Prog. Theor. Exp. Phys. 2018, 093H01 (18 pages)
DOI: 10.1093/ptep/pty091

Development of new radon monitoring systems
in the Kamioka mine
1

Kamioka Observatory, Institute for Cosmic Ray Research, University ofTokyo, Kamioka, Gifu 506-1205, Japan
Department of Physics, Gifu University, Gifu, Gifu 501-1193, Japan
3
Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes
for Advanced Study, University of Tokyo, Kashiwa, Chiba 277-8583, Japan∗
E-mail: pronost@km.icrr.u-tokyo.ac.jp
2

Received March 9, 2018; Revised July 13, 2018; Accepted July 28, 2018; Published September 27, 2018...................................................................................................................
Radioactivity from radon is a major threat for high-precision low-energy physics experiments like the ones in the Kamioka mine. We developed a new high-sensitivity radon
monitoring system and conducted systematic radon concentration measurements for the ﬁrst
time in Kamioka. The system consists of portable radon detectors with a capacity of 1 L
and new electronics based on a Raspberry Pi [Raspberry Pi documentation. (Available at:https://www.raspberrypi.org/documentation/)]. These radon detectors measure the radon in the
air with electrostatic collection and a PIN photo-diode. We measured the absolute humidity√
dependence of the 1-L radon detector for air as CF (AH ) = (12.86 ± 0.40) − (1.66 ± 0.19) AH(counts/day)/(Bq/m3 ). The background level of the 1-L radon detector is 0.65 ± 0.15 (stat.)counts/day. This corresponds to a detection limit of ∼ 0.4 Bq/m3 in a one-day measurement.
Data were collected for a period of more than one year with twenty-one 1-L radon detectors
in the Kamioka mine. They indicate seasonal and day–night variations in radon concentration
within the mine. These results also allow us to conﬁrm the stability of the new Raspberry Pi
electronics.
...................................................................................................................
Subject Index

1.

C43, H20

Introduction

222 Rn (called radon hereafter) is a radioactive gas abundant in most underground places. It is

produced in the 4n+2 decay chain of 238 U, the so-called “radium series”. Its decay chain is asα1follows: 222−−→
86 Rn −
3.82 d218 Po
84

α2

−−−−→
3.1 min

β−

β−

α4

22.26 yr5.01 d138.38 d

214 Pb
82

β−

−−−−→
26.8 min214 Bi
83

β−

−−−−→
19.9 min214 Po
84

α3

−−−−→
164.3 μs

210 Pb82

206
210−−−−→ 210
83 Bi −−−→ 84 Po −−−−→ 82 Pb, where α1 , α2 , α3 , and α4 are the different α particles inthis decay chain with energies of E(α1 ) = 5.590 MeV, E(α2 ) = 6.002 MeV, E(α3 ) = 7.833 MeV,
and E(α4 ) = 5.407 MeV [2]. Due to the production of α and β − particles over a long period, 222 Rn
is a serious source of background for low-energy physics experiments and underground experimentsin particular. In addition, as a radioactive gas, workers in underground environments can be exposed
to high concentrations of radon, which can lead to serious health issues [3]. These two issues suggest
the need to monitor the radon concentration in underground environments.
The Kamioka mine in the Gifu Prefecture of Japan hosts several high-precision, low-energy
experiments, including Super-Kamiokande [4], KamLAND [5], XMASS [6], CANDLES [7], and
NEWAGE [8]. To monitor the radon concentration in the air of the mine, we developed a precise
and cost-effective system using a 1-L detector with Raspberry Pi-based electronics.
© The Author(s) 2018. Published by Oxford University Press on behalf of the Physical Society of Japan.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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G. Pronost1,∗ , M. Ikeda1 , T. Nakamura2 , H. Sekiya1,3 , and S. Tasaka1

PTEP 2018, 093H01

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2.

