Stochastic Models for Radon Daily Time Series: Seasonality, Stationarity, and Long-Range Dependence Detection

Siino, Marianna; Scudero, Salvatore; D’Alessandro, Antonino

doi:10.3389/feart.2020.575001

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…(Cigolini et al., 2013 ; Iwata et al., 2018 ; Mentes, 2018 ; Morales‐Simfors et al., 2020 ; Papachristodoulou et al., 2020 ). Radon time series may present: (a) multiple seasonality (hourly, semidiurnal, diurnal, yearly) (D'Alessandro et al., 2020 ; Siino et al., 2019 ; Steinitz & Piatibratova, 2010 ; Steinitz et al., 2007 ), (b) non‐stationarity behavior (Barbosa et al., 2007 ), (c) long‐term memory (Donner et al., 2015 ; Siino et al., 2019 , 2020 ), (d) synchronization with other factors (Siino et al., 2019 ), (e) intermittence (Crockett et al., 2010 ), and (f) not constant variance over the time (Barbosa et al., 2007 ). For these reasons, radon time series were characterized here using different time series approaches such as ARMA, ARIMA, SARIMAX, and ARFIMA methods (Siino et al., 2019 , 2020 ; Stránský & Thinová, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

“…Frequency analyses were conducted using both Fast Fourier transform (FFT) and Wavelet transform (WT) analyses (Grinsted et al., 2004 ; Siino et al., 2019 ). The time evolution was studied using the Box‐Jenkins methodology, by means of the Autoregressive Integrated Moving Average (ARIMA) model for time series analysis and forecast (Siino et al., 2020 ; Stránský & Thinová, 2017 ). Three parameters ( p , d , q ) were needed to correctly describe an ARIMA model.…”

Section: Methodsmentioning

confidence: 99%

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Radon Dynamics and Effective Dose Estimation in a Touristic Volcanic Cave: La Cueva del Viento, Tenerife (Canary Islands, Spain)

et al. 2023

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La Cueva del Viento is a volcanic lava tube located in Tenerife Island (Canary Islands, Spain). Its touristic section, 180 m long, receives more than 28,200 visitants each year. According to the European and Spanish legislation, a radon monitoring program is required to minimize the radon exposition of workers, tourists, and cavers. In this work, we studied the radon concentration dynamics in the touristic section of the cave for ca. 1 year, using both passive and active radon detectors. Pluviometry and external air temperature played an important role in the seasonal and daily variations of indoor radon concentrations. Daily fluctuations during the dry season were analyzed using time series (Box‐Jenkins methodology) and frequency analysis (Fourier and Wavelet transforms) methods. The experimental radon time‐series was well‐fitted using a seasonal autoregressive integrated moving average model: Seasonal Auto‐Regressive Integrated Moving Average (2,0,1) (2,1,0) 24 , and its value, in a short‐time window (ca. 1 week) was conveniently forecasted. Finally, this work revealed that the annual effective doses received, during the observation period (1 year), by the touristic guides and visitors was ca. 2 mSv/yr and 4 μSv/hr, respectively. We concluded that the touristic exploitation of La Cueva del Viento is safe for both tourists and guides. However, based on our results, La Cueva del Viento had to be classified as a “Monitoring zone” and a regular monitoring program should be implemented.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Radon Dynamics and Effective Dose Estimation in a Touristic Volcanic Cave: La Cueva del Viento, Tenerife (Canary Islands, Spain)

et al. 2023

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“…This work demonstrated that it is possible to build a forecasting model that, used with active measurement devices, can provide an estimation of the variation of indoor radon activity concentration. Even if other forecasting models are available in the literature [30] the novelty of this study is to be used in conjunction with active instrumentation. Once implemented on a computer, this model can be used "in-field" to give immediate results that allows to estimate average radon concentrations in dwellings, to design remedial actions, and to evaluate their effectiveness.…”

Section: Discussionmentioning

confidence: 99%

ARMAX Forecast Model for Estimating the Annual radon Activity Concentration in Confined Environment by Short Measurements Performed by Active Detectors

Pepperosa

Remetti

Perondi

2022

IJERPH

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This work aims to implement a forecast model that, combined with the use of active instrumentation for a rather limited time, and with the knowledge of a set of data referring to the environmental parameters of the place to be monitored, can estimate the concentration of indoor radon activity for longer time periods. This model has been built through the MATLAB program, exploiting the theories of time series and, in particular, ARMAX models, to reproduce the variation in the concentration of radon activity. The model validation has been carried out by comparing real vs. simulated values. In addition, analytic treatment of input data, such as temperature, pressure, and relative humidity, can reduce the influence of sudden transients allowing for better stability of the model. The final goal is to estimate the annual radon activity concentration on the basis of spot measurements carried out by active instrumentation, such to avoid the need to measure for an entire calendar year by the use of passive detectors. The first experimental results obtained in conjunction with active radon measurement demonstrates the applicability of the method not only for forecasting future average concentrations, but also for optimizing remedial actions.

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Markov Chain transition probability modeling of radon gas records and future projection possibility determination

Bilici

Külahçı

Bilici

et al. 2023

Journal of Atmospheric and Solar-Terrestrial Physics

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Stochastic Models for Radon Daily Time Series: Seasonality, Stationarity, and Long-Range Dependence Detection

Cited by 8 publications

References 46 publications

Radon Dynamics and Effective Dose Estimation in a Touristic Volcanic Cave: La Cueva del Viento, Tenerife (Canary Islands, Spain)

Radon Dynamics and Effective Dose Estimation in a Touristic Volcanic Cave: La Cueva del Viento, Tenerife (Canary Islands, Spain)

ARMAX Forecast Model for Estimating the Annual radon Activity Concentration in Confined Environment by Short Measurements Performed by Active Detectors

Markov Chain transition probability modeling of radon gas records and future projection possibility determination

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