Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

Citation: Soriano-Gonzalez, R.; Martin, X.A.; Perez-Bernabeu, E.; Carracedo, P. Modeling and Optimization of NO2Stations in the Smart City of Barcelona. Appl. Sci. 2024,14, 10355. https://doi.org/ 10.3390/app142210355 Academic Editors: Mihaela Popa, S ,erban Raicu, Dorinela Costescu and Grzegorz Karo´n Received: 11 October 2024 Revised: 1 November 2024 Accepted: 8 November 2024 Published: 11 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Modeling and Optimization of NO2Stations in the Smart City of Barcelona Raquel Soriano-Gonzalez , Xabier A. Martin , Elena Perez-Bernabeu and Patricia Carracedo * Research Center on Production Management and Engineering, Universitat Politècnica de València, Ferrandiz-Carbonell, 03802 Alcoy, Spain; [email protected].es (R.S.-G.); [email protected] (X.A.M.); [email protected] (E.P.-B.) *Correspondence: [email protected].es Abstract: The growing problem of nitrogen dioxide (NO 2 ) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO 2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO 2 concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO 2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO 2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment. Keywords: NO 2 concentration; smart cities; KPI environmental; machine learning; intelligent algorithms 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 & Market Appl. Sci. 2024,14, 10355. https://doi.org/10.3390/app142210355 https://www.mdpi.com/journal/applsci Appl. Sci. 2024,14, 10355 2 of 22 Forecasts 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 µg/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. By using this algorithm, we aim to improve the representativeness of the data and provide a more accurate and comprehensive assessment of air quality in densely populated urban areas. This comprehensive and detailed approach will allow us to identify specific areas with higher concentration levels, elucidate the concentration behavior, and assess the effectiveness of current policies and initiatives to reduce pollution levels. Ultimately, this approach will provide valuable insights for designing specific and practical strategies to improve air quality and promote sustainable urban development in the city. The remainder of this paper is structured as follows. Section 2reviews the recent literature on the use of NO 2 indicators in sustainable mobility and its role as an indicator of pollution in smart cities. Section 3introduces the case study on Barcelona and the data sources used. Section 4presents the methodology used to achieve the aforementioned objectives, while Section 5presents the results obtained. Lastly, Section 6outlines the main conclusions and suggests potential avenues for future research. 2. Overview of NO2Concentrations 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. Subramaniam 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, Zhu 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, Gurjar et al. [14] warned about health risks in megacities, noting that some cities face higher risks due to high levels of pollutants like NO2, especially in South Asia. To address these issues, traffic reduction measures, such as pedestrian zones and LowEmission Zones (LEZs), have been implemented [ 15 ]. The effectiveness of these measures Appl. Sci. 2024,14, 10355 3 of 22 is 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, Ntafalias [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, Lebrusán and Toutouh [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. Golpayegani et al. [18] emphasized the need to address traffic-related NO 2 concentrations and how SMS solutions can contribute to reducing this pollution in urban areas. To evaluate cities, Angelakoglou 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 NO 2 , 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 Subramaniam et al. [12] , has been instrumental in developing effective pollution control strategies by predicting NO 2 concentrations more accurately. In particular, several works on atmospheric pollution and NO 2 concentrations in Barcelona aimed to understand the dynamics of key pollutants, such as NO, NO 2 , and O 3 . Malik and Tauler [20] utilized the Multivariate Curve Resolution– Alternating Least Squares technique to analyze temporal variations and diurnal profiles of these pollutants. Basagaña et al. [21] examined the impact of public transportation strikes on air quality, revealing increased NOx and black carbon levels during strikes. Gignac et al. [22] investigated the short-term effects of NO 2 exposure on cognitive and mental health, while Pierangeli et al. [23] estimated childhood asthma cases attributable to air pollution. Benavides et al. [24] developed accurate urban air quality models using operational prediction systems and specific dispersion models. Rodriguez-Rey et al. [25] evaluated traffic restriction measures in Barcelona, focusing on reducing NO 2 levels. Recently, Cican et al. [26] applied two ML techniques to predict the air quality in a city of Bucharest. In particular, the authors used advanced recurrent neural networks, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit models, which achieved improved performance over traditional methods. Wu et al. [27] introduced a novel deep learning model that combines Residual Neural Network, Graph Convolutional Network, and bidirectional LSTM architectures to improve the short-term regional predictions of NO 2 and O 3 concentrations in Shanghai (China). Similarly, Tao et al. [28] developed an ensemble ML model that incorporated deep learning to forecast NO 2 levels using data from 1609 air quality monitors in China. Lastly, El Mghouchi et al. [29] explored multivariable air quality predictions using five hybrid ML models to analyze the relationships between meteorological factors and particulate matter concentrations in Craiova (Romania). The growing body of research highlights the necessity of addressing air pollution, particularly NO 2 , in urban areas to safeguard public health and improve air quality. The combination of traffic reduction measures, the promotion of SMS, and the use of smart city tools are critical steps toward creating healthier urban environments. Active collaboration with local communities and policymakers is essential to ensure the successful implementation of these strategies, ultimately contributing to a more sustainable future for cities. 3. A Case of Study: Barcelona City This study extracts data from Barcelona’s Open Data Air Quality dataset, which stores the hourly measurements of various pollutants from the city’s eight air quality stations, along with daily meteorological data measured at four meteorological stations. Appl. Sci. 2024,14, 10355 4 of 22 3.1. Air Quality Stations To obtain the pollutant concentration data from each air quality station, the corresponding data for each year in the study period (2020–2023) must be downloaded from Open Data Barcelona (https://opendata-ajuntament.barcelona.cat/, accessed on 10 October 2024). These files include the station name, population, province, pollutant code, year, month, day, and hourly variables reflecting the concentration measurements for each pollutant. In addition to these files, it is necessary to download the location of each station and the pollutant codes. For our study, we focused on measurements of NO 2 , which has code 8 in the extracted datasets. The station locations related to NO 2 measurements are detailed in Table 1. Table 1. Air quality stations and their locations. Station ID Longitude Latitude 50 2.1874 41.38640 43 2.1538 41.38530 44 2.1534 41.39870 57 2.1151 41.38750 4 2.2045 41.40390 42 2.1331 41.37880 54 2.1480 41.42610 58 2.1239 41.41843 It is important to note that NO 2 concentrations are measured in µg/m 3 , and historical data in this format have been available since 2018. The average concentrations per year were close to the recommended limit set by the World Health Organization (WHO) at the time (25 µg/m 3 ). In December 2022, the WHO updated its global air quality guidelines, setting a new target of 10 µg/m 3 for the annual average and a maximum value of 25 µg/m 3 for a 24 h period [30]. 