Detector design

To measure the radon concentration in the air, we used the electrostatic collection method of thedaughter nuclei of 222 Rn, combined with the energy measurement of alpha particles from thedaughter-nuclei decays. This energy measurement is performed using a PIN photo-diode. Previous publications [9] showed that 90% of the isotopes produced in the 222 Rn-nuclei decay chain are
positive ions. Therefore, they can be collected on a PIN photo-diode on which a negative high voltage(HV) is applied.
α2 , α3 , and α4 , referring to the α particles from the 218 Po, 214 Po, and 210 Po decays, respectively,
can be detected by the PIN photo-diode if the polonium nucleus has been collected. Since 222 Rn
nuclei are not ions, they are unlikely to be collected on the PIN photo-diode; α1 are then unlikely to
be detected. Only α2 , α3 , and α4 measurements are performed.
Figure 1 shows a schematic of the new 1-L radon detectors that we use in the Kamioka mine;
this schematic is of the same type as that shown in Fig. 4 of Ref. [10]. These detectors are made ofstainless-steel cases, open at the bottom in order to allow air from the atmosphere to enter the detector.
A Hamamatsu S3590-09 PIN photo-diode is located at the top of the detector and is connected via afeed-through to an HV supplier, a preampliﬁer, and a shaping ampliﬁer. The air opening at the bottomof the detector is covered by three different layers. From inside to outside, these are a stainless-steel
mesh, a microporous membrane, and a black cover. The stainless-steel mesh is used as an electric
ground in continuity with the detector case. It consists of 100 mesh per inch. The microporous
membrane is used as a ﬁlter to remove dust from the incoming air and thus the daughter nuclei of theradon. This allows us to collect only polonium, bismuth, and lead positive ions from radon decays
occurring within the detector case. The microporous membrane used is a polypropylene membrane
produced by the CELGARDTM company (product’s name: CELGARD© 2400). The last cover is a
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Fig. 1. Schematic of a 1-L radon detector. Measurements are in mm.

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Fig. 3. Picture of the ampliﬁer circuit. View from above. Dimensions: 2 × 5 cm.

piece of black fabric, which is used to shield the inside of the detector, and thus the PIN photo-diode,from external light.
The ventilation rate of the 1-L radon detector has been calculated to be about seven times per hourwith natural ventilation. The computation was done using the theoretical equation and experimental
values from Ref. [11]. This large value is owing to the 80-mm-diameter ventilation window.

3.

Electronics

The HV supplier and the ampliﬁer circuit are described in Fig. 2, and a picture is shown in Fig. 3.
An HV of −120 V is supplied to the whole detector. This bias voltage allows positive ions from theradon decays to be collected on the PIN photo-diode. The value of the HV was determined as thevoltage for which the collection of 214 Po and 218 Po positive ions is at its maximum.
The charge from the PIN photo-diode is converted to voltage pulses by the preampliﬁer circuit. The pulse is then shaped using the operational ampliﬁer AD847JRZ [Ref:3/18

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Fig. 2. Circuit scheme of the HV supplier and ampliﬁer circuit. The combination of resistors R12 and R13
leads to a 7.5 gain. The ±12 V input signal is connected to CN1 (1,5,6); the positive output signal is connected
to CN1 (2,3); the HV is connected to CN2 (7,8). Connectors are set between JP1 (2,3), between JP1 (4,5), andbetween JP2 (2,3).

