Marine Pollution Bulletin 182 (2022) 113997

Contents lists available at ScienceDirect

Marine Pollution Bulletinjournal homepage: www.elsevier.com/locate/marpolbul

Identifying marine debris source position using adjoint marginal sensitivity
method and stranded beach litter data in Singapore
Hong Siang Gan a, Shunsuke Kanao b, Toru Sato b, *, Klaus Hellgardt a, Yusuke Uchiyama caDepartment of Chemical Engineering, Imperial College London, United Kingdom
Department of Ocean Technology, Policy and Environment, University of Tokyo, Japanc
Department of Civil Engineering, Kobe University, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Singapore
Marine debris
Stranded beach litter
Adjoint marginal sensitivity method
Volunteer beach clean-up groups
Citizen science data

Movement of marine debris is transboundary and complex, travelling vast distances and accumulating on
shorelines. These marine debris wash ashore as stranded beach litter. The objective of this work is to identify
release sources of marine debris accumulated along the Singapore coastlines collected by applying a timebackward adjoint marginal sensitivity method and citizen science data of stranded beach litter by a voluntary
beach clean-up group. A popular tourist hotspot on the opposite shore was estimated as a possible release source
contributing to the marine debris accumulation. This analytical result was validated by population density, in­dustry types, rainfall, and inference from product packaging labels. The use of the citizen science data also
illustrated potential as a data source for baseline monitoring and long-term cross-border research that influence
policymaking. Future research can be conducted in an expanded domain, considering monsoon effects andinstantaneous release events.

1. Introduction
Volunteers participating in many non-profit organisations havedecided to combat marine debris by picking up stranded beach litter incoastal clean-up sessions. Some of these highly committed clean-up
groups also perform data recording to track their efforts and archive
activity logs when they remove marine debris washed ashore. However,many individuals expressed a sense of futility as they return after a few
weeks to a beach renewed with trash. One volunteer reflected in an
online community that their efforts are “endless because the rubbish
never decreases”. Hence, an opportunity arose to engage these clean-up
groups to not only fix the symptoms but address the root of the problem.
Since collecting accumulation data of stranded beach litter over many
months at different positions is time-consuming and manpowerintensive, it may be rewarding if existing records kept by beach cleanup groups is re-highlighted by serving as an alternative contribution
through the adjoint method.
Within Southeast Asia, strong commitments have recently been
made to combat ocean-bound litter, such as the Indonesian Action Plan
(Ministry for Marine Affairs, Republic of Indonesia, 2017), Thailand's
Roadmap on Plastic Waste Management 2018–2030 (Ministry of Natural
Resources and Environment (MONRE), 2019), Malaysia's Roadmap

Towards Zero Single-Use Plastics 2018–2030 (Ministry of Energy, Sci­ence, Technology, Environment and Climate Change (MESTECC), 2018)
and the Association of Southeast Asian Nations Regional Action Plan for
Combating Marine Debris (ASEAN Secretariat, 2021). While these ini­
tiatives are expected to provide long-term and essential solutions to themarine debris problem, it remains unclear whether they can provide
urgent solutions.
A numerical approach such as the adjoint method can be used toestimate the pollution source via time-backward diffusion of adjoint
probability. Wilson and Liu (1994) illustrated this concept by showing
heuristically that the time-backward position and flux probabilities canbe obtained from their time-forward counterparts. Neupauer and Wilson(1999) formally derived a general form of the adjoint method torepresent the relationship described by Wilson and Liu (1994) andarrived at the same probabilities as the heuristic method for a onedimensional system. This result was extended to a multidimensional
groundwater system comprising of a rectangular two-dimensional
confined aquifer with pumping well (Neupauer and Wilson, 2001). It
was shown that the model can be extended to non-uniform and transient
flows (Neupauer and Wilson, 2002) and it can also delineate the possible
capture zone in 3D space (Neupauer and Wilson, 2004). Liu and Zhai
(2007) evaluated inverse methods and found the method described by* Corresponding author.
E-mail address: sato-t@k.u-tokyo.ac.jp (T. Sato).
https://doi.org/10.1016/j.marpolbul.2022.113997
Received 8 May 2022; Received in revised form 22 July 2022; Accepted 25 July 2022
Available online 10 August 20220025-326X/© 2022 Elsevier Ltd. All rights reserved.

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

the series of researches of Neupauer and Wilson to be suitable for indoor
air quality studies and applied the method in indoor air pollution sce­narios. Furthermore, it was shown that the method does not requireconcentration information. Instead, an array of threshold-based sensors
can be used for source identification.
For ocean contaminant transport, Mori et al. (2017) adopted the
adjoint marginal sensitivity method of Dimov et al. (1996) to success­fully predict the position and flux of CO2 seepage generated from a timeforward tidal current model. Sakaizawa et al. (2019) employed themethod to estimate a continuous CO2 seepage source with volumetricflux in three-dimensional unsteady flow. Kanao and Sato (2022) showedthat the method can be used for estimating multiple seepage sources andtheir fluxes. Due to the flexibility of the adjoint method, it can be applied
to contaminants other than CO2, such as marine plastics debris. Hui et al.
(2021) tried to identify marine plastics hotspots within the Tsushima
Strait, a narrow channel west of the Sea of Japan.
This paper aims to apply the adjoint marginal sensitivity method tomarine debris floating in the seas around Singapore, which is a new casestudy of the method that has not previously been performed, and to
identify the source locations of debris washed ashore on Singapore's
beaches, using beach clean-up data. To validate the adjoint simulation
result, circumstantial evidences are enumerated from social and meto­
cean aspects. If this attempt using the adjoint method is successful andwidely used, voluntary beach clean-up activities, as citizen science data,
can contribute to long-term reduction of plastic debris in cities identified
as sources.

release of C, at the same time, the time range of the observation of ψ *,which is defined in (2) below, and Π is the rectangular function.
The adjoint equation corresponding to the original advectiondiffusion equation of a pollutant is[]∂φ* ∂Vj φ*
∂
∂φ*
−
=
Dc
+ Π(x − xm : Δxm )Π(τ − 0 : Δtm ),
(2)
∂xj
∂τ
∂xj
∂xj
where φ*is the AMS [s], τ = tm − t is the backward time [s], t is theforward time [s], tm is the observation time [s], Δtm is the time range ofthe observation and, at the same time, the time range of the release of
φ*, xm is the observation position vector [m], and Δxm is the spatial
range of the observation position.
The debris velocity is considered the combination of ocean current
velocity and wind velocity, following Tong et al. (2021), who validated
this simple relationship by comparing the observation and their nu­
merical simulation:(3)

