This paper presents the development and application of a Computational Fluid Dynamics (CFD) model to simulate the dispersion of particulate matter, CO, and NO₂ from traffic activity in a highly busy area of Augsburg, Germany. The model was validated using high-precision air quality station data and meteorological data from a sensor network. The CFD model used the steady-state Reynolds-Averaged Navier-Stokes (RANS) approach to resolve the velocity field and simulate pollutant dispersion, with different molecular diffusion coefficients. A sensitivity analysis was performed to determine the most efficient computational mesh. The results showed that the model accurately predicted pollutant concentrations, with deviations of 11-27% from measurements and regression coefficients between 0.54 and 0.91 during the day. The model can be used to assess pollution levels at the street scale, identify areas of high contamination, and guide urban planning and air quality sensor network calibration. The study highlights the importance of CFD modeling in understanding and mitigating urban air pollution.This paper presents the development and application of a Computational Fluid Dynamics (CFD) model to simulate the dispersion of particulate matter, CO, and NO₂ from traffic activity in a highly busy area of Augsburg, Germany. The model was validated using high-precision air quality station data and meteorological data from a sensor network. The CFD model used the steady-state Reynolds-Averaged Navier-Stokes (RANS) approach to resolve the velocity field and simulate pollutant dispersion, with different molecular diffusion coefficients. A sensitivity analysis was performed to determine the most efficient computational mesh. The results showed that the model accurately predicted pollutant concentrations, with deviations of 11-27% from measurements and regression coefficients between 0.54 and 0.91 during the day. The model can be used to assess pollution levels at the street scale, identify areas of high contamination, and guide urban planning and air quality sensor network calibration. The study highlights the importance of CFD modeling in understanding and mitigating urban air pollution.