Journal of Environmental Radioactivity 139 (2015) 149e153
Contents lists available at ScienceDirect
Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Normal seasonal variations for atmospheric radon concentration: a sinusoidal model Koseki Hayashi a, Yumi Yasuoka a, *, Hiroyuki Nagahama b, Jun Muto b, Tetsuo Ishikawa c, Yasutaka Omori c, Toshiyuki Suzuki c, Yoshimi Homma c, Takahiro Mukai a a b c
Kobe Pharmaceutical University, 4-19-1, Motoyamakitamachi, Higashinada-ku, Kobe 658-8558, Japan Tohoku University, 6-3, Aza Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan Fukushima Medical University, 1, Hikarigaoka, Fukushima 960-1295, Japan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 May 2014 Received in revised form 30 September 2014 Accepted 12 October 2014 Available online
Anomalous radon readings in air have been reported before an earthquake activity. However, careful measurements of atmospheric radon concentrations during a normal period are required to identify anomalous variations in a precursor period. In this study, we obtained radon concentration data for 5 years (2003e2007) that can be considered a normal period and compared it with data from the precursory period of 2008 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. Then, we established a model for seasonal variation by fitting a sinusoidal model to the radon concentration data during the normal period, considering that the seasonal variation was affected by atmospheric turbulence. By determining the amplitude in the sinusoidal model, the normal variation of the radon concentration can be estimated. Thus, the results of this method can be applied to identify anomalous radon variations before an earthquake. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric radon Seasonal variation Sinusoidal model Atmospheric turbulence
1. Introduction Radon (222Rn), a radioactive gas with a half-life of 3.82 days, is continuously emanated in soil, rocks and water by the radioactive decay of 226Ra. Radon is released from the ground into the atmosphere, where it is transported mainly by turbulent diffusion or convection. Precursory anomalous radon concentrations in air and groundwater have been reported prior to earthquakes (Yasuoka et al., 2010). However, normal variations of the radon concentration in air are required to identify anomalous changes. Atmospheric radon concentrations have been estimated from data collected by some radioisotope institutes around Japan for exhaust monitoring measurements (Hatanaka et al., 2013; Nakamura et al., 2013; Tajika et al., 2013). For example, the atmospheric radon concentration at Fukushima Medical University in Fukushima City was measured from 2003 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. Some of authors reported anomalous variations in the daily minimum atmospheric radon concentration recorded in Fukushima from 2008 until March 2011, which can be considered as the precursor period (Hatanaka
* Corresponding author. E-mail address: [email protected] (Y. Yasuoka). http://dx.doi.org/10.1016/j.jenvrad.2014.10.007 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
et al., 2013). These atmospheric radon variations might be an indication of changes in the radon concentration due to exhalation by the anomalous pre-seismic crustal strain. Ozawa et al. (2012) reported spatial patterns of post-seismic crustal deformation caused by earthquakes in 2008 in the Pacific Ocean off the coast of Fukushima and Ibaraki Prefectures and an earthquake in 2010 off the coast of the Fukushima Prefecture (Fig. 3 in their paper). Time-series variations of atmospheric CO2 concentration in Japan have been analysed as secular and seasonal variations using trigonometric functions (Nakazawa et al., 1997; Japan Meteorological Agency, 2011). Bossew et al. (2008) analysed seasonal variations by fitting a sinusoidal model to indoor radon concentrations. In our previous study, the estimation of seasonal variations by fitting a sinusoidal model to the atmospheric daily minimum radon concentration was performed at four sites, including the Fukushima Prefecture in Japan (Nakamura et al., 2013). The common pattern for daily variation is as follows: the concentration gradually increases from mid-afternoon (15:00) to the next morning (06:00) and suddenly drops at sunrise, as the atmospheric radon concentration is strongly affected by variations at the top of the mixing layer (Garzon et al., 1986; Miles and Algar, € rfer et al. 1988; Yasuoka and Shinogi, 1994). Moreover, Porstendo (1994) reported that within the daily variation of the radon
150
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153
concentration, the daily minimum radon concentration is unaffected by topography. Therefore, to analyze a representative variation in the radon concentration around a certain area, the daily minimum radon concentration should be used. Sesana et al. (2003) and Zhang and Guo (2011) proposed that seasonal variations in the minimum radon concentration are matched by variations at the top of the mixing layer. In winter, higher values of the minimum radon concentration appear to reflect the lowering of the top of the mixing layer; whereas, in summer, lower values of the minimum radon concentration appear to reflect an increase in the height of the top of the mixing layer. Therefore, when analyzing the daily minimum radon concentration to establish the normal seasonal variation pattern, the pattern could be strongly affected by atmospheric turbulence, which is strongly linked to surface temperature variation (Trenberth, 1983). On the other hand, some researchers have proposed that the movement of radon is affected by the transport pathways of the air mass arriving at observation sites (Chambers et al., 2009; Zhu et al., 2012). The amount of radon exhaled from soil is enhanced by an increase in temperature. However, any change in the effective diffusion coefficient (0.99e1.07) is predicted to be small as long as the temperature of the soil stays within the range of 1 to þ28 C (Hosoda et al., 2009; Zhuo et al., 2006). If the amount of atmospheric radon is assumed to be constant at the observation site, the height of the mixing layer strongly affects the change in the atmospheric minimum radon concentration. The normal seasonal variation pattern is considered to be analogous to the sinusoid pattern (365-day period) observed for the inverse variation of mean surface temperatures (Trenberth, 1983; Nakamura et al., 2013). In this study, we used the daily minimum radon concentration observed in the Fukushima Prefecture from 2003 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. The obtained data were segregated based on two periods: the normal period from 2003 to 2007 and the precursor period from 2008 until the earthquake. The seasonal variation was calculated by applying a sinusoidal model to the daily minimum radon concentration during the normal period. Finally, we compared the two types of variations, whose radon concentrations from the de-trended levels were obtained by subtracting two type seasonal variations (before and after applying sinusoidal model) from the radon variation.
