Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/33188
Title: | Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations | Authors: | Weichenthal, Scott DONS, Evi Hong, Kris Y Pinheiro, Pedro O Meysman, Filip J R |
Issue Date: | 2021 | Publisher: | ACADEMIC PRESS INC ELSEVIER SCIENCE | Source: | ENVIRONMENTAL RESEARCH, 196 (Art N° 110389) | Abstract: | Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO2) concentrations using approximately 20,000 ground-level measurements in Flanders, Belgium combined with aerial images and deep neural networks. Our final model explained 79% of the spatial variability in NO2 (root mean square error of 10-fold cross-validation = 3.58 μg/m3) using only images as model inputs. This novel approach offers an alternative means of estimating large-scale spatial variations in ambient air quality and may be particularly useful for regions of the world without detailed emissions data or land use information typically used to estimate outdoor air pollution concentrations. | Keywords: | Citizen science;Convolutional neural networks;Deep learning;Nitrogen dioxide | Document URI: | http://hdl.handle.net/1942/33188 | ISSN: | 0013-9351 | e-ISSN: | 1096-0953 | DOI: | 10.1016/j.envres.2020.110389 | ISI #: | 000649620900007 | Rights: | 2020 Elsevier Inc. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S001393512031286X-main.pdf Restricted Access | Published version | 4.99 MB | Adobe PDF | View/Open Request a copy |
author_manuscript.pdf | Peer-reviewed author version | 671.39 kB | Adobe PDF | View/Open |
Weichenthal,2021 accepted.pdf | Non Peer-reviewed author version | 999.23 kB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
5
checked on Apr 22, 2024
Page view(s)
48
checked on Sep 6, 2022
Download(s)
8
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.