|Title||The assessment of local geological factors for the construction of a Geogenic Radon Potential map using regression kriging. A case study from the Euganean Hills volcanic district (Italy)|
|Publication Type||Articolo su Rivista peer-reviewed|
|Year of Publication||2022|
|Authors||Coletti, C., Ciotoli G., Benà E., Brattich E., Cinelli Giorgia, Galgaro A., Massironi M., Mazzoli C., Mostacci D., Morozzi P., Mozzi P., Nava J., Ruggiero L., Sciarra A., Tositti L., and Sassi R.|
|Journal||Science of the Total Environment|
|Keywords||air pollutant, Air Pollutants, article, assessment method, Bayes Theorem, concentration (composition), concentration (parameter), Dissolution, Euganean hill, Euganean Hills, Fault system, Finite difference method, gamma radiation, gamma spectrometry, Geo-statistics, Geogenic radon potential, geogenic radon potential map, geographic mapping, Geological factors, Geology, geostatistics, heat stress, interpolation, Italy, kriging, Landforms, mapping method, model, natural radioactivity, Padova, Potential map, Process optimization, radiation dose, radiation hazard, radiation monitoring, Radioactive, Radon, radon 220, Radon concentrations, Regression analysis, Regression-kriging, soil chemistry, Soil gas radon, Soil moisture, Soil permeability, soil pollutant, Soil Pollutants, soil property, Soils, spatial analysis, spatial regression, Thoron, Veneto, Weathering|
The assessment of potential radon-hazardous environments is nowadays a critical issue in planning, monitoring, and developing appropriate mitigation strategies. Although some geological structures (e.g., fault systems) and other geological factors (e.g., radionuclide content, soil organic or rock weathering) can locally affect the radon occurrence, at the basis of a good implementation of radon-safe systems, optimized modelling at territorial scale is required. The use of spatial regression models, adequately combining different types of predictors, represents an invaluable tool to identify the relationships between radon and its controlling factors as well as to construct Geogenic Radon Potential (GRP) maps of an area. In this work, two GRP maps were developed based on field measurements of soil gas radon and thoron concentrations and gamma spectrometry of soil and rock samples of the Euganean Hills (northern Italy) district. A predictive model of radon concentration in soil gas was reconstructed taking into account the relationships among the soil gas radon and seven predictors: terrestrial gamma dose radiation (TGDR), thoron (220Rn), fault density (FD), soil permeability (PERM), digital terrain model (SLOPE), moisture index (TMI), heat load index (HLI). These predictors allowed to elaborate local spatial models by using the Empirical Bayesian Regression Kriging (EBRK) in order to find the best combination and define the GRP of the Euganean Hills area. A second GRP map based on the Neznal approach (GRPNEZ) has been modelled using the TGDR and 220Rn, as predictors of radon concentration, and FD as predictor of soil permeability. Then, the two GRP maps have been compared. Results highlight that the radon potential is mainly driven by the bedrock type but the presence of fault systems and topographic features play a key role in radon migration in the subsoil and its exhalation at the soil/atmosphere boundary. © 2021 Elsevier B.V.
cited By 3