The Paris region is a densely populated metropolitan area that regularly experiences overrunning regulatory thresholds for nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10). In order to assess the effect of transport policies on air quality and exposure, integrated mobility – emissions – air quality modelling chains have been developed. However, little is known regarding the uncertainties associated with these modelling chains. This study thus aims (a) to develop a street-level modelling chain for air quality assessment based on an agent-based mobility model coupled with an emissions model and an air quality model, and (b) to perform a sensitivity analysis on the calculation of air pollutants concentrations. The developed modelling chain will also allow to evaluate air pollution exposure for the simulated population in future works. We thus develop a mobility-emissions–air quality modelling chain. Mobility and road traffic are simulated using the travel demand agent-based model, MATSim. We then use the outputs of the dynamic traffic assignment to estimate private car emissions at the street level and vehicle level, considering its fuel type and Euro standard. We complete these emissions with emissions inventories for other sectors. Road traffic emissions are assigned to streets in which air quality is simulated with the MUNICH street-network model (Kim et al. 2022). The urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM). In order to study the modelling chain sensitivity, we compare concentrations when using the background CTM model alone (Polair3D) vs the combined CTM and street air quality model. This sensitivity analysis highlights the concentration uncertainty resulting from the use of background pollution concentrations instead of pollution levels in the streets. By combining individual travel patterns and street-level pollution concentrations from this modelling framework, future research will focus on the assessment of personal exposure to air pollution for the region's population.