## 3 Results
### Estimation of OER and OEF  
The experimental data, collected by us and with all the necessary information available to calculate the emission factors, were derived from 46 emission stacks' data points but was limited to eight plants. Only two of them had odor controls systems, with different technologies. For this reason, it was not possible to individually analyze different odor abatement systems or odor control procedures, but they were calculated as a whole, to describe the reality of the sector. This dataset has been organized in order to obtain OER and OEF data. The data presented here consider only the conveyed odor emission from stacks; possible fugitive and diffuse emission are neglected.  
For the plants where collected data were enough, the global OER, from conveyed sources, and the related OEF, were calculated. A general overview of OER results is reported in Table 2. As depicted, the obtained yearly OER data span over several orders of magnitude. In addition, OEF shows a wide range of values, but appears fairly narrow compared to that of OER. Figure 1 reports the obtained OEFs, both on a linear and a logarithmic scale.  
In order to assess how OEF data are distributed, a Lilliefors (Kolomorov-Smirnov) normality test was conducted on the dataset [19] using MATLAB R2019b. The test was conducted on the data, as they were, and after their logarithmic transformation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Global** & **Total Stack** & **Plants with Stack** & **Fugitive** & **Ambient** \\  & **Data** & **Emissions** & **Emissions** & **Emissions** & **Air** \\ \hline N = & 113 & 85 & 47 & 12 & 10 \\ mean & 4625 & 4135 & 4494 & 11,085 & 130 \\ median & 1400 & 1500 & 1800 & 1775 & 125 \\ RSD\% & 7203 & 6779 & 10,538 & 146,497 & 569 \\ Min & 60 & 200 & 240 & 964 & 60 \\ Max & 49,000 & 37,000 & 20,000 & 49,000 & 220 \\ \hline \end{tabular}
\end{table}
Table 1: Overall view of emissions (data are in oue/m3). Global data reports of all available data, comprising fugitive emissions and ambient air values.  
\begin{table}
\begin{tabular}{c c c} \hline  & **OER [oug/s]** & **OER [oug/y]** \\ \hline N = & 8 & 8 \\ Arithmetic mean & \(1.3\times 10^{5}\) & \(1.8\times 10^{12}\) \\ Geometric mean & \(7.1\times 10^{4}\) & \(3.5\times 10^{11}\) \\ RSD \% & 74\% & 42\% \\ min & \(1.1\times 10^{4}\) & \(3.1\times 10^{10}\) \\ max & \(5.6\times 10^{5}\) & \(1.3\times 10^{13}\) \\ \hline \end{tabular}
\end{table}
Table 2: Results of OER calculations.  
The test on as-is data does not reject the null hypothesis, (\(p\)-value = 0.38), while it was rejected for the logarithm-transformed data (\(p\)-value = 0.046). From this information, we can assume that the OEF data show a log-normal distribution [20]. Normal distribution of emissions can be seen in Figure 2, where we report a QQplot of the log-normal distribution of the OEF data. Linearity of all quantiles, close to the diagonal line, with no evident outliers can be observed.  
Figure 1: OEF data for bitumen-related production sites on a logarithmic scale.  
Figure 2: QQplot of the logarithm-transformed OEF data. As mentioned in text, due to the nature of odor concentration measurements, the statistical calculations are based on the log-transformed data.  
Due to the log-normal distribution of the data, the geometric mean represents the best representation of expected value. Table 3 reports the obtained OEF for the bitumen-related product industry, with its confidence interval (coverage factor k = 1).