## 9 Statistical Methods in Studies on Odour Nuisance  
In airborne odour studies, the application of statistical methods is essential for the accurate interpretation of results, the assessment of environmental risks and the support of decision-making processes in odour emission management. The selection of appropriate data analysis techniques not only facilitates the description of basic measurement characteristics, but also enables the modelling of odour phenomena in time and space and the prediction of their occurrence under variable environmental conditions [57]. One of the basic tools used when analysing odour data is descriptive statistics. Arithmetic means, medians, standard deviations, quartiles and percentiles allow a synthetic characterisation of the distribution of odour intensity in a given area or time period [132]. These analyses form the basis for more advanced methods that enable initial detection of data structures, including the identification of outliers and asymmetric distributions typical of environmental datasets. To assess the intensity and frequency of odour occurrences over time, threshold exceedance frequency analysis is commonly used. This method enables the identification of periods of increased odour nuisance, which is particularly important for field monitoring and the validation of emission models [33]. It is often used when setting limit values for odour-emitting installations. The comparison of measurement results at different locations, under different conditions or before and after the use of odour abatement technologies requires the use of statistical significance tests. In practice, both parametric tests such as \(t\)-tests and analyses of variance (ANOVA) and non-parametric tests such as the Mann-Whitney U-test or the Kruskal-Wallis test are used, especially if the data do not meet the assumptions of normal distribution. Significance tests allow an objective evaluation of the effectiveness of mitigation measures and the differences in odour perception between different population groups [36]. Regression analysis is an advanced method that makes it possible to model relationships between environmental variables and the extent of odour nuisance. These models can include meteorological factors such as wind speed, temperature and humidity as well as technical parameters of the emission sources [133]. The regression enables the \begin{table}
\begin{tabular}{c c c c c} \hline
**Assessment Approach** & **Region/Country** & **Advantages** & **Limitations** & **Ref.** \\ \hline Exposure & & Less costly and labour-intensive than inspections or modelling. & Instantaneous results & \\ assessment—field olfactory using & & North America: USA & Captures human perception. Increases & meteorological conditions. Limited detection range. & [130, 131] \\  & & & public trust. & Technical constraints. & \\ \hline Exposure assessment— & & & & No standardisation. Requires large sample size. Prone to psychological and contextual biases. Sampling issues. & [57] \\ population surveys & & & & & \\ \hline Exposure & Europe: UK North America: & & & Data are anecdotal and \\ assessment—citizen complaints & & USA South America: & Inexpensive and easy to implement. & negative-biased. Results & [57] \\  & & New Zealand & & & \\ \hline Exposure assessment— & & & & Often misaligned with odour & \\ instrumental odour & & & & perception. Low sensor & \\ detection (e.g., & North America: Canada & Enables continuous and automated monitoring. & specificity. Requires prior & \\  & & & & knowledge of gas & \\  & & & Higher trust than sensory & composition. Affected by & \\  & & & & environmental conditions. & \\  & & & Needs calibration. Sensor & \\  & & & degradation over time. & \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._prediction of the variability of odour intensity and the identification of the most important determinants of odour events. In the context of analysing time series data, time series analysis techniques play an important role.