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Environ Eng Res > Volume 28(1); 2023 > Article
Thuamthansanga, Sahoo, and Tiwari: Estimation of 238U and 232Th in soil and water of prominent fault region of Mizoram

Abstract

The study investigates radon and thoron concentrations in soil and water at one of the most prominent faults in Mizoram state, India. The obtained isotope pair data were consequently used for estimating the uranium and thorium content of the region. An indigenously developed and calibrated ZnS(Ag) alpha based scintillation counter (Model: SMARTRnDuo, BARC, India) was deployed for assessing radon and thoron data. Thoron concentration was found to be higher than radon concentration in both soil and water. The isotope pair and their parent nuclei concentration in water were found to be higher than in soil. The uranium and thorium content in soil were estimated to be 17.5 and 22.6 Bqkg−1 respectively, but in water, they were estimated to be 41.6 and 124.8 Bqkg−1 respectively. Comparisons with global averages were also presented in detail and no radiological risk has been observed for the region. A continuous radon data was generated at Mizoram University, Aizawl, Mizoram (India) for cross-analysis with data at Mat fault. It was observed that radon data of the two locations behave similarly during geophysical phenomena, indicating that the region was seismically active. No geophysical properties of thoron were observed.

1. Introduction

In the early 1900s, exposure to radioactivity was considered to enhance good health and people were reported to drink radon rich water for that purpose [1]. It was not until the 1970s that a quantitative risk estimate for lung cancer could be established among underground miners, after diagnosing an overwhelming number of lung cancers among them [2]. Uranium mining became intensified during the 1940s–1960s in countries like Africa, Canada and the United States of America, where radon serve as a useful pathfinder to its parent nuclei [13]. In India, it was started in 1948 and in north-eastern India (NE India) in particular in 1950 [4]. No economic concentration was found in northeast India except in Meghalaya state [4]. Hence our study was confined to assessing the radioactivity profile of the region and geophysical properties of radon and thoron. Besides as a tracer to uranium deposit, some other discipline where radon has been studied includes earthquake prediction [511], tracer to a hidden fault [12, 13], health hazard [14, 15] etc. Radon is a radioactive noble gas and has three naturally accruing isotopes, Radon (222Rn: T1/2, 3.825 d, decay series of 238U), Thoron (220Rn: T1/2, 55.6 s, decay series of 232Th) and Actinon (219Rn: T1/2, 3.6 s, decay series of 235U). In the earth crust radon is produced by the process of emanation and then gets transported towards the surface by diffusion or advection [16, 17]. The latter two isotopes often get neglected in most studies due to their small half-life. As mentioned above approximately after the 1970s onwards (after intensified mining era) most radon and uranium studies were found to concentrate on reporting radiological background, risk estimation and their correlation [1826]. Several relevant papers estimating radon and uranium concentrations of a particular region or site were reported till date [2736]. Recent reporters like Bezuidenhout [27] performed mapping and estimation of radon risk in South Africa while Novikov et al. [28] reports radon and uranium content in groundwater of the Zaeltsovsky–Mochishch zone of Novosibirsk in Russia. Wang et al. [29] directly measure uranium content in order to map radon hazard region in Norway. Authors like Liu et al. in southern China [30], Fuhrmann et al. [31] in the USA and Yong et al. [32] in northwest China measured radon exhalation rate and fluxes from uranium tailing pond to determine whether it lies within world natural background. All other papers [3336] mainly focus on reporting radon and uranium concentrations in soil, water and air. The continuous measurement suggested that there was still a region whose radiation background was unknown for contributing or comparison to the global average. Approximately all recent studies on radon and uranium measurement focused on assessing their background radiation, health risk and comparison with the reported global average. Although extensive work has been done; radon monitoring and especially estimating its parent nuclei still has uncertainties reflected as well in the above recent reports [2736]. The main problem is that the radon exhalation process was influenced by several external factors such as physicochemical features of the soil (such as grain size, density, porosity, permeability etc), geophysical factors (morphology and movement of groundwater and nature of aquifers) and meteorological factors (temperature, pressure, humidity, rainfall and wind speed) [16, 17, 3739]. Without preventing or removing the external influence, the obtained result may be ambiguous and will lead to the wrong estimation. To overcome and minimize the above obstacles in the present study, the radon and thoron data were generated in-situ online in a fault (Mat fault). The selected fault was located in Serchhip District, Mizoram (India) and is the most prominent and active fault within the state [5]. Due to its loose soil formation, fault line provides an easy pathway for radon and hence its concentration was found to be more abundant and stable unless perturbed by external factors. Hence it serves as a suitable location for measuring radon data where problems due to physicochemical and geophysical features of the soil may be neglected as the radon concentration is abundant to detect and uniform unless perturbed. To handle the meteorological problem, the meteorological data were as well measured in-situ online along with the radon data. A ZnS(Ag) alpha scintillation counter named SMARTRnDuo developed and calibrated by Bhabha Atomic Research Centre, Mumbai (India) was used for measuring the radon and thoron data [16, 17, 37]. It was equipped with a device for recording temperature, humidity and pressure data automatically. Hence it will help in identifying the extent to which the isotope pair data were affected by meteorological factors thereby enhancing the accuracy of the result. To avoid meteorological influences, the data were sampled on a clear sunny day when the weather is calm. The precautions taken seem to be effective as no strong correlation was observed between radon and meteorological factors. A few literatures [4042] based on passive mode sampling were available from the northern and southern part of the state but none in the central region especially from a fault line. Besides the data were in-situ online and represent the real-time nature of radon which the passive method was incapable of. The obtained results were compared to those global averages given by UNSCEAR [43, 44] and IAEA [45, 46] and the estimated values were presented in detail. The study was extended to analyzing geophysical properties of the generated radon and thoron data. Since the isotope pair data were generated in an active fault they were also suitable for identifying geophysical phenomena of the region like seismic activity. The first officially recorded radon anomaly before seismic activity was in 1966 before the Tashkent earthquake in Russia [37]. This observation ignites optimism among researchers to predict earthquakes by monitoring radon anomaly and soon it becomes a global phenomenon that continues till dates [511, 3739, 4753]. Uncertainty and inaccuracy in prediction remained the main issue of present-day researchers, which is very well visible from some recent reports [3739, 4753]. Chowdhury et al. [38] and Sahoo et al. [39] from the east and north India, respectively observed unpredicted earthquakes and false radon anomaly peaks, though they observed a positive correlation between them. Authors like Omori et al. [47], Schekotov et al. [48] and Mohammad et al. [49] fails to correlate radon anomaly with parameters like tidal loading and atmospheric electromagnetic radiation before earthquakes. Quiescence in radon concentration was also observed by Muto et al. [50] before the 2018 Osaka earthquake. A positive correlation between radon anomaly and earthquakes was also recently reported by some authors [5153], where analytical methods like chaos method, decomposition methods, machine intelligence and stacking methods were successfully used to identify the radon anomaly. From the above discussion, existence of a causal relationship between radon anomaly and earthquakes might be undeniable but the results are still with uncertainties and controversial. Developing a network of monitoring stations with continuous online data was one of the methods suggested to reduce such uncertainties [39, 47, 50, 52]. In a hope to contribute to such network of monitoring stations, the authors generate in-situ online radon data (15 min cycles) at Mizoram University (MZU), Aizawl, Mizoram (India). This was then correlated with the isotope pair data of Mat fault in order to observe their geophysical characteristic and seismicity of the region. The correlation analysis shows that radon data of the two locations fluctuated in the same manner during geophysical phenomena. This shows that Mizoram University lies within an active region where radon data generated within its campus can suitably be used for seismic precursory studies in future. No geophysical properties of thoron were observed. A limited number of literatures [5, 12, 5459] describing seismicity of the region were also available, which totally based on passive sampling method. Since the data were passive in nature with a large sampling frequency (15, 30 days), it lacks behind the real-time nature which is vitally important in earthquake prediction studies for accuracy. But in the present study radon data was monitored online and get updated after every 15 min which enables us to observe the causal relationship between radon and geophysical phenomena in real-time and with high accuracy.

