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.

This paper presents the development of a device that creates two or more light pulses with a precision time delay in a range from microseconds to several seconds and for the purpose of the calibration of velocity of detonation (VOD) measurement instruments. The device was assembled, programmed, and tested for functionality. Measurements were conducted using a reference counter. First, a precision OCXO (oven-controlled crystal oscillator) was used as a clock source. For verification of programmed subroutines, a counter oscillator output port was used as a source of clock signal for the microcontroller. This enabled the cancellation of possible oscillator errors. The signal had to be converted from an AC sine signal to a signal with positive values only using a clamper circuit. By the proposed solution, a calculable standard of time delay between two light pulses was achieved. According to the obtained results, this device can be used to calibrate field measurement devices for VOD measurements in explosives or some other use where the measurement device records the time interval between multiple light pulses. This enables more confidence in measurement results, faster recognition of instrument drift and increases the quality of measurement.

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.