Identification and Comparison of Simple Predictive Models of Indoor Radon (222Rn) Activity Concentration Variations from Short-Term Measurements

Radon (222Rn) is a chemically inert (noble) gas, naturally occurring α-emitter radionuclide, and the direct progeny of 226Radium; it is produced in the uranium (238U) decay chain. Short-term measurements of the concentration of radon can be performed to identify locations and objects with potentially increased concentrations. The goal of this study is to present a comparison of models for the seasonal prediction of 222Rn active concentration variations from the results of 222Rn short-term measurements acquired by active instruments. Several predictive models are compared in this study, with estimation and validation datasets from 222Rn concentration measurements from two significant micro-locations: the St. Barbara mine and a ground-floor room of the University of Zagreb Faculty of Mining, Geology and Petroleum engineering (UNIZG-FMGPE) building, in Zagreb, Croatia. MATLAB version R2025b System identification and a Signal multiresolution analyzer were used for the estimation of predictive models and validation for mid-term prediction. This research provides one method for estimating the concentration variations from a smaller number of observations from 8 days of measurements. It shows that the best models for the estimation and prediction of radon concentration time series are the auto-regressive non-linear ARX model (NLarx), with a one-step-ahead prediction fit of up to around 90% for a minimum measurement duration of 8 days, 192 samples, and a 1 h floating mean for the estimation and ARMAX estimation from the reconstructed signal as a simple polynomial approximation of the original measurement signal, with a one-step-ahead prediction fit of almost 100%. The ARMAX model with a one-step-ahead predicted output gives excellent estimation of the MODWT and EMD reconstructed signal, which has approximately the same mean as the original signal and, thus, can be used for indirect prediction of the Rn mean. The NLarx model showed good results in the validation of Rn concentration variations; thus, this model is selected as the preferred model to predict Rn concentration variations from short-term measurements.