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

Abstract

<jats:title>Abstract</jats:title><jats:p>The population is continuously exposed to a background level of ionizing radiation due to the natural radioactivity and, in particular, with radon (<jats:sup>222</jats: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.</jats:p>

Indoor Radon Concentration and Risk Assessment in 27 Districts of a Public Healthcare Company in Naples, South Italy

“…Moreover, the radon concentrations measurements were conducted by applying a quality assurance system. The radon concentration data collected in this study could contribute to the radon map of Campania and to the validation of new analysis methods on the correlation between radon concentrations and geology [41,42].…”

Sorrentina Peninsula: Geographical Distribution of the Indoor Radon Concentrations in Dwellings—Gini Index Application

“…In this context, we offer a new method for the creation of interpolated maps (kriging) based on the Gini index [12,14] to characterize the distribution of Rn concentration. According to Loffredo et al, 2021, it is best practice to undertake data processing to ensure the hypothesis of stationarity (ergodicity).…”

“…where n is the number of experimental measurements, µ is the mean of the entire dataset, the index j denotes the available Gini indices in the column G jk , and G denotes all of the values in the matrix G ik [12,14]. This method appears to be beneficial for discovering larger-scale geographical correlations, which may then be related to geological features.…”

“…To address these constraints, several writers devised a new technique based on the calculation of the Gini index, a tool typically employed in the socio-economic sector for income distribution studies but with applicability in a wide range of disciplines [11,12].…”

“…To ensure the radon measurements were independent of the floor on which they were obtained, the first step was to normalize the data with respect to the ground floor. The Gini index was used to create the interpolated maps for this case study [12][13][14]. This index is bounded above because it can only take on a value of one, implying that it may make detecting quasi-stationarity distributions more difficult.…”