Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data
The advantages of our approach are that is yields <i>a</i>) an accurate estimation of indoor radon concentration even if the survey is not fully representative with respect to floor level and radon concentration in soil, and <i>b</i>) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171.
Authors
- Peter Bossew (ORCID: https://orcid.org/0000-0003-3924-8815)
- Eric Petermann (ORCID: https://orcid.org/0000-0001-8039-5660)
- Valeria Gruber (ORCID: https://orcid.org/0000-0002-9503-9383)
- Joachim Kemski
- Nils Suhr (ORCID: https://orcid.org/0000-0003-4380-4554)
- B. Hoffmann (ORCID: https://orcid.org/0000-0002-0444-4587)
Institutions
- Federal Office for Radiation Protection (DE)
- Austrian Agency for Health and Food Safety (AT)
Publication Details
- Journal
- Environmental Health Perspectives
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1289/ehp14171
- Citations
- 15
- Primary Topic
- Radioactivity and Radon Measurements
- Type
- article
- Field-Weighted Citation Impact
- 6.76
Funders
- Bundesamt für Strahlenschutz
- Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz