# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 4 Discussion  
NO\({}_{2}\) concentrations in Cagliari vary significantly, with some generated within the city--primarily from vehicle emissions--and others generated outside. In Europe, traffic emissions are the primary source of nitrogen oxides (NOx) across all levels--national, regional, and urban [39]. According to the 2019 environmental report provided by the Italian National Institute for Environmental Protection and Research (ISPRA), [40], the transport sector in Italy accounts for 46% of NOx emissions nationwide. In the Po Valley, however,  
Figure 7: Air quality comparison. The top panel displays the NO\({}_{2}\) concentration map for the AVG scenario in January 2019. In the bottom panel, after selecting a point on the map, the average hourly NO\({}_{2}\) concentrations throughout the day are compared across three scenarios (AVG, MIN, MAX) with measured values from the nearest monitoring station shown as a placeholder (in this case, the CENCA1 located on the left part of the map). In the AVG scenario, AERMOD input (NO\({}_{2}\) emissions) is calculated hourly by combining emission factors with the average vehicle fleet for each road in Cagliari. In the MIN scenario, the input (emission loads) is based on the average vehicle count minus its standard deviation, while the MAX scenario represents the average vehicle count plus the standard deviation, applied to each road and hour. The pink areas highlight Saturdays and Sunddays. the contribution from transport increases to 50%, based on data from the Eu-funded, Life PREPAIR project [41]. This figure rises further in densely populated areas, reaching 54% in Lombardy and as high as 70% in the city of Milan, according to INEMAR data [42]. In line with those general trends, our modeling results show that hourly concentrations of the NO2 pollutant due to the vehicular traffic range during the day between 28 and 55% of the total, with variations that depend on the hour considered. During rush hours (between 8 and 10.00 am and between 7 and 21 pm), the impact of vehicle emissions is highest. When observation points are sparse, as is often the case, a high-resolution air quality model can produce detailed, physically based pollutant maps. Conversely, with a dense monitoring network, spatial interpolation--relying solely on interpolation algorithms--may suffice. The AERQ modeling framework enhances air monitoring networks by offering deeper insights into local air pollution dynamics, identifying hidden hotspots, and improving the ability to predict events, as illustrated in Figures 6 and 7. Figure 8 presents Business Intelligence (BI) analysis as displayed in the Air Quality by Wind Direction interface. It shows hourly average NO2 concentrations for spring 2019 under different wind conditions: Mistral (upper left), Scirocco (upper right), West wind (lower left), and low wind (lower right). The algorithm evaluates pollutant levels only when wind speeds exceed 5.5 m/s for prevailing winds (Mistral, Scirocco, and West wind in Cagliari) or are below 1.5 m/s for low wind conditions. Key observations for spring 2019 include the following:  
1. Mistral occurred for 263 h with speeds above 5.5 m/s, averaging 7.86 m/s and peaking at 13.4 m/s. 2. Scirocco blew for 199 h, averaging 6.91 m/s and reaching a maximum of 10.8 m/s. 3. The West wind was recorded for 25 h, with an average speed of 7.6 m/s and a peak of 11.3 m/s. 4. Low-wind conditions were observed for 508 h, with an average speed of 1.17 m/s. These results highlight the relationship between wind and pollutant dispersion patterns in the region. Figure 8: Concentrations by wind direction: This figure presents pollutant concentrations under the three primary wind directions, along with no-wind conditions, for spring 2019. Under strong wind conditions, pollution hot spots appear primarily at major road intersections, where vehicles tend to decelerate or idle, intensifying pollution peaks. In these scenarios, pollutant plumes follow the wind direction.