111

Contributed paper

Potential of Crowdsourcing Approach on Monitoring
Radioactivity in Fukushima Prefecture
Wim Ikbal NURSAL*, Toshinori OKUDA and Toshihiro YAMADA
Graduate School of Integrated Arts and Sciences, Hiroshima University,
1-7-1 Higashi-Hiroshima, Hiroshima 739-8521, Japan*e-mail: wimnursal@hiroshima-u.ac.jp

Abstract
Crowdsourced data and professional scientists’ data were compared for agreement regarding air dose
rate levels and trends in air dose rate reduction to assess the value of public initiatives in radiation datacollection during nuclear crisis response. This study used seven KURAMA datasets from seven survey
periods to represent expert group data. To represent non-expert group data, we used seven datasets from
SAFECAST’s database the collection period of which was comparable to the KURAMA survey periods. A
simple linear regression model was separately applied to a pair of combination datasets from different
sources and also to a pair of first survey period datasets and subsequent datasets from the same source. The
R-squared of the models showed the non-scientist group data correlating well with the corresponding expert
data. The slopes of all the regression models, however, indicated that the air dose rate values measured by
non-expert group were about 40 to 70 percent lower than those of the expert group. The air dose rate
reduction trend from the crowd data showed a similar decreasing pattern compared to that of the expert
group, although the discrepancy in the magnitude of dose reduction between them was as high as 14 percent.
The discrepancy in air dose rate values suggest a careful interpretation of radiation information generated
solely from crowdsourced data. Nevertheless, given the strong linear relationship of crowd data with scientistgroups’ data, the superior number of data points during a crisis, and the flexibility and agility of selforganization, we argue that the public could be a great partner to scientist groups in radiation data collection.
Key words: contamination, crowdsourcing, Fukushima, monitoring, radioactivity, SAFECAST

1. Introduction
Crowdsourcing of radiation data or voluntary datacollection by lay people is a new approach to datacollection on radiation in Japan. SAFECAST, aninternational Civil Society Organization (CSO) for
citizen science and the environment, led such an
initiative in response to a lack of radiation information
available to the public soon after the Fukushima Daiichi
Nuclear Power Plant accident (Fukushima accident;
SAFECAST, 2011). SAFECAST still continues tocollect data and develop a methodology for radiation
data collection. It has gained interest and participationfrom people around the world. To date, SAFECAST’s
database has risen exponentially to 50 million records(data points) per July 2016 (SAFECAST, 2016).
The data collected by non-experts or non-scientistgroups has been undermined by other groups skepticalof the validity of the data collection methodology(Bordogna et al., 2014). In our view, there is no perfectmethod for anything, including data collection. On theother hand, data quality assessment of any source is
Global Environmental Research
20/2016: 111–118
printed in Japan

necessary before the data can be used for scientific or
policy-making purposes. From this stance, we would
like to assess how much the non-expert and expert dataagree with each other by narrowing the geographic focus
to Fukushima Prefecture area as the place most affectedby the Fukushima disaster.

2. Materials and Methods
2.1 Crowdsourced and Expert Data
Crowdsourced data mostly consist of radiation
measurements on the ground using a unique device
carried by a moving vehicle such as a car (Brown et al.,2016). The device, called a “bGeigie,” is a radiation
detector integrated with electronics designed by
SAFECAST for collecting necessary information,
including geographic coordinates, dates and times, andstoring this information in a flash memory card. Basedon its specifications, the bGeigie uses a pancake-type
Geiger-Muller detector (SAFECAST, 2013), widely
known as a “GM counter.”Expert data such as the Nuclear Regulatory Authority©2016 AIRIES112

W. I. NURSAL et al.

(NRA) or Japan Atomic Energy Agency’s (JAEA)
database include much thematic information. Not only
do they include air dose rate measurements but alsoradioactive concentrations in many media (such as soil,
fresh and marine water, the atmosphere and food). From
this database, we selected air dose rates measured by the
NRA through a car-borne survey known as KURAMA.
We chose these data because the data collection
methodology was quite similar to that of the citizen
science group, that is, the use of a car carrying a radiation
detector for measuring radiation. KURAMA has uses a
NaI(Tl) scintillator and recently a CsI(Tl) based scintillator
to detect and measure gamma radiation. Tsuda et al. (2015)
provided a thorough investigation on the air dose rate andenergy characteristics of this detector. The technicaldevelopment of the KURAMA system was described by
Tanigaki et al. (2013) and Tanigaki et al. (2015).

