Environmental Pollution xxx (2017) 1e6

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Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol

Sampling design and required sample size for evaluating
contamination levels of 137Cs in Japanese ﬁr needles in a mixed
deciduous forest stand in Fukushima, Japan*
Yurika Oba*, Toshihiro Yamada
Graduate School of Integrated Arts and Sciences, Hiroshima University, 1-7-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8521, Japana r t i c l e i n f o

a b s t r a c t

Article history:
Received 13 October 2016
Received in revised form
10 February 2017
Accepted 12 February 2017
Available online xxx

We estimated the sample size (the number of samples) required to evaluate the concentration of radiocesium (137Cs) in Japanese ﬁr (Abies ﬁrma Sieb. & Zucc.), 5 years after the outbreak of the Fukushima
Daiichi Nuclear Power Plant accident. We investigated the spatial structure of the contamination levels inthis species growing in a mixed deciduous broadleaf and evergreen coniferous forest stand. We sampled
40 saplings with a tree height of 150 cme250 cm in a Fukushima forest community. The results showed
that: (1) there was no correlation between the 137Cs concentration in needles and soil, and (2) the difference in the spatial distribution pattern of 137Cs concentration between needles and soil suggest thatthe contribution of root uptake to 137Cs in new needles of this species may be minor in the 5 years after
the radionuclides were released into the atmosphere. The concentration of 137Cs in needles showed astrong positive spatial autocorrelation in the distance class from 0 to 2.5 m, suggesting that the statisticalanalysis of data should consider spatial autocorrelation in the case of an assessment of the radioactive
contamination of forest trees. According to our sample size analysis, a sample size of seven trees was
required to determine the mean contamination level within an error in the means of no more than 10%.
This required sample size may be feasible for most sites.
© 2017 Elsevier Ltd. All rights reserved.

Keywords:
Fukushima nuclear accident
Natural forests
Abies ﬁrma
Inter-individual variation
Spatial autocorrelation
Moran's I

1. Introduction
Once radioactive materials are artiﬁcially released into a naturalenvironment, we need to promptly assess the contamination levelsof various organisms including humans (cf. Science Council ofJapan, 2011). The contamination levels of a variety of species
including wild plants (Papastefanou et al., 1989; Davids and Tyler,
2003; Yoshihara et al., 2013) and animals (McGee et al., 2000;
Moller et al., 2012; Hayama et al., 2013; Kubota et al., 2015;
Matsui et al., 2015) were assessed after the Chernobyl and
Fukushima nuclear reactor accidents.
Generically, the accumulation of pollutants into plant bodies isaffected by physiological and ecological plant characteristics as wellas environmental factors such as meteorological phenomena andsoil types (Baker, 1983). The use of plants as a biomonitor forenvironmental pollution needs to understand factors that inﬂuence*This paper has been recommended for acceptance by Maria Cristina Fossi.
* Corresponding author.
E-mail address: oyrk-2012@hiroshima-u.ac.jp (Y. Oba).

the accumulation of pollutants into plant body and to standardize
sampling methods, sample procedure and statistical analysis(Wagner, 1993).
Spatial structures of distributions of organisms often affect theresults of statistical test. In nature, organisms always have somefunctional spatial structures (Legendre and Legendre, 1998). The
ﬁrst law of geography, which is ‘everything is related to everythingelse, but near things are more related than distant things’ proposedby Tobler (1970), can apply to the spatial structure in ecology. For
example, plant populations often show aggregated spatial distribution caused by endogenous factors (e.g. dispersal, spatialcompetition) and/or exogenous factors (e.g. the heterogeneity ofenvironmental conditions) (Fortin and Dale, 2005). This spatial
distribution pattern is known as spatial autocorrelation. According
to Legendre (1993), spatial autocorrelation is loosely deﬁned as theproperty of random variables taking values, as pairs of locations acertain distance apart, that are more similar (positive autocorrelation) or less similar (negative autocorrelation) than expected for
randomly associated pairs of observations. When spatial autocorrelation exists among ecological data, there are some relationships
among the data depending on spatial distance. Because of the

http://dx.doi.org/10.1016/j.envpol.2017.02.023
0269-7491/© 2017 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Oba, Y., Yamada, T., Sampling design and required sample size for evaluating contamination levels of 137Cs in
Japanese ﬁr needles in a mixed deciduous forest stand in Fukushima, Japan, Environmental Pollution (2017), http://dx.doi.org/10.1016/
j.envpol.2017.02.023

