# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 1 Introduction  
In recent years, there has been a growing interest among municipal authorities worldwide in the study and research of smart and sustainable cities. This concept combines human and social capital with the city's infrastructure capital to achieve a sustainable, livable, and efficient city [1]. These cities strive to contribute to the Sustainable Development Goals [2] and attain a carbon-neutral future, as well as a better quality of life for their residents. To achieve these goals, cities are assessed in various areas to comply with national and international strategic plans for decarbonization and environmental sustainability. In this context, key performance indicators (KPIs) have been developed to measure the sustainability of cities, some of which are included in the list elaborated by the International Telecommunication Union in 2017 [3], as well as in various European projects conducted to evaluate smart cities [4]. Within the study of sustainable mobility in smart cities, particularly in terms of passenger and freight transport, aspects related to energy, environment, safety, and security are considered, taking into account the real-time socio-economic dimensions [5]. These aspects are part of the pillars included in the studies of KPIs for smart and sustainable cities, which should be used to evaluate defined objectives with high viability (i.e., data availability and accessibility) and a perceived high importance value by the indicator [6]. Specifically, Barcelona is one of the most populous cities in the European Union and was ranked among the top five smart cities in 2022, according to a study conducted by Juniper Research: \"Smart Cities: Key Technologies, Environmental Impact & MarketForecasts 2022-2026\" [7]. Despite being considered a smart city, analyses initiated in the city on pollutant concentrations indicate that it is well above the targets set by the European Parliament [8], both in terms of peak nitrogen dioxide (NO\({}_{2}\)) concentration values and the citywide average in \(\upmu\)g/m\({}^{3}\)[9]. Another issue with this indicator is whether the average values obtained in the city capture the true behavior of NO\({}_{2}\) across its entire extent. Considering that Barcelona is the second most populous city in Spain, with a population density of 16,339 inhabitants per km\({}^{2}\) and an area of 101.37 km\({}^{2}\)[10], using only eight air quality stations may not provide sufficient information about the behavior of pollutants in the city. This study stems from the need to create a KPI that accurately represents NO\({}_{2}\) concentrations throughout the city in a truthful and instantaneous manner [11]. Therefore, the study has three main objectives: (i) to study the behavior of NO\({}_{2}\) concentrations, (ii) to predict NO\({}_{2}\) concentrations considering the influence of meteorological variables, and (iii) to propose new distributions of air quality stations in the city to obtain information on real pollution levels. To achieve this, we employ a variety of Artificial Intelligence (AI) and Machine Learning (ML) models for analysis and prediction, alongside an optimization algorithm to relocate the air quality stations and provide an optimal distribution. This algorithm includes an original concept for estimating potential new stations based on the identification of sensitive points in the city and the effective area of action of each station. One of the limitations of our study lies in the limited number of air quality stations available in the city. This results in restricted coverage and can provide a biased view of the actual pollution situation in Barcelona, as the measurements are concentrated in a few locations and do not adequately reflect the spatial variability of pollution levels across the entire city. Our proposed optimization algorithm allows for the identification and prioritization of sensitive points in the city, where the presence of emission sources or the vulnerability of the population makes the information obtained more valuable.