Investigating the association between indoor radon concentrations and some potential influencing factors through a profile regression approach
Autor: | Lara Fontanella, Eugenia Nissi, Sergio Palermi, Luigi Ippoliti, Annalina Sarra |
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Rok vydání: | 2019 |
Předmět: |
Statistics and Probability
Association (object-oriented programming) Bayesian probability chemistry.chemical_element Radon 010501 environmental sciences 01 natural sciences Regression 010104 statistics & probability Human health Indoor air quality chemistry Econometrics Environmental science 0101 mathematics Statistics Probability and Uncertainty Cluster analysis Radioactive gas 0105 earth and related environmental sciences General Environmental Science |
Zdroj: | Environmental and Ecological Statistics. 26:185-216 |
ISSN: | 1573-3009 1352-8505 |
DOI: | 10.1007/s10651-019-00424-5 |
Popis: | 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. |
Databáze: | OpenAIRE |
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