## 10 Artificial Intelligence in the Measurement of Odour-Intensive Air Quality
However, MOS sensors are prone to cross-sensitivity and signal drift over time, which has traditionally \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Gas** & **Sensor Type** & **Description** & **Ref.** \\ \hline NH\({}_{3}\), H\({}_{2}\)S and their mixtures & Microsensor array (SnO\({}_{2}\)-CuO, SnO\({}_{2}\)-Pt); WENS & The WENS system with wireless communication used the ARTMAP classifier and the ART-based fuzzy estimator for classification and concentration prediction. 100\% classification accuracy and good drift corrections were achieved. & [153] \\ \hline Various volatile organic compounds & 14 MOX sensors (Figaro 75B602) & The data were subjected to regression analysis and PCA, which allowed quantification of trace VOC concentrations (ppb) with low MSE, confirming the effectiveness of AI in prediction. & [154] \\ \hline Acetone, ethanol, butanol, propanol & CNT–TiO\({}_{2}\) hybrid nanostructures & Classification with PCA and SVM achieved an accuracy of 97.5\%. The system worked effectively at room temperature and provided fast and cost-effective VOC detection. & [155] \\ \hline Formaldehyde, ethanol, acetone & TiO\({}_{2}\) nanofibres with PEDOT:PSS, PSS, Pry & Impedance analysis and PCA showed a high discrimination capability (variance 97.93\%), confirming the use of data analysis algorithms for gas detection. & [156] \\ \hline Acetone, chloroform, methanol & QCM sensors (NDK Ltd.) & PCA enabled effective discrimination of gases and their mixtures. However, the significant influence of humidity shows the need for further correction of the signals under variable conditions. & [157] \\ \hline H\({}_{2}\)S, NO\({}_{2}\) & 6 TGS sensors (Figaro) & Discriminant analysis was used to distinguish gases. The accuracy was dependent on the presence of CO\({}_{2}\) and humidity. & [158] \\ \hline Chemicals in the air & Mobile robots with gas sensors & The report covers strategies for mapping, tracking and localising odour sources. The importance of developing data processing algorithms and multimodal integration is emphasised. & [159] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Overview of the applications of the electronic nose in analysing basic environmental gases with AI algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Odour Source** & **Sensor Type** & **Description** & **Ref.** \\ \hline Recycling plant & 6 QMB, 6 MOS, chemical sensors (JIm Innovation) & The DOS system is based on multi-sensor signal processing and machine learning. Under field conditions, an R = 0.93 and high repeatability and stability were achieved, confirming its effectiveness in odour control. & [149] \\ \hline Waste & QCM (AltraSens, Germany) & The Odour Vector System used benchmark classification using machine learning algorithms to monitor odours (0–500 ou/m\({}^{3}\)) and evaluate filter efficiency. & [150] \\ \hline Sewage treatment plant & FOX 3000; 12 MOS & The electronic nose proved its usefulness as a warning system for VOC detection. Good repeatability and reproducibility were confirmed (14.8\% and 17.6\%). & [151] \\ \hline Building & KAMINA, 38 sensors on one chip & A data model was developed to predict odour intensity based on the correlation of sensory ratings with sensor responses. The variability of humidity was included in further predictive analyses. & [152] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._limited their utility in precise environmental measurements. AI-based techniques such as principal component analysis (PCA) for dimensionality reduction, combined with supervised learning models like artificial neural networks (ANNs) and gradient boosting machines (GBMs), have been effectively applied to calibrate and compensate for these limitations. In a study conducted by [PERSON] et al. [160], it was shown that the application of an e-nose based on MOS sensors combined with ANN algorithms allowed for the differentiation of odorous emissions originating from various waste management processes.