Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.
Black Carbon
Deep learning
Images
Land use regression
Ultrafine particles
Journal
Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
08
02
2023
revised:
28
06
2023
accepted:
19
07
2023
medline:
21
8
2023
pubmed:
7
8
2023
entrez:
6
8
2023
Statut:
ppublish
Résumé
Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto. We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.
Sections du résumé
BACKGROUND
Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.
OBJECTIVE
This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto.
METHODS
We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models.
RESULTS
In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm
CONCLUSION
Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.
Identifiants
pubmed: 37544265
pii: S0160-4120(23)00379-3
doi: 10.1016/j.envint.2023.108106
pii:
doi:
Substances chimiques
Particulate Matter
0
Air Pollutants
0
Environmental Pollutants
0
Soot
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
108106Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Scott Weichenthal reports financial support was provided by Health Effects Institute.