RT journal article
T1 Reducing Pollution Health Impact with Air Quality Prediction Assisted by Mobility Data
A1 Morales García, Juan
A1 Ramos Sorroche, Emilio
A1 Balderas Díaz, Sara
A1 Guerrero Contreras, Gabriel José
A1 Muñoz Ortega, Andrés
A1 Santa, Jose
A1 Terroso Sáenz, Fernando
A2 Ingeniería Informática
K1 Pollution
K1 Prediction
K1 Machine Learning
K1 Health
K1 Graph Neural Networks
K1 Internet of Things
K1 Road Traffic
AB Countries all around the world recognise the impact of air quality on public health, advocating for city centre decarbonisation and pollutant monitoring via Internet of Things technologies. Using data collected from these systems, it is possible to generate models that predict pollution based on regular patterns where mobility data can enhance the accuracy and robustness of these advanced machine learning models. This paper follows this approach, utilising vehicle traffic data from image recognition, on-site vehicle detectors, and synthetic data to maximise prediction accuracy in various urban environments. The results reveal that this proposal improves prediction for traffic-related pollutants, such as SO2 and PM2.5 , which are linked to severe respiratory diseases. These results also highlight the role of synthetic data in enhancing prediction performance under limited datasets.
PB IEEE
SN 2168-2194
YR 2024
FD 2024-11-28
LK http://hdl.handle.net/10498/34140
UL http://hdl.handle.net/10498/34140
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026