## 3 Results and Discussion
### Modelling Results
#### 3.5.2 Ship Emissions Impact
The decrease of O\({}_{3}\) concentration corresponding to an increase of NOx emissions is in accordance with the fact that although the Italian peninsula is almost entirely NO\({}_{\chi}\)-sensitive, there are well defined VOC-sensitive regions located over urban/industrial areas and harbours ([PERSON] et al., 2012). In such polluted area, O\({}_{3}\) production is more sensitive to VOC emissions, and then it decreases with increasing NOx emissions. In the same area, Mexico et al. (2012) found a similar O\({}_{3}\) decrease (about 5%). Model simulations of the effects of global ship emissions on European domain ([PERSON] et al., 2012) found that the effect of reducing ship emission in the Mediterranean Sea was a widespread O\({}_{3}\) titration close to major shipping lanes and harbour, especially in  
Figure 6: Average relative impact of ship emissions (%) on (**a**) NO\({}_{\chi}\), (**b**) O\({}_{3}\), (**c**) total PM\({}_{2.5}\), (**d**) total PM\({}_{10}\), (**e**) primary PM\({}_{2.5}\), and (**f**) secondary PM\({}_{2.5}\) obtained from BOLCHEM for the period June–October 2012.  
winter. During spring and summer, an increase of O3 was found in the South-Eastern Mediterranean Sea. Figure 7: Average relative impact of ship emissions (%) on (**a**) NOX, (**b**) O3, and (**c**) PM2.5 concentrations obtained from ADMS-Urban at 2 m for the whole period June–October 2012. ADMS-Urban was finally employed to evaluate the spatial distribution of ship-related concentrations of PM\({}_{2.5}\). The simulations were performed considering periods according to the two observed prevailing wind directions (see Section 2.1) based on the analysis of the measured data to facilitate the comparison between simulated concentration and findings derived from chemical characterization. Three different sets of simulations were performed:  
* prevailing winds from northern sectors (SIM_port hereinafter);
* prevailing wind from southern sectors (SIM_urb-ind hereinafter);
* finally, SIM_tot refers to the period comprising all the days considered in the previous two simulations. Averaged concentration of PM\({}_{2.5}\) measured and simulated at ASI position have been compared for the three set of simulations. Table 7 shows that modelled results agreed with measurements given that both shows larger concentration in the SIM_urb-ind case. However, simulations slightly underestimated measured concentrations probably due to different reasons. Emissions related to port activity (loading/unloading phase) and chemical reactions regarding secondary PM were not included in ADMS-Urban. Furthermore, the contribution of urban and industrial emissions was considered as background concentrations obtained from BOLCHEM and thus not explicitly reproduced. The impact at ASI position has been evaluated for the three sets of simulations and was 4.2% for the SIM_tot, 6.8% for the SIM_port, and 0.9% for SIM_urb-ind. The impact is in the range between values estimated by [PERSON] et al. (2011) and [PERSON] et al. (2022) for the same port at the same sampling site. Figure 8 finally synthesises the spatial impact of port emissions on the whole study area showed in % at 2 m for the three sets of simulations. Mean concentration distribution follows the prevailing wind direction with a decrease far away from the sources. Overall, Figure 8a shows the largest impact was located in the hotelling area. Figure 8b,c show that, when the prevailing winds were from southern sectors (SIM_urb-ind), the impact of the port emissions was localized within the port, while when the winds were from northern sectors (SIM_port) the impact of port was larger and confined in the urban area downwind to the sources up to a distance of about 2 km, after that it reduces to lower than 3%.