Abstract
Odor emissions are a common environmental problem that can have significant impacts on quality of life. Odor emissions are due to a variety of sources, including industrial processes, agriculture, and waste management facilities. When released into the air, these odorous compounds are often cause of nuisance and complaints.
Industrial plants responsible for the emission of odours are often characterised by the presence of stacks with relatively low height and small diameter. Therefore, it is important to simulate as precisely as possible the emissions from these stacks, because their impact may be close to the point of release.
In particular, the exit of these stacks is not always vertical and free, more often than not a rain cap may be present, or the terminal may be horizontal or with any inclination with respect to the vertical, including gooseneck tips pointing toward the ground. When sensitive receptors are close to the stacks, the terminal configuration may play an important role on the final impact.
There are some methods to consider non vertical or obstructed stacks within atmospheric dispersion models. Some methods require the adoption of a very low exit speed and the calculation of an equivalent diameter; one of these methods has been incorporated in AERMOD. Another well-known model, CALPUFF, allows nullifying the momentum flux factor. Finally, the Lagrangian particle model LAPMOD simulates exit terminals with any slope and direction thanks to the numerical plume rise algorithms adopted in its formulation.
In this work an inter- and intra-comparison of model results for different exit terminals have been performed. The results of different models (CALPUFF and LAPMOD) for stacks with the same exit terminal have been examined, as well as the results obtained for the same model with different stack terminals. The analysis was limited to CALPUFF and LAPMOD because they use exactly the same meteorological input deriving from CALMET. The comparison with AERMOD would have been interesting, but that means to use another set of meteorological data, making it more difficult to understand if possible different results are due to the way the stack terminal is simulated or to the meteorological input to the models.
