Modelling provides useful insights on air quality, especially when supported by monitoring data. State of the art air quality modelling can provide accurate predictions of emissions and air quality across the rail network, at locations not yet covered by monitoring. Modelling also allows future scenarios to be forecast as new rolling stock and traction technologies are introduced.
Under the CLEAR programme, RSSB has established new emission factors for rail. The work helps to improve our understanding by providing an updated and more accurate picture on the emissions rates of different train types. The new data has been incorporated into the National Atmospheric Emissions Inventory (NAEI) and London Atmospheric Emissions Inventory (LAEI).
As part of the T1186 project, an online mapping tool has been developed visualising the emissions across the GB rail network, incorporating the updated emission factors from T1187 as well as the latest rail activity data available from Network Rail live data feeds.
Initial trials of dispersion modelling software have also been carried out to understand the impact of emissions from rail on local air quality.
Resources
Rail Emissions and Air Quality Mapping
Fleet-Wide Assessment of Rail Emissions Factors
You may also be interested in:
CLEAR - Rail air quality mapping (T1186) - Research to produce a rail-specific mapping tool, with an up-to-date picture and improved resolution, of rail emissions across the GB rail network.
CLEAR: Fleet wide assessment of rail emissions factors (T1187) – Research to assess the national and local impact of rail on air quality more accurately.
National Atmospheric Emissions Inventory (NAEI) - The NAEI compiles estimates of emissions to the atmosphere from UK sources such as cars, trucks, power stations and industrial plant.
London Atmospheric Emissions Inventory (LAEI) - The LAEI is the key tool for air quality analysis and policy development in London.
Haven’t found what you’re looking for?
Get in touch with our Air Quality Lead for more information.
