Modeling of radon flux density depending on air temperature and humidity (using the example of the anomalous zone of Mount Beshtau)

  • P.A. Sidyakin Pyatigorsk Institute (branch) of the North Caucasus Federal University, 46 Ermolova Str., Pyatigorsk 357500, Russian Federation
  • A.A. Baryshnikov Pyatigorsk Institute (branch) of the North Caucasus Federal University, 46 Ermolova Str., Pyatigorsk 357500, Russian Federation
  • I.M. Pershin Pyatigorsk Institute (branch) of the North Caucasus Federal University, 46 Ermolova Str., Pyatigorsk 357500, Russian Federation
  • P.S. Miklyaev Sergeev Institute of Environmental Geoscience, Russian Academy of Sciences, 13/2 Ulanskiy lane, Moscow 101000
Keywords: radon, Mount Beshtau, radon flux density, radon anomaly, regression analysis, modeling

Abstract

Relevance. Studying radon transport mechanisms and identifying anomalies, such as on Mount Beshtau (Caucasian Mineral Waters region), where extremely high radon flux density (RFD) values have been recorded, is critically important for assessing radiation risk. The primary source of radon in buildings is its entry from soil, particularly in fault zones where convective transport is most prevalent. The aim of the study is to develop a mathematical model for predicting radon flux density based on meteorological and other parameters that potentially influence radon emission from rocks into the atmosphere. The study aims to analyze the influence of air temperature and humidity on the flux density within the identified radon anomaly on the western slope of Mount Beshtau. Methods. Data from radon activity, temperature, and humidity measurements taken between 2018 and 2020 were used for the analysis. A prototype mathematical model for predicting radon activity was developed using multiple linear regression. Results. A prototype mathematical model was developed that can predict radon activity based on temperature and humidity with a reliability of approximately 71 %. Validation of the model using independent data showed close agreement between the forecast and actual measured values. It was determined that improving prediction accuracy requires incorporating additional factors such as atmospheric pressure, soil moisture, soil temperature, and seismic activity. Work has begun on integrating these parameters into the model. The model has also been implemented as software.

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Published
2026-03-30
Issue
Vol 16 No 1 (2026): Geology and Geophysics of the South of Russia, 2026 №1
Section
Geophysics