Estudo da dispersão de gases odorantes ao redor de obstáculos através do modelo de pluma flutuante

Abstract

Atmospheric dispersion of odorous compounds can originateenvironment im-pacts, causing nuisance and health effects. One of the tools employed for studying these impacts are mathematical models, based on the solution of the pollutant transport equations. An important aspect is the ability of the models to include the effect of the presence of obstacles in the flow. Due to its easy and fast implementation gaussian models are employed but its original formulation doesn’t take into account the presence of obstacles, which presence augments turbulence generation and causes an increased dilution of the emitted com-pound. Ways to include these effects in gaussian models were the object of several authors. The present work studies the mathematical modeling of the atmospheric dispersion of odorous compounds using the fluctuating plume gaussian model along with sixdifferent approaches for taking the obstacles ef-fects into account. The fluctuating plume model states that atmospheric disper-sion of a pollutant is the result of turbulence and wind meandering. Both the concentration and fluctuation of the plume centerline will be the result of gauss-ian distributions. The methods employed permit the estimation ofthe impact caused by the pollutant giving results for concentrations during a specified av-eraging period, intermittency -defined as the time where the instant concentra-tion is higher than a defined threshold, and peak-to-mean ratio. The results are compared to wind tunnel measured data and show that the model is not com-pletely adequate for odor dispersion modeling, despiteshowing goodagree-ment with experimental datain terms ofaverage concentrationresults. The ob-stacle effects are well modeled in a qualitative manner. Intermittency results presented anunderestimate magnitude in regions near the sourceand are overestimated farther away from it. This result can be due to the obstacle mod-eling procedures or the dispersion parameters calculations. A modification in one of these dispersion parameters maintained the concentration and peak-to-mean results but provided an improved behavior for the intermittency results when compared to the wind tunnel data.