Eulerian-Lagrangian model for predicting odor dispersion using instrumental and human measurements

A Eulerian-Lagrangian model was used to predict the trajectory and spatial distribution of odor and odorants downwind from an industrial facility with multiple sources of odor emissions. Specifically, the model was used to simulate the dispersion of odor from a confined animal feeding operation (CAFO) under different meteorological conditions: (1) during daytime when the boundary layer is usually turbulent due to ground-level heating from solar short wave radiation, and (2) during the evening when deep surface cooling through long-wave radiation to space recreates a stable (nocturnal) boundary layer. Aerial photographs were taken of the CAFO, and the geographical area containing the odorant sources was partitioned into 10 m(2) grids. Relative odorant concentrations present at each grid point that corresponded to an odor source were measured on site and then entered into a database. The predicted odor dispersion distance was found to be greater at night-time than during daytime and was consistent with field reports from individuals living near the CAFO. The model utilizes single numbers that represent relative concentrations or intensities (e.g. from an electronic nose or human judgments) to simulate downwind dispersion. The advantages of this algorithm over standard Gaussian plume models are that: the velocity variances and covariances among its three components, integral time scale (a measure of eddy coherency), and complex boundary conditions (e.g. complex release points, surface boundary conditions) are explicitly considered. (c) 2004 Elsevier B.V. All rights reserved.