Premium
Radon Predictions with Geographical Information System Covariates: From Spatial Sampling to Modeling
Author(s) -
De Iaco Sandra,
Maggio Sabrina,
Palma Monica
Publication year - 2017
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12118
Subject(s) - covariate , multivariate statistics , variable (mathematics) , sampling (signal processing) , statistics , protocol (science) , environmental science , spatial analysis , radon , sample (material) , computer science , econometrics , mathematics , medicine , mathematical analysis , physics , alternative medicine , filter (signal processing) , pathology , quantum mechanics , computer vision , chemistry , chromatography
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.