Radon is known to be the main contributor to natural background radiation exposure and the second leading cause of lung cancer after smoking. Thus, radon prediction maps are strategic tools to support decisions regarding environmental and human health protection. In this paper, the convenience of using multivariate geostatistical methods to study the spatial distribution of radon soil concentration and assess high risk areas has been highlighted. A case study on sample data concerning radon-222 concentrations and covariates derived from a geographical information system (i.e. permeability, lithology, fault and polje) in Lecce district (Southern Italy) has been discussed. Geostatistical techniques, such as indicator-cokriging and indicator kriging for conditional probability analysis, have been applied in order to classify areas according to different radon levels and to provide probability maps of radon risk. Moreover, geostatistical bootstrap for quantifying spatial uncertainty has been performed.
RADON RISK ASSESSMENT THROUGH A MULTIVARIATE GEOSTATISTICAL APPROACH
CAPPELLO, CLAUDIA;MAGGIO, Sabrina;PALMA, Monica;POSA, Donato
2017-01-01
Abstract
Radon is known to be the main contributor to natural background radiation exposure and the second leading cause of lung cancer after smoking. Thus, radon prediction maps are strategic tools to support decisions regarding environmental and human health protection. In this paper, the convenience of using multivariate geostatistical methods to study the spatial distribution of radon soil concentration and assess high risk areas has been highlighted. A case study on sample data concerning radon-222 concentrations and covariates derived from a geographical information system (i.e. permeability, lithology, fault and polje) in Lecce district (Southern Italy) has been discussed. Geostatistical techniques, such as indicator-cokriging and indicator kriging for conditional probability analysis, have been applied in order to classify areas according to different radon levels and to provide probability maps of radon risk. Moreover, geostatistical bootstrap for quantifying spatial uncertainty has been performed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
