Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland

Georg Kropat; Francois Bochud; Michel Jaboyedoff; Jean-Pascal Laedermann; Christophe Murith; Martha Palacios Gruson; Sébastien Baechler

doi:10.1016/j.scitotenv.2014.09.064

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland

Sci Total Environ. 2015 Feb 1:505:137-48. doi: 10.1016/j.scitotenv.2014.09.064. Epub 2014 Oct 11.

Authors

Georg Kropat¹, Francois Bochud², Michel Jaboyedoff³, Jean-Pascal Laedermann², Christophe Murith⁴, Martha Palacios Gruson⁴, Sébastien Baechler⁵

Affiliations

¹ Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland. Electronic address: georg.kropat@chuv.ch.
² Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
³ Faculty of Geosciences and Environment, University of Lausanne, GEOPOLIS - 3793, 1015 Lausanne, Switzerland.
⁴ Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne, Switzerland.
⁵ Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland; Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne, Switzerland.

PMID: 25314691
DOI: 10.1016/j.scitotenv.2014.09.064

Abstract

Purpose: 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.

Methods: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map.

Results: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation.

Conclusions: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Keywords: Geology; Indoor radon; Kernel regression; Predictive mapping; Probability mapping; Switzerland.

MeSH terms

Air Pollutants, Radioactive / analysis*
Air Pollution, Radioactive / statistics & numerical data*
Radiation Monitoring / methods*
Radon / analysis*
Regression Analysis
Reproducibility of Results
Switzerland

Substances

Air Pollutants, Radioactive
Radon