Smart City Gnosys
Smart city article details
| Title | Modeling And Optimization Of No2 Stations In The Smart City Of Barcelona |
|---|---|
| ID_Doc | 37501 |
| Authors | Soriano-Gonzalez R.; Martin X.A.; Perez-Bernabeu E.; Carracedo P. |
| Year | 2024 |
| Published | Applied Sciences (Switzerland), 14, 22 |
| DOI | http://dx.doi.org/10.3390/app142210355 |
| Abstract | 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. © 2024 by the authors. |
| Author Keywords | intelligent algorithms; KPI environmental; machine learning; NO<sub>2</sub> concentration; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
10552  | 0.862 | Neo E.X.; Hasikin K.; Lai K.W.; Mokhtar M.I.; Azizan M.M.; Hizaddin H.F.; Razak S.A.; Yanto | Artificial Intelligence-Assisted Air Quality Monitoring For Smart City Management | PeerJ Computer Science, 9 (2023) |
| 58525 | 0.858 | Ilarri S.; Trillo-Lado R.; Marrodán L. | Traffic And Pollution Modelling For Air Quality Awareness: An Experience In The City Of Zaragoza | SN Computer Science, 3, 4 (2022) |
| 27176 | 0.857 | Kant S. | From Data To Decision-Making: Utilizing Decision Tree For Air Quality Monitoring In Smart Urban Areas | International Journal of Information Technology (Singapore), 17, 1 (2025) |
| 42864 | 0.856 | Varde A.S.; Pandey A.; Du X. | Prediction Tool On Fine Particle Pollutants And Air Quality For Environmental Engineering | SN Computer Science, 3, 3 (2022) |
| 37149 | 0.855 | Chen J. | Mitigating Nitrogen Dioxide Air Pollution: The Roles And Effect Of National Smart City Pilots In China | Energy, 263 (2023) |
| 49184 | 0.855 | Garrido-Hidalgo C.; Solmaz G.; Jacobs T.; Roda-Sanchez L. | Smart Beestricts: Improving The Spatial Resolution Of Air-Quality Data In Madrid Through Transfer Learning | International Journal of Geographical Information Science (2025) |
| 1955 | 0.853 | Alshuwaikhat H.M.; Basheer M.A.; AlAtiq L.T. | A Gis-Based Approach To Determining Optimal Location For Decentralized Inner City Smart Filters: Toward Net Zero Cities | Heliyon, 10, 11 (2024) |
| 58757 | 0.853 | Njaime M.; Abdallah F.; Snoussi H.; Akl J.; Chaaban K.; Omrani H. | Transfer Learning Based Solution For Air Quality Prediction In Smart Cities Using Multimodal Data | International Journal of Environmental Science and Technology, 22, 3 (2025) |
| 29758 | 0.85 | Ajnoti N.; Gehlot H.; Tripathi S.N. | Hybrid Instrument Network Optimization For Air Quality Monitoring | Atmospheric Measurement Techniques, 17, 6 (2024) |