Understanding soil gas radon spatial variations can allow the constnlctor of a new house to prevent radon gas flowing from the ground. [ndoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potenti al. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potenti al. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction perfolmances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data seI. The comparison of predictions was based on three measures of accuracy: (I) the mean absolute enor, (2) the me ansquared en'or of prediction; (3) the mean relative enor, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomali es with lithology and fault locations, no evidence of a strict conelation between type of outcropping ten'ain and radon anomalies was found, except in the western sector where there were granitic and gneissic tenain, Moreover, there was a clear conelation between radon anomalies and fault systems.

Mapping soil Gas Radon Concentration: A Comparative study of Geostatistical Methods

FALCONE, Giovanni
2007

Abstract

Understanding soil gas radon spatial variations can allow the constnlctor of a new house to prevent radon gas flowing from the ground. [ndoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potenti al. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potenti al. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction perfolmances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data seI. The comparison of predictions was based on three measures of accuracy: (I) the mean absolute enor, (2) the me ansquared en'or of prediction; (3) the mean relative enor, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomali es with lithology and fault locations, no evidence of a strict conelation between type of outcropping ten'ain and radon anomalies was found, except in the western sector where there were granitic and gneissic tenain, Moreover, there was a clear conelation between radon anomalies and fault systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/124232
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