## 3 Analysis of the Variation of the Air Quality in the Different Living Rooms of a Single Building
In our analysis, we employed machine learning techniques, particularly neural networks, to identify and model the relationships between different air quality parameters, including humidity, temperature, CO\({}_{2}\), and VOC. The neural networks were trained on off-the-shelf datasets, and the results were presented using data collected from CCS811 and BME680 sensors. Data preprocessing techniques were employed to ensure the quality and relevance of the input data. Consequently, predicted values were generated that exhibited a high degree of correlation with actual sensor measurements, as illustrated in the accompanying calibration graphs. The predicted trend was derived from a model trained on a substantial dataset of experiments conducted in controlled laboratory environments. The implementation of controlled conditions permitted the attainment of consistent measurements and the minimization of external variability, thereby facilitating more accurate predictions. It should be noted, however, that the actual trend presented in the figures is based on an experiment conducted in a living space, where a number of uncontrolled factors could potentially influence VOC levels. Such factors include the influence of daily activities, discrepancies in ventilation, and the existence of supplementary VOC sources, which are not always present or accounted for in laboratory conditions. The graphs illustrate the relation  
Figure 11: Variations in CO\({}_{2}\) levels (**a**) as well as VOC Levels and IAQ (**b**) throughout the day. ship between the measured and calibrated values of temperature, humidity, and CO\({}_{2}\) with VOC, thereby demonstrating the underlying dynamics of air quality indicators. They also highlight the challenges of applying laboratory-based models to real-world environments. Figure 12 illustrates the correlation between VOC concentrations and temperature using machine learning models. It presents expected versus actual correlations, highlighting the predictive capabilities of the model in capturing fluctuations in VOC levels in response to temperature changes. This figure aids in understanding the dynamic relationship between temperature variations and VOC emissions. Figure 13 explores the relationship between VOC concentrations and humidity levels through machine learning analysis. It depicts the expected versus observed correlations, providing insights into how humidity influences VOC emissions. The figure illustrates the model's capacity to identify patterns and dependencies between humidity and VOC levels. Figure 14 depicts the variation of VOC concentrations with CO\({}_{2}\) levels using machine learning techniques. It presents anticipated versus actual correlations, showcasing the model's efficacy in capturing the interplay between VOC emissions and CO\({}_{2}\) concentrations. This visual aids in understanding potential co-emission sources or interactions between VOCs and CO\({}_{2}\) in ambient air. Figure 12: Correlation between VOC and temperature. Figure 13: Correlation between VOC and humidity. The strength of these correlations varies depending on the specific IAQ parameters involved. For instance, the relationship between CO\({}_{2}\) and VOC concentrations is more complex, as both are influenced by different sources and activities within a living space. In contrast, humidity and VOC levels often show a more straightforward relationship, as changes in humidity can directly affect the emission rates of certain VOCs. In terms of statistical significance, the correlations observed in the controlled laboratory settings were robust, with high correlation coefficients indicating the strong predictive power of the model. However, as previously discussed, the actual trends in real-world environments, such as those observed in the living space experiments, may show discrepancies due to uncontrolled variables. This variability can lead to weaker correlations in practical applications, underscoring the need for caution when interpreting these figures in non-laboratory conditions.