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Statistical Estimate of Radon Concentration from Passive and Active Detectors in Doha

1
Faculty or Arts, Computing, Engineering and Sciences, Sheffield Hallam University, Sheffield S1 1WB, UK
2
Central Radiation Laboratory, Qatar Ministry of Municipality and Environment, Doha, Qatar
3
School of Mathematics, University of Leeds, Leeds LS2 9JT, UK
4
Statistics and Physics, Department of Mathematics, Qatar University, Doha, Qatar
*
Author to whom correspondence should be addressed.
Received: 22 March 2018 / Revised: 19 May 2018 / Accepted: 13 June 2018 / Published: 21 June 2018
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Abstract

Harnessing knowledge on the physical and natural conditions that affect our health, general livelihood and sustainability has long been at the core of scientific research. Health risks of ionising radiation from exposure to radon and radon decay products in homes, work and other public places entail developing novel approaches to modelling occurrence of the gas and its decaying products, in order to cope with the physical and natural dynamics in human habitats. Various data modelling approaches and techniques have been developed and applied to identify potential relationships among individual local meteorological parameters with a potential impact on radon concentrations—i.e., temperature, barometric pressure and relative humidity. In this first research work on radon concentrations in the State of Qatar, we present a combination of exploratory, visualisation and algorithmic estimation methods to try and understand the radon variations in and around the city of Doha. Data were obtained from the Central Radiation Laboratories (CRL) in Doha, gathered from 36 passive radon detectors deployed in various schools, residential and work places in and around Doha as well as from one active radon detector located at the CRL. Our key findings show high variations mainly attributable to technical variations in data gathering, as the equipment and devices appear to heavily influence the levels of radon detected. A parameter maximisation method applied to simulate data with similar behaviour to the data from the passive detectors in four of the neighbourhoods appears appropriate for estimating parameters in cases of data limitation. Data from the active detector exhibit interesting seasonal variations—with data clustering exhibiting two clearly separable groups, with passive and active detectors exhibiting a huge disagreement in readings. These patterns highlight challenges related to detection methods—in particular ensuring that deployed detectors and calculations of radon concentrations are adapted to local conditions. The study doesn’t dwell much on building materials and makes rather fundamental assumptions, including an equal exhalation rate of radon from the soil across neighbourhoods, based on Doha’s homogeneous underlying geological formation. The study also highlights potential extensions into the broader category of pollutants such as hydrocarbon, air particulate carbon monoxide and nitrogen dioxide at specific time periods of the year and particularly how they may tie in with global health institutions’ requirements. View Full-Text
Keywords: estimation; clustering; local regression; radon detection; spatio-temporal analyses; unsupervised modelling; visualisation estimation; clustering; local regression; radon detection; spatio-temporal analyses; unsupervised modelling; visualisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style

Mwitondi, K.; Al Sadig, I.; Hassona, R.; Taylor, C.; Yousef, A. Statistical Estimate of Radon Concentration from Passive and Active Detectors in Doha. Data 2018, 3, 22.

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