2. Materials and Method

2.1. Geology of the Study Area

Mizoram belongs to the Surma basin which is part of the greater Bengal Basin. The basin is an area of folded sediment, wider to the north and narrower to the south with many NE-SW and NW-SE trending lineaments/faults. The NE-SW Syllhet fault running from near Dhaka (Bangladesh) demarcates the northwestern boundary of the Surma basin while the Gumti fault cut across the basin (Fig. 1(b)). Among the NW-SE trending faults, Mat fault and Tuipui fault lies within Mizoram state at the southern part of the basin (Fig. 1(b)). Mat fault is one of the most prominent faults in Mizoram which obliquely cut across the Indo-Burmese Arc (NS-trend) and is traceable across the entire state from satellite and Google maps [5, 54]. According to the seismic hazard zonation map of India, northeast India lies at zone V, the highest seismic activity and is one of the six most seismically active regions of the world along with Taiwan, Japan, Mexico, California and Turkey [55]. The northeast region based on the distribution of its epicentres, fault plane solutions and geotectonic features was divided into five seismotectonic zones (Fig. 1(b)) [55]. They are as follows, (i) Eastern Himalayan collision zone (zone A) (ii) Indo-Myanmar subduction zone (zone B), (iii) Syntaxis zone of Himalayan arc and Burmese arc (Mishmi Hills, zone C) (iv) Plate boundary zone of the Shillong plateau and Assam valley (zone D) and (v) Bengal basin and plate boundary zone of Tripura-Mizoram fold belt (zone E). In the present study, Mat fault; being the most prominent fault within the state was selected for generating in-situ data (Fig. 1(b) and Fig. 1(c)).