Vj = Vjc + Cw Vjw ,where Vjc is the ocean current velocity [m/s] taken from HYbrid Coor­
dinate Ocean Model (HYCOM), Vjw is the wind velocity [m/s] from
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5,
and Cw is the wind coefficient.
Flux of the pollutant at any position k can be estimated as∫ tm
C(xm , t)dt
fk = ∫ |ttmm |− Δtm.
(4)* (x , τ)d τψ0|tm |− Δt0

2. Materials and methods

In fact, there is no need to solve (1) if we know all the numerators of(4) by the observation.
If the fluxes at a position estimated using multiple sensors are thesame, this position is the source point. However, numerical simulations
cannot escape errors, and Kanao and Sato (2022) introduced an evalu­
ation index SDN, which is the non-dimensional standard deviation of theresiduals for multiple source points:√̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
)2̅
∑N (
∑K
1
n=1 Cn Δtm −k=1 Ψ n,k f kN
SDN(K) =
,
(5)
N
∑
1
C
Δt
n
m
N

2.1. Adjoint marginal sensitivity method
Fig. 1 illustrates that marine debris is released from a yet unknown
source (“release source”), while stranded beach litter accumulation is anobserved variable observed on another shoreline (“sensor”). An adjoint
marginal sensitivity (AMS) is then released from this position in thetime-backward direction, with the minimization of an evaluation index,
to estimate the source position and flux.
The model equation of the advection-diffusion of a pollutant is)(()∂ C ∂ Vj C
∂
∂C
=
(1)
+
DC
+ Π(x − x0 : Δx0 )Π(t − 0 : Δt0 ),
∂xj
∂t
∂xj
∂xj

n=1

where K is the number of source points, N is the number of observation
points, Cn is the observed pollutant concentration, Δtm is release dura­

where C is the pollutant concentration [kg/m2], Dc is the eddy diffusivity[m2/s] of the pollutant, x0 is the source position vector [m], and Δx0 is
the spatial range of the source position, Δt0 is the time range [s] of thetion [s], f k is the average of release fluxes fk [kg/m2/s], and Ψ n, k is theadjoint probability [s] at the source position and time, defined as:∫ |tm,n |Ψ n,k =

|tm,n |− Δt0,k()ψ *n x0,k , τ dτ.

(6)

It is assumed that release flux is constant in the continuous releasemode and that multiple sourse positions begin the releases simulta­
neously. The spatially smallest value of the SDN signifies the least dif­ference between observed and estimated pollutant concentration andthe position of the smallest SDN indicates the source position.
2.2. Study site profile: Singapore
As shown in Fig. 2, the study site defined as the waters surrounding
Singapore (longitude: 103◦ to 105◦ , latitude: 0◦ to 1.75◦ ) serves as asuitable choice for a preliminary case study of the adjoint method. Witha small land area of 725.7 km2 and dense population of 5.69 million,
many residential, recreational, and commercial activities occur nearcoasts (Department of Statistics, 2020). For example, Jurong Island is apetrochemical hub situated on the southwest edge of Singapore, andLazarus Island and Sentosa Island are tourist hotspots in the south, while
East Coast Park is a recreational shoreline extending across the entire
southeast of the country. Hence, marine debris accumulation can have

Fig. 1. Illustration of time-forward advection-diffusion of marine debris andtime-backward advection-diffusion of adjoint marginal sensitivity (AMS),
where f is the release flux, C is the observed concentration, and ψ is thereleased AMS.
2

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

during simulation and generalised as the same cell. However, eachobservation is retained as data in different time ranges and the total
number of the observation data does not decrease. Although it is better
to use finer grids for the computational accuracy, the grid resolution of
the HYCOM, from which velocity data were interpolated to the 2 km
grids, is 1/12◦ , about 9 km and, therefore, smaller ones are not always
better. Unstructured grids can help to better represent topography, but
resolution is important anyway.
2.3. Data processing
The methodology comprises of data collection, data pre-processing,adjoint method simulation and post-processing. Since data sources
varied, transformation into a standard X-Y, i.e. East-West and SouthNorth, structured grid system with equal grid spacing and time inter­val was performed. The files were converted to appropriate input for­mats, followed by the simulation, and post-processing to generategraphs and maps for subsequent analysis. The processes are summarisedin Fig. 4.
Bathymetric data was obtained from HYCOM and Navy Coupled
Ocean Data (NCODA) Global 1/12◦ Analysis experiment GLBa0.08.
Working with marine debris clusters floating on the sea surface, theeastward and northward 2D current velocity components, u and v
respectively, were extracted from Global Ocean Forecasting System(GOFS) 3.1 41-layer HYCOM + NCODA Global 1/12◦ Analysis. (Naval

Fig. 2. Locations of Singapore, part of Malaysia, Indonesian islands, and theStrait of Malacca: 103.00◦ E to 105.00◦ E, 0.00◦ to 1.75◦ N.

an impact on multiple aspects of the activities in Singapore.
Additionally, the United Nations Environment Programme (2020)
reported a lack of major research focused on marine plastics in
Singapore, highlighting the lack of survey and monitoring, as well as
hotspots and source differentiation studies. Marine plastics source
identification and standardisation of their protocol were cited as
knowledge gaps. Thus, these various economic and social aspects,
environmental implication, along with the presence of many coastal
clean-up groups in the region, were reasons to select this region as thestudy site.
Fig. 3 shows the grid system of the numerical simulation and thepositions of the field observation data in the northern half of thecomputational domain. The adjoint simulation is two-dimensional (2D)
only on the sea surface. The narrow waters between the islands wereconverted to land areas because of the lack of the grid resolution and the
expected low flow rates. Since the entire domain was discretised into thestructured grids with a size of 2 km by 2 km, beach clean-up activities atdistinct but nearby positions were collapsed into the same position

Fig. 4. Pre-processing steps required to create standard input files for theadjoint method, followed by analysis.

Fig. 3. Northern half of the computational grid system, where grey and white cells indicate land and sea, respectively. Observation positions within a single
computational cell are spatially merged and indicated by orange diamonds: approximately, (130.7679◦ E, 1.25◦ N), (130.8214◦ E, 1.25◦ N), and (130.8929◦ E,
1.3214◦ N), which, however, contain 14 data points with different time ranges.
3

