Improving Air Quality Zoning through Deep Learning and Hyperlocal Measurements

According to the Air Quality Directive 2008/50/EC, air quality zoning divides a territory into air quality zones where pollution and citizen exposure are similar and can be monitored using similar strategies. However, there is no standardized computational methodology to solve this problem, and only...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Fernandez, Eduardo Illueca, Valera, Antonio Jesus Jara, Breis, Jesualdo Tomas Fernandez
Format: Article
Language:English
Subjects:
Air quality
artificial neural networks
atmospheric modeling
atmospheric modelling
Clustering
clustering algorithms
Deep learning
Neural networks
Zoning
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Summary:According to the Air Quality Directive 2008/50/EC, air quality zoning divides a territory into air quality zones where pollution and citizen exposure are similar and can be monitored using similar strategies. However, there is no standardized computational methodology to solve this problem, and only a few experiences in the Comunidad of Madrid based on CHIMERE-WRF. In this study, we propose a methodological improvement based on the application of deep learning. Our method uses the CHIMERE-WRF air quality modelling system and adds a step that uses neural networks architectures to calibrate the simulations. We have validated our method in the Region of Murcia. The results obtained are promising given the values of the Pearson coefficient, obtaining r = 0.94 for NO 2 and r = 0.95 for O 3 , improving 86 % and 29 % the performances reported in the state of the art. In addition, the cluster score improves after applying neural networks, demonstrating that neural networks improve the consistency of clusters compared to the current air quality zoning. This opened new research opportunities based on the use of neural networks for dimension reduction in spatial clustering problems, and we were able to provide recommendations for a new measurement point in the Region of Murcia Air Quality Network.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3374208