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Radon as Environment Diagnostic Tool and Global Electric Circuit Component

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17 January 2024

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18 January 2024

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Abstract
Abstract: Recent years demonstrate the increased attention to radon from two scientific directions. After neglecting radon as earthquake precursors in 1990-th it becomes again the subject of earthquake-forecast papers discussions due to growing networks of the radon monitoring in different countries, especially, the technologies of real-time radon measurements where the gamma-spectrometers become the leader of interest as the sources of 222Rn identification. The second fast developing direction is including the radon in the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) models as a source of the boundary layer ionization. And here we encounter with second direction which is not connected with the earthquake forecast problems. It is 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 these problems to present more complex view on radon as important element of our environment.
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Subject: Environmental and Earth Sciences  -   Geophysics and Geology

1. Introduction

Radon is odorless noble gas, it is radioactive, and belongs to the VIII group of the Mendeleev's periodic table. Its atomic number is 86, and it has three natural isotopes: 219Rn, 220Rn, and 222Rn. 219Rn is a member of action-uranium decay chain, so usually it is named Action with the symbol An. Its semi-decay period is equal 3.92 s. 220Rn is a member of Thorium decay chain and usually named as Thoron (Tn), its semi-decay period is 54.5 s. And the third, and actually most important isotope 222Rn from uranium-radium decay chain is radon itself, and the symbol Rn is attributed just to this isotope. Its semi-decay period is 3.823 days. One can see the decay trees of main radon isotopes in Figure 1, and major parameters of main radon isotopes and their progenies in Table 1. The priority of radon discovery as emanation of radium is given to German physicist Friedrich Ernst Dorn [1] which is dated by 1900. Thoron was opened by Rutherford and Owens one year earlier [2] and discovery of Action in 1903 is attributed to Andre Louis Debierne [3]. Some physicists including Rutherford proposed to name it “emanation” but finally because it is radium progeny, it was named Radon. Radon was the first chemical element showing the possibility to have isotopes. The mass concentration of radon in the Earth’s atmosphere is near 6×10-17%.
During its decay radon emits α-particles which are actually the helium nucleus. It should be noted that near 99% of the helium produced is the result of the alpha decay of underground deposits of minerals containing uranium or thorium. Radon radioactivity could be used as a tracer. In the case of earthquakes, it is used as the earthquake precursor because of possibility to register α particles emitted by increased radon volumetric concentration before strong earthquakes [4,5,6] within the earthquake preparation zone [7]. One can find a lot of reports on the radon as a precursor of strong earthquakes [8,9,10,11,12].
Before to consider the effects produced by radon in atmosphere, we should clarify the ways of its transport to the ground surface [13] and factors influencing its variability [14]. Between these factors we should consider radon activity dependence on weather (precipitation air pressure, relative humidity and air temperature), seasonal variability, space weather effects.
Another important factor is the using of different technologies of radon measurements and environment in which measurements are taken (soil, water or surface air layer). The advantages and flaws of alpha and gamma sensors for radon measurements, including gamma spectrometers should be considered while interpreting radon variations, especially before earthquakes.
To establish the role of radon in our environment we should also discuss its ionization abilities including its impact on the Global Electric Circuit [14]

