Seasonal variations of terrestrial gamma dose, natural radionuclides and human health.

The aim of the research was to study seasonal variations in gamma radiation and the statistical significance of these variations. Moreover, we compared in-situ and laboratory analyses of uranium, thorium, radium and potassium K-40 contents. Exposure to a low level of radiation is a minor (but still is) contributor to overall cancer risk therefore we compared doses generated by gamma radiation with overall cancer risk. The research was performed in SW Poland in two granitoid massifs -Strzelin and Karkonosze. The in-situ measurements were performed seasonally using gamma-ray spectrometer Exploranium with BGO detector and Radiometer RK-100. The laboratory measurements were performed using spectrometer with HPGe detector Canberra-Packard and alpha spectrometry technique. The general trend of seasonal variations of natural radionuclides, terrestrial ambient gamma dose (TGDR) and ambient gamma dose rate (AGDR) was difficult to identify. We noticed slightly increased values of all analysed parameters in warmer seasons, and lower in colder, although there were some exceptions. These exceptions were induced by precipitation and varied soil water content, but variations were mostly not statistically significant. The statistically important deviation from the trend was registered only in equivalent uranium data when the survey was carried out during or just after intensive precipitation. We observed a good positive correlation between in-situ and laboratory results (TGDR in situ/Lab r = 0.696), therefore, we recommend using in-situ measurements in a dense measuring grid before collecting selected soil samples to better evaluate the level of natural radiation in the environment. The average ambient gamma dose in the Karkonosze Massif was 0.52 mSv y-1 whereas in the Strzelin Massif was 0.39 mSv y-1. The overall cancer risk in Karkonoski county is higher than in Strzelin county. A connection between increased gamma radiation and higher overall cancer risk is possible but should be examined during more elaborated research.

Radon flux estimates, from both gamma radiation and geochemical data, to determine sources, migration pathways, and related health risk: The Campania region (Italy) case study.

An empirical method was applied to estimate the 222Rn fluxes distribution across the Campania region (Italy) by using both gamma-rays and U, Th, K concentrations in soils. As a first step, K, Th and U soil concentrations and 4 K, 238U and 232Th activity have been converted into their own specific activity to calculate the Terrestrial Gamma Dose Rate (TGDR). This latter has been then used to determine the 222Rn fluxes across the region. Regardless of the radiometric or geochemical origin, 222Rn fluxes reached, as expected, their maximum values in correspondence with the volcanic centres of Campania (Mt. Somma-Vesuvius, Phlegrean Fields, Mt. Roccamonfina). However, comparing the results obtained from the two different datasets, it was also possible to infer the existence of contributions to surficial 222Rn fluxes proceeding from both some underlying geological bodies and active seismogenic sources. In line with some national regulations, the 222Rn flux esteemed from gamma radiations was also used to assess the possible regional distribution of risk deriving from the indoor environmental exposure to 222Rn; results were compared with standardized incidence rates (SIRs) of lung cancer for an area on the south-western sector of Mt. Somma-Vesuvius showing a potential spatial relationship among flux data and SIRs.

Validation of a database of mean uranium, thorium and potassium concentrations in rock samples of Portuguese geological units, generated of literature data.

