Isobe et al. Microplastics and Nanoplastics
https://doi.org/10.1186/s43591-021-00013-z(2021) 1:16

Microplastics andNanoplastics

RESEARCH ARTICLE

Open AccessA multilevel dataset of microplastic
abundance in the world’s upper ocean andthe Laurentian Great Lakes
Atsuhiko Isobe1* , Takafumi Azuma2, Muhammad Reza Cordova3, Andrés Cózar4, Francois Galgani5,
Ryuichi Hagita6, La Daana Kanhai7, Keiri Imai8, Shinsuke Iwasaki9, Shin’ichro Kako10, Nikolai Kozlovskii11,
Amy L. Lusher12,13, Sherri A. Mason14, Yutaka Michida15, Takahisa Mituhasi2, Yasuhiro Morii16, Tohru Mukai17,
Anna Popova11, Kenichi Shimizu18, Tadashi Tokai19, Keiichi Uchida19, Mitsuharu Yagi18 and Weiwei Zhang20

Abstract
A total of 8218 pelagic microplastic samples from the world’s oceans were synthesized to create a dataset
composed of raw, calibrated, processed, and gridded data which are made available to the public. The raw
microplastic abundance data were obtained by different research projects using surface net tows or continuous
seawater intake. Fibrous microplastics were removed from the calibrated dataset. Microplastic abundance which
fluctuates due to vertical mixing under different oceanic conditions was standardized. An optimum interpolation
method was used to create the gridded data; in total, there were 24.4 trillion pieces (8.2 × 104 ~ 57.8 × 104 tons) of
microplastics in the world’s upper oceans.
Keywords: Microplastic abundance, 2D maps in the world’s ocean, Multilevel dataset

Introduction
Microplastics are being reported globally, but it is challenging to compare the data collected when different
methods and reporting criteria are followed (e.g., [1]).
Harmonized or standardized protocols are therefore recommended for collecting data in the future [2, 3]. Data
collected by previous studies are still valuable and efforts
to critically compare and evaluate these data are urgently
needed. Laboratory-based studies on damage to aquaticorganisms exposed to microplastics might be inaccurate
if microplastic concentration (e.g., weight per unit water
volume) estimates are much larger than the reality [4].
Analyzing microplastic abundance by synthesizing observation data from various oceanic basins will be helpful to bridge a gap between the laboratory-based studies
and threats in reality. Similarly, real data on microplastic* Correspondence: aisobe@riam.kyushu-u.ac.jp
1
Research Institute for Applied Mechanics, Kyushu University, 6-1
Kasuga-Koen, Kasuga 816-8580, Japan
Full list of author information is available at the end of the article

abundance in the oceans is needed to validate the accuracy of numerical models (e.g., [5, 6]).
A few studies have synthesized microplastic abundance
data for the world’s oceans to generate datasets. Eriksen
et al. [7] created a publicly available dataset of microplastic abundance based on data obtained from 680 surface net tows conducted by different researchers during
2007–2013. These data were standardized to reduce uncertainty derived from vertical mixing induced byoceanic turbulence, because abundance estimates basedon surface net tows are influenced by oceanic conditions: particle counts for light-weight microplastics,
which are produced mostly from polyethylene and polypropylene (polymers less dense than seawater, [8]), decrease (or increase) near the sea surface under stormy(or calm) oceanic conditions. They used a formula to estimate the vertical distribution of the particle counts [9],to deduce the total particle count throughout the entire
water column under wind speeds measured on the Beaufort scale. However, no description of the significant© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

wave heights required for the formula was provided in
Eriksen et al. [7]. Cózar et al. [10] synthesized microplastic abundance data obtained from 841 surface net tows
(442 wind-corrected samples), including a circumnavigation cruise of the earth. Published and unpublishedmicroplastic abundance data from 1979 through 2013(11,632 samples in total) were synthesized by van Sebilleet al. [6], although their dataset was not made available
to the public. They statistically standardized the data obtained by different researchers using a generalized additive model incorporating the year in which each study
was conducted, the geographical locations, and windspeeds given by an atmospheric reanalysis product.
Here, we provide a new dataset of pelagic microplastic
abundance in the world’s oceans which incorporates different sampling methods. The dataset includes bothpublished and unpublished microplastic abundance dataobtained from 2000 to 2019. The number of samples is~ 10-fold (n = 8218) higher than Eriksen et al. [7] andCózar et al. [10]. We standardized the data obtained bydifferent researchers in a physical manner. The dataset ispublicly available as the Supplementary data in a CSV
format.

