Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations

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.