A new Lagrangian-probabilistic approach for the study of pollutants diffusion: analytical modeling and experimental validation using the Copenhagen data set

A new Lagrangian-probabilistic approach for the study of pollutants diffusion: analytical modeling and experimental validation using the Copenhagen data set

In this paper a Lagrangian-probabilistic model for the study of pollutants diffusion in the atmosphere is presented. As opposed to the models available in literature, where the transition Probability Density Function of particles is Gaussian, in this new theoretical approach the transition is bilateral exponential. The model presented in this work, being based on an original formulation of the transition probability density, allows to represent and analytically calculate the mean concentration field produced by a time-varying point source. Comparing well results of the 1-D Laplace bilateral model (LBM) to those of the Gaussian approach, the three-dimensional model associated with PDFLB has also been developed. The 3-D modeling allows the representation of more complex scenarios, remaining the PDFLB function analytically flexible and easy to integrate. These peculiarities always lead to analytical solutions of the mean concentration field and allow to represent complex and time-varying scenarios, with low calculation requirements.The model based on Laplace bilateral PDF has been then validated using a three dimensional approach and considering actual meteorological and emissions scenarios. The many data collected in experimental campaign of Copenhagen, conducted in years 1978-1979, have been compared with model results, highlighting a good agreement in almost all test conditions. The good results obtained and presented in this study have opened new interesting perspectives for the approach proposed, such as the study of specific scenarios where pollutant emissions are characterized by instantaneous releases. These are typical situations of industrial equipment breakdown that can originate acute pollution scenarios, often characterized by a short transient phenomena. The possibility of using the model in these situations may make it suitable as an analytical tool of high value and flexibility for numerous engineering applications.