Overview and large-scale representative estimate of radon-222 flux data in China

Lei, Bo; Zhao, Lingjuan; Girault, Frédéric; Cai, Ziqi; Luo, Caiwu; Thapa, Sandeep; She, Jia-Xin; Perrier, Frédéric

doi:10.1016/j.envadv.2022.100312

Cited by 9 publications

(1 citation statement)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract