This study presents the application of CFD modeling for simulating pollutant dispersion from traffic emissions in a highly trafficked urban area in Augsburg, Germany. The CFD model was used to simulate the dispersion of particulate matter (PM), carbon monoxide (CO), and nitrogen oxides (NOx) based on traffic activity data from September 2018. The model used the steady-state RANS approach to solve the velocity field and the convection–diffusion equation to simulate pollutant dispersion. Meteorological data were obtained from a sensor network and compared with high-precision air quality station measurements. A sensitivity analysis was performed to determine the most efficient computational mesh for the model. The model was validated against measurements and showed good agreement, with a coefficient of determination (R²) of 0.82 for NOx and 0.54 for CO. The model was able to capture high spatial resolution of pollutant concentrations, showing variability in pollution levels at the street scale. The results demonstrated that the CFD model can provide accurate predictions of pollutant concentrations, which can be used to assess pollution levels in urban areas and inform pollution reduction strategies. The model also showed that high spatial resolution is essential for understanding pollutant dispersion in urban environments. The study highlights the potential of CFD modeling for urban air quality assessment and the importance of high-resolution data for accurate predictions. The model was validated against measurements and showed good agreement, with a coefficient of determination (R²) of 0.82 for NOx and 0.54 for CO. The model was able to capture high spatial resolution of pollutant concentrations, showing variability in pollution levels at the street scale. The results demonstrated that the CFD model can provide accurate predictions of pollutant concentrations, which can be used to assess pollution levels in urban areas and inform pollution reduction strategies. The model also showed that high spatial resolution is essential for understanding pollutant dispersion in urban environments. The study highlights the potential of CFD modeling for urban air quality assessment and the importance of high-resolution data for accurate predictions.This study presents the application of CFD modeling for simulating pollutant dispersion from traffic emissions in a highly trafficked urban area in Augsburg, Germany. The CFD model was used to simulate the dispersion of particulate matter (PM), carbon monoxide (CO), and nitrogen oxides (NOx) based on traffic activity data from September 2018. The model used the steady-state RANS approach to solve the velocity field and the convection–diffusion equation to simulate pollutant dispersion. Meteorological data were obtained from a sensor network and compared with high-precision air quality station measurements. A sensitivity analysis was performed to determine the most efficient computational mesh for the model. The model was validated against measurements and showed good agreement, with a coefficient of determination (R²) of 0.82 for NOx and 0.54 for CO. The model was able to capture high spatial resolution of pollutant concentrations, showing variability in pollution levels at the street scale. The results demonstrated that the CFD model can provide accurate predictions of pollutant concentrations, which can be used to assess pollution levels in urban areas and inform pollution reduction strategies. The model also showed that high spatial resolution is essential for understanding pollutant dispersion in urban environments. The study highlights the potential of CFD modeling for urban air quality assessment and the importance of high-resolution data for accurate predictions. The model was validated against measurements and showed good agreement, with a coefficient of determination (R²) of 0.82 for NOx and 0.54 for CO. The model was able to capture high spatial resolution of pollutant concentrations, showing variability in pollution levels at the street scale. The results demonstrated that the CFD model can provide accurate predictions of pollutant concentrations, which can be used to assess pollution levels in urban areas and inform pollution reduction strategies. The model also showed that high spatial resolution is essential for understanding pollutant dispersion in urban environments. The study highlights the potential of CFD modeling for urban air quality assessment and the importance of high-resolution data for accurate predictions.