Predictors of indoor radon levels in the Midwest United States

Uncovering hidden dangers in urban housing: Sources of indoor radon and associated health risks

Radon, a naturally occurring radioactive gas, tends to accumulate indoors and can reach hazardous concentrations that pose significant health risks. Globally, radon is recognized as the second leading cause of lung cancer after smoking. Despite its well-documented dangers, significant knowledge gaps persist regarding the sources of radon exposure and the resulting health risks. In this study, we examine the spatial and temporal distribution, sources, and health impacts of indoor radon in Ohio, USA-a state characterized by diverse geological and meteorological conditions and radon concentrations significantly exceeding the national average, contributing to approximately 900 lung cancer deaths annually. Our results reveal pronounced spatial heterogeneity, with elevated radon concentrations in central urban areas, and seasonal variability, with higher levels during winter. Using statistical analysis and structural equation modeling, we identify surface radiation sources, meteorological conditions, and building materials as key drivers of indoor radon accumulation. Furthermore, Monte Carlo simulations indicate that the estimated lifetime excess cancer risk for residents in the study area is 2.29 %, approximately double the standard safety threshold. These findings underscore the urgent need to raise awareness of radon-induced carcinogenesis and to implement effective mitigation strategies to safeguard public health.

Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics

Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This study advances methodologies for assessing residential radon exposure by leveraging a comprehensive dataset of 126,382 short-term (2-7 days) radon test results collected across North Carolina from 2010 to 2020. Employing a combination of linear regression and advanced machine learning techniques, including random forest models. Analysis through linear regression, linear mixed-effects models (LME), and generalized additive models (GAM) using the first-time tested radon levels reveals that elevation, proximity to geological faults, and soil moisture are pivotal in determining radon concentration. Specifically, elevation consistently shows a positive relationship with radon levels across models (linear regression: β = 0.12, p < 0.001; LME: β = 0.17, p < 0.001; GAM: β = 0.11, p < 0.001). Conversely, the distance to geological faults negatively correlates with radon concentration (linear regression: β = -0.11, p < 0.001; LME: β = -0.06, p < 0.001; GAM: β = -0.07, p < 0.001), indicating lower radon levels further from faults. Using the random forest model, our study identifies the most influential environmental predictors of first-time tested radon levels. Elevation is the most influential variable, followed by median instantaneous surface pressure and soil moisture in the upper 10 cm layer, illustrating the significant role of geological and immediate surface conditions. Additional important factors include precipitation, mean temperature, and deeper soil moisture levels (40-200 cm), which underscores the influence of climate on radon variability. Root zone soil moisture and the Normalized Difference Vegetation Index (NDVI) also contribute to predicting radon levels, reflecting the importance of soil and vegetation dynamics in radon emanation. By integrating multiple statistical models, this research provides a nuanced understanding of the predictors of radon concentration, enhancing predictive accuracy and reliability.

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany - a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

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

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 63  Bq / m 3 , a geometric mean of 41  Bq / m 3 , and a 95th percentile of 180  Bq / m 3 . The exceedance probabilities for 100 and 300  Bq / m 3 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.

Exploring statistical and machine learning techniques to identify factors influencing indoor radon concentration

Radon is a radioactive gas with a carcinogenic effect. The malign effect on human health is, however, mostly influenced by the level of exposure. Dangerous exposure occurs predominantly indoors where the level of indoor radon concentration (IRC) is, in its turn, influenced by several factors. The current study aims to investigate the combined effects of geology, pedology, and house characteristics on the IRC based on 3132 passive radon measurements conducted in Romania. Several techniques for evaluating the impact of predictors on the dependent variable were used, from univariate statistics to artificial neural network and random forest regressor (RFR). The RFR model outperformed the other investigated models in terms of R2 (0.14) and RMSE (0.83) for the radon concentration, as a dependent continuous variable. Using IRC discretized into two classes, based on the median (115 Bq/m3), an AUC-ROC value of 0.61 was obtained for logistic regression and 0.62 for the random forest classifier. The presence of cellar beneath the investigated room, the construction period, the height above the sea level or the floor type are the main predictors determined by the models used.

Evaluation of radon hazards at the rural settlements of Uzbekistan

The "passive" sorption detectors based on the activated charcoal together with scintillation spectrometry were utilized to measure radon flux density from the soil surface as well as volumetric activity of indoor radon at the dwellings of rural areas of Uzbekistan. Additionally, gamma dose rates as well as concentrations of natural radionuclides in soil and building materials samples were determined. Based on the values of natural radionuclides, common radiological indices have been calculated. It was found that varying greatly, 94% radon flux density values did not exceed 80 mBq/(m²·s), while volumetric activities of radon were in the range of 35-564 Bq/m³. The radium equivalent activity for studied soil and building materials samples were below the allowed limit of 370 Bq/kg. Computed gamma dose rates were in the range of 55.50-73.89 ƞGyh^-1 below the limit of 80 ƞGyh^-1 and annual effective dose rate 0.068-0.091 mSvy^-1, the average value of which was higher than the standard limit > 0.47 mSvy^-1. The gamma representative index range was 0.89-1.19 with an average of 1.002 which was higher than the standard limit of 1.0. The range of activity utilization index was equal to 0.70-0.86 with an average value 0.77 which was lower than the recommended level ≤ 2.0. And lastly, excess lifetime cancer risk index values were from 1.9 × 10^-4 to 2.5 × 10^-4 and were lower than the recommended value 2.9 × 10^-4 indicating low radiological risk. The results are consistent with the research conducted by other authors earlier, implying suitability of employing the method for the assessment of residential areas.

Burden of lung cancer attributable to household air pollution in the Chinese female population: trend analysis from 1990 to 2019 and future predictions

This study analyzes the long-term trend of the burden of lung cancer attributable to household air pollution in the Chinese female population, from 1990 to 2019, and make predictions for the next decade. Based the data from the 2019 Global Burden of Diseases (GBD 2019), the joinpoint regression model was used to reflect the temporal trend of the burden of lung cancer attributable to household air pollution, and an autoregressive integrated moving average (ARIMA) model was used to predict the burden of disease over the next decade. From 1990 to 2019, the age-standardized mortality and disability-adjusted life years (DALYs) rates of the Chinese female population were higher than the global rates, and the gap due to residential radon increased over time. The burden of lung cancer attributable to solid fuels has shown a significant downward trend while that due to residential radon has increased slightly overall, but remains lower than the former. The burden of lung cancer increased with age, and the peak age of DALYs rates changed from 70 < 75 years in 1990 to 75 < 80 years in 2019. The model predicted that the burden of lung cancer attributable to solid fuels will gradually decrease over the next decade, whereas the burden of lung cancer due to residential radon will gradually increase and surpass the burden due to solid fuels in 2023. Residential radon will become a more important factor of household air pollution than solid fuels in the next decade for the Chinese female population. Future interventions targeted at household air pollution are needed to reduce the burden of lung cancer.