## 3 Results and Discussion
### Comparative Analysis of the Investigated Scenarios
#### 3.2.1 Air Quality Analysis  
This subsection presents the air quality simulation results for the three scenarios under investigation, with a particular focus on PM\({}_{10}\) and PM\({}_{2.5}\) concentrations. The simulations were performed using the ENVI-met model at pedestrian height (z = 1.0 m) in order to realistically assess the exposure of campus users to atmospheric pollutants during daily activities. In the baseline scenario, PM\({}_{10}\) concentrations ranged from 0.68 to 1.22 \(\upmu\)g/m\({}^{3}\), while PM\({}_{2.5}\) levels varied between 0.914 and 1.321 \(\upmu\)g/m\({}^{3}\). The highest values were recorded near internal roadways characterized by intense traffic flows, poor ventilation, and the presence of impervious surfaces that promote dust resuspension. These findings confirm that urban morphology and surface material characteristics play a key role in determining the persistence of pollutants near the ground. The introduction of mitigation scenarios resulted in a systematic reduction of both pollutants. In particular, Scenario 1, which includes a 50% reduction in vehicular traffic combined with targeted interventions in vegetation and street layout, proved significantly more effective than Scenario 2, which involves a smaller traffic reduction (30%) but incorporates extensive greening and the use of permeable and photocatalytic materials. Specifically, the average PM\({}_{10}\) concentration decreased from 1.028 to 0.985 \(\upmu\)g/m\({}^{3}\) in Scenario 1 (\(-\)4.2%) and to 1.019 \(\upmu\)g/m\({}^{3}\) in Scenario 2 (\(-\)0.9%). For PM\({}_{2.5}\), levels dropped from 1.078 to 0.997 \(\upmu\)g/m\({}^{3}\) (\(-\)7.5%) in Scenario 1 and to 1.038 \(\upmu\)g/m\({}^{3}\) (\(-\)3.7%) in Scenario 2. These differences were also evident at the receptor level. At receptors 1, 3, 4, and 5--those most exposed to vehicular emissions--the reductions in Scenario 1 were particularly marked, reaching up to \(-\)11.5% for PM\({}_{2.5}\) (receptor 4) and \(-\)5.9% for PM\({}_{10}\) (receptor 5). By contrast, the same receptor 4 in Scenario 2 showed more modest reductions of \(-\)5.8% for PM\({}_{2.5}\) and \(-\)1.2% for PM\({}_{10}\). Even at receptors located in more ventilated or less exposed areas (e.g., receptors 6, 7, and 8), Scenario 1 consistently delivered more substantial improvements. For instance, at receptor 8, PM\({}_{10}\) decreased from 0.750 to 0.719 \(\upmu\)g/m\({}^{3}\) (\(-\)4.1%), and PM\({}_{2.5}\) decreased from 0.950 to 0.925 \(\upmu\)g/m\({}^{3}\) (\(-\)2.6%). In Scenario 2, the reductions at the same receptor were more limited: \(-\)0.8% for PM\({}_{10}\) and \(-\)1.6% for PM\({}_{2.5}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Receptor** & **Scenario** & **MRT (\({}^{\circ}\)C)** & **\(\Delta\)MRT (\({}^{\circ}\)C)** & **\(\Delta\)MRT (\%)** & **UTCI (\({}^{\circ}\)C)** & **\(\Delta\)UTCI (\({}^{\circ}\)C)** & **\(\Delta\)UTCI (\%)** \\ \hline
7 & Baseline & 55.590 & - & - & 27.610 & - & - \\
7 & Scenario 1 & 54.510 & 1.08 & 1.9 & 26.150 & 1.46 & 5.3 \\
7 & Scenario 2 & 54.270 & 1.32 & 2.4 & 25.647 & 1.96 & 7.1 \\ \hline
8 & Baseline & 55.190 & - & - & 27.500 & - & - \\
8 & Scenario 1 & 53.635 & 1.56 & 2.8 & 25.894 & 1.61 & 5.8 \\
8 & Scenario 2 & 54.610 & 0.58 & 1.1 & 26.387 & 1.11 & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Cont. All these data, represented in Figure 9, clearly illustrate the spatial distribution of PM\({}_{10}\) variation across the eight receptors and provide visual confirmation of the greater effectiveness of Scenario 1 in reducing airborne particulate matter. These results suggest that, while the material and greening interventions in Scenario 2 contribute to localized air quality improvements, the magnitude of traffic reduction remains the primary determining factor.