Multi-scale comparison of modeling chains for traffic-related air pollutant emission assessment

Traffic-related air pollutants pose major health risks in metropolitan areas, with Nitrogen Oxides ( N O x ) being particularly harmful. Reducing road traffic emissions through mobility management is crucial for achieving sustainable urban living. To assess such strategies, reliable modeling chains are essential. Traditionally, these chains couple traffic and pollutant emission models. However, this coupling can introduce significant uncertainty, particularly when traffic and pollution models are developed at different spatial scales. This paper addresses the challenge of examining the effects of spatio-temporal representation of traffic data on emission models. To this end, we compare emissions derived from two widely used European models, COPERT and HBEFA, using traffic data from the microscopic traffic model Symuvia and the agent-based model MATSim. Traffic outputs are aggregated at the trip, street, and square-cell grid levels. Despite significant variations in speed estimation due to mean speed calculation methodologies, emissions estimated at the trip level are generally closely aligned. At the street level, Symuvia is better suited for emission estimations, as it more accurately captures local variations and congested traffic conditions. Emissions estimated at the cell-grid level reveal high spatial variability, influenced by the street network form: higher values are often observed in city centers with MATSim, while Symuvia tends to show higher values on the ring road. Our findings indicate that MATSim+COPERT and Symuvia+HBEFA yield more consistent overall results, primarily due to the route assignment algorithm and the sensitivity of emission models to variations in traffic conditions. Emissions estimated with HBEFA, particularly when coupled with MATSim, tend to be higher than those from COPERT, largely due to differences in how stop-and-go traffic is represented. On the other hand, COPERT estimates are generally lower, especially when coupled with Symuvia, mainly due to the challenges of capturing congested conditions and speed variations with the mean speed representation. • Analysis of four modeling chains for road traffic pollutant emissions. • The MATSim+COPERT and Symuvia+HBEFA combinations are consistent for N O x estimation. • HBEFA tends to overestimate emissions due to inconsistencies in stop-and-go traffic. • COPERT underestimates emissions when paired with mesoscopic traffic models. • Trip-level emission estimates can provide a reliable benchmark for spatially estimated emissions.