2.2 Dataset Specification
This study uses all KURAMA data available from2011 to 2013, comprising seven datasets from sevensurveys (JAEA, 2014). The data collection effort started
and ended on a particular date and took around a week
to two months to complete. These datasets as well asother thematic data can be freely accessed through http://
emdb.jaea.go.jp/emdb/en/.
Since the non-expert group collected the data on an
irregular basis, SAFECAST’s database was divided into
seven datasets which measured close to or within a
KURAMA survey period. Each SAFECAST dataset
holds an accumulation of three months (90 days) of
measurements. Theoretically, the air dose rates of
Cs-137 do not significantly decrease within six months.
Table 1 introduces the selected datasets from both data
sources used in this study.
2.3 Unit of Analysis
The non-scientist group and scientist group did notnecessarily measure radiation exactly in the same
location. Therefore it is impossible to compare the air
dose rates from the two groups at any point in the study
area. To solve this spatial problem, we assume thatwhen both measurements were conducted at a distanceof less than 100 meters from each other, the ambient
doses measured did not differ significantly. This
assumption adopts the opinion of Andoh et al. (2015)
who argued that 90% of an air dose rate measured at a
specified location comes from a radius of 60 metersfrom the contaminated area. The idea was implemented
by representing the study area as a matrix of 100 metersquare grids. The index of grids follows the National
Standard Grid Square Framework (Ministry of Internal
Affairs and Communication, 1996). Each grid was
assigned a unique code. In each grid where two or moremeasurements existed, the air dose rate values wereaveraged.

2.4 Data Analysis
The easiest way to know how expert andcrowdsourced approaches compare in measuring
radiation would be by relating their data to each other,since the datasets are both about air radiation doses in
the open environment. For our comparison we adopted asimple linear regression analysis, which is a widelyused, very useful, straightforward statistical tool.
We performed linear regression analyses on two
dataset combinations: (1) between datasets fromdifferent data sources, the acquisition periods of which
are comparable, and (2) between datasets from the same
source that had different survey periods. In the latter
analysis, a radiation dose reduction rate from one survey
period to the next could be assumed. The degree of
decline in air dose rates may be a good way ofcomparing both methods in viewing the dynamics ofradioactivity in the study area.
Finally, we further examined the number ofobservation from both approaches across different kindsof land cover. We used the seventh vegetation survey
data from the Geospatial Information Authority of Japanto provide information about land cover in Fukushima
Prefecture.

3. Results
Figure 1 presents the pattern we used for comparing
air dose rate measurements by the citizen science groupand by national expert group at the same place. Eachpanel in the figure shows a significant number ofobservations concentrated in the lower range of air dose
rates and fewer observations in higher range values.
Similarly, the variation in air doses becomes broader as
air dose values increase.
Figure 1 illustrates that in all survey periods, the nonexpert data correlate quite well with the expert data. On
the other hand, the actual air dose rate values from nonexpert measurements seem to be lower than those of theTable 1 The KURAMA dataset and its analogous SAFECAST dataset used in this study.
KURAMA survey
1st
2nd
3rd
4th
5th
6th
7th

KURAMA’s Acquisition Dates
2011/06/06 – 2011/06/13
2011/12/05 – 2011/12/28
2012/03/13 – 2012/03/30
2012/08/20 – 2012/10/12
2012/11/05 – 2012/12/10
2013/06/12 – 2013/08/08
2013/11/05 – 2013/12/12

Analogous SAFECAST dataset
2011/05/26 – 2011/07/25
2011/11/02 – 2012/01/31
2012/03/21 – 2012/05/05
2012/08/01 – 2012/10/30
2012/10/09 – 2013/01/07
2013/05/12 – 2013/09/08
2013/10/09 – 2014/01/07