PTEP 2018, 093H01

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R2
2.2k
D1
1SS315

ADC_INC1
0.1u

D2
1SS315

SIG_IN

U2

C23
0.1u

74VHC374

CONT
D0

D7

D1

D6

D2

D5

D3

D4

JP1

C26
0.1u

3

Q2
2SK208D3
1SS315Q4
2SC1623

U8

50orm_In

C25
0.1u

MAX153CWP

1SIGNAL_IN

U1

R3
10k

3

Q1
2SC1623
C2
0.1u

R5
100

D4
1N41481AD8610
R1
51

C3
1000p

D5
1N4148R651
50orm_ADC_In

DGND

R7
51

INT

AGND

1

R4
10k

3

Q5
2SA812

R8
1k

+3.3V
P1

C31
100uF
GPIO_02

GPIO_02

I2C0_SDA

F1
1A
U12
U9

U7

74HC123

U3
R18

C5

R12

0.1u

6.2k
C8
1000p

0.1u

C13

1.5k
C14
1000p

C12
0.1u

0.1u

GPIO_04
GPIO_14
UART0_TXD
GPIO_15
UART0_RXD

U6A

TL3116

GPIO_17

0.1u
C22
1000p

74HC32

C15
1000p

R19

GPIO_18PWM074VHC14

R13

1.0k
R11
3.3k

GPIO_272.2k
GPIO_22

+1.2V
VREF
U4

R9
10k

C6
4.7uF

GPIO_23

74HC123
R14

U11
MAX8069ESA+

C16

3.3k
C17
1000p

VR1
10k

0.1u

GPIO_24
GPIO_10

U6B
GPIO_09
74HC32

C18
1000p

R10
100

U6E
74HC32

GPIO_08

C24
C27
100uF

C28
0.1u

U10

U5

74HC123

C29
100uF

C30
0.1u

C19
4.7k

C9
100uF
+5V_A

0.1u
R16

PWR
OSHR1608C1A

B2
MPZ1608S221A

GPIO_07

GPIO_11

SPI0_MISOGPIO_08

GPIO_07

SPI0_SCLKSPI0_CE0_N

SPI0_CE1_N
1-215307-3

0.1u

C20
1000p

-5V_A

MAX660

GPIO_09

SPI0_MOSIGPIO_25
GPIO_11

3.9k

+5V_D

GPIO_10

U6D
74HC32R15
B1
MPZ1608S221A

I2C0_SCLGPIO_03

74HC123C7C4GPIO_03

U6C
C10
100uF

C11
0.1u

74HC32

C21
1000p
R17
5.5k

Fig. 4. Circuit scheme of the ADC board used for the Raspberry Pi B, B+, 2B, and 3B systems. For Raspberry
Pi B+, 2B, and 3B, the circuit also contains the circuit shown in Fig. 5.

Analog Devices AD847 datasheet http://www.analog.com/media/en/technical-documentation/datasheets/AD847.pdf], inverted, and ampliﬁed. The ampliﬁcation gain of the ampliﬁer circuit is 7.5;
this can be reduced by adding a resistor at RR1 or increased by removing either R12 or R13. The
signal is then sent to an analog-to-digital-converter (ADC) board specially developed to be used with
Raspberry Pi systems.
In order to measure the equivalent-noise-charge (ENC) from the preampliﬁer, the PIN photodiode has been irradiated with γ -rays from an 241Am source. The 59.4 keV photoelectric peak wasmeasured, and allowed measurement of the value of the ENC as 530 electrons (FWHM).
The ADC board circuit is described in Fig. 4. It consists of a comparator circuit, a peak-holder
circuit, an 8-bit ADC (MAX153CWP) [12], a pulse timing circuit, and a latch circuit. It accepts an
input signal via a 50 -impedance LEMO connector with a maximal amplitude of 1000 mV. Thisamplitude is converted into a channel number by the peak-holder circuit and the 8-bit ADC, and thensent to the Raspberry Pi.
Raspberry Pi B, B+, 2B, and 3B [1] are currently used in the Kamioka mine. Due to the architectural differences between the different types of Raspberry Pi systems, two different versions of
the ADC board have been developed. The ﬁrst version, called ADC-B, is shown in Fig. 6. It is usedwith Raspberry Pi B, to which it is connected via a 26-GPIO-pins (General Purpose Input/Output)
connector. The second version, called ADC-B+, is shown in Fig. 7. It is used with Raspberry Pi B+,
2B, and 3B and is connected to the Raspberry Pi via a 40-GPIO-pin connector. The circuit of the
ADC-B+ is similar to that of the ADC-B; however, because of the additional pins, four additional
analog inputs are added to the circuit. These inputs accept signals with a maximal amplitude of 5000
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Q3
2SK208

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mV and process them by a 4-channel 10-bit ADC (ADC104S021CIMM) [13]. These additional
inputs are used to record environmental data (temperature, humidity, ﬂow rate, etc.).
The main part of the circuit is common to both versions and is described in Fig. 4; the additional
circuit for the four additional inputs is described in Fig. 5. These boards are manufactured by WIN
Electronics Co., Ltd.1

3.1.

Data acquisition

The ADC board is monitored by software to perform data acquisition. This software was developed
using C++ and uses the wiringPi framework [14] to communicate with the ADC board through the
GPIO-pin connector.
This software was developed to record data with ﬁxed time interval measurements. However, theCPU speed on the Raspberry Pi systems limits the minimal interval that can be used. To estimate
this minimal time interval, hardware-limitation tests have been performed using a Raspberry Pi 3B

1

http://www.win-ei.com.
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Fig. 5. Additional part of the circuit scheme of the ADC board used for the Raspberry Pi B+, 2B, and 3B
systems.