The European Atlas of Natural Radiation, recently published, contains a collection of maps of Europe showing the levels of natural sources of radiation. Among the lacunae of the Atlas are maps of U, Th and K concentrations in rocks due to lack of European-wide geochemical surveys of bedrock units. The objective of this paper is to investigate the usability of scattered geochemical data of rock samples for large-scale mapping of U, Th and K concentrations in geological units. For this purpose, geochemical data were compiled from literature sources to produce a geochemical database (LIT database) that includes 2817 entries of U, Th and K concentrations measured in rock samples of geological units outcropping in Portugal. Given the methodical heterogeneity within LIT database, the influence of the geochemical analysis techniques was assessed through a three-way analysis of variance (ANOVA) using geological units, geochemical analysis techniques and loss on ignition (LOI) as categorical variables. The percentage of variation explained by geological factors was large (>35%), while the percentage of variation explained by the geochemical analysis techniques and LOI was generally lower than 5%. The geological factors were the main source of variability in the data, followed by the error component which can be assumed to represent the true spatial variability of geochemical concentrations. The pairwise comparison of the least square (LS) means computed through the ANOVA for each geochemical analysis technique indicates that LIT database can be considered consistent within itself, thus, reliable. In order to validate the usability of literature data the terrestrial gamma dose rate (TGDR) calculated from LIT database (TGDRcalc) was compared to the TGDR displayed in the Radiometric Map of Portugal (TGDRobs). The correlation between TGDRcalc and TGDRobs was highly significant (p < 0.001) and the results of a paired sample t-test and Wilcoxon median tests indicate that the differences between the arithmetic means of TGDRcalc and TGDRobs were not statistically significant (p = 0.126 and p = 0.14, respectively). Distributions of TGDRcalc and TGDRobs were seemingly equal according to the Kolmogorov-Smirnov and Anderson-Darling tests. Although, systematic discrepancies between TGDRcalc and TGDRobs were observed for sedimentary rocks, the compatibility of the RMP and LIT databases can be considered acceptable, which implies that the estimation of the contents of terrestrial radionuclides using literature data for large-scale mapping of U, Th and K contents in geological units is reasonable.

A novel hybrid approach to the mapping and prediction of the terrestrial gamma dose rate distribution in the Central Anatolia Region of Turkey.

In this study, a new approach was suggested for the estimation and mapping of Terrestrial gamma dose rate (TGDR, in nGy h-1) in the Central Anatolia Region of Turkey by using the sequential Gaussian simulation (SGS) and Artificial neural network (ANN) methods together as a hybrid. In this hybrid approach (SGS-ANN), different from the classical spatial examinations, each spatial pixel (500×500m2) were calculated separately by evaluating the activity concentrations of terrestrial radionuclides that directly affects TGDR (226Ra, 232Th and 40K, in Bq kg-1) terrestrial coordination (X and Y, in meter). Therefore, the local changes of TGDR distributions that were estimated for the study area could be determined in appropriate precision without the smoothing effect. The performance evaluation of SGS-ANN approach was conducted by comparing the results for the same study area of Ordinary kriging (OK) method which is frequently used in the literature. According to the validation diagram that was created with the observed and estimated TGDR values, the Pearson's r correlation coefficient was obtained as 0.30 and 0.65, RMSE as 31.41 nGy h-1 and 25.79 nGy h-1, MAE as 24.50 nGy h-1 and 21.29 nGy h-1 and mean error as 5.97 nGy h-1 and -1.32 nGy h-1 for the OK method and the SGS-ANN approach, respectively.

Estimation of radon flux spatial distribution in Rize, Turkey by the artificial neural networks method.

In this study, average radon flux distribution in the Rize province (Turkey) was estimated by the artificial neural networks (ANN) method. For this purpose, terrestrial gamma dose rate (TGDR), which is defined as an important proxy in determining radon flux distribution, was used. Input parameters that were used for ANN were the natural radionuclide (238U, 232Th and 40K) activity values in soil samples taken from 64 stations in Rize Province, data from ambient gamma dose rates (AGDR) directly affecting the distribution of radon flux and data of geographical coordinates. Randomly chosen 42 stations were used for ANN training and data from 22 stations were used for testing the ANN model. Performance test results gave a Pearson's r value of 0.60 (p < 0.001) and RMSE of 0.296. The area that was used for the model was divided into grids of 100 m by 100 m and a spatial distribution map was composed by using ANN predicted radon flux rates at grid nodes, whereby natural radionuclide values and Ordinary Kriging predicted values of external gamma dose rates were used for composing the map.

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PURPOSE: The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. METHOD: Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. RESULTS: The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. CONCLUSION: Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland.