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SYSTEMATIC REVIEW article
Front. Public Health
Sec. Radiation and Health
Volume 12 - 2024 |
doi: 10.3389/fpubh.2024.1460295
This article is part of the Research Topic Radon and Related Health Effects: From Exposure to Risk Assessment and Policies View all 3 articles
Systematic review of statistical methods for the identification of buildings and areas with high radon levels
Provisionally accepted- 1 Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
- 2 Western Switzerland Center for Indoor Air Quality and Radon (croqAIR), Transform Institute, School of Architecture of Fribourg, HES-SO University of Applied Sciences and Arts Western Switzerland, Fribourg, Switzerland
- 3 National Center for Radiation Protection and Computational Physics, Viale Regina Elena, 299, National Institute of Health (ISS), Rome, Lazio, Italy
- 4 Department of Radon and Radioecology, Austrian Agency for Health and Food Safety (AGES), Vienna, Vienna, Austria
- 5 Bureau d'Etude et d'expertise du Radon, IRSN, PSE-ENV, SERPEN, BERAD. 31 Av. de la Division Leclerc,, Institut de Radioprotection et de Sûreté Nucléaire, Fontenay-aux-Roses, France
Radon is a natural and radioactive noble gas, which may accumulate indoors and cause lung cancers after long term-exposure. Being a decay product of Uranium 238, it originates from the ground and is spatially variable. Many environmental (i.e. geology, tectonic, soils) and architectural factors (i.e. building age, floor) influence its presence indoors, which make it difficult to predict. However, different methods have been developed and applied to identify radon prone areas and buildings. This paper presents the results of a systematic literature review of suitable statistical methods willing to identify buildings and areas where high indoor radon concentrations might be found. The application of these methods is particularly useful to improve the knowledge of the factors most likely to be connected to high radon concentrations. These types of methods are not so commonly used, since generally statistical methods that study factors predictive of radon concentration are focused on the average concentration and aim to identify factors that influence the average radon level. In this paper, an attempt has been made to classify the methods found, to make their description clearer. Four main classes of methods have been identified: descriptive methods, regression methods, geostatistical methods, and machine learning methods. For each presented method, advantages and disadvantages are presented while some applications examples are given. The ultimate purpose of this overview is to provide researchers with a synthesis paper to optimize the selection of the method to identify radon prone areas and buildings.
Keywords: Radon prone areas and building, Public Health, statistic, geostatistics, machine learning
Received: 05 Jul 2024; Accepted: 02 Sep 2024.
Copyright: © 2024 Rey, Antignani, Baumann, Di Carlo, Loret, Gréau, Gruber, Goyette Pernot and Bochicchio. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Joan F. Rey, Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
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