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person. A continuous sample of odorous air was exhausted from the auditorium through a glass tube to an odor test station. One-half of the air flow was heated 1–7 K, and the other half was unheated. The odor panel was asked to compare the odor intensity of the two air flows. The air flow judged to have the strongest odor was stepwise dilluted with clean air (air temperature difference maintained). The dilution required to provide the same odor intensity in the two air flows was estimated. At air temperatures 23–32 °C no significant influence of temperature on perceived intensity of body odor was found. The ventillation requirement in auditoria and similar spaces is likely to be independent of the temperature level, provided that the occupants are kept thermally neutral or cooler, so that little or no perspiration occurs.
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doi:10.1016/j.snb.2004.05.067 
Copyright © 2004 Elsevier B.V. All rights reserved.
Eulerian-Lagrangian model for predicting odor dispersion using instrumental and human measurements
Available online 25 July 2004.
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Abstract
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 m2 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.
Author Keywords: Odor dispersion; Odorant concentration; Odor intensity; Prediction of odor downwind
