The annual exposure to indoor radon and thoron imparts a major contribution to inhalation doses received by the public. In this study, we report results of time integrated passive of indoor radon and thoron concentrations that were carried out in Rohilkhand region with health risk to the dwellers in the region.  In present study, Solid State Nuclear Track Detectors (SSNTDS ) based twin chamber dosimeter with LR-115 track detector were used for estimating Radon (222Rn) and Thoron (220Rn) gas concentration levels in the dwellings of Moradabad city. The average Radon and thoron concentration levels in the studied dwellings were found to vary from 13.5 to 21.8 Bq m-3 and thoron concentrations is found to vary from 11.7 to 19.5 Bqm-3 and its corresponding geometric mean of equilibrium-equivalent 222Rn and 220Rn concentration were found 7.07 to 1.7 Bqm-3 . The total annual effective dose due to the exposure to radon and thoron was found to vary from3.7 to 6.2 mSv/y whereas from thoron found to vary from 0.3 to 0.61 mSv/y.

Mining activities are generally known to enhance the concentration of primordial radionuclides in the environment thereby contributing immensely to human exposure to ionizing radiation of terrestrial origin. Thus, the abandoned Tin and Cassiterite mining site in Oyun, Kwara State, Nigeria, is believed to cause radiological implications on local residents.  Assessment of radon concentration in surface water from the study area was carried out using RAD7-Active Electronic detector big bottle system. In order to ascertain the risk or hazard incurable in consuming such water, 12 samples were analysed and used in the estimation of annual effective dose of radon. The measured maximum and minimum radon concentrations were found to be 44.95 and 21.03 Bq/L with average of 35.86 Bq/L. These values are quite greater than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) recommended limits of 11.1.Bq/L. The estimated total effective dose (AEDEtotal) was found to be within the range of 206.52 and 441.41 μSvy-1, and an average of 352.20 μSvy-1 for Adults, 283.30 and 605.47 μSvy-1, and average of 483.10 μSvy-1 for Children, and finally, 321.70 and 687.47 μSvy-1 with average of 548.64 μSvy-1 for Infants, respectively. These values were higher than the recommended limit of 100 µSvy-1 and 200 µSvy-1 for adult and children respectively. Furthermore, worries should be noted about the probabilistic cumulative effect on the consumers of such water if the ingestion is for an extended period of time.

The aim of this paper is to assess the radon concentration of surface and ground waters around Bismuth mining site located in Edu, Kwara State, Nigeria, in order to ascertain its radiological risk. Seventeen (17) water samples were collected and analyzed for radon concentration using a calibrated Rad7-Active Electronic Detector Durridge. The Radon concentration for surface water ranged from 16.23±3.45 Bq/l to 24.71±4.51 Bq/l with a mean of 19.14±3.98 Bq/l while that of ground water ranged from 21.59±3.29 to 27.93±5.74 Bq/l with a mean of 24.16±4.21 Bq/l. The concentration results were used to estimate the annual effective doses. The mean total annual effective dose obtained by summing the dose due to inhalation and ingestion for surface water samples were 187.97 μSvy-1, 257.84 μSvy-1 and 292.77 μSvy-1 for adult, children and infants respectively. Also, the mean effective doses for ground water samples were of 237.25 μSvy-1, 325.44 μSvy-1 and 369.53 μSvy-1 for adult, children and infants respectively. Both the radon concentration and the effective dose due to its inhalation and ingestion were higher than the recommended limit of 11.1 Bq/l and 100 μSvy-1 respectively for all samples. Therefore, consumption of the water in this area poses serious health risk as the water is not safe for all age groups considered. Therefore, it is advised that the water from both sources be treated before consumption.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the ‘Sistema de Información sobre Ocupación del Suelo de España’ (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

We discuss 15 years (2000–2015) of daily-integrated PM₂.₅ samples from the Cape Grim Station. Ion beam analysis and positive matrix factorisation are used to identify six source-type fingerprints: fresh sea salt (57%); secondary sulfate (14%); smoke (13%); aged sea salt (12%); soil dust (2.4%); and industrial metals (1.5%). An existing hourly radon-only baseline selection technique is modified for use with the daily-integrated observations. Results were not significantly different for days on which >20 hours were below the baseline radon threshold compared with days when all 24 hours satisfied the baseline criteria. This relaxed daily baseline criteria increased the number of samples for analysis by almost a factor of two. Two radon baseline thresholds were tested: historic (100 mBq m⁻³), and revised (50 mBq m⁻³). Median aerosol concentrations were similar for both radon thresholds, but maximum values were higher for the 100 mBq m⁻³ threshold. Back trajectories indicated more interaction with southern Australia and the Antarctic coastline for air masses selected with the 100 mBq m⁻³ threshold. Radon-only baseline selection using the 50 mBq m⁻³ threshold was more selective of minimal terrestrial influence than a similar recent study using wind direction and back trajectories. The ratio of concentrations between terrestrial and baseline days for the primary sources soil, smoke and industrial metals was 3.4, 2.6, and 5.5, respectively. Seasonal cycles of soil dust had a summer maximum and winter minimum. Seasonal cycles of smoke were of similar amplitude for terrestrial and baseline events, but of completely different shape: peaking in autumn and spring for terrestrial events, compared to summer for baseline conditions. Seasonal cycles of industrial metals had a summer maximum and winter minimum. A significant fraction of the Cape Grim baseline smoke and industrial metal contributions appeared to be derived from long-term transport (>3 weeks since last terrestrial influence).

Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R²), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R² = 0.9415, RMSE = 0.0589, and MAE = 0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.

The performance of the radon (²²²Rn)-deficit technique has been evaluated at a site in which a complex DNAPL mixture (mostly hexachlorocyclohexanes and chlorobenzenes) has contaminated all four layers (from top to bottom: anthropic backfill, silt, gravel and marl) of the soil profile. Soil gas samples were collected at two depths (0.8 m and 1.7 m) in seven field campaigns and a total of 186 ²²²Rn measurements were performed with a pulse ionization detector. A statistical assessment of the influence of field parameters on the results revealed that sampling depth and atmospheric pressure did not significantly affect the measurements, while the location of the sampling point and ground-level atmospheric temperature did. In order to remove the bias introduced by varying field temperatures and hence to be able to jointly interpret ²²²Rn measurements from different campaigns, ²²²Rn concentrations were rescaled by dividing each individual datum by the mean ²²²Rn concentration of its corresponding field campaign. Rescaled ²²²Rn maps showed a high spatial correlation between ²²²Rn minima and maximum contaminant concentrations in the top two layers of the soil profile, successfully delineating the surface trace of DNAPL accumulation in the anthropic backfill and silt layers. However, no correlation could be established between ²²²Rn concentrations in superficial soil gas and contaminant concentration in the deeper two layers of the soil profile. These results indicate that the ²²²Rn-deficit technique is unable to describe the vertical variation of contamination processes with depth but can be an effective tool for the preliminary characterization of sites in which the distance between the inlet point of the sampling probe and the contaminant accumulation falls within the effective diffusion length of ²²²Rn in the affected soil profile.

Radon is a natural radioactive gas present in the environment, which is considered as the second most important lung cancer cause worldwide. Currently, radon gas is under focus and was classified as contaminant of emerging concern, which is responsible for serious biological/health effects in human. In presented work we propose the numerical model and analysis method for radon diffusion rate measurements and radon transport parameters determination. The experimental setup for radon diffusion was built in a classical, two chamber configuration, in which the radon source and outlet reservoirs are separated by the sample being tested. The main difference with previously known systems is utilization of only one radon detector, what was achieved by a careful characterization of the Rn-222 source and development of a numerical model, which allows for exact determination of radon transport parameters by fitting simulated radon concentration profile in the outlet reservoir to experimental data. For verification of the developed system, several insulation materials commonly used in building industry and civil engineering, as well as, common building materials (gypsum, hardened cement paste, concrete) were tested for radon diffusion rate through these barriers. The results of radon transmittance, permeability and diffusion coefficients for investigated materials are in compliance with values known previously from the literature. The analysis method is fast and efficient, and requires measurement period varying from a dozen or so hours up to 2–3 days depending on material properties. The described method is entirely based on a numerical analysis of the proposed differential equation model using freely available SCILAB software and experimental data obtained during sample measurements.

Two radon measurement stations located to the north and south of a NORM (Naturally Occurring Radioactive Materials) repository of phosphogypsum (southwest of Europe) were used to monitor radon behavior during 2018. The stations are located at opposing sides of the repository, one in Huelva City to the north and other one in a rural area to the south. This setup aimed to identify the influence of the NORM repository on each station and use radon levels as a marker of atmospheric transport in the local area. To achieve this, a comparison was carried out with other coastal stations in the south of Spain, finding higher average concentrations in Huelva City, ~3.3 Bq m⁻³. Hierarchical clustering was applied to identify days with different radon patterns at each Huelva station, detecting possible local radon transport events from the repository. Three events were investigated with WRF (Weather Research and Forecasting) and FLEXPART-WRF (FLEXible PARTicle dispersion model). It was found that both sampling sites required atmospheric stagnant conditions to reach high radon concentration. However, under these conditions the urban station showed high radon regardless of wind direction while the rural station also required radon transport from the repository, either directly or indirectly.