2. Radon production, transport and gas migration

Every of components of the upper cover of our planet (mantle, crust and soil) contains some amount of uranium or radium – origins of radon. For example, every square 2.5 km of soil to a depth of 15 cm contains about 1g of radium, which releases radon into the atmosphere. Only the longest-lived isotope of radon, 222Rn (daughter product of 226Ra, series 238U), whose half-life is 3.8 days, is capable of migrating over any significant distances separately from its parent radionuclides. The concentration of radon in the pores of rocks depends on the uranium (radium) content in them and the emanating ability of the rocks. The release of radon from the solid phase into the pore space (emanation) occurs mainly due to the energy of radioactive recoil. Radon atoms, formed due to alpha decay from radium, experience radioactive recoil and move in the medium. Some of them remain in the solid rock matrix, while some enter pores and cracks and acquire the ability to migrate further. The proportion of radon atoms released into the pore space depends on the distribution of parent radium in the solid phase, the size of solid particles and pores, rock porosity, the content of film and capillary moisture in the pores and other factors affecting the range of recoil atoms in the medium [15,16].
The transfer of radon in the system of pores and cracks in the lithosphere occurs primarily through two main processes - diffusion and advection. Diffusion is the molecular transfer of radon atoms, it occurs constantly and everywhere if there is a radon concentration gradient, and is most common at the lithosphere-atmosphere interface. The low speed of the diffusion process, combined with the relatively short half-life of radon, significantly limits the distance of its diffusion transfer. Radon can be transported in the lithosphere by diffusion no more than 10 m before the decay of 222Rn atoms reduces its concentration to a level indistinguishable from the background. At the same time, in areas located outside fault zones, calculations using the classical diffusion model show satisfactory agreement with the measured values of the radon concentration and radon exhalation in the surface soil gas [17,18]. Advection is the volumetric transport of gases under the influence of a wide variety of external forces acting in the lithosphere. The speed and spatial scale of advective transfer of radon is disproportionately greater than diffusion; however, this type of transfer can only be developed in large pores and in fractured fault zones, where the development of intense volumetric gas transfer is possible. The advective gas transport is developed both locally in cracks in the unsaturated zone due to changes in atmospheric pressure, fluctuations in groundwater levels, changes in wind speed and other surface factors, and more globally in fault zones in the presence of significant temperature and pressure gradients. First of all, such conditions are created in areas of modern volcanism in conditions close to the surface of uncooled magma chambers, where volcanic gases are discharged onto the surface [19]. It has also been suggested that changes in stress/strain on fault zones caused by seismic activity may cause crustal fluids to migrate by advection up faults, carrying radon to the surface [20]. In addition, radon anomalies can arise as a result of natural convection of atmospheric air in fault zones in the near-surface part of the lithosphere (above the local erosion base) due to the temperature difference between inside and outside the mountain range and the surrounding atmosphere (the “stack” effect). This process is not specific to fault zones and occurs in any permeable environments (layers of highly permeable sediments, zones of exogenous fracturing, karst cavities, mine workings) provided there is a temperature gradient between the mountain range and the atmosphere, as well as a difference in heights (outcrops of permeable zones at different elevations above sea level). The rate of convective air transfer at high temperature gradients can reach significant values, which causes the formation of strong radon anomalies even at relatively low contents of uranium and radium in rocks.
Radon is a rare gas with an average concentration in the lithosphere n·~1018 mg/kg, and it is not able to form its own gas phase, therefore radon advection transport occurs as part of a gas mixture contained in pores and cracks (the so-called "geogas"). These are mainly CO2, CH4, H2S, H2 and other lithospheric gases, which are sometimes called “carrier gases” of radon [21]. It should be remembered that the “carrier” of radon is not any specific gases, but a general gas mixture, “geogas” that fills the pores and the cracks and moves into them.
The permeability of faults for gas transfer is significantly heterogeneous and depends on many factors, such as the intensity of modern tectonic movements, characteristics of the fracture filler material, water saturation of fractures, the permeability of surface sediments and soils overlying fault zones [22,23,24]. As a result, radon anomalies above fault zones often represent a chain of individual elongated or isometric degassing spots, apparently confined to the most permeable segments of faults and their intersection points [22]. In such anomalous patches, as a rule, local concentrations of radon in soil gas exceed the levels that would be expected based on the decay of uranium and radium contained in soils [21,25,26,27,28]. In most cases, radon concentrations in the soil air above fracture zones slightly exceed the background (up to 2-4 times), which can be satisfactorily explained by increased emanation and more active transfer of gases in fracture zones compared to undisturbed lithosphere blocks. However, there are also strong anomalies, with radon levels exceeding the background by 10-20 times or even several orders of magnitude [29]. According to recently obtained data, such anomalies are associated primarily with the processes of natural convection of atmospheric air in the near-surface part of the lithosphere [30]. A number of large radon anomalies have been recorded over fault zones where uranium ores occur at depths of 100-500 m or more [21,31,32,33], which suggests the presence in these cases of powerful deep gas flows with which radon is transported from the bowels of the earth over very long distances. 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 [21,34], 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. Calculations show that theoretically, thanks to this mechanism, rapid transport of radon in the water-saturated lithosphere from the interior to the surface of the earth over distances of 100 - 500 m is possible. Bubble transport in some cases actually determines gas exchange in the aquatic environment, for example, in local swamp ecosystems or in the thickness of ocean waters However, there are significantly fewer facts that convincingly indicate the widespread development of this process in fault zones. The correlations between soil radon and the main components of “geogas” (CO2, CH4) mentioned by some authors [35] do not in themselves indicate the presence of a bubble transfer mechanism. Experimental observation of bubbles in faults is challenging due to small spatial, short time scales and limited observation conditions [36]. The distance of bubble transfer of 222Rn through a porous material filled with water, obtained in a laboratory experiment, did not exceed 4-5 m, which is at least two orders of magnitude less than the theoretical values [37]. In addition, it is obvious that this mechanism can only be realized under conditions of high gas saturation of water, otherwise the gas will dissolve in water and bubbles simply will not form. All this limits the possible role of bubble transport in the formation of radon anomalies in fault zones. A number of authors believe that the above-mentioned strong radon anomalies are not associated with the transfer of radon from deeply buried uranium ores, but are determined by secondary near-surface halos of uranium and radium dispersion [38].
Radon anomalies in fault zones are also characterized by significant temporal variability, including periodic rhythms (seasonal, daily) and non-periodic bursts, as well as sudden changes in the mode and pattern of fluctuations. In most studies, changes in the moisture of the near-surface layer in which measurements are taken are considered as the main cause of seasonal fluctuations in radon, both in fault zones and beyond ones. In the paper [39] various patterns of seasonal fluctuations in radon along the San Andreas fault system (Central California, USA) are demonstrated. Four types of anomalous sites were identified in which radon variations were characterized by maximums in winter, maximums in summer, alternation of winter and summer maximums, and sudden non-rhythmic changes in the nature of radon fluctuations. The authors explained seasonal variations in radon by changes in the moisture saturation of surface sediments (depending on the permeability of sediments, infiltrating rainwater reached the depth of the detectors installation in summer or winter). The sharp and sudden variations were explained by changes in seismic stresses during the preparation and implementation of earthquakes. In addition, the anomalous seasonal radon fluctuations of radon in fault zones were established, associated with a change in the direction of movement of convective air flows. The change in air movement direction is a result of a seasonal inversion of the temperature gradient between inside and outside the mountain range which can also be characterized by maximums in summer or in winter depending on elevation about sea level of anomalies sites [30,40]. The seasonal cycle is superimposed by non-periodic fluctuations associated with other reasons, including changes in stress/strain in fault zones caused by seismic and volcanic activity. Thus, a number of studies have recorded a sharp change in the concentration of radon in groundwater and soil gas before strong earthquakes, volcanic eruptions and/or immediately after them [41,42,43,44]. The response of the field of radon concentrations to the changes of seismic stresses and deformations cannot yet be considered fully studied; the maximums and minimums of radon concentrations do not always coincide with the time of occurrence of earthquakes. The significant uncertainty is also introduced by the factor of distance from the earthquake source. However, deformations of the environment both during the preparation of an earthquake and during its implementation and propagation of seismic waves undoubtedly create additional pressure gradients and also affect the permeability of the environment, creating additional radon migration paths, which can cause radon emissions into the atmosphere in fault zones during earthquakes, which is confirmed by observational results. The most powerful radon anomalies are observed in areas characterized by both high seismic/volcanic activity and the development of uranium ores or rocks with uranium mineralization.
Special place occupies the problem of radon transport to the surface of ocean and rivers. The “geogas” theory resolves one more problem in discussion of possibility to observe radon over the ocean surface. As a matter of fact, we observe air ionization effects initiated by radon decay both over the land and ocean. The gas migration from the ocean bottom resolves this problem and hydrocarbon’s marine exploration proofs the presence of carrier gases (at least methane) in the ocean. This problem was not considered so widely as radon transport over land. Nevertheless, one can find publications demonstrating the radon presence both in near shore waters [45] and in the open ocean [46]. The intensive fluxes of carbon dioxide – the main radon carrier from the ocean bottom, also can be considered as a radon arising over the ocean surface [47].

3. Multifactor sources of the radon variability

Like any natural phenomenon that interacts with the environment, radon is exposed to various factors, the separation of which is a non-trivial task. Just listing these factors shows the complexity of the task before us. Nevertheless, we will try to do this:
  • Various sources of radon (surface layer and deep sources, local anomalies)
  • Ways for bringing radon to the surface (diffusion, radon transport by geogas and fluids)
  • Place and environment where measurements are taken (underground, in soil, in water, on the surface indoors, on an open surface)
  • Atmospheric influences (air humidity, air temperature, atmospheric pressure, air movements – advection and convection)
  • Gravitational deformations (diurnal tides, monthly and seasonal variations)
  • Method of measurement (alpha sensors, gamma sensors, gamma spectrometers)
  • Seismically quiet and seismically active regions
Looking at the list above, it becomes clear: in order to isolate radon variations associated with the earthquake preparation process, you need to learn to filter out all other types of variations listed in the first 6 points. Moreover, these points are not independent. Each of them is influenced by one or more other factors.
In this paragraph we will try, as a brief overview, to at least give some idea of the causes of radon variations. All examples will demonstrate that the observed variations are the combination of factors mentioned above.