2.2. Procedure for Measurement

An indigenously developed and calibrated ZnS(Ag) alpha based scintillation counter named SMARTRnDuo (Model: SMARTRnDuo, BARC, Mumbai, India) was deployed for measuring radon and thoron data [16, 17]. The monitor is operable in three modes; radon, thoron and alpha modes. Only radon and thoron modes were applied in the present study. In thoron mode, the monitor retrieves counts of radon, thoron and additive concentration of the isotope pair. But in radon mode, the monitor gives counts and concentration of radon only as thoron is prevented from entering the scintillation cell. The obtained isotope pair data counts were converted into their respective concentrations using Eq. (1) [46].
(1)
CRn=C3EVe-λt
where C is the net count rate (count per second, s−1) of 222Rn or 220Rn, E is the efficiency of counting, V is volume of the sampler (m3), λ is the decay constant of 222Rn or 220Rn, t is the time delay post-sampling (s) and 3 represent the three alphas in the respective decay chain of 222Rn and 220Rn.
The site was selected in such a way that the data might be suitable for approximating the isotope pair concentrations of the region and as well for revealing their geophysical properties. For that, 9 location spots were selected across and along Mat fault from where radon and thoron data were generated (Fig. 1(c)). To obtain radon flux, the instrument was operated in radon mode using an accumulator chamber (2.1 × 10−4 m3) with 15 min cycle for 3 h in each of the spots (Fig. 2(a)). The accumulated radon concentration of each spot was least fitted and then averaged out to get the radon concentration built-up rate C(t). Then it was subsequently substituted in Eq. (2) to get radon flux of the region [46].
(2)
C(t)=C0+kAVft
where C0 is the initial concentration (Bqm−3), k is the factor by which the initial flux drops while the gas inside the accumulator passes through state of uniform mixing prior to deployment to the state of diffusive mixing post to deployment, A is the surface area of the opening of the accumulator (m2), V is the effective volume of the sampling device (m3), f is the flux of radon (Bqm−2s−1) and t is the measurement time (s).
To obtain thoron flux, first, we need the thoron equilibrium concentration. Using the thoron equilibrium concentration, thoron flux of the soil-air interface will be estimated. For that, the accumulator chamber was placed in one of the sampling spots and connected to the monitor. Now the monitor was operated in thoron mode with 15 min cycle for 1 h at each spot (Fig. 2(b)). Average of the last three readings from all the spots were taken as the thoron equilibrium concentration of the region. The first reading of each spot has been neglected to avoid corruption in the data due to external sources. The equilibrium concentration was then substituted in Eq. (3) to estimate thoron flux of the region [46].
(3)
f=CeqVλA
where Ceq is the 220Rn equilibrium concentration (Bqm−3), V is the effective volume of the sampling device (m3), λ is the thoron decay constant and A is the surface opening area of the accumulator (m2).
To estimate the 238U concentration, first, the radon production rate (Jm) was obtained from the soil sample. For that soil samples from the 9 selected spots (Fig. 1(c)) were collected with a frequency of once a month, between May, 2018 and October, 2018. The soil samples were put in a metal cylinder (5.0 × 10−4 m3) called the mass exhalation chamber. The detector probe of the monitor was then mounted on it using the provided slide tight mechanism which prevents it from leakage (Fig. 2(c)). Now, the build-up radon concentration was monitored with 60 min cycle for 24 h and was least fitted to obtain the radon build-up rate C(t). The build-up rate was then subsequently substituted into Eq. (4) to obtain the radon mass exhalation rate of the region. The mass exhalation rate was further substituted into Eq. (5) for retrieving 226Ra content of the soil samples [46].
(4)
C(t)=(JmMV)t+C
where Jm is the radon mass exhalation rate (Bqkg−1s−1), M is mass of the soil sample (kg), V is the volume of the mass exhalation chamber (m3) and C0 is the radon concentration at t = 0.
(5)
Jm=REλ
where R is 226Rn content of the soil in Bqkg−1, E is the emanation coefficient of 222Rn (0.1–0.3 in soil) [46], λ is the radioactive decay constant of 222Rn.
On the other hand, the 232Th content of the soil was estimated using equation (6) after substituting the thoron flux from Eq. (3) [46].
(6)
f=λLRρE
where f is the 220Rn flux at the soil-air interface, λ is the 220Rn decay constant, L is the diffusion length of 220Rn in soil (0.013 m) [46], R is the 224Ra content in the soil, ρ is the density of the soil matrix and E is the emanation coefficient of 220Rn (0.14) [46].
For assessing radon and thoron data from water, all water sources near the vicinity of the above 9 spots were located. A total of 5 such water spots were selected for a sampling spot. The water samples were collected in a glass bottle (2.2 × 10−4 m3) attached with a bubbler. For this, the glass bottle containing the water sample was connected to the monitor in place of the accumulator as shown in Fig. 2(a). The pump was then turned ON for 3 min such that it may cause bubbling in the sample water through the bubbler. These actions will push most of the radon gas dissolves in the water into the scintillation cell through the connecting tube. After that, the monitor was run in radon mode for 1 h with 15 min cycle. The first reading was discarded to avoid corruption in the data. Averages of the last three readings were taken as radon concentrations of the sample water. The exact same procedure was followed for retrieving thoron concentration in water except that the monitor was operated in thoron mode.
The obtained radon and thoron concentrations were then substituted in Eq. (7) for retrieving 238U and 232Th content of the water, respectively [46].
(7)
E=VCMR
where E is the radon or thoron emanation coefficient, V is the effective volume of the sampling device (m3), C is the radon or thoron concentrations (Bqm−3), M is the total mass of the sample (kg) and R is the 226Ra or 224Ra content of the sample water (Bqkg−1). Since, the 226Ra and 224Ra are in equilibrium concentrations with their parent nuclei, they may be used for depicting 238U and 232Th concentrations of the region, respectively.
To study radon and thoron anomalies due to geophysical phenomena, data of the isotope pair were continuously generated with 15 min cycle between January, 2017 and March, 2017. An accumulator chamber of 5.0 × 10−4 m3 and a soil probe of length 5 cm were connected to the SMARTRnDuo in a closed-loop manner to sample the sub-soil sample gas (Fig. 2(a)). For sampling with a soil probe, the accumulator chamber shown in Fig. 2(a) was replaced with a soil probe of length 5 cm. The sample gas was sampled for 47 days and 33 days using the soil probe and an accumulator chamber, respectively during the said period. The surface radon and thoron gases were drawn into the scintillation cell by the inbuilt pump at the rate of 5–7 L/min for 5 min for each 15 min cycle. During the 5 min sampling, counting of the alpha particles was simultaneously carried out by the monitor. The sample gas was drawn in through a progeny filter preventing progeny of both the gases and trace gases (Ch4, Co2 etc.) from entering into the scintillation cell. Hence the 5 min simultaneous sampling and counting attributes to the sum of radon and thoron concentration of the sample gas. The following 5 min was delayed such that the short-lived (55.6 s) thoron may decay out. After that, counting of alpha particles was resumed for another 5 min, which attributes to the radon concentrations and some marginal long-lived alpha particles. Subsequently, the thoron count was obtained by subtracting the last 5 min counts from the first 5 min counts. In this manner, the radon and thoron data were continuously monitored for 24 h [16, 17].

3. Results and Discussion

3.1. 238U, 232Th, 222Rn and 220Rn Content of the Region

The radon and thoron concentrations of the soil were found to be 5,649.4 and 11,858.2 Bqm−3, respectively while their concentrations in water were observed to be 7,557.0 and 12,091.2 Bqm−3, respectively (Table 1). At the soil-air interface, radon and thoron fluxes were found to be 0.016 and 1.25 Bqm−2s−1, respectively with radon mass exhalation rate of 7.36 × 10−6 Bqkg−1h−1 (Table 1). The 238U and 232Th content of the soil were estimated to be 17.5 and 22.6 Bqkg−1, respectively (Table 1). On the other hand, the 238U and 232Th content in water were estimated to be 41.6 and 124.8 Bqkg−1, respectively (Table 1). When compared to that of the worldwide average given by UNSCEAR and IAEA [4346] the obtained isotope pair concentrations in soil and water, fall within the range (103–105 Bqm−3in soil) given by IAEA [46]. Also, the radon and thoron concentrations in water were respectively higher than their concentrations in soil (Table 1). But the thoron concentration in both the media was higher than its isotope pair (Table 1). The isotope pair fluxes were also in close agreement with the worldwide average (15–20 mBqm−2s−1 for radon and 1–1.9 Bqm−2s−1 for thoron) given by UNSCEAR [44]. The 238U and 232Th content of the soil was lower than that of the worldwide average (35 and 30 Bqkg−1 for 238U and 232Th, respectively) given by UNSCEAR [43]. But their concentrations in water were higher than the reported average [43]. The higher 232Th content reflects the observed higher concentrations and flux of its daughter nuclei to its isotope pair in both the media. Again when compared to that of the critical value set by IAEA [45] (1,000 Bqkg−1) no radiological hazards from 238U and 232Th were observed in the region. The obtained result was in close agreement with the previous studies [4042] of the region and some recent reports from southern India [13, 35] where no radiological risk due to radon and thoron were observed. It also confirmed that radon and thoron data of fault and non-fault areas were in close agreement in the present study region.

