Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping
Radon, a radioactive gas produced through the decay of uranium in the earth's crust, poses a significant health risk when it accumulates to high concentrations indoors. This study focuses on identifying areas at higher risk of radon accumulation in Norway by employing a data-driven approach bas...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Environmental Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666765723000947 |
| _version_ | 1797448766849024000 |
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| author | Ying Wang Hendrik Paasche Vikas Chand Baranwal Marie-Andrée Dumais Alexandros Stampolidis Frode Ofstad Marco Brönner |
| author_facet | Ying Wang Hendrik Paasche Vikas Chand Baranwal Marie-Andrée Dumais Alexandros Stampolidis Frode Ofstad Marco Brönner |
| author_sort | Ying Wang |
| collection | DOAJ |
| description | Radon, a radioactive gas produced through the decay of uranium in the earth's crust, poses a significant health risk when it accumulates to high concentrations indoors. This study focuses on identifying areas at higher risk of radon accumulation in Norway by employing a data-driven approach based on geogenic factors, particularly the distribution of uranium on the ground surface.Utilizing two types of uranium measurements and employing a statistical methodology, we classify bedrock geology based on their average uranium content. The classification process integrates Self-organizing maps (SOM) with K-means clustering, facilitating the creation of a country-scale extrapolation. The resulting uranium map is merged from the high-resolution airborne uranium map and the extrapolated uranium map.While acknowledging the presence of uncertainties, our study offers valuable insights into geogenic radon risk, serving as a valuable resource for radon studies and mitigation efforts. Furthermore, the methodology employed in this study is characterized by its flexibility and scalability, enabling future updates and refinements to enhance radon risk assessment and management strategies. |
| first_indexed | 2024-03-09T14:15:13Z |
| format | Article |
| id | doaj.art-e4a9f22eee684c19bb6ac019246e1b41 |
| institution | Directory Open Access Journal |
| issn | 2666-7657 |
| language | English |
| last_indexed | 2024-03-09T14:15:13Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental Advances |
| spelling | doaj.art-e4a9f22eee684c19bb6ac019246e1b412023-11-29T04:24:59ZengElsevierEnvironmental Advances2666-76572023-12-0114100436Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mappingYing Wang0Hendrik Paasche1Vikas Chand Baranwal2Marie-Andrée Dumais3Alexandros Stampolidis4Frode Ofstad5Marco Brönner6Geological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, Norway; Corresponding author.Geological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, Norway; UFZ - Helmholtz Centre for Environmental Research, Department Monitoring and Exploration Technologies, Permoserstr. 15 04318, Leipzig, GermanyGeological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, NorwayGeological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, NorwayGeological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, Norway; Aristotle University of Thessaloniki, Dept. of Geophysics, University Campus 54124, Thessaloniki, GreeceGeological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, NorwayGeological Survey of Norway (NGU), Leiv Eirikssons vei 39 7040, Trondheim, NorwayRadon, a radioactive gas produced through the decay of uranium in the earth's crust, poses a significant health risk when it accumulates to high concentrations indoors. This study focuses on identifying areas at higher risk of radon accumulation in Norway by employing a data-driven approach based on geogenic factors, particularly the distribution of uranium on the ground surface.Utilizing two types of uranium measurements and employing a statistical methodology, we classify bedrock geology based on their average uranium content. The classification process integrates Self-organizing maps (SOM) with K-means clustering, facilitating the creation of a country-scale extrapolation. The resulting uranium map is merged from the high-resolution airborne uranium map and the extrapolated uranium map.While acknowledging the presence of uncertainties, our study offers valuable insights into geogenic radon risk, serving as a valuable resource for radon studies and mitigation efforts. Furthermore, the methodology employed in this study is characterized by its flexibility and scalability, enabling future updates and refinements to enhance radon risk assessment and management strategies.http://www.sciencedirect.com/science/article/pii/S2666765723000947Radon riskNatural radiationMachine learningUranium concentrationGeospatial analysisGeology |
| spellingShingle | Ying Wang Hendrik Paasche Vikas Chand Baranwal Marie-Andrée Dumais Alexandros Stampolidis Frode Ofstad Marco Brönner Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping Environmental Advances Radon risk Natural radiation Machine learning Uranium concentration Geospatial analysis Geology |
| title | Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping |
| title_full | Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping |
| title_fullStr | Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping |
| title_full_unstemmed | Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping |
| title_short | Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping |
| title_sort | extrapolating a uranium map of norway implications for country scale geogenic radon risk mapping |
| topic | Radon risk Natural radiation Machine learning Uranium concentration Geospatial analysis Geology |
| url | http://www.sciencedirect.com/science/article/pii/S2666765723000947 |
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