3.1. Daily radon variations

In this paragraph we will consider two types of radon daily variations: underground and in air. For underground measurements we will use the results of three most recent publications [48,49,50]. Regardless, in the publications [48,49] the active air movements in caves and wells play important role, the results in general are in good agreement: daily radon variations are controlled by atmospheric parameters as one can see from the Figure 2.
We see the positive correlation with air temperature, and negative correlation with relative humidity and air pressure. The main maximum in radon variations is formed in early afternoon hours, but sometimes we can observe the smaller early morning (3 h) maximum which will be discussed later.
The seasonal difference is expressed only in different magnitude of variations but the correlation characteristics with atmospheric parameters are the same.
Daily variations of radon in air are also controlled by the atmosphere behavior, but the main factor is the Global Boundary Layer (GBL) dynamics [51]. This effect was detailly considered in [52] and is presented in the Figure 3.
From the Figure 3a we see that Nocturnal Boundary Layer (NBL) is located near 100-300 m altitude and vertical motions are suppressed due to the cooling at the surface. We can see this from experimental measurements of daily aerosol dynamics (Figure 3b): the very dense aerosol layer is formed after sunset near 100 m height. Air cooling results in a stable temperature stratification and in the formation of a thin boundary layer isolating the surface from the residual layer above where turbulence decays. The model (Figure 3c) and experimental measurements (Figure 3d) show that the NBL is characterized by very high radon concentrations and significant vertical concentration gradients. Over the night, radon is emitted constantly (upper panel of Figure 3d) and, due to the stability of the NBL, it is accumulating close to the surface. After sunrise due to intensive vertical convection radon is washed out from the near ground layer and reaches altitudes up to 2 km (bottom panel of Figure 3d).
Returning to Figure 2 even the underground measurements connected with atmosphere “feel” the radon increased concentration what is reflected in small maxima mentioned in Figure 2 description.
In the studies of the air electric conductivity [56] the same night-time radon concentration maximum is marked as a main feature of the radon in air concentration (Figure 4).

3.2. Seasonal radon variations

To come to some conclusion regarding the possible seasonal variations of radon we used both results of our measurements and published in the scientific literature from different regions of the globe: Mt. Beshtau, North Caucasus [57], Northern Altai [58], Black sea coastal area [59], Israel [60] and Italy [61].
Authors of {57} and {60] make conclusion that radon concentration follows the air temperature and its maximum reached during local summer (Figure 5 [57]). Actually, we see the same effect as for daily variation: positive correlation with air temperature and negative correlation with the air pressure. Here two new features could be added: Such variations are characteristic for measurements over the fault (both exhalation rate and radon in air) while average background sites from both sides of the fault do not show changes in exhalation rate (curve b in the panel 1). The positive correlation with the temperature difference between the outside air temperature and temperature in mine where the measurements were takes immediately implies the conclusion that we deal with the pumping effects due to the vertical convection initiated by the temperature difference.
The authors of [60] make the similar conclusions indicating that the atmospheric effects are characteristic to the shallow (few meters underground) radon measurements. They discriminate the air temperature and air pressure effects as follows:
  • Radon within a rock media (as measured by gamma detectors) is driven by the surface temperature gradient to a depth of 100 m, with the same daily cycle, and a specific time lag.
  • Radon in the measuring air space of open boreholes (as measured by alpha detectors) is driven by pressure. It varies in anti-correlation with the intra-seasonal pressure waves and the semi-daily pressure periodicity.
In [60] another important problem is raised: the difference between the alpha and gamma detectors technology in radon monitoring which will be discussed lower.
The publications [58,59,61,62] provide the opposite result in radon seasonal variations: winter maximum and summer minimum. In Figure 5 are shown the yearly radon measurements for year 2016 in very distant locations: Black Sea shore (38° E) and Gorny Altai in Siberia (85.5° E). Variations show surprising similarity: deep minimum in summer season and large sharp intensive variations during winter. Both measurement sites were located in basement isolated from atmospheric variations of air temperature.
In Italy (Aquila) [61] radon measurements in 2006 were also provided in basement, but unlike the first two sites radon activity was measured by gamma-spectrometer, and again we see the late summer minimum, and negative correlation with the air temperature (Figure 7).
Concluding this paragraph, we should state that the seasonal variations of radon activity is controlled by the air temperature both in open space and closed basement sites but with opposite sign of correlation. The explanation of this fact will be item for future studies. The control experiment which could be recommended is the radon measurements at the equator (for example, Singapore or Hawaii) where the temperature is constant through all the year.

3.3. Radon variations and solar activity

It is very difficult find the long series of radon measurements through the whole solar cycle. One of the most interesting is the paper [63] where the authors calculated the spectra of radon variations within the solar cycle. They found several characteristic periods of radon variations, and naturally, the main peak was near the solar rotation period: 12.39 year-1= 29.3 days. What is the most interesting, the positive night-time radon variation was established which physical mechanism was discussed in [52] and depicted in Figure 2. Actually, the increased radon concentration in near ground layer of atmosphere generates the positive deviations in the ionosphere (Figure 8).
The similar period of 28.5 days was revealed in the long-term radon measurements (2012-2017) at Gorny Altai [58]. It is not a dominating spectrum line in the long-time radon activity registration. The strongest in the observed spectrum is period 450 days which up to now has no any reasonable explanation.
Period of continuous observation of the radon activity at Gorny Altai (almost half a solar cycle duration) gives opportunity to look for correlation between the RVA and solar activity. The comparison of solar radio flux F10.7 and RVA is presented in Figure 9.
We can clearly see at the end of observational period the counter-directional trends of the solar activity and VAR. Radon activity grows while approaching to the minimum of solar activity. Counter-directional trend is only on the face of it, in reality the picture is more complex as it can be seen in Figure 10. The clear negative correlation is revealed in the beginning if decay phase of the solar cycle in 2014, and in period of approaching to the minimum in 2016. Between them we see the oscillation character of the cross-correlation coefficient, probably modulated by the seasonal radon variations.