3.2. Correlation of Radon with Geophysical Phenomena

To study the geophysical properties of radon and thoron, a 4 m3 shed enclosing the sampling spot was erected at Mizoram University, Aizawl, Mizoram (India). The dimension of the shed was selected in such a way, to take care, the 1 m diffusion length of radon from all sides. The soil probe or accumulator chamber was placed at the centre of the shading and connected to the monitor as mentioned in section 2. The main purpose of the shading was to minimize the meteorological influence on radon flux, such that any anomaly of radon data may be regarded as due to geophysical phenomena. A statistical t-test (at 95% confidence level) was performed to observe the meteorological influence on the isotope pair data and masking effect among themselves. Rainfall and wind speed data which the monitor fails to record were obtained from the regional meteorological centre IMD, Guwahati, India. Details of the correlation are given in Table 2 and depicted in Fig. 3. From Table 2 the weak positive correlation between rainfall and wind speed (r = 0.27, p = 0.016) indicates that rainfall was mostly accompanied by wind. The positive correlation between rainfall and humidity (r = 0.37, p = 0.0007) also shows that wind and humidity were the direct results of rainfall. The weak reverse correlation between rainfall and pressure (r = −0.23, p = 0.04) may be due to the accompanying wind reducing pressure at the ground surface. The weak reverse correlation between temperature and humidity may also be regarded as rain and wind being its accompanying factors which consequently reduced the air temperature. Thoron shows a negative correlation with rainfall (r = −0.35, p = 0.0014) and humidity (r = −0.33, p = 0.003), a positive correlation with pressure (r = 0.49, p = 4 × 10−6) and has no significant correlation with temperature and wind speed. The reverse correlation between thoron, rainfall and humidity may be due to the capping effect where wet soil obstructs thoron from escaping towards the earth surface [58]. The positive correlation between thoron and pressure seems ambiguous because radon’s poor atmospheric gas is pushed into the upper layer of the earth and hence diluting its concentrations during raise in pressure [12]. At the same time, no significant correlation was observed between radon and all the meteorological factors. It may also be noted that most of the correlations were weak and negligible. The reason behind the ambiguous correlation may be attributed to the provided shading to the monitoring station which prevents the monitoring gas from interference by meteorological factors. This is the nature and condition of the monitored gas that we required because in this condition we can assume that radon and thoron were free of meteorological influence. In such conditions, we can safely assume that any radon and thoron anomaly was due to geophysical phenomena.
A total of 7,160 data were recorded for each isotope between January, 2017 and March, 2017. Thoron data was neglected for geophysical studies as the data remain constant throughout the measuring period and have no geophysical meaning (Fig. 4(a) and (b)). Exclusively average of all the diurnal and nocturnal radon data was taken after removing the peak period data. This average value was taken as the base count (CB) of radon for the study period (Fig. 4(c) and (d)). And it was assumed as the count of radon data in the absence of any geophysical or meteorological perturbation. In the real-time data curve, the CB line was represented by a horizontal line passing through zero (Fig. 4(c) and (d)). The rise of any radon counts was a measure from this value (CB).
Now the radon anomaly peaks were identified using the mean plus ‘n’ times standard deviation (SD) method (where n = 1, 2, 3,...). The radon variation was considered as an anomaly peak when it crosses +2.6SD or the anomaly line (AL) (Fig. 4(c) and (d)). This turned out to be 1.1 times and 3 times the CB (xmean) for radon data at 5 cm depth and soil-air interface respectively (Fig. 4(c) and (d). Using the above method 21 radon anomaly peaks were observed during the period. Seismic data of the study period were assessed from USGS archive (United State Geological Survey, https://eartquake.usgs.gov/earthquakes/map/) and were selected using Dobrovolsky and Fleihcher criteria [55] given by Eq. (8) and (9), respectively.
(8)
D=100.43MkmD=100.813M16.6km   for   M3
(9)
D=100.48M1.66km   for   M3
Using Eq. (8) and (9) 46 earthquakes were selected and all of them were located within 1,000 km radius from the monitoring station. Details of the selected earthquakes were given in Table 3 and inserted as vertical lines in Fig. 4. Upon analysis, no post-cursory radon peak was observed but only a precursory one. The recorded earthquakes have an occurrence time range of 0:39:02 min – 8 days after radon anomalies. Out of the 46 earthquakes 23 of them occur within 1 day from the anomaly peaks; 8 within 2 days; 3 within 3 days; 5 within 4 days; 2 within 5 days; 1 within 6 days; 2 within 7 and 8 days from the peaks (Table 3). In general, most earthquakes occur close to the anomaly peaks with an average of 2.4±2.3 days after the peaks. In other words, it may be stated as 50% of the earthquakes occur within 2 days after the radon anomaly peaks; 39% of them between 3–5 days after the anomaly peaks and 11% of them after 5 days from the anomaly peaks. Thoron data, on the other hand, shows no anomaly peaks. Hence correlation of thoron with seismic activity was neglected in the study.
The observation supports and agrees well with the experimentally demonstrated analytical model of Sahoo and Gaware [60] at the sub-soil. Their model suggested that due to relatively low radon concentration at the sub-soil, perturbation in its concentration due to external source was more pronounced compared to that of the deep soil where it attains asymptotic value. The study affirmed the significance of monitoring sub-soil radon anomaly as premonitory gas to seismic activity within 1,000 km radius from the monitoring site. Correlations of air and soil radon data with other parameters like tidal loading, atmospheric electromagnetic radiation, meteorological etc for seismic precursor were recently proposed by some others [38, 39, 4753, 59]. The study agrees well in observing radon anomaly especially to those authors [38, 39, 52, 59] who also observed radon anomaly at 2σ above the mean concentrations.
To compare the in-situ data at Mat fault and the continuous data at Mizoram University, radon data was generated with a frequency of once a month between May, 2018 and October, 2018 at Mat fault. At the same time, radon data was continuously generated at Mizoram University with 15 min cycle. As positive correlation between radon anomaly at Mizoram University and earthquakes was observed in the above section. Any further radon anomaly observed at Mizoram University will be considered as a result of geophysical phenomena. Now the in-situ online data of Mat fault were categorized into the anomaly and non-anomaly period data based on the continuous data of Mizoram University. For this, mean plus ‘n’ times standard deviation was applied for identifying radon anomaly as mentioned above. And the radon anomaly was observed at 2σ from the mean. Such that whenever radon data was generated at Mat fault by the time the continuous data was above 2σ, the in-situ data were taken as anomaly period data otherwise non-anomaly period data. In this manner, radon data generated on August 29, 2018 and October 09, 2018 were taken as anomaly period data. While that of May 30, 2018; June 28, 2018; July 27, 2018 and September 25, 2018 were considered as non-anomaly period data (Fig. 5(a)). In Fig. 5(a) the anomaly period was indicated by a vertical red line. Now average of the anomaly period and non-anomaly period data were taken for each and every spot. It was observed that in 60% (3 out of 5 spots) of the measuring spots soil radon data was higher during anomaly period than that of the non-anomaly period (Fig. 5(c)). On the other hand, in all the measuring spots (100%) the radon counts in water were higher during anomaly period to that of the non-anomaly period (Fig. 5(d)). Hence it can be concluded that radon data at the continuous monitoring station and the fault varies in similar manner during geophysical phenomena. Such that, the continuous data of Mizoram University may safely be adopted for geophysical studies in future. Finally, it concluded that radon anomaly can be easily detected in Mizoram University because the university was located in fault region.