3.2. Meteorological Stations To obtain data on the meteorological variables measured in Barcelona during the study period, the corresponding files for each year are downloaded from Open Data Barcelona. Each file contains the measurement date, hour (if applicable), station code, meteorological variable code, and the corresponding value. A separate file from Open Data provides the station identifiers and coordinates, while another file in the same repository lists the codes for meteorological variables and the units in which they are measured. Table 2displays the stations’ identifiers and coordinates. Table 2. Meteorological stations and their locations. Station ID Longitude Latitude D5 2.12379 41.41864 X2 2.18847 41.38943 X4 2.16775 41.38390 X8 2.10540 41.37919 The selected variables for the analysis of meteorological data are Daily Mean Temperature (TM), measured in °C; Daily Mean Relative Humidity (HRM), measured in %; Daily Mean Atmospheric Pressure (PM), measured in hPa; Accumulated Daily Precipitation (PPT), measured in mm; Daily Solar Radiation (RS24h), measured in MJ/m 2 ; Daily Mean Wind Speed at 10 m (VVM10), measured in m/s; and Daily Mean Wind Direction at 10 m (DVM10), measured in degrees. Each variable’s maximum, minimum, and mean values are measured at each meteorological station. For the study, we work with the mean value of each variable. Station X2 only has records for the temperature and relative humidity Appl. Sci. 2024,14, 10355 5 of 22 variables throughout the entire period, and the reasons for this data gap are unknown. Due to the lack of data, this station is not considered in the study. Figure 1shows the location of all the air quality and meteorological stations in Barcelona that are considered in the study. Figure 1. Spatial distribution of air quality stations (red) and meteorological stations (green). 4. Methodology The methodology employed in this study to achieve the proposed objectives is described in this section. Various tools are used to conduct the descriptive analysis, predict NO 2 behavior, and optimize the distribution of stations using an optimization algorithm. In particular, the descriptive analysis is performed using Python 3.10. The libraries NumPy [ 31 ], Pandas [ 32 ], Seaborn [ 33 ], and Matplotlib [ 34 ] are used for data analysis and visualization. The Folium library [ 35 ] is used to work with the map of Barcelona and generate heat maps. The Geocoder library is used to obtain coordinates for points of interest in Barcelona. The Scikit-learn library [ 36 ] is used for clustering analysis concentrations at the stations. This same library is necessary to apply ML models to predict concentrations at each station. As for the optimization algorithm, it is implemented using Python 3.10 with support of Numpy and Pandas libraries to perform array manipulations and manage the input datasets, respectively. 4.1. Descriptive Analysis A detailed analysis of the measured NO 2 concentrations across the city is conducted to identify and observe patterns over different time frames, including daily and weekly behavior. Various graphical representations are created to explore the temporal dynamics of NO2concentrations [37]. First, daily averages are analyzed to examine general trends in NO 2 behavior, investigating daily and weekly variability, showing the influence of traffic emissions and weather conditions [ 38 ]. Additionally, weekly patterns are explored to identify potential cyclical behaviors influenced by factors such as urban mobility and industrial activity. Finally, a spatial analysis is then conducted to distinguish the behavior of NO 2 concentrations at individual monitoring stations. This approach examines the spatial and temporal representativeness of NO 2 monitoring stations in urban settings, highlighting the importance of capturing local variations to obtain a complete picture of air quality [ 39 ]. The analysis is further complemented by using meteorological variables obtained from stations distributed throughout the city. A wind rose is generated to visualize and understand the predominant wind directions and speeds in the city. This tool is essential for identifying local climate patterns and variations in different parts of the city [ 40 ]. This analysis helps in understanding how meteorological conditions contribute to the dispersion of atmospheric pollutants, Appl. Sci. 2024,14, 10355 6 of 22 highlighting the influence of urban climates on air quality [ 41 ]. Additionally, integrating meteorological data with air quality models is crucial to obtain a more accurate view of pollutant dispersion in urban areas [42]. As the final step of the descriptive analysis, a cluster analysis is performed to study the behavior of NO 2 concentrations at the monitoring stations. The hourly average values of NO 2 concentrations at each station during the