Radon generation and migration from the soil toward the surface are natural processes that can lead to radon entry in buildings, thus constituting a health risk. The analysis and the modelling of these processes can be thought of as the contribution of different proxies representing the geological radon source (GRS) (e.g., geology, soil properties, radionuclide content), and the pathways (e.g., faults, karst) that favour the geological radon migration (GRM) in the subsoil. The aggregation of these quantities can be used to construct a geogenic radon hazard index (GRHI) map that can be understood as a measure of the susceptibility of an area to increased indoor radon concentration for geogenic reasons (Radon Priority Areas, RPA). A number of direct and indirect models have been developed in order to create GRHI maps of a certain region by using both deterministic and probabilistic models. Here, we propose a bottom- up procedure through the integration of different factors (predictors and/or proxies) and by weighs their importance. In particular, we first propose to construct a GRHI map of the whole Italian territory using a GIS-based (spatial) multicriteria decision analysis (SMCDA). SMCDA uses the Analytical HierarchyProcess (AHP) to assess the importance of the factors and to derive their relative weights and, consequently, it determines the overall final scores. Lithologies of the National Geological Map of Italy (1:1000000) were reclassified in few homogeneous classes and ranked according to the associated mean content of uranium, thorium and potassium available from GEMAS (http://gemas.geolba.ac.at/) and FOREGS (http://weppi.gtk.fi/publ/foregsatlas/index.php) database by using a multivariate statistical approach. In this way the intermediate map of the GRS was obtained. SMCDA was then applied by using the GRS map and the maps of other factors, such as the fine fraction of the soil (LUCAS top- soil database, https://esdac.jrc.ec.europa.eu/projects/lucas), the fault density map (Italian national/regional datasets), the map of the karst areas (https://www.whymap.org/whymap/EN/Maps_Data/Wokam/wokam_node_en.html) and the map of the heat flow of Italy. All these factors were standardised by using fuzzy classification to transform input data to a 0/1 scale. The standardised factors are weighted by using AHP and then summed to obtain the final GRHI map. All maps are constructed at the same grid resolution of the European Atlas of Natural Radiation (10x10km) (https://remon.jrc.ec.europa.eu/About/Atlas-of- Natural-Radiation) published by the Joint Research Centre (JRC) of the European Commission.
Mapping the Geogenic Radon Hazard Index of Italy
Giustini F;Ciotoli G
2021
Abstract
Radon generation and migration from the soil toward the surface are natural processes that can lead to radon entry in buildings, thus constituting a health risk. The analysis and the modelling of these processes can be thought of as the contribution of different proxies representing the geological radon source (GRS) (e.g., geology, soil properties, radionuclide content), and the pathways (e.g., faults, karst) that favour the geological radon migration (GRM) in the subsoil. The aggregation of these quantities can be used to construct a geogenic radon hazard index (GRHI) map that can be understood as a measure of the susceptibility of an area to increased indoor radon concentration for geogenic reasons (Radon Priority Areas, RPA). A number of direct and indirect models have been developed in order to create GRHI maps of a certain region by using both deterministic and probabilistic models. Here, we propose a bottom- up procedure through the integration of different factors (predictors and/or proxies) and by weighs their importance. In particular, we first propose to construct a GRHI map of the whole Italian territory using a GIS-based (spatial) multicriteria decision analysis (SMCDA). SMCDA uses the Analytical HierarchyProcess (AHP) to assess the importance of the factors and to derive their relative weights and, consequently, it determines the overall final scores. Lithologies of the National Geological Map of Italy (1:1000000) were reclassified in few homogeneous classes and ranked according to the associated mean content of uranium, thorium and potassium available from GEMAS (http://gemas.geolba.ac.at/) and FOREGS (http://weppi.gtk.fi/publ/foregsatlas/index.php) database by using a multivariate statistical approach. In this way the intermediate map of the GRS was obtained. SMCDA was then applied by using the GRS map and the maps of other factors, such as the fine fraction of the soil (LUCAS top- soil database, https://esdac.jrc.ec.europa.eu/projects/lucas), the fault density map (Italian national/regional datasets), the map of the karst areas (https://www.whymap.org/whymap/EN/Maps_Data/Wokam/wokam_node_en.html) and the map of the heat flow of Italy. All these factors were standardised by using fuzzy classification to transform input data to a 0/1 scale. The standardised factors are weighted by using AHP and then summed to obtain the final GRHI map. All maps are constructed at the same grid resolution of the European Atlas of Natural Radiation (10x10km) (https://remon.jrc.ec.europa.eu/About/Atlas-of- Natural-Radiation) published by the Joint Research Centre (JRC) of the European Commission.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