3.4. How to measure radon

The history of radon monitoring is very long and starts from ionization chambers, through gas analyzers, to widely used now alpha sensors and sophisticated gamma spectrometers. Now these devices look like complex stations measuring in addition air temperature, air pressure and relative humidity, have smart software and possibility to be controlled and send information remotely. The separate class of devices is small portable gadgets to measure indoor radon in sanitarian purposes.
Another type of instrument categories is the passive and active measurements. The first option does not need any operator intervention when instrument can operate autonomously and even remotely. The second option involves active operator actions when the air should be pumped into the instrument and this portion of air needs the manual chemical analysis.
The problems of radon measurements and discussions about it is very old but it seems that the paper [63] put the final point in this discussion: the authors demonstrated the victory by clear advantage of the gamma sensors which sensitivity 2-4 order higher than the same of alpha. The gamma sensor is able to monitor temporal radon variations directly within the geological media without the time delay required for the radon to move and reach equilibrium within the air volume where the alpha detector is located: cave, tunnel, basement or narrow borehole. The readers can familiarize themselves with this publication, but we want to add something what was not mentioned in it.
First, the most important thing especially when we use radon variations as an earthquake precursor. During the years of defamation of physical precursors of earthquakes [65], opponents of forecasts argued that radon was not a harbinger of earthquakes because its anomaly often cannot be registered. But the problem is not in absence of radon anomaly before earthquake but in alpha-particle emitted by radon free path in air, which is near 5 cm. It means that the sensor measuring pre-earthquake increase of radon flux should sit directly within this flux, and few meters away it will see nothing. It is quite natural that not knowing the location of active fault it is very difficult to “catch” pre-earthquake anomaly. Contrary to alpha emission, the gamma emission is long range and easily penetrating. It means that the gamma sensor will be able to register the radon precursor everywhere within the earthquake preparation zone.
Second important advantage is the possibility to use instead of gamma sensor the gamma spectrometer. What is this advantage in details? Radon itself does not emit gamma quants. Gamma emission is a result of its daughter products. Different radon isotopes (Table 1) produce different daughter products (Figure 1), which, in turn, emit gamma emission producing the rich energy spectrum. We consider that the main isotope to be used as a precursor is 222Rn which daughter products are 214Pb and 214Bi They emit gamma lines with energies 295 and 352 keV for 214Pb and 609, 1120 and 1764 keV for 214Bi. So, if we will select from the total gamma spectrum only these lines, we will identify the 222Rn with 100% probability. More details are possible to find in [61].
One more advantage of gamma spectrometer put in isolated room that it has no daily amplitude variations correlated with ambient air temperature contrary to alpha sensor. It is shown in Figure 11 where are compared the data series registered by alpha sensor Rad 7 and gamma spectrometer PM4 [61].

3.5. How to distinguish the soil and tectonic origin radon

As mentioned above, radon is formed during the decay of radium contained in all layers of the earth's crust, from the “granite layer” lying at depths of several kilometers to shallow soils. A natural limitation on the distance over which radon can be transported in the earth's crust is its relatively short average lifetime, determined by radioactive decay and amounting to 5.5 days. With real speeds of advective transfer of gases in cracks unsaturated with water in the earth's crust, apparently averaging no more than a few meters per day, and in extreme cases up to 25-35 m/day, the distance over which radon can be transported to the surface of the earth with the help of advection it averages 20-30 m, in extreme cases perhaps up to 200 m.
However, during radon monitoring, the concentration of radon in soil gas is recorded, as a rule, in near-surface conditions, at a depth of no more than 1 m. In this regard, every time when interpreting the results of radon monitoring, the question arises - what is the nature of the radon that we register with our sensors? Is it formed directly in near-surface soils, in fact, in the area where the measuring device is located, or is all or some part of the recorded radon not of local origin, but arrives through advective transport to the surface along faults from greater depths? In the case of some other gases, for example, He, CO2 or CH4, the answer to the question of the depth and genesis of the gas can to a certain extent be given by the isotope ratios of helium and carbon. However, in the case of radon, such isotopic tracers are absent. Radon atoms formed directly at the surface of the earth and in the deep parts of the earth’s crust are no different from each other.
At the same time, it is possible to distinguish between radon of soil and tectonic (deeper) origin based on the analysis of data from simultaneous monitoring of radon concentration in soil gas at a depth of 0.5 to 1 m and radon exhalation rate from the soil surface.
As we have established during experiments on radon monitoring, in the case when radon is formed directly in the near-surface soil layer, an inverse correlation is observed between the concentration of radon in soil gas and the rate of radon exhalation from the soil surface: with an increase in radon exhalation from the surface, its concentration in the soil gas decreases (top panel of Figure 12). This is logical, because the more radon that flows out of the soil, the less of it remains in the soil air. This type of correlation is typical for areas located outside fault zones, characterized, as a rule, by a relatively thick layer of soils overlying bedrock, where diffusive transfer of radon predominates [17,18,66]. Most often, fluctuations in soil radon under such conditions are caused by changes in soil permeability, which is associated, in turn, with fluctuations in air temperature and soil moisture. A decrease in permeability leads to a increase in radon exhalation and an increase in the concentration of radon in soil gas, and vice versa, a decrease in soil permeability causes an increase in exhalation and a decrease in the concentration of radon in the soil.
In the case when radon is transferred to the near-surface zone along cracks from deeper horizons, including radon of tectonic origin, the nature of the correlation between the concentration of radon in soil gas and the rate of radon exhalation from the surface is of the opposite nature. There is a direct correlation between these parameters (lower panel of Figure 12). This is due to the fact that in this case radon enters the near-surface layer, where measurements are taken, with an advective gas flow from a certain depth, which leads to a synchronous change in both the radon concentration at a depth of 0.5-1.0 m and the exhalation speed of radon from the earth's surface. This type of correlation is observed in highly permeable zones of tectonic faults [18,30]. Under such conditions, high-amplitude synchronous fluctuations in the concentration of radon in soil gas and exhalation of radon from the surface are observed, which, as a rule, are closely correlated with air temperature.
Additional information about the sources of radon is provided by measurements of the content of 226Ra, the parent of radon, in the near-surface soil layer where the sensors are located. Thus, the totality of information about fluctuations in radon concentration in soil gas, the rate of radon exhalation from the soil surface and the radium content in these soils makes it possible attempts to separate radon of soil and tectonic origin during gas-dynamic monitoring. The first experience of such studies shows their high promise [18,40].

4. Radon as diagnostic means and an earthquake precursor

From discussion above we see that radon is reacting at the variations of atmospheric parameters. This means that by solving the inverse problem we can try to determine atmospheric parameters based on measurements of radon variations [67]. In this publication Dr. Robertson demonstrates the different atmospheric borders and air movements where radon can be used as a tracer (Figure 13).
As it was mentioned above, now radon emanation is used as a tracer of the upper border of the Global Boundary Layer of atmosphere.
Gamma emission within the energy band of 214Bi 484-800 keV, the daughter of 222Rn was used to monitor the spatial distribution of crustal activity in Japan during 8 years [68]. The gamma scintillation counter RE-100 was installed close to the earth surface while moving by car or Shinkansen bullet train on the route from Kyoto to Tokyo. This monitoring showed a long-term increasing trend of radon concentration in Inagawa Town, Hyogo Prefecture from around the end of 2001 with a rate of 16/count /min/year. It was revealed also the increased by 22% level of radon emanation in particular regions near Kyoto.

