Radon transport carried by geogas: prediction model

Chen, Xiaojie; Liu, Yong; Jiang, Yourui; Feng, Shaokong

doi:10.1007/s11356-023-28616-4

Cited by 9 publications

(3 citation statements)

References 260 publications

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“…Conventional models of advection, much less gas diffusion, cannot explain these facts, since this requires unrealistically high transport rates, especially in water-saturated media. In this regard, the hypothesis of radon bubble transport has been proposed [21,33], according to which radon transfer can occur due to "geogas" bubbles rising upward in water-filled cracks. As they rise, the bubbles "collect" gases dissolved in the water, including radon, transferring them from the liquid phase to the gas phase.…”

Section: Radon Production Transport and Gas Migrationmentioning

confidence: 99%

Radon Variability as a Result of Interaction with the Environment

Pulinets,

Mironova,

Miklyaev

et al. 2024

Atmosphere

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Recent years have seen increased attention given to radon from two scientific directions. After neglecting radon as an earthquake precursor in the 1990s, it has become the subject of discussions in earthquake-forecast papers due to growing networks of radon monitoring in different countries, particularly the technologies of real-time radon measurements where gamma spectrometers are of great interest as sources of 222Rn identification. The second fast-developing direction involves radon in Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) models as a source of boundary layer ionization. Here we address the second topic, which is not connected with the earthquake forecast problems, namely, the role of air ionization by radon as a source of the Global Electric Circuit (GEC) modification. In this publication, we try to unite all of these problems to present a more complex view of radon as an important element in our environment. Special attention is paid to the dependence of radon variability on environmental conditions.

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Section: Radon Production Transport and Gas Migrationmentioning

confidence: 99%

Radon Variability as a Result of Interaction with the Environment

Pulinets,

Mironova,

Miklyaev

et al. 2024

Atmosphere

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“…But in the faults and in porous media model of radon migration include gas permeability of soils related to the total porosity, water saturation, and arithmetic of fraction (since 1980s). Most of the fluid flow in fractured rock tends to be carried by a tiny of the all fractures (Chen et al 2023). Based on to the difference in water content, the fractures are divided roughly into two categories: unsaturated and saturated.…”

Section: Model Assumptions and Boundary Conditionsmentioning

confidence: 99%

“…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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Measurement and characteristics of radon flow in an extremely arid region based on a closed-system earth-air model

Li,

Wang

2024

Journal of Environmental Management

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Radon transport carried by geogas: prediction model

Cited by 9 publications

References 260 publications

Radon Variability as a Result of Interaction with the Environment

Radon Variability as a Result of Interaction with the Environment

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

Measurement and characteristics of radon flow in an extremely arid region based on a closed-system earth-air model

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