ANALYZING THE EFFECTIVENESS OF TRADITIONAL RADON PREDICTION METHODS IN GEORGIA USING INDOOR RADON DATA FROM THE GEORGIA RADON EDUCATION PROGRAM

The EPA Map of Radon Zones predicts radon risk for each county in the U.S. based on the USGS Map of Geologic Radon Potential and EPA risk standards. The Georgia Radon Education Program, which distributes complimentary indoor radon test kits and risk information to the public, uses the EPA Map of Radon Zones as a prediction guide. The U.S. Geological Survey designated regions of low, moderate, and high radon risk based on rock type, uranium (U) data from the 1970’s National Uranium Resource Evaluation (NURE) program, and the limited radon test results then available. Analysis of radon (Rn) data collected by the Georgia Radon Education Program indicate that the USGS radon prediction method based on general rock type and U stream sediment data from the NURE program is ineffective.

Stream sediment data in the NURE national database contain n = 11,154 data points within Georgia with a reported U value. The Georgia Radon Education Program data set currently has n = 16,025 located sample points with Rn values. Both data sets are found to have a somewhat log-normal distribution. Using GIS, U and Rn data points were assigned to one of 34 “primary rock type” polygons found in the Digital Geologic Map of Georgia provided by the USGS and the Georgia Department of Natural Resources (GDNR). The digital map was updated in 1999 from the compilation published by the Georgia Geologic Survey in 1976 as the Geologic Map of Georgia. Preliminary statistical analysis indicates little correlation between rock type and elevated U and/or Rn values. This may indicate that the categorizations of rock types during compilation of the Geologic Map of Georgia are in need of revision and/or refinement of scale. The USGS protocols used in predicting Rn appear to have also suffered from this issue. Further analysis will investigate local correlations of U and Rn with rock type that may have been obscured by the relatively coarse-scale of the data. The absence of a strong correlation between regions with elevated U and regions with elevated Rn indicate that the indoor Rn data may be controlled by other factors beyond the scope of this study, including home age, construction style, and ventilation.