Urban emission and air quality modeling informed by microscopic traffic data

Despite decades of regulatory progress, air pollution, continues to pose a major public health burden in European cities. The transport sector remains a dominant contributor to urban air pollution (particularly to NO₂), yet its impacts under real-world driving conditions are still insufficiently quantified. Attribution modeling studies form an important pillar for improving this understanding. However, commonly used approaches most often still rely on static emission inventories, which fail to take the spatial and temporal variability of true traffic dynamics into account. For this reason, modeling errors are often dominated by the emission uncertainties even at street-scale resolution.

This study presents a digital twin framework for urban air quality modeling that integrates the microscopic traffic simulation model SUMO with the urban microscale dispersion model CAIRDIO. In SUMO each vehicle is modeled explicitly on individual interactively managed routes, enabling to dynamically represent real-world traffic conditions, such as congestion patterns leading to emission spikes. Traffic flows and speeds in the network are continuously adjusted at calibration points with available real-time traffic measurements. Emissions are derived directly from simulated traffic data and imported into the CAIRDIO model, which computes urban flow, dispersion and air chemistry based on realistically evolving boundary conditions for meteorological and air composition.

We showcase an application study on the city of Leipzig, for which real-city weather conditions and NOx dynamics are modeled at the neighborhood scale over a period of one week. Based on the results, we assess the contribution of traffic-related emissions to ambient NO₂ levels in different urban micro environments (high traffic sites, residential areas) and discuss the impact of variable atmospheric conditions on dispersion and chemical evolution characteristics. Representation of diurnal peak NO₂ timing and magnitude in the data-driven emission modeling approach is further evaluated against a conventional modeling approach using available static emissions. Finally, the tool’s capability for scenario-based assessment of, e.g., traffic rerouting impacts is demonstrated, which can quantitatively support the implementation of traffic management strategies, infrastructure modifications, and urban air quality mitigation measures.