Abstract
Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research.
Highlights
The growing threat of terrorism [1], the Fukushima nuclear accident (2011) [2] and the Eyjafjallajökull volcanic eruption (2010) [3] are significant events with a detrimental impact on public health and several industries including aviation and transport
This paper has presented the problem and importance of estimating atmospheric dispersion events, a review of source term estimation (STE) algorithms using static or mobiles sensors, and a brief review on boundary tracking and source seeking
Static sensors have been the dominant method of STE in the literature, for emergency response applications arguably due to their benefit of early detection
Summary
The growing threat of terrorism [1], the Fukushima nuclear accident (2011) [2] and the Eyjafjallajökull volcanic eruption (2010) [3] are significant events with a detrimental impact on public health and several industries including aviation and transport. What these events have in common is the dispersal of hazardous material into the atmosphere. For an accurate forecast, several variables are needed as an input to the model including, but not limited to: meteorological data, the strength of the release and its location. The strength, location and time of the release are often unknown, and should be inferred from relevant sensor measurements
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