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Y. Oba, T. Yamada / Environmental Pollution xxx (2017) 1e6

relationships among the data, these data are not independent ofeach other (Legendre and Legendre, 1998) and the lack of independence among the data affect the result of statistical analysis(e.g. underestimation of the variance of the mean) (Fortin and Dale,
2005). However, if we obtain information about spatial autocorrelation, its information can assist in designing sampling methods(e.g. the distance between observations). In addition, the information can be used to adjust the underestimated variance (Cressie,
1993), and this adjustment leads to more reliable estimation of theappropriate number of samples (Petersen and Calvin, 1986).
Therefore, understanding the spatial structure of observations
plays an important role in good sample design and statisticalanalysis (Legendre et al., 2002). However, the spatial structure ofradioactive contamination of forest tree species has been rarely
studied.
When investigating environmental pollution such as aerial andsoil pollution, tree leaves are often used as biomonitors (Sawidis
et al., 1995; Moreno et al., 2003; Tomasevi
c et al., 2004; Maher
et al., 2008). In the case of radioactive contamination, manystudies have been conducted examining the accumulation of radiocesium (137Cs) in leaves, especially conifer needles. In this study,
we investigated the contamination levels in needles of Japanese ﬁrtrees (Abies ﬁrma Sieb. & Zucc.) contaminated by 137Cs released
from the Fukushima Daiichi Nuclear Power Plant (FDNPP) to clarify
the spatial structure of the radioactive pollution of this species.
A. ﬁrma is a common large evergreen coniferous tree species offorests in Fukushima. It is a shade-tolerant species whose saplings
are abundant in shaded understory. This paper addresses following
objectives; (1) to clarify the concentration of 137Cs in current year
needles of this species 5 years after the FDNPP accident; (2) toexamine whether spatial autocorrelation exists among trees
contaminated with 137Cs; and (3) to estimate the number of samples that were necessary and sufﬁcient (i.e. required sample size) toassess the contamination level of this tree species using the resultsof spatial analysis. The ﬁnal goal of this study is to provide information about sampling methods for the assessment of contaminated forest trees.

2. Materials and methods
2.1. Study sites
The study was conducted in a deciduous broadleaf (Quercus
crispula and Cornus macrophylla) and evergreen coniferous (Pinus
densiﬂora and A. ﬁrma) mixed forest located in Soma, Fukushima
Prefecture (N37450 41, E140 470 52) (Fig. 1). In a preliminary survey,
two plots (20 m  20 m) set in the forest showed that a tree densityof 2825 trees (over 5 cm in DBH) ha1 and basal area of
45.5 m2 ha1. 24 tree species were recorded in the plots. The plots
were dominated by Q. crispula and A. ﬁrma accounting for 32.7%
and 12.8% of total tree number, respectively. The trees of Q. crispula,
Quercus serrata, P. densiﬂora and A. ﬁrma made up canopy layer at
about 20 m from the ground. No bare land was located in the forest.
Soil types of the study site is brown forest soil. The forest was
located approximately 45 km northwest of the FDNPP. According to
the nearest meteorological observation point (approximately 12 km
southwest of the study site), the average annual precipitation from1981 to 2010 was 1361.1 mm and the average annual temperaturefrom 1981 to 2010 was 10.0  C (Japan Meteorological Agency, 2016).
According to the results of the third airborne monitoring survey
conducted from 31 May to 2 July in 2011, the initial deposition of137
Cs around the study site was 100e300 kBq/m2 (MEXT, 2011).

