Predicting outdoor ultrafine particle number concentrations, particle size, and noise using street-level images and audio data.
Audio
Deep learning
Images
Noise
Ultrafine particles
Journal
Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
03
06
2020
revised:
02
08
2020
accepted:
05
08
2020
pubmed:
18
8
2020
medline:
12
1
2021
entrez:
18
8
2020
Statut:
ppublish
Résumé
Outdoor ultrafine particles (UFPs) (<0.1 µm) may have an important impact on public health but exposure assessment remains a challenge in epidemiological studies. We developed a novel method of estimating spatiotemporal variations in outdoor UFP number concentrations and particle diameters using street-level images and audio data in Montreal, Canada. As a secondary aim, we also developed models for noise. Convolutional neural networks were first trained to predict 10-second average UFP/noise parameters using a large database of images and audio spectrogram data paired with measurements collected between April 2019 and February 2020. Final multivariable linear regression and generalized additive models were developed to predict 5-minute average UFP/noise parameters including covariates from deep learning models based on image and audio data along with outdoor temperature and wind speed. The best performing final models had mean cross-validation R
Identifiants
pubmed: 32805577
pii: S0160-4120(20)31999-1
doi: 10.1016/j.envint.2020.106044
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
106044Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.