Fig. 2. Daily variations of the residual radon concentration during the four seasons (subtracting the linear trend and converting the radon concentration from the ionization current).
2. Methods A gas-flow ionization chamber has been used to date to monitor radioisotope leakage in exhaust air from the Radioisotope Institute at Fukushima Medical University (N 37.69 , E 140.47 ), located 240 km northenortheast of Tokyo (Fig. 1). The continuous and automatic measurements every hour of atmospheric radon concentration are made with a DGM-101 flow-type ionization chamber (Hitachi-Aloka Medical Ltd., Tokyo, Japan) with an effective volume of 0.014 m3. Outdoor air from approximately 7 m above the ground is brought into the radioisotope facility (1616 m3) with a flow rate of 7848 m3 h1 through forced draft fans. The ventilation system is operated on a 24-hour basis. The exhaust air passes through a highefficiency particulate air filter to prevent radioisotope leakage, and this filter removes radon progenies. Then, the exhaust air is led into the ionization chamber with a flow rate of 0.0065 m3 min1. Tajika et al. (2013) have reported that the variation of the atmospheric radon concentration can be determined using data obtained by a gas-flow ionization chamber for cases of a negligibly small radioisotope leakage. According to Seya et al. (2014), the meteorological statistics for any 5-year period can be used as representative data for the
Residual radon concentration (Bq m-3)
6
2003 2004 2005
Date (year) 2006 2007 2008 2009
Normal period (R2 = 0.92)
4
2010 2011
Precursor period
Mw 9.0
Data (Ri)
2 0
Mean
-2 Seasonal variation (Si)
-4 -6
Fig. 1. A map showing the Fukushima Medical University (FMU: white circle) (N 37.69 , E 140.47 ) and the epicentre of the 2011 Tohoku-Oki Earthquake (Mw.9.0: black star) (depth 24 km, N 38.10 , E 142.86 ) in Japan.
0
365
730
1095 1460 1825 2190 2555 2920 3285 Time t (day)
Fig. 3. Comparison between the time-series variation of the residual radon concentration (Ri: black line) and the seasonal variation (Si: blue thick line). The time at the start of the observation (1 January 2003) was set as zero. R2 indicates the coefficient of determination between Ri and Si during the normal period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
Residual radon concentration (Bq m-3)
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153
3 Winter 2 1
Autumn
Spring
0
Regression curve R2 = 0.95
Mean
-1 Summer -2 -3
0
30
10 20 Temperature (°C)
Fig. 4. Linear regression of the normal air temperature variation versus the seasonal variation of the residual radon concentration Si.
evaluation of average conditions, such as the average radionuclide concentration in surface air over the year. Therefore, we used the radon data of 5 years to represent the normal period for the evaluation of average conditions. Data for leap days (29 February) are not included in the dataset for simplicity. Missing data were linearly interpolated. The percentage of missing data was 0.3%. 3. Analysis
Residual radon concentration (Bq m-3)
3
Seasonal variation (Si) Model of seasonal variation (Smi) R2 = 0.95
2
was used to obtain the normal seasonal variation of the residual radon concentration Si by applying the exponential smoothing method, subtracting the linear trend and converting it to the radon concentration from the ionization current (Tajika et al., 2013). The simple exponential smoothing function of the software package (SPSS, 1994), which is most similar to an autoregressive integrated moving average (ARIMA) model with zero orders of autoregression, one order of differencing, one order of moving average and no constant, was used. Next, the daily minimum data were used to obtain the time-series variation of the residual radon concentration (Ri) by applying the exponential smoothing method, subtracting the linear trend and converting it to the radon concentration from the ionization current (Tajika et al., 2013). The variations in Si and Ri are shown in Fig. 3. 