Original Article

A geospatial approach to the prediction of indoor radon vulnerability in British Columbia, Canada

  • Journal of Exposure Science and Environmental Epidemiology (2016) 26, 554565 (2016)
  • doi:10.1038/jes.2015.20
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Received:
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Abstract

Radon is a carcinogenic radioactive gas produced by the decay of uranium. Accumulation of radon in residential structures contributes to lung cancer mortality. The goal of this research is to predict residential radon vulnerability classes for the province of British Columbia (BC) at aggregated spatial units. Spatially referenced indoor radon concentration data were partitioned into low, medium, and high classes of radon vulnerability. Radon vulnerability classes were then linked to environmental and housing data derived from existing geospatial datasets. A Balanced Random Forests algorithm was used to model environmental predictors of indoor radon vulnerability and values at un-sampled locations across BC. A model was generated and evaluated using accuracy, precision, and kappa statistics. The influence of predictor variables was investigated through variable importance and partial dependence plots. The model performed 34% better than a random classifier. Increased probabilities of high vulnerability were associated with cold and dry winters, close proximity to major river systems, and fluvioglacial and colluvial soil parent materials. The Kootenays and Columbia-Shuswap regions were most at risk. Here, we present a novel method for predictive radon mapping that is broadly applicable to regions throughout the world.

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Acknowledgements

We would like to thank Paul Schiarizza of BC Ministry of Energy and Mines for consultation on geological categorization and Dr. Chuck Bulmer of the BC Ministry of Forests and Range for consultation on available soil datasets. We would also like to thank the BC Lung Association, Northern Health Authority, Donna Schmidt Foundation and Peter Chataway for sharing their data. This work has been supported by the Social Sciences and Humanities Research Council of Canada and the Natural Sciences and Engineering Research Council of Canada.

Author information

Affiliations

  1. Spatial Pattern Analysis and Research Laboratory, Department of Geography, University of Victoria, Victoria, British Columbia, Canada

    • Michael C Branion-Calles
    •  & Trisalyn A Nelson
  2. Environmental Health Services, BC Centre for Disease Control, Vancouver, British Columbia, Canada

    • Sarah B Henderson
  3. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada

    • Sarah B Henderson

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to Michael C Branion-Calles.