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Few studies have considered the real-world impact of changes in traffic signal timings on air pollution and pedestrian exposure with most only drawing their conclusion from vehicle emission models alone. Here, we consider two distinct cycle timings at a junction in London, UK, model the impact using a traffic microsimulation and a NOx emissions model, and compare these results with NOx and other air pollution measurements collected during a two-week field study at the junction.Our models predict that extending the cycle time leads to a 23% decrease in NOx emissions within a 15 m radius of the junction itself. When the wind direction was such that our sensors were downwind of the junction a 21% decrease in traffic and background-adjusted NOx concentrations were seen, suggesting that the intervention was successful. However, when the sensors were upwind of the junction, we observed an increase of 23% in adjusted NOx concentrations. Similar patterns were found for the other pollutants NO2, lung deposited surface area, black carbon and CO2 we measured. This indicates that meteorology was by far the greatest determinant of roadside concentrations during our two-week study period.Looking at pedestrian exposure for pedestrians waiting to cross the road, we found that their NOx exposure increased by 46% as waiting times to cross the road increased and that potential small reductions in air pollution were offset by increases in waiting times on the main road.The study demonstrates the need to go beyond assessing the impact of hyper-local traffic interventions on vehicle emissions. Real-world trials over extended periods are required to evaluate the impact of meteorology and changes to air pollution concentrations and pedestrian exposures.

The large-eddy simulation (LES) model uDALES is used to evaluate the predictive skill of the operational air quality model SIRANE. The use of LES in this study presents a novel approach to air quality model evaluation, avoiding sources of uncertainty and providing numerical control that permits systematic analysis of targeted parametrisations and assumptions.A case study is conducted over South Kensington, London with the morphology, emissions, meteorological conditions and boundary conditions carefully matched in both models. The dispersion of both inert (NOx) and reactive (NO, NO2 and O3) pollutants under neutral, steady-state conditions is simulated for a south-westerly and westerly wind direction. A quantitative comparison between the two models is performed using statistical indices (the fractional bias, FB, the normalised mean squared error, NMSE, and the fraction in a factor of 2, FAC2).SIRANE is shown to successfully capture the dominant trends with respect to canyon-averaged concentrations of inert NOx (FB = −0.08, NMSE = 0.08 and FAC2 = 1.0). The prediction of along-canyon velocities is shown to exhibit sources of systematic error dependant on the angle of incidence of the mean wind (FB = −0.18). The assumption of photostationarity within SIRANE (deviations from equilibrium of up to 170% exist close to busy roads) is also identified as a significant source of systematic bias resulting in over- and underpredictions of NO2 (FB = −0.18) and O3 (FB = 0.14) respectively. The validity of the assumed uniform in-canyon concentration is assessed by analysing the pedestrian, leeward and windward concentrations resolved in uDALES. The use of canyon-averaged concentrations to predict pedestrian level exposure is shown to result in significant underestimations. Linear regression is used to effectively capture the relationship between pedestrian- and canyon-averaged concentrations in uDALES. Correction factors are derived (m≈1.62 and R2=0.92 for inert NOx) that are capable of significantly improving SIRANE's ability to assess pedestrian level exposure for the conditions investigated in this study. The prevalence of intersections and advective nature of the shear layer are highlighted as important differences between modelling real heterogeneous urban morphology and idealised infinite canyons. Keywords: Urban air quality, Model evaluation, Large-eddy simulation, Street canyon modelling, Traffic emissions, Pollutant dispersion