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Coastal accumulation of microplastic particles emitted from the Po River, Northern Italy: Comparing remote sensing and hydrodynamic modelling with in situ sample collections

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Microplastic research has mainly concentrated on open seas, while riverine plumes remain largely unexplored despite their hypothesized importance as a microplastic source to coastal waters. This work aimed to model coastal accumulation of microplastic particles (1–5 mm) emitted by the Po River over 1.5 years. We posit that river-induced microplastic accumulation on adjacent coasts can be predicted using (1) hydrodynamic-based and (2) remote sensing-based modelling. Model accumulation maps were validated against sampling at nine beaches, with sediment microplastic concentrations up to 78 particles/kg (dry weight). Hydrodynamic modelling revealed that discharged particle amount is only semi-coupled to beaching rates, which are strongly mouth dependent and occur within the first ten days. Remote sensing modelling was found to better capture river mouth relative strength, and accumulation patterns were found consistent with hydrodynamic modelling. This methodology lays groundwork for developing an operational monitoring system to assess microplastic pollution emitted by a major river.
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Marine Pollution Bulletin
journal homepage: www.elsevier.com/locate/marpolbul
Coastal accumulation of microplastic particles emitted from the Po River,
Northern Italy: Comparing remote sensing and hydrodynamic modelling
with in situ sample collections
Elizabeth C. Atwood
a,b,
, Francesco M. Falcieri
c
, Sarah Piehl
d
, Mathias Bochow
d,e
,
Michael Matthies
f
, Jonas Franke
a
, Sandro Carniel
c
, Mauro Sclavo
c
, Christian Laforsch
d
,
Florian Siegert
a,b
a
RSS Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany
b
Ludwig-Maximilians-Universität Munich, GeoBio-Center, Großhadernerstr. 2, 82152 Martinsried, Planegg, Germany
c
Consiglio Nazionale delle Ricerche – Istituto di Scienze Marine (CNR-ISMAR), Arsenale-Tesa 104, Castello 2737/F, 30122 Venezia, Italy
d
University Bayreuth, Dept. Animal Ecology I, Universitätsstr. 30, 95440 Bayreuth, Germany
e
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
f
University of Osnabrück, Institute of Environmental Systems Research, Barbarastr. 12, 49069 Osnabrück, Germany
ARTICLE INFO
Keywords:
Beach sediment
River plume
FT-IR
ROMS
Landsat-8
Sentinel-2
ABSTRACT
Microplastic research has mainly concentrated on open seas, while riverine plumes remain largely unexplored
despite their hypothesized importance as a microplastic source to coastal waters. This work aimed to model
coastal accumulation of microplastic particles (1–5 mm) emitted by the Po River over 1.5 years. We posit that
river-induced microplastic accumulation on adjacent coasts can be predicted using (1) hydrodynamic-based and
(2) remote sensing-based modelling. Model accumulation maps were validated against sampling at nine beaches,
with sediment microplastic concentrations up to 78 particles/kg (dry weight). Hydrodynamic modelling revealed
that discharged particle amount is only semi-coupled to beaching rates, which are strongly mouth dependent and
occur within the first ten days. Remote sensing modelling was found to better capture river mouth relative
strength, and accumulation patterns were found consistent with hydrodynamic modelling. This methodology
lays groundwork for developing an operational monitoring system to assess microplastic pollution emitted by a
major river.
1. Introduction
Marine plastic litter has long been recognized as an environmental
problem (Azzarello and van Vleet, 1987;Law and Thompson, 2014;
Sheavly and Register, 2007) but only recently has begun to receive
international attention at a level adequate to the potential severity of
the threat (G7 Germany, 2015;GESAMP, 2016;UNEP, 2016). Micro-
plastics, commonly defined as particles < 5 mm in diameter (Galgani
et al., 2013), are increasingly proving to be ubiquitous in all water
systems. Roughly 70 to 80% of marine debris comes primarily from
land-based sources (Wagner et al., 2014), much being passively col-
lected in waterways which eventually flow to the sea. Mani et al. (2015)
found river water concentrations up to 3.9 million particles/km
2
in
metropolitan areas along the Rhine River. Annual input of plastic par-
ticles to the Great Laurentian Lakes is estimated at 9.8 thousand tonnes
(Hoffman and Hittinger, 2017). Despite the fact that freshwater systems
are at least as severely contaminated as the oceans (Dris et al., 2015),
large rivers have to date received relatively little attention (Mani et al.,
2015;Wagner et al., 2014). An estimated 1.15 and 2.41 million tonnes
enter the oceans each year from rivers alone (Lebreton et al., 2017),
representing up to 50% of land based plastic emissions estimate, which
ranges from 4.8 to 12.7 million tonnes (Jambeck et al., 2015). Once
microplastics reach coastal waters, their dispersion and transportation
pathways are governed by ocean and atmosphere dynamics; our un-
derstanding of these physical processes is still limited. Some authors
suggest that how these processes influence microplastic transport may,
to some extent, be comparable to well-studied suspended sediment
transportation systems (Zhang, 2017), which could offer a more es-
tablished framework for modelling suspended microplastic transporta-
tion.
https://doi.org/10.1016/j.marpolbul.2018.11.045
Received 24 July 2018; Received in revised form 21 September 2018; Accepted 19 November 2018
Corresponding author at: RSS Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany.
E-mail address: atwood@rssgmbh.de (E.C. Atwood).
Marine Pollution Bulletin 138 (2019) 561–574
0025-326X/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T
Microplastic transportation pathways are characterized by complex
dynamics due to processes such as movement mechanisms (windage
and sinking velocities) as well as changes in physical and chemical
characteristics (loss of structural integrity, fragmentation and ag-
gregation, see review Andrady, 2017) as well as interactions with biota
(Law and Thompson, 2014). A combined hydrodynamic-Lagrangian
transportation model effort would therefore be surely dependent upon
necessary simplifying assumptions, as well as the quality of the hy-
drodynamic forcing data. Such models, with different degrees of rea-
lism, have been recently utilized to hindcast potential sources of
stranded plastic litter in the Indian Ocean (Bouwman et al., 2016;
Duhec et al., 2015), Aegean Sea (Politikos et al., 2017) and Adriatic Sea
(Carlson et al., 2017). To date, little attention has been placed on local-
scale river plume microplastic transport modelling in coastal seas
(Browne et al., 2010;Carlson et al., 2017;Zhang, 2017). It is important
to bear in mind that, due to the intrinsic model simplifications, dis-
persion pathways computed based on modelling results can accumulate
errors over longer distances and times. Generally, modelling results
should be considered qualitative rather than quantitative until vali-
dated against an independent dataset. A different type of model based
on remote sensing acquisitions offers multiple depictions of the river
plume that inherently include actual environmental conditions. While
such an image displays the complex coastal ocean environment of the
surface layer, it nevertheless offers restricted information for below the
water surface and only represents the snapshot time period when the
image was acquired.
In this paper, we implement and compare these two different types
of models to assess how microplastics from a major river are spreading
into a semi-enclosed sea and accumulate along its coastline. The ob-
jective is to create a coastal microplastic exposure map, which depicts
accumulation of particles emitted from the Po River along the outer
delta and covering southward the coastal area still under strong influ-
ence from the main Po River plume. Model (1) is a Lagrangian particle
transportation model forced by a state-of-the-art hydrodynamic model,
while model (2) is based on satellite remote sensing of river plume form
and intensity along the coastline. We hypothesize that both models are
able to capture coastal patterns in river plume emitted microplastic
accumulation. Model results are validated against sediment sampling
for microplastics from beaches with varying river plume exposure
gradients. Development of a system to model coastal accumulation of
microplastic debris from rivers would represent a very useful tool for
agencies responsible for monitoring and reporting this pollution, as well
as organization of clean-up activities and remediation strategies.
2. Materials and methods
2.1. Study area
The Adriatic Sea separates the Italian peninsula and Balkan coast,
extending 800 km from the connection with the Ionian Sea over the
Strait of Otranto northwest toward the Venice Lagoon (Fig. 1). The
prevailing currents flow counterclockwise from the Strait of Otranto
along the Balkan coastline and return southward with the Western
Adriatic Current (WAC) along the Italian coastline (Artegiani et al.,
1997a, 1997b;Carniel et al., 2016). The North Adriatic sub-basin is
defined as the shallow area north of the 100 m isobath (Fig. 1).
The Po River provides the largest riverine influx to the Adriatic Sea,
averaging daily 1500 m
3
/s with streamflow ranging between 100 m
3
/s
and 11,550 m
3
/s (Falcieri et al., 2014). Being the longest river in Italy,
the Po River drainage area (74,000 km
2
) encompasses much of the
northern region of the country, with > 20 million inhabitants, and in-
cludes many large cities as well as areas of intensive industrial and
agricultural activities (lower left inset Fig. 1). The river splits into many
sub-rivers before flowing into the Adriatic Sea, the main recognized
arms of which are the Po di Maistra, della Pila, delle Tolle, di Gnocca
(or della Donzella) and di Goro (upper right inset Fig. 1). Additionally,
there exist many side channels and lagoons, which also carry a portion
of the river water to the sea. Notable among these side channels are the
Busa di Scirocco and di Tramontana. The delta is an actively changing
system with shifting sandbars that can obstruct outflow from a parti-
cular mouth (Simeoni and Corbau, 2009) and thus increase the outflow
elsewhere. The highest river discharge occurs in the spring, associated
with high precipitation and snow-melt runoff, and the lowest in autumn
(Falcieri et al., 2014).
Both wind regime and freshwater influx play a deciding role in
North Adriatic circulation patterns (Bignami et al., 2007;Bolaños et al.,
2014;Falcieri et al., 2014). There are three main recognized wind re-
gimes: Bora, Scirocco and Mistral. Bora events consist of strong, dry,
northeasterly winds that tend to occur more often during the winter
months, which together with low river discharge results in a small Po
River plume that remains close to the coastline (Boldrin et al., 2009;
Falcieri et al., 2014). As mentioned above, a Scirocco event comprises
warm, humid, east-southeasterly winds that tend to occur more often
during the spring to fall. This wind regime together with high river
discharge results in a wider plume that can extend far across the
Adriatic Basin. Mistral events are the least powerful of the wind regimes
and are defined based on winds coming from the northwest, which have
been found to minorly enhance WAC flow into the Ionian Sea (Bignami
et al., 2007).
