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

Abstract

The radon isotope (222Rn, half-life 3.8 days) is a radioactive byproduct of the 238U decay chain. Because radon is the second biggest cause of lung cancer after smoking, dense maps of indoor radon concentration are required to implement effective locally based risk reduction strategies. In this regard, we present an innovative method for the construction of interpolated maps (kriging) based on the Gini index computation to characterize the distribution of Rn concentration. The Gini coefficient variogram has been shown to be an effective predictor of radon concentration inhomogeneity. It allows for a better constraint of the critical distance below which the radon geological source can be considered uniform, at least for the investigated length scales of variability; it also better distinguishes fluctuations due to environmental predisposing factors from those due to random spatially uncorrelated noise. This method has been shown to be effective in finding larger-scale geographical connections that can subsequently be connected to geological characteristics. It was tested using real dataset derived from indoor radon measurements conducted in the Sorrentina Peninsula in Campania, Italy. The measurement was carried out in different residences using passive detectors (CR-39) for two consecutive semesters, beginning in September–November 2019 and ending in September–November 2020, to estimate the yearly mean radon concentration. The measurements and analysis were conducted in accordance with the quality control plan. Radon concentrations ranged from 25 to 722 Bq/m3 before being normalized to ground level, and from 23 to 933 Bq/m3 after being normalized, with a geometric mean of 120 Bq/m3 and a geometric standard deviation of 1.35 before data normalization, and 139 Bq/m3 and a geometric standard deviation of 1.36 after data normalization. Approximately 13% of the tests conducted exceeded the 300 Bq/m3 reference level set by Italian Legislative Decree 101/2020. The data show that the municipalities under investigation had no influence on indoor radon levels. The geology of the monitored location is interesting, and because soil is the primary source of Rn, risk assessment and mitigation for radon exposure cannot be undertaken without first analyzing the local geology. This research examines the spatial link among radon readings using the mapping based on the Gini method (kriging).

Gini Method Application: Indoor Radon Survey in Kpong, Ghana

“…This could be due to the composition of the bedrock (schists, quartzite and phyllites, unaltered schists and sandstone). The Loffredo et al 2021 [30] survey conducted in the Sorrento Peninsula, Italy, found indoor radon concentrations in dwellings higher than these results. Also, in the Adelikhah et al, 2021 work [20] conducted in dwellings in Mashhad, Iran, indoor radon concentrations in dwellings were higher than our results.…”

“…This might be due to the common bedrock formation of these two study areas. The Togo formations (schists, quartzite and phyllites, unaltered shale and sandstone) are rocks forming the Akwapim range of hills trending northeast from the coast West of Accra through Kpong, Aburi, and Anum into the Republic of Togo [30]. The kriging map was made using the Gini method and estimated using the geographic distribution of the measurement points over the Kpong area.…”

“…In addition, a relationship between the geogenic potential of radon and indoor concentrations has been shown [39,40]. To verify the correlation between indoor radon concentration and geogenic potential radon, the dependence on the floor should be eliminated by normalizing the data [30,41]. Finally, other studies have confirmed how the intensive use of some building materials with high concentrations of 232 Th, 226 Ra and 40 K increase the risk of radon potential [22,42].…”

“…Applying interpolation techniques like kriging, for example, might be challenging because these techniques must be deployed after data pretreatment [27]. A new method for the realization of maps based on the calculation of the Gini index has recently been developed and shown [28][29][30]. It can tell the difference between variations generated by environmental influences and those caused by random spatially uncorrelated noise.…”

“…With the voluntarily cooperation of the locals, the indoor radon measuring survey at the Kpong was made possible. The measurements were taken in 96 dwellings, all of which were on the ground floor; for this reason in the specific case, the radon concentration variability is independent of the floor, otherwise the data should be normalized [30]. Dependence on the floor is one of the determining factors in the estimates of radon concentration, as well demonstrated in the literature.…”