study period are used to identify common patterns among the stations [ 43 ]. For this analysis, the K-means algorithm is selected, which requires specifying the number of clusters in advance [ 44 ]. To determine the optimal number of clusters, the elbow method is employed [ 45 ]. This method involves plotting the number of clusters against inertia (the sum of squared distances within each cluster), and the point where a significant change in the inertia decrease occurs indicates the optimal number of clusters. In addition, agglomerative hierarchical clustering is applied, a method that does not require specifying the number of clusters initially [ 46 ]. This algorithm treats each point as an individual cluster and successively merges the closest clusters, creating a hierarchical structure. The result is represented by a dendrogram, where the branches indicate the clusters, and the height of each union reflects the Euclidean distance [ 47 ]. In our study, the Euclidean distance is used as the metric that captures the separation between points in an n-dimensional space, providing a clear representation of how NO 2 concentrations vary according to the location of the monitoring stations. 4.2. Behavior Prediction of NO2 Afterward, the NO 2 concentration predictions for each station are analyzed using different methodologies. The objective is to determine which methodology best approximates the data for each particular station. Drawing on several studies [ 12 , 48 , 49 ], we decide to utilize the following methods: 1. K-Nearest Neighbors (KNN): This algorithm is based on the idea that data points with similar characteristics tend to have similar output values. It works by finding the K closest points in the training dataset and predicting the output value based on the majority of the K-nearest neighbors [50]. 2. Decision Tree: This model uses a decision tree to make predictions. Each internal node represents a feature, each branch represents a decision rule, and each leaf represents the prediction result [51]. 3. Support Vector Regression (SVR): SVR is a regression technique based on support vectors that seeks to find an optimal regression function within a feature space. It uses a supervised learning approach to predict output values [52]. 4. Random Forest: Random Forest is an ensemble of decision trees, where each tree votes for the predicted output. The final prediction is determined by selecting the output with the most votes. By combining multiple trees, the risk of overfitting is reduced, and the prediction accuracy is improved [53]. 5. Artificial Neural Network (ANN): ANN is a model inspired by the structure and functioning of the human brain. It consists of a network of interconnected artificial neurons used for making predictions. The model learns from training data by adjusting the synaptic weights of neurons [54]. All the prediction models proposed in this study were validated with the Holdout method, where a 70% data split was used for training (training) and the remaining 30% for testing (test). This division made it possible to evaluate the predictive capacity of the models [ 55 ]. By applying these methodologies to predict NO 2 concentrations at various stations, we aim to identify the best-suited approach for each station based on its specific characteristics and patterns. This analysis will contribute to a better understanding of the performance of different prediction techniques in capturing the complexities and variations of NO 2 concentrations across different locations. To compare the effectiveness of each method at the stations, we use evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R 2 ). These metrics Appl. Sci. 2024,14, 10355 7 of 22 help determine which models offer the best approximation to the observed data at each station. For more details about these statistics, readers are referred to Hrust et al. [56]. 