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Identifying radon-prone building typologies by marginal modelling

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Listed:
  • Riccardo Borgoni
  • Valeria Tritto
  • Daniela de Bartolo

Abstract

Radon is a naturally occurring decay product of uranium known to be the main contributor to natural background radiation exposure. It has been established that the health risk related to radon exposure is lung cancer. In fact, radon is considered to be a major leading cause of lung cancer, second only to smoking. In this paper, we identified building typologies that affect the probability of detecting indoor radon concentration above reference values, using the data collected within two monitoring campaigns recently conducted in Northern Italy. This information is fundamental both in prevention, i.e. when the construction of a new building is planned and in mitigation, i.e. when a high concentration detected inside buildings has to be reduced. A spatial regression approach for binary data was adopted for this goal where some relevant covariates on the soil were retrieved by linking external spatial databases.

Suggested Citation

  • Riccardo Borgoni & Valeria Tritto & Daniela de Bartolo, 2013. "Identifying radon-prone building typologies by marginal modelling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2069-2086, September.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2069-2086
    DOI: 10.1080/02664763.2013.804906
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    References listed on IDEAS

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    1. Riccardo Borgoni & Ann Berrington & Peter Smith, 2012. "Selecting and fitting graphical chain models to longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 715-738, April.
    2. Riccardo Borgoni & Piero Quatto & Giorgio Somà & Daniela Bartolo, 2010. "A geostatistical approach to define guidelines for radon prone area identification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 255-276, June.
    3. Rob Foxall & Adrian Baddeley, 2002. "Nonparametric measures of association between a spatial point process and a random set, with geological applications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 165-182, May.
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