4.1. Radon activity as measure of tectonic stress

If to filter radon variations caused by meteorological factors and air movements the question arises: the residual variations of radon concentration (including variations before earthquakes), both increasing and decreasing are connected only with the transport of radon through the new ways of migration, or rock deformation itself may change the radon emanation effectiveness? The paper [69] gives the answer on this question. It presents the results of laboratory experiments the effects of radon emanation changes after mechanical and thermal damage of various granite representatives of the upper crust. In comparison with other experiments using the one-dimensional loading the authors of [69] use the three-dimensional deformation when the samples were placed under natural conditions (controlled confinement and pore pressure) and then they were flushed with pore gas. Their results show that radon emanation increases up to 170 ± 22% at the last moments before the sample rupture. At the same time the heating of the sample to 850°C shows that thermal fracturing irreversibly decreases emanation by 59–97% due to the amorphization of biotites hosting radon sources. So, we can conclude that the temporal radon variations before earthquakes are result of two effects: new ways of gas (and fluids) migration and changes of radon emanation from solid body under increasing stress and temperature.
Is there any possibility to check the stress-radon release relation not only in laboratory experiments but in natural conditions besides earthquakes? The closest to the seismic cycle conditions and well controlled experiments were produced with transient deformation near reservoir lakes [70]. Is reported the electric potential variations, radon emanation and deformation measurements recorded since 1995 in the French Alps in the vicinity of two artificial lakes which have strong seasonal variations in water level of more than 50 meters. In both emptying and filling of water reservoirs during transitions period the increased radon emanation was observed.
In [71] the authors tested dependence of the radon emanation intensity on the tectonic faults parameters. Emanation survey results for Central Mongolia and Baikal region show that faults and their key parameters, such as size rank, internal structure peculiarities, dynamic formation conditions, and seismic activity, have a significant effect on radon activity. Additional analysis of the radon survey data from other regions confirms the discovered regularities. Dependence of radon emanation intensity on fault parameters is shown in the Figure 14.
The correction of atmospheric chemical potential (ACP) parameter (to be discussed lower) was derived from studies of radon ionization effects on the lower atmosphere [72] and it was demonstrated that it can be used as a radon activity proxy [73]. It follows with high level of correlation the tectonic shear traction [74] what was checked by the mutual global monitoring. Figure 15 demonstrates the variations of ACP (blue and green) and share traction around the time of Fukushima earthquake 16 March 2022.
We obtained quite enough proofs that radon reacts on the earth’s crust deformation: these are the laboratory experiments [69], the natural monitoring of the tectonic fault’s activity [71], in artificial stress initiation due to large water reservoirs filling and emptying [70], by global monitoring of shear stress with the radon proxy [74]. The paper volume limitations do not permit us bring more examples but even from the example provided it is clear that radon-stress effect can be used in practical applications including the short-term earthquake forecast.
Organization of earthquakes forecast using radon variations is not a subject of present paper. We only will demonstrate what forecast parameters can be estimated using the radon variations.

4.2. Radon as earthquake precursor

For correct forecast we need to determine three main parameters: time, location and magnitude. We have plenty examples of pre-earthquake radon anomalies. Some recent examples one can find in [75,76,77]. But for real forecast these values should be determined with the sufficient precision. What does it mean? For example, the leading time of pre-earthquake anomaly should be sufficiently stable. Otherwise, the time spread makes parameter value insignificant. Of course, in different areas of seismic activity the leading time value can be different, but for the given place it should be stable. In the Figure 16 are shown results of radon in air monitoring in Azov and Black seas area.
One can clear see that the main maximum of radon pre-earthquake variation for both cases have the leading time near 6 days.
It is difficult to find epicenter position from the single sensor radon measurements. In this case we can use the radon proxy – ACP which is calculated from the assimilative atmospheric models and with its help we can obtain its spatial distribution within the zone of earthquake preparation. In the Figure 17 is demonstrated ACP spatial distribution map one day before the M6.3 earthquake 34 km of Herat, Afghanistan.
The same approach of ACP distribution is used for the earthquake magnitude estimation assuming that radius of ACP anomaly is the order of Dobrovolsky earthquake preparation zone radius [78] determined as:
R(km)=100.43M (1)
where M – earthquake magnitude
This estimate is based on the fact that spatial radon distribution determined statistically from many publications on radon monitoring in seismically active regions follows the Dobrovolsky law of magnitude-size relationship [79]. It is demonstrated in the Figure 18.
Another indicator for earthquake magnitude estimation can be the amplitude and duration of the radon anomaly, but this question needs more statistical studies.

5. Radon as a component of Global Electric Circuit

The Global Electric Circuit (GEC) exists due to two major processes: creating of the potential difference nearly 250 kV between the ionosphere at altitude ~80 km and ground surface created by the global thunderstorm activity [82] and existence of air conductivity which provides the fair weather vertical current from the ionosphere to the Erath’s surface due to air ionization by external sources (galactic cosmic rays, solar proton events, magnetospheric electrons and protons and solar electromagnetic emission) and internal source – natural ground radioactivity, where radon plays the major role [14,83].
To estimate the radon contribution in the air ionization is not a simple task because the real global distribution of radon is very rough. Nevertheless, such attempt was made in [84]. The author used the chemistry-climate model SOCOLv3 [85] considering ionization by solar energetic particles during an extreme solar proton event (SPE), galactic cosmic rays (GCR), and terrestrial radon (222Rn).
Contribution of radon in air ionization is calculated as:
IR=((CRn-222 х 10-3)/5.69 х 1015)* ρ (2)
where CRn-222 is the ratio of the mass of 222Rn to the mass of dry air; 5.69 x 1015 Bq - conversion factor between mBq/(m2 x s) and g/(m2 x s) (1g 222Rn in the calculation corresponds to 5.69 x 1015 Bq); ρ - air density (kg/m3).
The global distribution of the ionization rate at the altitude 1000 hPa (near ground surface) according to the model distribution of radon emanation is presented in Figure 19.
It should be mentioned that at the regional level exist models based on the real measurements. The radon activity map for Russia is presented in the Figure 20 [86].
Figure 19 shows that the ionization rates from radon do not exceed values of the order of 6 ion pairs /cm3/s and the highest ionization rates caused by radon emissions are observed in individual foci in the territories of Eurasia, part of Africa and the west coast of North America. The average values of radon ionization rates obtained in the SOCOL v.3 model were compared with other results obtained previously in other models [87,88]. The comparison showed good consistency of results in terms of the order of magnitude and distribution of radon on the surface. Since the ground surface in ocean areas is covered by water, there is very low level of ionization caused by radon over the surface of the oceans. Only in coastal areas close to the continents is there an increased level of ionization from 222Rn, due to radon transport by rivers [89].
According to [90] we calculate the air conductivity as
σ = n*e*(μ- + μ+), (3)
where: σ – specific conductivity (Sm/m); n – total number of ion pairs from all included sources (cm3); e – elementary charge (C); μ- + μ+ - mobility of positive and negative ions (in our work we assume an equal number of negative and positive ions). Three separate numerical experiments were carried out in which conductivity was calculated for each of the three ionization sources in order to estimate the contribution of each of the considered natural ionization sources to the overall conductivity of the atmosphere. The calculation results are presented in Figure 21. To calculate ionization rates from fluxes of galactic cosmic rays (GCR), solar cosmic rays (SCR) and solar proton events (SPE), the CRAC model: CRII was used [90,91]. Figure 21 shows calculations of atmospheric conductivity caused by 222Rn, GCR and SPS through the SOCOLv3 model. In Figure 21, two SPEs are considered, one on 01/17/2005 and another SPE of the GLE (ground level enhancement) type on 01/20/2005. SCR flows are considered on undisturbed/quiet days from January 1 to January 15, 2005.
From Figure 21 it is clear that above 50 hPa, the predominant contribution to the ionization rate is made by ionization from the event of January 20, 2005. Ionization from radon is the main contributor to conductivity only in the layer of the atmosphere that is closest to the earth's surface, somewhere below 850-900 hPa. In general, ionization has an exponential dependence and grows from the earth’s surface, where it has an average global value of the order of 10-13, and to the upper boundary of the model atmosphere, where the conductivity value grows to values of the order of 10-7, which corresponds to observations and previously obtained numerical results [88]. To compare the specific conductivity during the disturbed period (for January 17 and 20, 2005), the specific conductivity was also calculated for ionization from SCR during the quiet period. The time period from January 1 to January 15, 2005 was chosen as the quiet period. It can be seen from the figure that the conductivity during the quiet period differs from the conductivity during the disturbed period by approximately two to three orders of magnitude, depending on the height. This modeling study took into account all the main natural sources of atmospheric ionization and took into account the contribution to atmospheric conductivity from a solar event compared to quiet conditions.
Looking at the results of modeling a reader who is not in the know will not notice anything unusual, while there is something what never was acknowledge before, it is that air ionization by radon produce essential impact on the GEC parameters. On the Figure 22 the computed latitudinal distribution of the vertical fair-weather current is shown at the longitude near 1° W.
The difference between calculated fair-weather current density with taking into account only GCR ionization and fair-weather current density with taking into account radon and GCR effect is about 0.2–0.6 pA/m2 and appears in the 222Rn active regions, see [14]. It is essential contribution which should create the local anomaly of the ionosphere potential.
We made the model calculations using commonly accepted radon concentration on the ground surface nearly 3 Bq/m3 but in the Figure 16 we see values close to 60 Bq/m3 which were measured at 2 m altitude about ground surface. It is more than order of magnitude larger of radon concentration used in calculations. Some time ago we provided radon measurements in closed box to prevent the wind effects at 3 levels: -70 cm, 0 cm and 100 cm in relation to ground surface in two different regions of Mexico [92]. The results are presented in Table 2. Altitude -70 cm is the level where the original ground radon concentration was measured. From these values at the ground surface, we see concentration reaching 288 Bq/m3 what is two orders of magnitude larger than accepted in calculations. At the altitude 100 cm radon concentration are similar to those presented in Figure 16 from Azov and Black Sea shore areas.
The future direction of our work in radon ionization ability will be concentrated on areas of increased radon concentration to calculate the local anomalies of the ionospheric potential.

