Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: An application to Switzerland
The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information.
•Kernel regression was used to map indoor radon concentration in Switzerland.
•Our model explains 28% of the variations of radon concentration data.
•Maps were generated considering different architectural elements and geology.
•Maps showing the local probability to exceed 300 Bq/m3 were proposed.
•We developed a confidence index to assess the reliability of the probability map.
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