An Outline of Lagrangian Stochastic Dispersion Models
231
are briefl y presented as examples of LSDM possible applications. These examples
illustrate the wide fi eld of application of this kind of modeling technique: going from
basic model evaluation to impact assessment analysis and to scenarios’ studies, both
in complex terrain and in urban environment.
Lagrangian models are proved to be reliable and accurate tools not only for the
description and investigation of the physical processes determining the dispersion of
pollutants in the atmosphere but also for supporting the assessment and control of the
air quality and the protection of the environment and public health.
REFERENCES
Anfossi D. 1985. Analysis of plume rise data from fi ve TVA Steam Plants. J. Clim. App. Met.
24:1225–1236.
Anfossi D., E. Ferrero, G. Brusasca, A. Marzorati, and G. Tinarelli. 1993. A simple way of
computing buoyant plume rise in a Lagrangian stochastic dispersion model for airborne
dispersion. Atmos. Environ. 27 A, 1443–1451.
Anfossi D., E. Ferrero, D. Sacchetti, and S. Trini Castelli. 1996. Comparison among empirical
probability density functions of the vertical velocity in the surface layer based on higher
order correlations. Bound. Lay. Meteorol. 82, 193–218.
Anfossi D., E. Ferrero, G. Tinarelli, and S. Alessandrini. 1997. A simplifi ed version of the correct boundary conditions for skewed turbulence in Lagrangian particle models. Atmos.
Environ. 31:301–308.
Anfossi D., E. Canepa, and H. van Dop. 2004. Plume Rise. Chapter 6 of AIR QUALITY
MODELING Theories, Methodologies, Computational Techniques, and Available
Databases and Software. Vol. I Fundamentals, P. Zannetti, Ed., The EnviroComp
Institute and the Air & Waste Management Association.
Anfossi D. and W. Physick. 2005. Lagrangian Particle Models. Chapter 11 of AIR QUALITY
MODELING—Theories, Methodologies, Computational Techniques, and Available
Databases and Software. Vol. II—Fundamentals, P. Zannetti, Ed., The EnviroComp
Institute and the Air & Waste Management Association.
Anfossi D., D. Oettl, G. Degrazia, and A. Goulart. 2005. An analysis of sonic anemometer
observations in low wind speed conditions. Bound. Lay. Meteorol. 114:179–203.
Anfossi D., S. Alessandrini, S. Trini Castelli, E. Ferrero, D. Oettl, and G. Degrazia. 2006.
Tracer dispersion simulation in low wind speed conditions with a new 2-D Langevin
equation system. Atmos. Environ. 40:7234.45.
Anfossi D., G. Tinarelli, S. Trini Castelli, E. Ferrero, D. Oettl, G. Degrazia, and L. Mortarini.
2009a. Well mixed condition verifi cation in windy and low wind speed conditions. In
press on Int. J. Environ. Pollut.
Anfossi D., G. Tinarelli, S. Trini Castelli, and G. Belfi ore. 2009b. Proposal of a new Lagrangian
particle model for the simulation of dense gas dispersion. In press on Int. J. Environ.
Pollut.
Baerentsen J.H. and R. Berkowicz. 1984. Monte-Carlo simulation of plume diffusion in the
convective boundary layer. Atmos. Environ. 18:701–712.
Briggs G.A. 1975. Plume rise predictions. In: Lectures on air pollution and environmental
impact analyses. D. Haugen, ed., Workshop proceedings, Boston, MA, September
29–October 3, pp. 59–111, American Meteorological Society, Boston, MA.
Brusasca G., G. Tinarelli, and D. Anfossi. 1992. Particle model simulation of diffusion in low
wind speed stable conditions. Atmos. Environ. 4:707–723.
Carvalho J., D. Anfossi, S. Trini Castelli, and G. A. Degrazia. 2002a. Application of a
model system for the study of transport and diffusion in complex terrain to the TRACT
experiment. Atmos. Environt. 36:1147–1161.
