Personal measurement studies and modelling investigations are used to examine pollutant exposure for pedestrians in the urban environment: each presenting various strengths and weaknesses in relation to labour and equipment costs, a sufficient sampling period and the accuracy of results. This modelling exercise considers the potential benefits of modelling results over personal measurement studies and aims to demonstrate how variations in fleet composition affects exposure results (presented as mean concentrations along the centre of both footpaths) in different traffic scenarios. A model of Pearse Street in Dublin, Ireland was developed by combining a computational fluid dynamic (CFD) model and a semi-empirical equation to simulate pollutant dispersion in the street. Using local NOx concentrations, traffic and meteorological data from a two-week period in 2011, the model were validated and a good fit was presented. To explore the long-term variations in personal exposure due to variations in fleet composition, synthesised traffic data was used to compare short-term personal exposure data (over a two-week period) with the results for an extended one-year period. Personal exposure during the two-week period underestimated the one-year results by between 8% and 65% on adjacent footpaths. The findings demonstrate the potential for relative differences in pedestrian exposure to exist between the north and south footpaths due to changing wind conditions in both peak and off-peak traffic scenarios. This modelling approach may help overcome potential under- or over-estimations of concentrations in personal measurement studies on the footpaths. Further research aims to measure pollutant concentrations on adjacent footpaths in different traffic and wind conditions and to develop a simpler modelling system to identify pollutant hotspots on our city footpaths so that urban planners can implement improvement strategies to improve urban air quality.