## 4 Results and Discussion
### Data Pre-Processing, Correlations, and Trends
The weak correlations with meteorological variables at the hourly level suggest that meteorological influences on PM concentrations may be more evident over longer temporal resolutions (e.g., daily or seasonal) or may require more complex modelling approaches (e.g., non-linear interactions or lagged effects) to fully capture their impact. The weaker correlations that exist between PM\({}_{2.5}\) and PM\({}_{10}\) and wind direction, temperature and sun brightness are explained by a few factors. The first of them is represented by the topography of this city. Craiova is located in the plain of Oltenia which is relatively flat. The lack of valleys can contribute to the accumulation of pollutants and leads to the absence of strong air flows. Also, the flat topography combined with unplanned urbanization leads to areas where polluted air accumulates, especially during winter when thermal inversions are frequent. Another factor is represented by the local pollution sources (thermal power stations, construction sites, busy traffic, illegal burning) bring perturbations of the dispersive effects of wind on air pollution. In a study for Spanish urban regions [118], the authors found weak correlations between PM concentrations and meteorological parameters and suggested that other sources might be dominant. Figure 5: A heatmap for Pearson \(p\)-values. In summary, benzene, CO, NO\({}_{2}\), NO\({}_{\omega}\), and ethylbenzene emerge as the most relevant predictors of PM\({}_{2.5}\) and PM\({}_{10}\) levels in this context, while humidity, temperature, and precipitation may act as moderating factors with limited immediate influence. Figure 6 provides a comprehensive analysis of seasonal PM trends, outliers, and correlations with other variables, including seasonality and monthly variations. The key observations are the following:  
1. **PM\({}_{\mathbf{2.5}}\) and PM\({}_{\mathbf{10}}\) trends over time--subplot (a)** - Observation: PM\({}_{2.5}\) (red) and PM\({}_{10}\) (blue) show high variability with sharp peaks. - Insight: many events show simultaneous peaks, indicating shared sources or conditions. 2. **Average PM\({}_{\mathbf{2.5}}\) and PM\({}_{\mathbf{10}}\) levels by month--subplot (b)** - March (Month 3) has the highest average concentrations of both PMs. - Declining trend toward April (Month 4) and lowest levels that month. - There is likely a seasonal/meteorological influence or source activity pattern that peaks in March. 3. **Average PM\({}_{\mathbf{2.5}}\) and PM\({}_{\mathbf{10}}\) Levels by Season--subplot (c)** - Winter (Season 1) has the highest PM levels, followed by Spring (Season 2). - Insight: this confirms seasonal dependency, possibly due to heating emissions in winter or atmospheric stagnation in cold months. - Less vertical mixing and inversion layers in winter. 4. **Correlation of PM\({}_{\mathbf{2.5}}\) and PM\({}_{\mathbf{10}}\) with other variables--subplot (d)** - **Strongest Positive Correlations:** - Benzene (0.82 PM\({}_{\mathbf{2.5}}\), 0.61 PM\({}_{\mathbf{10}}\)): likely from vehicular/industrial emissions. - CO (0.61 PM\({}_{\mathbf{2.5}}\) & PM\({}_{\mathbf{10}}\)): strong indicator of combustion sources. - Ethylbenzene, m/p/o-Xylene, NO\({}_{\omega}\), NO\({}_{2}\), NO: moderate to strong correlations, all linked to traffic and combustion. Figure 6: PMs trends and correlations with season, month, and the studied variables for the shorter period. **Negative Correlations:**  
- Season (\(-0.39\)) & Month (\(-0.25\)): PM levels decline as the months progress, showing a seasonal decay. - Wind velocity (\(-0.19\) to \(-0.20\)): wind helps disperse pollutants, reducing PM levels. - Temperature (\(-0.29\)): warmer temperatures are associated with lower PMs, likely due to better vertical mixing and less heating. - Ozone (\(-0.34\) for PM\({}_{10}\)): indicates inverse behaviour--high PM days might coincide with low ozone due to atmospheric chemistry. **Low or No Correlation:**  
- Wind direction: as expected, direction alone is not a strong indicator.