# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 2 Materials and Methods
### Calibration and Validation of the Model
Together the MSE, MSEP, and R\({}^{2}\) give a fuller picture of model performance: ideally, a model with a low MSEP and MSE and high R\({}^{2}\) has accurate predictions and a good fit. Figure 2: The average hourly distribution throughout the day for March and April 2019. The measured data exhibit a regular daily cycle with two peaks and two minima (average: 25.84; standard deviation: 14.78). The high standard deviation indicates significant variability within the sample. The black line represents the measured NO\({}_{2}\) values, the blue line indicates the best run, and the red dotted line corresponds to the first run. patterns, temperatures, and atmospheric stability, which, in turn, affect the dispersion of pollutants. Incorporating this effect in the model helped better simulate local air quality in the Cagliari domain by adjusting for these localized climate effects. Once calibrated, the model was then run for the entirety of 2019, capturing seasonal and daily variations in vehicle emissions and atmospheric conditions. Air pollution peaked during winter and fall due to increased traffic and low-wind conditions that trap pollutants. Summer experienced cleaner air thanks to lighter traffic, high-pressure systems, and stronger winds. Daily patterns remained consistent across seasons, with air pollution peaking between 8-10 a.m. and 7-10 p.m. In winter and fall, however, model estimates at 6 p.m. tended to be higher than observed values, likely due to micro-atmospheric factors causing numerical anomalies. The MSE, MSEP, and R\({}^{2}\) values calculated for the four seasons demonstrate a strong alignment between the simulated results and the observed experimental data, highlighting the reliability of the proposed modeling system. Specifically, the model achieved the following performance metrics:  
1. Winter: MSE = 36.03, MSEP = 1.25, R\({}^{2}\) = 0.72;
2. Autumn: MSE = 62.53, MSEP = 1.71, R\({}^{2}\) = 0.73;
3. Spring: MSE = 27.4, MSEP = 1.03, R\({}^{2}\) = 0.74;
4. Summer: MSE = 21.5, MSEP = 1.42, R\({}^{2}\) = 0.72. These results validate the model's capability to accurately estimate the environmental impact of vehicular traffic in the city of Cagliari across all seasons. Notably, the lower MSE and MSEP values observed in spring and summer suggest better predictive accuracy during these periods, although the R\({}^{2}\) values remain consistently high across all seasons, reflecting strong agreement between predictions and observations. Given its robust performance, the model is well suited for integration into a web-based platform. This enables stakeholders to simulate and analyze various traffic and pollution scenarios, supporting data-driven decision making to mitigate environmental impacts. The platform can generate detailed seasonal maps by leveraging high-resolution output, enabling the in-depth spatial analysis and visualization of pollutant distributions and traffic-related emissions throughout the year. Near major roads, the model showed that traffic-induced mechanical mixing dominated over mixing driven by atmospheric turbulence, especially under calm cross-road wind conditions that made plumes rise more significantly. The model tended to underestimate pollutant concentrations during southeast and east winds coming from the sea, highlighting specific areas where the more detailed characterization of traffic and atmospheric conditions (resulting in a stronger calibration) could enhance accuracy.