Radon (Rn) is a potentially toxic gas in soil which may affect human health. Assessing Rn levels in soil gas usually requires enormous efforts in terms of time and costs, since the sampling protocol is very complex. In most cases, the variable under study is sparsely sampled over the domain and this could affect the reliability of the spatial predictions. For this reason, it is useful to incorporate, into the estimation procedure, some auxiliary variables, correlated with the in soil gas Rn concentrations (primary variable) and more densely available over the domain. On the basis of this latter aspect, it is even better if the covariates are derived from a geographical information system (GIS). In this article, the Rn sampling protocol used during a measurement campaign planned over a risk area is described and the process of deriving GIS covariates considered as secondary information for predicting the primary variable is clarified. Then, multivariate modeling and prediction of the Rn concentrations over the domain of interest are discussed and a comparative study regarding the performance of the prediction procedures is presented. Rn prone areas are also analyzed with respect to urban and school density. All these aspects can clearly support decisions on environmental and human safeguard.
Radon Predictions with Geographical Information System Covariates: From Spatial Sampling to Modeling
DE IACO, Sandra;MAGGIO, Sabrina;PALMA, Monica
2017-01-01
Abstract
Radon (Rn) is a potentially toxic gas in soil which may affect human health. Assessing Rn levels in soil gas usually requires enormous efforts in terms of time and costs, since the sampling protocol is very complex. In most cases, the variable under study is sparsely sampled over the domain and this could affect the reliability of the spatial predictions. For this reason, it is useful to incorporate, into the estimation procedure, some auxiliary variables, correlated with the in soil gas Rn concentrations (primary variable) and more densely available over the domain. On the basis of this latter aspect, it is even better if the covariates are derived from a geographical information system (GIS). In this article, the Rn sampling protocol used during a measurement campaign planned over a risk area is described and the process of deriving GIS covariates considered as secondary information for predicting the primary variable is clarified. Then, multivariate modeling and prediction of the Rn concentrations over the domain of interest are discussed and a comparative study regarding the performance of the prediction procedures is presented. Rn prone areas are also analyzed with respect to urban and school density. All these aspects can clearly support decisions on environmental and human safeguard.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