2.2. Sample design
The Po Delta field campaign was conducted from 4 to 25 June 2016,
during which both water and sediment samples were taken. Water
sample locations were selected to cover the main Po River, recognized
river mouths and important subsidiary river mouths as well as the
plume, ranging from near-coast waters to the plume outer edge (in-
dicated by surface waters with salinity > 30 PSU). At each station,
water samples used to estimate microplastic concentrations were col-
lected from a small boat using a specially designed mini-manta trawl
(300 μm mesh, further details available in S1 of the Supplementary
material). A total of 24 water stations were sampled, the locations of
which are indicated in Fig. 2 of the Results. The trawl net was rinsed
before each sample collection by running the net without the cod end
through the water for 5 min at the sampling location. One trawl pass per
location was conducted alongside the boat for an average of 20 min and
only when wind conditions were below Beaufort 2 (light breeze,
6–11 km/h). Samples were stored in glass jars until further processing
in the lab. During trawling, in situ measurements were collected for sea
surface temperature (°C) and salinity (PSU). Water clarity measure-
ments (visibility depth with a Hydrobios secchi disk) were conducted
both before and after each trawl. Additionally, 2 L water samples were
concurrently collected from the water surface (top 40 cm) for later
determination of the water parameters chlorophyll-A(Chl-A) and sus-
pended particulate matter (SPM). Water samples were also processed
for measurement of colored dissolved organic matter (CDOM, or
Gelbstoff), but due to very low detected CDOM levels, these data were
determined not useful for building a regionally calibrated remote sen-
sing algorithm. Samples were kept dark while being stored in a cooler
with ice until filtering later that the same day.
Chl-Asamples were hand-pump filtered using Whatman GF/F glass
microfiber filters (0.7 μm pore size), following the IOC and SCOR
(1994) protocol. Filters were then wrapped in aluminum and stored at
−20 °C for the duration of the field campaign, after which they were
stored at −80 °C until further processing. Chl-Awas extracted with
96% ethanol and analyzed with a JASCO FP-8600 fluorometer at an
excitation wavelength of 435 nm and an emission wavelength of
670 nm. The fluorometer was calibrated using a photometer (JASCO V-
670) and a Chl-Astandard (C6144-1MG, Sigma-Aldrich). After the first
measurements, samples were acidified with HCl and again measured to
subtract phaeopigments from the chlorophylls to get concentration of
Chl-Ain mg/L following the JGOFS protocol (UNESCO, 1994).
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
562
SPM samples were hand-pump filtered using pre-weighed cellulose
acetate filters with 0.45 μm pore size, air dried and stored in aluminum
foil (Lindell et al., 1999). Filters were further dried in a 60–80 °C oven
for 2 h and allowed to cool in a desiccator before weighting on a Sar-
torius R 200 D.
Surface reflectance measurements concurrent to each trawl were
taken following the measurement methodology from Mobley (1999)
and Fargion and Mueller (2000). An ASD FieldSpec 3 Hi-Res spectro-
meter was fitted with an 8° optic lens and set to measure raw digital
numbers over an averaging of 50 rapid measurements. For each sam-
pling location, a minimum of five measurement cycles were taken with
the goal to collect as many cycles as possible during trawling. Each
cycle consisted of a downwelling irradiance measurement over a white
reference (nadir angle), an upwelling plus a sky radiance measurement
both made following Mobley geometry (135° azimuth angle from sun,
40° off nadir for water and 40° off zenith for sky; Mobley, 1999), and
lastly a repeated downwelling irradiance measurement to control for
potential changes in lighting intensity conditions over the measurement
cycle. All spectral measurement angles were estimated by hand and
controlled by a second observer with a preset adjustable triangle.
Measurement integration times were optimized for each measurement
cycle in order to maximize signal. Downwelling irradiance was mea-
sured over a 90% Spectralon®white reference panel. Processing of raw
digital numbers into remote sensing reflectance is discussed further in
Section 2.5.
Sediment samples were collected from nine beaches in order to serve
as a validation dataset for the hydrodynamic and remote sensing models
(sample locations are indicated in the Results). Beach sample locations
were selected so that three each of low, medium and high river plume
impact areas would be represented. Estimates of river impact were based
on the hydrodynamic modelling accumulation map (more details below
in Section 2.4). At each location, samples were taken along the extreme
high tide line, following protocols from Moreira et al. (2016) and Turra
et al. (2014), and were only conducted between high tide cycles. The
extreme tide line was defined visually as the area with the largest ac-
cumulation of drift material, which was found to always be a clearly
separate line to the last high tide line. Samples were taken at equal in-
tervals along a 100 m transect line, where the first 10 m were walked
along the straight transect line and then turned at 90° for placement
along the meandering drift line. Samples were taken with a 25 × 25cm
stainless steel quadrat and sampled to a depth of 5 cm. Wet weight of the
samples were recorded and then sieved over 1 mm stainless steel mesh
(matching model assumptions from the hydrodynamic model, more de-
tails below). Additionally, two 1 L bottles where filled with unsieved
sand from the same transect line for later processing in the lab to convert
the wet weight to dry weight.
Fig. 1. Adriatic Sea overview map, showing bathymetry (contour lines follow 50 m depth intervals) along with large coastal cities and bordering countries: AL -
Albania, ME - Montenegro, BIH - Bosnia and Herzegovina, HR - Croatia, SI - Slovenia, IT - Italy. Lower left inset shows Po River watershed (yellow dashed line) with
large inland cities, as well as the Brenta (dark green line) and Adige (light green line) rivers. The Po Delta is displayed in the upper right inset, showing all five major
river mouths (Maistra, Pila, Tolle, Gnocca and Goro) as well as important side channels (Tramontana and Scirocco) and dense aquaculture areas. (For interpretation
of the references to color in this figure legend, the reader is referred to the web version of this article.)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
563
2.3. Microplastic sample processing
Water samples were first fractionated into two size classes:
5 mm–500 μm and 500–300 μm. To remove organic matter (which
would disturb spectroscopic analysis) from the microplastic water
samples, samples of the size class 500–300 μm were treated with en-
zymatic purification (Löder et al., 2017) and wet peroxide oxidation
(Masura et al., 2015). For the latter class (size 5 mm–500 μm), samples
with high organic content were treated solely with wet peroxide oxi-
dation. All potential microplastic particles > 500μm were visually pre-
sorted, photographed and stored for further analysis with Attenuated
Total Reflectance (ATR) Fourier Transform Infrared (FT-IR) spectro-
scopy. For a full quantitative analysis of the fraction < 500 μm, samples
were split. One subsample was filtered onto aluminum oxide mem-
branes (Whatman Anodisc filters) and analyzed with Focal Plane Array
(FPA) based Micro-FT-IR spectroscopy. The rest of the subsamples were
filtered onto glass fiber filters (grade MN 85/90 BF) and analyzed with
a newly developed shortwave infrared (SWIR) close-range imaging
spectroscopy methodology called PlaMAPP (Schmidt et al., 2018) using
a HySpex SWIR-320 m-e sensor (Norsk Elektro Optikk AS). This method
allows counting microplastic particles, classifying the plastic type and
determining particle size in a semi-automated way. Determination of
plastic type is done by comparing the wavelength positions of the
spectral absorption bands (local minima in the spectral signatures) of
the recorded image spectra to those of plastic spectra from a reference
spectral library.
Sediment samples along the 100 m transect were pooled, then pro-
cessed by drying at 55 °C and separated from inorganic material using a
zinc chloride solution (density 1.6–1.8 g/cm
3
). The supernatant, which
included both organic material and potential polymer particles, was
collected using a self-made mote spoon (stainless steel, mesh size < 1
mm), rinsed with 98% ethanol and transferred into glass petri dishes.
All potential microplastic particles were visually separated from or-
ganic material under a stereomicroscope (Leica M50 with cold light
source Leica KL 300 LED, Leica Microsystems), photographed (attached
Olympus DP26 camera, 5 Megapixel, Olympus Corp.) and identified to
polymer type using ATR FT-IR spectroscopy.
Spectra of all potential microplastic particles > 500 μm, from both
water and sediment samples, were recorded with a Tensor 27 FT-IR
spectrometer (Bruker Optik GmbH) from 8 co-added scans within a
spectral range from 4000 to 400 cm
−1
and a spectral resolution of
8 cm
−1
. Background scans were performed after every 10th measure-
ment. Spectra were identified using the OPUS v7.5 software, correlating
measured spectra against reference spectra from a custom in-house li-
brary (containing polymer spectra as well as spectra from both natural
and lab materials used during sampling and processing, see Löder et al.,
2015). Spectra of all potential microplastic particles < 500 μm were
collected using the Tensor 27 FT-IR spectrometer further equipped with
Fig. 2. Overview of water microplastic samples (diamonds, blue scale) and sediment microplastic samples (circles, pink scale) collected during the June 2016 field
campaign. A total of 24 water locations and 9 beach locations were sampled, only beach locations are labeled (black text). Water samples are reported as particles/m
3
while sediment samples are reported as particles per dry weight kg (DW kg). River mouths are labeled in dark blue and dense aquaculture areas within lagoons are
indicated. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
564
a Hyperion 3000 FT-IR microscope that had a 15× cassegrain objective
and a 64 × 64 FPA detector mounted. Spectra were obtained in trans-
mission mode and measurement settings were as published by Löder
and Gerdts (2015). Obtained chemical images were analyzed with the
ImageLab v2.26 software and the BayreuthParticleFinder tool (devel-
oped during the project together with Epina Software Lab GmbH),
which automatically highlights potential polymer particles on the
chemical image obtained from the FT-IR measurements of the filter.
Given that polymer spectra can diverge, dependent on factors such as
particle size, thickness, color, polymer additives or adsorbed chemicals,
all automatically detected particles were further manually controlled
afterwards.
2.4. Hydrodynamic model
Simulations for microplastic dispersal from the Po River were per-
formed from 1 January 2015 to 15 June 2016, to coincide with the field
sampling campaign. A 3D Individual Based Lagrangian tracking model
(ICHTHYOP; Lett et al., 2008) was implemented to simulate the dis-
persion of virtual microplastic particles (VMP) due to 3D currents and
water column thermohaline structure. In the model, VMP behave as a
Lagrangian drifter under the effect of horizontal/vertical advection and
dispersion as well as buoyancy force due to the difference between the
particle and surrounding water density. Particles were assigned a
spherical shape (diameter of 1 mm) and density of 0.91 g/mL. Density
was chosen to correspond with the averaged density of virgin poly-
ethylene (both high and low density) and polypropylene, which to-
gether account for over 48% of EU demand (PlasticsEurope, 2014) and
represent the majority of sampled microplastic debris (Imhof et al.,
2013;Zbyszewski and Corcoran, 2011). Horizontal dispersion was in-
cluded with a turbulent dissipation rate of є = 10
−7
m
2
/s
3
, in agree-
ment with turbulent kinetic energy observations in the Adriatic Sea.
VMP were tracked for a total of 60 days, in excess of Adriatic particle
half-life model estimates (Liubartseva et al., 2016) and drifter mean
half-life observations (Poulain, 2001) of circa 40 days.
