Passive sampling-based assessment of NO2 dispersion modelling approaches in a complex urban traffic hotspot

Abstract

Rapid urban expansion and sustained growth in road traffic in Prague have led to elevated nitrogen dioxide (NO₂) concentrations in complex street-canyon settings, demanding accurate microscale dispersion modelling to guide effective health and policy interventions. This study evaluates the performance of two commonly applied urban dispersion models, ADMS-Urban and GRAL, driven by the 2024 hourly meteorological data, traffic emissions, and building geometries. Model outputs were evaluated against the matched 2024 monthly passive sampling data collected across five receptor locations in the Smichov district. Measured overall mean NO₂ concentrations varied substantially across sites. ADMS-Urban exhibited marked site-independent bias, overestimating concentrations at background site Graficka (e.g., 32.3 versus 22 μg/m³ observed) while underestimating levels at traffic-dominated site Plzenska (40.3 versus 54.6 μg/m³). In contrast, GRAL consistently underestimated concentrations acrosss receptors but with smaller discrepancies (typically within 2.5–11.2 μg/m³). When evaluated across all paired receptor-month observations, GRAL demonstrated stronger statistical agreement, achieving higher correlation (e.g., r = 0.59 at Graficka receptor point) and lower root mean square error (6.09 μg/m³ at Gymnasium receptor point). Taylor diagram analysis and geospatial mapping further confirmed GRAL's improved representation of concentration gradients and localized hotspots, particularly within complex street-canyon geometries. By comparison, ADMS-Urban produced larger and less consistent deviations, especially along densely built traffic corridors. The findings underline the critical role of model selection and receptor-specific evaluation when assessing urban air quality in heterogeneous urban environments.