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Research Laboratory, 2021; Metzger et al., 2017; Cummings andSmedstad, 2013; Cummings, 2005) and combined using the Climate
Data Operators (CDO) code package along the time axis into a single
NetCDF file.
Coordinate transformation was performed to convert the HYCOM
native Mercator-curvilinear horizontal grid to a structured X-Y grid
system preserving velocity data corresponding to each point. The data
sources are interpolated to proceed at 0.1 hourly time step between 01
Jun 2020 and 01 Dec 2020.
Near-surface sea wind velocity was incorporated to account for the
impacts of wind velocities on the sea surface debris movement. Dataset
was obtained from ECMWF ERA5 reanalysis product at standard refer­ence height of 10 m above ocean surface (Copernicus Climate Change
Service (C3S), 2017; Hersbach et al., 2020). The ERA5 reanalysis has thehighest temporal and spatial resolution: 6-hourly daily data and 0.25◦
spatial resolution (Gualtieri, 2021), and the key distinction of covering
the desired study site. Particularly, the “10m u-v components of wind”
was selected instead of the equivalent neutral winds. Equivalent neutral
winds are calculated assuming a neutral atmospheric stratification,
hence when wind stability is low, the calculated wind speeds can beexceedingly large. Stability conditions over open sea is commonly nonneutral (Baas et al., 2015) and wind speeds around Singapore are usuallylarge so equivalent wind estimated will be higher than normal winds(Liu and Tang, 1996), making the former variable more applicable.
Li et al. (2021) suggested that the Indian Ocean, due to its
geographical location and monsoon climate, has a significant impact on
microplastics in the Pacific Ocean through the Straits of Malacca. Jonget al. (2022) systematically quantified the effect of monsoons on
microplastic distributions in beaches and mangroves of Singapore andconcluded that microplastic abundance strongly correlated with wind­age. Therefore, although their target is microplastics, it is presumed thatthe effects of monsoon winds are even more important for our target,
floating debris. Fig. 5 shows an example of the velocity field, containing
current and wind converted from HYCOM and ERA5, respectively, to thegrid of this study at the start of the simulation, 01-June 2020. This day isalmost the beginning of the Southwest Monsoon season, during which
the wind velocity tends to be predominantly southwest to northeast.
However, as explained later in 4.3, the velocity direction changes fromtime to time.
Weight-concentration raw data of marine debris were obtained froma voluntary beach clean-up group, Singapore Beach Warriors (SGBW), as
shown in Table 1. The total weights were standardised by area coverage
into kg/m2 basis and used as C(xm, t) in (4).
Each time range between two stranded beach litter collection eventsat the same position is defined as a separate data. Therefore, there are 16
observation points used in the adjoint method: namely N = 16 in (5). It isalso assumed that marine debris is continuously accumulated on thebeaches within these time ranges, meaning that the AMS was released in
the time-backward simulation in the same continuous manner as that ofTable 1Marine debris beach clean-up data with courtesy of Singapore Beach Warriors(SGBW).
LocationStart of collection(YYYY-MM-DD
HH:MM)End of collection(YYYY-MM-DD
HH:MM)

Total
weight(kg)

Area
coverage(m2)

East CoastPark(Zone C)
East CoastPark(Zone E)
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah
Tanah
Merah

2020-07-24
04:00

2020-07-25
03:00

32

2080

2020-07-23
04:00

2020-07-25
11:00

30

3420

2020-07-20
10:00
2020-07-26
04:00
2020-08-01
03:30
2020-08-08
03:30
2020-08-09
03:30
2020-08-10
03:30
2020-08-15
03:30
2020-08-22
03:30
2020-08-29
11:00
2020-09-05
03:30
2020-09-06
03:30
2020-09-19
03:30
2020-09-19
03:00
2020-10-31
10:00

2020-07-26
04:00
2020-08-01
03:30
2020-08-08
03:30
2020-08-09
03:30
2020-08-10
03:30
2020-08-15
03:30
2020-08-22
03:30
2020-08-29
11:00
2020-09-05
03:30
2020-09-06
03:30
2020-09-19
03:30
2020-09-19
03:00
2020-10-31
10:00
2020-11-29
10:00

131

2900

194.5

2900

309.352900

270.422900

209.65

2900

200

2900

253.03

2900

212.1

2900

140.53

2900

127.3

2900

504.662900

125.95

2900

78.35

2900

332.422900

the debris accumulation. Actually, marine debris is accumulated on thebeach intermittently or continuously. To investigate the validity of thecontinuous accumulation assumption, the results of sensitivity analysis
for the amplitude and period of the debris accumulation will be dis­cussed later in Sections 3.3.3 and 3.3.4, respectively. Intermittent
accumulation may be regarded as an extreme case of periodic
accumulation.
The start time of the adjoint simulation, τ = 0 in (2) and (4), was set
to be later than the latest collection time in Table 1, 2020-11-29 10:00.
Each observation point has different tm and Δtm.
Settings and conditions are prepared prior to conducting the simu­
lation in accordance with the various conditions that must be satisfied.
Table 2 summarises the settings, followed by brief descriptions of thefulfilled conditions.

Fig. 5. Example of velocity vector map including wind and current, which were interpolated from ECMWF-ERA5 and HYCOM, respectively, to the grids of this study,
based on (3) with Cw = 0.04. The vectors are shown every three cells in south-north direction.
4

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Table 2
Conditions and parameters for horizontal 2D adjoint simulation.
Parameters

ValuesLongitude/latitude
Grid spacing
Time span
Time increment
Release candidates
Eddy diffusivity Dc
Wind coefficient Cw

Lon: 103◦ –105◦ /lat: 0◦ –1.75◦
2 km × 2 km
01 Jun 2020 to 01 Dec 2020
0.1 hourAll coasts + open boundaries
65.0 m2/s
0.04

Table 3
Release fluxes and positions estimated by the adjoint method in the cases where
the number of source positions is changed from 3 to 1.
Case where thenumber of source
positions ischangedEstimated
source positions
in cell number

Estimated
fluxes [kg/m2/s]

Backward time τ when
the temporal change of
SDN reaches a plateau
[day]

K=3

78, 292.2451 ×
10− 5
1.5408 ×
10− 10− 2.9748 ×
10− 9
1.1505 ×
10− 101.1335 ×
10− 101.2437 ×
10− 10146.6759, 6951, 76

From the velocity data obtained from HYCOM, it was found that the
maximum absolute velocity throughout the entire time-range andcomputational domain is 0.9249 m/s in the eastward direction. Hence,with predefined settings of time increment of 0.1 hourly (360 s) and grid
spacing of 2.0 km, the Courant–Friedrichs–Lewy (CFL) Condition(Courant et al., 1967) is satisfied as the Courant number is 0.1665(<1.0). The eddy diffusivity Dc and the wind coefficient Cw were set to be
63.5 m2/s following Isobe et al. (2009) and 0.04 following Tong et al.
(2021), respectively.
The largest observation timespan is 2020-09-06 03:30:00 to 202009-19 03:00:00, as shown in Table 1. This is equivalent to 7,450,200 s
minus 6,328,800 s in the backward time and, therefore, the time span
shown in Table 2 is acceptably long.
The source candidates in Table 2 mean that any point, either multiple
or single, can be selected as the source positions from all the points on
the coastlines and the open boundaries.