Methods –description of the dataset
Categorization of data

Different from the datasets mentioned above, the datain the present study were categorized as raw, calibrated, processed, and gridded data, similar to satellite
products (https://climatedataguide.ucar.edu/climatedata/nasa-satellite-product-levels). Raw data (hereinafter referred to as Level-0 data) were mostly obtained by surface net tows and are provided as“particle count per unit seawater volume (partly, perunit area)”. First, these raw data were calibrated tothe abundance of microplastics (< 5 mm), except fibrous microplastics (filaments and fibers), as a quality
control (Level 1). Second, to reduce uncertainty derived from vertical mixing, integrating microplastic
abundance vertically from the sea surface to the infinitely deep layer yielded processed data for both thetotal particle count (Level 2p) and weight (Level 2w),
over the entire water column per unit area, where thesubscripts ‘p’ and ‘w’ represent the particle count andweight, respectively. Third, the Level-2p and -2w data
were gridded to obtain the particle counts (Level 3p)
and weight (Level 3w) per unit area using an
optimum interpolation method (OIM). Last, these
gridded data were converted to monthly particle
counts (Level 3 pm; ‘m’ represents monthly data) andweights (Level 3wm) per unit seawater volume in theuppermost layer. The present paper describes the detailed procedures to create this multilevel dataset.

Page 2 of 14

Level 0 –raw data

Data from 27 research projects conducted during theperiod from 2000 through 2019 (Table 1) were used tocreate the Level-0 data on pelagic microplastic abundance in the world’s oceans and the Laurentian Great
Lakes. We synthesized the data collected during the past
20 years to represent the ‘current status’ of microplastic
abundance, because a long-term trend is undetectable insuch a short period, as shown by Law et al. [26], whoprovided a time series of plastic-debris abundance from1986 to 2008, and because long term change is not acommon scheme for floating plastics and microplastics[11, 26, 33–35]. In total, 23 of the 27 projects collected
microplastics only by surface net towing, but Projects#13 and #26 (Table 1) collected data via continuous seawater intake at a depth of 3 m (#12 partly included seawater intake; Table 1): Nonetheless, the target of thesetwo projects was microplastics over several tens of μm
in size (see ‘Mesh size’ in Table 1). Thus, as defined in
the present study, the surface layer included seawaterfrom the sea surface to a depth of 3 m. The Projects #25
and #27 collected data via continuous seawater intake atthe depth deeper than 3 m, so that these data were included only in the Level-0 and Level-1 (shown next)
data. The number of samples obtained after 2014 wassmaller than that before 2014, but observations wereconducted over all seasons (Supplementary Fig. 1).
Except for duplicated data (the same location, time/
date/year, and observer) which were removed because of
no relation to dataset reliability, we used all data obtained by these 27 projects to ensure that the amountthereof was sufficiently large, although parts of theseprojects adopted procedures that differed from the latest
guidelines. Almost all projects adopted a tow net with amesh size of 0.2–0.3 mm to collect floating objects, including microplastics. The maximum size of the plastic
debris was not recorded in the majority of the projects.
We here assumed that plastic debris reported in all projects listed in Table 1 was categorized as microplastics(< 5 mm, as per [8]) unless otherwise stated. This assumption is justified because, for instance, more than
90% of the plastic debris particles collected by surface
net tows in Project #9 were < 5 mm. Likewise, microplastics (< 5 mm) accounted for > 93.7% of all particles in
Project #3 despite the upper size limit of 50 mm in collecting plastic fragments (Supplementary Figure 2). Nine
projects conducted surface net tows without a flowmeter, and measured the seawater volume passing
through the net (Table 1). The absence of a flowmeter
may have led to alternations in the volume passing
through the net by ocean currents at towing speeds of 2
~ 3 knots. However, a large amount of data was averaged, which can be expected to reduce the deviations
due to ambient ocean currents flowing in different

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

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Table 1 Data sources and measurement procedures
Project
No.

Reference

Area

Sampling
method

Mesh
size
[mm]

Numberof data

Without Flowmeter Identification Unit
fiber (%)

(1)

Law et al. [11]eastern North Pacific

Na

0.335

2529

NRb

W/Oc

Vd

pieces/km2(2)

T/V Umitaka, Japan(unpublished)eSouthern Ocean,
Pacific

N

0.35

128

100f

Wg

FTIR

pieces/m3(3)

Ministry of the Environment,
Japan (unpublished)hEast Asian seas

N

0.35

312

100f

W

FTIR

pieces/m3(4)Collignon et al. [12]the Mediterranean

Wi

0.2

38

NR

W/O

V

pieces/100m2(5)

Cózar et al. [10]world’s ocean

N

0.2

194

100f

W

V

pieces/km2(6)

Cózar et al. [13]the Mediterranean

N

0.2

39

93.6

W

V

g/km2

(7)

Cózar et al. [14]Arctic Ocean

Mj

0.5

42

100f

W/O

V

pieces/km2(8)

Doyle et al. [15]Bering Sea

M

0.505

271

80

W

FTIR

pieces/m3(9)Eriksen et al. [7]world’s ocean

N

0.33

679

100k

W/O

V

pieces/km2(10)Goldstein et al. [16]eastern North Pacific

N

0.333

147

100k

W

V

pieces/m3(11)

de Lucia et al. [17]the Mediterranean

M

0.5

4

NR

W

V

pieces/m3(12)Lusher et al. [18]Arctic Ocean

M & Im

0.333

21

100l

W

FTIR

pieces/m3(13)

Lusher et al. [19]eastern North
Atlantic

I

0.25n

652

4

–

Raman

pieces/m3(14)

