A modelling methodology for the analysis of radon potential based on environmental geology and geographically weighted regression

  • Authors:

  • Antonio Pasculli;Sergio Palermi;Annalina Sarra;Tommaso Piacentini;Enrico Miccadei

  • Affiliations:

  • Department of Engineering and Geology, University G. d'Annunzio, Chieti-Pescara, V.le dei Vestini 31, 65013 Chieti, Italy;Agency of Environmental Protection of Abruzzo (ARTA), V.le G. Marconi, 178, 65127 Pescara, Italy;Department of Economics, University G. d'Annunzio, Pescara, V.le Pindaro, 42, 65127 Pescara, Italy;Department of Engineering and Geology, University G. d'Annunzio, Chieti-Pescara, V.le dei Vestini 31, 65013 Chieti, Italy;Department of Engineering and Geology, University G. d'Annunzio, Chieti-Pescara, V.le dei Vestini 31, 65013 Chieti, Italy

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2014

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Abstract

Many countries have promoted environmental studies and established national radon programmes in order to identify those geographical areas where high indoor exposure risk of people to this radioactive gas are more likely to be found (often referred to as 'radon-prone areas'). Traditionally, the evaluation of radon potential has been pursued by means of global inference techniques. Conversely, in this paper we present a novel modelling approach, based on well established environmental software, best suited to capture the spatial variability of local relationships between indoor radon measurements and some environmental geology-related factors. The proposed strategy consists of three stages. First, a multilevel model based standardisation of indoor radon data should be carried out in order to reduce the building related variability. Then, the global and local autocorrelation indexes have to be employed to highlight the role of the local effects. The last step implies the use of the Geographically Weighted Regression(GWR) to show the differences in associations between indoor radon and the geological factors across space. The method was tested using an available geo-referenced dataset including both radon indoor measurements and geological data related to the territory of an Italian region (Abruzzo). The results are encouraging, although there are several critical issues to be addressed.