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Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models
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  • Published: 05 March 2026

Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models

  • Heechan Lee1,2,3,
  • Dakotah Maguire2,
  • Jeremy Logan4,
  • Greeshma Agasthya1,
  • Shaheen Dewji1 &
  • …
  • Heidi A. Hanson2 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental sciences
  • Risk factors

Abstract

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

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Data availability

Indoor radon measurement data for Pennsylvania were obtained from the Pennsylvania Department of Environmental Protection (PA DEP) which is publicly available.All datasets used in this study are publicly available from the original data providers: indoor radon measurement from the Pennsylvania Department of Environmental Protection, elevation from the USGS GMTED2010 product, soil characteristics from the USDA NRCS gNATSGO database, geochemical variables from the USGS Geochemical and Mineralogical Survey, hydrologic landscape data from USGS, meteorological variables from the Daymet database, and demographic and housing characteristics from the U.S. Census Bureau (Decennial Census and American Community Survey). Detailed information on data sources and preprocessing workflows is provided in the method paper. 44Data are available from the authors upon reasonable request.

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Acknowledgements

This work was supported by the Office of Biological and Environmental Research’s Biological Systems Science Division. This manuscript has been authored by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy and Award AWD-002827 between UT-Battelle and the Georgia Tech Research Corporation. This research used resources of CADES at the Oak Ridge National Laboratory, which is supported by the US Department of Energy’s Office of Science under Contract No. DE-AC05-00OR22725.

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Authors and Affiliations

  1. Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 770 State Street, Atlanta, GA, 30332, USA

    Heechan Lee, Greeshma Agasthya & Shaheen Dewji

  2. Advanced Computing for Health Sciences Section, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37830, USA

    Heechan Lee, Dakotah Maguire & Heidi A. Hanson

  3. Department of Environmental and Occupational Health, University of California, Irvine, 856 Health Sciences Quad, Irvine, GA, 92697, USA

    Heechan Lee

  4. Data Engineering Group, Data and AI Section, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37830, USA

    Jeremy Logan

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Contributions

HL conducted the study design, processed the data, performed the analyses, interpreted the results, drafted the manuscript, and contributed to its editing. DM and JL conducted analysis design, data processing, and manuscript editing. GA and SD contributed to the study design and manuscript revisions. HH was responsible for study design, project supervision, interpretation of results, and critical revision of the manuscript.

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Correspondence to Heidi A. Hanson.

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Lee, H., Maguire, D., Logan, J. et al. Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37891-3

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  • Received: 09 June 2025

  • Accepted: 27 January 2026

  • Published: 05 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-37891-3

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Keywords

  • Radon
  • Geology
  • Prediction model
  • Machine learning
  • ZCTA-level predictions
  • Environmental health
  • Exposome
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