Pan et al. [20]western North Pacific M

0.33

18

91.1

W/O

Raman

pieces/km2(15)

Pedrotti et al. [21]the Mediterranean

M

0.33

33

100

W/O

FTIR

pieces/km2(16)

Reisser et al. [22]Waters around
Australia

N&M

0.33

57

93.6

W/O

FTIR

pieces/km2(17)Suaria, G., C. G., et al. [23]the Mediterranean

N

0.2

74

100f

W

FTIR

pieces/m3(18)

Zhang et al. [24]Bohai Sea

M

0.33

11

73

W

FTIR

pieces/m3(19)

Zhao et al. [25]East China Sea

N

0.333

15

16.8

W/O

V

pieces/m3(20)

Law et al. [26] owestern North
Atlantic & Caribbean
Sea

N

0.335

2280

NR

W/O

V

pieces/km2(21)

Mason et al. [27]Lakes Erie & Ontario

M

0.333

130

98

W

FTIR

pieces/km2(22)Indonesian Institute of Science(unpublished)Java Sea

N

0.35

16

NR

W

FTIR

pieces/m3(23)

Ifremer (unpublished)eastern North
Atlantic & theMediterranean

M & Bp

0.3

256

NR

W

FTIR

pieces/m3(24)

Pacific Geographical Institute &Maritime State Univ.
(unpublished)

Sea of Japan

N & Pq

0.1

21

100l

W

FTIR

pieces/m3(25)Kanhai et al. [28] reastern Atlantic

I

0.25

76

0 ~ 100s–

FTIR

pieces/m3

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 4 of 14

Table 1 Data sources and measurement procedures (Continued)
Project
No.

Reference

Area

Sampling
method

Mesh
size
[mm]

Numberof data

Without Flowmeter Identification Unit
fiber (%)

(26)

Yakushev et al. [29]Arctic Ocean

N&I

0.2,
0.1t

108

0 ~ 100

W/O

FTIR, μFTIRu

pieces/m3(27)Kanhai et al. [30] v

Arctic Ocean

I

0.25

58

0

–

FTIR

pieces/m3

aNeuston net, b Not recorded, c Without a flowmeter, d Visual identification, e Partly published in Isobe et al. [31] and Isobe et al. [5], f Fibrous microplastics werediscarded by this project., g With a flowmeter, h Partly published in Isobe et al. [32], i WP2 net, j Manta net, k The authors stated that the “vast majority” ofcollected microplastics were fragments. l The abundance without fibrous microplastics was provided by the coauthor. m Intake seawater, n The lower size limit inthis project, o 88% of fragments collected in this project were smaller than 10 mm, while fragments between 5 and 10 mm in size account for approximately 5%of all microplastics shown in Supplementary Fig. 2. Thus, 83% (0.88 × 0.95) was categorized as microplastics < 5 mm in size. p Bongo net, q Plankton net, r These
data were included only in Levels 0 and 1 data because the intake depth of 11 m was largely different from other studies. s The proportions of fragments were
given at each station (see Level_1_2.csv of Supplementary data). t 0.1-mm was used for the continuous seawater intake. u μFTIR is used for the continuous
seawater intakev
These data were included only in Levels 0 and 1 data because the intake depth of 8.5 m was largely different from other studies

directions. Fourth, attenuated total reflection Fouriertransform infrared spectrophotometer (ATR-FTIR),μFTIR, or Raman spectroscopy were not used to accountfor non-plastic materials in 10 projects conducted
mostly in the early 2010s. Identification by the naked
eye and/or using a stereomicroscope may have led to an
overestimation of the particle counts < 2 mm (which
accounted for 66.2% of all particles; see Supplementary
Fig. 2) by approximately 50% [5]. Meanwhile, identification using a stereomicroscope has also led to an underestimation of particle counts < 50 μm with a statisticalsignificance [36]. However, the targets of the previous
studies in Table 1 were microplastics larger than several
hundreds of μm in size, thus these early projects may
have overestimated the particle count by approximately
30% (~ 66.2% × 50%). Both sizes and surface areas of
microplastics show a continuous distribution [37] and,thus, the overestimation in small microplastics could beobserved even if equivalent lengths computed from areas(e.g., [38]) were used for a measure of microplastic size.
The microplastic abundance metric for the Level-0
data is the particle count per unit seawater volume(pieces m− 3). Abundance was measured directly using aflowmeter (12 projects) or intake water (4 projects).
However, 11 projects measured abundance per unit area,
which was computed by converting flowmeter (projects#5, #6 and #21) or global navigation satellite system data
(projects #1, #4, #7, #9, #14, #15, #16, and #20). The seawater volume for each of these 11 projects was computed by multiplying the area by tow depth (half theheight of the tow net). The abundance in Project #6 wasgiven by weight. For consistency, this was converted intoa particle count according to the Eqs. (4)~(7) shown
later, although Project #6 converted from the weight toa particle count in a statistical manner.
Level 1 – calibration by removal of fibrous microplastics

