An investigation of 222Rn exhalation rates from backfill mill tailings influenced by the different parameters in underground uranium mines. (February 2023)

• Record Type: Journal Article
• Authors: Sahu, Patitapaban; Beg, Imran Athar; Panigrahi, Durga Charan
• Is Part Of: Radiation physics and chemistry. Volume 203(2023)Part B
• Publication Date: 2023-02
• Subjects: Radon exhalation -- Smart-radon-monitor system -- Multivariate analysis -- 226Ra content; Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2022.110648
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: 222 Rn surface exhalation and mass exhalation rates measured from the backfill mill tailings using a new method comprising the smart-radon-monitor system and accumulation chamber in underground uranium mines are presented in this paper. The range values of 222 Rn surface and mass exhalation rates were 0.6–4 Bq m −2 s −1 and 28–71 Bq kg −1 h −1, respectively. Further, Multivariate regression models involving radium content, porosity, moisture saturation and surface area to fit the 222 Rn surface and mass exhalation of the backfill materials were developed in this paper. The curve fitting results showed that multivariate models could explain 86% of the variation of surface exhalation rate and 69% of the variation of mass exhalation rate from the backfill materials. This study also revealed that the 222 Rn surface exhalation rate calculated based on the method of concentration gradient of 222 Rn in soil, found to be about two times more than that of the measured surface exhalation rate. However, the theoretical 222 Rn exhalation rate was prone to various uncertainties including a variation of moisture saturation, the occurrence of adsorption and advection in the backfill mill tailings. Therefore, the values of the 222 Rn exhalation rate measured inside underground uranium mines are more realistic. Highlights: Models concerning influencing factors to fit 222 Rn exhalations were established. Models explained 86% of surface exhalation and 69% of mass exhalation variations. Measured 222 Rn exhalation rates were more realistic than calculated ones.