# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 5 Conclusions  
Atmospheric dispersion modelling has a key role in the study of environmental odors. Unlike the study of macro-pollutants, for odor impact assessment, the calculation of the short-term peak odor concentrations is fundamental. For this purpose, in this article a simplified and analytical Fluctuating Plume Model [54] is tested. It is fundamental to point out the merits of this model: it is very simple, and the equations of the mean and variance concentration can be worked out analytically. This model was here used to verify if it can provide reliable data, not only for the mean concentration, but also for \(\sigma_{C}\), \(I_{c}\) and \(R_{90}\). Moreover, for the calculation of the peaks, PDFs are necessary: Gamma and Modified Weibull PDFs were tested here. Different conclusions can be drawn from the present work. The difficultly of Gaussian models to consider the horizontal meandering that is naturally present in nature was corroborated. For the scope of this work, wind direction data were tuned in order to fit the plume transversal limits. Seventy-two percent of the simulated mean concentration values fell within the range of the _FAC2_ with respect to the experimental ones. Good results are also obtained looking at the linear regression line. Moreover, the model here presented and developed, has the aim to calculate concentration variance, in order to use it for the estimation of the peak-to-mean factor \(R_{90}\). Both the results for \(\sigma_{C}\) and \(I_{c}\) provide an overestimation of the experimental data. This result is strictly linked with the data losses due to the non-ideality of the measurement system: the non-theoretically instantaneous sampling (0.1 Hz) and the response time \(\tau\) contribute to the flattening of the instant concentration data and so cause a decrease of the sample variance. The propagation of the errors from \(I_{c}\) data to \(R_{90}\) is very complex due to the introduction of the relationships of the chosen PDF. Nonetheless, the obtained data for \(R_{90}\) show a dispersed behavior with respect to the experiment, but the ranges covered are quite repeatable: the P/M obtained values are 1-3 for the Gamma PDF and 1.5-4 for theModified Weibull PDF, with experimental values ranging from 1.4 to 3.6. This finding can be linked to the \(R_{90}\) trend with respect to \(I_{c}\): depending on the PDF, different maximum values are reached: three and four for the Gamma and the Modified Weibull, respectively. The obtained results highlight the variability of ambient odor concentration fluctuations, which cause great confusion in complaints from citizens close to industrial activities. The model here presented is theoretically designed for neutral/slightly stable situations: in this work, the comparison was made only in neutral conditions. A future perspective could be the use of different atmospheric stability situation to test this kind of analytical models. One goal is to make it usable even in less ideal conditions. On the other hand, a limitation of the work presented here, is its inapplicability in calm wind conditions: unfortunately in these conditions the Gaussian plume models are not applicable due to the mathematical formulation that requires the presence of the wind speed term in the denominator. A further crucial point that has to be taken into consideration when \"high-frequency\" concentration data are used: spectral losses are implicit when using non-ideal sensors. Unluckily these considerations are very scarce in the odor nuisance research field: the considerations made in this paper could be a first step for further reflections for the development of future experimental field campaigns. In the most advanced models, the PDFs need to be evaluated in a consistent statistical way. For this reason, it is also crucial to deepen the scientific knowledge on probability density functions, which can well describe the ambient instantaneous concentration.