Estimation of Seasonal Correction Factors for Indoor Radon Concentrations in Korea.

Long-term exposure to high radon concentration exerts pathological effects and elicits changes in respiratory function, increasing an individual's risk of developing lung cancer. In health risk assessment of indoor radon, consideration of long-term exposure thereto is necessary to identify a relationship between indoor radon exposure and lung cancer. However, measuring long-term indoor radon concentration can be difficult, and a statistical model for predicting mean annual indoor radon concentrations may be readily applicable. We investigated the predictability of mean annual radon concentrations using national data on indoor radon concentrations throughout the spring, summer, fall, and winter seasons in Korea. Indoor radon concentrations in Korea were highest in the winter and lowest in the summer. We derived seasonal correction and seasonal adjustment factors for each season based on the method proposed by previous study. However, these factors may not be readily applicable unless measured in a specific season. In this paper, we separate seasonal correction factors for each month of the year (new correction factors) based on correlations between indoor radon and meteorological factors according to housing type. To evaluate the correction factors, we assessed differences between estimated and measured mean annual radon concentrations. Roughly 97% of the estimated values were within ±40 Bq/m³ of actual measured values in detached houses, and roughly 85⁻87% of the estimated values were within ±40 Bq/m³ of the measured values in other residences. In most cases, the seasonal correction factors and the new correction factors had slightly better agreement than the seasonal adjustment factor. For predicting mean annual radon concentrations, the seasonal correction factors or seasonal adjustment factors can be of use when actual measurements of indoor radon concentrations for a specific season are available. Otherwise, the new correction factors may be more readily applicable.