Including fibrous microplastics can cause a pseudo difference in microplastic abundance estimates obtained

from different projects; while one group of projects provided abundance data for microplastics including fiber,
another group omitted fibrous microplastics from theirestimates. Fibrous microplastics were unlikely to havebeen quantified precisely, unless clean-air devices wereused to prevent airborne contamination during samplingor processing, or airborne contamination was removed
by a blank test [39, 40]. In addition, sampling gear, suchas a tow net made from synthetic fibers, might be asource of contamination. Thus, some of the projects (#2,
#3, #5, #7, and #17) excluded fibrous microplastics whencreating their datasets. Meanwhile, fibrous microplastics
constituted a non-negligible fraction of microplastics
collected in the ocean close to the coast (projects #13
and #18), or in an estuary (Project #19).
We excluded the fibrous microplastics from the original data as a data quality control to reduce the pseudo
difference in synthesizing the data obtained by the various projects. In total, 21 of 27 projects provided nonfibrous microplastic proportions (Table 1); multiplying
these proportions given in the Level-0 data resulted in
the Level-1 data excluding fibrous microplastics (piecesm− 3). The relatively high ratios in Table 1 suggest thatfibrous microplastics were a minor component of all
microplastics, particularly in the open ocean; textile fibers made from polyester or polyamide are heavier thanseawater and are unlikely to move a long distance fromland. Recently, Suarial et al. [41] showed that 79.5% of fibers recording in the world’s ocean are cellulosic, and12.3% are of animal origin. Therefore, the ratios were assumed to be 100% for all projects in which the ratios of
non-fibrous microplastics were not recorded (projects#1, #4, #11, #20, #22, and #23).
Level 2p – processing for wind/wave correction

The Level-1 data were standardized to obtain the total
particle count, by vertically integrating microplastic
abundance over the entire water column using the windspeed and significant wave heights during each

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 5 of 14

microplastic survey (‘wind/wave correction’ [5, 32]). This
processing step was applied because abundance data of
buoyant microplastics from surface net tows vary depending on the oceanic turbulence under different ocean
conditions [9, 42, 43].
The vertical distribution of the microplastic concentration (N) can be approximated as follows:wz

N ¼ N 0 eA 0 ;ð1Þ

where N0 denotes the particle count per unit seawater
volume around the sea surface (z = 0), which corresponds to the Level-1 data in the present study; w is theterminal rise velocity of the microplastics (5.3 mm s − 1),which was obtained experimentally [43]; and z is the vertical axis, measured upward from the sea surface. The
vertical diffusivity A0 was calculated as:
A0 ¼ 1:5u kH s ;ð2Þ

where u∗ represents the friction velocity of water (=ﬃ
pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
C d ρa =ρw W 10 ); k is the von Karman constant (0.4); Hs
is significant wave height; and W10 is wind speed at 10m from the sea surface [9]. In the present study, the air
density (ρa), the seawater density (ρw), and drag coefficient (Cd) are set to 1.25 kg m− 3, 1025 kg m− 3, and 1.2 ×
10− 3 (4 m s− 1 < W10 < 11 m s− 1 in Large and Pond [44]),
respectively, so that u∗≈ 0.0012 W10. The daily windspeed data, provided by the Japanese Ocean Flux Data
Sets with Use of Remote Sensing Observations (JOFURO [45];), were obtained from multiple satellite observations for the period 1988–2013. In addition, daily
wind-speed data acquired by the Advanced Scatterometer (ASCAT) [46] from 2014 to the present were used.
Daily significant wave heights were computed using the
University of Miami wave model (version 1.0.1 [47];)over the world’s oceans within ±80° latitude to reduce
assumptions of wave properties (e.g., wave speed ofdominant wave) included in the parameterization (e.g.,[9]). However, the readers who prefer theparameterization rather than the wave model can replace
the modeled wave heights given in the supplementary
data (Level-012.csv) with other choices. The wave model
was driven by the wind data obtained by the J-OFURO
and ASCAT. These wind-speed and wave-height data,
which were gridded with a 0.25° horizontal resolution inlatitude and longitude, were used for the Eq. (2) on thesame date and at the same location as the actual observations of each project listed in Table 1.
Vertically integrating Eq. (1) from the sea surface (z =0) to an infinitely deep layer (z → − ∞ ) yields the total
particle count of microplastics per unit area (M) as
follows:

M ¼ N 0 A0 =w:

ð3Þ

The result thus obtained, in pieces/km2, is independent of oceanic conditions. However, dependence of theterminal rise velocity (w) on the total particle count (M)was examined as shown later in the first subsection in
Results and discussion.
Level 2w – conversion from particle count to weight

The Level-2p particle count was converted to weight inaccordance with Isobe et al. [5]. Each microplastic fragment was assumed to be a flat cylinder with a basediameter and height of δ and γδ, respectively, where δ isthe maximum size of the fragments, and γ is an adjustable constant (0.4) selected through trial and error to beconsistent with the microplastic weight measured directly using a mass scale [5]. We approximated the size
distribution of the total particle count of microplastics
as follows:υðδ Þ ¼ βδe−αδ ;ð4Þwhere α (0.83 mm − 1) represents the reciprocal of themode size (1.2 mm) obtained by Project #2 across theSouthern Ocean and western Pacific, and β is calculated
from Eq. (4) as follows:
R δ2

