## 4 Experiments
### Dataset
The plots in Figure 4 illustrate the distribution of the equivalent sound pressure level (LAeq) under different conditions: holidays versus non-holidays (left) and weekdays versus weekends (right). In the left plot, which compares the LAeq distributions on holidays versus non-holidays, both curves exhibit similar shapes, although a slight difference in peak density is  
Figure 4: Equivalent sound pressure level (LAeq) distribution under different time conditions. **Left**: comparison between holidays and non-holidays. **Right**: comparison between weekends and weekdays. A slight reduction in noise levels is observed during non-working days, suggesting an impact of human activity on noise pollution. Figure 3: Time series of the average noise level (LAeq) recorded by all stations in Madrid during the study period. Seasonal patterns are observed, as long with weekends, and an abrupt drop in 2020, associated with the reduction in mobility due to the COVID-19 pandemic. evident. Non-holiday days generally show marginally higher noise levels, as indicated by the rightward shift of the peak. This suggests that human activities--such as traffic and work--contribute to increased noise levels on regular days. In the right plot, comparing weekdays and weekends, a similar pattern is observed. Weekdays display a slightly higher density of high LAeq values compared to weekends. This supports the idea that routine activities during the workweek generate more noise than those typical of weekends and holidays. While these differences are subtle, they may be significant for a predictive model that incorporates such temporal factors. It is also plausible that noise patterns shifted after the COVID-19 pandemic. The Figure 5 presents the LAeq distribution before and after the pandemic. It is evident that, overall, post-pandemic noise levels (orange curve) tend to be lower than pre-pandemic levels (blue curve). The peak of the post-pandemic distribution is shifted to the left, indicating a higher density of values in lower sound intensity ranges. Although this is not the primary objective of the study, we provide evidence suggesting that mobility restrictions and changes in social and work habits during and after the pandemic have contributed to a reduction in environmental noise levels in the city of Madrid, as will be discussed in subsequent sections. The differences in the shape and position of the curves emphasize the importance of incorporating social and economic changes into predictive models of noise pollution. To train Graph Convolutional Networks (GCNs), a spatiotemporal graph must be constructed, considering the nearest distances to each station. Various connectivity criteria were tested, including linking each node to its 10 and 20 nearest neighbors. Performance was evaluated using 10 and 20 neighbors, with results showing a deterioration of approximately 3.33% and 0.33% (RMSE), respectively. Connections to 15 neighbors yielded slightly better results for some GCN models. Therefore, this criterion was selected for graph construction. The resulting graph is shown in Figure 6.  
The choice of geodesic distance (Haversine) is based on several practical and methodological considerations. First, since the measurement stations are relatively close to one another (with typical distances under 20 km), the spherical Earth distance offers a good approximation of spatial connectivity relevant to acoustic propagation without introducing significant distortion. Second, the Haversine metric is computationally efficient, which simplifies preprocessing and facilitates scaling to large datasets. Finally, several studies  
Figure 5: Distribution of noise level (LAeq) before and after the COVID-19 pandemic. A shift towards lower values is observed after the pandemic, suggesting a generalized reduction in urban noise pollution due to changes in mobility and social activity.