In many environmental sciences, several correlated variables are observed at some locations of the domain of interest, then appropriate modeling and prediction techniques for multivariate spatial data are necessary. This paper aims to highlight the convenience of using multivariate geostatistical methods to study the spatial distribution of radon soil concentration, to map and assess high risk areas. Indeed, this soil gas, due its nature, is known to be carcinogen: many studies have demonstrated that risk of lung cancer increases substantially with the exposure to higher radon concentrations. In analyzing radon concentrations, it is relevant to consider the available data regarding the geology, geomorphology and soil type since this gas is released during the decay of some radioactive elements found in rocks and soil. Thus, the application of multivariate geostatistical techniques, such as indicator-cokriging and indicator kriging for conditional probability analysis, is convenient to classify areas according to radon levels and to provide probability maps of radon risk. Note that in this paper geostatistical bootstrap for quantifying spatial uncertainty has been performed. A case study on sample data concerning radon-222 concentrations, permeability, lithology, fault and polje in Lecce district (Southern Italy) is proposed. Radon risk prediction maps for the probability to exceed certain threshold values, conditioned to specific soil type, can be useful especially for regions with no or only few measurements of soil gas radon.
A geostatistical approach for radon risk prediction
CAPPELLO, CLAUDIA;DE IACO, Sandra;PALMA, Monica;PELLEGRINO, DANIELA
2014-01-01
Abstract
In many environmental sciences, several correlated variables are observed at some locations of the domain of interest, then appropriate modeling and prediction techniques for multivariate spatial data are necessary. This paper aims to highlight the convenience of using multivariate geostatistical methods to study the spatial distribution of radon soil concentration, to map and assess high risk areas. Indeed, this soil gas, due its nature, is known to be carcinogen: many studies have demonstrated that risk of lung cancer increases substantially with the exposure to higher radon concentrations. In analyzing radon concentrations, it is relevant to consider the available data regarding the geology, geomorphology and soil type since this gas is released during the decay of some radioactive elements found in rocks and soil. Thus, the application of multivariate geostatistical techniques, such as indicator-cokriging and indicator kriging for conditional probability analysis, is convenient to classify areas according to radon levels and to provide probability maps of radon risk. Note that in this paper geostatistical bootstrap for quantifying spatial uncertainty has been performed. A case study on sample data concerning radon-222 concentrations, permeability, lithology, fault and polje in Lecce district (Southern Italy) is proposed. Radon risk prediction maps for the probability to exceed certain threshold values, conditioned to specific soil type, can be useful especially for regions with no or only few measurements of soil gas radon.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