4. Conclusions

The study shows that radon and thoron concentrations and fluxes of the region were in agreement with the worldwide averages reported by IAEA and UNSCEAR [4346]. The thoron concentration in soil and water were found to be higher than radon. The 238U and 232Th contents of the soil were observed to be lower than the worldwide average [43]. However, their concentrations in water were observed to be higher than the worldwide average [43] but far below the critical value given by IAEA [43] respectively. Hence, no radiological risk due to the isotope pair and their parent nuclei was observed for the region. The study also highlights the advantages of monitoring sub-soil radon data as a premonitory gas to nearby earthquakes. The seismic activities were found to succeed the radon anomaly peaks within 2.4±2.3 days on average and with a range of 00:39:02 min–8 days. It has been observed that 50% of the earthquakes occurred within 2 days from the anomaly peaks, 39% between 3–5 days from the peaks and 11% after 5 days from the anomaly peaks. The inter-correlation analysis also shows that data generated at the fault region (Mizoram University) could also be used for seismic study, avoiding the hardship in assessing the fault line. Despite the short study period, the observation clearly reveals that the region was seismically active and suitable for monitoring radon data as a forecasting gas. Although this region has been declared as the second-highest seismically active region of the world, Mizoram in particular has no continuous online data in the past. Hence, the author hopes that the present study will serve as a significant baseline data for future reference. We also hope that the obtained result might conveniently be replicated to other similar tectonic zones around the globe.

Acknowledgment

This work was supported financially by DAE-BRNS, BARC, Mumbai, India [Sanction Order No.:36(4)/14/66/2014-BRNS/36024 Dt.26.02.2016. This article was presented at 2nd Annual Convention of North East (India) Academy of Science & Technology (NEAST) held on 16–18 November 2020 in Mizoram, India.

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

Authors Contributions

T.T.T.S (PhD student) carried out the fieldwork, performed data analysis, wrote and revised the manuscript. B.K.S (Scientist) provided research ideas and designs the experiment. R.C.T (Professor) guide the research work and acquired the research grant.

References

1. Ball TK, Cameron DG, Colman TB, Roberts PD. Behaviour of radon in the geological environment: a review. Q J Eng Geol Hydrogeol. 1991;24:169–182. https://doi.org/10.1144/GSL.QJEG.1991.024.02.01
crossref

2. Swedjemark GA. The history of radon from a Swedish perspective. Radiat Prot Dosim. 2004;109:421–426. https://doi.org/10.1093/rpd/nch318
crossref

3. Khattak NU, Khan MA, Ali N, Abbas SM. Radon Monitoring for geological exploration: A review. J Himal Earth Sci. 2011;44:91–102.


4. Gupta R, Sarangi AK. Emerging trend of uranium mining: The Indian scenario. In : International Symposium on Uranium production and raw materials for the nuclear fuel cycle-supply and demand, economics, the environment and energy security; 20–24 June 2005; Vienna. p. 1563–0153.


5. Jaishi HP, Singh S, Tiwari RP, Tiwari RC. Radon and thoron anomalies along Mat fault in Mizoram, India. J Earth Syst Sci. 2013;122:1507–1513. https://doi.org/10.1007/s12040-013-0361-4
crossref

6. Shapiro MH, Melvin JD, Tombrello TA, Mendenhall MH, Larson PB, Whitcomb JH. Relationship of the 1979 Southern California radon anomaly to a possible regional strain event. J Geophys Res Solid Earth. 1981;86:1725–1730. https://doi.org/10.3133/ofr81449_vol2
crossref

7. King CY. Gas geochemistry applied to earthquake prediction: An overview. J Geophys Res Solid Earth. 1986;91:12269–81.
crossref

8. Igarashi G, Wakita H. Groundwater radon anomalies associated with earthquakes. Tectonophysics. 1990;180:237–254. https://doi.org/10.1016/0040-1951(90)90311-U
crossref

9. Yasuoka Y, Shinogi M. Anomaly in atmospheric radon concentration: a possible precursor of the 1995 Kobe, Japan, earthquake. Health Phys. 1997;72:759–761. https://doi.org/10.1097/00004032-199705000-00012
crossref pmid

10. Zmazek B, Živčić M, Vaupotič J, Bidovec M, Poljak M, Kobal I. Soil radon monitoring in the Krško Basin, Slovenia. Appl Radiat Isot. 2002;56:649–657. https://doi.org/10.1016/s0969-8043(01)00255-x
crossref pmid

11. Vaupotič J, Riggio A, Santulin M, Zmazek B, Kobal I. A radon anomaly in soil gas at Cazzaso, NE Italy, as a precursor of an ML= 5.1 earthquake. Nukleonika. 2010;55:507–511.


12. Singh S, Jaishi HP, Tiwari RP, Tiwari RC. Variations of soil radon concentrations along Chite Fault in Aizawl district, Mizoram, India. Radiat Prot Dosim. 2014;162:73–77. https://doi.org/10.1093/rpd/ncu221
crossref

13. Nalukudiparambil J, Gopinath G, Ramakrishnan RT, Surendran AK. Groundwater radon (222 Rn) assessment of a coastal city in the high background radiation area (HBRA), India. Arab J Geosci. 2021;14:1–7. https://doi.org/10.1007/s12517-021-07082-7
crossref

14. Auvinen A, Mäkeläinen I, Hakama M, et al. Indoor radon exposure and risk of lung cancer: a nested case-control study in Finland. J Natl Cancer Inst. 1996;88:966–972. https://doi.org/10.1093/jnci/88.14.966
crossref pmid

15. Baysson H, Tirmarche M, Tymen G, et al. Indoor radon and lung cancer in France. Epidemiology. 2004;15:709–716. https://doi.org/10.1097/01.ede.0000142150.60556.b8
crossref pmid

