Modelling the bimodal distribution of indoor gamma-ray dose-rates in Great Britain

Kendall, G.M.; Chernyavskiy, P.; Appleton, J.D.; Miles, J.C.H.; Wakeford, R.; Athanson, M.; Vincent, T.J.; McColl, N.P.; Little, M.P.. 2018 Modelling the bimodal distribution of indoor gamma-ray dose-rates in Great Britain. Radiation and Environmental Biophysics, 57 (4). 321-347. 10.1007/s00411-018-0752-7

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Abstract/Summary

Gamma radiation from naturally occurring sources (including directly ionizing cosmic-rays) is a major component of background radiation. An understanding of the magnitude and variation of doses from these sources is important, and the ability to predict them is required for epidemiological studies. In the present paper, indoor measurements of naturally occurring gamma-rays at representative locations in Great Britain are summarized. It is shown that, although the individual measurement data appear unimodal, the distribution of gamma-ray dose-rates when averaged over relatively small areas, which probably better represents the underlying distribution with inter-house variation reduced, appears bimodal. The dose-rate distributions predicted by three empirical and geostatistical models are also bimodal and compatible with the distributions of the areally averaged dose-rates. The distribution of indoor gamma-ray dose-rates in the UK is compared with those in other countries, which also tend to appear bimodal (or possibly multimodal). The variation of indoor gamma-ray dose-rates with geology, socio-economic status of the area, building type, and period of construction are explored. The factors affecting indoor dose-rates from background gamma radiation are complex and frequently intertwined, but geology, period of construction, and socio-economic status are influential; the first is potentially most influential, perhaps, because it can be used as a general proxy for local building materials. Various statistical models are tested for predicting indoor gamma-ray dose-rates at unmeasured locations. Significant improvements over previous modelling are reported. The dose-rate estimates generated by these models reflect the imputed underlying distribution of dose-rates and provide acceptable predictions at geographical locations without measurements.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1007/s00411-018-0752-7
ISSN:	0301-634X
Date made live:	27 Sep 2018 14:05 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/521053

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