# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 2 Overview of NO\({}_{2}\) Concentrations  
The growing concentration of urban populations has led to environmental challenges, such as increased air pollution, which affects health in urban settings. A primary contributor to this pollution is NO\({}_{2}\), primarily emitted by diesel engines, which poses serious environmental and human health risks. [PERSON] et al. [12] highlighted that NO\({}_{2}\) significantly contributes to global warming, the greenhouse effect, and climate change. This pollutant is also a primary cause of acid rain, which harms aquatic and terrestrial ecosystems. Regarding human health, [PERSON] et al. [13] showed that exposure to elevated NO\({}_{2}\) is linked to respiratory diseases and lung cancer, with significant mortality rates from these diseases in a studied population in Hefei, China. Women with respiratory diseases appeared more susceptible to air pollution than men. Additionally, [PERSON] et al. [14] warned about health risks in megacities, noting that some cities face higher risks due to high levels of pollutants like NO\({}_{2}\), especially in South Asia. To address these issues, traffic reduction measures, such as pedestrian zones and Low-Emission Zones (LEZs), have been implemented [15]. The effectiveness of these measuresis often questioned, and this is where smart city tools play a crucial role by providing data to scientifically evaluate their impact. Various approaches have been proposed to evaluate smart city interventions. For instance, [PERSON] [16] proposed a seven-step methodology for assessing the impact of these interventions, emphasizing the importance of a comprehensive analysis of the city's long-term vision and cooperation among all stakeholders. Additionally, [PERSON] and [PERSON] [17] analyzed the effectiveness of an LEZ in Madrid, demonstrating its capability to significantly reduce air pollution and noise in the city. Analyzing Shared Mobility Systems (SMSs) is another critical approach to addressing urban transportation challenges. [PERSON] et al. [18] emphasized the need to address traffic-related NO2 concentrations and how SMS solutions can contribute to reducing this pollution in urban areas. To evaluate cities, [PERSON] et al. [19] developed a repository of 75 KPIs in six dimensions, including environmental aspects and concentrations of air pollutants. This repository can serve as a basis for assessing the impact of solutions aimed at improving air quality and reducing pollution in urban environments. The mentioned articles highlight the importance of addressing gas pollutant concentrations, such as NO2, in the context of smart and sustainable cities. A holistic approach involving all stakeholders and interconnected entities is essential to evaluate the impact of smart city interventions and achieve greater efficiency in the urban mobility system. The application of AI and ML techniques, as shown by [PERSON] et al. [12], has been instrumental in developing effective pollution control strategies by predicting NO2 concentrations more accurately. In particular, several works on atmospheric pollution and NO2 concentrations in Barcelona aimed to understand the dynamics of key pollutants, such as NO, NO2, and O3. [PERSON] and [PERSON] [20] utilized the Multivariate Curve Resolution-Alternating Least Squares technique to analyze temporal variations and diurnal profiles of these pollutants. [PERSON] et al. [21] examined the impact of public transportation strikes on air quality, revealing increased NOx and black carbon levels during strikes. [PERSON] et al. [22] investigated the short-term effects of NO2 exposure on cognitive and mental health, while [PERSON] et al. [23] estimated childhood asthma cases attributable to air pollution. [PERSON] et al. [24] developed accurate urban air quality models using operational prediction systems and specific dispersion models. [PERSON] et al. [25] evaluated traffic restriction measures in Barcelona, focusing on reducing NO2 levels. Recently, [PERSON] et al.