16. Thuamthansanga T, Sahoo BK, Tiwari RC, Sapra BK. A study on the anomalous behaviour of Radon in different depths of soil at a tectonic fault and its comparison with time-series data at a distant continuous monitoring station. SN Appl Sci. 2019;1:683
crossref

17. Thuamthansanga T, Tiwari RC, Sahoo BK, Datta D. Analysis of Meteorological Influence on Exhalation of 222Rn and 220Rn Gases at Mat Fault. USA: Nova Science Publishers, Inc.; 2020. p. 17


18. Singh NP, Singh M, Singh S, Virk HS. Uranium and radon estimation in water and plants using SSNTD. Nucl Tracks Radiat Meas. 1984;8:483–486. https://doi.org/10.1016/0735-245X(84)90147-9
crossref

19. Ramola RC, Singh S, Virk HS. Uranium and radon estimation in some water samples from Himalayas. Int J Radiat Appl Instrum Nucl Tracks Radiat Meas. 1988;15:791–793. https://doi.org/10.1016/1359-0189(88)90252-x
crossref

20. Heath MJ. Radon in the surface waters of southwest England and its bearing on uranium distribution, fault and fracture systems and human health. Q J Eng Geol Hydrogeol. 1991;24:183–189. https://doi.org/10.1144/GSL.QJEG.1991.024.02.02
crossref

21. Singh AK, Sengupta D, Prasad R. Radon exhalation rate and uranium estimation in rock samples from Bihar uranium and copper mines using the SSNTD technique. Appl Radiat Isot. 1999;51:107–113. https://doi.org/10.1016/s0969-8043(98)00152-3
crossref pmid

22. Denagbe SJ. Radon-222 concentration in subsoils and its exhalation rate from a soil sample. Radiat Meas. 2000;32:27–34. https://doi.org/10.1016/S1350-4487(99)00213-9
crossref

23. Charlton P, Kotrappa P. Uranium prospecting for accurate time-efficient surveys of radon emissions in air and water, with a comparison to earlier radon and He surveys. In : Proceedings of the 2006 International Radon Symposium; 17–20 September 2006; Kansas City. p. 1–9.


24. Siaka YT, Bouba O. Indoor radon measurements in the uranium regions of Poli and Lolodorf, Cameroon. J Environ Radioact. 2014;136:36–40. https://doi.org/10.1016/j.jenvrad.2014.05.001
crossref pmid

25. Thivya C, Chidambaram S, Thilagavathi R, et al. Occurrence of high uranium and radon in hard rock aquifers of South India-Evaluating the temporal and spatial trends. Groundw Sustain Dev. 2015;1:68–77. https://doi.org/10.1016/j.gsd.2016.01.003
crossref

26. Zhou Q, Liu S, Xu L, et al. Estimation of radon release rate for an underground uranium mine ventilation shaft in China and radon distribution characteristics. J Environ Radioact. 2019;198:18–26. https://doi.org/10.1016/j.jenvrad.2018.12.010
crossref pmid

27. Bezuidenhout J. Estimating indoor radon concentrations based on the uranium content of geological units in South Africa. J Environ Radioact. 2021;234:106647 https://doi.org/10.1016/j.jenvrad.2021.106647
crossref pmid

28. Novikov DA, Dultsev FF, Kamenova-Totzeva R, Korneeva TV. Hydrogeological conditions and hydrogeochemistry of radon waters in the Zaeltsovsky-Mochishche zone of Novosibirsk, Russia. Environ Earth Sci. 2021;80:1–1. https://doi.org/10.1007/s12665-021-09486-w
crossref

29. Wang Y, Brönner M, Baranwal VC, Paasche H, Stampolidis A. Data-driven classification of bedrocks by the measured uranium content using self-organizing maps. Appl Geochem. 2021;132:105074 https://doi.org/10.1016/j.apgeochem.2021.105074
crossref

30. Liu X, Li X, Lan M, Liu Y, Hong C, Wang H. Experimental study on permeability characteristics and radon exhalation law of overburden soil in uranium tailings pond. Environ Sci Pollut Res. 2021;28:15248–15258. https://doi.org/10.1007/s11356-020-11758-0
crossref

31. Fuhrmann M, Benson CH, Likos WJ, et al. Radon fluxes at four uranium mill tailings disposal sites after about 20 years of service. J Environ Radioact. 2021;237:106719 https://doi.org/10.1016/j.jenvrad.2021.106719
crossref pmid

32. Yong J, Liu Q, Wu B, Chen H, Feng G, Hu Y. Measurement and spatial distribution pattern of natural radioactivity in a uranium tailings pond in Northwest China. J Radiat Res Appl Sci. 2021;14:344–352. https://doi.org/10.1080/16878507.2021.1964314
crossref

33. Olsthoorn B, Rönnqvist T, Lau C, et al. Indoor radon exposure and its correlation with the radiometric map of uranium in Sweden. Sci Total Environ. 2021;151406 https://doi.org/10.1016/j.scitotenv.2021.151406
crossref

34. Baykara O, Dogru M. Measurements of radon and uranium concentration in water and soil samples from East Anatolian Active Fault Systems (Turkey). Radiat Meas. 2006;41:362–367. https://doi.org/10.1016/j.radmeas.2005.06.016
crossref

35. Suman G, Reddy KV, Reddy MS, Reddy CG, Reddy PY. Radon and thoron levels in the dwellings of Buddonithanda: a village in the environs of proposed uranium mining site, Nalgonda district, Telangana state, India. Sci Rep. 2021;11:1–9. https://doi.org/10.1038/s41598-021-85698-1
crossref pmid pmc

36. Sukanya S, Noble J, Joseph S. Factors controlling the distribution of radon (222Rn) in groundwater of a tropical mountainous river basin in southwest India. Chemosphere. 2021;263:128096 https://doi.org/10.1016/j.chemosphere.2020.128096
crossref pmid

37. Thuamthansanga T, Sahoo BK, Tiwari RC. Study of pre-seismic thoron anomaly using empirical mode decomposition based Hilbert–Huang transform at Indo-Burman subduction region. J Radioanal Nucl Chem. 2021;330:1571–1582. https://doi.org/10.1007/s10967-021-08001-6
crossref

38. Chowdhury S, Deb A, Nurujjaman M, Barman C. Identification of pre-seismic anomalies of soil radon-222 signal using Hilbert–Huang transform. Nat Hazards. 2017;87:1587–606. https://doi.org/10.1007/s11069-017-2835-1
crossref

39. Sahoo SK, Katlamudi M, Barman C, Lakshmi GU. Identification of earthquake precursors in soil radon-222 data of Kutch, Gujarat, India using empirical mode decomposition based Hilbert Huang Transform. J Environ Radioact. 2020;222:106353 https://doi.org/10.1016/j.jenvrad.2020.106353
crossref pmid

40. Zoliana B, Rohmingliana PC, Sahoo BK, Mishra R, Mayya YS. Measurement of radon concentration in dwellings in the region of highest lung cancer incidence in India. Radiat Prot Dosim. 2016;171:192–195. https://doi.org/10.1093/rpd/ncw056
crossref

41. Rohmingliana PC, Vanchhawng L, Thapa RK, et al. Measurement of indoor concentrations of radon and thoron in Mizoram, India. Sci Vis. 2010;10:148–152.


