Abstract
In this paper, the presence of seasonality, stationarity, and long-range memory structures are detected in daily radon measurements in a permanent monitoring station in central Italy. The transient dynamics and the seasonality structure are identified by power spectral analysis based on the continuous wavelet transformation and a clear 1-year periodicity emerges. The stationarity in the data is assessed with the Dickey Fuller test; the decay of the estimated autocorrelation function and the estimated Hurst exponent indicate the presence of long-range dependence. All the main characteristics of the data have been properly included in a modelling structure. In particular, an autoregressive fractionally integrated moving average (ARFIMA) model is estimated and compared with the classical ARMA and ARIMA models in terms of goodness of fit and, secondarily, of forecast evaluation. An autoregressive model with a non-integer values of the differencing parameter ($d=0.278$) resulted to be the most appropriate on the basis of Akaike Information Criterion, the diagnostic on the residuals, and the Root Mean Squared Error. The results suggest that there is statistically-significant evidence for not rejecting the presence of long memory in the radon concentration. The radon measurements are better characterised as being stationary, but with long memory and so the statistical dependence decays more slowly than an exponential decay.
Highlights
The monitoring of soil radon (222Rn) emission is a relevant topic for the risk that this radioactive gas poses to human health and for its relationship with environmental and geological processes
We briefly describe the spectral analysis in the time–frequency domain based on continuous wavelet transformation following the notation in Daubechies (1992)
The PTRL station is in a framework of near real-time monitoring of soil radon emission to study earthquake preparatory processes, the Italian radon monitoring network (IRON) (Cannelli et al, 2018)
Summary
The monitoring of soil radon (222Rn) emission is a relevant topic for the risk that this radioactive gas poses to human health and for its relationship with environmental and geological processes. (Pinault and Baubron, 1996; Piersanti et al, 2015; Siino et al, 2019b) All these factors have a different effect on the signal, as they can result either in a trend, seasonal, or stochastic component. Anomalies can be the result of weather episodes which cannot be explained by meteorological variables. Whatever the cause, these anomalies can be masked within the signal, and a way to bring them to light would be to de-noise the signal from the trend and/or periodic components (Baykut et al, 2010; Siino et al, 2019b; D’Alessandro et al, 2020). As a matter of fact, it is a challenging task to untangle and properly quantify all of these effects on the radon fluctuations because Rn time series present generally a nonstationary behavior, not constant variability over time and a long-term memory (Donner et al, 2015)
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