4.3. Optimal Location of Stations Lastly, a new placement of stations at strategic locations is proposed to improve the NO 2 pollution index in Barcelona, and obtain a more representative average of NO 2 concentrations across the city. This is supported by the analysis since it has been determined that the current 8 monitoring stations are insufficient, as they do not cover a large or representative area of the city. To determine the potential new locations, we rely on the report from the European Commission’s Mobility Observatory, using the Eltis method [ 57 ], which recommends selecting 40 sensitive locations in the city distributed as follows: (i) 5 locations near highways, (ii)5 locations near ring roads, (iii) 10 locations near access roads to the city center, (iv) 10 locations near sensitive facilities (schools, hospitals, residences, etc.), (v) 5 locations in low-income neighborhoods, and (vi) 5 locations in recreational areas (sports facilities, parks, museums, etc.). The points chosen for this study are shown in Appendix A, Table A1. These strategically selected points cover the entire metropolitan area and are added to the existing stations to form an expanded set of air quality monitoring stations (Figure 2). This approach allows for a more detailed understanding of NO 2 concentrations in the city, facilitating the implementation of effective measures to improve air quality in critical areas and throughout Barcelona. These results will support future research and environmental management actions, contributing to more effective policies to reduce NO 2 pollution and its adverse impacts. Figure 2. Initial situation of air quality stations (red) and sensitive points for the possible location of new stations (blue). To optimize the placement of air quality monitoring stations in Barcelona, we consider the effective radius of each station, which varies according to traffic levels in the area [ 58 ]. We choose an average radius of 500 m due to the city’s high traffic intensity. This approach also aims to maximize city area coverage, minimizing the overlap between stations to avoid redundant measurements and ensure no areas are left uncovered. However, the budget constrains the number of stations that can be installed, necessitating a balance between maximizing coverage and managing the costs of implementation and maintenance of the stations. This problem can be modeled as a Capacitated Dispersion Problem (CPD) [59] , which aims to maximize the minimum distance between elements. Various approaches have been proposed in the literature to solve the CDP, but it is common to employ heuristics Appl. Sci. 2024,14, 10355 8 of 22 and metaheuristics to solve large-scale instances in short computing times [60] . This study uses an adapted version of the approach proposed by [ 61 ], which has been proven to generate high-quality solutions within short computational times. Algorithm 1outlines the main steps of our algorithm. Algorithm 1 Biased-randomized algorithm. 1: function BR-CDP(I,β,tmax,itmax) 2: S←DESTRUCTIVEHEURISTIC(I) 3: S←LOCALSEARCH(S,itmax) 4: while time ≤tmax do 5: Snew ←DESTRUCTIVEHEURISTIC(I,β) 6: Snew ←LOCALSEARCH(Snew,itmax) 7: if f(Snew)>f(S)then 8: S←Snew 9: end if 10: end while 11: return S 12: end function The algorithm receives as input an instance comprising the stations and the distances between stations, denoted by I , as well as the maximum execution time tmax and the maximum number of iterations without improvement itmax . The algorithm generates a feasible initial solution S by applying a destructive heuristic followed by a local search operator. The destructive heuristic and local search procedures are presented next. At this point, this initial solution becomes the best-found solution so far. Next, the algorithm performs a multistart procedure to generate new solutions until a maximum execution time is reached. In each iteration, the algorithm generates a new solution Snew using a biased-randomized version of the destructive heuristic combined with the local search operator. The biased-randomized heuristic introduces a slight modification in the greedy constructive behavior, which provides a certain degree of randomness while maintaining the logic behind the constructive heuristic. The biased-randomized version considers each element in the edges list with a probability that follows a geometric distribution with a single parameter β∈( 0,1 ) , which controls the relative level of greediness present in the randomized behavior of the algorithm [ 62 ]. By employing a biased-randomized version of the constructive heuristic, multiple alternative solutions can be generated without losing the logic behind the original heuristic. Next, the algorithm compares the newly generated solution to the best-known solution. If the new solution has a lower objective function