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Fig. 7. Picture of the ADC board version B+, used for Raspberry Pi B+, 2B, and 3B systems. View from above.
Dimensions: 5.5 × 8.5 cm.

equipped with a 48-MB/s TOSHIBA 32-GB micro-SD card. Table 1 summarizes the number oftimes that the actual interval was different than the expected interval.
From these results, we can see that the CPU speed of the Raspberry Pi cannot perform 1 measurement per second as this conﬁguration leads to obtaining fewer data than when asking for 1
measurement every 2 seconds. Hence, we determined that the minimal interval that can be usedwithout critical data loss is 2 seconds.
Additional tests showed that electronics are able to support without loss an input of ∼ 23 kHz in
the case of Raspberry Pi B and B+, and of ∼ 28 kHz in the case of Raspberry Pi 2B and 3B. At thisrate, the current version of the software needs to be run with a 4-sec (or less) interval in order toavoid data loss. Taking into account the calibration factor shown later, and assuming a humidity of
10 g/m3 , this rate theoretically corresponds to a maximal radon concentration of ∼ 260 MBq/m3 inthe case of Raspberry Pi B and B+, and of ∼ 315 MBq/m3 in the case of Raspberry Pi 2B and 3B.
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Fig. 6. Picture of the ADC board version B, used for Raspberry Pi B systems. View from above. Dimensions:
5.5 × 8.5 cm.

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Table 1. Summary of the study of the minimal time interval measurement. The “Different
intervals” columns show the ratio (third column) and number of times per day (fourthcolumn) that the actual interval was not the same as the expected interval. Only statisticaluncertainties are shown.
Expectedinterval

4.

Different intervals

1.59 ± 0.50 sec
2.01 ± 0.21 sec
3.01 ± 0.15 sec
4.00 ± 0.13 sec
5.00 ± 0.10 sec%# per day59.6201 ± 1.1182%
0.2717 ± 0.0008%
0.1292 ± 0.0005%
0.0356 ± 0.0002%
0.0296 ± 0.0002%

51 806 ± 967
117 ± 137 ± 1
8 ± 0 (0.05)
5 ± 0 (0.02)

Calibration of the radon detector

Previous studies showed that the 222 Rn daughter nuclei can be collected and neutralized by watermolecules in the air [16]. Therefore, the detection efﬁciency depends on the absolute humidity in the environment. A study of the humidity dependence of the calibration factor CF (in(counts/day)/(Bq/m3 )) was thus performed. The calibration system was a closed loop including a 1-L detector, a humidity meter, a radon source, and an air pump. A refrigerator was used tocontrol the dew-point temperature of the system. A schematic of the calibration system is shownin Fig. 8. The calibration was performed over two different periods. First, we measured the highhumidity dependence (> 6 g/m3 ); then, we measured the low-humidity dependence (< 1 g/m3 ).
The humidity was measured with a MICHELL sensor (PCMini52) during the ﬁrst campaign and witha VAISALA dew-point meter (DMT340) during the second. The MICHELL sensor measured therelative humidity and air temperature, whereas the VAISALA sensor measured the dew point and the
air temperature. The conversion to absolute humidity was performed using the VAISALA humidity
formula in the [−20, 50]◦ C approximation [17]. The actual radon concentration was measured witha Pylon scintillator counter (Lucas cell 300A) [18]. The accuracy of the Pylon scintillator counterhas been estimated to be ±4%; this value is used as a systematic error in the calibration.
The peak from 218 Po α decay can be overlapped with the peak from 216 Po α decay in the Th-series
decay chains. Hence, the number of counts used to compute the radon concentration is the integralof the peak from the 214 Po α decays. This approach was also used in Ref. [19].
Figure 9 shows the measured calibration factors in (counts/day)/(Bq/m3 ) as a function of the√absolute humidity. The data were found to be best described by f (x) = p0 − p1 × x, as in Ref.
[19]. The ﬁt result is shown as:

CF (AH ) = (12.86 ± 0.40) − (1.66 ± 0.19) AH ,(1)with CF being the calibration factor in (counts/day)/(Bq/m3 ) and AH being the absolute humidity
in g/m3 . The uncertainties include both statistical and systematic errors. This ﬁt result is consistent
with the previous study performed in Ref. [10], as is shown in Fig. 9 with the data points from Fig. 6
in Ref. [10].
In order to measure the background level of the 1-L detector, we sealed a 1-L detector in astainless-steel container purged with pure air. The background level was measured over one month
as 0.65 ± 0.15 α3 counts/day. This allowed us to determine the detection lower limit with Currie’s
method [20] as ∼ 0.4 Bq/m3 for a 10 g/m3 humidity, for one day of measurement.
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1 sec
2 sec
3 sec
4 sec
5 sec

Actual meaninterval

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Calibration Factor[(214Po/day)/(Bq/m3)]

20
18
16
14
12
10
8
χ2 / ndf
p0
p1

6
4

2.84 / 2
12.86 ± 0.40
1.66 ± 0.19

2
0

10-1

1

10
Absolute Humidity [g/m3]

Fig. 9. 1-L detector-calibration factor in (counts/day)/(Bq/m3 ) as a function of the absolute humidity. The
uncertainties of the absolute humidity for the two last points are 0.06 g/m3 and 0.12 g/m3 (from left to √right)
and are therefore not visible here. The red solid line shows the ﬁt with a function f (AH ) = p0 − p1 × AH ,with AH being the absolute humidity and p0 and p1 being the function’s parameters. The white markers referto the data points from Ref. [10]. These data points were not used to determine the ﬁt function.