42. Chhangte LZ, Rohmingliana PC, Sahoo BK, Sapra BK, Pachuau Z, Zoliana B. Determination of Radon Mass Exhalation Rate in the Region of Highest Lung Cancer Incidence in India. Radiat Environ Med. 2019;8:113–117.


43. UNSCEAR. Sources and effects of ionizing radiation. New York: United Nation Press; 2000. p. 83


44. UNSCEAR. Ionizing Radiation: Sources and Biological Effects: Exposures to Radon and Thoron and their decay products. New York: United Nation Press; 1982. p. 141


45. IAEA. Application of the concepts of exclusion, exemption and clearance. Vienna: International Atomic Energy Agency; 2004. p. 1–20.


46. IAEA. Measurement and Calculation of Radon Releases from NORM Residues. Vienna: International Atomic Energy Agency; 2013. p. 1–74.


47. Omori Y, Nagahama H, Yasuoka Y, Muto J. Radon degassing triggered by tidal loading before an earthquake. Sci Rep. 2021;11:1–10. https://doi.org/10.1038/s41598-021-83499-0
crossref pmid pmc

48. Schekotov A, Hayakawa M, Potirakis SM. Does air ionization by radon cause low-frequency atmospheric electromagnetic earthquake precursors? Nat Hazards. 2021;106:701–714. https://doi.org/10.1007/s11069-020-04487-7
crossref

49. Mohammed DH, Külahcı F, Muhammed A. Determination of possible responses of Radon-222, magnetic effects, and total electron content to earthquakes on the North Anatolian Fault Zone, Turkiye: an ARIMA and Monte Carlo Simulation. Nat Hazards. 2021;8:1–20. https://doi.org/10.1007/s11069-021-04785-8
crossref

50. Muto J, Yasuoka Y, Miura N, et al. Preseismic atmospheric radon anomaly associated with 2018 Northern Osaka earthquake. Sci Rep. 2021;11(1)1–8. https://doi.org/10.1038/s41598-021-86777-z
crossref pmid pmc

51. Kamislioglu M. The use of chaotic approaches for the nonlinear analysis of soil radon gas (222 Rn) known as an earthquake precursor: finite ımpulse response (FIR) application. Arab J Geosci. 2021;14:1–6. https://doi.org/10.1007/s12517-021-06983-x
crossref

52. Dhar S, Randhawa SS, Kumar A, et al. Decomposition of continuous soil–gas radon time series data observed at Dharamshala region of NW Himalayas, India for seismic studies. J Radioanal Nucl Chem. 2021;327:1019–1035. https://doi.org/10.1007/s10967-020-07575-x
crossref

53. Mir AA, Çelebi FV, Rafique M, et al. Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function. Pure Appl Geophys. 2021;7:1–5. https://doi.org/10.1007/s00024-021-02736-9
crossref

54. Jaishi HP, Singh S, Tiwari RP, Tiwari RC. Temporal variation of soil radon and thoron concentrations in Mizoram (India), associated with earthquakes. Nat Hazards. 2014;72:443–454. https://doi.org/10.1007/s11069-013-1020-4
crossref

55. Jaishi HP, Singh S, Tiwari RP, Tiwari RC. Correlation of radon anomalies with seismic events along Mat fault in Serchhip District, Mizoram, India. Appl Radiat Isot. 2014;86:79–84. https://doi.org/10.1016/j.apradiso.2013.12.040
crossref pmid

56. Jaishi HP, Singh S, Tiwari RP, Tiwari RC. Analysis of soil radon data in earthquake precursory studies. Ann Geophys. 2014;57:0544 https://doi.org/10.4401/ag-6513
crossref

57. Jaishi HP, Singh S, Tiwari RP, Tiwari RC. Soil-gas Thoron Concentration Associated with Seismic Activity. Chiang Mai J Sci. 2015;42:972–979.


58. Singh S, Jaishi HP, Tiwari RP, Tiwari RC. A study of variation in soil gas concentration associated with earthquakes near Indo-Burma Subduction zone. Geoenviron Disasters. 2016;3:22 https://doi.org/10.1186/s40677-016-0055-8
crossref

59. Singh S, Jaishi HP, Tiwari RP, Tiwari RC. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies. Pure Appl Geophys. 2017;174:2793–2802. https://doi.org/10.1007/s00024-017-1556-4
crossref

60. Sahoo BK, Gaware JJ. Radon in ground water and soil as a potential tracer for uranium exploration and earthquake precursory studies. SRESA’s Int J Life Cycle Reliab Saf Eng. 2016;5:21–29.


Fig. 1
(a) Schematic map of India (b) Northeast India and its geotectonic setup (c) formation of 9 locations grid at Mat fault (edited after Jaishi et al. [56]).
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Fig. 2
Set up of SMARTRnDuo for sampling radon data (a) at the continuous monitoring station (b) at Mat fault and (c) at the Lab for mass exhalation.
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Fig. 3
Analysis plot between radon isotopes (radon and thoron) and Meteorological data recorded at Mizoram University, Aizawl (India), showing the extent of meteorological influence on the isotope pair data by displaying the correlation strength r (Pearson’s correlation coefficient value) between the isotope pair and each meteorological parameters. Plot of radon data versus (a) temperature (b) pressure (c) rainfall (d) humidity and (e) wind speed. Plot of thoron data versus (f) temperature (g) pressure (h) rainfall (i) humidity and (j) wind speed.
/upload/thumbnails/eer-2021-106f3.gif
Fig. 4
Sampling time versus 15 min cycle (a) thoron data at 5 cm depth, (b) thoron data at soil-air interface, (c) radon data at 5 cm depth and (d) radon data at soil-air interface, along with earthquakes data during the measurement period within 1,000 Km radius from the monitoring station (represented by vertical lines).
/upload/thumbnails/eer-2021-106f4.gif
Fig. 5
Plot of sampling dates versus (a) continuous radon data and (b) continuous thoron data between May, 2018 and October, 2017 at Mizoram University; (c) Average radon count in soil at Mat fault during anomaly period and non-anomaly period and (d) Average radon count in water at Mat fault during anomaly period and non-anomaly period.
/upload/thumbnails/eer-2021-106f5.gif
Table 1
Detail Estimated Value of 238U and 232Th Concentrations and Fluxes of Their Daughter Nuclei in Soil and Water at Mat Fault
Soil Water ratio



