Smart solutions for urban health risk assessment: A PM
Air quality
Early warning
Graph convolutional network
Health risk
Smart cities
Spatiotemporal forecasting
Journal
Chemosphere
ISSN: 1879-1298
Titre abrégé: Chemosphere
Pays: England
ID NLM: 0320657
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
10
02
2023
revised:
28
04
2023
accepted:
28
05
2023
medline:
26
6
2023
pubmed:
5
6
2023
entrez:
4
6
2023
Statut:
ppublish
Résumé
Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM
Identifiants
pubmed: 37271471
pii: S0045-6535(23)01338-3
doi: 10.1016/j.chemosphere.2023.139071
pii:
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
139071Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.