Due to the differences in the HV feed-through manual manufacturing and in the PIN photo-diodesettings, the detector response is slightly different for each detector. In order to take this into account,each detector was individually calibrated. This calibration was done in a high-radon-concentration
environment (> 500 Bq/m3 in the mine) where each detector was deployed together with a reference
detector (ionization chamber detector, SAPHYMO AlphaGUARD PQ2000) for several days. The
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Fig. 8. Schematic of the 1-L Rn detector calibration system. “Dew” indicates the position of the humidity
sensor, “MF” indicates the position of the mass ﬂow controller used to monitor the air ﬂow in the loop, “P”
indicates the position of the air pump, “Ref” indicates the position of the sampling point used to measure
the actual radon concentration, and “226 Ra” indicates the position of the 226 Ra source. The arrows show thedirection of the air ﬂow.

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stability of this calibration was checked with the deployment of a “reference” detector over the fullcalibration period. The relative deviation between the AlphaGUARD detector and a 1-L detector isused to determine a correction factor, which is applied to the detector result. The correction factors
used for the detectors presented in this paper are shown in Fig. 10. The error of the measured
correction factor is dominated by the statistics due to the relatively short measurement period forquick deployment of ∼ 10 detectors.

5.

Data analysis

A framework, based on C++ and on ROOT/CERN [21], was developed to automatically analyze thedata from many different radon detectors in the Kamioka mine. Using a standard approach from
Ref. [22], the spectrum obtained from the ADC board is analyzed. Cuts are deﬁned to integrate thespectrum between four intervals, three of which correspond to the main α decays from the 222 Rn
decay chain (α2 , α3 , and α4 ) and the last to the α decay of 212 Po (E(αTh ) = 8.784 MeV) in the
232 Th-series decay chains. The measurement of the number of counts from 212 Po α decays allows
estimation of the contamination from 216 Po α decays (E = 6.788 MeV) in the 218 Po α-decay peak,as explained in Sect. 4. α particles from 212 Po are denoted by αTh in the following. The cuts aredeﬁned separately for each detector because small variations of the gain between each ampliﬁer canlead to variations in the peak positions. Figure 11 shows an example of the cuts performed for one
detector.
The main difﬁculties with the automatic analysis of dozens of detectors is the possible ﬂuctuationof the peak positions. Each time a detector HV is restarted or the PIN photo-diode or ampliﬁer isreplaced, the gain can vary, leading to a change in the peak position. The software is thus able todetermine whether the peak positions change and raises an error if this happens. Human action is thenneeded to determine the new peak positions. In normal operation, this maintenance is not needed formore than two years. It is mainly needed after power failure in the mine, and if the PIN photo-diodeor the ampliﬁer is replaced.
Data analyses are performed on a server in the Kamioka network. However, thanks to their higher
performances, data analyses can also be performed directly on the Raspberry Pi 2B and 3B systems.
This allows the use of these electronics as standalone units outside of the Kamioka network.
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Fig. 10. Correction factor for the detectors used in Sect. 6. The x-axis shows the detector number. The correction
factor for the reference detector is shown in red (detector #0).

Count

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1600

α3

α2

α4

1400

αTh

1200
1000

600
400
200
0
0

50

100

150

200

250
bin

Fig. 11. Spectrum from one 1-L radon detector. The red lines show the cuts used to determine the peak
positions. This detector is using a brand-new PIN photo-diode (less than one month exposure), hence theabsence of a 210 Pb peak (α4 ).

Fig. 12. Map of the experimental area of the Kamioka mine. The red points refer to the detectors’ locations.
The numbers indicate the devices used in this article. The blue point refers to the anemometer’s position.

6.

Results

Twenty-one 1-L detectors using Raspberry Pi electronics are monitoring the radon concentration in
the air at various positions in the Kamioka mine. In this section, the measurements from a selectionof these detectors are shown and discussed. Figure 12 shows the positions of these 21 detectors in
the Kamioka mine, and Fig. 13 zooms in on the Super-Kamiokande area.

6.1.