Simulations were based on the simplifying assumption of a constant
concentration of 10 microplastic particles/m
3
in river waters, as re-
ported in previous observations from the Po River (van der Wal et al.,
2015;Vianello et al., 2015). Given that the river is represented as a
point source inside the hydrodynamical model, VMP were released at
the surface along straight 500 m transects located 250 m in front of each
river mouth, with the goal being to mimic a direct discharge from the
river itself. Po River mouths included Maistra, Pila, Tolle, Gnocca and
Goro plus the Busa di Scirocco (given its presence in the hydrodynamic
model). VMP were released over the entire simulation period at hourly
intervals from all six locations, and the total number of VMP released at
each mouth was determined based on the water discharge distribution
among the main branches of the Po River.
Once released, a VMP was considered beached if it passed closer
than 250 m from the coastline. This fixed distance was set based on the
model spatial resolution (half the horizontal grid size) and in con-
sideration that the model has difficulty representing complex nearshore
processes. VMP were tagged with release date and river mouth, so that
relative contribution from each river mouth could later be assessed.
Once identified as beached, the VMP was removed from the dataset.
VMP resuspension after beaching was not accounted for in the model,
given the still existing amount of uncertainty surrounding this process
(Hardesty et al., 2017;Zhang et al., 2017). This approach could lead to
a small overestimation of beaching rates, but it was decided that a
simplifying approach was preferable to setting an arbitrary factor
meant to represent resuspension and similar nearshore processes.
ICHTHYOP simulations were run offline using as physical forcing an
elaboration of the UNIVPM-Regione Marche operational hydrodynamic
model that covers the northern Adriatic Sea (horizontal resolution of
500 m; 12 vertical sigma layers). The model (ROMS, Regional Ocean
Modelling System; Haidvogel et al., 2008;http://myroms.org) was
implemented in a coupled version with a surface wave model (SWAN,
Simulating WAves Nearshore model; Booij et al., 1999;http://swan.
tudelft.nl) through the COAWST (Coupled-Ocean-Atmosphere-Wave-
Sediment Transport Modelling System; Warner et al., 2010;Warner
et al., 2008). Surface forcings were derived from COSMO-I7, a local
implementation of the Lokal Model (Steppeler et al., 2003) developed
in the framework of the COSMO Consortium (http://cosmo-model.org)
and run by the Agenzia Regionale per la Prevenzione, l'Ambiente e
l'Energia dell'Emilia Romagna - Servizio Idro-Meteo-Clima (ARPA ER-
SIMC). The UNIVPM-Regione Marche model implementation was
chosen because it was the only freely available and operationally run-
ning forecast model with a high horizontal resolution for the Adriatic
Sea.
A coastal reference grid was developed for displaying the distribu-
tion of beached particles along the Po Delta shore. To avoid artificial
“shadowing” effects from corners of the hydrodynamic model grid cells
located along the coastline, a smoothed grid was established based
rather on the coastline. This grid was created with ArcGIS v9.31 soft-
ware by projecting the coastline 250 m offshore, separating this into
500 m segments and buffering each segment with 250 m, producing
grid cells variable in both shape and surface but without sharp angles or
abrupt changes in direction. After post processing, distribution maps of
estimated accumulation could be defined for each day up to the entire
simulation period. Beach sediment sampling transect locations were
placed as close as possible to the middle of the modelled accumulation
pixel.
2.5. Near-range spectral measurements and remote sensing model
The remote sensing model for quantifying coastal exposure to riv-
erine-based microplastic particles was based on the assumption that
suspended microplastic particles are transported by the same mechan-
isms as other passive, suspended water constituents for which well-es-
tablished remote sensing methodologies exist. With the goal being to
optimally capture river plume water reflectance characteristics, near-
range spectral measurements were used to build regionally calibrated
remote sensing spectral reflectance water parameter algorithms for
different satellite platforms.
First, raw digital number measurements from the spectroradiometer
of downwelling irradiance plus upwelling and sky radiance were con-
verted to irradiance, E(z, λ) in units of W/(m
2
nm), and radiance, L
(z,θ,φ,λ) in units of W/(m
2
sr nm), using the software package RS
3
version 6.4.0 from ASD Inc. Radiance measurements were visually
checked for abnormal behavior (such as saturation or detector jumps)
before being converted to remote sensing reflectance (R
RS
) following
the methodology described by Heim (2005):
+ =
+
+
RL r xL
Esr(0 , ) (0 , ) ( )
(0 , ) [ ]
RS
total wa sky
down
1
where R
RS
(0+,λ) is the remote sensing reflectance directly above the
water surface (0+) for a given wavelength (λ), L
total
is the above water
(upwelling) radiance measurement, r
wa
is the proportion of directly
back-reflected skylight at the air-water interface (taken here to be
0.021, following Heim, 2005), L
sky
the sky radiance, and E
down
the
downwelling irradiance measurement.
The ASD R
RS
dataset from the field campaign together with the in
situ SPM measurements were used to calibrate various candidate algo-
rithms to the Po River region and for a given satellite sensor, the best of
which was then selected as the optimal regionally calibrated empirical
algorithm for the time series analysis. Four separate algorithms for
spectral detection of SPM were considered: (i) Jørgensen (1999) based
on the CZCS band 3 detecting in the range 540–560 nm, (ii) Dekker
(1993) based on in situ spectrometer measurements at 706 nm, and two
different SPOT-3 ratio-based algorithms from Doxaran et al. (2002)
based on (iii) band 3 (780–890 nm) divided by band 1 (500–590 nm)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
565
and (iv) band 3 divided by band 2 (610–680 nm). All calibrated models
were assessed for quality via Root Mean Square Error (RMSE) as well as
goodness of fit statistics following the methods of the Ocean Color
Group (Campbell and O'Reilly, 2005). This allowed determination of
the best-calibrated SPM algorithm for Po River water with a particular
satellite. Both the “Baseline” and “Calibrated” fits were also assessed for
data overfitting using a leave-one-out cross-validation (LOOCV) tech-
nique (Michaelsen, 1987). Further details regarding the calibration and
validation process are available in S2 of the Supplementary material.
Landsat 8 (L8), a joint mission of the U.S. Geological Survey (USGS)
and National Aeronautics and Space Administration (NASA), is
equipped with two push-broom sensors, the Operational Land Imager
(OLI) and the Thermal Infrared Sensor (TIRS), which provide multi-
spectral images with 30 m spatial resolution. The Po Delta study area is
located in the overlap region between two Landsat flight paths, thus
reducing the revisit time for this particular study to 7 days. The
European Space Agency (ESA) Sentinel-2 (S-2) mission is a constellation
of two identical satellites that are equipped with a push-broom
MultiSpectral Instrument (MSI) sensor. S-2 provides multispectral
images with 10, 20 and 60 m spatial resolution depending on the
spectral band. S-2 has a revisit time of up to 2–3 days at midlatitudes.
Usable images from L8 and S-2 acquired between 1 January 2015 and
30 June 2016 were compiled. Other platforms with coarser image
spatial resolution (≥300 m) but proving daily (MODIS) to 2-day
(Sentinel-3) acquisitions with much greater Signal-to-Noise Ratio (SNR)
were considered but not implemented given that our goal was to cap-
ture the fine river plume structure as close to the coastline as possible.
Different atmospheric correction algorithms were tested to mini-
mize the introduction of artifacts to the bands needed for detection of
various water parameters, which was accomplished through compar-
ison with concurrent in situ R
RS
spectrometer measurements (further
details in S3 and S4 of the Supplementary material).
The L8 and S-2 acquisitions were processed with a hierarchical
object-based image analysis (OBIA) developed with eCognition soft-
ware (Trimble Navigation Ltd.) to remove land, cloud, boats, white caps
and breaking waves. The masked images were then used to create SPM
concentration maps, which showed how the river plume was spreading
into the surface coastal waters over the examined time period.
For each acquisition date, non-coastline pixels were masked and the
remaining utilized as the basis for creating the coastline riverine mi-
croplastic exposure map. This was accomplished by converting pixel
values to a similarity ratio using the average SPM concentration from
all five river mouths for that acquisition date. The goal was to display
how similar a given coastline pixel was to a pure river water pixel,
which was then used to indicate influence from river plume waters
along the coastline. Data were binned into hexagons to allow for
combination of images with differing footprints as well as spatial re-
solution, at diameters of both 30 m and 100 m. This was accomplished
using the “hexbin” package within the R software package (R Core
Team, 2016). The first diameter represents the minimum allowable
resolution and the second to match the sediment sampling scheme as
well as easier visualization of the entire Po Delta coastline. Gaps in the
dataset, produced through masking areas such as cloud cover or
breaking waves, were filled in the time series using a combination of
Nearest Neighbor Filtering and temporal linear interpolation. This was
done again in R using the packages “raster”, “rgdal”, “rgeos”, “sp” and
“spacetime”. SPM values between L8 and S-2 images were compared
using standardized differences to check for any inherent bias between
the different sensors. The time series was then summed to create a
composite image of river plume influence along the Po Delta coastline
for the entire modelled time period.
Po River gauge measurements were obtained for the modelling
period from ARPA ER, taken at Pontelagoscuro. Wind regime in front of
the Po Delta was estimated by extracting the zonal and meridional wind
components from the COSMO I7 dataset (forcing field used in the hy-
drodynamic model) for eight points located 20 km in front of the
coastline. For each point, the daily average magnitude and heading
were first computed, and then all eight points averaged to obtain a
single value representative of the whole area. Significant wind regime
events were identified as days with an average wind speed over 5 m/s
and consistently blowing from northeast (Bora), southeast (Scirocco) or
northwest (Mistral).
2.6. Validation of the modelled microplastic exposure maps
Modelled microplastic accumulation values from both the remote
sensing time series as well as the hydrodynamic particle tracking were
compared to in situ beach sediment microplastic concentrations to as-
sess model validity as well as identify weaknesses and strengths of each
modelling method. Comparisons were made using both Pearson's
Correlation r as well as Spearman's Rank Coefficient ρ. All calculations
were carried out using R software. Model maps were also compared to
one another by unit-base normalizing (also known as feature scaling)
each map and then comparing difference values at regular latitudinal
intervals along the coastline.
3. Results and discussion
3.1. Water parameter sampling
Water parameter field measurements are presented in Table 1. Chl-
Ameasurements fell within 0.005–0.043 mg/L, and SPM values covered
a moderate range as compared with ARPA ER monitoring measure-
ments of SPM from Pontelagoscuro (for the time period January 2015
to June 2016, these ranged from 12 to 372 mg/L). Secchi depth mea-
surements only reached a maximum of 163 cm, all located along the
outer edge of the river plume.
