Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling

This study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO₂ concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO₂ concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO₂ annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models.

The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization.

Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.