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Radiation Protection Dosimetry Advance Access originally published online on July 25, 2006
Radiation Protection Dosimetry 2007 123(1):83-94; doi:10.1093/rpd/ncl082
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases

Claus E. Andersen1,*, Ole Raaschou-Nielsen2, Helle Primdal Andersen3, Morten Lind3, Peter Gravesen4, Birthe L. Thomsen2 and Kaare Ulbak5

1 Risø National Laboratory, DK-4000 Roskilde, Denmark
2 Danish Cancer Society, Institute of Cancer Epidemiology, Strandboulevarden 49, DK-2100 Copenhagen, Denmark
3 National Survey and Cadastre, Rentemestervej 8, DK-2400 Copenhagen NV, Denmark
4 Geological Survey of Denmark and Greenland, Øster Voldgade 10, DK-1350 Copenhagen K, Denmark
5 National Institute of Radiation Hygiene, Knapholm 7, DK-2730 Herlev, Denmark

* Corresponding author: claus.andersen{at}risoe.dk

Received September 29, 2005, amended May 17, 2006
  Abstract

A linear regression model has been developed for the prediction of indoor 222Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case–control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R2 of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R2 = 10%).


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