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

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. •Citizen science can be used to collected air pollution data over broad spatial scales.•We combined NO2 data collected through citizen science with aerial image and deep learning models to predict annual average NO2 concentrations in Flanders, Belgium.•The final models explained the majority of spatial variations in NO2 using only images as inputs.•This method may be useful in regions lacking land use data or emissions information.