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Review
. 2020 Jun 10;17(11):4134.
doi: 10.3390/ijerph17114134.

Development of a Geogenic Radon Hazard Index-Concept, History, Experiences

Peter Bossew  1 , Giorgia Cinelli  2 , Giancarlo Ciotoli  3 , Quentin G Crowley  4 , Marc De Cort  2 , Javier Elío Medina  5 , Valeria Gruber  6 , Eric Petermann  1 , Tore Tollefsen  2
Affiliations
Review

Development of a Geogenic Radon Hazard Index-Concept, History, Experiences

Peter Bossew et al. Int J Environ Res Public Health. .

Abstract

Exposure to indoor radon at home and in workplaces constitutes a serious public health risk and is the second most prevalent cause of lung cancer after tobacco smoking. Indoor radon concentration is to a large extent controlled by so-called geogenic radon, which is radon generated in the ground. While indoor radon has been mapped in many parts of Europe, this is not the case for its geogenic control, which has been surveyed exhaustively in only a few countries or regions. Since geogenic radon is an important predictor of indoor radon, knowing the local potential of geogenic radon can assist radon mitigation policy in allocating resources and tuning regulations to focus on where it needs to be prioritized. The contribution of geogenic to indoor radon can be quantified in different ways: the geogenic radon potential (GRP) and the geogenic radon hazard index (GRHI). Both are constructed from geogenic quantities, with their differences tending to be, but not always, their type of geographical support and optimality as indoor radon predictors. An important feature of the GRHI is consistency across borders between regions with different data availability and Rn survey policies, which has so far impeded the creation of a European map of geogenic radon. The GRHI can be understood as a generalization or extension of the GRP. In this paper, the concepts of GRP and GRHI are discussed and a review of previous GRHI approaches is presented, including methods of GRHI estimation and some preliminary results. A methodology to create GRHI maps that cover most of Europe appears at hand and appropriate; however, further fine tuning and validation remains on the agenda.

Keywords: European map of geogenic radon; geogenic radon hazard index; geogenic radon potential.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
General workflow of multivariate classification approach to construct a geogenic radon hazard index (GRHI) [49]. TGDR—terrestrial gamma dose rate.
Figure 2
Figure 2
Approaches A and B.
Figure 3
Figure 3
Physical predictors and proxies (see text).
Figure 4
Figure 4
Consistency between quantity GRHI calculated in regions A and B from different sets of predictors, Y(A) and Y(B).
Figure 5
Figure 5
Conceptual difference between classical (generalized) regression and machine learning.
Figure 6
Figure 6
Classification of geological units according to the Neznal-GRP; from [112].
Figure 7
Figure 7
GRHI map created by multiple regression (from [68]).
Figure 8
Figure 8
GRHI map created by machine learning (MARS) (from [68]).
Figure 9
Figure 9
Raw PCA result. Loading plot, showing the coefficients of each variable for the first component versus the coefficients for the second component. This graph shows which variables have the largest effect on each component. Percentages: Explained variance (in percentages) of first principal components F1 and F2 (From [30]).
Figure 10
Figure 10
GRHI map derived from the first principal component (From [30]).
See this image and copyright information in PMC

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