CALPUFF is a multi-layer, multi-species non-steady-state puff dispersion model that simulates the effects of time- and space-varying meteorological conditions on pollution transport, transformation and removal. CALPUFF can be applied on scales of tens to hundreds of kilometers. It includes algorithms for subgrid scale effects (such as terrain impingement), as well as, longer range effects (such as pollutant removal due to wet scavenging and dry deposition, chemical transformation, and visibility effects of particulate matter concentrations).
Modelling of the Dispersion of atmospheric pollutants is today a routine method in environmental air quality management. In the particular case of environmental odour emissions, dispersion models have become indispensable given the difficulty of obtaining a reliable value of odour concentration in immision.
The use of dispersion models helps in the prediction of the impacts on air quality from industrial emission at their sources and it is a valuable argument to propose effective control strategies.
It is also important to consider that the cost of a model usually increases with its complexity and necessary computational resources, as follows:
Eulerian model >> Lagrangian model >> Gaussian model
There is a tendency to label the quality of the models according to their complexity. This sometimes causes errors in the choice of dispersion model, since such a choice should be based on the adequacy of the model to the case study. From this point of view, a model based on the gaussian solution could be sufficient to solve a complex problem and vice versa, an eulerian model may not be adequate for a simple study. The key is to align the selection criteria and validate methods and results.
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