value, the best-known solution is updated. Once the stopping criterion is met, the algorithm returns the best-found solution. Algorithm 2shows the destructive heuristic procedure. Initially, the heuristic assumes all stations are opened. Then, the edges connecting the stations are sorted in ascending order according to their distance between the stations. Next, an iterative process begins, where certain stations are removed from the solution. At each iteration, an edge is selected from the list of edges in a greedy or biased-randomized manner. The facility to be removed is chosen randomly from the two stations connected by the selected edge, and the edges connected to the deleted facility are also removed from the list of edges. This procedure is repeated until the percentage of open stations falls below the required threshold. Then, the last facility that was removed is reintroduced in the solution to preserve its feasibility, and the initial solution is returned by the procedure. The local search procedure is depicted in Algorithm 3. This procedure involves removing the oldest station from the solution and reconstructing the solution with a station not currently included. It is important to note that a removed station will not be considered for generating a new solution until all older stations (i.e., those added earlier) have been eliminated from the solution. This approach facilitates efficient space exploration while avoiding redundancy in the search process. The procedure continues until a maximum number of iterations without improvement is reached. Appl. Sci. 2024,14, 10355 9 of 22 Algorithm 2 Destructive heuristic procedure. 1: function DESTRUCTIVEHEURISTIC(I,β) 2: S←V 3: edges ←GETEDGES(I) 4: edges ←SORT(edges) 5: while ISFEASIBLE(S)do 6: e∗←SELECTEDGE(edges,β) 7: i∗←SELECTNODE(e∗) 8: S←DROP(S,i∗) 9: end while 10: S←ADD(S,i∗) 11: return S 12: end function Algorithm 3 Local search procedure. 1: function LOCALSEARCH(S,itmax) 2: S′←S 3: noImprov ←0 4: while noImprov <itmax do 5: noImprov ←noImprov +1 6: u←OLDESTSELECTEDNODE(S) 7: S←DROP(S,u) 8: u∗ ←SELECTBESTNODE(V\S) 9: S←ADD(S,u∗) 10: if f(S)>f(S′)then 11: S′←S 12: noImprov ←0 13: end if 14: end while 15: return S′ 16: end function Given the budget constraints on the number of stations that can be installed, we also analyze the impact of the percentage of open stations, which is controlled by a parameter m . When m is set to 0.1, it indicates that only 10% of the stations are open; conversely, setting m to 0.9 means that 90% of the air quality stations are operational. Our objective is to identify the optimal combination of area coverage percentage and overlap among these stations, resulting in the most efficient air quality monitoring network. To achieve this, we utilize a Pareto frontier to evaluate the effects of the parameter m on both the percentage of area coverage and the percentage of overlap between stations. By generating the Pareto frontier, we can discern the trade-offs between the percentage of covered area and the percentage of overlap, enabling us to identify the best configurations that balance maximizing coverage with minimizing redundancy. 5. Computational Results In this section, we present the computational results derived from the methodologies outlined in the previous section. The analysis encompasses the evaluation of NO 2 concentration predictions, and the optimization of the location of stations. 5.1. Behavior of Measured NO2Concentrations The obtained results highlight the importance of temporal and spatial variability in air quality analysis, demonstrating how local conditions and weekly traffic patterns can have a significant impact on NO2concentrations. Figure 3shows the average value per year for each time slot at each station. Although most stations exhibit a similar pattern, with peak concentrations observed around 9:00 a.m. and another local peak in the late hours, the concentration ranges differ across stations. Similarly, Figure 4shows the average value per year for each time slot, considering Appl. Sci. 2024,14, 10355 16 of 22 Figure 12. Minimum scenario: New locations of the minimum stations that should be considered as a result of the problem. To compare the NO 2 concentration KPI calculated in both scenarios with the current situation, we will use the annual mean NO 2 concentration measured across all stations