6. Conclusions

In this research we tried to create the comprehensive picture of radon variations under action of different factors. We demonstrated that meteorological effects have important contribution on radon variations. It was revealed that effect also depends on the sensor location: closed space or directly connected with atmosphere (even in caves). Convection direction depending on the temperature difference between outside and inside the room where the sensor is located my change the sign of dependence on air temperature and pressure. Main results of this consideration are the following:
  • Meteorological effects depend not only on the pure variations of meteorological parameters but from methodology and location of radon measurements (pumping effect), difference of outside and inside (where the sensor installed) temperatures
  • Daily radon variability (nighttime maximum) is determined by daily dynamics of the Global Boundary Layer
  • Two types of seasonal variations of radon (summer maximum or minimum) need further clarification. More probable the summer maximum is a result of measurements site location. The summer minimum should be checked by the long-time measurements at geodetic equator where the air temperature is not changing round the year
Tectonic activity also has different options of action on radon concentration. It is stress working on microlevel and changing the level of radon emanation increasing it up 177% (in laboratory experiments), and increase of the crust temperature leading to the decrease of radon emanation. Another way is the large-scale deformations creating the new ways of gas transport within the crust and leading to changing the levels of radon exhalation at the ground surface. It means that radon exhalation intensity should depend on the earthquake source mechanism: extension, compression or shear. It means tha during preparation period we can observe the increase or decrease of radon flux intensity, or even not changing.
It was demonstrated that there are two types of radon origin:
  • The surface radon contained in radium grains of the soil
  • Tectonic radon coming from deeper layers of the crust. The problem of many publications is that dependence of surface radon on meteorological parameters is applied to the tectonic radon creating mish-mash in data interpretation
  • One of new and important results is the way of discrimination of the surface an tectonic radon. It is simple but effective: for the surface radon the radon concentration and exhalation are in counterphase, while for the tectonic radon they are in phase.
The solar activity effects on radon activity were practically not studied in the literature and here we made two contributions:
  • We supplemented described in [62] the oscillation of radon intensity within the solar cycle with maximum period of 12.5 years was demonstrated which is modulated by radon changes in local time. The maximum of radon activity is observed during night-time hours what coincides with variations of the Total Electron Content provided by radon activity.
  • The study of long-term observations of radon activity in Gorny Altai imply the possible anticorrelation of solar and radon activity within the solar cycle. Nevertheless, more long observations analysis are necessary to make a more definite conclusion.
Advantage of gamma spectrometry for 222Rn monitoring and discriminating from other radon isotopes and daughter products was demonstrated.
It was underlined the role of radon in environment monitoring applications. It is used as a tracer for determining of upper boundary of Global Boundary Layer and as earthquake precursor.
One of the more significant results of this publication is demonstration of importance of radon contribution to the vertical current-ionospheric potential of the global electric circuit. It opens the way to further improvement of the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model where the GEC plays important role in Atmosphere-Ionosphere Coupling mechanism.
All results mentioned above are not considered as final ones and will be improved and developed in future works.

Acknowledgements

The research of SP was carried out with the support of the Ministry of Science and Higher Education of the Russian Federation (theme \Monitoring", state registration No. 122042500031-8). The research of IM was completed in accordance with state order of St. Petersburg State University. The work of PM was accomplished as part of government assignment No. 1220224001059. The study of TP was supported by state assignment of Lomonosov Moscow State University "Solving of problems of nuclear energy and environmental safety problems, as well as diagnostics of materials using ionizing radiation" (Project Reg. No. 122030200324-1)