3.2. Microplastic sampling
Water microplastic samples analyzed by ATR FT-IR and SWIR
spectroscopy ranged from 1 to 84 particles/m
3
(Fig. 2), with the highest
concentrations being found along the outer river plume edge, within
the main arm of the river (Po della Pila) and the side channel Busa di
Tramontana. The Maistra and central Tolle river mouths both had very
low concentrations, < 6 particles/m
3
. Repeated measures from a par-
ticular river section, such as where Po delle Tolle separates from Pila or
where Tolle splits into three channels before entering the Adriatic, in-
dicated large variability from one sampling time to another.
Some of the highest in situ water microplastic measurements were
found along the outer edge of the Po River plume, which suggests that
either microplastic concentrations in the open Adriatic are at least
comparable with those from the river, or that there are local accumu-
lation processes occurring along the front between fresh river water and
much higher salinity ocean water. Given that rivers are considered one
of the main sources of plastic debris to the ocean (Jambeck et al., 2015;
Lebreton et al., 2017) together with evidence that the Adriatic Sea is a
highly dissipative system (Horvat, 2015), the latter hypothesis is more
likely. Furthermore, concentrations found in this study are an order of
magnitude higher than values measured by Suaria et al. (2016) in the
open Adriatic Sea. Using the median in situ measured microplastic
Table 1
Measured water parameters during the field campaign. Chlorophyll-A(Chl-A)
and suspended particulate matter (SPM) reported in mg/L, Secchi depth
average from before and after trawl in cm.
Chl-A(mg/L) SPM (mg/L) Secchi (cm)
Mean/Median 0.011/0.009 30.2/21.1 67/51
Standard deviation 0.008 29.4 36
Maximum 0.043 127.9 163
Minimum 0.005 7.7 29
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
566
concentration from this study together with average Po River discharge
(1500 m
3
/s) and estimates of microplastic particle count to weight in
the Adriatic (1.68 to 3.00 mg/particle; Suaria et al., 2016;van der Wal
et al., 2015;Vianello et al., 2015), a rough estimate of floating micro-
plastic released by the Po River ranges between 2.2 and 3.8 t per day.
This translates to between 785 and 1402 t/yr, coming close to the es-
timates of 1349 t/yr by Liubartseva et al. (2016), although it should be
noted the latter estimate is based on vertical water column integrated
estimates of all plastic debris (both macro and microplastics). Our es-
timate should be taken with care given that the microplastic sampling
method utilized in this study only sampled microplastics floating at the
water surface, which has been shown to often underestimate total
floating microplastic concentrations (Brunner et al., 2015;Kooi et al.,
2016).
The beach sediment microplastic samples (Fig. 2) ranged from 0 to
78 particles per dry weight (DW) kg. The highest measurement by far
was on the northernmost beach, Caleri, where a total of 3080 micro-
plastic particles were identified for the entire transect (Table 2). Poly-
styrene (PS), acrylonitrile butadiene styrene (ABS) and styrene acrylo-
nitrile (SAN) were found to have similar spectral signatures, thus were
pooled into a group called styrene-based polymers to avoid potential
confusion between these types. The same was true for the polymer types
ethylene vinyl alcohol (EVOH) and ethylene vinyl acetate (EVA).
Polyethylene (PE), polypropylene (PP) and the styrene polymer group
made up > 97% of all particles sampled on six beaches (Boccasette, Pila
North 1, Pila South, Allagamento, Barricata and Goro). Beach sediment
particles identified as belonging to the styrene polymer group were
most often found in their foamed form, which is not surprising con-
sidering that the non-foamed polystyrene is less dense than seawater.
The remaining three beaches had either an increased contribution from
EVOH/EVA or, in the case of Pila North 2, elevated contributions for
the polymer types polyamide (PA) and polyethylene terephthalate
(PET).
The top three contributing polymer types from the beach sediment
microplastic concentrations were PE, styrene-based polymers and PP, in
step with general trends observed in both the Po River (van der Wal
et al., 2015) and the Mediterranean Sea (Suaria et al., 2016) as well as
coastal (Zhang, 2017) and global oceans (Andrady, 2017). PE and PP
make up between 45 and 50% of total global plastic production
(PlasticsEurope, 2016). Higher occurrence of other plastic types, espe-
cially the heavier polymers such as EVOH, PVAL, PET (polyethylene
terephthalate) and PVC (polyvinylchloride), were found at Caleri, Le-
vante and Pila North 2 (Fig. 2 and Table 2). Caleri in particular was
found to have the most extreme microplastic concentration, exceeding
the measurement by Munari et al. (2017) of 21 particles/DW kg at
Volano, just south of the Po Delta. It is important to note that a possible
explanation for this could be different sampling locations, as this study
sampled the extreme high tide line in contrast to the most recent high
tide line. Our measurements were lower than those made in the Venice
Lagoon (672–2175 particles/DW kg; Vianello et al., 2013), although it
should be noted that smaller size classes were under investigation in the
study by Vianello et al. and it is often the case that particle abundances
increase with decreasing size class (Imhof et al., 2018;Lee et al., 2013).
The two northernmost beaches surveyed in this study (Caleri and Le-
vante) were located close to either a public parking lot or a harbor.
Heavier particles are known to be transported more slowly than parti-
cles which are less dense than surrounding seawater (Cable et al.,
2017), which include the plastic types EVOH, PVAL, PET and PVC in
their virgin form. This suggests that the higher concentration rates more
likely result from local sources, rather than longer distance transpor-
tation by the Po River plume or other Adriatic currents.
3.3. Time series data and hydrodynamic model accumulation
River discharge and wind speed, overlain with wind regimes, are
shown in Fig. 3 together with total daily beached VMP and a pictorial
overview of satellite acquisition coverage (discussed in more detail in
the following Section 3.4). The highest observed daily wind speeds
(Fig. 3a) occurred in February, March and November 2015, and March
2016, which all corresponded to northeast winds (Bora events, light
blue bars in Fig. 3). Scirocco events (southeast wind, green bars) were
observed to have less strong wind speeds. Both Mistral and Scirocco
events were found to have occurred less frequently than Bora events.
Comparing Po River average daily outflow with the total daily
beached VMP (Fig. 3b), a loose connection between streamflow and
number of beached VMP was evident. This comes as no great surprised
since Po River streamflow was inherently linked to daily particle release
rate in the model. High beaching rates in February, June and October
2015 were observed to follow high river discharge events, but this
pattern was not always present. Beaching peaks in July 2015 and
January 2016 did not correlate with high river discharge events,
hinting that beaching is not only driven by the amount of released VMP
but also by the surface current field close to the coast and winds.
Of all VMP released, only 18% were found to beach during the si-
mulations. The ratio of released-to-beached VMP for each mouth was
highly variable. Po della Pila, Busa di Scirocco and Po di Gnocca river
mouths were found to beach < 10% of all VMP released, while Po di
Maistra and delle Tolle presented higher rates (26% and 19% respec-
tively). By far the highest rate of beaching was determined for the
southernmost river mouth, Po di Goro, with 94% of all released VMP
being found to have beached. In Fig. 4a, the percentage of beached VMP
from a particular river mouth are compared with the total VMP beached
for each model run day. The other river mouths (Maistra, Pila, Scirocco,
Tolle and Gnocca) display similar behavior in that the majority of
Table 2
Sediment microplastic overview for all 9 beaches sampled, listed north to south. Percent contribution from each plastic type identified is listed: PE polyethylene, PP
polypropylene, PS polystyrene (
1
also includes ABS acrylonitrile butadiene styrene and SAN styrene acrylonitrile), PA polyamide, EV accounts for EVOH ethylene
vinyl alcohol and EVA ethylene vinyl acetate, PEST polyester, PET polyethylene terephthalate, PVC polyvinyl chloride, PUR polyurethane, PVAL polyvinyl alcohol,
SBR styrene butadiene rubber, C/U accounts for either composite particles or unknown plastic types. Total microplastic particles found as well as particles/DW kg is
indicated for each beach sampled.
Beach % contribution Tot. part. Part./DW kg
PE PP PS
1
PA EV PEST PET PVC PUR PVAL SBR C/U
Caleri 45.0 8.6 28.0 < 1 18.0 < 1 < 1 < 1 < 1 0 0 < 1 3080 78.8
Levante 62.2 14.6 16.4 < 1 5.7 < 1 < 1 < 1 < 1 < 1 < 1 < 1 2032 59.4
Boccasette 42.9 13.2 42.9 0 < 1 0 0 0 0 0 0 < 1 182 3.9
Pila North 1 27.0 14.8 54.8 0 < 1 0 < 1 0 0 0 0 < 1 115 2.2
Pila North 2 60.2 9.7 20.4 1.9 0 < 1 4.9 0 < 1 0 0 < 1 103 3.6
Pila South 45.7 18.9 34.1 0 1.4 0 0 0 0 0 0 0 440 8.4
Allagamento 10.0 5.0 85.0 0 0 0 0 0 0 0 0 0 20 0.5
Barricata 19.2 13.8 66.3 < 1 < 1 0 0 0 0 0 0 0 652 14.3
Goro 52.0 19.0 27.8 0 < 1 0 0 0 0 0 0 < 1 248 5.2
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
567
beaching occurs within the first 3 days and was then followed by a
sudden drop to low values, remaining close to zero after about 10 days.
The Po di Goro mouth, on the other hand, also displayed high beaching
rates in the first 5 days but thereafter a more gradual decline, reaching
zero levels after circa 20 days. Thus, VMP released by the Po di Goro
mouth were able to reach the coastline for a longer period of time (up to
30 days after release, as shown in Fig. 4a) and thus had higher prob-
ability to be beached than VMP released from the other mouths. Fig. 4b
depicts the percentage of beached VMP per river mouth as compared to
the total VMP released by the same river mouth. Here the much larger
percentage of VMP to become beached from the total released by the Po
di Goro mouth was quite clear, with over 34% of all VMP released from
the river mouth being beached within the first three days after release.
The elevated beaching rates of the Po di Maistra and delle Tolle were
also more clearly depicted.
The hydrodynamic model beaching accumulation map for the entire
simulation period is shown in Fig. 5. VMP release points in front of river
mouths are indicated by the red arrows. Higher beached VMP accu-
mulation was evident locally around each of the river mouth release
points (Fig. 5a), as well as along the southern coast of the Po Delta and
extending along the southward coast. The highest accumulation areas
were modelled to be just south of the Po della Pila river mouth, and
near to the Po di Gnocca and di Goro river mouths. The individual
distribution from each river mouth is depicted in Fig. 5b, showing that
the VMP beaching rates for all mouths remain quite local except for the
southernmost Po di Goro mouth.