Potential of Crowdsourcing Approach on Monitoring Radioactivity in Fukushima Prefecture

113

Fig.
diagramsof non-scientistgroup air dose rate
data from(SAFECAST)
to scientistgroup data (KURAMA),
collected
Fig.1 1Correlation
Correlation
diagramsof non-scientist
group
air doserate datafrom (SAFECAST)
to scientist
during parallel acquisition times. The figure is organized into seven panels according to the survey times of the KURAMA system.

group data (KURAMA), collected during parallel acquisition times. The figure is organized into seven
panels according to the survey times of the KURAMA system.

professional group measurements in all observation
periods. Represented by the slope of the figure, thediscrepancy level between the crowdsourcing andprofessional approached a factor of 0.65 in the firstsurvey period (June 2011), followed by 0.56 in thesecond survey period (December 2011), 0.51 in the third
period (March 2012) 0.70 in the fourth period (August –October 2012), 0.38 in the fifth period (November –December 2012), 0.68 in the sixth period (June – August2013), and 0.48 in the seventh survey period (November– December 2013).
To discover how similar the non-expert and expert
methods were in depicting air dose rate trends, the data of
the same group in the first survey period were paired withthose of a subsequent survey period. Figures 2 and 3
present the estimated slopes, showing that both expert andnon-expert data demonstrated a continuous decreasing
trend from the first survey period towards the latest. The
first panel of Fig. 2 (the first row and first column of the
figure) of the air dose rate from the second KURAMA
survey had decreased by about 25% in comparison to thefirst KURAMA survey. The subsequent panels of thesame figure show that air dose rate in the third survey
measurement by the expert group had decreased by about
36%, the fourth by 46%, the fifth by 56%, the sixth by
60%, and the seventh by 61%. During the same period, as
illustrated by the first panel of Fig. 3, the percentage bywhich the second period of SAFECAST measurements
had decreased compared to the first measurement period
was 34% . Correspondingly, as shown in subsequent
panels of the figure, the air dose rate in the third
measurement period by the citizen science group had
decreased by 50%, the fourth by 47%, the fifth by 64%,
the sixth by 65%, and the seventh by 72%.
Based on Fig. 4, in the early measurement period of
2011, the citizen science group collected more radiation
data than the national authority. The number ofobservations, however, fell off in the following years.
Both radiation measurement approaches showed
relatively significant numbers of observations in urban
and suburban environments but a lack of observations inforested areas throughout the survey periods.

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W. I. NURSAL et al.

Fig. 2 Correlation diagram of air dose rates between the first and the subsequent surveys of the expert group.

Fig. 2 Correlation diagram of air dose rates between the first and the subsequent surveys of the expert
group.

Fig. 3 Correlation diagrams of the air dose rates between the first selected measurement period and subsequent periods of the citizen
scienceFig. 3 group.
Correlation diagrams of the air dose rates between the first selected measurement period andsubsequent periods of the citizen science group.

Potential of Crowdsourcing Approach on Monitoring Radioactivity in Fukushima Prefecture

115

Fig. 4 N umbers of observations provided by crowdsourcing and the expert survey across several land cover types in Fukushima Prefecture
Fig.
Numbersof observations provided by crowdsourcing and the expert survey across several land
after
the4Fukushima
accident.

