catch of scolytid beetles dispersing outward through rings of pheromone traps, J. Chem. Ecol. 25, 985–1005.Google Scholar
  37. 37.
    Egan, B.A. and Mahoney, J.R.: 1972, Numerical modeling of advection and diffusion of urban source pollutants, J. Appl. Meteorol. 11, 312–322.Google Scholar
  38. 38.
    Sykes, R.I. and Henn, D.S.: 1992, Large-eddy simulation of concentration fluctuations in a dispersing plume, Atmos. Environ. 26A, 3127–3144.Google Scholar
  39. 39.
    Pielke, R.A.: 1994, Mesoscale Meteorological Modeling, Academic Press, San Diego.Google Scholar
  40. 40.
    Nadaoka, K., Nihei, Y. and Yagi, H.: 1999, Grid-averaged Lagrangian LES model for multiphase turbulent flow. International, J. Multiphase Flow 25, 1619–1643.Google Scholar
  41. 41.
    Saiki, E.M., Moeng, C.-H. and Sullivan, P.P.: 2000, Large-eddy simulation of the stably stratified planetary boundary layer, Boundary-Layer Meteorol. 95, 1–30.Google Scholar
  42. 42.
    Gifford, F.A.: 1959, Statistical properties of a fluctuating plume dispersion model. In: F.N. Frankiel and R.A. Shepard (eds.), Advances in Geophysics, Vol. 6, Atmospheric Diffusion and Air Pollution, p. 117, Academic Press, New York.Google Scholar
  43. 43.
    Elkinton, J.S., Schal, C., Ono, T. and Cardé, R.T.: 1987, Pheromone puff trajectory and upwind flight of male gypsy moths in a forest, Physiol. Entomol. 12, 399-406.Google Scholar
  44. 44.
    Brady, J.H., Gibson, G. and Packer, M.J.: 1989, Odour movement, wind direction, and the problem of host finding by tsetse flies, Physiol. Entomol. 14, 369–380.Google Scholar
  45. 45.
    Rumbo, R. and Kaissling, K.-E.: 1989, Temporal resolution of odour pulses by three types of pheromone receptor cells in Antherea polyphemus, J. Comp. Physiol. A. 165, 281–291.Google Scholar
  46. 46.
    Mafra-Neto, A. and Cardé, R.T.: 1996, Dissection of the pheromone-modulated flight of moths using the single-pulse response as a template, Experientia 52, 373–379.Google Scholar
  47. 47.
    Aylor, D.E.: 1976, Estimating peak concentrations of pheromones in the forest. In: J.F. Anderson and M.K. Kaya (eds.), Perspectives in Forest Entomology, pp. 177–188. Academic Press.Google Scholar
  48. 48.
    Aylor, D.E., Parlange, J.-Y. and Granett, J.: 1976, Turbulent dispersion of disparlure in the forest and male gypsy moth response, Environ. Entomol. 5, 1026–1032.Google Scholar
  49. 49.
    Mylne, K.R.: 1992, Concentration fluctuation measurements in a plume dispersing in a stable surface layer, Boundary-Layer Meteorol. 60, 15–48.Google Scholar
  50. 50.
    Anonymous: 1968, Meteorological fundamentals for atmospheric transport and diffusion studies. In: D.H. Slade (ed.), Meteorology and Atomic Energy,, pp. 13–63, U.S. Atomic Energy Commission.Google Scholar
  51. 51.
    Stacey, M.T., Cowen, E.A., Powell, T.M., Dobbins, E., Monismith, S.G. and Koseff, J.R.: 2000, Plume dispersion in a stratified, near-coastal flow: measurements and modeling, Continental Shelf Res. 20, 637–663.Google Scholar
  52. 52.
    Pasquill, F. and Smith, F.B.: 1983, Atmospheric Diffusion, 3rd edn., Halsted Press, New York.Google Scholar
  53. 53.
    Luenberger, D.G.: 1979, Introduction to Dynamic Systems, John Wiley, New York.Google Scholar
  54. 54.
    Jazwinski, A.H.: 1970, Stochastic Processes and Filtering Theory, Academic Press, San Diego.Google Scholar
  55. 55.
    Roberts, O.F.T.: 1923, The theoretical scattering of smoke in a turbulent atmosphere, Proc. Roy. Soc. A 104, 640–654.Google Scholar
  56. 56.
    Tang, J.D., Charlton, R.E., Cardé, R.T. and Yin, C.-M.: 1992, Diel periodicity and influence of age and mating on sex pheromone titer in gypsy moth, Lymantria dispar, J. Chem. Ecol. 18, 749–760.Google Scholar
  57. 57.
    Germano, M., Piomelli, U., Moin, P. and Cabot, W.H.: 1991, A dynamic subgrid-scale eddy viscosity model, Phys. Fluids A 3, 1760–1765.Google Scholar
  58. 58.
    Moin, P., Squires, K., Cabot, W. and Lee, S.: 1991, A dynamic subgrid-scale model for compressible turbulence and scalar transport, Phys. Fluids A 3, 2746–2757.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Jay A. Farrell
    • 1
  • John Murlis
    • 2
  • Xuezhu Long
    • 2
  • Wei Li
    • 3
  • Ring T. Cardé
    • 4
  1. 1.Department of Electrical EngineeringUniversity of CaliforniaRiversideU.S.A
  2. 2.School of Public PolicyUniversity College LondonU.K
  3. 3.Department of Computer ScienceCalifornia State UniversityBakersfieldU.S.A
  4. 4.Department of EntomologyUniversity of CaliforniaRiversideU.S.A

Personalised recommendations