4. Results and discussion 4.1. Sinusoidal model Trenberth (1983) reported that two sinusoids (in a 365-day period) can describe the mean surface temperatures in the U.S. and solar radiation. The seasonal variation of the sinusoidal model is determined by the phase shift, and there is a phase lag of 27.5 days between the two sinusoids. In other words, the phase shift of solar radiation is Spring Equinox Day, and the phase shift of surface temperature lags 27.5 days behind from Spring Equinox Day. The solar radiation in the northern hemisphere reaches its maximum at the summer solstice in June and minimum at the winter solstice in December. Therefore, the normal seasonal variation pattern is affected by atmospheric turbulence, which is strongly linked to the surface temperature variation. The phase shift of the seasonal radon variation was regarded to lag 27.5 days behind from Autumnal Equinox Day because of the inverse variation of the surface temperature. Therefore the point we emphasize in this paper is that the phase shift for the normal seasonal radon variation is approximately 70 days. The air temperature data were then established, as no mean surface temperature data were recorded in Fukushima. As shown in Fig. 4, we found through linear regression with the coefficient of determination (the square of the Pearson productemoment correlation coefficient: R2 ¼ 0.95) that the
6 Residual radon concentration (Bq m-3)
Fig. 2 shows the daily variation in residual radon concentration of each season, which were averaged over 5 years. We assumed that the four seasons began on the dates as follows: spring, 1 March; summer, 1 June; autumn, 1 September and winter, 1 December. The variations for the four seasons follow a common pattern. The decrease in the radon concentration after sunrise results from the generation of high turbulence in the daytime mixing layer. These results serve to strengthen the reports that the minimum radon concentration is normally reached in the late afternoon, when the mixing layer is fully developed (Sesana et al., 2003; Zhang and Guo, 2011). We used the daily minimum data to estimate the representative variations around a certain area. The seasonal mean residual radon concentration value for each day was calculated using the daily minimum data. For example, by taking the mean of the daily minimum values for 1 January of each year from 2003 to 2007, the mean daily minimum value for 1 January was calculated. Then, the seasonal mean value for each day
1
2003 2004 2005
Mean
4
-3
J (0)
F
M
A
M
J
J
A
Month (Elapsed time t (day))
S
O
N
D (365)
Fig. 5. Comparison of the seasonal variation for the residual radon concentration Si and the model of the seasonal variation Smi, which was averaged over 5 years. R2 indicates the coefficient of determination between Si and Smi.
Precursor period
Mw 9.0
2 0
-6 0
-2
2010 2011
Data (Ri)
Mean Model of seasonal variation (Smi)
-4
-1
Date (year) 2006 2007 2008 2009
Normal period (R2 = 0.88)
-2
0
151
365
730
1095 1460 1825 2190 2555 2920 3285 Time t (day)
Fig. 6. Comparison between the time-series variation of the residual radon concentration (Ri: black line) and the seasonal variation (Smi: red broken line). The time at the start of the observation (1 January 2003) was set as zero. R2 indicates the coefficient of determination between Ri and Smi during the normal period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
152
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153 Precursor period
Normal period
5
5
a)
Mw 9.0
3 2 1
+3σ
0 De-trended level
-1 -2 2003
2005
2007 Year
2009
-3σ
2011
Precursor period Mw 9.0
4 Radon variation (Bq m-3)
Radon variation (Bq m-3)
4
Normal period
b)
3 2 1
+3σ
0 De-trended level
-1 -2 2003
2005
2007
Year
2009
-3σ
2011
Fig. 7. Time-series variations of the radon concentrations from the de-trended levels. The downward arrow indicates the date of the 2011 Tohoku-Oki Earthquake in Japan. The light lines indicate the variations during the normal period, whereas the dark lines indicate the variations during the precursor period. The broken line indicates the 3 times standard deviation (±3s) obtained from the variation during the normal period. a) The blue lines indicate the variation Rn, which was obtained by subtracting Si from Ri shown in Fig. 3b) The red lines indicate the variation Rm, which was obtained by subtracting Smi from Ri shown in Fig. 6. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
correlation between Si and normal air temperature variation is high (data from the Japan Meteorological Agency, 2014). Si was considered to be affected by the surface temperature variation, which is linked to air temperature variation. If the amount of atmospheric radon is assumed to be constant at the observation site, the height of the mixing layer strongly affects the change in the atmospheric minimum radon concentration. According to the results of Fig. 4, the normal seasonal variation pattern is considered to be analogous to the sinusoid pattern (365day period) obtained by fitting the inverse variation of mean surface temperatures. We determined the normal seasonal variation of the residual radon concentration by fitting Si to a sinusoidal model. The model f (t) of the seasonal variation is assumed to be given by.