as the KPI. Each new station will be assigned an average concentration value using the KNN algorithm, which has been previously utilized. In this case, the average value for each new station will be determined by the proportional mean distance to the three nearest current stations. The values for the nearest current stations for the studied period are shown in Appendix A, Table A2. After applying the algorithm, the values obtained for each new station are included in Appendix A, Table A3. Figure 13 shows the created heat maps to facilitate visual comparison, considering the annual mean concentration value for each station for 2023. The comparison of these maps reveals that the optimal scenario covers a larger portion of the city and provides a more representative depiction of the current situation. Although the algorithm’s assigned values do not account for traffic conditions or other urban characteristics, they are considered a reasonable approximation for comparing the scenarios. Using the values assigned to the new stations, the NO 2 concentration KPI for the study period is calculated for the different scenarios. The values obtained for each case are shown in Table 5. Throughout the study period, both the minimum and optimal scenarios yield higher KPI values. For the minimum scenario, the KPI increases by 4% to 6%, while for the optimal scenario, the KPI increases by 6% to 9% compared to the current situation. This confirms that the current scenario may be underestimating this indicator for the city and may not capture all the necessary information to accurately represent reality. Table 5. Comparison of the current NO2concentration KPI values (µg/m3) with two scenarios. 2020 2021 2022 Current Scenario [NO2] 23.3372 23.3249 26.4427 Minimum Scenario [NO2] 24.3717 24.0780 28.2378 Optimal Scenario [NO2] 25.3184 25.2244 28.2392 Increase in the Minimum Scenario (%) 4.43 3.23 6.78 Increase in the Optimal Scenario (%) 8.49 8.14 6.79 Appl. Sci. 2024,14, 10355 17 of 22 (a) (b) (c) 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. 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. Appl. Sci. 2024,14, 10355 18 of 22 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. Author Contributions: 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. Funding: This work has been partially funded by the Spanish Ministry of Science and Innovation (PID2022-138860NB-I00 and RED2022-134703-T), the project SUN (HORIZON-CL4-2022-HUMAN01-14-101092612), the project UP2030 (HORIZON-MISS-2021-CIT-02-01-101096405) as well as by the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020–2023 (grant 21S09355-001). Funding for open access charge: CRUE-Universitat Politècnica de València. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original data presented in the study are openly available in [Open Data Barcelona] at [https://opendata-ajuntament.barcelona.cat/] (accessed on 10 October 2024) . Conflicts of Interest: The authors declare no conflicts of interest. Appendix A. Tables Appendix A.1. Location of the 40 Potential New Air Quality Stations Table A1. Location of the 40 potential new air quality stations. ID Item Address 1 Location near highway 1 Carrer número 62 2 Location near highway 2 Carrer de portbou 3 Location near highway 3 Carrer de la mare de deu del port 1 55 Location near highway 4 Gran via Cortes Catalans 5 Location near highway 5 Passeig de García Gracia 15 6 Location ring road 1 Carrer de Can Móra (Ronda Dált 7 Location ring road 2 Carrer de Scala Dei (Ronda de Dalt 8 Location ring road 3 Carrer de Cuzco (Ronda del litoral) 9 Location ring road 4 Carrer de Vicenc Montal (Ronda del litoral) 10 Location ring road 5 Carrer de Aristides Maillol (Ronda del litoral 11 Location city access road 1 Carrrer de Sancho de Ávila 1 ( Av. Meridiana) 12 Location city access road 2 Carrer de Padilla 167 (Av. Cortes Catalanas) 13 Location city access road 3 Carrer de Valencia (Cruce Calle de Padillal) 14 Location city access road 4 Carrer de Bac de Roda (Gran via de las Corts Catalanas) 15 Location city access road 5 Carrer de Rogent (Av. Meridiana) 16 Location city access road 6 Carrer de Badajoz (Av. Diangonal) 17 Location city access road 7 Carre de Bailen (Av. Diagonal) 18 Location city access road 8 Carrer de Balmes (Gran Via Corts Catalanes) 19 Location city access road 9 Passeo de Fabra i Puig (Av. Meridiana) 20 