Air quality (AQ) is a growing concern, especially in urban
areas where high-density populated regions are exposed to
frequent exceedances of regulated pollutants. To take action in
reducing citizen exposure to pollution, a reliable assessment of
the pollutants’ ambient concentrations across the city is
required.
Street-scale AQ models are designed to capture the typical
spatial variability that pollutants exhibit in the urban
morphology. Such urban models are generally nested to
regional AQ models and use the information of traffic
emissions, together with meteorological conditions, and a
geometric description of the building’s layout, to provide an
estimation of the dispersion of target pollutants at the street
scale. However, results of urban AQ models are subjected to
uncertainties, mainly due to the multiscale behavior of the
phenomenon and to the challenges of characterizing the wind
flow within street-canyons, which encompasses multiple
emission sources and the downscaling of meteorological
variables.
To minimize these uncertainties, we present a data-fusion
method that combines the model results, obtained using the
CALIOPE-Urban [1] model, with publicly available
observations from the official monitoring network in Catalonia
(XVPCA). This method is derived to preserve the spatial
variability of the urban model. As a test case, we then present
annual bias-corrected results of the NO2 levels across the city
of Barcelona for the year 2019. Results correspond to the
legislated annual mean and the 19th daily maximum value of the
year.
