Validation and optimization of the ATMO-Street air quality model chain by means of a large-scale citizen-science dataset

Detailed validation of air quality models is essential, but remains challenging, due to a lack of suitable high- resolution measurement datasets. This is particularly true for pollutants with short-scale spatial variations, such as nitrogen dioxide (NO2). While street-level air quality model chains can predict concentration gradients at high spatial resolution, measurement campaigns lack the coverage and spatial density required to validate these gradients. Citizen science offers a tool to collect large-scale datasets, but it remains unclear to what extent such data can truly increase model performance. Here we use the passive sampler dataset collected within the largescale citizen science campaign CurieuzeNeuzen to assess the integrated ATMO-Street street-level air quality model chain. The extensiveness of the dataset (20.000 sampling locations across the densely populated region Flanders, 1.5 data points per km(2)) allowed an in-depth model validation and optimization. We illustrate generic techniques and methods to assess and improve street-level air quality models, and show that considerable model improvement can be achieved, in particular with respect to the correct representation of the small-scale spatial variability of the NO2-concentrations. After model optimization, the model skill of the ATMO-Street chain significantly increased, passing the FAIRMODE model quality threshold, and thus substantiating its suitability for policy support. More generally, our results reveal how a "deep validation " based on extensive spatial data can substantially improve model performance, thus demonstrating how air quality modelling can benefit from one-off large-scale monitoring campaigns.

Publication year:2022

Keywords:A1 Journal article

Accessibility:Open