2.2. Samples and 137Cs measurements
The sampling was carried out in March 2016. First, we selected
40 saplings varying in height from 150 cm to 250 cm. We recorded
the coordinates of each tree by a level survey using a compass and alaser rangeﬁnder (TruePulse 200, Centennial, Colorado, USA) andmeasured the tree height with a measuring tape.
Previous studies have commonly reported a large interindividual variability of 137Cs in the same tree species. For
example, Ertel and Ziegler (1991) reported that the needles of Picea
abies (L.) Karst which was most highly contaminated with 137Cs had
contamination levels two orders of magnitude higher than the least
contaminated ones in a southeast Bavarian forest in 1985. A large
intra-individual variability in an individual tree is one of the factorsthat a large inter-individual variability is generated. For example, inthe case of evergreen coniferous species, the contamination levelsof needle-leaves in an individual tree showed a large variation
between branches depending on the age of branches (Sombre et al.,1994), the size of trees (Thiry et al., 2002) and the height at which
the branch was located (Yoshihara et al., 2014). Generally, a large
variation among environmental samples masks the effect being
examined by statistical tests and lessens the power of statisticaltests (Cohen, 1988). Therefore, we used a sampling method
designed to exclude the factors causing a large intra-individual
variability (tree size, branch age and branch height) to decrease
the variation between individuals. Branch growth in A. ﬁrma species occurs over spring and summer, sprouting from the ends of theprevious year's growth. Buds are produced from summer to winterand remain dormant until branch growth starts again the following
spring. This growth pattern allows us to identify branch age bymeasuring the number of nodal units. We sampled a lateral branchof the annual trunk that had grown in 2015 to standardize thesampling branch age (current year branches) and position on an
individual tree. At the same time, we removed the litter layer andcollected soils at a depth of 0e5 cm within a 15 cm radius from thebase of trees using a soil core sampler (450  51 mm in height,
100 ml in volume). One soil sample was taken per tree. Sampled
branches were oven-dried at 65  C for 72 h and needles were
removed from the branches. The needles were pulverized using a
powdering machine (Tube Mill control, IKA Japan K.K., Osaka,
Japan) and packed into a plastic container (U-9 container, 50 ml).
Soil samples were oven-dried at 80  C for 72 h and sieved using a
2 mm mesh sieve to remove plant roots and pebbles. After that, soil
samples were packed into a plastic container (U-9 container,
50 ml).
The 137Cs concentration in samples was determined using a low
background germanium detector (GEM30-70, Seiko EG & G Co.,
Ltd., Tokyo, Japan). The detection efﬁciency of gamma-rays are reported separately (Shizuma et al., 2016). The measuring time was
10,000 s for each sample. Measurement uncertainty averaged under 10% for all samples. The 137Cs concentration was converted tothat on the sampling date based on the physical decay rate.

2.3. Statistical analysis
The concentration of 137Cs in needles and soil were bothtransformed to a natural logarithm to meet the criteria of normal
distribution (Kolmogorov-Smirnov Test, needles: p ¼ 0.072, soil:
p ¼ 0.2). We calculated the sample size required from the sample
variance as follows. We tested the spatial patterns of 137Cs concentrations from sampled tree needles and soils for spatial autocorrelation using Moran's I statistics. The Moran's I statistic at

Please cite this article in press as: Oba, Y., Yamada, T., Sampling design and required sample size for evaluating contamination levels of 137Cs in
Japanese ﬁr needles in a mixed deciduous forest stand in Fukushima, Japan, Environmental Pollution (2017), http://dx.doi.org/10.1016/
j.envpol.2017.02.023

Y. Oba, T. Yamada / Environmental Pollution xxx (2017) 1e6

3

Fig. 1. Maps showing the location of the sampling site and the Fukushima Daiichi Nuclear Power Plant (FDNPP), Fukushima, Japan. Gray parts represent Fukushima Prefecture.

distance class d (I (d)) is calculated from the following equation (1):PPðW1ðdÞ Þ ni¼1 nj¼1 wij ðdÞðxi  xÞ xj  x
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ;
I ðdÞ ¼
Pn
2
1
ðx

xÞ
i
i¼1
n

(1)

where wij (d) is the distance class connectivity matrix that indicates
whether a pair of sampling locations are in distance class d, xi and xj
are the values of the 137Cs concentrations (x) at sampling location iand j, and W (d) is the sum of wij (d) (Fortin and Dale, 2005). Values
for the Moran's I statistics run from 1 to þ1 (Fortin and Dale,
2005). Positive values indicate positive autocorrelation, and negative values show negative autocorrelation. If the value is close to 0,
there is no signiﬁcant autocorrelation (Fortin and Dale, 2005). Fifty
distance classes with 2.5 m distance intervals were used. The signiﬁcance of the Moran's I statistics were obtained by a randomization test of 5000 permutations. This analysis was carried out
using the software package R version 3.3.2 (R Core Team, 2016).
We calculated the sample size required (nreq) using the equation
by Petersen and Calvin (1986) as follows:.
2
nreq ¼ t0:95
s2 D2 ;

q¼ 1þ2(2)where t0.95 is the Student's t statistic with n-1 degrees of freedom atthe 95% probability, s2 is the sample variance of the 137Cs concentration in needles, and D is the speciﬁed error limit. D denotes theallowable 95% conﬁdence interval of the mean. In this study, we
estimated the nreq to be the D smaller than or equal to 10% and 20%of the sample mean. When a positive spatial autocorrelation exists,
the observed sample variance is known to be underestimatedcompared with the true variance (Fortin and Dale, 2005). The underestimation leads us to accept a smaller nreq than really exists. We
corrected the underestimation of observed sample variance when asigniﬁcant positive autocorrelation was found as follows. First, we
calculated the correction factor (q) following Cressie (1993). The
correction factor is expressed by I (d) (Moran's I statistics) as:




I ðdÞ
1
I ðdÞ2
1  I ðdÞn11
2
1  I ðdÞnn
ð1  I ðdÞ Þ2(3)

Then equation (2) was modiﬁed as:.
2
nreq ¼ t0:95
qs2 D2(4)

3. Results
3.1. 137Cs concentration in needles and soil
The maximum (minimum) values of 137Cs concentrations in theneedles of lateral branches of annual trunk of Japanese ﬁr and soil
under the sampled trees were 7020 (156) Bq/kg-DW and 38,100(3940) Bq/kg-DW, respectively. The geometric mean 137Cs concentration in the needles was 1530 Bq/kg-DW, whereas in the soil
the mean was 10,500 Bq/kg-DW. The standard deviation of 137Csconcentration in needles and soil ranged from 738 to 3190 Bq/kgDW and from 6340 to 17,600 Bq/kg-DW, respectively. There was no
signiﬁcant correlation between the 137Cs concentration in needles
and soil (Pearson's correlation coefﬁcient r ¼ 0.217, p ¼ 0.179).
The locations and contamination levels of needles taken fromtarget trees and soil are shown in Fig. 2. The Moran's I correlogramof the 137Cs concentration in needles (Fig. 3a) showed strong signiﬁcant positive autocorrelation at the distance class from 0 to
2.5 m (Moran's I autocorrelation coefﬁcient, I (d) ¼ 0.375, p < 0.01).
In addition, 6 other signiﬁcant autocorrelations were found. The
Moran's I of soil showed no signiﬁcant spatial autocorrelation at the
0e2.5 m distance class (Fig. 3b). The pattern of Moran's I spatial
correlogram of the 137Cs concentration of needles showed a quite
different pattern from that of soils (Fig. 3a and b).
3.2. The number of required samples
Because the 137Cs concentration in needles showed a signiﬁcantPlease cite this article in press as: Oba, Y., Yamada, T., Sampling design and required sample size for evaluating contamination levels of 137Cs in
Japanese ﬁr needles in a mixed deciduous forest stand in Fukushima, Japan, Environmental Pollution (2017), http://dx.doi.org/10.1016/
j.envpol.2017.02.023

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Y. Oba, T. Yamada / Environmental Pollution xxx (2017) 1e6

Fig. 2. The locations of sampled trees of A. ﬁrma in the study area (n ¼ 40). The size of circles shows (a) the concentration of 137Cs in current year needles of lateral branches ofannual trunk growing in 2015, (b) the concentration of 137Cs in soil (0e5 cm) under the trees. Note that the size of circles shows different concentration of 137Cs between (a) and (b).

positive autocorrelation at the distance class of less than 2.5 m, we
corrected the observed variance of the 137Cs concentration inneedles to calculate the required sample sizes. After correction, therequired sample sizes were calculated as seven and two forallowable margins of error of 10% and 20% in the sample mean at
the 95% probability level, respectively.

4. Discussion
The contamination levels of 137Cs, 5 years after the FDNPP accident, in A. ﬁrma sapling current year needles that sprouted in2015 were shown in Fig. 2. Kato et al. (2015) reported that theconcentrations of 137Cs in throughfall and stemﬂow were low about
2 years after the FDNPP accident, especially in a mixed broadleaved stand. We thus assumed that the possibility of external
contamination and absorption from the surface of needles in thisstudy was low. From the results that (1) there was no correlation
between the 137Cs concentration in sampled needles and soil, and(2) there was a difference in the spatial distribution pattern of the137
Cs concentration between needles and soil, we deduce that thecontribution of root uptake to the concentration of 137Cs in newneedles in this study may be small 5 years after the radionuclides
were released into the atmosphere. Nishikiori et al. (2015) also
indicated that there was a small contribution of root uptake tocontamination in new leaves of Cryptomeria japonica that sprouted
in 2012 in forest located approximately 160 km southwest of the
FDNPP, and that 137Cs in new leaves was translocated from othertree parts. We conclude that the 137Cs concentration in new needles
in the present study may be largely explained by the translocationof radiocesium within the tree body.
As far as we know, this is the ﬁrst study to examine the geostatistical characteristics of 137Cs in trees. Our study showed astrong positive spatial autocorrelation of 137Cs in trees at the distance class within 2.5 m. Although the underlying mechanism
creating the spatial autocorrelation remains unclear, a spatial
autocorrelation over a short distance might be applicable to othertree species growing in a similar environment. Spatial autocorrelation affects accurate sampling in that the presence of a signiﬁcant
positive autocorrelation violates the independence of observations,
resulting in an underestimation of the variance of sampling means

Please cite this article in press as: Oba, Y., Yamada, T., Sampling design and required sample size for evaluating contamination levels of 137Cs in
Japanese ﬁr needles in a mixed deciduous forest stand in Fukushima, Japan, Environmental Pollution (2017), http://dx.doi.org/10.1016/
j.envpol.2017.02.023

Y. Oba, T. Yamada / Environmental Pollution xxx (2017) 1e6

5

the variance of 137Cs between trees may become unnecessarily
large. A large variation between individuals may be reduced by awell-designed sampling method that excludes factors caused by alarge intra-individual variability (Fisher, 1966). It is therefore
necessary to standardize the characteristics of sampled branches
for the assessment of radioactive contamination of forest trees. This
allows us to minimize variation generated by poor sampling
methods, resulting in a small variance between trees. According toour sample-size analysis based on the observed variance between
trees, the sample sizes required to fall within the 95% conﬁdence
limit of the population mean of 137Cs concentrations, with allowable margins of error of 10% and 20% of the population mean, wereseven and two, respectively. These sample sizes would be feasible
for many case studies. Additional studies are needed to test if ourresults are applicable to other forest stands and other tree species.
We found the spatial structure of radioactive contamination oftrees growing in a mixed deciduous broadleaf and evergreen
coniferous forest. In the present study, however, the results were
derived from only one forest stand. Several studies reported thatthe dynamics of 137Cs in forest ecosystems depend on the type offorest stand (Melin et al., 1994; Goor and Thiry, 2004; Komatsu
et al., 2016). Thus, based on the present analysis, further investigations are needed to clarify the spatial structure of contamination of various forest stands, including stands in plantation andnatural forests. Additionally, using these results, it is necessary todevelop methods of scaling up from a stand-level evaluation to aforest-level evaluation in assessing the overall contaminated forest
areas in future work.
Acknowledgements
Fig. 3. Spatial correlograms of the concentration of 137Cs in (a) needles of lateralbranches of annual trunks, and (b) soil under the sampling trees. Open circles show
signiﬁcant Moran's I statistics (p < 0.01) and closed circles show nonsigniﬁcant ones.
Signiﬁcance of Moran's I statistics was obtained by the randomization test of 5000
permutations. Distance class is at intervals of 2.5 m. Straight line shows Moran's I ¼ 0.

(Fortin and Dale, 2005). According to Fortin and Dale (2005), it isimportant to understand the characteristics of spatial autocorrelation before the start of research. This understanding will make itpossible to design sampling methods that avoid spatial autocorrelation, or to reduce the effect of spatial autocorrelation on following
analyses. They suggest, therefore, that a pilot study for the design ofsampling is required. However, the assessment of radioactive
contamination of the environment is often urgent (Science Councilof Japan, 2011). Therefore, the best sampling methods would bedesigned to allow the analysis of a spatial pattern of radioactive
contamination of forest trees at the same time that an assessmentof contamination levels of forest trees is conducted. For example,
the locations of trees are recorded to examine whether a signiﬁcant
positive spatial autocorrelation is present in a study plot. If a signiﬁcant positive spatial autocorrelation is detected, an observed
sample variance can be corrected using a correction factor calculated using Moran's I statistics. Our results showed six additionalsigniﬁcant autocorrelations in the 137Cs concentration in needles insome distance classes. However, the processes underlying these
autocorrelations are unclear. Further investigations are needed toclarify them.
The accumulation of 137Cs into tree leaves is affected by various
factors. Yoshihara et al. (2014) reported that the concentration of137
Cs in leaves differed between leaves near the top of the tree andthose in the lower parts of the tree. In addition, the concentrationsof 137Cs in leaves were affected by the leaf age (Sombre et al., 1994;
Yoshida et al., 2004). If the sampling design ignores these factors,

We thank Prof. K. Shizuma at the Graduate School of Engineering, Hiroshima University for his support in sample measurements
and data analysis. The present research was supported through theHiroshima University Phoenix Leader Education Program for the“Renaissance from Radiation Disaster”, funded by the Ministry of
Education, Culture, Sports, Science and Technology.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.envpol.2017.02.023.
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Please cite this article in press as: Oba, Y., Yamada, T., Sampling design and required sample size for evaluating contamination levels of 137Cs in
Japanese ﬁr needles in a mixed deciduous forest stand in Fukushima, Japan, Environmental Pollution (2017), http://dx.doi.org/10.1016/
j.envpol.2017.02.023