υ dδ
M
β ¼ R δ 2δ 1
¼ 
δ ;

1
1
−αδ
dδ
− α α þ δ e−αδ δ 2
δ 1 δeð5Þ1where M represents the Level-2p data for each project inTable 1 (Eq. (3)), and the operator ½ f ðδÞδδ 21 correspondsto f(δ2) − f(δ1).
Then, we calculated the microplastic weight (W) for
particle sizes between δ1 (0.3 mm) and δ2 (5 mm), as
follows:
 2
δ
W ¼
ργδ
πυ dδ
2
δ1
 4
 δ2
4δ 3 12δ 2 24δ 24−αδ δ¼ −ργβπ e;þ 2 þ 3 þ 4 þ 5
α
α
α
α
α
δ1
Z δ2

ð6Þ
or concisely expressed as:
W ¼ −ργβπ e−αδX5δ2θn δ 5−n;n¼1αn δ 1

ð7Þ

where θn = θn − 1(6 − n), θ0 = 0.2, ρ denotes the plastic
density (~ 1.0 g cm− 3) close to polyethylene and polypropylene which are majority of plastic polymers collectedin surface net tows in the ocean [48], W is weight perunit area (g/km2). Based on all microplastics collected in
Project #2, Isobe et al. [5] estimated that theIsobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 6 of 14

microplastic weight approximated by Eq. (7) was 85.3%of the actual weight.
For comparison, we also created an alternative weightdata by using a statistical manner given by the Project#6 as follows:
log10 W g km−2 ¼ 1:22 log10 M pieces km−2 −4:04;
ð8Þ
where M represents the Level-2p data as in Eq. (5). The
weight obtained by Eq. (8) (WEq(8)) is expressed approximately by W in Eq. (7) as follows:
log10 W Eq:ð8Þ ¼ 1:2 log10 W Eq:ð7Þ −2:0:ð9Þ

The dataset converted using Eq. (7) is referred to as theLevel-2w1, while Eq. (8) created the Level-2w2 data. The
difference between the Level-2w1 and 2w2 data was described in the first subsection in Results and discussion.
Level 3p and 3w – gridded data through OIM

The total particle count (Level 2p) and weight (Level
2w1 and w2) per unit area were interpolated to the gridded data (Level 3p, 3w1, and 3w2) using an OIM. Although OIM algorithms have been established by several
research projects, the method of Daley [49] and Kakoet al. [46] was adopted in the present study as follows:
XN
Ag ¼ Bg þðOi −Bi ÞW i ;ð10Þ
i¼1
where Ag (Bg) is an analysis (first guess) value to be interpolated to a grid cell, g, 5° × 2° in longitude and latitude, and Oi (Bi) is an observed (first guess) value given
at observation point i, and Wi denotes a weight function
at observation point i; there are N observation points.
The optimum weight, computed so as that the errors included in observed (O) and first guess (B) values in Eq.
(10) are unbiased and uncorrelated to generate gridded
data free of biases, can be expressed as
XN XNBμBij þ μO
ð11Þ
ij W i ¼ μig ;j¼1
i¼1
where μi,j (or μi,g) is a coefficient of error correlation between grid points i and j (or g); superscripts B and O denote observed and first guess values, respectively; μO
i; j isan identity matrix (1 only if i = j, otherwise 0); and μBi; j isestimated to ber2

μB ¼ e

r2

z
−m
2 − 2
Lm Lz

;

ð12Þ

where rz (rm) denotes the zonal (meridional) distance between two arbitrary points (i–j, and i–g in Eq. (11)), and
Lz (Lm) is the decorrelation scale in the zonal (meridional) direction [46, 50]. In the present study, the decorrelation scales of 1000 and 500 km were chosen for Lz

and Lm, respectively, through trial and error.
Interpolation was not conducted at grid cells having
fewer than observed data points within the decorrelation
scales. Zero was used as the first-guess value over theentire domain.
Level 3 pm and 3wm – gridded monthly surface
concentration data

The total particle count (Level 3p) and weight (Level3w) of microplastics in the grid cells are available forcomputing the concentration (N0 in Eq. (3)) under thevarious wind/wave conditions. For instance, the Levels
3p and 3w1 data were converted to the surface concentration for each month, under the average wind speed
and wave height for the period 1993–2018. To be sure,the seasonal variation of surface microplastic abundanceshould be validated by field surveys in the actual ocean,and so this is a subject of future research beyond thepresent study. Nonetheless, these data should allow for
accurate laboratory-based studies on impact to aquaticorganisms exposed to microplastics, so that microplastic
concentrations used for exposures are comparable withthose in reality. In addition, these data may be capableof predetermining appropriate months and locations of afield campaign to collect sufficiently large numbers of
microplastics. The wind speed and wave height data
used to create the Level-2 dataset were averaged
monthly for the period 1993–2018. Using Eqs. (2) and(3), we converted abundance at Level 3p and 3w1 (M inthe equations) to the Level-3 pm and -3wm surface concentrations, respectively, for each month using themonthly averaged wind speed and wave height. Other
parameters, such as terminal rise velocity, were the sameas those in creating the Level-2 dataset.

