# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 6 Conclusions  
This study evaluates the suitability of using the daily average concentration of NO\({}_{2}\) as a KPI to assess air quality in a smart city like Barcelona. This evaluation builds on the availability of high-quality initial data that accurately reflect the actual concentration levels across the city as measured by monitoring stations. After analyzing the behavior of NO\({}_{2}\) using the available data, important relationships are evident between the concentration measured at each station and its location. This is not only linked to traffic in the area but also to the city's meteorological conditions.  
Moreover, the study highlights the importance of station placement, as an inadequate distribution could result in a distorted KPI: overestimated if the stations are concentrated in high-traffic areas, or underestimated if they are mainly located in residential zones. For this reason, strategic points are identified in the city where measuring the NO\({}_{2}\) concentration would provide significant added value. Given that the optimal solution for the distribution of air quality monitoring stations could be very costly, a more conservative alternative is also proposed that minimizes investment. This solution only requires the installation of two additional stations and the relocating of some existing ones to achieve more representative results. Furthermore, it is observed that when the stations are better distributed, the NO\({}_{2}\) KPI value exceeds the thresholds set by the WHO. This suggests that Barcelona needs the continuous and precise monitoring of NO\({}_{2}\) levels to quantify the effects of the policies implemented in the city, enabling informed decision-making that improves air quality.  
Figure 13: Heat map comparison with annual average NO\({}_{2}\) concentrations in 2023. (**a**) Heat map with the initial stations. (**b**) Heat map with minimum scenario. (**c**) Heat map with the 39 stations.  
As a future line of research, it would be highly relevant to correlate real-time traffic data in the city with NO\({}_{2}\) concentration data and consider the population density in each zone. This integration would allow for a better understanding of the relationship between vehicular flow, atmospheric pollutant concentrations, and the population exposed to this pollution, providing a more comprehensive perspective on the impact of traffic on air quality in Barcelona.  
Conceptualization, R.S.-G. and P.C.; methodology, P.C.; software, R.S.-G. and X.A.M.; validation, P.C., R.S.-G. and X.A.M.; formal analysis, R.S.-G.; investigation, P.C., R.S.-G. and X.A.M.; resources, R.S.-G. and P.C.; data curation, R.S.-G.; writing--original draft preparation, R.S.-G. and X.A.M.; writing--review and editing, P.C. and E.P.-B.; visualization, P.C., R.S.-G. and X.A.M.; supervision, P.C.; project administration, E.P.-B.; funding acquisition, E.P.-B. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Ministry of Science and Innovation (PID2022-138860 NB-I00 and RED2022-134703-T), the project SUN (HORIZON-CLA-2022-HUMAN-01-14-101092612), the project UP2030 (HORIZON-MISS-2021-CIT-02-01-101096405) as well as by the Barcelona City Council and Fundacio \"la Caixa\" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001). Funding for open access charge: CRUE-Universitat Politecnica de Valencia.  
Not applicable.  
The original data presented in the study are openly available in [Open Data Barcelona] at [[[https://opendata-ajuntament.barcelona.cat/](https://opendata-ajuntament.barcelona.cat/)]([https://opendata-ajuntament.barcelona.cat/](https://opendata-ajuntament.barcelona.cat/))] (accessed on 10 October 2024).  
The authors declare no conflicts of interest.