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Hierarchical generalised latent spatial quantile regression models with applications to indoor radon concentration

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Abstract

Radon-222 is a noble gas arising naturally from decay of uranium-238 present in the earth’s crust. In confined spaces, high concentrations of radon can become a serious health concern. Hence, experts widely agree that prolonged exposure to this gas can significantly increase the risk of lung cancer. A range of variables, such as geological factors, soil properties, building characteristics, the living habits of dwellers and meteorological parameters, might have a significant impact on indoor radon concentration and its variability. In this paper, the effect of various factors that are believed to influence the indoor radon concentrations is studied at the municipal level of L’Aquila district (Abruzzo region, Italy). The statistical analysis is carried out through a hierarchical Bayesian spatial quantile regression model in which the matrix of explanatory variables is partially defined through a set of spatial common latent factors. The proposed model, here referred to as the Generalized latent-spatial-quantile regression model, is thus appropriate when some covariates are indicators of latent factors that can be used as predictors in the quantile regression and the variables are supposed to be spatially correlated. It is shown that the model has an intuitive appeal and that it is preferable when the interest is in studying the effects of covariates on one or both the tails of the response distribution, as in the case of indoor radon concentrations. Full probabilistic inference is performed by applying Markov chain Monte Carlo techniques.

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Notes

  1. Member states of European Union, for example, have to establish national reference levels for annual average indoor radon concentration not exceeding the value of 300 Bq/m3 fixed by the Directive 2013/59/Euratom.

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Acknowledgments

We are grateful to the reviewers for their useful comments and suggestions which have significantly improved the quality of the paper. L. Fontanella, L. Ippoliti, A. Sarra and P. Valentini acknowledge the financial support of MIUR, Ministero dell’Istruzione, dellUniversità e della Ricerca, FIRB (Futuro in Ricerca) research project Statistical modeling of environmental phenomena: pollution, meteorology, health and their interactions—StEPhI Project. This paper is dedicated to the memory of our colleague G. Visini.

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Authors and Affiliations

  1. Department of Economics, University G. d’Annunzio, V.le Pindaro, 42, 65127, Pescara, Italy

    Lara Fontanella, Luigi Ippoliti , Annalina Sarra & Pasquale Valentini

  2. Agency of Environmental Protection of Abruzzo (ARTA), V.le G. Marconi, 178, 65127, Pescara, Italy

    Sergio Palermi

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  1. Lara Fontanella
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  2. Luigi Ippoliti
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  3. Annalina Sarra
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  4. Pasquale Valentini
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  5. Sergio Palermi
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Correspondence to Luigi Ippoliti .

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Fontanella, L., Ippoliti , L., Sarra, A. et al. Hierarchical generalised latent spatial quantile regression models with applications to indoor radon concentration. Stoch Environ Res Risk Assess 29, 357–367 (2015). https://doi.org/10.1007/s00477-014-0917-0

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