The first version of the Pan-European Indoor Radon Map

Elío, Javier; Cinelli, Giorgia; Bossew, Peter; Gutiérrez-Villanueva, José Luis; Tollefsen, Tore; Cort, Marc De; Nogarotto, Alessio; Braga, Roberto

doi:10.5194/nhess-19-2451-2019

Cited by 23 publications

(15 citation statements)

References 41 publications

(60 reference statements)

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“…In the future work, we will focus on the additional radon measurements in the typical residential buildings from other types of houses. 28 (12) 28( 11) 27( 7) 23( 6) 32( 14) SN1030…”

Section: Discussionmentioning

confidence: 99%

“…So, it is necessary to convert indoor radon map to a dose map. One of the examples is presented as a plan to develop models that allow correction from groundfl oor dwellings to the real situation, accounting data from the national buildings database [12]. In Serbia, national typology of residential buildings is based on the results from the monography "National typology of residential buildings of Serbia" by a group of authors from the Faculty of Architecture [13].…”

Section: Introductionmentioning

confidence: 99%

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Radon variability due to floor level in two typical residential buildings in Serbia

et al. 2020

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AbstractIt is well known that one of the factors that influence the indoor radon variability is the floor level of the buildings. Considering the fact that the main source of indoor radon is radon in soil gas, it is expected that the radon concentration decreases at higher floors. Thus at higher floors the dominant source of radon is originating from building materials, and in some cases there may be deviations from the generally established regularity. In such sense, we chose one freestanding single-family house with loft and other 16-floor high-rise residential building for this study. The indoor radon measurements were performed by two methods: passive and active. We used passive devices based on track-etched detectors: Radtrak2 Radonova. For the short-term indoor radon measurements, we used two active devices: SN1029 and SN1030 (manufactured by Sun Nuclear Corporation). The first device was fixed in the living room at the ground level and the second was moved through the floors of the residential building. Every measuring cycle at the specified floor lasted seven days with the sampling time of 2 h. The results show two different indoor radon behaviours regarding radon variability due to floor level. In the single-family house with loft we registered intense difference between radon concentration in the ground level and loft, while in the high-rise residential building the radon level was almost the same at all floors, and hence we may conclude that radon originated mainly from building materials.

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“…In the future work, we will focus on the additional radon measurements in the typical residential buildings from other types of houses. 28 (12) 28( 11) 27( 7) 23( 6) 32( 14) SN1030…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radon variability due to floor level in two typical residential buildings in Serbia

et al. 2020

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“…The geochemical merging of lithologies is necessary to have sufficient IRC or SRC sample size per geological unit or for computational handling. In an example shown in [94], 178 units of the One Geology map were simplified into 28 units following a scheme proposed by [95].…”

Section: Databasesmentioning

confidence: 99%

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

Bossew

Cinelli

Ciotoli

et al. 2020

IJERPH

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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.

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“…Hence, 222 Rn is considered as one of the main causes of lung cancer (Yoon et al 2016). Accordingly, the EU has launched an initiative to map Rn concentrations in natural soils and rocks (Elío et al 2019). The purpose is to determine Rn risk areas, where newly built houses have to be protected against infiltration from the subsurface strata into the basement.…”

Section: Introductionmentioning

confidence: 99%

Weather control in radon flux time series from Schleswig-Holstein, Germany

2020

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Indoor radon exposure is a serious hazard to human health. The radon concentration in surface air varies spatially as a result of the uranium content in the underlying rocks. However, there exist also considerable knowledge gaps about temporal variations. Here we document the high temporal variability of radon flux from exhalation in high-resolution (hourly) time series from a site near Kleinneudorf, Schleswig-Holstein, Germany. By means of advanced techniques of statistical time series analysis, we show a close association between radon and meteorological variables (air temperature and air pressure). We identify four principal weather regimes that lead to different radon exhalation modes. For each of the modes, we construct a statistical linear model for radon prediction via the meteorological variables and their derivatives or time-lagged versions. The model explains between 53 and 86 percent of the variance. Many model deviations consist in excessively high measured radon values and hint at nonlinear effects. Other model deviations hint at non-meteorological forcing.

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The first version of the Pan-European Indoor Radon Map

Cited by 23 publications

References 41 publications

Radon variability due to floor level in two typical residential buildings in Serbia

Radon variability due to floor level in two typical residential buildings in Serbia

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

Weather control in radon flux time series from Schleswig-Holstein, Germany

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