title: Quantile regression and Bayesian cluster detection to identify radon prone areas.
abstract: Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.
authors: Department of Economics, Viale Pindaro, 42 -65127 Pescara, G. d'Annunzio University, Italy. Electronic address: asarra@dmqte.unich.it.
authors: Annalina
authors: A
authors: Sarra
authors: Department of Legal and Social Sciences, Viale Pindaro, 42 -65127 Pescara, G. d'Annunzio University, Italy.
authors: Lara
authors: L
authors: Fontanella
authors: Department of Economics, Viale Pindaro, 42 -65127 Pescara, G. d'Annunzio University, Italy.
authors: Pasquale
authors: P
authors: Valentini
authors: Agency of Environmental Protection of Abruzzo (ARTA), V.le G. Marconi, 178, 65127 Pescara, Italy.
authors: Sergio
authors: S
authors: Palermi
mesh_terms: D000396:Air Pollutants, Radioactive / Q000032:analysis*; D016902:Air Pollution, Indoor / Q000032:analysis / Q000706:statistics & numerical data*; D006798:Housing; D007558:Italy; D008956:Models, Chemical; D011834:Radiation Monitoring / Q000379:methods*; D011886:Radon / Q000032:analysis*; D062206:Spatial Analysis
chemical_list: D000396:Air Pollutants, Radioactive; D011886:Radon
keywords: Bayesian cluster detection; Bayesian spatial quantile regression; Building factors; Radon; Standard house