K=2

54, 6458, 69K=158, 69142.50140.83two sources is the same as that in the case with one source, suggestingthat both the cases are possible and, in such a case, the case with thelarger number of sources should be adopted so as not to lose any possible
sources.
The two unique release source positions are denoted on the struc­tured X-Y grid system in Fig. 6, where the grid indexes I and J mark thepositions (54, 64) and (58, 69).
3.2. Elimination of observation outliers

2.4. Assumptions and limitations

Since there are numerous other places contributing small amount ofwastes into the sea, the beach litter recorded may include debris released
from sources other than the two points shown in Fig. 6. This means thatobservations contributed by other unidentified sources may mislead theresults. To avoid this inconvenience, we tried to eliminate observation
points that did not detect debris released from the two sources. For thispurpose, we used the adjoint simulation result of SDN(K = 1) shown in
3.1 and examined the detectability of observation points for each of thetwo sources, regardless of the position giving the minimum SDN.
Fig. 7 shows the relationship between the concentration observed by∑each sensor Cn and the estimated concentration Δt1m Kk=1 Ψ n,k f k defined in

Because the adjoint method can cope with either instantaneous orcontinuous release, the suitability depends on whether a temporally
single pollutant-release event is expected, such as typhoon or flood. Lo
et al. (2020) evaluated the effects of Typhoon Mangkhut in 2018 on
marine debris pollution including macro debris and microplastics inHong Kong and found that, for macro debris, 11.4-fold increase wasobtained before and after the cyclone. Nevertheless, in this study,
continuous release of marine debris was assumed. It is likely that marine
debris does not accumulate at a constant rate across all the time.
Therefore, a sensitivity analysis using fluctuating deposition rates was
conducted in Section 3.3 to examine the effect of this assumption on thesimulation results.
Next, the pollutant of interest is marine debris clusters instead of a
specific target such as plastics, which poses a challenge in defining thepollutant characteristics. The diffusion coefficient DC in (2) cannot be
accurately defined because different clusters of debris must havedifferent values of diffusivity. The same also holds for the wind coeffi­
cient Cw in (3). Thus, a sensitivity analyses were conducted in Section
3.3 for these values.
Finally, it was assumed that there is no backwash of stranded beachlitter into the sea. It is likely that waves depositing litter onto the shore
also remove some litter over time, but accounting for such a factor isinfeasible. Thus, the recorded concentration of stranded beach litter isused to represent the total marine debris washed ashore.

(5), assuming each position of the two sources: namely, (54, 64) in (a)and (58, 69) in (b). If there is a large disparity between them, it suggeststhat such an observation Cn contains marine debris coming from sources
other than the assumed single point. In this study, outliers that exceed
one standard deviation were removed for each of the two selected source
positions. Since they are simulations for K = 1, observation points that
detected debris released from both the two sources may be eliminated.
However, those that detected debris from one of the two sources defi­
nitely remain, serving our purpose. For (54, 64) in (a) and (58, 69) in (b),11 and 6 observation points remained, respectively.

3. Results and discussion

74 km

3.1. Detection of source positions using all observation data
For the adjoint method, it is possible to assume multiple source
points. Hence, we performed simulations assuming that marine debris
was released from three concurrent positions (K = 3), two concurrent
positions (K = 2) and one position (K = 1). The preliminary results asshown in Table 3 indicated that the case of three source positions is aninvalid scenario because the flux of one of the sources is negative, as
shown in Table 3. One of the estimated source positions in the case with212 kmFig. 6. Estimated positions of the marine debris release sources, which areindicated by orange diamonds: Position (54, 64) and Position (58, 69),
approximately, (103.9642◦ E, 1.1429◦ N) and (104.0357◦ E, 1.2321◦ N),
respectively.
5

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

(a)

(b)

Fig. 7. Relationships between observed and estimated concentrations for Position (54, 64) in (a) and for Position (58, 69) in (b). The dashed lines and the squares
indicate the standard deviations and outliers, respectively.

After removing the outlier observation points, an adjoint simulationof SDN(K = 1) was performed again, using the remaining observation
points for each of the two sources.
Fig. 8 indicates the temporal changes of the spatial minimum of theevaluation index SDN, using the remaining 11 and 6 observation pointsin (a) and (b), respectively. Although 11 and 6 points were originallyselected for the position (54, 64) in (a) and (58, 69) in (b), we searched
for new source positions in these SDN(K = 1) simulations. As thesimulation progressed, an estimated source position changed over time.
However, after about 145th day in (a) and 150th day in (b), plateaus of
the SDN values appeared, and SDN became the minimum at a single
position during each plateau. Therefore, these positions are considered
the renewed sources. The I-J coordinates of the renewed source positions
are shown in Table 4. Although slight changes are found for Position 2
between the results before and after the removal of outlier observation
points, it can be concluded that the two origins of the marine debris areat the northern coast of Batam, which is an Indonesian island situatednear the south of Singapore. Since scientific validation for this result is
not very easy, we will discuss this from social and metocean points ofview later in Section 4.
It can be seen that the SDN value in Fig. 8(a) is larger than the SDN
value in (b) over the entire simulation time. This is probably because thenumber of observation points in (a) is larger than that in (b), resulting inlarge errors when calculating SDN defined by (5). It is also thought that,for the same reason, the plateaus appeared earlier in (a) than (b).

Table 4Comparison of source positions before and After the removal of outlier obser­
vation points.
Estimated
positions

Before the removal of outlier
observation points

After the removal of outlier
observation points

Position 1
Position 2

58, 6954, 6458, 6956, 673.3. Sensitivity analysis
As discussed in Sections 2.3. and 2.4, certain assumptions that can
impact the validity of the results must be addressed. It is paramount tounderstand the effects of these input parameters on target variables toeliminate the possibility that the simulation settings change the results
significantly. Three input parameters for the simulations were investi­
gated: namely, the standard eddy diffusion coefficient DC, the windcoefficient Cw, and the AMS release ways. The results of the sensitivity
analyses are summarised in Table 5 and Fig. 9, which show that theeffects are largest for changes of the wind coefficient. Elaboration foreach of the three analyses follows.
3.3.1. Wind coefficient
The effects when wind coefficient Cw in (3) was doubled and halved
were studied. The analysis showed that changing wind coefficient

(a)

(b)

Fig. 8. Temporal change of the spatially minimum SDN using remaining 11 and 6 observation points in (a) and (b), respectively.
6

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Table 5
Sensitivity analysis results in Case (1): wind coefficient, Case (2): diffusivity, Case (3): sinusoidal amplitude of AMS source term, and Case (4): sinusoidal period of AMS
source term, tabulated by percentage deviation from original cases.
Targetvariable

Wind coefficient Cw
Lower(0.02)

Original(0.04)

Min. SDN− 36.89%30.05 %
− 38.03
%
69,65

0%

Time
Flux
Location

0%
0%
58,69

Diffusion coefficient DC (m2/s)

AMS source term

Higher
(0.08)

Lower(32.7)

Original(65.3)

Higher(130.6)

Original(Constant)

