Modeling and predicting mean indoor radon concentrations in Austria by generalized additive mixed models

Alber, Oliver; Laubichler, Christian; Baumann, Sebastian; Gruber, Valeria; Kuchling, Sabrina; Schleicher, C. M.

doi:10.1007/s00477-023-02457-6

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model included geologic, meteorological and construction variables. Similar approaches have been used to estimate IRC in other study areas (Demoury et al, 2013;Vienneau et al, 2021;Alber et al, 2023). The log-linear regression model was fitted with equation 3.…”

Section: Regression Analysismentioning

confidence: 99%

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Domínguez Durán,

Sandoval Garzón,

Huguet

2023

Preprint

View full text Add to dashboard Cite

Abstract. Radon (222Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard that uses inferential statistical methods to estimate indoor radon concentration’s (IRC) spatial distribution for a target area. We demonstrate the functionality of the dashboard by modeling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured were the highest reported in the country, with a geometric mean of 91 ±14 Bq/m3 and a maximum concentration of 407 Bq/m3. In 57 % of the residences RC exceeded the WHO's recommendation of 100 Bq/m3. A prediction map for houses registered in Bogotá’s cadaster was built in the dashboard by using a log-linear regression model fitted with the in situ measurements, together with meteorological, geologic and building specific variables. The model showed a cross-validation Root Mean Squared Error of 56.5 Bq/m3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 present a statistically significant increase of 72 % compared to those built after 1980 (p-value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution.

show abstract

Section: Regression Analysismentioning

confidence: 99%

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Domínguez Durán,

Sandoval Garzón,

Huguet

2023

Preprint

View full text Add to dashboard Cite

show abstract

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data

Petermann,

Bossew,

Kemski

et al. 2024

Environ Health Perspect

View full text Add to dashboard Cite

Background: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Objectives: Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that control the indoor radon concentration, such as the availability of geogenic radon or floor level. Furthermore, the sample size usually does not allow estimation with high spatial resolution. We propose a model-based approach that allows a more realistic estimation of indoor radon distribution with a higher spatial resolution than a purely data-based approach. Methods: A multistage modeling approach was used by applying a quantile regression forest that uses environmental and building data as predictors to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany. Based on the estimated probability distribution function, a probabilistic Monte Carlo sampling technique was applied, enabling the combination and population weighting of floor-level predictions. In this way, the uncertainty of the individual predictions is effectively propagated into the estimate of variability at the aggregated level. Results: The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of , a geometric mean of , and a 95th percentile of . The exceedance probabilities for 100 and are 12.5% (10.5 million people affected) and 2.2% (1.9 million people affected), respectively. In large cities, individual indoor radon concentration is generally estimated to be lower than in rural areas, which is due to the different distribution of the population on floor levels. Discussion: The advantages of our approach are that is yields a ) 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 b ) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171

show abstract

Modeling of indoor ²²²Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia

Domínguez Durán,

Sandoval Garzón,

Huguet

2024

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Radon (222Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard (http://ircmodelingdashboard.eu.pythonanywhere.com/, last access: 17 April 2024) that uses inferential statistical methods to estimate the spatial distribution of indoor radon concentration (IRC) for a target area. We demonstrate the functionality of the dashboard by modeling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured was the highest reported in the country, with a geometric mean of 91±14 Bq m−3 and a maximum concentration of 407 Bq m−3. In 57 % of the residences, RC exceeded the WHO's recommendation of 100 Bq m−3. A prediction map for houses registered in Bogotá's cadaster was built in the dashboard by using a log-linear regression model fitted with the in situ measurements, together with meteorological, geologic and building-specific variables. The model showed a cross-validation root mean squared error of 57 Bq m−3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 presents a statistically significant increase of 72 % compared to IRC of those built after 1980 (p value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution.

show abstract

Modeling and predicting mean indoor radon concentrations in Austria by generalized additive mixed models

Cited by 3 publications

References 39 publications

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data

Modeling of indoor ²²²Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia

Contact Info

Product

Resources

About

Modeling and predicting mean indoor radon concentrations in Austria by generalized additive mixed models

Cited by 3 publications

References 39 publications

Indoor 222Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Indoor 222Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data

Modeling of indoor 222Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia

Contact Info

Product

Resources

About

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Indoor ²²²Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia

Modeling of indoor ²²²Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia