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http://hdl.handle.net/1942/33188
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DC Field | Value | Language |
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dc.contributor.author | Weichenthal, Scott | - |
dc.contributor.author | DONS, Evi | - |
dc.contributor.author | Hong, Kris Y | - |
dc.contributor.author | Pinheiro, Pedro O | - |
dc.contributor.author | Meysman, Filip J R | - |
dc.date.accessioned | 2021-01-27T13:12:58Z | - |
dc.date.available | 2021-01-27T13:12:58Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2021-01-25T15:36:41Z | - |
dc.identifier.citation | ENVIRONMENTAL RESEARCH, 196 (Art N° 110389) | - |
dc.identifier.issn | 0013-9351 | - |
dc.identifier.uri | http://hdl.handle.net/1942/33188 | - |
dc.description.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. | - |
dc.description.sponsorship | This work was supported by an NSERC Discovery Grant and a CIHR Foundation Grant (Weichenthal PI) and a postdoctoral scholarship from FWO Research Foundation Flanders (Dons). | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.rights | 2020 Elsevier Inc. All rights reserved. | - |
dc.subject.other | Citizen science | - |
dc.subject.other | Convolutional neural networks | - |
dc.subject.other | Deep learning | - |
dc.subject.other | Nitrogen dioxide | - |
dc.title | Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations | - |
dc.type | Journal Contribution | - |
dc.identifier.volume | 196 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 110389 | - |
dc.identifier.doi | 10.1016/j.envres.2020.110389 | - |
dc.identifier.pmid | 33129861 | - |
dc.identifier.isi | 000649620900007 | - |
dc.identifier.eissn | 1096-0953 | - |
local.provider.type | PubMed | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.contributor | Weichenthal, Scott | - |
item.contributor | DONS, Evi | - |
item.contributor | Hong, Kris Y | - |
item.contributor | Pinheiro, Pedro O | - |
item.contributor | Meysman, Filip J R | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2022 | - |
item.fullcitation | Weichenthal, Scott; DONS, Evi; Hong, Kris Y; Pinheiro, Pedro O & Meysman, Filip J R (2021) Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations. In: ENVIRONMENTAL RESEARCH, 196 (Art N° 110389). | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 0013-9351 | - |
crisitem.journal.eissn | 1096-0953 | - |
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 |
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