Location city access road 10 Travesia de las Cortes 21 Sensitive location 1 Hospital del mar (Passeig Maritim 25) 22 Sensitive location 2 Hospital de Nens. Carrer Consell de Cent, 437) 23 Sensitive location 3 Hospital dos de Maig (carrer dos de Maig, 301) 24 Sensitive location 4 Hospital Quironsalud Barcelona (Plaza Alfonso Comins 5) 25 Sensitive location 5 Hospital Universitari Dexeus (Calle de Sabino Arana19) 26 Sensitive location 6 Universida de Barcelona (Av. Del Doctor Marañón) 27 Sensitive location 7 Universidad Abierta de Cataluña (Av. Del Tibidabo 31) 28 Sensitive location 8 Centro de dia La Torre Setze ( Calle la Torre 16) 29 Sensitive location 9 Residencia de mayores Felizvita (Av. Josep Tarradellas 38) Appl. Sci. 2024,14, 10355 19 of 22 Table A1. Cont. ID Item Address 30 Sensitive location 10 Centro residencial Bonaire. (Carrer Alt de Pedrell 100) 31 Low-income neighborhood location 1 Carrer del Vesuvi/Nou Barris 32 Low-income neighborhood location 2 Passeig d’Úrrutia/Nou Barris 33 Low-income neighborhood location 3 Calle Huelva/Barrio San Martí 34 Low-income neighborhood location 4 Cami Noy de la RAmbla/Barrio del Raval 35 Low-income neighborhood location 5 Carrer de la Pedrosa/Barrio Trinitat Nova 36 Recreation area location 1 Complejo deportivo Municipal Mar Bella (Av. del litoral) 37 Recreation area location 2 Camp Nou. Calle de Aristides Maillol 12 38 Recreation area location 3 Parque de Montjuic. C/Lleida 35 39 Recreation area location 4 Parque de la Ciutadella. Paseo de Pujades 22 40 Recreation area location 5 Parque Güell. Carrer de Olot 5 Appendix A.2. Current Stations: Location and Annual Mean NO2Concentration in (µg/m3) Table A2. Current stations: location and annual mean NO2concentration in (µg/m3). 2020 2021 2022 ID Longitude Latitude [NO2] [NO2] [NO2] 50 2.1874 41.3864 22.9552 24.5184 27.4114 43 2.1538 41.3853 34.1354 36.8185 25.7095 44 2.1534 41.3987 31.2408 30.8547 41.4748 57 2.1151 41.3875 17.5895 18.2762 34.1493 4 2.2045 41.4039 27.9477 25.4330 31.0970 42 2.1331 41.3788 23.1318 21.6456 21.7994 54 2.1480 41.4261 21.3454 20.2746 20.8797 58 2.1239 41.4184 8.3513 8.7778 9.0200 Appendix A.3. New Scenario Stations: Location and Annual Mean NO2Concentration in (µg/m3) Table A3. New scenario stations: location and annual mean NO2concentration in (µg/m3). 2020 2021 2022 ID Longitude Latitude [NO2] [NO2] [NO2] 1 41.3618 2.1381 25.0244 25.2807 25.9081 2 41.3758 2.1269 23.3542 23.0103 25.4241 3 41.3536 2.1504 29.2182 29.5320 28.2716 55 41.3728 2.1463 29.1856 29.5732 27.5267 5 41.3896 2.1674 30.0775 31.4250 31.4502 6 41.4014 2.1140 15.7675 15.8991 23.2605 7 41.4392 2.1564 20.5333 19.9485 22.6254 8 41.4374 2.2083 24.8566 23.8632 27.4571 9 41.3829 2.1830 25.1718 26.3397 27.6735 12 41.4004 2.1838 26.3689 26.2020 31.6768 15 41.4205 2.1864 24.6427 23.7635 27.2174 16 41.3954 2.1996 26.5924 25.7325 30.9999 18 41.3975 2.1520 31.1350 31.0611 38.4371 19 41.4325 2.1631 21.0723 20.4905 23.5300 20 41.3976 2.1325 23.8908 23.4705 32.1344 21 41.3808 2.1722 28.8873 30.3454 30.3644 22 41.3973 2.1738 28.8754 30.1216 31.5276 23 41.4106 2.1770 27.3635 26.9645 33.3680 24 41.4156 2.1385 18.9812 18.6260 21.5304 25 41.3855 2.1266 22.8310 22.8241 27.0318 28 41.4044 2.1487 29.9043 30.0493 33.9573 29 41.3833 2.1427 29.1355 29.4766 27.7437 30 41.4235 2.1710 26.2113 24.9961 30.1150 Appl. Sci. 2024,14, 10355 20 of 22 Table A3. Cont. 2020 2021 2022 ID Longitude Latitude [NO2] [NO2] [NO2] 31 41.4465 2.1832 26.3096 24.9517 30.0425 33 41.4177 2.1967 27.2233 26.1888 32.0277 35 41.4495 2.1919 24.2373 23.3618 26.4753 36 41.3985 2.2093 27.2300 25.7422 31.2939 38 41.3650 2.1669 29.6844 31.1761 30.4976 40 41.4135 2.1532 27.6891 27.6332 29.7088 50 41.3853 2.1538 34.1354 36.8185 25.7095 57 41.3875 2.1151 17.5895 18.2762 34.1493 4 41.4039 2.2045 27.9477 25.4330 31.0970 54 41.4261 2.1480 21.3454 20.2746 20.8797 58 41.4184 2.1239 8.3513 8.7778 9.0200 References 1. Toli, A.M.; Murtagh, N. The concept of sustainability in smart city definitions. Front. Built Environ. 2020,6, 77. [CrossRef] 2. Sustainable Development Goals. Available online: https://bit.ly/2R8siwl (accessed on 17 July 2024). 3. International Telecommunication Union. Available online: https://unece.org/fileadmin/DAM/hlm/documents/Publications/ U4SSC-CollectionMethodologyforKPIfoSSC-2017.pdf (accessed on 17 July 2024). 4. 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