2p ðt þ 4Þ ; 365
f ðtÞ ¼ a sin
(1)
where f (t) (Bq m3) is the residual radon concentration, t (day) is the time elapsed after the start of the observation (1 January is zero), a (Bq m-3) is the amplitude, 4 (day) is the phase shift and 2p radians is equivalent to 360 and 365-day (1 year). The model of the seasonal variation Smi is predicted by Eq. (1) and fits Si (a ¼ 2.1 (Bq m-3), 4 ¼ 72 (day)) with a coefficient of determination of R2 ¼ 0.95. Si and Smi are plotted in Fig. 5. The fact that the phase shift of Smi was approximately 70 days suggests that the normal seasonal variation pattern was affected by the surface temperature variation. Therefore, the time-series variation of the residual radon concentration Ri would be affected by atmospheric turbulence, which is strongly linked to the surface temperature variation. A strong correlation was obtained between the time-series variation of data Ri during the normal period and the seasonal variation Si (R2 ¼ 0.92), as observed in Fig. 3. Moreover, a strong correlation was found between the time-series variation of data Ri during the normal period and the model of seasonal variation Smi (R2 ¼ 0.88), as observed in Fig. 6. 4.2. Anomalous variation before the 2011 Tohoku-Oki Earthquake The time-series variation of the radon concentration (Rn) from the de-trended level was obtained by subtracting the seasonal variation (Si) from Ri. The time-series variation of the radon concentration (Rm) from the de-trended level based on the model was obtained by subtracting the model of seasonal variation (Smi) from Ri. The standard deviation was calculated using Rn and Rm for the normal period. Fig. 7 shows Rn and Rm over the observation period and the 3 times standard deviation (±3s). Variations of both Rn and
Rm can be extracted showing a pre-seismic change from 2008 until the 2011 Tohoku-Oki Earthquake, and a strong correlation (R2 ¼ 0.93) between them was found. We suggest that this preseismic change in the atmospheric radon concentration was related to the change in the crustal strain over the area (Ozawa et al., 2012). 5. Conclusions We used the data for the daily minimum atmospheric radon concentration in areas around Fukushima as representative concentrations to reduce the effects of topography. We determined that applying a sinusoidal model with a phase shift of approximately 70 days to the seasonal data variation is possible. The timeseries variation of the residual radon concentration Ri was found to be affected by atmospheric turbulence, which was strongly linked to the surface temperature variation. Because radon concentration was mainly affected by atmospheric turbulence, the normal variation of the daily minimum radon concentration could be estimated by determining the amplitude in the sinusoidal model with a phase shift of approximately 70 days. For the case of a simple variation of the daily minimum radon concentration, estimating the pattern of variation was easy. Finally, using the radon data, detecting preseismic anomalies in the radon concentration before the 2011 Tohoku-Oki Earthquake was possible. We suggest that fitting the sinusoidal model to other seasonal data can be performed to further understand variations in anomalous radon concentrations before earthquakes, and the model will be able to be adapted to predict earthquakes in future. Acknowledgement The suggestions of 3 anonymous reviewers were very helpful in clarifying the paper. This study was supported by the Observation and Research Program for the Prediction of Earthquakes and Volcanic Eruptions (1223) from the Ministry of Education, Culture, Sports, Science and Technology, Japan. References Bossew, P., Dubois, G., Tollefsen, T., 2008. Investigations on indoor radon in Austria, part 2: geological classes as categorical external drift for spatial modelling of the radon potential. J. Environ. Radioact. 99, 81e97. Chambers, S., Zahorowski, W., Matsumoto, K., Uematsu, M., 2009. Seasonal variability of radon-derived fetch regions for Sado Island, Japan, based on 3 years of observations: 2002e2004. Atmos. Environ. 43, 271e279.
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153 Garzon, L., Juanco, J.M., Perez, J.M., Fernandez, J.M., Arganza, B., 1986. The universal Rn wave. An approach. Health Phys. 51, 185e195. Hatanaka, H., Yasuoka, Y., Muto, J., Nagahama, H., Suzuki, T., Homma, Y., Sakashita, M., Kobayashi, Y., Mukai, T., 2013. Atmospheric radon concentration linked to crustal strain prior to and/or after earthquakes. In: Bessho, K., Tagami, K., Takamiya, K., Miura, T. (Eds.), Proceedings of the 14th Workshop on Environmental Radioactivity. High Energy Accelerated Research Organization (KEK), Tsukuba, pp. 379e385 (in Japanese, with English abstract). Hosoda, M., Sorimachi, A., Yasuoka, Y., Ishikawa, T., Sahoo, S.K., Furukawa, M., Hassan, N.M., Tokonami, S., Uchida, S., 2009. Simultaneous measurements of radon and thoron exhalation rates and comparison with values calculated by UNSCEAR equation. J. Radiat. Res. 50, 333e343. Japan Meteorological Agency, 2011. Annual Report on Atmospheric and Marine Environment Monitoring, No.11 (Observation Results for 2009). Japan Meteorological Agency, Tokyo (in Japanese). Japan Meteorological Agency, 2014. Meteorological Information. http://www.data. jma.go.jp/obd/stats/etrn/index.php (accessed 26.09.14.) (in Japanese). Miles, J.C.H., Algar, R.A., 1988. Variations in radon-222 concentrations. J. Radiol. Prot. 8, 103e105. Nakamura, S., Hayashi, K., Yasuoka, Y., Nagahama, H., Muto, J., Omori, Y., Ishikawa, T., Suzuki, T., Homma, Y., Ihara, H., Mukai, T., 2013. Sinusoidal model for the annual variation of atmospheric radon concentration in Japan. In: 2nd G-ever (AsiaPacific Region Global Earthquake and Volcanic Eruption Risk Management) International Symposium, Sendai, 19 Oct. 2013, pp. 165e168. http://g-ever.org/ en/symposium/file/2ndG-EVER_symposium_abstracts_2013101 7.pdf (accessed 26.09.14.). Nakazawa, T., Ishizawa, M., Higuchi, K., Trivett, N.B.A., 1997. Two curve fitting methods applied to CO2 flask data. Environmetrics 8, 197e218. Ozawa, S., Nishimura, T., Munekane, H., Suito, H., Kobayashi, T., Tobita, M., Imakiire, T., 2012. Preceding, coseismic, and postseismic slips of the 2011 Tohoku earthquake, Japan. J. Geophys. Res. 117, B07404.