Results and discussion
Sensitivity of parameter choices on microplastic
abundance

Because of limited available knowledge regarding microplastics in the ocean, the present study had to makesome parameter choices for processing the data at eachlevel. Here we demonstrate how microplastic abundance
depends on the choices made by using different parameters such as terminal rise velocities (w) in Eq. (3) andformulae to convert from the total particle count toweight.
The early plastic projects ca. 2010s may have overestimated the particle count by approximately 30% becauseof misidentification of small fragments in the absence ofspectrometry. To quantify how the overestimation diminished the quality of the dataset, the Level-2p data
were created from the Level-1 so that the particle counts
were reduced by 30% in the projects without spectrometry (Table 1). It was found that the total particle countIsobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 7 of 14

Fig. 1 Sensitivity of parameters on the deduced microplastic abundance. (a) The relationship between the Level 2p data (solid line) and the samedata but for the terminal rise velocity of 0. 009 m s− 1 (dash-dot-dash line) and 0.019 m s− 1 (dashed line). (b) The relationship between the Level2w1 data (solid line) and 2w2 data (dash-dotted line)

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

averaged over the world’s ocean in the Level-2p data was
reduced approximately by 7%.
Replacing the terminal rise velocity of (Reisser et al. [43];w = 0.0053 m s− 1) with those experimentally estimated byKooi et al. [42] and Poulain et al. [38] decreased the total
particle count (M). Kooi et al. [42] estimated 0.009 m s− 1
and 0.019 m s− 1 for microplastics with sizes of 0.5 ~ 1.5 mm
and 1.5 ~ 5 mm, respectively, while the experimental velocities for microplastics with sizes of 1 ~ 5 mm in [38]; their Fig.
1B) had nearly the same magnitude as those in Kooi et al.
[42]. When w in Eq. (3) was replaced with 0.009 m s− 1, thetotal particle count (M0.009) was simply converted to
M0.009 = (0.0053/0.009) M = 0.59 M, where M represents
Level-2p data (Fig. 1a). Likewise, M0.019 = 0.28 M (Fig. 1a).
The weight of microplastics (W in Eq. (7)) depends significantly on the choice of the formula to convert from the total
particle count to weight. When the statistical manner of Eq.

Page 8 of 14(8) was adopted for the conversion, the weight in Level-2w1
data decreased to 2 ~ 20% in the range of 102 ~ 107 g km− 2(Eq. (9); Fig. 1b). This is probably because the particle counts
in smaller microplastic sizes from Project #6 (their Fig. 3)
were more abundant than those observed in Project #2 (Supplementary Fig. 2). The size distributions are unlikely to behomogeneous in the world’s ocean and, therefore, it should
be noted that the current estimate of weight includes uncertainty as shown in Fig. 1b. Therefore, for reference, thepresent study created Level-2w2 data using Eq. (8) inaddition to Level-2w1 data. Likewise, the gridded data
through the OIM using Level-2w2 data were created as
Level-3w2 data.
2D maps and statistics

The present study’s objective was to generate a new,publicly available dataset and facilitate microplastic

Fig. 2 Microplastic abundance at (a) Level 0 and (b) Level 1. Abundance is represented by the colors in the scales shown at the bottom ofeach panel

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

research based on actual and reliable ocean data. Although further and more detailed interpretations, analyses, and processing are expected to be carried out by
researchers who download the dataset, we present twodimensional (2D) maps with brief explanations of thefeatures of the dataset.
Figure 2a and b provide 2D maps of the Level-0
and Level-1 data, respectively, including the microplastic abundance obtained by Project #21, conductedin the Great Lakes of the United States. Microplastic
surveys have been conducted in the seas around theUnited States, European countries, such as the Mediterranean Sea and the eastern North Atlantic, andJapan. Approximately 46% of microplastic surveys
have been conducted in the mid-latitude ocean between 30°N and 60°N, while low-latitude surveys of
the Indian Ocean and western Pacific (between 30°S

Fig. 3 Same as Fig. 2, but for (a) Level 2p and (b) Level 2w1

Page 9 of 14

and 30°N, and 40°E and 180°E, respectively) accountfor only 5% of all data.
Integrating the microplastic abundance over the entire water column yielded 2D maps of the total particle count (Level 2p; Fig. 3a) and weight (Level 2w1;
Fig. 3b), after removing effects of winds/waves during
the observations. Note that the Great Lakes and 2019
data were excluded because of a lack of wind/wave
data among the satellite data. Nonetheless, 679 survey
positions were added to Fig. 2, because Project #9
originally provided vertically-integrated microplastic
abundance data after the wind/wave correction, andthose data are not included among the Levels-0 and-1 data.
The gridded data created by the OIM were displayedin 2D maps of the total particle count (Level 3p; Fig. 4a)and weight (Level 3w1; Fig. 4b), which coveredIsobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 10 of 14

