Summary and recommendations from the workshop 'Integrating measurements and atmospheric-dispersion modelling to enhance the UK response to radiological atmospheric releases'.

Effective preparedness and response to an atmospheric release following a radiological incident relies on information concerning the source, transport and eventual removal of the contaminant. A notable improvement to emergency preparedness and response in the UK to airborne releases of radiological contaminants can be achieved through the integration of information sources, in particular environmental radiological measurements and atmospheric-dispersion modelling. A one-day workshop was organised by the UK Met Office and the University of Bristol, comprising private nuclear facility operators, public bodies, academia and others, on 6 February 2020 in Bristol, UK. The workshop reviewed the current capabilities and challenges of measurements and modelling of airborne radiological contaminants and their integration, and identified improvement pathways. This memorandum provides a summary of recommendations from the workshop.

Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach.

For the first time since the Chernobyl accident, detectable concentrations of ruthenium-106 were measured across Europe in September and October 2017. The source of this radioactive cloud remains unconfirmed. In this paper we present a forensic inverse modelling study to simultaneously estimate the source location, timing and magnitude of the unexpected ruthenium-106 release using 473 measurements of atmospheric concentration. To do this, we introduce a novel method, which estimates the uncertainty in the often unknown transport error using a Markov chain Monte Carlo approach. We corroborate the conclusions of other studies which suggest the source location is in the Southern Ural region of Russia, where the Mayak nuclear complex is located. Assuming that the Mayak nuclear complex is the most plausible release location, the method estimates that 441±13 TBq was released 12:00-18:00 UTC 24 September 2017, assuming a six hour release window.

An early warning system to predict and mitigate wheat rust diseases in Ethiopia.

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.