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€rfer, J., Butterweck, G., Reineking, A., 1994. Daily variation of the radon Porstendo concentration indoors and outdoors and the influence of meteorological parameters. Health Phys. 67, 283e287. Sesana, L., Caprioli, E., Marcazzan, G.M., 2003. Long period study of outdoor radon concentration in Milan and correlation between its temporal variation and dispersion properties of the atmosphere. J. Environ. Radioact. 65, 147e160. Seya, N., Hashimoto, M., Nemoto, K., Shimizu, T., Takasaki, K., 2014. Applicability of meteorological statistics over a 5-year period to evaluation of annual average of radionuclide concentration in surface air: based on meteorological statistics for 20 years at Oarai Research and Development Center, JAEA. Jpn. J. Health Phys. 49, 29e38 (in Japanese, with English abstract). SPSS, 1994. SPSS for Windows-trend. Release 6.1. SPSS Inc., Chicago. Tajika, Y., Yasuoka, Y., Nagahama, H., Suzuki, T., Homma, Y., Ishikawa, T., Tokonami, S., Mukai, T., Janik, M., Sorimachi, A., Hosoda, M., 2013. Radon concentration of outdoor air: measured by an ionization chamber for radioisotope monitoring system at radioisotope institute. J. Radioanal. Nucl. Chem. 295, 1709e1714. Trenberth, K.E., 1983. What are the seasons? Bull. Am. Meteor. Soc. 64, 1276e1282. Yasuoka, Y., Nagahama, H., Ishikawa, T., 2010. Anomalous Radon Concentration Prior to an Earthquake e a Case Study on the 1995 Kobe Earthquake, Japan. LAP Lambert Academic Publishing, Saarbrücken, Germany. Yasuoka, Y., Shinogi, M., 1994. The variation of atmospheric 222Rn concentration in Kobe. Radioisotopes 43, 688e694. Zhang, L., Guo, Q., 2011. Observation and analysis of atmospheric radon in Qingdao, China. J. Radiol. Prot. 31, 129e134. Zhu, C., Yoshikawa-Inoue, H., Matsueda, H., Sawa, Y., Niwa, Y., Wada, A., Tanimoto, H., 2012. Influence of Asian outflow on Rishiri Island, northernmost Japan: application of radon as a tracer for characterizing fetch regions and evaluating a global 3D model. Atmos. Environ. 50, 174e181. Zhuo, W., Iida, T., Furukawa, M., 2006. Modeling radon flux density from the earth's surface. J. Nucl. Sci. Technol. 43, 479e482.
Contents lists available at ScienceDirect
Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Normal seasonal variations for atmospheric radon concentration: a sinusoidal model Koseki Hayashi a, Yumi Yasuoka a, *, Hiroyuki Nagahama b, Jun Muto b, Tetsuo Ishikawa c, Yasutaka Omori c, Toshiyuki Suzuki c, Yoshimi Homma c, Takahiro Mukai a a b c
Kobe Pharmaceutical University, 4-19-1, Motoyamakitamachi, Higashinada-ku, Kobe 658-8558, Japan Tohoku University, 6-3, Aza Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan Fukushima Medical University, 1, Hikarigaoka, Fukushima 960-1295, Japan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 May 2014 Received in revised form 30 September 2014 Accepted 12 October 2014 Available online
Anomalous radon readings in air have been reported before an earthquake activity. However, careful measurements of atmospheric radon concentrations during a normal period are required to identify anomalous variations in a precursor period. In this study, we obtained radon concentration data for 5 years (2003e2007) that can be considered a normal period and compared it with data from the precursory period of 2008 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. Then, we established a model for seasonal variation by fitting a sinusoidal model to the radon concentration data during the normal period, considering that the seasonal variation was affected by atmospheric turbulence. By determining the amplitude in the sinusoidal model, the normal variation of the radon concentration can be estimated. Thus, the results of this method can be applied to identify anomalous radon variations before an earthquake. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric radon Seasonal variation Sinusoidal model Atmospheric turbulence
1. Introduction Radon (222Rn), a radioactive gas with a half-life of 3.82 days, is continuously emanated in soil, rocks and water by the radioactive decay of 226Ra. Radon is released from the ground into the atmosphere, where it is transported mainly by turbulent diffusion or convection. Precursory anomalous radon concentrations in air and groundwater have been reported prior to earthquakes (Yasuoka et al., 2010). However, normal variations of the radon concentration in air are required to identify anomalous changes. Atmospheric radon concentrations have been estimated from data collected by some radioisotope institutes around Japan for exhaust monitoring measurements (Hatanaka et al., 2013; Nakamura et al., 2013; Tajika et al., 2013). For example, the atmospheric radon concentration at Fukushima Medical University in Fukushima City was measured from 2003 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. Some of authors reported anomalous variations in the daily minimum atmospheric radon concentration recorded in Fukushima from 2008 until March 2011, which can be considered as the precursor period (Hatanaka
* Corresponding author. E-mail address: [email protected] (Y. Yasuoka). http://dx.doi.org/10.1016/j.jenvrad.2014.10.007 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
