## 2 Materials and Methods
### Data Collection and Monitoring Campaign  
On 31 May 2023, a comprehensive and structured monitoring campaign was conducted to collect essential data for the estimation of traffic-related emissions and for the  
Figure 2: (**a**) Satellite image of the study area within the city; (**b**) 2D view of the investigated area. simulation of air quality within the university campus. Vehicular and pedestrian flows were recorded throughout the entire day to represent the full cycle of campus activity. Air quality measurements were specifically concentrated in the time window between 8:00 a.m. and 2:00 p.m., corresponding to peak mobility hours, including the morning arrival of students, faculty, and staff; lunchtime circulation; and transitions between classes and administrative functions. Two Traffic Monitoring Stations (TMS1 and TMS2) were set up at the main intersection of the campus in order to intercept all characteristic vehicular flows along road Sections A, B, and C, as shown in Figure 3. At each station, a Miovision Scout(r) (Miovision Technologies Incorporated, Kitchener, Ontario, Canada) device was installed--an autonomous, video-based monitoring system equipped with high-efficiency sensors and LTE connectivity. The two units, owned by the Department of Civil Engineering and Architecture at the University of Catania, enable the accurate detection of directional traffic flow and vehicle classification, distinguishing between private cars, motorcycles, buses, and vans. The collected data enabled the development of detailed origin-destination matrices and the segmentation of vehicular flows along the three internal road sections. The recorded values are summarized in Table 2 and were subsequently input into the ENVI-met simulation model as stationary linear emission sources using the \"Sources\" module of the Database Manager. Each source was parameterized based on traffic volume, vehicle fleet composition, and emission rates calculated for each segment. Simultaneously, air quality measurements were carried out using Aeroqual S500 portable sensors (Aeroqual Ltd, Auckland, New Zealand), placed both at the TMS1 and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Section** & **Total** & **\% Private Cars** & **\% Motorcycles** & **\% Buses** & **\% Vans** \\ \hline A & 2736 & 81 & 17 & 1 & 1 \\ B & 1811 & 87 & 12 & 1 & - \\ C & 1314 & 63 & 35 & - & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Road flow values for the three main sections are inserted in the “Sources” area. Figure 3: Road sections A, B, and C and Traffic Monitoring Stations (TMS1 and TMS2). TMS\({}_{2}\) stations and at six additional locations corresponding to specific receptors, described in Section 2.6. The selection of these receptors was based on the following criteria: proximity to the main entrances of academic buildings and student residences and the presence of pedestrian crossings and high-traffic footpaths. At each of these locations, particulate matter concentrations (PM\({}_{10}\) and PM\({}_{2,5}\)) were measured through sampling conducted between 8:00 a.m. and 2:00 p.m., with the aim of assessing the local environmental quality and enabling subsequent comparison with simulation results. Additionally, continuous monitoring was performed at the TMS\({}_{1}\) station for the entire duration of the same time window in order to support the validation process of the ENVI-met model, as described in Section 2.5. For the air quality component as well, the entire experimental setup was designed with attention to spatial and temporal consistency to ensure robustness and reliability in the model-based analyses. The Aeroqual S500 sensors were configured to acquire data at high sampling frequencies, allowing the detection of short-term variations in pollutant concentrations.