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Investigating the association between indoor radon concentrations and some potential influencing factors through a profile regression approach

  • Lara Fontanella
  • Luigi Ippoliti
  • Annalina SarraEmail author
  • Eugenia Nissi
  • Sergio Palermi
Article

Abstract

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.

Keywords

Bayesian Profile Regression Building characteristics Cluster profile Indoor radon concentration Lithology 

Notes

Acknowledgements

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.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lara Fontanella
    • 1
  • Luigi Ippoliti
    • 2
  • Annalina Sarra
    • 2
    Email author
  • Eugenia Nissi
    • 2
  • Sergio Palermi
    • 3
  1. 1.Department of Legal and Social SciencesUniversity G. d’AnnunzioPescaraItaly
  2. 2.Department of EconomicsUniversity G. d’AnnunzioPescaraItaly
  3. 3.Agency of Environmental Protection of Abruzzo (ARTA)PescaraItaly

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