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Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.

Spatial distribution of concentrations of radon gas in the soil is important for defining high risk areas because geogenic radon is the major potential source of indoor radon concentrations regardless of the construction features of buildings. An area of southern Italy (CatanzaroLamezia plain) was surveyed to study the relationship between radon gas concentrations in the soil, geology and structural patterns. Moreover, the uncertainty associated with the mapping of geogenic radon in soil gas was assessed. Multi-Gaussian kriging was used to map the geogenic soil gas radon concentration, while conditional sequential Gaussian simulation was used to yield a series of stochastic images representing equally probable spatial distributions of soil radon across the study area. The stochastic images generated by the sequential Gaussian simulation were used to assess the uncertainty associated with the mapping of geogenic radon in the soil and they were combined to calculate the probability of exceeding a specified critical threshold that might cause concern for human health. The study showed that emanation of radon gas radon was also dependent on geological structure and lithology. The results have provided insight into the influence of basement geochemistry on the spatial distribution of radon levels at the soil/atmosphere interface and suggested that knowledge of the geology of the area may be helpful in understanding the distribution pattern of radon near the earth's surface.

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Soils can exhibit a complex range of physical, mineralogical, and chemical features depending on many interrelated factors such as parental rock composition and mineralogy, climate, topography, vegetation amounts and types, water infiltration versus runoff, soil moisture, organic matter, presence and types of anthropogenic contaminants, and many others. Radioactivity as measured on these complex systems is consequently perturbed by several mixed effects. In order to find out a possible (potential) relationship among soil properties and experimental data attributable to radioactivities of K, Th, U and Rn a detailed investigation has been performed in the Cecita Lake basin (Sila Grande, Calabria, Southern Italy). Most of the soil types outcropping in the Cecita Lake surroundings belong to the Entisol and Inceptisol orders [USDA (United States Department of Agriculture), 2006. Keys to Soil Taxonomy. 10th edit., USDA, Soil Survey Staff, Natural Resources Conservation Service, Washington D.C., 333 pp.], representing relatively young, poorly to moderately differentiated soils, showing features strongly dependent on the nature of the parent rock and the climatic conditions. Disintegrations contributed by K, Th, U and Rn measured respectively in % (K), ppm (U and Th) and kBq/m 3 (Rn), have been related to 13 a priori known soil unit groups with well-defined general features and spatial position. The data have been analysed by using graphical and numerical statistical procedures able to manage compositional data in a correct sample space. The paper summarises the results of this research and highlights the conclusions drawn from these investigations, particularly concerning i) the modelling of the high U variability, a behaviour that tends to homogenise the differences potentially attributable to soil features, with the exception of situations where uranium could be enriched due to adsorption onto iron oxi-hydroxides and/or clay minerals or concentrated in argillic horizons due to illuviation; ii) the discovering of the discriminative effect of the K/Th ratio values, particularly for soil groups where Th behaviour, as other tetravalent actinides, is strongly affected by the presence of mineral colloids or where the presence of clays affects the trapping of K; iii) the clustering of the a priori known soil groups in four new sets characterised by internal similarities for Rn values for which the morphology appears to be the most important discriminative effect.