Radiation and radioactive isotopes form part of our natural environment. Elevated levels of these radioactive isotopes in the environment can pose a threat to our health. A greater proportion of the natural radiation is from the radioactive gas radon. Although it cannot be detected by human senses, radon and its progenies are of health concern as it can cause lung cancer when inhaled over a period of time. This study sought to provide baseline indoor radon data, the life time risk of lung cancer and its interpretation within a suburb of Ghana. Solid State Nuclear Track Detector (LR-115 type II) was deployed in 82 homes within a suburb for a period of three months (September 2017-January 2018). Indoor radon concentration (IRC) for the suburb was within the range of 4.1-176.3 Bq m −3 . With mean 57±39 Bq m −3 . The mean radon exposure to the dwellers was recorded as 0.12±0.08 WLMy −1 resulting in 0.7±0.5 mSvy −1 effective dose to the lung with an excess lifetime cancer risk of 0.39±0.26%. There was a positive correlation between indoor radon concentration and the building type and the association was significant with a P value of 0.047.

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).

Seasonal radon levels have been studied in dwellings and soils in selected areas in Ga East, Greater Accra Region of Ghana using LR-115-type II (SSNTDs). This study was conducted to determine the seasonal correlation between soil and dwelling radon concentrations. Detectors were exposed from January to March and April to June, for dry and wet seasons, respectively. Overall, indoor radon was 133.4 ± 6.7 Bqm−3 and 72.1 ± 3.6 Bqm −3 for wet and dry seasons. The estimated annual effective dose to the lung received by the occupants at Paraku Estate, Dome, and Kwabenya was 6.9 ± 0.4, 7.2 ± 0.5, and 9.8 ± 0.8 mSvy−1 for the wet season and 3.8 ± 0.2, 4.3 ± 0.2, and 4.6 ± 0.3 mSvy−1 for the dry season. On average, the soil radon concentration was found to be 0.96 ± 0.07 kBqm−3 and 2.24 ± 0.01 kBqm−3 for wet and dry seasons. To determine the correlation between soil and dwelling radon, a positive Pearson correlation coefficient value R = (0.74) and R = (0.66) was obtained representing the dry and wet seasons. To test the statistical significance between soil and dwelling radon, P < 0.05 was obtained, indicating a statically significant relationship between the two.

222Rn concentration indoors was measured in 40 dwellings in the Obuasi municipality, a gold-mining town in the Ashanti Region of Ghana using the LR 115 type II strippable detectors for the two major seasons in Ghana, rainy and dry. The detectors were placed in the bed rooms of dwellers for 6 months each. Average indoor radon concentration varied from 63.9 to 364.9 Bqm−3 with a mean of 152.2 ± 10.9 Bqm−3 in the rainy season and 26.1–119.0 Bqm−3 with a mean of 50.5 ± 3.9 Bqm−3 in the dry season. The effective dose of 3.90 ± 0.3 mSvy−1 for the rainy season and for the dry season, effective dose of 0.6 mSvy−1 were recorded. The seasonal variation of 222Rn concentration indoors showed higher values in the rainy season than the dry season. A dependence was observed between the type of building materials used in building and the indoor radon level.