cover types in Fukushima Prefecture after the Fukushima accident.
(supported by a belt strap locked to the hand grip inside
4. Discussion
the car). It thus faces either to the left or the right side
4.1 Agreement in Dose Rate Measurementsof the car. Because of this placement, some number of
The large number of observations in the lower range
photons coming from behind the detector might beof air dose rates shown in Fig. 1 was probably due to theblocked by the body of the car. Also, due to the physicallarge extent of the contaminated area with such a range
design of the detector, such that a thick steel case coversof air dose rates and partly because access to the highly
its back side, the direction the detector is facing would
contaminated part of Fukushima Prefecture wasaffect the number of photons coming into the detector’s
restricted. Meanwhile, the high variation in the high air
window. Second, as we recognized previously, seasonaldose rate regions might be due to the detector types used.
conditions might also influence the response of GM
Knoll (2010) stated that application of this kind ofcounters. The slopes of the regression lines shown in thedetector is less useful at high counting rates because of apanels associated with winter or early springwell-known dead time phenomenon that necessitates
measurements were relatively smaller than the slopes
application of a dead time correction. Any bGeigie
in other panels. The shielding effect of snow that
instrument utilizes a dead time compensation formula in
had affected the measurements had already been
its counting system (SAFECAST, 2014). Anothercompensated for in the expert measurements databasepossible cause of high variation in the upper range of air(JAEA, 2014). Since similar efforts have not made yetdose rate measurements is a seasonal factor, together
with the citizen science group data, it is likely that thewith the detector sensitivity factor mentioned earlier. It is
snow-shielding effect have shown up as a lower slope
quite clear from the figure that the magnitude of variation
values in the regression lines.
was relatively larger in the survey periods of December
Both the citizen science group and expert group data
2011, March 2012, November – December 2012, andinclude natural background radiation. Natural
November – December 2013. During these periods, there
background radiation may include radiation fromcould have been some amount of snow cover in parts ofterrestrial sources such as uranium, thorium and radium,
Fukushima Prefecture when measurements wereand extraterrestrial phenomena such as cosmic rays.
undertaken. Tanigaki et al. (2015) found that air dose
The detector used by the expert group was not designed
readings by the KURAMA-II system were greatly
to detect cosmic rays, therefore cosmic rays may also
affected by heavy snow occurrence. The sixth panel ofhave influenced the discrepancy between the air dose
the June – August survey period, when measurements
rate values of the two measurement approaches. The
were conducted in summer, is an exception, but there is aintercept values in Fig. 1, ranging from 0.06 to 0.17,
chance of outliers also affecting the variation.
might reflect the influence of cosmic rays on the citizen
The coefficient correlation in Fig. 1 signifies that thescience group data. In highly contaminated areas, thenon-expert data can be estimated from the expert groupcontribution of total background radiation to air dose
data. Although both data are well correlated, the nonrate mesurement values would not be substantial andexpert measurements of air dose rate values of can beneither would that of cosmic rays. On the other hand,
40% – 70% lower than the values of the expert groupthey become significant when measurements aremeasurements. We suspect that at least two factors that
undertaken in low to very low contaminated areas ormight contribute to the discrepancy. First, it might beunder normal conditions.
due to the how the detector is mounted on the car. The
bGeigie is usually set on either side of a car window

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W. I. NURSAL et al.

1.2
Citizen science group

Slope values

1

Scientist group

0.8
0.6
0.4
0.2
0

Jun-2011

Dec-2011

Mar-2012

Aug-2012 - Nov-2012 - Jun-2013 - Nov-2013 Oct-2012
Dec-2012 Aug-2013 Dec-2013

Survey Period
Fig. 5 AFig.
ir dosethroughout
the survey
period the(2011-2013),
taken fromthe slope of regression
model
5 rateAirreduction
dose ratetrends
reduction
trendsthroughout
survey period(2011-2013),
taken fromthe between
slope ofthe firstsurvey (June 2011) and second survey (December 2011), third survey (March 2012), fourth survey (August 2012 – October 2012),
regression
model between
the first survey(Junesurvey2011)(Juneand2013secondsurvey(December2011),
survey2013 –fifth
survey (November
2012 – December2012), sixth– August
2013)
and seventh
surveythird(November
December2013). fourth survey (August 2012 – October 2012), fifth survey (November 2012 – December(March 2012),

