Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment

Marzio Invernizzi,Selena Sironi,Federica Capra,Roberto Sozzi,Laura Capelli

doi:10.3390/app11083310

Abstract

For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, R90, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, σC, and concentration intensity, Ic, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of R90 is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6.

Highlights

Odor is a property of a mixture of substances able to stimulate the olfactory sense
The main sources of environmental odors are the emissions of those odorous gases into the Planetary Boundary Layer (PBL) from industrial and agricultural activities [3,4,5]
In the PBL, like any other meteorological variable, the instantaneous concentration of odorous gases, more or less fluctuates, in time and space around to the local mean value, depending on the turbulence level of the atmosphere [8,9]. This is the reason why fluid dynamics researchers attribute to each PBL variable, and to the concentration of a passive scalar, the character of a stochastic variable completely defined by an appropriate Probability Density Function (PDF) [10,11,12]

Summary

Introduction

Odor is a property of a mixture of substances able to stimulate the olfactory sense. In the human nose, there are millions of receptors and the smell perception arises from the combination of the reactions of the nasal receptors to a wide range of odorous gases (H2S, NH3, VOCs, and more) [1,2]. In the PBL, like any other meteorological variable, the instantaneous concentration of odorous gases, more or less fluctuates, in time and space around to the local mean value, depending on the turbulence level of the atmosphere [8,9]. This is the reason why fluid dynamics researchers attribute to each PBL variable, and to the concentration of a passive scalar, the character of a stochastic variable completely defined by an appropriate Probability Density Function (PDF) [10,11,12]. In order to completely know the statistics of the instantaneous concentration of a gas, it would be necessary to know its PDF or, equivalently, its infinite statistical moments, for each of which, it is theoretically possible to write an appropriate partial differential equation describing its budget [8,9,12,13]

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Conclusion