## 4 Results and Discussion
### Data Pre-Processing, Correlations, and Trends
Figure 3: PMs versus all studied variables, excluding missing values, for the shorter period. Furthermore, Figures 4 and 5 illustrate the Pearson correlations and \(p\)-values between PM concentrations and all selected variables during the shorter period. The \(p\)-value tests the null hypothesis \"There is no linear correlation between two variables X and Y if r = 0\". If the \(p\)-value is small (typically \(p\) < 0.05), we reject the null hypothesis and we accept that there is a statistically significant linear relationship between the two variables. If the \(p\)-value is large (\(p\) > 0.05), we fail to reject the null hypothesis, and consequently, we cannot conclude that there is a linear relationship (even if r is high or low). For PM\({}_{2.5}\), the strongest correlations are observed with benzene (r = 0.82) and CO (r = 0.80), followed by NO\({}_{2}\) and NO\({}_{\rm X}\) (r = 0.59), ethylbenzene (r = 0.55), o-Xylene (r = 0.51), and ozone (inverse correlation, r = \(-\)0.50). Moderate correlations are found with m-Xylene (r = 0.48), NO (r = 0.44), and p-Xylene (r = 0.42). In contrast, weaker correlations are noted with relative humidity (r = 0.05), toluene (r = 0.07), wind direction (r = 0.08), and an inverse correlation with precipitation (r = \(-\)0.09). Notably, no significant correlation is found between PM\({}_{2.5}\) and other variables. These findings suggest that compounds such as benzene and CO are likely key contributors to PM\({}_{2.5}\) formation, while factors like humidity and precipitation may play a role in suppressing its levels. Benzene, as a VOC emitted by the incomplete combustion of fuels in traffic and industry, contributes chemically to PM\({}_{2.5}\). CO contributes indirectly to PM\({}_{2.5}\) (as a tracer, not as a precursor). Humidity and precipitation can decrease the PM\({}_{2.5}\) level (chemical context and atmospheric conditions are very important in this process). OH radicals represent the key to the formation of secondary organic compounds, which include secondary organic aerosols (an important component of PM\({}_{2.5}\)). This process is favored by solar radiation, high temperature, and the presence of ozone and NO\({}_{\rm X}\). For PM\({}_{10}\), like PM\({}_{2.5}\), the strongest correlations are observed with the same set of variables, although the correlation coefficients are slightly lower. Benzene and CO show the highest correlations (r = 0.61), followed by NO\({}_{2}\) (r = 0.55), NO\({}_{\rm X}\) (r = 0.53), and ethylbenzene (r = 0.50). Moderate correlations are found with o-Xylene (r = 0.47), m-Xylene (r = 0.46), p-Xylene (r = 0.41), NO (r = 0.37), and O\({}_{3}\) (inverse correlation, r = \(-\)0.33). In contrast, weaker  
Figure 4: A heatmap for Pearson correlation. correlations are noted with wind direction (r = 0.02), temperature (r = 0.03), sun brightness (inverse correlation, r = \(-\)0.03), toluene (r = 0.06), and precipitation (inverse correlation, r = \(-\)0.10). Notably, no significant correlation is found between PM\({}_{10}\) and the remaining variables. These results indicate that pollutants such as benzene and CO may contribute to PM\({}_{10}\) levels. At the same time, meteorological factors like wind direction, temperature, and sun brightness could potentially help reduce PM\({}_{10}\) concentrations, especially hourly. This correlation analysis indicates that, over short-term (hourly) time scales, PM\({}_{2.5}\) exhibits stronger correlations with pollutant variables than PM\({}_{10}\), likely due to its finer nature and stronger association with combustion-related sources. PM\({}_{10}\), being coarser, may be influenced by additional non-combustion sources (e.g., dust, resuspension), which are less directly linked to the measured air pollutants.