Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Journal: Environmental health perspectives

PMID: 39292674

Abstract

BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas.

Authors

Eric Petermann

Section Radon and NORM, Federal Office for Radiation Protection (BfS), Berlin, Germany.
Peter Bossew

Section Radon and NORM, Federal Office for Radiation Protection (BfS), Berlin, Germany.
Joachim Kemski

Sachverständigenbüro Dr. Kemski, Bonn, Germany.
Valeria Gruber

Department for Radon and Radioecology, Austrian Agency for Health and Food Safety, Linz, Austria.
Nils Suhr

Section Radon and NORM, Federal Office for Radiation Protection (BfS), Berlin, Germany.
Bernd Hoffmann

Section Radon and NORM, Federal Office for Radiation Protection (BfS), Berlin, Germany.

Keywords

Air Pollutants, Radioactive Air Pollution, Indoor Germany Housing Humans Machine Learning Models, Statistical Radiation Monitoring Radon

External Resources

View on PubMed Access via DOI PubMed (39292674)

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Abstract

Authors

Keywords

External Resources

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