10 %amplitude

30 %amplitude

50 %amplitude

Pi/2
periodPi/20
periodPi/200period18.71%
0.18 %
76.54
%
57,67

− 4.94%0.18 %
4.60 %

0%2.45 %

0%0.08 %

0.02 %

0.03 %

0.03 %

0.08 %

0.02 %

0%0%

0.18 %
0.37 %

0%0%

0.00 %− 0.02 %0.00 %− 0.01 %0.00 %− 0.01 %

59,69

59,69

58,69

59,69

58,69

58,69

58,69

0.00 %− 0.01%58,69

0.00 %− 0.02%58,69

0.00 %− 0.01%
58,69

Fig. 9. Results of sensitivity analysis in debris release flux in Case (1): wind coefficient, Case (2): diffusivity, Case (3): sinusoidal amplitude of AMS source term, andCase (4): sinusoidal period of AMS source term, tabulated by percentage deviation from original cases.

significantly altered the estimated source position, as shown in Fig. 9.
Tong et al. (2021) mentioned that suitable Cw values are between 0.03
and 0.05 and the value tested in this sensitivity analysis, 0.02 and 0.08,
are out of their recommendation. The large discrepancy can be attrib­
uted to this: however, in any case, Cw is a very influential parameter andneeds further investigation.

Sc = γΔt + sin

(π )
nt ,
2

(7)

where γ is the AMS flux, which was set to be 1.0, nt is the time step, andΔt is the time increment [s].
Then, analyses were conducted to vary the release across time using
the sinusoid function shown in Fig. 10. Ultimately, it is recognised thatany variations in AMS source term did not influence the results, as theestimated source position remains unchanged, and the effects on marine
debris flux and time was minimal, as shown in Fig. 9.
In this study, the AMS was released constantly in the time-backward
simulation as a base case, suggesting that this assumption is acceptable.

3.3.2. Diffusion coefficient
It is found that, although the range of the diffusivity DC set in thissensitivity analysis is almost the maximum and minimum values pro­
posed by Isobe et al. (2009), the changes in DC did not have a significant
impact on the estimated release source positions with only smallchanges in flux and time, as shown in Fig. 9. This may be because
advection dominated for the debris transport rather than diffusion.

3.3.4. AMS source term period
Finally, the effects of the period of the release flux of AMS were
examined. A sensitivity analysis was conducted by increasing the period
π/2 by one magnitude, followed by two magnitudes, as shown in Fig. 11.
After conducting the analysis, despite two orders of magnitude in­crease in the sinusoidal period, it was indicated that there was no effecton the simulation results, as the estimated source position remains un­changed, and the effects on marine debris flux and time is minimal, as
shown in Fig. 9. This result is also suggestive in the sense that we do not

3.3.3. AMS source term amplitude
The temporal shape of the AMS source term Sc in (1) means how
debris is temporally accumulated on the beach. The source term in (1)has the shape of a rectangular function, indicating a temporally constantrelease of the AMS. Now we consider the form of a sinusoid:

Fig. 10. Sensitivity analysis parameters for sinusoidal AMS source term at 10 %, 30 % and 50 % amplitude, compared to constant base case. The unit of the verticalaxis is second and the original constant value of 360 s corresponds to one time step based on (7), while the horizontal axis indicates time steps.
7

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Fig. 11. Sensitivity analysis parameters for sinusoidal AMS source term at 50 % amplitude, with period π/2, π/20, and π/200 proceeding with time. The unit of thevertical axis is second and the horizontal axis indicates time steps.

that the situation is exacerbated by an increasing population and eco­nomic growth. Importantly, it was noted that the city had weak regu­
latory enforcement and inadequate waste-handling facilities. In terms of
tourism, 2018 statistics showed that Batam has the highest monthly
arrival by an order of magnitude compared to the second largest city
(BPS-Statistics Indonesia, 2019). It was confirmed that the high tourism
rate correlates to an increased amount of solid waste generation (Mar­tins and Cró, 2021).
These waste-generating tourism and industrial sectors may notnecessarily lead to greater release of marine debris if handled appro­
priately. Well-developed high population cities usually enjoy
economies-of-scale in waste management infrastructure that allows
them to better manage wastes using financial capital generated by thesesame sectors. However, a rapid assessment by the World Bank (Shuker
and Cadman, 2018) showed that Batam did not have adequate waste
management infrastructure that can allow municipal agents to prevent
wastes release directly to the coast. As a result, 902.4 tons/day of waste
are generated with 101.1 tons/day (11.20 %) unhandled. Nine hotspots
were identified that can be considered “serious” debris release sources,
based on frequency and volume (Shuker and Cadman, 2018).
Along the northern coast of Batam, which is the identified source
position, there are two major rivers highlighted in Fig. 12. Both rivers
are situated next to urban areas, where the build-up around the westernriver is much denser and urbanised, compared to the build-up aroundthe eastern river.
Overall, it is speculated that the lack of waste management infra­
structure discussed in various literature supports the idea that waste
generated by economic sectors (e.g., tourism, electronics, shipbuilding)eventually enters the two highlighted rivers, and fails to be removed dueto the inadequate downstream waste retrieval facilities, resulting inmarine debris release into the sea.

need to pay attention to the time variation of the debris washed ashore.
4. Validation and discussion from social and metocean points ofview
This paper sets out to accomplish the identification of marine debris
release source using the adjoint method and evaluates the opportunities
to engage voluntary beach clean-up groups as a data source for the
method. The following sections will focus on validation of the calculated
results by showing circumstantial evidences from the social and meto­
cean points of view.
Batam, the identified marine debris release source, is an industrial
boomtown situated approximately 20 km from Singapore, as shown in
Fig. 1. It is the largest city in the Indonesian province of Riau Islands
having the highest total population and urban density, as shown in
Table 6. The size and proximity of the position suggests an increased
likelihood of waste transport from this region to Singapore. However,Schuyler et al. (2021) found that population density was an inadequate
primary indicator for marine debris generation due to a non-linear andhighly variable relationship. Therefore, a nuanced approach with arange of factors was recommended and additional factors such as in­dustry type and waste generation, waste management infrastructure,
rainfall, monsoon seasonality and product packaging labels are dis­cussed extensively in the following sections.
4.1. Industry type and waste generation
The economic growth of Batam is reliant on tourism, electronics
manufacturing, as well as heavy industries such as steel and oil rig
fabrication, equipment production and shipbuilding (Batam Center,2002). Yuvita and Hadi (2019) found that the shipyard, electronics, andhousehold wastes remain a problem in Batam City and suggests that theefforts to collect solid wastes have been insufficient. Lee (2018) added

4.2. Annual rainfall
The rainfall level around Batam is high all year round, increasing the
likelihood of land-based wastes transport into the ocean. The consis­
tently high rainfall supports the simulation assumption of a continuous
release of marine debris, instead of an instantaneous event such as
typhoon or floods.
Due to the COVID-19 pandemic, tourism and industrial sectors suf­
fered reduced activity within the simulation period. Therefore, given
Batam's aforementioned inadequate waste management infrastructureand existing unhandled wastes, the consistent rainfall could generate aTable 6
Populations and visitor arrivals of Batam, compared to other relevant areas in
the computational domain (BPS-Statistics Indonesia, 2019).
Position

Population

Urban density

Monthly visitor arrivals

Batam
BintanKarimun
Lingga1,196,396
481,120
253,457
98,633800/km2
450/km2
170/km2
44/km2~172,000~14,625~8950(N.A.)

