<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The population is continuously exposed to a background level of ionizing radiation due to the natural radioactivity and, in particular, with radon (<sup>222</sup>Rn). Radon gas has been classified as the second leading cause of lung cancer after tobacco smoke [1]. In the confined environment, radon concentration can reach harmful level and vary accordingly to many factors. Since the primary source of radon in dwellings is the subsurface, the risk assessment and reduction cannot disregard the identification of the local geology and the environmental predisposing factors. In this article, we propose a new methodology, based on the computation of the Gini coefficients at different spatial scales, to estimate the spatial correlation and the geographical variability of radon concentrations. This variability can be interpreted as a signature of the different subsurface geological conditions. The Gini coefficient computation is a statistical tool widely used to determine the degree of inhomogeneity of different kinds of distributions. We generated several simulated radon distributions, and the proposed tool has been validated by comparing the variograms based on the semi-variance computation with those ones based on the Gini coefficient. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration. Indeed, it allows to better constrain the critical distance below which the radon geological source can be considered as uniform at least for the investigated length scales of variability; it also better discriminates the fluctuations due to the environmental predisposing factors from those ones due to the random spatially uncorrelated noise.</p></abstract>

The population is continuously exposed to a background level of ionizing radiation due to the natural radioactivity and, in particular, with radon (222Rn). Radon gas has been classified as the second leading cause of lung cancer after tobacco smoke [1]. In the confined environment, radon concentration can reach harmful level and vary accordingly to many factors. Since the primary source of radon in dwellings is the subsurface, the risk assessment and reduction cannot disregard the identification of the local geology and the environmental predisposing factors. In this article, we propose a new methodology, based on the computation of the Gini coefficients at different spatial scales, to estimate the spatial correlation and the geographical variability of radon concentrations. This variability can be interpreted as a signature of the different subsurface geological conditions. The Gini coefficient computation is a statistical tool widely used to determine the degree of inhomogeneity of different kinds of distributions. We generated several simulated radon distributions, and the proposed tool has been validated by comparing the variograms based on the semi-variance computation with those ones based on the Gini coefficient. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration. Indeed, it allows to better constrain the critical distance below which the radon geological source can be considered as uniform at least for the investigated length scales of variability; it also better discriminates the fluctuations due to the environmental predisposing factors from those ones due to the random spatially uncorrelated noise.

A new geostatistical tool for the analysis of the geographical variability of the indoor radon activity

Nukleonika

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The population is continuously exposed to a background level of ionizing radiation due to the natural radioactivity and, in particular, with radon (222Rn)....

A new geostatistical tool for the analysis of the geographical variability of the indoor radon activity

Published Online: May 29, 2020

Page range: 99 - 104

Received: Dec 02, 2019

Accepted: Jan 16, 2020

DOI: https://doi.org/10.2478/nuka-2020-0015

Keywords
Geographical variability, Geostatistics, Gini coefficient, Lorenz curve, Radon concentration, Variogram

© 2020 Filomena Loffredo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A new geostatistical tool for the analysis of the geographical variability of the indoor radon activity

Published Online: May 29, 2020

Page range: 99 - 104

Received: Dec 02, 2019

Accepted: Jan 16, 2020

DOI: https://doi.org/10.2478/nuka-2020-0015

KeywordsGeographical variability, Geostatistics, Gini coefficient, Lorenz curve, Radon concentration, Variogram

© 2020 Filomena Loffredo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Geographical variability, Geostatistics, Gini coefficient, Lorenz curve, Radon concentration, Variogram