Air pollution mapping and variability over five European cities

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO₂ and PM_2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO₂, PM₂.₅) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring.

To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method’s capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation—particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO₂ and BC than PM_2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM₂. ₅ (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.