Analysing radon accumulation in the home by flexible M-quantile mixed effect regression
- 50 Downloads
Radon is a noble gas that occurs in nature as a decay product of uranium. Radon is the principal contributor to natural background radiation and is considered to be one of the major leading causes of lung cancer. The main concern revolves around indoor environments where radon accumulates and reaches high concentrations. In this paper, a semiparametric random-effect M-quantile model is introduced to model radon concentration inside a building, and a way to estimate the model within the framework of robust maximum likelihood is presented. Using data collected in a monitoring survey carried out in the Lombardy Region (Italy) in 2003–2004, we investigate the impact of a number of factors, such as geological typologies of the soil and building characteristics, on indoor concentration. The proposed methodology permits the identification of building typologies prone to a high concentration of the pollutant. It is shown how these effects are largely not constant across the entire distribution of indoor radon concentration, making the suggested approach preferable to ordinary regression techniques since high concentrations are usually of concern. Furthermore, we demonstrate how our model provides a natural way of identifying those areas more prone to high concentration, displaying them by thematic maps. Understanding how buildings’ characteristics affect indoor concentration is fundamental both for preventing the gas from accumulating in new buildings and for mitigating those situations where the amount of radon detected inside a building is too high and has to be reduced.
KeywordsEnvironmental radioactivity Building factors Radon-prone areas Hierarchical mixed models Penalised splines Lombardy region
The work of Nicola Salvati has been carried out with the support of the project InGRID 2Grant Agreement No 730998, EU) and of project PRA_2018_9 (‘From survey-based to register-based statistics: a paradigm shift using latent variable models’). The authors were further supported by the MIUR-DAAD Joint Mobility Program (57265468).
- Borgoni R, Tritto V, Bigliotto C, de Bartolo D (2011) A geostatistical approach to assess the spatial association between indoor radon concentration, geological features and building characteristics: the Lombardy case, Northern Italy. Int J Environ Res Public Health 8:1420–1440CrossRefGoogle Scholar
- Darby S, Hill D, Auvinen A, Barros-Dios J, Baysson J, Bochicchio F, Deo H, Falk R, Forastiere F, Hakama M, Heid I, Kreienbrock L, Kreuzer M, Lagarde F, MSkelSinen I, Muirhead C, Oberaigner W, Pershagen G, Ruano-Ravina A, Ruosteenoja E, Rosario AS, Tirmarche T, Tomsek L, Whitley E, Wichmann H, Doll R (2005) Radon in homes and risk of lung cancer: collaborative analysis of individual data from 13 European case–control studies. Br Med J 330(6485):223–226CrossRefGoogle Scholar
- Gates A, Gundersen L (1992) Geologic controls on radon. Geological Society of America, Washington, DC (Special Paper 271)Google Scholar
- Geraci M (2018) Additive quantile regression for clustered data with an application to children’s physical activity. arXiv:1803.05403
- Green B, Miles J, Bradley E, Rees D (2002) Radon atlas of England and Wales. Report nrpb-w26, Chilton NRPBGoogle Scholar
- Kreienbrock L, Kreuzer M, Gerken M, Dingerkus M, Wellmann J, Keller G, Wichmann H (2001) Case-control study on lungcancer and residential radon in western Germany. Am J Epidemiol 89(4):339–348Google Scholar
- Krewski D, Lubin MAJH, Zielinski JM, Catalan V, Field R, Klotz J, Letourneau E, Lynch C, Lyon J, Sandler D, Schoenberg D, Steck J, Stolwijk C, Weinberg C, Wilcox H (2005) Residential radon and risk of lung cancer: a combined analysis of seven North American case-control studies. Epidemiology 16(4):137–145CrossRefGoogle Scholar
- Organization WH (2009) WHO handbook on indoor radon: a public health perspective. WHO Library Cataloguing-in-Publication DataGoogle Scholar
- R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- USEPA (1992) National residential radon survey: summary report. Technical Report EPA/402/R-92/011, United States Environmental Protection Agency, Washington, DCGoogle Scholar
- Yu K, Lu Z, Stander J (2003) Quantile regression: applications and current research areas. Statistician 52(3):331–350Google Scholar