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Figure 1. The decay trees of the main radon isotopes finished by stable elements. Left panel 222Rn chain (red circle); middle panel 220Rn named thoron (red circle); right panel 219Rn named actinon (turquoise circle). All images copyright © 2008-2024 the International Association of Certified Home Inspectors, Inc. (InterNACHI). https://www.nachi.org/gallery/.
Figure 1. The decay trees of the main radon isotopes finished by stable elements. Left panel 222Rn chain (red circle); middle panel 220Rn named thoron (red circle); right panel 219Rn named actinon (turquoise circle). All images copyright © 2008-2024 the International Association of Certified Home Inspectors, Inc. (InterNACHI). https://www.nachi.org/gallery/.
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Figure 2. One-week measurement results of radon concentration (3 h moving average) in soil at 1.2 and 1.6 m, air temperature, air relative humidity and air pressure in typical spring days (April 13–20). The similarity of three publications results is due to the fact that the caves and wells have the direct contact with atmosphere [50].
Figure 2. One-week measurement results of radon concentration (3 h moving average) in soil at 1.2 and 1.6 m, air temperature, air relative humidity and air pressure in typical spring days (April 13–20). The similarity of three publications results is due to the fact that the caves and wells have the direct contact with atmosphere [50].
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Figure 3. a – schematic presentation of the GBL daily dynamics [51]; b – lidar measurements of aerosol concentration in air [53]; c – modelling of the radon concentration S0 in local time as a function of GBL dynamics [54]; d – upper panel – radon in air concentration, bottom panel – equivalent mixing height during 12 days in April-May 2011 [55].
Figure 3. a – schematic presentation of the GBL daily dynamics [51]; b – lidar measurements of aerosol concentration in air [53]; c – modelling of the radon concentration S0 in local time as a function of GBL dynamics [54]; d – upper panel – radon in air concentration, bottom panel – equivalent mixing height during 12 days in April-May 2011 [55].
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Figure 4. Diurnal variation of radon and its progenies in air [56].
Figure 4. Diurnal variation of radon and its progenies in air [56].
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Figure 5. 1) – radon exhalation rate in the fault (a) and mean values outside the fault; 2) variation of radon in air over the fault; 3) air temperature (e – on monitoring site, monthly measurements (Тair), f – data from Mineralnye Vody weather station (MVWS), g – average annual temperature inside the mine Tmine =11.5°C 4) temperature difference between the outside air temperature and temperature in mine; 5) atmospheric pressure.
Figure 5. 1) – radon exhalation rate in the fault (a) and mean values outside the fault; 2) variation of radon in air over the fault; 3) air temperature (e – on monitoring site, monthly measurements (Тair), f – data from Mineralnye Vody weather station (MVWS), g – average annual temperature inside the mine Tmine =11.5°C 4) temperature difference between the outside air temperature and temperature in mine; 5) atmospheric pressure.
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Figure 6. Upper panel – Radon volumetric activity (RVA) at Gorny Altai radon monitoring site; bottom panel – relation of RVA to yearly mean at Black Sea radon monitoring site.
Figure 6. Upper panel – Radon volumetric activity (RVA) at Gorny Altai radon monitoring site; bottom panel – relation of RVA to yearly mean at Black Sea radon monitoring site.
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Figure 7. Upper panel – RVA relation to the yearly mean at two sites: Gran Sasso (blue) and Coppito (red) near the L’Aquila city; lower panel – maximum (red) and minimum (blue) air temperature. Gratitude to Giampaolo Giuliani.
Figure 7. Upper panel – RVA relation to the yearly mean at two sites: Gran Sasso (blue) and Coppito (red) near the L’Aquila city; lower panel – maximum (red) and minimum (blue) air temperature. Gratitude to Giampaolo Giuliani.
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Figure 8. Left panel – spectral lines of the gamma radon emission versus local time. Modified from [62], right panel – nocturnal positive ionospheric anomaly before off the coast of southern Kamchatka M7.5 earthquake 25 March 2020.
Figure 8. Left panel – spectral lines of the gamma radon emission versus local time. Modified from [62], right panel – nocturnal positive ionospheric anomaly before off the coast of southern Kamchatka M7.5 earthquake 25 March 2020.
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Figure 9. Upper panel – Solar flux F10.7; bottom panel – daily mean of RVA.
Figure 9. Upper panel – Solar flux F10.7; bottom panel – daily mean of RVA.
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Figure 10. Moving cross-correlation coefficient (R) of the 10.7 index series and the radon volumetric activity series. Sampling frequency is 1 sample/day, sliding time window 200 days, confidence correlation at significance level 0.01, Rcr = 0.1.
Figure 10. Moving cross-correlation coefficient (R) of the 10.7 index series and the radon volumetric activity series. Sampling frequency is 1 sample/day, sliding time window 200 days, confidence correlation at significance level 0.01, Rcr = 0.1.
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Figure 11. Air temperature, Rad7 and PM-4 time series [61].
Figure 11. Air temperature, Rad7 and PM-4 time series [61].
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Figure 12. Fluctuations in the rate of radon exhalation (JRn) and radon concentration in soil gas (CRn) in the area with radon of soil origin outside the fault zones (upper panel) and in the area with convective transfer of radon in the fault zone (lower panel).
Figure 12. Fluctuations in the rate of radon exhalation (JRn) and radon concentration in soil gas (CRn) in the area with radon of soil origin outside the fault zones (upper panel) and in the area with convective transfer of radon in the fault zone (lower panel).
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Figure 13. Schematic illustration of the various atmospheric transport processes for which 222Rn and its radioactive decay products are used as tracers.
Figure 13. Schematic illustration of the various atmospheric transport processes for which 222Rn and its radioactive decay products are used as tracers.
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Figure 14. Diagram illustrating the effect of dynamic formation conditions of faults in Mongolia and Baikal region on maximum radon activity values (KQ max) identified for each group of tectonic faults. Gray scale represents radon activity levels of faults according to the accepted classification [71].
Figure 14. Diagram illustrating the effect of dynamic formation conditions of faults in Mongolia and Baikal region on maximum radon activity values (KQ max) identified for each group of tectonic faults. Gray scale represents radon activity levels of faults according to the accepted classification [71].
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Figure 15. Average of near- and intermediate-field of ACP (unfiltered—blue; filtered—green) and shear-traction field (red) in the epicentral area of the 16 March 2022 Fukushima earthquake, Japan (time shown with grey vertical dashed line). The ACP follows the temporal evolution of the shear traction field before the earthquake, while the spike in ACP occurs at the same time and shear traction increases.
Figure 15. Average of near- and intermediate-field of ACP (unfiltered—blue; filtered—green) and shear-traction field (red) in the epicentral area of the 16 March 2022 Fukushima earthquake, Japan (time shown with grey vertical dashed line). The ACP follows the temporal evolution of the shear traction field before the earthquake, while the spike in ACP occurs at the same time and shear traction increases.