Fig. 4 Same as Fig. 2, but for (a) Level 3p and (b) Level 3w1

approximately 60% of the entire ocean. Note that thegrid cells remain white in Fig. 4 when there were fewer
than two observed data points within the decorrelation
scales. In addition to the interior of the midlatitude
subtropical gyres, including the so-called ‘Great Garbage Patch’ (e.g., [51]) areas, a large number of pelagic microplastics were detected in the seas around
Europe, the East Asian seas, and the eastern Indian

Ocean. The sum of the particle count (weight) of
microplastics was estimated at 24.4 trillion pieces(8.2 × 104 ~ 57.8 × 104 tons) (Table 2), which was larger than the conservative estimate of Eriksen et al.
[7]; 5 trillion pieces, and 25 × 104 tons especially forthe particle count. However, the present estimates arealso conservative because gridded data were mostly
absent for the western Indian Ocean and South China

Table 2 Microplastic abundance: Level-3p and -3w data (Fig. 4). These values were obtained from grid cells where more than twovalues exited (i.e., all grid cells except the white areas). Total abundance was computed so that values were representative of each
5°-longitude × 2°-latitude grid cell. The particle count (weight) per unit area was rounded to the 1000 (10)Total particle countWeight (3w2 ~ 3w1)

Average

113,000 pieces km−2

130 ~ 2670 g km−2

Maximum (2.5°E, 53.0°N)

5,300,000 pieces km−2

14,580 ~ 126,000 g km−2

Total abundance

13

2.44 × 10(24.4 trillion) pieces(8.2 ~ 57.8) × 104 tons

Isobe et al. Microplastics and Nanoplastics(2021) 1:16

Sea, where the South Asia, Southeast Asia, and China
generate approximately 68% of all mismanaged plastic
waste worldwide [52].
The surface concentrations, represented by the particle
count (weight) per unit seawater volume are shown in
Fig. 5a and b (Fig. 5c and d) for February and August,respectively, as exemplified by the monthly data. The
particle count and weight increased in the Northern
Hemisphere during the boreal summer under calm
oceanic conditions. At the same time, the seasonality of
microplastic abundance was not remarkable in theSouthern Hemisphere, probably due to the relatively
small amount of pelagic microplastics. The annuallyaveraged abundance (both particle count and weight)
and maximum values over the entire domain are listed
in Table 3.

Conclusion –recommendations for future surveys
Microplastics are oceanic pollutants that have yet to bearchived sufficiently for mapping climatological state orvariability over the world’s oceans, despite observations
dating back to the 1970s [53]. The present study
attempted to create state-of-the-art 2D maps of microplastic abundance, based on published and unpublisheddata. However, protocols for microplastic field surveys
have only recently become available (e.g., [2, 3]), so thesharing and synthesis of observed data, which could facilitate ocean plastic studies, has only just begun. The

Page 11 of 14

field campaigns that must be prioritized to further advance marine-plastic-pollution research are discussed
below.
First, locations where large amounts of mismanaged
plastic waste are discharged should be intensively studied. In particular, a notable shortcoming of the present
dataset is the lack of microplastic data for the Indian
Ocean and the seas around Southeast Asia (including
the South China Sea). Besides waters close to landmasses, surveys in the subtropical convergence zones approximately across the 30°–latitude in both hemispheres
should be prioritized to determine the total amount ofplastics in the world’s oceans.
Second, microplastic abundance in the subsurface
layer of the ocean should be explored. Recent observations of pelagic microplastics have revealed that a
non-negligible fraction of microplastics exists in thesubsurface layers of coastal waters [36], and in intermediate and abyssal layers of the open ocean [30, 54,55]. It has been suggested that biofouling [56], inclusion within marine aggregates [57–60], and inclusion
within fecal pellets [61] allow microplastics lighter
than seawater to settle in the abyssal ocean. Thus,
microplastic abundance in the ocean is likely to bemuch greater than estimated. Three-dimensional mapsof microplastic abundance, rather than the 2D maps
presented here, are required to determine the ultimate fate of marine plastic debris.

Fig. 5 Same as Fig. 2, but for (a) Level 3 pm in February, (b) Level 3 pm in August, (c) Level 3wm in February, and (d) Level 3wm in AugustIsobe et al. Microplastics and Nanoplastics(2021) 1:16

Page 12 of 14

Table 3 Microplastic abundance: Level-3 pm and -3wm data (Fig. 5). The average, standard deviation, and maximum values in thetable were computed based on the abundance values for all months
Particle count (pieces m−3)

Weight (mg m−3)

Average

0.3

7.8

Maximum (2.5°E, 53.0°N, May)

59.4

1405.3

Third, field survey protocols of very small microplastics (< 300 μm) urgently required further development
and optimization. The lower size limit of ocean microplastics investigated to date is dependent on both themesh size of tow nets used in field surveys and the operational limitations of the equipment, such as FTIR.
However, some studies have reported the existence ofvery small microplastics down to several tens of μm inthe open ocean [38, 55, 62] and coastal waters [36].
Moreover, the drifting of nanoplastics (< 1 μm) in theocean was suggested [63]. It is plausible that very small
microplastics and nanoplastics could exist in the marine
environment, if degradation and fragmentation proceed
continuously in nature. Besides these very small microplastics, Tokai et al. [37] reported that 60% of microplastic particles with the size between 0.4 mm and 1 mm
pass through the 0.333-mm mesh of surface samplingnets. The fate of plastic debris will remain obscure unless these missing plastic particles are quantified in thewater column and bottom sediments.

