## 10 Artificial Intelligence in the Measurement of Odour-Intensive Air Quality  
Recent advances in artificial intelligence (AI) have significantly enhanced the capabilities of air quality monitoring systems, particularly in the field of odour nuisance assessment. Traditional analytical techniques, although reliable, often prove insufficient when faced with the complexity and variability of odorous emissions. AI offers a transformative approach, enabling the intelligent interpretation of multidimensional sensor data, thereby increasing both the accuracy and responsiveness of air quality monitoring systems related to odour control. Electronic noses (e-noses), as sensor systems designed to mimic the human sense of smell, constitute one of the primary platforms where AI integration has led to substantial advancements. E-noses utilise arrays of gas sensors to detect a wide range of volatile organic compounds (VOCs) associated with odorous emissions. However, the signals generated by these sensors are often nonlinear, overlapping and sensitive to environmental factors such as humidity and temperature. Machine learning algorithms, particularly support vector machines (SVMs), random forests, and convolutional neural networks (CNNs), are widely used to classify odour types, distinguish between complex gas mixtures, and predict odorant concentrations based on e-nose signals. For example, [PERSON] et al. [144] successfully employed an e-nose system combined with SVM to monitor odorous emissions from a municipal wastewater treatment plant, achieving high classification accuracy across various operational phases and environmental conditions. Their study demonstrated the feasibility of real-time odour monitoring using AI-enhanced e-nose technologies in complex industrial environments. Table 9 contains examples of the use of AI to support the monitoring of odour-intensive objects with an electronic nose and Table 10 provides an overview of the applications of the electronic nose in the analysis of basic environmental gases, taking AI algorithms into account. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Odour Source** & **Sensor Type** & **Description** & **Ref.** \\ \hline Landfill gas & 16 tin oxide sensors & \begin{tabular}{c} The study used artificial neural networks (ANNs), \\ including MLP and RBF models, for quantitative odour \\ prediction (0–200 ou/m\({}^{3}\)). A strong correlation and very \\ low prediction errors were achieved: MSE 0.000410 (MLP) \\ and 0.000755 (RBF). \\ \end{tabular} & [145] \\ \hline Waste disposal, landfills & 6–8 tin oxide sensors & \begin{tabular}{c} The system used data processing techniques and predictive \\ models to determine odour concentration with an accuracy \\ of 96.4\% and a coefficient of R\({}^{2}\) = 0.90172. The analysis \\ included the comparison of the sensory data with the meteorological conditions. \\ \end{tabular} & [146] \\ \hline \begin{tabular}{c} Composting plant \\ \end{tabular} & 6 tin oxide sensors & \begin{tabular}{c} E-nose with supervised data processing and PCA analysis \\ enabled the classification of compost odours and the \\ calibration of emissions up to 1500 ou/m\({}^{3}\). The system \\ recognised carbon filter breakthroughs in real time. \\ \end{tabular} & [147] \\ \hline \begin{tabular}{c} Poultry farm \\ \end{tabular} & \
\begin{tabular}{c} 12 sensors (MOS, SnO\({}_{2}\), \\ WO\({}_{3}\)) + humidity and temperature sensors \\ \end{tabular} &
\begin{tabular}{c} The “Odour Expert” system uses AI to classify farm \\ odours. Field tests on 14 farms showed a high level of \\ agreement with the ratings of the sensory panel (R = 0.93). \\ \end{tabular} & [148] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Examples of the use of AI to support the monitoring of odour-intensive objects with an electronic nose. Metal oxide semiconductor (MOS) sensors, extensively used due to their high sensitivity and low cost, play a crucial role in odour-monitoring applications.