Radon concentration in the mine

Figure 14 shows the radon concentrations from January 2016 to October 2017 in the tunnels of
the mine from the detectors #1 and #2 in Fig. 12 (directly exposed to the air of the mine). The
humidity in these areas ranges from 8–12 g/m3 . A clear seasonal variation in the radon concentration
is observed. In the approximate period between April to November, the radon concentration is high(> 1000 Bq/m3 ), whereas it is low (< 500 Bq/m3 ) between November and April. In the Atotsu tunnel,
the main access tunnel to the Super-Kamiokande area, we measure the speed and direction of thewind with an anemometer, whose position is indicated in Fig. 12. A correlation is observed between
the radon concentration in the Atotsu tunnel and the direction of the wind, as shown in Fig. 15. The
radon concentration in the Atotsu tunnel is high when the wind comes from inside the mine and
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800

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Fig. 14. Radon concentration (2-day-averaged) measured in Kamioka’s mine areas without fresh-air input.
The dotted lines indicate the 148-Bq/m3 US limit for indoor radon concentration [3]. The top ﬁgure (Atotsu
tunnel) starts around 7 April 2016 as the detector was not deployed in the mine beforehand. The error bars
include statistical uncertainties, as well as the uncertainty on the detector’s correction factor.

low when it comes from outside. Our interpretation is that fresh air with low radon concentration isbrought into the mine when wind is coming in from outside, whereas the high-radon-concentration
air is pushed out of the mine when the wind is blowing in the opposite direction. An increase of
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Fig. 13. Zoom in on the Super-Kamiokande experimental area. The red points indicate the detectors’ locations.
The numbers are an indication for the detectors used in this article, with number 1 showing a reference from
Fig. 12. The blue lines indicate the airtight doors’ positions.

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the radon concentration is observed from 10 January 2017. This increase is due to a temporary
modiﬁcation of the water ﬂow in the mine, causing radon-rich water to be near the detector.
In fall (October–November) and spring (April), daily ﬂuctuations of the radon concentration are
observed. Figure 16 illustrates this phenomenon. The radon concentration typically increases during
daytime (usually from noon) and decreases during nighttime. Our interpretation of this phenomenon
is that the temperature outside the mine ﬂuctuates around the value leading to a change of the winddirection in the tunnel.

6.2.

Radon concentration in the fresh air

As shown in the previous section, the radon concentration in the air in the mine is very high. Therefore,
to reduce the exposure of workers and experiments, fresh air needs to be brought into the experimental
areas. This fresh air is brought through a 1.8-km pipe [4] going through the Atotsu tunnel. This pipe
brings 100 m3 of fresh air per minute into the mine. The humidity in the pipe is kept between 2.5
and 7 g/m3 by a dryer.
We positioned two detectors to monitor the radon concentration in this pipe. One was placed at
the input outside of the mine, in the so-called “Rn hut”, which houses a large compressor used
to send the fresh air, and the other detector was placed at one of the outputs of the pipe in the
Super-Kamiokande dome. Figure 17 shows the concentrations measured by these detectors fromJanuary 2016 to October 2017. A seasonal variation is observed in this ﬁgure, with an average radon
concentration of 50.7 ± 1.0 Bq/m3 during summer, and 19.7 ± 0.2 Bq/m3 during the rest of theyear. This observation suggests a feedback of the high-radon-concentration air from Atotsu tunnel.
Figure 18 shows the hourly averaged radon concentration for both detectors over August 2016 andsuggests that both detectors measured the same concentration and that there are no leaks in the pipe
(i.e., no additional radon coming from the air in the mine).
Figure 18 also indicates variations between daytime and nighttime radon concentrations in the fresh
air. Such variations were observed over the whole year. During a typical day, the radon concentration
tends to increase during the nighttime and decrease during the daytime. This phenomenon comes
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Fig. 15. Correlation between the hourly averaged radon concentration measured in the Atotsu tunnel and the
speed of the wind in this tunnel. Positive values indicate an air ﬂow coming from outside of the mine andnegative values indicate the opposite. The color scale shows the number of measurements in each bin.

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from the stratiﬁcation of the atmosphere during the nighttime, and from the convection due to theSun’s radiation during the daytime.

6.3.