The hydrodynamic modelling results suggest that surface currents
play a more deciding role in determining beaching rates, with the
number of particles being released by the river only semi-coupled to
beach accumulation. Surface currents in the northern Adriatic are de-
termined by wind regime and freshwater influx, the Po River being the
main contributor (Falcieri et al., 2014). VMP tracks from different river
mouths revealed beaching rates of up to 18% for all modelled river
mouths, with the exception of the southernmost mouth Po di Goro. This
is a result of the Goro freshwater plume likely being held closer to the
shoreline by the other plumes, thus allowing plume water to interact
with the coastline for a longer period of time. For the other river
mouths, VMP beaching was found to occur within 10 days following
release, and beaching rate estimates suggests that over 80% of the
microplastic particles being released by the Po River are being dis-
persed to the open Adriatic Sea system.
3.4. Remote sensing model accumulation
Results of all four assessed SPM algorithms are presented in Table 3,
where the algorithm basis is listed along with the fitted algorithm and
model fit statistics (RMSE, LOOCV-RMSE, bias). Model fit statistics
were found to be reduced by an order of magnitude through the cali-
bration/validation for both the Jørgensen and Dekker algorithms, only
slight improvement was achieved for one of the band-ratio Doxaran
algorithms while the other was found to be a non-significant predictor
for the Po River water. Given the observed overlap of the Chl-Are-
flectance peak at 560 nm with the SPM signal saturation between 550
and 700 nm, the Dekker algorithm was selected as preferable to the
Jørgensen algorithm (further details in S5 and S6 of the Supplementary
material). Furthermore, the Dekker algorithm was found to be a sig-
nificant predictor for both L8 as well as S-2 data.
A total of 26 usable images from L8 and S-2 (12 and 14 respectively)
were compiled covering the modelling time period (as shown in Fig. 3
and Table 4). Usable images from two out of the total eighteen months
considered could not be obtained. Of the compiled usable satellite
images, five instances of Bora/low discharge were captured, as well as
three instances of Scirocco/high discharge, only two instances of Bora/
Fig. 3. (a) Average daily wind speed (m/s) in front of the Po Delta. Colored bars highlight wind events: Bora (light blue), Scirocco (green) and Mistral (light gray).
Horizontal dashed line depicts the 5 m/s wind speed threshold. (b) Average daily Po River outflow (m
3
/s) at Pontelagoscuro (dark blue line, left axis) compared with
the average daily total of beached virtual microplastic particles (VMP, rose line, right axis). VMP were tracked in the model a total of 60 days, with the first day that
satisfied this condition indicated by the vertical dashed black line. Usable satellite acquisitions are depicted by arrows (Landsat 8: L8, red; Sentinel-2: S-2, blue) along
the temporal horizontal axis, ticked below to separate months. Wind events displayed as in (a). (For interpretation of the references to color in this figure legend, the
reader is referred to the web version of this article.)
Fig. 4. Percent beached virtual microplastic particles (VMP) from each river
mouth in comparison to (a) total daily beached VMP and (b) total daily released
VMP. Days after release are depicted along the horizontal axis. Release events
after April 15th, 2016, are not included since these were run for < 60 days.
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
568
high and Mistral/high, and one instance each of Scirocco/low and
Mistral/low discharge conditions. Images were atmospherically cor-
rected (further details in S3 and S4 of the Supplementary material) and
the optimal calibrated regional empirical algorithm for each sensor was
implemented to create a time series of SPM river plume maps.
Examples from satellite image masking and implementation of the
calibrated SPM algorithm are shown in Fig. 6. High/low river discharge
was classified as daily average river discharge over/below the median
discharge rate for the entire modelling period (1210 m
3
/s). From the
acquisition with high discharge, the strong effect of a wind event on
river water transportation was quite evident. In the case of high dis-
charge together with southeasterly Scirocco winds (Fig. 6b), plume
water can be observed being pushed northward of the Po della Pila
mouth. With northwesterly Mistral winds (Fig. 6c), the plume shape
appears to be more heavily influenced by river outflow, with high river
discharge producing a plume extending further into the Adriatic. But in
the case of northeasterly Bora winds (Fig. 6a), the high discharge plume
was kept closer to the coastline while primarily spreading high SPM
waters toward the south. A somewhat different pattern was observed
for the acquisitions concurrent to low discharge. The Bora wind event
on January 16, 2016, was observed to again retain the plume close to
the southern coastline (Fig. 6d). Plume form under low discharge and
Scirocco wind was only demonstrated with one acquisition (Fig. 6e).
SPM signal from the river water on this date were quite low, making the
plume difficult to detect, but through utilizing a different stretch the
plume could be observed to extend further into the Adriatic. The Mistral
wind together with low discharge (Fig. 6f) was observed to retain the
river plume close to the coastline, but much smaller than was observed
with high discharge. Standard difference comparison between L8 and S-
2 images revealed a slight sensor bias, in that detected L8 SPM values
tended to be less (< 2 mg/L) than detected S-2 SPM values. This
amount represented < 2% of the SPM range measured in the field
(Table 1) and was thus taken to be negligible.
Results of the remote sensing composite hexagon binning processing
are presented in Fig. 7, with red indicating coastal areas of high river
water influence and green areas with less. Strong river water influence
was detected around all five river mouths (Maistra, Pila, northern and
central Tolle, Gnocca and Goro) as well as the Busa di Tramontana and
di Scirocco. The southern arm of the Po delle Tolle was observed to
have a lesser influence, while the northern section of coastline between
river mouths presented very low rates of river water influence. Coast-
line sections near to the Po della Pila mouth and southward were ob-
served to have higher rates, with the highest influence evidently being
along the coastal section just north of Po della Pila. An area of very high
river water influence (red) was detected between Po della Pila and Busa
di Tramontana, which corresponds to an additional river mouth flowing
out from the lagoon that was first observed during the field campaign.
Coastal exposure modelling using SPM derived from remote sensing
images was able to well capture the signal of sediment heavy river
plume waters spreading along the coastline (Fig. 6 and Fig. 7). Plume
Fig. 5. (a) Distribution map for virtual microplastic particle (VMP) beaching accumulation over the entire 1.5-year simulation period, VMP release locations in front
of river mouths are indicated by the red arrows. Color scale (beige low, red high) indicates total particles beached over entire modelling period. (b) Beached VMP for
each river mouth displayed separately, color scale (blue low, red high) indicates percentage of total VMP beached from that particular river mouth. (For inter-
pretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 3
Calibrated algorithms for suspended particulate matter (SPM). Algorithm spectral basis and publication is indicated in the first column, standard fit algorithm in the
second column together with model fit statistics: root mean square error (RMSE) and bias. Baseline and satellite specific algorithms are listed in the following
columns, with fitted algorithm listed followed by fit statistics (RMSE, leave-one-out cross-validation RMSE, bias) in parentheses. Relationships that were found to be
non-significant (α ≥ 0.05) during fitting are indicated with N/A. The satellite sensor band used is also indicated,e.g. Landsat 8 band 3 centered at 560 nm is indicated
by b3
560
.
Algorithm basis Standard fit Baseline fit Landsat 8 Sentinel-2
Band at 555 nm (Jørgensen, 1999) 0.09 + 56.19 b
555
(154.91; 148.22)
exp(1.47 + 0.60 b
555
)
(40.66; 29.98; 27.67)
exp(1.46 + 0.60 b3
560
)
(40.64; 29.35; 27.67)
exp(1.45 + 0.60 b3
561
)
(40.65; 29.63; 27.67)
Band at 706 nm (Dekker, 1993) 2.69 + 3.31 b
706
(561.16; 488.92)
exp(1.92 + 0.79 b
706
)
(40.45; 21.72; 27.67)
exp(1.82 + 0.66 b4
655
)
(40.50; 22.91; 27.67)
exp(1.91 + 0.78 b5
706
)
(40.45; 21.65; 27.67)
SPOT bands XS3 (cen. 835 nm) and XS1
(cen. 545 nm) (Doxaran et al., 2002)
exp(3.01 + 3.13 XS3
835
/ XS1
545
)
(27.37; 21.06)
exp(2.37 + 3.25 XS3
835
/ XS1
545
)
(26.29; 29.62; 16.43)
N/A exp(2.39 + 3.57 b8
843
/ b3
561
)
(26.42; 29.79; 16.49)
SPOT bands XS3 (cen. 835 nm) and XS2
(cen. 645 nm) (Doxaran et al., 2002)
exp(2.56 + 5.31 XS3
835
/ XS2
645
)
(83.15; 50.92)
N/A N/A N/A
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
569
exposure was found to be highest locally around the five main river
mouths (Maistra, Pila, Tolle, Gnocca and Goro), as well as by side
channels (Scirocco, Tramontana). Different amounts of river plume
exposure were determined for the three arms of the Tolle river mouth,
with the highest signal coming from the middle arm and the lowest
from the southern arm. Evidence of an extra river mouth with strong
outflow between Tramontana and Pila from the remote sensing analysis
follows observations made while collecting the field data. The Po della
Pila mouth is supposed to transport over 60% of the entire river dis-
charge (Correggiari et al., 2005), but based on the SPM exposure map,
this river mouth appears to be on par with the effects from the Po delle
Tolle and Busa di Tramontana. A persistent sand bank was observed at
the opening of this river mouth, both in the remote sensing images and
during sampling in the field. In images taken during high SPM events, it
is clear that flow out of Po della Pila is being split into a northern and
southern portion after encountering this sand bar. If flow is indeed
being slowed out of Po della Pila by the presence of this sand bar, this
would provide a mechanism to explain why the flow is high out of the
Busa di Tramontana and the unnamed outlet just south of Tramontana.
Although this can only be definitively tested with in situ hydrodynamic
measurements, the potential of using remote sensing SPM images for
identifying fine-scale river mouth dynamic patterns is nevertheless well
demonstrated here. The time series was able to capture multiple ac-
quisitions of Bora events with low river discharge and one instance with
high river discharge. In all events, the river plume is observed to stay
closer to the Italian coastline with Bora wind, following results and
model predictions made by Falcieri et al. (2014). This is in stark con-
trast to the situation observed with Scirocco together with high dis-
charge, where the river plume can be observed to extend further east
and north (Fig. 6). River plume dynamics during Mistral events appear
to be controlled more by river discharge than wind regime. The re-
lationship between wind regime and freshwater outflow on northern
Adriatic circulation patterns is complex, but remote sensing images of
the river plume can certainly serve as a useful tool for testing hy-
potheses.
Very low in situ water microplastic concentrations were found for
the Po di Maistra and delle Tolle mouths, as well as the Busa di
Scirocco. Two of these river mouths, namely Maistra and Scirocco, were
observed to also have low river plume influence from the remote sen-
sing accumulation map. Maistra is expected to have the smallest out-
flow of all river mouths (Correggiari et al., 2005), and was thus also
found to have the smallest impact from the hydrodynamic accumula-
tion modelling (Fig. 5). The low in situ water microplastic concentration
measurement from Po delle Tolle is less easily clarified, as this mouth
was found to have a substantial influence by both the hydrodynamic
and remote sensing accumulation models. There was also a discrepancy
between the measured in situ concentrations from the middle Tolle
mouth and before the Tolle arm divides into three. This suggests that
Table 4
Temporal satellite image coverage from January 2015 to June 2016. Total images from each satellite (Sentinel-2: S-2; Landsat 8: L8) are listed in the table, note that
S-2 images first became available July 2015. Satellite acquisitions also depicted in Fig. 4.
