Methods for Estimation of Seasonal Indoor Radon Concentration Trend and Mean with Trend Decomposition and Distribution Fitting

Radon (222Rn) is a noble gas and an α-emitter radionuclide. Indoor radon levels are measured because it is a carcinogen. This study aimed to explore new methods for estimating and predicting the seasonal trend and mean radon concentration based on short-term measurements. The methodologies used in this study included trend decomposition and distribution fitting. Measurement datasets of 222Rn activity concentration were collected from two representative sites using active instruments, and data analysis and estimations were conducted using MATLAB version R2025b and Excel. The results showed that the prediction of the long-term seasonal trend component was not dependent on the length of measurement or location and could be described by a second-order polynomial AR model with constant coefficients. This study demonstrated simple ways to estimate the seasonal Rn concentration mean from short-term datasets (8 to 16 days) using distribution fits. In conclusion, the seasonal concentration mean can be estimated from short-term measurements of a minimum of 8 days and and minimum 16 days for trend estimates. From these two measurements, one year apart, it is possible to estimate the seasonal radon mean and approximate its wave-like trend. Measurements shorter than 8 days may not provide sufficient information for conclusions and are therefore unreliable.