期刊
ENVIRONMENTAL RESEARCH
卷 196, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2020.110389
关键词
Nitrogen dioxide; Citizen science; Deep learning; Convolutional neural networks
资金
- NSERC Discovery Grant
- CIHR Foundation
- FWO Research Foundation Flanders (Dons)
A novel approach was developed to estimate outdoor nitrogen dioxide concentrations using ground-level measurements, aerial images, and deep neural networks. The model explained 79% of spatial variability in NO2 concentrations and may be particularly useful for regions lacking detailed emissions data or land use information.
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 ?g/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.
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