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Figure 16. Charts of radon volumetric activity fluctuations in the near-surface atmosphere: a – 38 days before the earthquake in the Sea of Azov; b – 32 days before the earthquake in the Black Sea [77].
Figure 16. Charts of radon volumetric activity fluctuations in the near-surface atmosphere: a – 38 days before the earthquake in the Sea of Azov; b – 32 days before the earthquake in the Black Sea [77].
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Figure 17. Spatial distribution of ACP one day before the M6.3 earthquake in Afghanistan on 15 October 2023. Epicenter position is shown by yellow star, white circle indicates the earthquake preparation zone for M6.3.
Figure 17. Spatial distribution of ACP one day before the M6.3 earthquake in Afghanistan on 15 October 2023. Epicenter position is shown by yellow star, white circle indicates the earthquake preparation zone for M6.3.
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Figure 18. (a) The distance from the precursor to the epicenter as a function of the earthquake magnitude. Geochemical precursors are denoted by filled circles; the resistance from different sources, by dashes and crosses; telluric currents, by triangles; radon, by arrows; and light effects, by open circles according. Modified from [78]. (b) The distance from the precursor to the epicenter as a function of the earthquake magnitude for geochemical data. Modified from [79]. Opened and filled squares denote measurements of radon and other gaseous anomalies, respectively. Continuous thin lines show the relation between the deformation radius and magnitude for deformations of 10-7to 10-9 in accordance with the empirical equation (1). Thick line represents the empirical dependence derived in [80] as a result of calibrating the maximal distance between the measured anomaly and epicenter for a given magnitude on the basis of the shear dislocations law for earthquakes. The dashed line shows the typical size of the rupture zone of an active fault as a function of magnitude in accordance with the empirical equation of Aki and Richards [81].
Figure 18. (a) The distance from the precursor to the epicenter as a function of the earthquake magnitude. Geochemical precursors are denoted by filled circles; the resistance from different sources, by dashes and crosses; telluric currents, by triangles; radon, by arrows; and light effects, by open circles according. Modified from [78]. (b) The distance from the precursor to the epicenter as a function of the earthquake magnitude for geochemical data. Modified from [79]. Opened and filled squares denote measurements of radon and other gaseous anomalies, respectively. Continuous thin lines show the relation between the deformation radius and magnitude for deformations of 10-7to 10-9 in accordance with the empirical equation (1). Thick line represents the empirical dependence derived in [80] as a result of calibrating the maximal distance between the measured anomaly and epicenter for a given magnitude on the basis of the shear dislocations law for earthquakes. The dashed line shows the typical size of the rupture zone of an active fault as a function of magnitude in accordance with the empirical equation of Aki and Richards [81].
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Figure 19. Global distribution of atmospheric ionization rates at an altitude of 1000 hPa caused by 222Rn emissions averaged over January 2005, calculated using the SOCOLv3 chemical-climate model. Reprinted from [85], with permission from Karagodin A.V.
Figure 19. Global distribution of atmospheric ionization rates at an altitude of 1000 hPa caused by 222Rn emissions averaged over January 2005, calculated using the SOCOLv3 chemical-climate model. Reprinted from [85], with permission from Karagodin A.V.
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Figure 20. Radon hazard map of Russia for 1995 [86].
Figure 20. Radon hazard map of Russia for 1995 [86].
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Figure 21. Left panel: Global average ionization rates from various sources. Red dotted lines: Ionization rates from the SPE on the 17th (large hatching) and 20th (fine hatching) January 2005; Purple dotted line: SCR ionization rate on undisturbed/quiet days (January 1-15, 2005); Green curve: Ionization rate from GCR (averaged for January 2005); Blue curve: Ionization rate from 222Rn (averaged over January 2005); Right panel: Global average contribution of various ionization sources to the total atmospheric conductivity. Red dotted lines: conductivity caused by ionization from the ATP on the 17th (large shading) and 20th (fine shading) of January 2005; Purple dotted line: conductivity caused by ionization from SCR on undisturbed/quiet days (January 1-15, 2005); Green curve – conductivity calculated using ionization from GCR (averaged for January 2005); Blue curve: conductivity calculated using ionization from 222Rn (averaged over January 2005). Reprinted from [84], with permission from Karagodin A.V.
Figure 21. Left panel: Global average ionization rates from various sources. Red dotted lines: Ionization rates from the SPE on the 17th (large hatching) and 20th (fine hatching) January 2005; Purple dotted line: SCR ionization rate on undisturbed/quiet days (January 1-15, 2005); Green curve: Ionization rate from GCR (averaged for January 2005); Blue curve: Ionization rate from 222Rn (averaged over January 2005); Right panel: Global average contribution of various ionization sources to the total atmospheric conductivity. Red dotted lines: conductivity caused by ionization from the ATP on the 17th (large shading) and 20th (fine shading) of January 2005; Purple dotted line: conductivity caused by ionization from SCR on undisturbed/quiet days (January 1-15, 2005); Green curve – conductivity calculated using ionization from GCR (averaged for January 2005); Blue curve: conductivity calculated using ionization from 222Rn (averaged over January 2005). Reprinted from [84], with permission from Karagodin A.V.
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Figure 22. Fair-weather current density calculated for June 2005. Red line - fair-weather current with taking into account only GCR ionization effect. Black line – fair-weather current with taking into account radon and GCR effect. The figure is modified from [14].
Figure 22. Fair-weather current density calculated for June 2005. Red line - fair-weather current with taking into account only GCR ionization effect. Black line – fair-weather current with taking into account radon and GCR effect. The figure is modified from [14].
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Table 1. Uranium decay products.
Table 1. Uranium decay products.
nuclide historic name (short) historic name (long) decay mode half life MeV product of decay
222Rn Rn Radon α 3.8235 d 5.590 218Po
220Rn Tn Thoron α 55.6 s 6.4047 224Ra
219Rn An Actinon α 3.96 s 6.946 223Ra
218Po RaA Radium A
Polonium		 α
β− 		3.10 min 6.1150.265 214Pb218At
218At Astatine α
β−		 1.5 s 6.8742.883 214Bi218Rn
218Rn α 35 ms 7.263 214Po
214Pb RaB Radium B β− 26.8 min 1.024 214Bi
214Bi RaC Radium C β α− 19.9 min 3.2725.617 214Po210Tl
214Po RaC' Radium C' α 0.1643 ms 7.883 210Pb
Table 2. Radon concentration values in Bq⋅m−3: average (Av) and relative standard deviations (RSD) in percentage (Av ± RSD (%)), maximum (Max) and minimum (Min)values obtained at 70 cm under surface (−70cm), at the surface (0cm) and at 100 cm height in air [92].
Table 2. Radon concentration values in Bq⋅m−3: average (Av) and relative standard deviations (RSD) in percentage (Av ± RSD (%)), maximum (Max) and minimum (Min)values obtained at 70 cm under surface (−70cm), at the surface (0cm) and at 100 cm height in air [92].
Location -70 cm 0cm 100 cm
Max Min Av ± RSD Max Min Av±RSD Max Min Av ± RSD
Cuernavaca Bqm-3
% original soil radon		 4813
100		 873
100		 2249 ±71
100		 288
6		 86
10		 179±48
8		 37
0.8		 20
2.3		 29 ± 6
1.3
Las Cruces Bqm-3
% original soil radon		 3197
100		 500
100		 1574 ±64
100		 159
5		 59
12		 106 ± 33
6.7		 18
0.5		 17
3.4		 18±4
1.1
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