## 3 Results and Discussion
### Correlation Analysis of Different Pollutant Concentrations  
The analysis of interactions between the studied substances and PM\({}_{10}\) revealed strong correlations between the concentrations of the individual PAHs, which may indicate similar sources of these compounds (Figure 6, Tables S10, S12, S14, S16, S18, S20, S22, and S24, Supplementary Materials). The correlation coefficients between PAHs and PM\({}_{10}\) at stations 1, 4, 5, 7, and 8 are moderately low. This indicates a certain degree of co-variation between these pollutants; however, the relationship is not strong, suggesting that PM\({}_{10}\) in these locations originates from different sources. On the other hand, statistically significant strong correlations between PAHs and PM\({}_{10}\) were observed at station 6. A strong correlation was also found between the cold and warm seasons (R\({}^{2}=0.966\)) for the analyzed PAHs and PM\({}_{10}\) across all monitoring stations. The observed spatial variability in PAH-PM\({}_{10}\) correlations highlights the complexity of emission sources and atmospheric processes influencing air pollution in different locations. The strong correlations at some stations, contrasted with weaker relationships elsewhere, underscore the importance of considering local emission profiles and environmental conditions. This site-specific insight extends previous findings by demonstrating that uniform regulatory approaches may not be equally effective across regions with distinct pollution sources. The results of the study conducted by [PERSON] et al. (2018) [24] in Zagreb confirm significant relationships between PAH concentrations and different particulate matter fractions (PM\({}_{10}\), PM\({}_{2\cdot 5}\), and PM\({}_{1}\)). It was observed that a larger portion of PAHs is associated with smaller particle fractions, especially during the winter season. In the study conducted by [PERSON] et al. (2022) [25], strong and statistically significant correlations were found between PAH concentrations and particulate matter fractions PM\({}_{2\cdot 5}\) and PM\({}_{10}\). These correlations indicate the combined influence of local emission sources and environmental conditions on pollution levels. It was observed that PAHs with medium and high molecular weights (MMW and HMW) exhibited higher correlation coefficients with particulate matter fractions than low molecular weight (LMW) PAHs. This is because LMW PAHs tend to exist in the gaseous phase, whereas MMW and HMW PAHs are more strongly associated with particulate matter. All PCA results are presented in Supplementary Materials (Tables S1-S16). The results of the principal component analysis showed that at all stations, two factors explained over 90% of the data variance (Figure 6). According to this finding, the first two components provide important information relevant to all the analyzed monitoring stations. Factor 1 was strongly associated with BaA, BaP, BBF, BjF, BkF, and IP compounds. The analyzed PAHs negatively correlated with PC1 in each case. Factor 2 was associated with PM\({}_{10}\) (except at stations 4, 6, and 7) and DBahA at stations 3, 4, and 7. Literature identifies BaP, IP, and DBahA as indicators of emissions related to vehicles and gasoline exhausts. BkF also indicates emissions from coal combustion and natural gas emissions. BaP also points to stationary emission sources using heating oils as fuel [7; 26; 27; 28]. PC1 represents the dominant component, explaining a significant portion of the total variance. All the analyzed PAHs show quite similar variable contributions, suggesting that they are interconnected and may originate from similar emission sources. PC1 reflects a widely spread, universal source of pollution, such as transportation or biomass burning, operating throughout the studied region. In contrast, the lower loadings for PC2 indicate that this component explains only a small part of the variance in PAHs.