2026-04-18T06:11:22Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/705552025-05-31T01:10:55Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Calo Oliveira, Sergio author Bistaffa, Filippo author Jonsson, Anders author Gómez, Vicenç author Viana, Mar author 2025-05-29T08:59:25Z 2025-05-29T08:59:25Z 2024 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. http://hdl.handle.net/10230/70555 Air quality Graph neural networks Graph signal reconstruction Spatial air quality prediction in urban areas via message passing