2026-04-14T05:09:40Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/705552025-05-31T01:10:55Zcom_2072_6col_2072_452952
RECERCAT
author
Calo Oliveira, Sergio
author
Bistaffa, Filippo
author
Jonsson, Anders
author
Gómez, Vicenç
author
Viana, Mar
2025-05-31T01:10:55Z
2025-05-31T01:10:55Z
2025-05-29T08:59:25Z2025-05-29T08:59:25Z2024
http://hdl.handle.net/10230/70555
Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant contributions: 1) We formalize the air pollution prediction problem as a Maximum A Posteriori (MAP) estimate within the framework of a Markov Random Field and 2) we propose a message-passing algorithm, which stands out as an efficient solution that surpasses the current state of the art. The experimental procedure has been carried out using the case study of the city of Barcelona, based on a dataset extracted from the BCN Open Data portal.
© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess
Air quality
Graph neural networks
Graph signal reconstruction
Spatial air quality prediction in urban areas via message passing
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion