## 10 Artificial Intelligence in the Measurement of Odour-Intensive Air Quality
These results suggest that proper data processing using AI can significantly reduce signal drift effects and improve the reliability of MOS sensor networks in long-term monitoring campaigns. Photo-ionisation detectors (PIDs), known for their high sensitivity in detecting VOCs, are increasingly being integrated into intelligent odour monitoring systems. Although PIDs provide valuable information on the total concentration of ionisable compounds, they inherently lack the selectivity to identify specific odorous compounds. In this context, AI-driven data fusion methods, including ensemble learning and deep learning architectures, are employed to integrate PID signals with data from additional sensors, such as temperature and humidity sensors. [PERSON] and [PERSON] [161] described the application of a PID-based system supported by recurrent neural networks (RNN) to predict odour peaks in the vicinity of petrochemical facilities. Their approach enabled early detection of potential odour complaints, demonstrating the added value of AI in enhancing the specificity and predictive power of PID-based monitoring systems. In addition to gas-phase sensors, particulate matter (PM) sensors have also found applications in odour air quality monitoring, particularly in industrial and agricultural environments where odorous emissions are often associated with particulate releases. Low-cost PM sensors, which typically estimate particle concentrations based on light scattering principles, can be significantly improved through AI-based signal processing techniques. [PERSON] et al. [162] developed a predictive model based on long short-term memory (LSTM) networks that successfully identified particulate-bound odour events from livestock operations using PM sensor data. Their work demonstrated that AI can effectively distinguish odour episodes associated with particulate emissions from background environmental variability, thus expanding the functional capabilities of PM sensors within odour monitoring frameworks. Beyond individual sensor technologies, artificial intelligence plays a pivotal role in the optimisation of entire odour monitoring networks. Reinforcement learning algorithms are increasingly being explored to dynamically adapt sensor deployment, optimise sampling strategies, and predict spatial patterns of odour dispersion in real time. Coupling machine learning models with atmospheric dispersion models such as AERMOD and WRF-Chem further enhances the predictive capabilities of odour management systems, enabling early warning and rapid response to odour events. A notable example is the work of [PERSON] and [PERSON] [163], where machine learning models were integrated with dispersion simulations to optimise odour mitigation strategies in large industrial complexes. Overall, the application of artificial intelligence across diverse sensing technologies--including electronic noses, MOS sensors, PID detectors, and PM sensors--represents a breakthrough in the field of odour air quality assessment. AI enables not only improved measurement accuracy and resilience but also the extraction of valuable insights from complex, high-dimensional environmental datasets. As sensor technologies continue to advance and computational power becomes increasingly accessible, the integration of AI-based analytics will undoubtedly remain central to the evolution of intelligent, real-time odour monitoring systems in the coming years. In recent years, the development of AI-based tools, including quantitative structure-property/activity relationship (QSPR/QSAR) methods, has significantly improved the prediction of odour thresholds for a variety of organic compounds. An example of such an approach is the Monte Carlo method implemented in the CORAL software (CORAL, 2016), which allows the identification of structural promoters for increased or decreased odour sensitivity of molecules based on their SMILES representations [164].