title: Mapping geogenic radon potential by regression kriging.
abstract: Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.
authors.affiliation: Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest, Hungary.
authors.forename: László
authors.initials: L
authors.lastname: Pásztor
authors.affiliation: Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100, Hungary. Electronic address: sz_k_zs@yahoo.de.
authors.forename: Katalin Zsuzsanna
authors.initials: KZ
authors.lastname: Szabó
authors.affiliation: Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest, Hungary.
authors.forename: Gábor
authors.initials: G
authors.lastname: Szatmári
authors.affiliation: Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest, Hungary.
authors.forename: Annamária
authors.initials: A
authors.lastname: Laborczi
authors.affiliation: Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary.
authors.forename: Ákos
authors.initials: Á
authors.lastname: Horváth
mesh_terms: D016902:Air Pollution, Indoor / Q000706:statistics & numerical data*; D006814:Hungary; D011834:Radiation Monitoring / Q000379:methods*; D011886:Radon / Q000032:analysis*; D012990:Soil Pollutants, Radioactive / Q000032:analysis*; D062206:Spatial Analysis
chemical_list: D012990:Soil Pollutants, Radioactive; D011886:Radon
keywords: Auxiliary environmental variables; Physical soil properties; Regression kriging; Soil gas radon; Spatial prediction