Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks.
Convolutional neural networks
Deep learning
Land use regression
Ultrafine particles
Journal
Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
01
04
2019
revised:
13
05
2019
accepted:
29
05
2019
pubmed:
12
6
2019
medline:
24
3
2020
entrez:
12
6
2019
Statut:
ppublish
Résumé
We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite images and deep convolutional neural networks as a means of extending the spatial coverage of these models. Our findings demonstrate that this method can be used to expand the spatial scale of LUR models, thus providing exposure estimates for larger populations. The cost of this approach is a small loss in precision as the training data are themselves modelled values.
Identifiants
pubmed: 31185385
pii: S0013-9351(19)30302-0
doi: 10.1016/j.envres.2019.05.044
pii:
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
108513Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.