DIPLOS - Dispersion of Localised Releases in a Street Network

DIPLOS is an EPSRC-funded collaborative project between the Universities of Reading, Southampton and Surrey, and will run from Jan 2014 to Aug 2017. DIPLOS is focused on performing wind tunnel experiments and high-resolution numerical simulations to produce high quality datasets that will then be used to develop and implement parametrizations for dispersion processes in an operational model.

Why DIPLOS?

The security threat level from international terrorism, introduced by the UK Security Service, has been classified as either "severe" or "critical" for much of its six-year history, and currently remains as "substantial" (source: MI5 web site). Part of the risk posed by terrorist threats involves potential releases of air-borne chemical, biological, radiological or nuclear (CBRN) material into highly populated urbanised areas. Smoke from industrial accidents within or in the vicinity of urban areas also pose risks to health and can cause widespread disruption to businesses, public services and residents. The Buncefield depot fire of 2005 resulted in the evacuation of hundreds of homes and closure of more than 200 schools and public buildings for two days; consequences would have been much more severe if prevailing meteorological conditions had promoted mixing or entrainment of the smoke plume into the urban canopy.

In both these scenarios it is crucial to be able to model, quickly and reliably, dispersion from localised sources through an urban street network in the short range, where the threat to human health is greatest. However, this is precisely where current operational models are least reliable because our understanding and ability to model short-range dispersion processes is limited. The contribution that DIPLOS will make is: (i) to fill in the gaps in fundamental knowledge and understanding of key dispersion processes, (ii) to enable those processes to be parametrized for use in operational models, and (iii) to implement them into an operational model, evaluate the improvement and apply the model to a case study in central London.