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s43591-021-00013-z.
Additional file 1: Supplementary Fig. 1 Number of microplastic
surveys conducted (see also Table 1). The upper panel shows thenumber in each year from 2000 to 2019, while the lower panel
represents the number during each month for the same period.
Additional file 2: Supplementary Fig. 2 Size distribution of
microplastics collected by Project #2. Bar height represents the particle
count per unit seawater volume. Note that the bar width is 0.1, 1, and 10
mm for microplastics < 5, 5–10, and 10–50 mm, respectively. The dots
indicate cumulative ratios computed for microplastics of 50 mm
downward. Plastic fragments > 5 (2) mm in size account for 6.3% (33.8%)of all fragments.
Additional file 3: All data generated are available in supplementary
information files (Level012.csv, Level3.csv, Level3pm.csv, andLevel3wm.csv).

Funding
AI was supported by the Environmental Research and Technology
Development Fund (JPMEERF18S20201) of the Ministry of the Environment,
Japan, and by SATREPS of Japan International Cooperation Agency andJapan Science and Technology Agency. Data from IFREMER was collected
within the MSFD and supported by the French ministry of Environment.
Availability of data and materials
All data generated are available in supplementary information files(Level012.csv, Level3.csv, Level3pm.csv, and Level3wm.csv).

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Research Institute for Applied Mechanics, Kyushu University, 6-1
Kasuga-Koen, Kasuga 816-8580, Japan. 2Training Vessel Kagoshima maru,
Faculty of Fisheries, Kagoshima University, 4-50-20 Shimoarata, Kagoshima
890-0056, Japan. 3Research Center for Oceanography, Indonesian Institute of
Sciences, Jl. Pasir Putih 1, Ancol Timur, Jakarta 14430, Indonesia.
4
Departamento de Biología, University of Cadiz and European University of
the Seas (SEA-EU), Instituto Universitario de Investigación Marina (INMAR),E-11510 Puerto Real, Spain. 5IFREMER, Laboratoire LER/PAC, immeuble
Agostini ZI Furiani, 20600 Bastia, France. 6Training and research Vessel
Umitaka maru, Tokyo University of Marine Science and Technology, 4-5-7
Konan, Minato-ku, Tokyo 108-8477, Japan. 7Department of Life Sciences, The
University of the West Indies, St. Augustine Campus, W.I, Trinidad andTobago. 8School of Fisheries Sciences, Hokkaido University, 3-1-1, Minato-cho,
Hakodate, Hokkaido 041-8611, Japan. 9Civil Engineering Research Institute forCold Region, 1-3-1-34 Toyohira, Sapporo 062-8602, Japan. 10Department ofEngineering, Ocean Civil Engineering Program, Kagoshima University,
Kagoshima 890-0054, Japan. 11Pacific Geographical Institute, Far Eastern
Branch of Russian Academy of Sciences, Radio 7, 690041 Vladivostok, Russia.
12
Norwegian Institute for Water Research, Gaustadalléen 21, Oslo, Norway.
13
Department of Biological Sciences, University of Bergen, Postboks 7803,
5020 Bergen, Norway. 14Pennsylvania State University, The Behrend College,
4701 College Dr, Erie, PA 16563, USA. 15Atmosphere and Ocean ResearchInstitute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8564,
Japan. 16Faculty of Fisheries, T/S Nagasaki-Maru, Nagasaki University, 1-14
Bunkyo machi, Nagasaki city, Nagasaki 852-8521, Japan. 17Faculty of Fisheries
Sciences, Hokkaido University, 3-1-1, Minato-cho, Hakodate, Hokkaido
041-8611, Japan. 18Institute of Integrated Science and Technology, Nagasaki
University, 1-14 Bunkyo machi, Nagasaki city, Nagasaki 852-8521, Japan.
19
Tokyo University of Marine Science and Technology, 4-5-7 Konan,
Minato-ku, Tokyo 108-8477, Japan. 20National Marine Environmental
Monitoring Center, Linghe Street 42, Dalian 116023, China.
Received: 2 March 2021 Accepted: 16 July 2021

Acknowledgements
This work was supported by Ministry of the Environment, Japan. The IDEA
Consultants Inc. helped collect microplastic data observed by the
researchers.

Authors’ contributions
All authors contributed to microplastic sampling in their field surveys, andcreated the Level-0 data. SK and SI contributed to generate wind/wave data.
AI and SK created Level-1, 2, and 3 data, and contributed to write the manuscript. All authors read and approved the final manuscript.

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