Platform Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Total
L8 1 0 1 2 1 0 0 0 1 1 1 0 1 0 1 1 0 1 12
S-2 – – – – – 1 2 3 1 0 1 1 1 1 0 1 2 24
Fig. 6. Combined effect of different wind regimes (Bora, Scirocco or Mistral) with differing river discharge conditions on river plume transportation along the
Western Adriatic. River discharge is termed “high” (panels a, b, c) or “low” (panels d, e, f) depending on daily discharge relative to the median (1210 m
3
/s) over the
entire simulation period. Wind events were classified based on wind direction (indicated by wind compass in each column, pointing in the direction that wind is
blowing) and strength (winds in excess of 5 m/s). Suspended Particulate Matter (SPM) values, ranging from low in blue to high in red, depict river plume shape.
Masked pixels are depicted in dark blue, land in light gray (outside of area of interest in dark gray). (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
570
further accumulation processes may be occurring within the Tolle sub-
arm which have not been captured by the model, and thus warrants
further investigation than was feasible within the scope of this study.
3.5. Model validation results
No significant relationship was found when comparing the in situ
beach sediment microplastic concentrations to the nearest hydro-
dynamic model grid cell (p > 0.10 for Pearson's r and Spearman's ρ).
Removal of beach locations that were under more influence from beach
tourism and nearby aquaculture (namely Caleri, Levante, Boccasette
and Barricata) resulted in a stronger correlation with the hydrodynamic
results: Pearson's r= 0.79 and Spearman's ρ = 0.80 (p < 0.07 in both
cases). Comparison of in situ beach sediment microplastic concentra-
tions with the nearest remote sensing model 30 m hexagon revealed a
moderate negative correlation, with Pearson's r= −0.58 (p = 0.05).
No significant correlation was found at the 100 m hexagon resolution.
Focusing the comparison to beaches with lesser influence from beach
tourism and nearby aquaculture did not reveal an improved correlation.
Removal of the styrene-based polymers from the in situ beach sediment
microplastic concentrations was also considered, given that the ma-
jority of the styrene-based polymer group was composed of foamed
polystyrene. This particular styrene polymer form is highly buoyant and
thus very susceptible to windage during transport as well as potential
higher susceptibility for further particle fractionation during beach
sediment lab processing. Despite these considerations, removal of this
group from in situ beach sediment microplastic concentrations was not
found to provide any further model improvement.
Comparison between the two models is depicted in Fig. 8, where the
normalized remote sensing exposure map is shown next to the hydro-
dynamic model accumulation map (Fig. 8a). General tendencies for
lower normalized values along the coastline north of Pila di Maistra and
south of Lido di Volano were similar between the two model results.
Strong river mouth signal from Pila, the southern Tolle, Gnocca and
Goro were also evident in both maps. Visual dissimilarities were most
evident for the river mouths Maistra, Tramontana and Scirocco, where
a strong signal was registered by the remote sensing model but not by
the hydrodynamic model. In Fig. 8b, the difference of the normalized
values (remote sensing normalized values, RS
norm
, minus hydrodynamic
normalized values, HD
norm
) are displayed as a bar chart aligned along
the latitudinal axis. The comparison was made along the full overlap
extent of both maps and the distribution is indicated in Fig. 8b with one
and two standard deviation gray shaded areas. A slight positive bias is
observed, meaning that the RS
norm
values tend to be higher than the
HD
norm
values, with 95% of all values lying between −0.07 and 0.49.
Areas of exceptional variation, indicated by bars lying outside the
shaded gray area, were notably the coastline located between Pila and
Scirocco and between the northern and central Tolle mouths.
Validation of both accumulation models against all in situ mea-
surements did not produce a significant relationship. This is likely due
to additional microplastic processes (such as biofouling or sinking) and
sources outside of the Po River water which were not included in either
model. Artifacts may also have been introduced to the correlation
through the in situ sediment sampling scheme. In an effort to circum-
vent potential temporal variability, the extreme high tide line was
chosen for the field sampling over the most recent high tide line.
Another factor to acknowledge is the assumption of beaching occurring
after a particle passes within 250 m of the coastline, representing a
substantial simplification of nearshore currents but which was neces-
sary with the given modelling tools. A slightly significant correlation
was found between the hydrodynamic accumulation map and in situ
samples from beaches which were only accessible by boat and not lo-
cated next to a large harbor. An inverse relationship between amount of
beach litter and distance to nearest parking lot has already been es-
tablished in the Adriatic (Munari et al., 2017), suggesting that beach
tourism poses a significant plastic litter source not included in the
models. The remote sensing river plume exposure model was not found
to have a significant relationship with the in situ samples but was very
useful in identifying which river mouths were significant outflow con-
tributors during the simulation period. This information can be useful
in the set-up of future ocean current models of the Po Delta. A number
of factors not incorporated into either the hydrodynamic or the remote
sensing model may largely explain the missing correlation. Refuse re-
sulting from the major shipping corridors which cross the Adriatic are
posited to account for 20% of all marine plastic litter introduced each
year to the sea and the Po River for only 13.5% thereof (Liubartseva
et al., 2016). Windage of particles was not accounted for in the hy-
drodynamic model, which can provide drift speeds up to 25% larger
than the current speed (Chubarenko et al., 2016). After particles be-
come beached, wind transportation may move particles laterally or
further inland (Munari et al., 2016). Microplastic particle aging within
the marine environment was also not represented, including processes
of biofouling, further fragmentation, flocculation and aggregation, all
which are recognized as important dynamic parameters influencing
residence times and transportation pathways (Zhang, 2017). VMP
density was simplified to 0.91 g/mL, representative of the average of
virgin PE (both high and low density) and PP, which was a necessary
assumption given the scope of the study. It would be quite interesting to
test how differing particle density for each major plastic type would
affect modelled coastal accumulation patterns, especially for the
polymer groups most represented in the sediment samples (PE, PP and
PS). Seasonality was accounted for in the hydrodynamic model through
changing the amount of VMP released dependent upon Po River out-
flow, but the concentration of microplastic particles was held constant
during the entire modelling period. It has been established that river
mouth concentrations of microplastic particles can vary by up to three
orders of magnitude at different times of the year (Lebreton et al., 2017)
and that storm water runoff events can significantly increase river
Fig. 7. Composite hexagon (100 m) map of SPM time series, colored by
summed daily similarity values to river water. High rates of river plume in-
fluence (red) are observed at all five major river mouths and the Busa di
Tramontana and di Scirocco, around Po della Pila. Low river plume influence
(green) can be observed along the northern coast of the delta. (For inter-
pretation of the references to color in this figure legend, the reader is referred to
the web version of this article.)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
571
mouth microplastic load (Zhang, 2017). Beaching in this study's hy-
drodynamic model follows simplifying assumptions made in other
studies (Lebreton et al., 2012;Politikos et al., 2017), since the me-
chanisms controlling onshore-offshore transport of microplastic parti-
cles remain unclear (Critchell et al., 2015;Hardesty et al., 2017;Hinata
et al., 2017;Moreira et al., 2016). Despite this, these mechanisms likely
play a driving role in determining small-scale and temporal variation in
sediment microplastic deposition rates (Carlson et al., 2017;Hinata
et al., 2017;Schulz et al., 2017;Zhang, 2017).
4. Conclusions and outlook
In situ sampling of both Po River and Adriatic Sea waters revealed
microplastic concentrations up to 84 particles/m
3
and beach sediment
concentrations up to 78 particles/DW kg. The hydrodynamic modelling
approach was able to identify differing beaching rates between various
river mouths and suggested that particle beaching mostly occurred
within the first 10 days of release. Particles which do not beach within
this initial time period (over 80% of all VMP emitted by the Po River)
were transported away from the Po Delta coastline. Po River emitted
particles that were moved offshore remained offshore, likely due to the
continual freshwater input creating water density boundaries that in-
hibit westward transport. Especially the Po di Goro mouth was identi-
fied as effecting higher beaching rates over a much longer stretch of
coastline. The suspended sediment remote sensing approach was able to
well represent river mouth relative strength, such as the smaller con-
tribution from the southernmost Po delle Tolle river arm or the much
larger contribution of Busa di Tramontana in river outflow.
Microplastic accumulation exposure maps were constructed from both
approaches, which were found to be similar to one another but were not
found to have a significant relationship to in situ beach sampling. This
relationship changed when beaches that were closer to public parking
lots and harbors were removed, suggesting that microplastic sources
which were not accounted for in either modelling approach are also
large contributors to beach microplastic accumulation.
There remain many uncertainties still in our understanding of the
transportation and accumulation mechanisms of microplastics
(Hardesty et al., 2017) and with this study we offer some insight into
these mechanisms within the coastal environment. From the hydro-
dynamic modelling, we see how particles not beached within the first
10 days are transported away from the coastline. The hydrodynamic
model also offers a continual track of VMP transportation and could be
used to study VMP distribution in the open sea. The remote sensing
model presents snapshots of surface river plume form at a finer spatial
resolution over a larger area than computationally feasible with ex-
isting ocean current models. River plume exposure during the model-
ling period could be well captured but this is difficult to translate to
actual microplastic accumulation rates. Model assimilation of remote
sensing data into ocean current simulation models has begun to gain
traction in other oceanographic modelling areas (Miyazawa et al.,
2013;Stroud et al., 2009;Zhang et al., 2014), with up to 40% im-
provements in model forecast root square error. Hardesty et al. (2017)
have already suggested the great improvements possible to our under-
standing of microplastic transportation pathways through integrating
simulation model and empirical observations.
