## 3 Results and Discussion  
The methodology proposes to be as precise in spatial resolution as the analysis's cartography is precise. In this case, the spatial resolution of this analysis was that of the neighborhood-level territorial division.  
Building upon the project's initial aim to detail emissions at the neighborhood level, we achieved not only this objective but exceeded it by reaching a resolution at the route level, tracking the specific journeys of emitting vehicles. From this highly precise resolution, we scaled down to calculate emissions across entire zones. These results surpass the original expectations, achieved through an interdisciplinary methodology that integrates emission chemistry, GIS-based route calculations, and other scientific approaches. This comprehensive methodology provides an innovative and detailed framework for emission analysis at multiple spatial scales.  
To represent the results, the mean and standard deviation were chosen as the key reference points, serving as the breakpoints for analysis. Therefore, these are the results:  
More than the 60% of the neighborhoods had less pollution than the mean. Only 15% are significantly above average, which are notably higher compared to the rest of the data (Table 3).  
Figure 3: Example of sections extracted. Before on the left, after on the right.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emitting:** & **CO\({}_{2}\)** & **NOx** & **PM10** \\ \hline \(<\)mean \(-\) stdv/2 & 32\% & 29\% & 32\% \\ \(<\)mean & 34\% & 37\% & 36\% \\ \(<\)mean + stdv/2 & 16\% & 14\% & 9\% \\ \(<\)mean + stdv & 3\% & 5\% & 9\% \\ \(>\)mean + stdv & 15\% & 15\% & 14\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results: percentage of the neighborhoods emitting.  
The three most polluting neighborhoods inside the top 15% are ARRANCAPINS, SANT PAU, and CAMPANAR, located in the center-west of the city. These neighborhoods have the characteristics of being located at the busiest entrances to the city and in the center of the city, where they have the greatest combination of public transport (Figure 4).  
On the other hand, the neighborhoods with the lowest polluting emissions inside the bottom 30% are FAVARA, NAZARET, and L'ILLA PERDUDA. These are characterized by having a small amount of public transport and light traffic (Figure 4).  
The methodology developed categorized/coded each process. Therefore, the quantification of the different emissions from daily transport trips at different scales was obtained with high spatial resolution. It is important to note that these emissions do not include emissions from commercial private sources, as the information from large trucks from private companies are not integrated into ODM. This is because the data source is the 50,000 surveyed people, of which less than 1% of those responses correspond to trucks.  
The application of the methodology developed to the Valencia case study shows that the total amount of daily GHG emissions is 1279 t CO\({}_{2}\) eq., and the total amount of other pollutant emissions that affect air quality are 3.38 t NOx and 0.14 t of PM10 per day. In terms of GHG emissions, the major transport mode is the private car (Figure 5):  
To compare the results, we searched for information within the European Commission's Covenant of Majors [21]. The result of this daily methodology by neighborhoods seen from an annual perspective over the entire city showed 910,310 tonnes of CO\({}_{2}\) emissions. The \"Plan de acicon por el clima\" (PACES) offered a value of 1,121,456 for the entire city in 2019. That value refers to the emissions of all the city's transport. The difference in values could be due to the lack of mobility of private business transport trucks, for example those that enter and leave the port of Valencia daily. These data, not being public, could not be taken into account for the analysis, and the lack of them was mentioned above.  
Figure 4: Total emissions per neighborhood. (**a**) CO\({}_{2}\), (**b**) NOx, (**c**) PM.