Radon concentration in the Super-Kamiokande area

As shown in Fig. 12, several radon detectors are settled in the Super-Kamiokande area. Figure 19
shows the radon concentration measured in the dome of the Super-Kamiokande experiment fromJanuary 2016 to October 2017. The positions of the detectors in the Super-Kamiokande area are
indicated in Fig. 13. The humidity in this area ranges from 6–8 g/m3 . The periods without data
points are periods at which the detectors were not running due to power cuts or maintenance or
because the detector was yet to be deployed. The concentration plots show the same patterns ofradon-concentration variation, indicating that they are in the same atmosphere. The differences in
the absolute value of the radon concentration can be explained by the positions of these detectors. In
this dome, from the entrance of the Super-Kamiokande area to the opposite side, the concentrations
are shown by the ﬁgures (a), (b), (c), and (d) (detectors #5, #6, #7, and #8, respectively). The radon
concentration measured near the tunnel side of the dome is lower than the one measured on the otherside of the dome. This phenomenon is due to the position of the radonless air output, which is locatedon this side of the dome. The radon concentration in the air inside the tank of Super-Kamiokande is
not affected by the variation of radon concentration in the dome [23].
A detector is settled in the entrance room of the Super-Kamiokande dome (detector #9). This room
is closed by two iron doors, and acts as a transfer airlock between the tunnel and the dome, in order toprevent the radon-rich air from the tunnel entering the dome. Figure 20(a) shows the 2-day-averaged
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Fig. 16. Hourly averaged radon concentration (bottom) and wind velocity (top) measured in the Atotsu tunnel(detector #1) from 8–16 April.

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Fig. 18. Hourly averaged radon concentration measured at the input (plain circles, black) and output (red) of
the fresh-air pipe.

concentration measured by this detector from January 2016 to October 2017. Figure 20(b) shows
the hourly averaged concentration measured by the detector in May 2016.
Daily ﬂuctuation of the radon concentration is observed. The radon concentration increases during daytime, and decreases during nighttime. This phenomenon is due to the radon-rich air fromthe tunnel entering the room when workers (including shifters) enter the Super-Kamiokande area.
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Fig. 17. 2-day-averaged radon concentration measured at the input (top) and output (bottom) of the fresh-air
pipe. The detector at the input of the fresh-air pipe was deployed at the end of June 2016, hence the absenceof data beforehand. The error bars include statistical uncertainties, as well as the uncertainty on the detector’s
correction factor.

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Fig. 19. 2-day-averaged radon concentration measured in the Super-Kamiokande dome. The dotted line indicates the 148-Bq/m3 US limit for indoor radon concentration [3]. The error bars include statistical uncertainties,
as well as the uncertainty on the detector’s correction factor. Detectors #5, #6, and #8 were deployed in April
2016, hence the absence of data before this month.

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During nighttime the radon concentration decrease as the injection of radon-rich air from the tunnel
is reduced. Conﬁrmation of this interpretation arises from the observation of the low radon concentration during Japanese national holidays (such as 3–5 May in Fig. 20(b)), and on Saturdays andSundays, when few workers enter the area.
On 20 October 2016, a sudden increase in the radon concentration can be observed in Fig. 20(a),as well as in Fig. 19. The door between Atotsu tunnel and the entrance room was left open overnight
while the radon concentration in the tunnel was about 1700 Bq/m3 , as shown in Fig. 14(a). In Figs. 19
and 20(a), another radon concentration increase is observed in February 2017, due to an interruptionof the aeration system of the entrance room.

7.

Summary

New 1-L radon detectors and Raspberry Pi electronics have been developed for radon monitoringat the Kamioka Observatory. As of December 2017, twenty-one 1-L detectors using Raspberry Pi
electronics are being used to monitor the radon concentration in the air in the mine. A total of 28 ofthese devices are used within the research facility (the Kamioka mine and surface buildings). These
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Fig. 20. Radon concentration measured in the entrance room of the Super-Kamiokande dome (detector #9).
The dotted line indicates the 148-Bq/m3 US limit for indoor radon concentration [3]. The error bars include
statistical uncertainties, as well as the uncertainty on the detector’s correction factor.

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Table 2. Summary table of the average radon concentrations in different important areas of themine. The second row indicates the number of detectors measuring the atmosphere of each area.
The third row indicates the yearly averaged radon concentration. The fourth to seventh rows
indicate the four seasonally averaged radon concentrations. Winter is deﬁned as being between 21
December and 21 March; spring between 21 March and 21 June; summer between 21 June and
21 September; and fall between 21 September and 21 December.
Super-K
dome