et al., 2013). These atmospheric radon variations might be an indication of changes in the radon concentration due to exhalation by the anomalous pre-seismic crustal strain. Ozawa et al. (2012) reported spatial patterns of post-seismic crustal deformation caused by earthquakes in 2008 in the Pacific Ocean off the coast of Fukushima and Ibaraki Prefectures and an earthquake in 2010 off the coast of the Fukushima Prefecture (Fig. 3 in their paper). Time-series variations of atmospheric CO2 concentration in Japan have been analysed as secular and seasonal variations using trigonometric functions (Nakazawa et al., 1997; Japan Meteorological Agency, 2011). Bossew et al. (2008) analysed seasonal variations by fitting a sinusoidal model to indoor radon concentrations. In our previous study, the estimation of seasonal variations by fitting a sinusoidal model to the atmospheric daily minimum radon concentration was performed at four sites, including the Fukushima Prefecture in Japan (Nakamura et al., 2013). The common pattern for daily variation is as follows: the concentration gradually increases from mid-afternoon (15:00) to the next morning (06:00) and suddenly drops at sunrise, as the atmospheric radon concentration is strongly affected by variations at the top of the mixing layer (Garzon et al., 1986; Miles and Algar, € rfer et al. 1988; Yasuoka and Shinogi, 1994). Moreover, Porstendo (1994) reported that within the daily variation of the radon
150
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153
concentration, the daily minimum radon concentration is unaffected by topography. Therefore, to analyze a representative variation in the radon concentration around a certain area, the daily minimum radon concentration should be used. Sesana et al. (2003) and Zhang and Guo (2011) proposed that seasonal variations in the minimum radon concentration are matched by variations at the top of the mixing layer. In winter, higher values of the minimum radon concentration appear to reflect the lowering of the top of the mixing layer; whereas, in summer, lower values of the minimum radon concentration appear to reflect an increase in the height of the top of the mixing layer. Therefore, when analyzing the daily minimum radon concentration to establish the normal seasonal variation pattern, the pattern could be strongly affected by atmospheric turbulence, which is strongly linked to surface temperature variation (Trenberth, 1983). On the other hand, some researchers have proposed that the movement of radon is affected by the transport pathways of the air mass arriving at observation sites (Chambers et al., 2009; Zhu et al., 2012). The amount of radon exhaled from soil is enhanced by an increase in temperature. However, any change in the effective diffusion coefficient (0.99e1.07) is predicted to be small as long as the temperature of the soil stays within the range of 1 to þ28 C (Hosoda et al., 2009; Zhuo et al., 2006). If the amount of atmospheric radon is assumed to be constant at the observation site, the height of the mixing layer strongly affects the change in the atmospheric minimum radon concentration. The normal seasonal variation pattern is considered to be analogous to the sinusoid pattern (365-day period) observed for the inverse variation of mean surface temperatures (Trenberth, 1983; Nakamura et al., 2013). In this study, we used the daily minimum radon concentration observed in the Fukushima Prefecture from 2003 until March 2011, when the 2011 Tohoku-Oki Earthquake occurred. The obtained data were segregated based on two periods: the normal period from 2003 to 2007 and the precursor period from 2008 until the earthquake. The seasonal variation was calculated by applying a sinusoidal model to the daily minimum radon concentration during the normal period. Finally, we compared the two types of variations, whose radon concentrations from the de-trended levels were obtained by subtracting two type seasonal variations (before and after applying sinusoidal model) from the radon variation.
Fig. 2. Daily variations of the residual radon concentration during the four seasons (subtracting the linear trend and converting the radon concentration from the ionization current).
2. Methods A gas-flow ionization chamber has been used to date to monitor radioisotope leakage in exhaust air from the Radioisotope Institute at Fukushima Medical University (N 37.69 , E 140.47 ), located 240 km northenortheast of Tokyo (Fig. 1). The continuous and automatic measurements every hour of atmospheric radon concentration are made with a DGM-101 flow-type ionization chamber (Hitachi-Aloka Medical Ltd., Tokyo, Japan) with an effective volume of 0.014 m3. Outdoor air from approximately 7 m above the ground is brought into the radioisotope facility (1616 m3) with a flow rate of 7848 m3 h1 through forced draft fans. The ventilation system is operated on a 24-hour basis. The exhaust air passes through a highefficiency particulate air filter to prevent radioisotope leakage, and this filter removes radon progenies. Then, the exhaust air is led into the ionization chamber with a flow rate of 0.0065 m3 min1. Tajika et al. (2013) have reported that the variation of the atmospheric radon concentration can be determined using data obtained by a gas-flow ionization chamber for cases of a negligibly small radioisotope leakage. According to Seya et al. (2014), the meteorological statistics for any 5-year period can be used as representative data for the
Residual radon concentration (Bq m-3)
6
2003 2004 2005
Date (year) 2006 2007 2008 2009
Normal period (R2 = 0.92)
4
2010 2011
Precursor period
Mw 9.0
Data (Ri)
2 0
Mean
-2 Seasonal variation (Si)
-4 -6
Fig. 1. A map showing the Fukushima Medical University (FMU: white circle) (N 37.69 , E 140.47 ) and the epicentre of the 2011 Tohoku-Oki Earthquake (Mw.9.0: black star) (depth 24 km, N 38.10 , E 142.86 ) in Japan.
