Vehicle emission models alone are not sufficient to understand full impact of change in traffic signal timings

Abstract

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 NO_x emissions model, and compare these results with NO_x 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 NO_x 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 NO_x 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 NO_x 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 NO_x 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.