Exploring the Feasibility, Challenges, and Limitations of the URBAIR ® Second-Generation Gaussian Model for Sustainable Regional Air Quality Simulations

Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR ® as a computationally efficient alternative for long-term regional air quality simulations. URBAIR ® was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM 10 ) and nitrogen dioxide (NO 2 ) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R 2 values ranging mostly between 0.2–0.6, RMSE between 16–36 µg.m −3 , and mean bias from −8 to 5 µg.m −3 , indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change.