0
365
730
1095 1460 1825 2190 2555 2920 3285 Time t (day)
Fig. 3. Comparison between the time-series variation of the residual radon concentration (Ri: black line) and the seasonal variation (Si: blue thick line). The time at the start of the observation (1 January 2003) was set as zero. R2 indicates the coefficient of determination between Ri and Si during the normal period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
Residual radon concentration (Bq m-3)
K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153
3 Winter 2 1
Autumn
Spring
0
Regression curve R2 = 0.95
Mean
-1 Summer -2 -3
0
30
10 20 Temperature (°C)
Fig. 4. Linear regression of the normal air temperature variation versus the seasonal variation of the residual radon concentration Si.
evaluation of average conditions, such as the average radionuclide concentration in surface air over the year. Therefore, we used the radon data of 5 years to represent the normal period for the evaluation of average conditions. Data for leap days (29 February) are not included in the dataset for simplicity. Missing data were linearly interpolated. The percentage of missing data was 0.3%. 3. Analysis
Residual radon concentration (Bq m-3)
3
Seasonal variation (Si) Model of seasonal variation (Smi) R2 = 0.95
2
was used to obtain the normal seasonal variation of the residual radon concentration Si by applying the exponential smoothing method, subtracting the linear trend and converting it to the radon concentration from the ionization current (Tajika et al., 2013). The simple exponential smoothing function of the software package (SPSS, 1994), which is most similar to an autoregressive integrated moving average (ARIMA) model with zero orders of autoregression, one order of differencing, one order of moving average and no constant, was used. Next, the daily minimum data were used to obtain the time-series variation of the residual radon concentration (Ri) by applying the exponential smoothing method, subtracting the linear trend and converting it to the radon concentration from the ionization current (Tajika et al., 2013). The variations in Si and Ri are shown in Fig. 3. 4. Results and discussion 4.1. Sinusoidal model Trenberth (1983) reported that two sinusoids (in a 365-day period) can describe the mean surface temperatures in the U.S. and solar radiation. The seasonal variation of the sinusoidal model is determined by the phase shift, and there is a phase lag of 27.5 days between the two sinusoids. In other words, the phase shift of solar radiation is Spring Equinox Day, and the phase shift of surface temperature lags 27.5 days behind from Spring Equinox Day. The solar radiation in the northern hemisphere reaches its maximum at the summer solstice in June and minimum at the winter solstice in December. Therefore, the normal seasonal variation pattern is affected by atmospheric turbulence, which is strongly linked to the surface temperature variation. The phase shift of the seasonal radon variation was regarded to lag 27.5 days behind from Autumnal Equinox Day because of the inverse variation of the surface temperature. Therefore the point we emphasize in this paper is that the phase shift for the normal seasonal radon variation is approximately 70 days. The air temperature data were then established, as no mean surface temperature data were recorded in Fukushima. As shown in Fig. 4, we found through linear regression with the coefficient of determination (the square of the Pearson productemoment correlation coefficient: R2 ¼ 0.95) that the
6 Residual radon concentration (Bq m-3)
Fig. 2 shows the daily variation in residual radon concentration of each season, which were averaged over 5 years. We assumed that the four seasons began on the dates as follows: spring, 1 March; summer, 1 June; autumn, 1 September and winter, 1 December. The variations for the four seasons follow a common pattern. The decrease in the radon concentration after sunrise results from the generation of high turbulence in the daytime mixing layer. These results serve to strengthen the reports that the minimum radon concentration is normally reached in the late afternoon, when the mixing layer is fully developed (Sesana et al., 2003; Zhang and Guo, 2011). We used the daily minimum data to estimate the representative variations around a certain area. The seasonal mean residual radon concentration value for each day was calculated using the daily minimum data. For example, by taking the mean of the daily minimum values for 1 January of each year from 2003 to 2007, the mean daily minimum value for 1 January was calculated. Then, the seasonal mean value for each day
1
2003 2004 2005
Mean
4
-3
J (0)
F
M
A
M
J
J
A
Month (Elapsed time t (day))
S
O
N
D (365)
Fig. 5. Comparison of the seasonal variation for the residual radon concentration Si and the model of the seasonal variation Smi, which was averaged over 5 years. R2 indicates the coefficient of determination between Si and Smi.