Deeper understanding of microplastic sources, pathways and accu-
mulation areas is intrinsic to our ability to mitigate introduction of this
pollutant to limnic and marine systems as well as organize clean-up
activities. International agreements are already in place forbidding
deposition of litter into the Mediterranean marine environment (Mistri
et al., 2017;Munari et al., 2016), yet despite these steps this enclosed
sea continues to have particularly high concentrations of marine debris
Fig. 8. (a) Remote sensing hexagon-binned (100 m) exposure map (left) next to hydrodynamic model accumulation map (right), both datasets have been unit-based
normalized (green low to red high). (b) Difference normalized remote sensing model (RS
norm
) to normalized hydrodynamic model (HD
norm
), aligned along the
latitudinal axis. Percentage all observations (obs.) at one standard deviation (1-σ, 68%, dark gray) or two (2-σ, 95%, light gray) is indicated, and river mouth position
along bar chart is shown in blue italics. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
572
(Cozar et al., 2015;Suaria et al., 2016). Other modelling efforts within
the Adriatic suggest that land-based sources of marine litter contribute
the majority of marine litter entry into the sea each year (Munari et al.,
2017). National borders are not a component of marine plastic debris
transportation pathway mechanisms and finding middle ground in na-
tional agendas to support concerted legislation efforts are difficult. In
situ microplastic sampling and sample processing is costly, thus mod-
elling offers a methodology for upscaling point measurements to larger
areas than could be feasibly sampled (Hardesty et al., 2017). Fresh-
water systems, in particular rivers, have been slower to receive the
same microplastic research attention as attributed to marine systems
(Wagner et al., 2014). Evidence exists that even low-density tourism
can still create heavy consumer plastic pollution (Free et al., 2014).
Methods for identifying marine debris sources and forecasting accu-
mulation areas have already been put forward as a method to reduce
the cost and optimize the effort of remediation activities (Krelling et al.,
2017;UNEP, 2016). This study demonstrates the strengths and weak-
nesses of two separate modelling approaches, providing further tools
aiming to answer the suggestion of Hardesty et al. (2017) to develop
multipart solutions which can be applied at both local and regional
scales to effect change.
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.marpolbul.2018.11.045.
Acknowledgements
The authors would like to thank Sandra Lohberger for providing
advice and input to both the analysis and writing of the paper. This
work was very kindly supported by numerous lab technicians and in-
terns. Sabela Rodríguez Castaño, Sophia Wisböck and Moritz Altenbach
in particular provided much appreciated support in image processing.
Both Veronika Mitterwallner and Lena Löschel played very important
roles in the preparation and analysis of the microplastic samples, and
Heghnar Martirosyan and Annika Heymann are thanked for their help
with ATR measurements. We would like to extend our thanks to our
brave boat captains, Claudio and Sandro, who were willing to take a
trio of crazy scientists repeatedly out into the open ocean. This study
was partly funded by the German Federal Ministry for Economic Affairs
and Energy (Bundesministerium für Wirtschaft und Energie, or BMWi)
via the DLR Space Administration under the grant numbers 50EE1301
and 50EE1269, and by the Italian National Flagship Program RITMARE
of the Italian Ministry of Education, University and Research.
References
Andrady, A.L., 2017. The plastic in microplastics: a review. Mar. Pollut. Bull. 119, 12–22.
https://doi.org/10.1016/j.marpolbul.2017.01.082.
Artegiani, A., Paschini, E., Russo, A., Bregant, D., Raicich, F., Pinardi, N., 1997a. The
Adriatic Sea general circulation. Part I: Air-sea interactions and water mass structure.
J. Phys. Oceanogr. 27, 1492–1514. https://doi.org/10.1175/1520-0485(1997)
027<1492:TASGCP>2.0.CO;2.
Artegiani, A., Paschini, E., Russo, A., Bregant, D., Raicich, F., Pinardi, N., 1997b. The
Adriatic Sea general circulation. Part II: Baroclinic circulation structure. J. Phys.
Oceanogr. 27, 1515–1532. https://doi.org/10.1175/1520-0485(1997)
027<1515:TASGCP>2.0.CO;2.
Azzarello, M.Y., van Vleet, E.S., 1987. Marine birds and plastic pollution. Mar. Ecol. Prog.
Ser. 37, 295–303. https://doi.org/10.3354/meps037295.
Bignami, F., Sciarra, R., Carniel, S., Santoleri, R., 2007. Variability of Adriatic Sea coastal
turbid waters from SeaWiFS imagery. J. Geophys. Res. Oceans 112, C03S10. https://
doi.org/10.1029/2006JC003518.
Bolaños, R., Sørensen, J.V.T., Benetazzo, A., Carniel, S., Sclavo, M., 2014. Modelling
ocean currents in the northern Adriatic Sea. Cont. Shelf Res. 87, 54–72.
Boldrin, A., Carniel, S., Giani, M., Marini, M., Bernardi Aubry, F., Campanelli, A., Grilli,
F., Russo, A., 2009. Effects of bora wind on physical and biogeochemical properties of
stratified waters in the northern Adriatic. J. Geophys. Res. Oceans 114, 1492. https://
doi.org/10.1029/2008JC004837.
Booij, N., Ris, R.C., Holthuijsen, L.H., 1999. A third-generation wave model for coastal
regions: 1. Model description and validation. J. Geophys. Res. Oceans 104,
7649–7666.
Bouwman, H., Evans, S.W., Cole, N., Yive, Nee Sun Choong Kwet, Kylin, H., 2016. The
flip-or-flop boutique: marine debris on the shores of St Brandon's rock, an isolated
tropical atoll in the Indian Ocean. Mar. Environ. Res. 114, 58–64.
Browne, M.A., Galloway, T.S., Thompson, R.C., 2010. Spatial patterns of plastic debris
along estuarine shorelines. Environ. Sci. Technol. 44, 3404–3409. https://doi.org/10.
1021/es903784e.
Brunner, K., Kukulka, T., Proskurowski, G., Law, K.L., 2015. Passive buoyant tracers in
the ocean surface boundary layer: 2. Observations and simulations of microplastic
marine debris. J. Geophys. Res. Oceans 120, 7559–7573. https://doi.org/10.1002/
2015JC010840.
Cable, R.N., Beletsky, D., Beletsky, R., Wigginton, K., Locke, B.W., Duhaime, M.B., 2017.
Distribution and modeled transport of plastic pollution in the Great Lakes, the world's
largest freshwater resource. Front. Environ. Sci. 5 (10377). https://doi.org/10.3389/
fenvs.2017.00045.
Campbell, J.W., O'Reilly, J.E., 2005. Metrics for Quantifying the Uncertainty in a
Chlorophyll Algorithm: Explicit Equations and Examples Using the OC4.v4 Algorithm
and NOMAD data. Ocean Color Bio-optical Algorithm Mini-workshop, Durham, New
Hampshire. 27–29 Sept. 2005.
Carlson, D.F., Suaria, G., Aliani, S., Fredj, E., Fortibuoni, T., Griffa, A., Russo, A., Melli, V.,
2017. Combining litter observations with a Regional Ocean model to identify sources
and sinks of floating debris in a semi-enclosed basin: the Adriatic Sea. Front. Mar. Sci.
4, 1–16. https://doi.org/10.3389/fmars.2017.00078.
Carniel, S., Benetazzo, A., Bonaldo, D., Falcieri, F.M., Miglietta, M.M., Ricchi, A., Sclavo,
M., 2016. Scratching beneath the surface while coupling atmosphere, ocean and
waves: analysis of a dense water formation event. Ocean Model 101, 101–112.
https://doi.org/10.1016/j.ocemod.2016.03.007.
Chubarenko, I., Bagaev, A., Zobkov, M., Esiukova, E., 2016. On some physical and dy-
namical properties of microplastic particles in marine environment. Mar. Pollut. Bull.
108, 105–112. https://doi.org/10.1016/j.marpolbul.2016.04.048.
Correggiari, A., Cattaneo, A., Trincardi, F., 2005. The modern Po Delta system: lobe
switching and asymmetric prodelta growth. Mar. Geol. 222-223, 49–74. https://doi.
org/10.1016/j.margeo.2005.06.039.
Cozar, A., Sanz-Martín, M., Martí, E., Ignacio González-Gordillo, J., Ubeda, B., Gálvez,
J.Á., Irigoien, X., Duarte, C.M., 2015. Plastic accumulation in the Mediterranean Sea.
PLoS One 10, e0121762. https://doi.org/10.1594/PANGAEA.842054.
Critchell, K., Grech, A., Schlaefer, J., Andutta, F.P., Lambrechts, J., Wolanski, E., Hamann,
M., 2015. Modelling the fate of marine debris along a complex shoreline: lessons from
the Great Barrier Reef. Estuar. Coast. Shelf Sci. 167, 414–426. https://doi.org/10.
1016/j.ecss.2015.10.018.
Dekker, A.G., 1993. Detection of Optical Water Quality Parameters for Eutrophic Waters
by High Resolution Remote Sensing. Proefschrift Vrije Universiteit Amsterdam,
Amsterdam, The Netherlands (237 pp).
van der Wal, M., van der Meulen, M., Tweehuijsen, G., Peterlin, M., Palatinus, A., Viršek,
M.K., Coscia, L., Kržan, A., 2015. SFRA0025: Identification and Assessment of
Riverine Input of (Marine) Litter. Eunomia Research & Consulting (208 pp).
Doxaran, D., Froidefond, J.-M., Lavender, S., Castaing, P., 2002. Spectral signature of
highly turbid waters: application with SPOT data to quantify suspended particula-
tematter concentrations. Remote Sens. Environ. 81, 149–161.
Dris, R., Imhof, H., Sanchez, W., Gasperi, J., Galgani, F., Tassin, B., Laforsch, C., 2015.
Beyond the ocean: contamination of freshwater ecosystems with (micro-)plastic
particles. Environ. Chem. 12, 539. https://doi.org/10.1071/EN14172.
Duhec, A.V., Jeanne, R.F., Maximenko, N., Hafner, J., 2015. Composition and potential
origin of marine debris stranded in the Western Indian Ocean on remote Alphonse
Island, Seychelles. Mar. Pollut. Bull. 96, 76–86. https://doi.org/10.1016/j.
marpolbul.2015.05.042.
Falcieri, F.M., Benetazzo, A., Sclavo, M., Russo, A., Carniel, S., 2014. Po River plume
pattern variability investigated from model data. Cont. Shelf Res. 87, 84–95. https://
doi.org/10.1016/j.csr.2013.11.001.
Fargion, G.S., Mueller, J.L., 2000. Ocean Optics Protocols for Satellite Ocean Color Sensor
Validation, Revision 2: Sensor Intercomparison and Merger for Biological and
Interdisciplinary Ocean Studies (SIMBIOS) Project Technical Memoranda. NASA/TM-
2000-209966/REV2, Rept-2000-04041-0/REV2, NAS 1.15:209966/REV2. NASA,
NASA Goddard Space Flight Center, Greenbelt, MD, USA (194 pp). https://ntrs.nasa.
gov/search.jsp?R=20000097063.
Free, C.M., Jensen, O.P., Mason, S.A., Eriksen, M., Williamson, N.J., Boldgiv, B., 2014.
High-levels of microplastic pollution in a large, remote, mountain lake. Mar. Pollut.
Bull. 85, 156–163. https://doi.org/10.1016/j.marpolbul.2014.06.001.
G7 Germany, 2015. Leaders' Declaration G7 Summit, 7–8 June 2015. G7 Germany,
Schloss Elmau, Germany (23 pp).