8

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

These indications point to the possibility that these products areprimarily sold, and thus used and disposed, in their respective countries.
In some cases, the origin required no guesswork, such as the bottle
shown in the lower right of Fig. 13 printed specifically with the manu­
facturer's name and address in Batam. Although one can reasonably
argue that the product might not be disposed in the same place as its
production because of international exports. However, many national
brands do not expect a lot of sales beyond their immediate region,
especially when marketed in a native language. Therefore, the over­whelmingly large number of marine debris packaging in the Indonesianlanguage suggests that the litter disposal originated from Indonesiansources, further corroborating the analysis results that Batam is a releasesource.

Malaysia

4.5. Availability and quality of marine debris data
Beyond successfully estimating the release source, this study also
raised an adjacent, but equally important issue, regarding the challengesof obtaining suitable marine debris concentration data for the adjoint
method. As this paper has shown, the adjoint method uses a dataset ofpollutant concentration that are recurring either in position or time. The
source of these studies can be academia, for-profit organisations, andnon-profit organisations. In the ideal case, the simulation would be fed
with the accumulating marine debris concentration of many specificpositions, and each of these positions would be observed and frequently
sampled at many points in time. However, the reality is that the processof acquiring this data is fraught with difficulties in the aspects ofaccessibility (privately held data which are not shared), pertinence (datais incompatible with the method), and quality (data is complete but
inconsistent or incorrect).
In literature, many studies are hyper-localised and conducted forshort periods of time. Presumably, this is because the studies aredesigned to answer specific regional questions, or the methods forstudying marine debris and beach-stranded litter are prohibitively timeconsuming and expensive. For instance, researchers may be required tocharter expensive ship expeditions and deploy personnel on ships tovisually count marine debris floating on the sea surface. They may also
wish to study the accumulation of stranded beach litter on a remoteisland, which requires returning regularly to monitor and record thedata. Innovative methods like applying artificial intelligence on cameras
to recognise and count litter are being investigated but have not reachedmaturity (Kako et al., 2018). As a result, attempts to amalgamate data
sources become almost impossible. Browne et al. (2015) extensively
discussed this problem of variations and incompatibilities across indi­vidual beach debris studies and found that very few can be adequately
compared due to differences in debris types, sampling methods andsampling areas.
Additionally, access to these data can be restricted by various con­cerns. For example, academia may not be ready to share such data until
after publications complete, some government sectors have a policy ofnot sharing data with the public, and several for-profit organisations
cited “privacy concerns” as a reason for denying the request, while manyother organisations simply did not respond to the enquiry.
These challenges are important to address, with data being funda­
mental to the adjoint method simulation. As discussed in 4.3, one rec­ommended future work requires the study of monsoon season variability
which requires recurring data at the same positions across an entire
year. Obtaining such a range from a single source, or by amalgamating
incompatible sources, is a tall order. Therefore, solutions should be
explored.

Fig. 12. Terrain map showing the prominent rivers in the northern coast of
Batam, which is the identified release source. The orange rectangles indicate
prominent rivers.

land-based release of previously accumulated wastes and daily usage inthe form of surface run-off into rivers even during the pandemic.
4.3. Tidal and monsoon effects
The region has two general monsoon seasons, Northeast Monsoon
from December to early March and Southwest Monsoon from June to
September, with inter-monsoon periods in the transition months (Haire,2021). This simulation used data in June to November 2020, whichcorresponds to the Southwest Monsoon going into the Inter-monsoon
period. During this period, the sea and wind velocity direction tend
towards the northeast from southwest, which also supports the calcu­
lated results.
However, it is important to highlight that the interactions between
ocean current, wind, and monsoon seasonality are complex. These
combined effects lead to variability in the flow direction from time totime and from one sea to another. In other words, the flow of marine
debris may not always be in the expected general direction of the overallmonsoon period. Therefore, there is incentive to conduct the same
simulation process for Northeast monsoon period, as well as the intermonsoon periods, both separately and within one extensive simula­tion. This will allow an understanding of the effects of monsoon sea­sonality and tidal interactions on the eventual fate of marine debris. The
main implication is that the release source identified may not always be
Batam if marine debris movement was in a different monsoon period.
4.4. Product label origin
Beach clean-up volunteers remarked that many pieces of productpackaging are written in the same language, suggesting similar origins.
Even though the clean-up group did not painstakingly record the data of
every single product label, the majority of these brand origins can still
reveal additional information about their production, usage, anddisposal. For example, a search of “NoDrop” yielded an Indonesia-based
brand origin, manufacturer, and sales channels. Similarly, even withposition-based search settings turned off, 9 out of 9 of the first page
Google search results for “Generasi Baru aerosol spray” led to Indone­
sian sales platforms only.

4.6. Citizen science in beach clean-up groups
In this study, a release source was estimated in line with evidence
using data from a beach clean-up group with a debris monitoring pro­gramme. The use of beach clean-up group data resolves many of the9H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Fig. 13. Selective samples of marine debris representing often identified product label languages taken from SG Beach Warriors.