2012), sixth survey (June 2013 – August 2013) and seventh survey (November 2013 – December 2013).
4.2 Reduction of Air Dose Rates
Improvement for more extensive monitoring of
The non-expert data show a decreasing trend in air
forested land is imperative in the long term, sincedose rates, and so do the expert group data, as discernedradioactive matter is highly concentrated in the forestsfrom the slopes of the regression lines in Figs. 2 and 3,(Hashimoto et al., 2012). Improvement of therespectively. We took the slopes from Figs. 2 and 3, andcrowdsourcing approach may be accomplished byused them to see their performance in the course of thesegmenting the volunteers (Rossiter et al., 2015) or
survey periods, as illustrated in Fig. 5. It clearly showsdeveloping a tasking system (Boulos et al., 2011)that the slopes from both non-professional scientists andprioritizing areas that have not yet been visited or need toexpert groups are going in the same direction, which has
be revisited. SAFECAST has been developing a fixedstarted leveling since the last period of the survey. The
sensor network installed in fixed locations and positions as
difference in the air dose rate reduction between the twoan alternative system for monitoring the environment withmethods based on their regression slopes ranges fromless effort from human operators. This new instrument
0.5% to 14%. We believe that this difference came about
will continuously update the air dose rate in near real time.
as a consequence of detector characteristics andmeasurement outcomes.
5. Conclusion
4.3 Radiation Measurements in Forested Areas
It is reasonable for any measurement which depends
primarily on human or so-called human-centric sensor
types (Srivastava et al., 2012) to have limited movement
throughout a wide landscape, particularly a landscapethat has a complex physical form or condition, such as
various forms of land cover, terrain or road access as in
Fukushima Prefecture. What we can learn from thecitizen science approach is that the response of the
public toward provision of radiation information in theearly period of the crisis was very positive given thatmany observations were done in urban and suburban
environments (Fig. 4).

This study extends the discourse about the quality of
information that could be acquired by citizen
participation in science. We investigated the radiation
data of SAFECAST and data managed by the NRA and
JAEA for agreement on air dose rate values, reduction in
air dose rates from 2011 to 2013, and the number ofmeasurements across several land cover types. We
presented evidence that the air dose rate values fromcrowdsourced radiation measurements are well correlatedwith scientist group measurements. The real air dose rate
values from the citizen science groups, however, werelower than those of the expert groups, ranging from 40%
up to 70% lower. We also assessed trends in air dose

Potential of Crowdsourcing Approach on Monitoring Radioactivity in Fukushima Prefecture

rates indicated by the slope of the linear regression model
between two datasets of different survey periods but fromthe same source. The result showed that the trend of air
dose reduction generated from citizen science group data
followed the same direction of the trend provided fromscientist group data. The magnitude of air dose rate
reduction of the citizen group data toward the expert
group data is lower than that of the expert group, thediscrepancy between which can be as high as 14%. We
discussed some factors that might cause suchdiscrepancies in measurement values, which are mainly
associated with GM counter characteristics andsensitivity. We provided evidence that a crowdsourcing
approach to radiation data is responsive to a crisis.
Especially for urban and suburban areas, a crowdsourcing
approach could potentially be relied on to provide
radiation information after a nuclear accident.
Given the significant discrepancy in air dose rate
values, we would like to suggest that the radiation
information provided by citizen science, especially frommeasurements with GM counters would need
supplemental and comparative material with a brief
explanation on the existing discrepancies. Regarding theutilization of citizen science data, we would emphasize
the importance of preprocessing or pre-analysis stages
including data selection and conversion, before further
using them for generating information. Since the air
dose values show discrepancies with a seasonal pattern,data selection based on period of measurement is crucial.
The selection of datasets based on the detector type fromthe SAFECAST database is important as well because
the database may contain numerous measurements using
a variety of detectors. Each detector has uniqueconversion factors to other measurement units.
We argue that, since both data sources have quite agood linear relationship, the use of citizen sciencethrough the SAFECAST database to provide radiation
information is worth consideration. It is particularly
beneficial in areas lacking data due to government
resource limitations or because national monitoring is nolonger required there.

Acknowledgement
We would like to express our sincere gratitude to thereviewers for their constructive comments. We are in
debt to and sincerely thank certain prominent
SAFECAST members: Pieter Franken, Nick Dolezal andJoe Moross for helping us understand and use their data.
We also want to express our gratitude toward the Japan
Atomic Energy Agency (JAEA) for providing publicaccess to their data. We also thank Prof. Kiyoshi
Shizuma from Hiroshima University for valuableknowledge and input to improve this study. The
Hiroshima University Phoenix Leader Education
Program (Hiroshima Initiative) for the Renaissance from
Radiation Disaster funded by the Ministry of Education,
Culture, Sports, Science, and Technology financially
supported this study.

117

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