Literature DB >> 29350190

Development and validation of a high-resolution mapping platform to aid in the public awareness of radiological hazards.

Peter G Martin1, Dean Connor1, Oliver D Payton1, Macarena Leal-Olloqui1, Anya C Keatley1, Thomas B Scott1.   

Abstract

The distribution, quantification and exposure-related effects of radiation in the environment, arising from both natural and anthropogenic sources, is of great (and growing) concern for global populations. Recent events at the Fukushima Daiichi Nuclear Plant (FDNPP) have further highlighted the importance of developing radiation mapping technologies that not only contribute to the continued assessment of contamination, but can serve as an educational tool for members of the public regarding both its behaviour and extent. With an even greater number of people possessing smart-phone technology, a lightweight and portable 'connected system' has been developed to demonstrate to users the calibrated radioactive dose rate in an area, viewable in real-time through a dedicated phone application. As well as allowing for system users to be alerted where variations in dose rate are experienced, the combined results from multiple systems are viewable through a custom-built desktop application-permitting the output obtained via any number of units to be similarly displayed in real-time. A successful initial trialling of the system is described at a former tin mine in Cornwall (south-west England)-known to exhibit low, but identifiable radiation anomalies in discrete areas. Additional applications outside of its educational usage are also discussed.

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Year:  2018        PMID: 29350190     DOI: 10.1088/1361-6498/aaa914

Source DB:  PubMed          Journal:  J Radiol Prot        ISSN: 0952-4746            Impact factor:   1.394


  1 in total

1.  Radiological Mapping of Post-Disaster Nuclear Environments Using Fixed-Wing Unmanned Aerial Systems: A Study From Chornobyl.

Authors:  Dean T Connor; Kieran Wood; Peter G Martin; Sevda Goren; David Megson-Smith; Yannick Verbelen; Igor Chyzhevskyi; Serhii Kirieiev; Nick T Smith; Tom Richardson; Thomas B Scott
Journal:  Front Robot AI       Date:  2020-01-17
  1 in total

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