222Rn 220Rn ratio 222Rn 220Rn ratio 222RnW/222RnS 220RnW/220RnS
Bqm−3 5,649.4 11,858.2 2.1 7,557.0 12,091.2 1.6 1.3 1.02
Bqm−2s−1 0.016 1.25 79.3

Soil Water ratio



238U 232Th ratio 238U 232Th ratio 238UW/238US 232ThW/232ThS
Bqkg−1 17.5 22.6 1.3 41.6 124.8 3 2.4 5.5
Table 2
Details of Correlation between Meteorological Parameters and Radon and Thoron Data Recorded between January, 2017 and March, 2017
Temperature (°C) Pressure (Pa) Rainfall (mm) Humidity (%) Wind speed (Kmh−1) 222Rn 220Rn
Temperature (°C) r 0.12 −0.22 −0.26 0.08 −0.18 0.11
Sig. 0.30 0.05 0.02 0.46 0.11 0.33

Pressure (Pa) r −0.23 −0.34 −0.44 0.05 0.49
Sig. 0.04 0.002 5.9 × 10−5 0.66 4 × 10−6

Rainfall (mm) r 0.37 0.27 0.16 −0.35
Sig. 0.0007 0.016 0.15 0.0014

Humidity (%) r 0.30 −0.04 −0.33
Sig. 0.008 0.73 0.003

Wind speed (Kmh−1) r −0.03 −0.15
Sig. 0.8 0.17

222Rn r −0.03
Sig. 0.8
Table 3
Details of the Selected Earthquakes within 1,000 km Radius Using Debrovolsky and Fleischer Criteria [55] Represented by Vertical lines in Fig. 4
Dates of Radon Peak Dates of Earthquakes Lat, Long Depth (km) Magnitude Distance (km)
10-01-2017 07:47:00 11-01-2017 18:51:14 28.3, 94.1 10 3.3 527
12-01-2017 15:02:07 26.5, 95.4 75 4.7 413

15-01-2017 04:48:00 17-01-2017 20:52:16 27.6, 88.6 10 3.6 592
16-01-2017 08:03:23 18-01-2017 07:16:10 23.9, 93 27 3.7 39
17-01-2017 06:47:43 18-01-2017 08:33:17 24.5, 94.8 22 4.2 233

19-01-2017 08:47:23 19-01-2017 15:29:42 28.9, 88.2 10 4.1 729
19-01-2017 20:48:36 28.1, 92.6 40.21 4.1 494

20-01-2017 07:31:41 21-01-2017 03:19:18 19.8, 94.7 71.98 4.3 487

22-01-2017 08:19:33 23-01-2017 15:03:05 30.8, 78.2 10 3.5 1629
24-01-2017 17:55:38 25.6, 91.7 15 3.4 229
24-01-2017 23:44:29 25.5, 94.6 50 3.1 277
29-01-2017 14:39:04 24.8, 92.8 10 3.2 119
29-01-2017 03:06:08 25.9, 96.4 41.14 4.5 457
31-01-2017 10:58:42 26.4, 93.5 22 3.2 308
31-01-2017 11:46:04 31.5, 94.1 32.7 4.5 885

04-02-2017 06:18:26 05-02-2017 18:24:59 27.9, 93.8 10 3.8 477
08-02-2017 13:44:28 26.9, 92.9 15 3.6 353
08-02-2017 02:16:16 22.5, 94.7 121 4.5 253

10-02-2017 10:04:12 11-02-2017 23:42:51 23.9, 91.8 10 3.5 90

12-02-2017 08:56:11 12-02-2017 09:35:30 25.6, 90.8 10 4.5 280

16-02-2017 08:11:00 16-02-2017 20:43:10 26.2, 92.8 20 3.6 274
19-02-2017 00:23:42 26.6, 93 30 3.4 320

19-02-2017 04:19:45
23-02-2017 18:35:17
24-02-2017 01:46:07 23.7, 94.5 82 3.4 187
24-02-2017 03:09:16 27.3, 88.1 150 3.5 606
24-02-2017 17:32:49 24.1, 93.4 20 5.2 85
25-02-2017 02:30:44 28.7, 96 36 3.2 644
25-02-2017 05:30:44 28.7, 96 10 3.5 644
25-02-2017 12:32:19 24.1, 92.1 33 4 70
27-02-2017 09:07:47 27.3, 85.9 10 5 786
27-02-2017 09:51:45 27.3, 85.9 10 4.7 786

03-03-2017 00:42:58 04-03-2017 05:08:13 24.3, 94.2 70 3.5 168
04-03-2017 07:41:52 25.2, 94.6 70 5 255
04-03-2017 12:20:44 25.5, 90.9 10 3.3 265
06-03-2017 03:00:06 25.1, 95.1 89.95 4.3 294
07-03-2017 15:29:16 26.8, 90.5 30 4.1 405
07-03-2017 15:58:56 26.9, 89.1 10 4 503
09-03-2017 08:25:15 25, 94.2 36 4.1 210

13-03-2017 00:14:45 13-03-2017 19:49:06 17.3, 95.9 10 5.1 785
14-03-2017 22:09:56 27.2, 96.7 54.22 4.4 573

17-03-2017 05:24:42
18-03-2017 00:56:28
21-03-2017 07:03:31
21-03-2017 21:10:44 24.9, 92.1 37 3.9 142

22-03-2017 04:28:14
24-03-2017 02:35:17
25-03-2017 03:31:45
25-03-2017 07:35:55 25, 95.1 82 5 284
26-03-2017 05:10:34 25.8, 99.9 33.07 4.6 769
26-03-2017 05:25:06 25.9, 99.8 27.61 5 764
27-03-2017 03:12:09 27.3 88.6 10 4.6 569
27-03-2017 06:40:25 25.9, 100 10 4.1 779
28-03-2017 15:48:49 26.5, 93.5 10 3 319
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