Monte Carlo modeling of scintillation detectors for continuous underground radon monitoring

Haquin, G.; Zafrir, Hovav; Ilzycer, D.; Weisbrod, Noam

doi:10.1016/j.jenvrad.2021.106693

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…It is also noticeable that there is a lag in Rn profiles with depth, suggesting a delayed response of Rn flow downward within the bedrock because of the heating of the surface. The lag depends on the downward velocity of the Rn movement in the surrounding bedrock [30,51,52]. Another study [17] also showed similar results from Rn monitoring at 10 m depth.…”

Section: Daily Profilesmentioning

confidence: 62%

“…It is essential to restate that the subground temperature in the shallow depth of a few meters (10 m in our case) is already constant [21,30,31] (Figures 3 and 4a), and the energy that induces Rn movement in the rock column comes from the temperature on the surface, which heats the surface layer of the ground like an inverted pan. Radon molecules in the shallow surface are energized by the heat of the sun and forced to flow downwards, escaping the heat source above [51,52]. The delayed response of Rn with depth reflects the time it takes for Rn to travel downwards as a result of the kinetic energy that was given by the external surface temperature.…”

Section: The Temperature Dependency Of the Rn Flow Within The Borehol...mentioning

confidence: 99%

“…An experimental system was set up in the Northern part of Israel (Figure 1); three Rn gamma-ray detectors were inserted into the airspace of an abandoned borehole, which was built as an iron pipe 20 radius, which descends sealed to 80 m depth and has a perforated extension of more than 30 m.; two Rn gamma detectors were set up at depths of 10 and 60 m, the third under the water table at 88 m, and an additional Rn alpha detector at 40 m depth [21,30,31,51]. Inclusively, it was proven in our previous studies that the Rn gamma detectors at depth monitor the gas flow within the surrounding bedrock [52]. Semi-diurnal, diurnal, multiday, and seasonal Rn temporal variation, and their relationship to surface temperature, were studied.…”

mentioning

confidence: 99%

“…2023, 15, x FOR PEER REVIEW 3 of 18 detectors were set up at depths of 10 and 60 m, the third under the water table at 88 m, and an additional Rn alpha detector at 40 m depth [21,30,31,51]. Inclusively, it was proven in our previous studies that the Rn gamma detectors at depth monitor the gas flow within the surrounding bedrock [52]. Semi-diurnal, diurnal, multiday, and seasonal Rn temporal variation, and their relationship to surface temperature, were studied.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Novel Assessment of the Surface Heat Flux Role in Radon (Rn-222) Gas Flow within Subsurface Geological Porous Media

Benkovitz,

Zafrir,

Reuveni

2023

Remote Sensing

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At present, Rn subsurface flow can be described only by diffusion and advection transportation models within porous media that currently exist. Even though the temperature is a strong driving force in climate and gas thermodynamics, the impact of the surface heating is missing from all gas flow models within geological porous media. In this work, it is shown that heating the ground surface by the sun, every day up to a maximum temperature at noon, creates a downward vertical temperature gradient related to the constant temperature in the upper shallow layer whose measured thickness is several meters. Undersurface, the Rn gas in the porous media is propelled in nonlinear dependency by the surface temperature gradient to flow downward, up to a measured depth of 100 m, revealing a daily periodicity with time delay depending on depth, similar to the diurnal cycle of the surface temperature. Moreover, regression analysis applied with the data implies a non-linear relationship between Rn and the temporal surface temperature. The relationship is non-linear and the best fit for it from a thermodynamic point of view is an exponential dependency. From now on, it will be possible according to the model to predict and extract, if required, by the time series of the surface-measured parameters (the ambient temperature and pressure), the semi-diurnal, diurnal, multiday, and seasonal Rn temporal variation at a shallow depth.

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Section: Daily Profilesmentioning

confidence: 62%

Section: The Temperature Dependency Of the Rn Flow Within The Borehol...mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Assessment of the Surface Heat Flux Role in Radon (Rn-222) Gas Flow within Subsurface Geological Porous Media

Benkovitz,

Zafrir,

Reuveni

2023

Remote Sensing

Self Cite

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“…The latest research advancements on radon involve utilizing radon time series data to measure the radon flow (Alhmadi and Abdullah, 2021;Girault et al 2022;Haquin et al 2021;Muhammad and Külahcı 2022;Rafique et al 2022;Chen et al 2023;Lei et al 2023;Wang et al 2024). With underground systems, the prediction of radon's behaviour depends strongly on the identification of patterns hidden in the associated time series.…”

Section: Introductionmentioning

confidence: 99%

Fractal discrete fracture network modeling of radon gas concentration in underground tunnels under Książ Castle in Poland

Fijałkowska–Lichwa,

Ajayi

2024

Bull Eng Geol Environ

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The numerical modeling of radon concentrations in the fault zone of the underground excavations at Książ Castle was conducted using a stochastic Discrete Fracture Network (DFN) model. Due to the difficulties related with obtaining the exact fractures in a rock mass, the novel approach used in this study incorporates the stochastic model with known site data. The analysis utilized a dataset comprising long-term measurements of 222Rn activity concentration and geodetic measurements for twelve faults in the Książ unit. The parameters considered in the DFN model are: fracture length, Peclet number (Pe = 0.1 and 1.0, respectively), advection velocities (from 10–8 m/s to 10–6 m/s and from range from 10–7 m/s to 10–4 m/s, respectively), radon diffusion (D = 2.1 × 10–61/s), radon decay constant (λ = 1/s), and radon gas generation (q) along the fractures within the range of 1.5 × 10–3 Bq/m3·s to 3.5 × 10–3 Bq/m3·s. The calibration process obtained the best fit when the radon generation rate was uniformly distributed through the rock mass in addition to incorporating a higher value of radon generation rate (q = 3.0 × 10–3 Bq/m3·s) where elevated radon concentrations have been measured. The modeling results also confirmed that the radon generation rate should always be higher where elevated radon activity concentrations were measured regardless of the measurement period. For the indicated “area” the radon generation rate should be higher from 25% to 37.5% between May–October and 18.5% to 40% between November–April. The influence of fracture zones on the recorded radon activity concentrations was noticeable up to a depth of 15 m. Within this range, the highest values of 222Rn activity concentration, ranging from 1,600 Bq/m3 to 2,000 Bq/m3, were consistently observed regardless of the season. However, as the depth increased, the values of 222Rn activity concentration decreased from 800 Bq/m3 to 400 Bq/m3 and became more dispersed.

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Effect of atmospheric temperature on underground radon: A laboratory experiment

Haquin¹,

Zafrir²,

Ilzycer³

et al. 2022

Journal of Environmental Radioactivity

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Monte Carlo modeling of scintillation detectors for continuous underground radon monitoring

Cited by 5 publications

References 21 publications

A Novel Assessment of the Surface Heat Flux Role in Radon (Rn-222) Gas Flow within Subsurface Geological Porous Media

A Novel Assessment of the Surface Heat Flux Role in Radon (Rn-222) Gas Flow within Subsurface Geological Porous Media

Fractal discrete fracture network modeling of radon gas concentration in underground tunnels under Książ Castle in Poland

Effect of atmospheric temperature on underground radon: A laboratory experiment

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