## 9 Statistical Methods in Studies on Odour Nuisance
Methods such as autoregressive integrated moving average (ARIMA) modelling allow the identification of trends, seasonality and anomalies in odour data [134]. This type of analysis is suitable for long-term odour monitoring and the early detection of deviations from the normative emission values. Multivariate analysis, including principal component analysis (PCA) and cluster analysis, enables the reduction in data complexity by identifying patterns and groups within datasets with a large number of variables. These techniques are particularly useful in studies that monitor different emission types or different physico-chemical characteristics associated with odours simultaneously. In studies that require a spatial view of the distribution of odour concentrations, geostatistical methods play an important role. Kriging interpolation and variogram analysis allow the creation of odour distribution maps and the identification of spatial odour-nuisance patterns [135]. These methods also facilitate the integration of measurement results into atmospheric dispersion models. In recent years, machine learning techniques have become increasingly important in odour research. Algorithms such as random forests, neural networks and support vector machines (SVMs) enable the prediction of odour intensity based on complex input datasets that include meteorological and technical parameters as well as results of subjective odour assessments [136]. Machine learning is particularly effective in modelling non-linear relationships and in the automatic classification of odour emission sources. Statistical methods for data analysis in odour nuisance research are characterised by high versatility and adaptability to different data types and research objectives. Their appropriate selection and application enable the generation of highly reliable results, which are essential for environmental impact assessments and the development of effective odour mitigation strategies. The statistical methods used for the analysis and interpretation of air quality data related to odour are summarised in Table 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Method** & **Description** & **Ref.** \\ \hline Descriptive statistics & Data characterisation: mean, median, standard deviation, quartiles. & [137] \\ \hline Frequency analysis & Determination of the frequency of odour threshold exceedances over time. & [138] \\ \hline Statistical significance tests & Comparison of data groups (e.g., ANOVA, Student’s \(t\)-test, non-parametric tests). & [115] \\ \hline Regression models & Assessment of relationships between environmental variables and odour nuisance. & [139] \\ \hline Time series analysis & Identification of trends, seasonality and forecasting of changes. & [140] \\ \hline Multivariate analysis & Data clustering and dimensionality reduction (PCA, cluster analysis). & [141] \\ \hline Geostatistical methods & Analysis of the spatial distribution of odours (kriging, dispersion models). & [142] \\ \hline Machine learning & Prediction and classification of odour nuisance (neural networks, random forests). & [143] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical instruments for measuring odour nuisance.