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

Featured Application The dispersion model here presented can be a first approach for the definition of a simple screening model for odor impact assessment with non-constant peak-to-mean, in case Gaussian Plume model is fit to the aim. 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, R-90, 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, sigma(C), and concentration intensity, I-c, 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 R-90 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.

Automated summary

The presented dispersion model serves as a first approach for odor impact assessment, providing information on instantaneous concentration statistics. It was tested and found acceptable based on comparison with experimental data. While the simulated mean concentrations were accurate, the model tended to overestimate standard deviation and concentration intensity.