issues highlighted with marine debris data sources. First, beach clean-up
activities are recurrent in nature, providing a span of data at the same
position across time. Second, data collection from such groups are often
purely for archival reasons. Since the groups do not use the data forother functions, it removes conflicting interests that may drive data
withholding. Third, it adds an element of public education andempowerment as the pool of volunteers change, oftentimes consisting of
citizens with little exposure to the technical aspect of the marine debris
problem.
The idea is not new. In Singapore, the Reach-Inspire-SynergiseEmpower, R.I.S.E. network consists of 44 organisations contributing
clean-up logs to a central database (Singapore Public Hygiene Council,
2018). Many institutions also work with International Coastal Cleanup
Singapore (ICCS) as part of a global network coordinated by the USbased The Ocean Conservancy, which crystallises an annual report on
the state of the issue (Sivasothi, 2021). Individual groups also have theirown data collection practices to varying extent. However, in the context
of the adjoint method applicability, R.I.S.E network was not able toprovide records of the time and duration of clean-up activities, ICCS did
not have sufficiently frequent data points, and individual beach clean-up
groups often use inconsistent practices within their own internal datacollection from session to session, such as selectively weighing somedebris, or recategorising the same waste under different categories indifferent clean-up sessions.
It is proposed to engage and empower beach clean-up groups as acornerstone of the data collection processes. As these beach clean-up
efforts are voluntary, such proposals should not be organisational or
directive in nature. One future work area could be a review of theexisting beach clean-up groups operating in Singapore, assessing thequality and consistency of data collection, highlighting key issues, andrationalising a set of best practices, such as collecting beach litter withina defined quadrat, standardising manpower distribution, weighing ofdry mass or wet mass, and other such factors.
Another recommendation is to implement a practice of metadata
inclusion. Drawing inspiration from bioinformatics, metadata – which isdata about data – can also be recorded (European Bioinformatics Insti­tute, 2021). As highlighted before, one of the reasons for data in­compatibility is due to inter-studies differences in methods andmaterials. Thus, descriptions, such as the number of tools used, partic­
ipants profiles, condition of environment, can be introduced to help
researchers determine if conditions are similar enough for two or moredatasets to be combined.
Finally, moving beyond addressing the data challenges, the next stepwould be an implementation of long-term adjoint simulations based on
sufficient beach clean-up data. Several use cases are available forconsideration. Data can be built up over several years to be used for abaseline monitoring study to drive policy decision, perform trend
analysis, and evaluate the effectiveness of action plans. The same data
can also be used for specific tracking after instantaneous release event,for example, to locate the release source following a significant increasein stranded beach litter accumulation, such as natural events, releases,
or illegal dumping of wastes.
5. Conclusion
This paper marks the first application of the adjoint marginal
sensitivity method to detect possible release sources of marine debris toregional waters around Singapore. The Northern Coast of Batam, Prov­
ince of Riau Islands, Indonesia, was estimated to be the marine debris
release source. This calculated result was validated substantially withthe high population density, strong presence of tourism and heavy in­
dustries, inadequate waste management infrastructure, heavy yearly
rainfall, and inference from product packaging labels.
This study presents several new opportunities. Beyond immediate
10

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

release sources, the computational domain can be greatly expanded tofurther investigate transboundary flow across Malaysia, and central andeastern Indonesia. Additionally, the complexity of the monsoon seasons
with tidal levels causes significant changes in flow direction and ve­
locity. Hence, further studies can be carried out to profile the marine
debris transport differences between Southwest monsoon and Northeast
monsoon in the region. It must also be noted that the method is also
applicable for marine debris compositions and other pollutants,
enabling targeted future investigations such as plastic bottles andproduct packaging.
Furthermore, perhaps more importantly, it was shown that beachstranded marine debris data collected from volunteer beach clean-up
groups can complement research institutes for effective usage in casestudies. This has substantial implications for the development of citizen
science data policy, which will contribute significantly to a field withlimited availability of large datasets and a numerical method which
requires consistent data across many positions and time.
It was recommended to engage, review, and empower these beachclean-up volunteers to begin and/or improve data collection consistency
and quality. Many volunteer beach clean-up groups in Singapore prac­
tice some form of data collection, but data quality and consistency are
low, and the data is underutilised. Observations include selectively
weighing of collected litter, not recording duration or distance covered,and categorising the same objects differently from session to session.
These recommendations may help to circumvent the aforementioned
challenges associated with accessibility, pertinence, and quality,
allowing opportunities to evaluate and drive policies, to raise publicawareness and to allow further research within and beyond the region.

Browne, M., Chapman, M., Thompson, R., Amaral Zettler, L., Jambeck, J., Mallos, N.,
2015. Spatial and temporal patterns of stranded intertidal marine debris: is there a
picture of global change? Environ. Sci. Technol. 49, 7082–7094.
Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth Generation of ECMWF
Atmospheric Reanalyses of the Global Climate. Copernicus Climate Change Service
Climate Data Store (CDS).
Courant, R., Friedrichs, K., Lewy, H., 1967. On the partial difference equations ofmathematical physics. IBM J. Res. Dev. 11, 215–234.
Cummings, J.A., 2005. Operational multivariate ocean data assimilation. Q. J. R.
Meteorol. Soc. 131, 3583–3604.
Cummings, J.A., Smedstad, O.M., 2013. Variational data assimilation for the global
ocean. In: Data Assimilation for Atmospheric, Oceanic And Hydrologic Applications,II, pp. 303–343.
Department of Statistics Republic of Singapore</collab>, Ministry of Trade andIndustry, 2020. Population Trends 2020. ISSN 2591-8028.
Dimov, I., Jaekel, U., Vereecken, H., 1996. A numerical approach for determination ofsources in transport equations. Comput. Math. Appl. 32, 31–42.
European Bioinformatics Institute, 2021. Available at. https://www.ebi.ac.uk/training/online/courses/bioinformatics-terrified/what-makes-a-good-bioinformatics-database/describing-data-consistently/. (Accessed 9 October 2021).
Gualtieri, G., 2021. Reliability of ERA5 reanalysis data for wind resource assessment: acomparison against tall towers. Energies 14, 4169.
Haire, P., 2021. The currents in Singapore Strait are extremely complex. Available at: htt
ps://www.tidetechmarinedata.com/news/the-complex-currents-in-singapore-strait.
(Accessed 9 June 2021).
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J.,
Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S.,
Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G.,
Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R.,
Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I.,
Vamborg, F., Villaume, S., Thépaut, J., 2020. The ERA5 global reanalysis. Q. J. R.
Meteorol. Soc. 146, 1999–2049.
Hui, J., Kanao, S., Sato, T., Sawano, Y., 2021. Numerical tracking of floating marine
plastic in the sea of Japan using time-backward probabilistic method. In: San Diego,
OCEANS Conference.
Isobe, A., Kako, S., Chang, P.-H., Matsuno, T., 2009. Two-way particle tracking model for
specifying sources of drifting objects: application to the East China Sea shelf.
J. Atmos. Ocean. Technol. 26, 1672–1682.
Jong, M.-C., Tong, X., Li, J., Xu, Z., Chng, S.H.Q., He, Y., Gin, K.Y.-H., 2022.
Microplastics in equatorial coasts: pollution hotspots and spatiotemporal variations
associated with tropical monsoons. J. Hazard. Mater. 424, 127626.
Kako, S., Isobe, A., Kataoka, T., Yufu, K., Sugizono, S., Plybon, C., Murphy, T., 2018.
Sequential webcam monitoring and modeling of marine debris abundance. Mar.
Pollut. Bull. 132, 33–43.
Kanao, S., Sato, T., 2022. Development of a numerical method to detect multiple
positions of CO2 seepage on the seafloor. Comput. Fluids 232, 105195.
Lee, P.O., 2018. Reconciling Economic And Environmental Imperatives in Batam. ISEAS
Publishing, Singapore. ISBN: 9789814818339.
Li, C., Wang, X., Liu, K., Zhu, L., Wei, N., Zong, C., Li, D., 2021. Pelagic microplastics insurface water of the Eastern Indian Ocean during monsoon transition period:
abundance, distribution, and characteristics. Sci. Total Environ. 755, 142629.
Liu, T.W., Tang, W., 1996. Equivalent Neutral Wind, California: NASA Contractor Report,
NASA-CR-203424, Document ID: 19970010322.
Liu, X., Zhai, Z., 2007. Inverse modeling methods for indoor airborne pollutant tracking:literature review and fundamentals. Indoor Air 17, 419–438.
Lo, H.-S., Lee, Y.-K., Po H.-K., B., Wong, L.-C., Xu, X., Wong, C.-F., Wong, C.-Y., Tama, N.
F.-Y., Cheung, S.-G., 2020. Impacts of Typhoon Mangkhut in 2018 on the depositionof marine debris and microplastics on beaches in Hong Kong. Sci. Total Environ. 716,
137172.
Martins, A.M., Cró, S., 2021. The impact of tourism on solid waste generation andmanagement cost in Madeira Island for the period 1996–2018. Sustainability 13,5238.
Metzger, E., Helber, R., Hogan, P., Posey, P., Thoppil, P., Townsend, T., Wallcraft, A.,
2017. Global Ocean Forecast System 3.1 Validation Testing, Naval ResearchLaboratory, NRL/MR/7320-17-9722.
Ministry for Marine Affairs, Republic of Indonesia, 2017. Indonesia's Plan of Action on
Marine Plastic Debris 2017-2025. The Government of the Republic of Indonesia.
Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC),
2018. Malaysia's roadmap towards zero single-use plastics, 2018-2030. Available at.
https://www.malaysia.gov.my/portal/content/30918. (Accessed 9 October 2021).
Ministry of Natural Resources and Environment (MONRE), Thailandcollab, 2019.
Thailand's roadmap on plastic waste management, 2018-2030. Available at. https://enviliance.com/regions/southeast-asia/th/th-waste/th-plastic-waste. (Accessed 9
October 2021).
Mori, C., Sato, T., Oyama, H., Kano, Y., 2017. Development of numerical estimation
method of the location and amount of materials seeping from the sea floor. J. Jpn.
Soc. Nav. Archit. Ocean Eng. 26, 203–212 (in Japanese).
Naval Research Laboratory, 2021. HYCOM Consortium for Data-Assimilative Ocean
Modeling, GOFS 3.1. Available at. https://www7320.nrlssc.navy.mil/GLBhycomci
ce1-12/. (Accessed 9 October 2021).
Neupauer, R.M., Wilson, J.L., 1999. Adjoint method for obtaining backward-in-time
location and travel time probabilities of a conservative groundwater contaminant.
Water Resour. Res. 35, 3389–3398.