© 2010 by Taylor and Francis Group, LLC
231
are briefl y presented as examples of LSDM possible applications. These examples
illustrate the wide fi eld of application of this kind of modeling technique: going from
basic model evaluation to impact assessment analysis and to scenarios’ studies, both
in complex terrain and in urban environment.
Lagrangian models are proved to be reliable and accurate tools not only for the
description and investigation of the physical processes determining the dispersion of
pollutants in the atmosphere but also for supporting the assessment and control of the
air quality and the protection of the environment and public health.
REFERENCES
Anfossi D. 1985. Analysis of plume rise data from fi ve TVA Steam Plants. J. Clim. App. Met.
24:1225–1236.
Anfossi D., E. Ferrero, G. Brusasca, A. Marzorati, and G. Tinarelli. 1993. A simple way of
computing buoyant plume rise in a Lagrangian stochastic dispersion model for airborne
dispersion. Atmos. Environ. 27 A, 1443–1451.
Anfossi D., E. Ferrero, D. Sacchetti, and S. Trini Castelli. 1996. Comparison among empirical
probability density functions of the vertical velocity in the surface layer based on higher
order correlations. Bound. Lay. Meteorol. 82, 193–218.
Anfossi D., E. Ferrero, G. Tinarelli, and S. Alessandrini. 1997. A simplifi ed version of the correct boundary conditions for skewed turbulence in Lagrangian particle models. Atmos.
Environ. 31:301–308.
Anfossi D., E. Canepa, and H. van Dop. 2004. Plume Rise. Chapter 6 of AIR QUALITY
MODELING Theories, Methodologies, Computational Techniques, and Available
Databases and Software. Vol. I Fundamentals, P. Zannetti, Ed., The EnviroComp
Institute and the Air & Waste Management Association.
Anfossi D. and W. Physick. 2005. Lagrangian Particle Models. Chapter 11 of AIR QUALITY
MODELING—Theories, Methodologies, Computational Techniques, and Available
Databases and Software. Vol. II—Fundamentals, P. Zannetti, Ed., The EnviroComp
Institute and the Air & Waste Management Association.
Anfossi D., D. Oettl, G. Degrazia, and A. Goulart. 2005. An analysis of sonic anemometer
observations in low wind speed conditions. Bound. Lay. Meteorol. 114:179–203.
Anfossi D., S. Alessandrini, S. Trini Castelli, E. Ferrero, D. Oettl, and G. Degrazia. 2006.
Tracer dispersion simulation in low wind speed conditions with a new 2-D Langevin
equation system. Atmos. Environ. 40:7234.45.
Anfossi D., G. Tinarelli, S. Trini Castelli, E. Ferrero, D. Oettl, G. Degrazia, and L. Mortarini.
2009a. Well mixed condition verifi cation in windy and low wind speed conditions. In
press on Int. J. Environ. Pollut.
Anfossi D., G. Tinarelli, S. Trini Castelli, and G. Belfi ore. 2009b. Proposal of a new Lagrangian
particle model for the simulation of dense gas dispersion. In press on Int. J. Environ.
Pollut.
Baerentsen J.H. and R. Berkowicz. 1984. Monte-Carlo simulation of plume diffusion in the
convective boundary layer. Atmos. Environ. 18:701–712.
Briggs G.A. 1975. Plume rise predictions. In: Lectures on air pollution and environmental
impact analyses. D. Haugen, ed., Workshop proceedings, Boston, MA, September
29–October 3, pp. 59–111, American Meteorological Society, Boston, MA.
Brusasca G., G. Tinarelli, and D. Anfossi. 1992. Particle model simulation of diffusion in low
wind speed stable conditions. Atmos. Environ. 4:707–723.
Carvalho J., D. Anfossi, S. Trini Castelli, and G. A. Degrazia. 2002a. Application of a
model system for the study of transport and diffusion in complex terrain to the TRACT
experiment. Atmos. Environt. 36:1147–1161.
© 2010 by Taylor and Francis Group, LLC