Precursor period
Mw 9.0
2 0
-6 0
-2
2010 2011
Data (Ri)
Mean Model of seasonal variation (Smi)
-4
-1
Date (year) 2006 2007 2008 2009
Normal period (R2 = 0.88)
-2
0
151
365
730
1095 1460 1825 2190 2555 2920 3285 Time t (day)
Fig. 6. Comparison between the time-series variation of the residual radon concentration (Ri: black line) and the seasonal variation (Smi: red broken line). The time at the start of the observation (1 January 2003) was set as zero. R2 indicates the coefficient of determination between Ri and Smi during the normal period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
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K. Hayashi et al. / Journal of Environmental Radioactivity 139 (2015) 149e153 Precursor period
Normal period
5
5
a)
Mw 9.0
3 2 1
+3σ
0 De-trended level
-1 -2 2003
2005
2007 Year
2009
-3σ
2011
Precursor period Mw 9.0
4 Radon variation (Bq m-3)
Radon variation (Bq m-3)
4
Normal period
b)
3 2 1
+3σ
0 De-trended level
-1 -2 2003
2005
2007
Year
2009
-3σ
2011
Fig. 7. Time-series variations of the radon concentrations from the de-trended levels. The downward arrow indicates the date of the 2011 Tohoku-Oki Earthquake in Japan. The light lines indicate the variations during the normal period, whereas the dark lines indicate the variations during the precursor period. The broken line indicates the 3 times standard deviation (±3s) obtained from the variation during the normal period. a) The blue lines indicate the variation Rn, which was obtained by subtracting Si from Ri shown in Fig. 3b) The red lines indicate the variation Rm, which was obtained by subtracting Smi from Ri shown in Fig. 6. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
correlation between Si and normal air temperature variation is high (data from the Japan Meteorological Agency, 2014). Si was considered to be affected by the surface temperature variation, which is linked to air temperature variation. If the amount of atmospheric radon is assumed to be constant at the observation site, the height of the mixing layer strongly affects the change in the atmospheric minimum radon concentration. According to the results of Fig. 4, the normal seasonal variation pattern is considered to be analogous to the sinusoid pattern (365day period) obtained by fitting the inverse variation of mean surface temperatures. We determined the normal seasonal variation of the residual radon concentration by fitting Si to a sinusoidal model. The model f (t) of the seasonal variation is assumed to be given by.
2p ðt þ 4Þ ; 365
f ðtÞ ¼ a sin
(1)
where f (t) (Bq m3) is the residual radon concentration, t (day) is the time elapsed after the start of the observation (1 January is zero), a (Bq m-3) is the amplitude, 4 (day) is the phase shift and 2p radians is equivalent to 360 and 365-day (1 year). The model of the seasonal variation Smi is predicted by Eq. (1) and fits Si (a ¼ 2.1 (Bq m-3), 4 ¼ 72 (day)) with a coefficient of determination of R2 ¼ 0.95. Si and Smi are plotted in Fig. 5. The fact that the phase shift of Smi was approximately 70 days suggests that the normal seasonal variation pattern was affected by the surface temperature variation. Therefore, the time-series variation of the residual radon concentration Ri would be affected by atmospheric turbulence, which is strongly linked to the surface temperature variation. A strong correlation was obtained between the time-series variation of data Ri during the normal period and the seasonal variation Si (R2 ¼ 0.92), as observed in Fig. 3. Moreover, a strong correlation was found between the time-series variation of data Ri during the normal period and the model of seasonal variation Smi (R2 ¼ 0.88), as observed in Fig. 6. 4.2. Anomalous variation before the 2011 Tohoku-Oki Earthquake The time-series variation of the radon concentration (Rn) from the de-trended level was obtained by subtracting the seasonal variation (Si) from Ri. The time-series variation of the radon concentration (Rm) from the de-trended level based on the model was obtained by subtracting the model of seasonal variation (Smi) from Ri. The standard deviation was calculated using Rn and Rm for the normal period. Fig. 7 shows Rn and Rm over the observation period and the 3 times standard deviation (±3s). Variations of both Rn and
Rm can be extracted showing a pre-seismic change from 2008 until the 2011 Tohoku-Oki Earthquake, and a strong correlation (R2 ¼ 0.93) between them was found. We suggest that this preseismic change in the atmospheric radon concentration was related to the change in the crustal strain over the area (Ozawa et al., 2012). 5. Conclusions We used the data for the daily minimum atmospheric radon concentration in areas around Fukushima as representative concentrations to reduce the effects of topography. We determined that applying a sinusoidal model with a phase shift of approximately 70 days to the seasonal data variation is possible. The timeseries variation of the residual radon concentration Ri was found to be affected by atmospheric turbulence, which was strongly linked to the surface temperature variation. Because radon concentration was mainly affected by atmospheric turbulence, the normal variation of the daily minimum radon concentration could be estimated by determining the amplitude in the sinusoidal model with a phase shift of approximately 70 days. For the case of a simple variation of the daily minimum radon concentration, estimating the pattern of variation was easy. Finally, using the radon data, detecting preseismic anomalies in the radon concentration before the 2011 Tohoku-Oki Earthquake was possible. We suggest that fitting the sinusoidal model to other seasonal data can be performed to further understand variations in anomalous radon concentrations before earthquakes, and the model will be able to be adapted to predict earthquakes in future. Acknowledgement The suggestions of 3 anonymous reviewers were very helpful in clarifying the paper. This study was supported by the Observation and Research Program for the Prediction of Earthquakes and Volcanic Eruptions (1223) from the Ministry of Education, Culture, Sports, Science and Technology, Japan. References Bossew, P., Dubois, G., Tollefsen, T., 2008. Investigations on indoor radon in Austria, part 2: geological classes as categorical external drift for spatial modelling of the radon potential. J. Environ. Radioact. 99, 81e97. Chambers, S., Zahorowski, W., Matsumoto, K., Uematsu, M., 2009. Seasonal variability of radon-derived fetch regions for Sado Island, Japan, based on 3 years of observations: 2002e2004. Atmos. Environ. 43, 271e279.
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