Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

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...

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Bibliographic Details
Main Authors: Marshall Lloyd, Arman Ganji, Junshi Xu, Alessya Venuta, Leora Simon, Mingqian Zhang, Milad Saeedi, Shoma Yamanouchi, Joshua Apte, Kris Hong, Marianne Hatzopoulou, Scott Weichenthal
Format: Article
Language:English
Published: Elsevier 2023-08-01
Series:Environment International
Subjects:
Ultrafine particles
Black Carbon
Deep learning
Images
Land use regression
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412023003793

Internet

http://www.sciencedirect.com/science/article/pii/S0160412023003793

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