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

Abstract

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.

Risk Factors for Childhood Leukemia: Radiation and Beyond

“…To overcome these limitations, a new study is ongoing which includes a larger number of cases and controls using an extended calendar period and an extended set of indoor gamma-ray measurements from the United Kingdom Childhood Cancer Study [UKCCS (105)]. For a better estimation of indoor gamma ray dose rates, several ad hoc models were explored and the results were published (106)(107)(108). Some of the most recent studies have been summarized by Mazzei-Abba et al (99) in 2019 and their review includes a comprehensive discussion about methodological differences, limitations, and challenges that have to be faced when evaluating the findings of these studies.…”

Summary of Radiation Research Society Online 66th Annual Meeting, Symposium on “Epidemiology: Updates on epidemiological low dose studies,” including discussion

“…The analysis was conducted on a subset of this data, during 1980-2006, and provided results consistent with extrapolation from high-dose rate risk models. Several parametric models had been employed by Kendall et al (Kendall et al 2018) to estimate radiation dose rates. Compared to these parametric models the proposed machine learning methods were less prone to overfitting, avoided limiting parametric assumptions, could include variables of various types simultaneously, and flexibly modelled non-linearities.…”

Epidemiological studies of natural sources of radiation and childhood cancer: current challenges and future perspectives

“…The performance of models used to estimate exposure to gamma radiation has been rarely assessed. In GB, gamma exposure was assessed using a simple county district mean, but in recent years several alternative prediction models based on gamma measurements in GB (48,52,53) have been devised.…”

“…The best performance was found for a linear model based on weighted sums of gamma dose rates among neighbouring measurement points and other simple models (53), which might be used in future studies. In France, the model used to estimate gamma exposure was based on Warnery et al (41) and validated against an independent set of 8,839 dwelling indoor measurements (54).…”