CRediT authorship contribution statement
Hong Siang Gan: Methodology, Formal analysis, Visualization,
Investigation, Writing – original draft. Shunsuke Kanao: Software.
Toru Sato: Conceptualization, Supervision, Writing – review & editing.
Klaus Hellgardt: Project administration. Yusuke Uchiyama:
Resources.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The authors do not have permission to share data.
Acknowledgements
The authors would like to thank Faith Tan for providing data that
contributed to the writing of this paper, Joleen Chan and N. Sivasothi,
and Pei Rong Cheo of Department of Biological Sciences, National
University of Singapore, and Karenne Tun of Coastal and Marine Branch,
National Biodiversity Centre, National Parks Board, Singapore for theirkind support.
References
ASEAN Secretariat, 2021. ASEAN Regional Action Plan for Combating Marine Debris in
the ASEAN Member States 2021 – 2025. ASEAN Secretariat, Jakarta.
Baas, P., Bosveld, F.C., Burgers, G., 2015. The impact of atmospheric stability on thenear-surface wind over sea in storm conditions. Wind Energy 19, 187–198.
Batam Center, 2002. Investing in Batam. https://www.batam-center.web.id/geninfo_in
vesting.html. (Accessed 9 June 2021).
BPS-Statistics Indonesia, 2019. Statistical Yearbook of Indonesia 2019, Jakarta. ISSN/
ISBN: 0126-2912.

11

H.S. Gan et al.

Marine Pollution Bulletin 182 (2022) 113997

Neupauer, R.M., Wilson, J.L., 2001. Adjoint-derived location and travel time
probabilities for a multidimensional groundwater system. Water Resour. Res. 37,
1657–1668.
Neupauer, R.M., Wilson, J.L., 2002. Backward probabilistic model of groundwatercontamination in non-uniform and transient flow. Adv. Water Resour. 25, 733–746.
Neupauer, R.M., Wilson, J.L., 2004. Numerical implementation of a backward
probabilistic model of ground water contamination. Ground Water 42, 175–189.
Sakaizawa, R., Sato, T., Mori, C., Oyama, H., Tsumune, D., Tsubono, T., Kano, Y., 2019.
Position and flux estimation of the unexpected seepage of CO2 purposefully stored ina subseabed geological formation. Int. J. Greenhouse Gas Control 84, 131–146.
Schuyler, Q., Wilcox, C., Lawson, T., Ranatunga, R., Hu, C., , Global Plastics Project
Partners, Hardesty, B., 2021. Human population density is a poor predictor of debris
in the environment. Front. Environ. Sci. 9, 583454.
Shuker, I.G., Cadman, C., 2018. Indonesia - marine debris hotspot rapid assessment:
synthesis report. Available at:. The World Bank http://documents.worldbank.org/curated/en/983771527663689822/pdf/126686-29-5-2018-14-18-6-SynthesisRe
portFullReportAPRILFINAL.pdf. (Accessed 25 June 2020).

Singapore Public Hygiene Council, 2018. R.I.S.E. - champions network of the KeepSingapore Clean Movement. Available at. https://www.publichygienecouncil.sg/init
iatives/rise. (Accessed 9 October 2021).
Sivasothi, N., 2021. International coastal cleanup Singapore. Available at: http://coastal
cleanup.nus.edu.sg/. (Accessed 9 October 2021).
Tong, X., Jong, M., Zhang, J., You, L., Gin, K., 2021. Modelling the spatial and seasonal
distribution, fate and transport of floating plastics in tropical coastal waters.
J. Hazard. Mater. 414, 125502.
United Nations Environment Programme, 2020. Status of research, legal and policy
efforts on marine plastics in ASEAN+3: a gap analysis at the interface of science, law
and policy. Available at: https://wedocs.unep.org/20.500.11822/33383. (Accessed
9 October 2021).
Wilson, J.L., Liu, J., 1994. Backward tracking to find the source of pollution. In: Water
Management Risk Remediation, 1, pp. 181–199.
Yuvita, D.S., Hadi, W., 2019. Study of economic waste exchange in Batam City. Jurnal
Sains dan Seni ITS 8, 2337–3520.

12