In the event of a malicious or accidental release of airborne chemical agents, the emergency services need rapid information on the location and type of release. Traditionally, this required the deployment of large numbers of chemical sensors, which is prohibitively time consuming and expensive. DYCE sought to mitigate this limitation through the development of deployment planning tools that react to gathered data and instruct the dynamic redeployment of a limited set of wireless sensor nodes, thereby optimising their data gathering capability. The solution proposed in this project addresses the needs of military and blue light responders for rapid, reliable on-scene analysis of contaminant dispersion in an urban or industrial environment.
The overall aim of DYCE was therefore to develop the capability to detect and identify airborne chemical agents and estimate the location and strength of their source. This capability was designed to operate in complex industrial and urban environments where the sensors are faced with the need to identify very low concentrations, often in the presence of high levels of benign pollutants and rapidly changing turbulent meteorological conditions.
The work was undertaken by a consortium led by SELEX Galileo and included academic partners, the Universities of Surrey and Reading, plus the sensor developers, Owlstone. The sensors were designed to be small, easily deployable and able to provide rapid concentration measurements of a wide range of chemical agents. SELEX Galileo led the project and developed the system design and communications. The Universities of Reading and Surrey developed inverse algorithms for using the concentrations to infer the location and strength of the source of the chemical agent.
The system is based on a small network of ad-hoc deployable sensor nodes that are able to monitor and react to changing local conditions and chemical data content to enable end-users to adjust sensor locations dynamically in order to optimise network performance. The solution is novel in that it addresses the combined issues of:
- 1. Optimising data gathering through intelligent organisation of the sensor network.
- 2. Monitoring localised changing environmental conditions and understanding its repercussions on the data-gathering mission.
- 3. Operating effectively in complex urban and industrial environments by incorporating wireless communications needs into the deployment strategy.
- 4. Developing a deployment planning solution to optimise the data gathering mission given a constrained or unconstrained asset base.
The algorithm developed by the Universities of Reading and Surrey was based on a formal maximum likelihood inverse method. It consisted of a forward model, which, given the source strength and location, predicts the dispersion of the chemical agent to give the concentration field. The difference between the forward model predictions (with the current estimate of the source strength and location) and the measured concentrations is then formed into a cost function, accounting for error estimates. A next estimate at the source strength and location is then computed by the algorithm. The process is repeated until the cost function is minimized, and the most likely source strength and location is found. The process automatically provides estimates of the uncertainty in its output.
Controlled dispersion experiments the EnFlo wind tunnel at the University of Surrey, were carried out both with and without an array of model buildings. This involved flow visualisation, single and multi-point, simultaneous concentration measurement, and laser-Doppler anemometry. The latter was particularly important in determining the flow speeds in the street network between the model buildings. The data were used to assess new models for dispersion in urban areas and to test the inverse algorithm. Related field work demonstrated the capability of the Owlstone sensor systems and the associated deployment and incident management facility. Overall, the DYCE project demonstrated a capability for rapid identification of the type and concentration of a chemical agent and estimation of the location and source of that agent. The complete system had the potential to operate successfully in complex industrial and urban environments.
Rudd, A. C., Belcher, S. E., Robins, A. G., & Lepley, J. J. (2012, July). An Inverse Method for Determining Source Characteristics for Emergency Response Applications. Boundary-Layer Meteorology, 144(1), 1-20.
J. J. Lepley, D. Lloyd, A. Robins, A. Wilks, A. Rudd and S. Belcher, Dynamic sensor deployment for the monitoring of chemical releases in urban environments (DYCE), SPIE Security, Defence and Sensing, Orlando, Florida, April 2011, p8018.
J.J. Lepley, A Rapidly Deployable Chemical Sensing Network for the Real-Time Monitoring of Toxic Airborne Contaminant Releases in Urban Environments, SPIE Security, Defence and Sensing, Orlando, Florida, April 2010.
A. Wilks, Development of a Chemical ID System Compatible within the Hydra Distributed, Multimodal, Networked Surveillance System, Chemical Detection Systems for Security Applications - Further Needs and New Technologies, Sensors & Instrumentation KTN, IoP, 30th September 2009.
A. Rudd, S. Belcher and A. Robins, An inverse modelling technique for emergency response application, 13th Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Paris, France, 1-4 June 2010.