Factors influencing radon transport in the soils of Moscow

Influence of earthquake on radon exhalation law at the beach of the uranium tailing reservoir under high temperature environment

In this paper, the influence of an earthquake on radon exhalation rate of uranium tailings reservoir beach under high temperature environment is studied by using a self-made integrated simulation test device for natural disasters, and a scale model test based on similarity and dimensional laws. The results show that, (1)When the peak acceleration reaches 0.6g, the radon exhalation rate increases sharply with the increase of peak acceleration, and tends to be gentle after 1.0g. (2)Under the action of high temperature, the radon exhalation rate increases rapidly with the increase of high temperature time, and gradually becomes flat after the 4th hour. (3)Compared with loading the earthquake condition only, the coupling effect of high temperatures and earthquakes causes a greater degree of damage to the beach surface of a uranium tailings reservoir under the same acceleration conditions, and the fissure rate and radon exhalation rate of the beach surface are substantially increased.

Predicting radon flux density from soil surface using machine learning and GIS data

Several machine learning algorithms including artificial neural networks (ANN), random forest (RF) and multivariate adaptive regression splines (MARS) were used to construct a radon flux density (RFD) map of Moscow for the purpose of finding which one of them would be the best for radon delineation. Predictors used included geological soil classes for quaternary and some pre-quaternary sediment types, elevations of quaternary and pre-quaternary layers, 226Ra content in soil, ambient dose equivalent rate (ADER), distances to geodynamically active zones and lineaments. Training of the models was performed using previously collected radon flux density data from approximately ten thousand of measurements over 756 sites. ANN and RF algorithms produced the best maps with high correlation coefficients and low mean squared error, while MARS failed to get a high correlation coefficient and low mean squared error. Predictions made using RF were found to be more conservative due to higher prediction values of RFD, while those made using ANN were likely more realistic in their prediction value distribution, leading to the conclusion that RF is better for the purposes of delineation, while ANN is better for predicting average RFD values. Based on the constructed maps, the main factors affecting the flow of radon in the city were determined.