Super-K
entrance

AtotsutunnelFresh-airpipe

Detectors

4

1

1

2

64.2 ± 0.3
56.0 ± 0.6
63.8 ± 0.6
79.2 ± 0.8
55.2 ± 0.5

137.7 ± 1.2
72.1 ± 1.3
146.9 ± 2.6
194.4 ± 3.5
136.0 ± 2.5

1082.6 ± 10.5
209.6 ± 5.0
1261.1 ± 23.3
1739.1 ± 32.1
791.0 ± 14.3

28.6 ± 0.3
14.1 ± 0.3
21.5 ± 0.5
50.7 ± 1.0
22.7 ± 0.4

Averaged
radon
conc.
(Bq/m3 )Year
WinterSpringSummerFall

detectors and electronics have now been used for more than one year with stable data acquisition, assummarized in Table 2. We observed seasonal variations of the radon concentration in the air of themine. Day–night variations of the radon concentration in the fresh air brought into the mine havealso been observed.
Acknowledgements
The authors would like to thank Mitsuhiro Nakamura, from the Fundamental Particle Physics Laboratory,
Graduate School of Science of Nagoya University, for his help in the development of the preampliﬁer circuit.
We would like to express our sincere gratitude to Prof. Takao Iida, from the Graduate School of Engineeringof Nagoya University, who gave us adequate advice and discussion on the development of the radon detector.
We would also like to thank the Super-Kamiokande Collaboration for their help in conducting this study. We
gratefully acknowledge the cooperation of the Kamioka Mining and Smelting Company.

References[1] Raspberry Pi documentation. (Available at: https://www.raspberrypi.org/documentation/)[2] Brookhaven National Laboratory, National Nuclear Data Center. (Available at:
http://www.nndc.bnl.gov/.)[3] EPA website. (Available at: https://www.epa.gov/radon/health-risk-radon/.)[4] S. Fukuda et al. [Super-Kamiokande Collaboration], Nucl. Instrum. Meth. Phys. Res. A
501, 418 (2003).
[5] A. Gando et al. [KamLAND-Zen Collaboration], Phys. Rev. Lett. 110, 062502 (2013).
[6] K. Abe et al. [XMASS Collaboration], Nucl. Instrum. Meth. Phys. Res. A 716, 78 (2013).
[7] S. Umehara et al., Phys. Rev. C 78, 058501 (2008).
[8] K. Nakamura et al., Prog. Theor. Exp. Phys. 2015, 043F01 (2015).
[9] P. Kotrappa, S. K. Dua, P. C. Gupta, and Y. S. Mayya, Health Phys. 41, 35 (1981).
[10] S. Tasaka and Y. Sasaki, Radioisotopes 45, 741 (1996).
[11] T. Iida, Y. Ikebe, T. Hattori, H. Yamanishi, S. Abe, K. Ochifuji, and S. Yokoyama, Health Phys.
54, 139 (1988).
[12] Maxim MAX153 datasheet. (Available at: https://datasheets.maximintegrated.com/en/ds/MAX153.pdf.)[13] Texas Instruments ADC104S021 datasheet. (Available
at: http://www.ti.com/lit/ds/symlink/adc104s021.pdf.)[14] Wiring Pi, GPIO Interface library for the Raspberry Pi. (Available at: http://wiringpi.com/.)[15] T. Sato et al., Earth Planets Space 56, BF03353071 (2014).
[16] K. D. Chu and P. K. Hopke, Environ. Sci. Tech. 22, 711 (1988).
[17] VAISALA humidity conversion formulas, B210973EN-F (2013) (Available at https://www.vaisala.
com/sites/default/ﬁles/documents/Humidity_Conversion_Formulas_B210973EN-F.pdf).
[18] PYLON® manual A900071.
17/18

Downloaded from https://academic.oup.com/ptep/article-abstract/2018/9/093H01/5107827 by University of Tokyo Library user on 14 March 2019

Area

PTEP 2018, 093H01

G. Pronost et al.

18/18

Downloaded from https://academic.oup.com/ptep/article-abstract/2018/9/093H01/5107827 by University of Tokyo Library user on 14 March 2019[19] K. Hosokawa, A. Murata, Y. Nakano, Y. Onishi, H. Sekiya, Y. Takeuchi, and S. Tasaka, Prog. Theor.
Exp. Phys. 2015, 033H01 (2015).
[20] L. A. Currie, Anal. Chem. 40, 586 (1968).
[21] R. Brun and F. Rademakers, Nucl. Instrum. Meth. Phys. Res. A 389, 81 (1997).
[22] Y. Takeuchi, K. Okumura, T. Kajita, S. Tasaka, H. Hori, M. Nemoto, and H. Okazawa, Nucl. Instrum.
Meth. Phys. Res. A 421, 334 (1999).
[23] Y. Nakano et al., Nucl. Instrum. Meth. Phys. Res. A 867, 108 (2017).