County-level indoor radon concentration mapping and uncertainty assessment in South Korea using geostatistical simulation and environmental factors

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

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).

Mapping indoor radon hazard in Germany: The geogenic component

Indoor radon is considered as an indoor air pollutant due to its carcinogenic effect. Since the main source of indoor radon is the ground beneath the house, we utilize the geogenic radon potential (GRP) and a geogenic radon hazard index (GRHI) for predicting the geogenic component of the indoor Rn hazard in Germany. For this purpose, we link indoor radon data (n = 44,629) to maps of GRP and GRHI and fit logistic regression models to calculate the probabilities that indoor Rn exceeds thresholds of 100 Bq/m³ and 300 Bq/m³. The estimated probability was averaged for every municipality by considering only the estimates within the built-up area. Finally, the mean exceedance probability per municipality was coupled with the respective residential building stock for estimating the number of buildings with indoor Rn above 100 Bq/m³ and 300 Bq/m³ for each municipality. We found that (1) GRHI is a better predictor than GRP for indoor radon hazard in Germany, (2) the estimated number of buildings above 100 Bq/m³ and 300 Bq/m³ in Germany is ~2 million (11.6% of all residential buildings) and ~ 350,000 (1.9%), respectively, (3) areas where 300 Bq/m³ exceedance is greater than 10% comprise only 0.8% of the German building stock but 6.3% of buildings with indoor Rn exceeding 300 Bq/m³, and (4) most urban areas and, hence, most buildings (77%) are located in low hazard regions. The implications for Rn protection are twofold: (1) the Rn priority area concept is cost-efficient in a sense that it allows to find the most buildings that exceed a threshold concentration with a given amount of resources, and (2) for an optimal reduction of lung cancer risk areas outside of Rn priority areas must be addressed since most hazardous indoor Rn concentrations occur in low to medium hazard areas.