Recent figures suggest that emissions are responsible for around 64,000 deaths in the UK each year with air pollution killing nearly as many people as smoking. A further 29,000 deaths were also linked to air pollution through exacerbation of other conditions such as cancer, diabetes and chronic lung disease. In Britain, 98 deaths in every 100,000 can now be attributed to inhaled pollutant chemicals.
To tackle these health concerns, UK local authorities have declared over 500 Air Quality Management Areas, of which 44 are declared due to pollution from roads within National Highways’ network. Nitrogen Dioxide (NO2) is the main pollutant, followed by particulate matter (PM).
Variable speed limits (VSLs) are already delivering major reductions in congestion and improvements in safety across the UK’s motorway network. This project will seek to identify how the use of this VSL technology can also impact on vehicle emissions.
The project, commissioned by National Highways and led by Amey Consulting, researched the improvement air quality by dynamically changing speed limits on the Strategic Road Network (SRN). Making use of existing smart motorway infrastructure, it explored the introduction of flow management to control the speed of traffic on the motorway when poor air quality is predicted, to reduce the effects of poor air quality on health. Prediction work measured traffic congestion with existing traffic detectors, weather conditions from the Met Office, and pollution levels from new air quality sensors. How resulting pollution disperses near these major roads was then modelled.
Sensors reported critical data every minute to deliver air quality predictions at a much higher resolution than previously, with the ability to determine how varying the speed limit can improve air quality in the local vicinity. Frequent modelling and prediction enabled the introduction of ‘air quality’ speed restrictions, only when necessary, in response to each predicted poor air quality event.
Collecting the various data sources and predicting where and when poor air quality occurred was handled by Amey Consulting’s AQATANE system. Developed in collaboration with Newcastle University, it integrates multiple data sources and enables continuous modelling of dispersion in real-time through three modelling components; a traffic congestion model, an emissions model and pollution dispersion modelling. Envirowatch’s “e-Mote” sensors will monitor and report data on nitric oxide (NO), nitrogen dioxide (NO2) and carbon monoxide (CO) levels, as well as trialling new low-cost PM sensors.
Nicander provided their curo360 system to demonstrate the setting of variable message signs and mandatory speed limits when poor quality events were predicted and an alert raised by the AQATANE system; this process was automated through the use of curo360’s rules engine and business process management functionality. curo360 collected and displayed congestion and device status data from National Highways’ NTIS system.
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