Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

Abstract

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 chemistry transport models. 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.

Keywords

• Air quality
• artificial neural networks
• atmospheric modeling
• clustering algorithms
• deep learning