
doi: 10.3390/app142210355
The growing problem of nitrogen dioxide (NO2) 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 NO2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO2 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 NO2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.
Technology, smart cities, QH301-705.5, T, Physics, QC1-999, 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles, KPI environmental, Engineering (General). Civil engineering (General), Intelligent algorithms, Chemistry, machine learning, Machine learning, NO<sub>2</sub> concentration, 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades, intelligent algorithms, NO2 concentration, TA1-2040, Biology (General), QD1-999, Smart cities
Technology, smart cities, QH301-705.5, T, Physics, QC1-999, 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles, KPI environmental, Engineering (General). Civil engineering (General), Intelligent algorithms, Chemistry, machine learning, Machine learning, NO<sub>2</sub> concentration, 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades, intelligent algorithms, NO2 concentration, TA1-2040, Biology (General), QD1-999, Smart cities
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