Investigating the association between indoor radon concentrations and some potential influencing factors through a profile regression approach
Radon-222 is a naturally occurring radioactive gas arising from the decay of Uranium-238 present in the earth’s crust. The knowledge of the radon effects on human health is generating a growing attention by national and international authorities aimed at assessing the exposure of people to this radioactive gas and identifying building types and geographic areas where high indoor radon concentrations (IRCs) are likely to be found. However, given its multi-factorial dependence and the substantial regional variation, the analysis of IRC is not a simple task. There have been several efforts to evaluate the impact of the major influencing factors on IRCs. In this paper we illustrate how the complex relationships between the IRCs and a set of associated variables can be analysed using profile regression, a Bayesian non-parametric model for clustering responses and regressors simultaneously. Analyzing a geo-referenced database of annual IRCs for the Abruzzo region (Central Italy), we show that the proposed methodology allows to identify clusters of buildings according to their proneness to IRCs and that, through cluster assignment, it is possible to disentangle the effect of regressors on IRC and predict its levels for specific combinations of the explanatory variables.
KeywordsBayesian Profile Regression Building characteristics Cluster profile Indoor radon concentration Lithology
The authors would like to thank the Editor-in-Chief, the Associate Editor and the referees for their helpful comments and suggestions. LI, LF and EN were partially funded by the grant MIUR, Ministero dell’Istruzione, dell’Università e della Ricerca, PRIN research project 2015 “Environmental processes and human activities: capturing their interactions via statistical methods”-EphaStat. The authors also thank Dr. Roberto Luis Di Cesare of ARTA Abruzzo for making the maps in ArcGis.
- Darby S, Hill D, Auvinen A, Barros-Dios JM, Baysson H, Bochicchio F, Deo H, Falk R, Forastiere F, Hakama M, Heid I, Kreienbrock L, Kreuzer M, Lagarde F, Mäkeläinen I, Muirhead C, Oberaigner W, Pershagen G, Ruano-Ravina A, Ruosteenoja E, Rosario AS, Tirmarche M, Tomàsek L, Whitley E, Wichmann HE, 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(7485):223CrossRefGoogle Scholar
- ISPRA (2012) Cartografia geologica d’italia alla scala \(1:50.000\). in: Servizio geologico d’italia e progetto carg. Technical reportGoogle Scholar
- Kreienbrock L, Kreuzer M, Gerken M, Dingerkus G, Wellmann J, Keller G, Wichmann HE (2001) Case-control study on lung-cancer and residential radon in Western Germany. Am J Epidemiol 89(4):339–348Google Scholar
- Krewski D, Lubin JH, Zielinski JM, Alavanja M, Catalan VS, Field RW, Klotz JB, Letourneau EG, Lynch CF, Lyon JI, Sandler DP, Schoenberg JB, Steck DJ, Stolwijk JA, Weinberg C, Wilcox HB (2005) Residential radon and risk of lung cancer: a combined analysis of 7 North American case-control studies. Epidemiology 16(4):137–145CrossRefGoogle Scholar
- Molitor J, Brown IJ, Chan Q, Papathomas M, Liverani S, Molitor N, Richardson S, Van Horn L, Daviglus ML, Dyer A, Stamler J, Elliott P, INTERMAP Research Group (2014) Blood pressuredifferences associated with optimal macronutrient intake trial forheart health (OMNIHEART)-like diet compared with a typical Americandiet. Hypertension 64:1198–1204CrossRefGoogle Scholar
- Palermi S, Pasculli A (2008) Radon mapping in abruzzo, italy. In: Proceedings of 4th Canadian conference on Geohazards Quèbec City CanadaGoogle Scholar
- Palermi S, Carnesale L, Buccella G, Rancitelli D, Sulli G, Benedetti F, Capannolo R, Gianfelice G, Di Giansante A (2012) Indagine per la mappatura del radon in abruzzo. In:Proceedings del V Convegno Nazionale sugli Agenti FisiciGoogle Scholar
- R Development Core Team (2017) R: a language and environment for statistical computing. Technical report. R Foundation for Statistical Computing, Vienna. http://www.R-project.org
- Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288Google Scholar
- Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagaña X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Gražulevičiene R, Gutzkow K, Julvez J, Keun HC, Kogevinas M, McEachan RRC, Meltzer HM, Sabidò E, Schwarze PE, Siroux V, Sunyer J, Want E, Zeman F, Nieuwenhuijsen MJ (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122:535–544CrossRefGoogle Scholar
- World Health Organization (2009) WHO handbook on indoor radon: a public health perspective. World Health Organization, GenevaGoogle Scholar