Galgani, F., Hanke, G., Werner, S., Oosterbaan, L., Nilsson, P., Fleet, D., Kinsey, S.,
Thompson, R.C., van Franeker, J., Vlachogianni, T., Scoullos, M., Veiga, J.M.,
Palatinus, A., Matiddi, M., Maes, T., Korpinen, S., Budziak, A., Leslie, H., Gago, J.,
Liebezeit, G., 2013. Guidance on Monitoring of Marine Litter in European Seas: A
Guidance Document Within the Common Implementation Strategy for the Marine
Strategy Framework Directive. Publications Office of the European Union,
Luxembourg.
GESAMP, 2016. Sources, Fate and Effects of Microplastics in the Marine Environment:
Part Two of a Global Assessment. Rep. Stud. GESAMP 93. IMO, FAO, UNESCO-IOC,
UNIDO, WMO, IAEA, UN, UNEP, UNDP Joint Group of Experts on the Scientific
Aspects of Marine Environmental Protection, Rome, Italy (221 pp).
Haidvogel, D.B., Arango, H., Budgell, W.P., Cornuelle, B.D., Curchitser, E., Di Lorenzo, E.,
Fennel, K., Geyer, W.R., Hermann, A.J., Lanerolle, L., Levin, J., McWilliams, J.C.,
Miller, A.J., Moore, A.M., Powell, T.M., Shchepetkin, A.F., Sherwood, C.R., Signell,
R.P., Warner, J.C., Wilkin, J., 2008. Ocean forecasting in terrain-following co-
ordinates: formulation and skill assessment of the Regional Ocean Modeling System.
J. Comput. Phys. 227, 3595–3624.
Hardesty, B.D., Harari, J., Isobe, A., Lebreton, L., Maximenko, N., Potemra, J., van Sebille,
E., Vethaak, A.D., Wilcox, C., 2017. Using numerical model simulations to improve
the understanding of micro-plastic distribution and pathways in the marine en-
vironment. Front. Mar. Sci. 4 (30). https://doi.org/10.3389/fmars.2017.00030.
Heim, B., 2005. Qualitative and Quantitative Analyses of Lake Baikal's Surface-waters
Using Ocean Colour Satellite Data (SeaWiFS). Doctoral Thesis. (142 pp).
Hinata, H., Mori, K., Ohno, K., Miyao, Y., Kataoka, T., 2017. An estimation of the average
residence times and onshore-offshore diffusivities of beached microplastics based on
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
573
the population decay of tagged meso- and macrolitter. Mar. Pollut. Bull. 122, 17–26.
https://doi.org/10.1016/j.marpolbul.2017.05.012.
Hoffman, M.J., Hittinger, E., 2017. Inventory and transport of plastic debris in the
Laurentian Great Lakes. Mar. Pollut. Bull. 115, 273–281. https://doi.org/10.1016/j.
marpolbul.2016.11.061.
Horvat, P., 2015. MICRO 2015 Seminar, Piran, Slovenia. May 2015.
Imhof, H.K., Ivleva, N.P., Schmid, J., Niessner, R., Laforsch, C., 2013. Contamination of
beach sediments of a subalpine lake with microplastic particles. Curr. Biol. 23,
R867–R868. https://doi.org/10.1016/j.cub.2013.09.001.
Imhof, H.K., Wiesheu, A.C., Anger, P.M., Niessner, R., Ivleva, N.P., Laforsch, C., 2018.
Variation in plastic abundance at different lake beach zones - a case study. Sci. Total
Environ. 613-614, 530–537. https://doi.org/10.1016/j.scitotenv.2017.08.300.
IOC, SCOR, 1994. Protocols for the Joint Global Ocean Flux Study (JGOFS) Core
Measurements. IOC Manuals and Guides 29 (181 pp).
Jambeck, J.R., Geyer, R., Wilcox, C., Siegler, T.R., Perryman, M., Andrady, A., Narayan,
R., Law, K.L., 2015. Plastic waste inputs from land into the ocean. Science 347,
768–771. https://doi.org/10.1126/science.1260352.
Jørgensen, P.V., 1999. Standard CZCS Case 1 algorithms in Danish coastal waters. Int. J.
Remote Sens. 20, 1289–1301. https://doi.org/10.1080/014311699212731.
Kooi, M., Reisser, J., Slat, B., Ferrari, F.F., Schmid, M.S., Cunsolo, S., Brambini, R., Noble,
K., Sirks, L.-A., Linders, T.E.W., Schoeneich-Argent, R.I., Koelmans, A.A., 2016. The
effect of particle properties on the depth profile of buoyant plastics in the ocean. Sci.
Rep. 6. https://doi.org/10.1038/srep33882.
Krelling, A.P., Souza, M.M., Williams, A.T., Turra, A., 2017. Transboundary movement of
marine litter in an estuarine gradient: evaluating sources and sinks using hydro-
dynamic modelling and ground truthing estimates. Mar. Pollut. Bull. 119, 48–63.
https://doi.org/10.1016/j.marpolbul.2017.03.034.
Law, K.L., Thompson, R.C., 2014. Oceans. Microplastics in the seas. Science 345,
144–145. https://doi.org/10.1126/science.1254065.
Lebreton, L.C.-M., Greer, S.D., Borrero, J.C., 2012. Numerical modelling of floating debris
in the world's oceans. Mar. Pollut. Bull. 64, 653–661. https://doi.org/10.1016/j.
marpolbul.2011.10.027.
Lebreton, L.C.M., van der Zwet, J., Damsteeg, J.-W., Slat, B., Andrady, A., Reisser, J.,
2017. River plastic emissions to the world's oceans. Nat. Commun. 8, 15611. https://
doi.org/10.1038/ncomms15611.
Lee, J., Hong, S., Song, Y.K., Hong, S.H., Jang, Y.C., Jang, M., Heo, N.W., Han, G.M., Lee,
M.J., Kang, D., Shim, W.J., 2013. Relationships among the abundances of plastic
debris in different size classes on beaches in South Korea. Mar. Pollut. Bull. 77,
349–354. https://doi.org/10.1016/j.marpolbul.2013.08.013.
Lett, C., Verley, P., Mullon, C., Parada, C., Brochier, T., Pierrick, P., Balnke, B., 2008. A
lagrangian tool for modelling ichthyoplankton dynamics. Environ. Model. Softw. 23,
1210–1214.
Lindell, T., Pierson, D., Premazzi, G., Zilioli, E. (Eds.), 1999. Manual for Monitoring
European Lakes Using Remote Sensing Techniques. Off. for Off. Publ. of the Europ.
Communities, Luxembourg (161 pp).
Liubartseva, S., Coppini, G., Lecci, R., Creti, S., 2016. Regional approach to modeling the
transport of floating plastic debris in the Adriatic Sea. Mar. Pollut. Bull. 103,
115–127. https://doi.org/10.1016/j.marpolbul.2015.12.031.
Löder, M.G.J., Gerdts, G., 2015. Methodology used for the detection and identification of
microplastics—A critical appraisal. In: Bergmann, M., Gutow, L., Klages, M. (Eds.),
Marine Anthropogenic Litter. Springer International Publishing, Cham, pp. 201–227.
Löder, M.G.J., Kuczera, M., Mintenig, S., Lorenz, C., Gerdts, G., 2015. Focal plane array
detector-based micro-Fourier-transform infrared imaging for the analysis of micro-
plastics in environmental samples. Environ. Chem. 12, 563. https://doi.org/10.1071/
EN14205.
Löder, M.G.J., Imhof, H.K., Ladehoff, M., Löschel, L.A., Lorenz, C., Mintenig, S., Piehl, S.,
Primpke, S., Schrank, I., Laforsch, C., Gerdts, G., 2017. Enzymatic purification of
microplastics in environmental samples. Environ. Sci. Technol. 51, 14283–14292.
https://doi.org/10.1021/acs.est.7b03055.
Mani, T., Hauk, A., Walter, U., Burkhardt-Holm, P., 2015. Microplastics profile along the
Rhine River. Sci. Rep. 5 (17988). https://doi.org/10.1038/srep17988.
Masura, J., Baker, J., Foster, G., Arthur, C., 2015. Laboratory Methods for the Analysis of
Microplastics in the Marine Environment: Recommendations for Quantifying
Synthetic Particles in Waters and Sediments Technical Memorandum NOS-OR&R-48.
NOAA Marine Debris Program, NOAA Marine Debris Division, Silver Spring, MD,
USA (39 pp).
Michaelsen, J., 1987. Cross-validation in statistical climate forecast models. J. Clim. Appl.
Meteorol. 26, 1589–1600.
Mistri, M., Infantini, V., Scoponi, M., Granata, T., Moruzzi, L., Massara, F., de Donati, M.,
Munari, C., 2017. Small plastic debris in sediments from the Central Adriatic Sea:
types, occurrence and distribution. Mar. Pollut. Bull. 124, 435–440. https://doi.org/
10.1016/j.marpolbul.2017.07.063.
Miyazawa, Y., Murakami, H., Miyama, T., Varlamov, S.M., Guo, X., Waseda, T., Sil, S.,
2013. Data assimilation of the high-resolution sea surface temperature obtained from
the Aqua-Terra satellites (MODIS-SST) using an ensemble Kalman filter. Remote Sens.
5, 3123–3139. https://doi.org/10.3390/rs5063123.
Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface
measurements. Appl. Opt. 38, 7442. https://doi.org/10.1364/AO.38.007442.
Moreira, F.T., Prantoni, A.L., Martini, B., de Abreu, M.A., Stoiev, S.B., Turra, A., 2016.
Small-scale temporal and spatial variability in the abundance of plastic pellets on
sandy beaches: methodological considerations for estimating the input of micro-
plastics. Mar. Pollut. Bull. 102, 114–121. https://doi.org/10.1016/j.marpolbul.2015.
11.051.
Munari, C., Corbau, C., Simeoni, U., Mistri, M., 2016. Marine litter on Mediterranean
shores: analysis of composition, spatial distribution and sources in north-western
Adriatic beaches. Waste Manag. 49, 483–490. https://doi.org/10.1016/j.wasman.
2015.12.010.
Munari, C., Scoponi, M., Mistri, M., 2017. Plastic debris in the Mediterranean Sea: types,
occurrence and distribution along Adriatic shorelines. Waste Manag. 67, 385–391.
https://doi.org/10.1016/j.wasman.2017.05.020.
PlasticsEurope, 2014. Plastics – The Facts 2014. An Analysis of European Plastics
Production, Demand and Waste Data. PlasticsEurope (33 pp).
PlasticsEurope, 2016. Plastics – THE FACTS 2016. An Analysis of European Plastics
Production, Demand and Waste Data. PlasticsEurope (38 pp). http://www.
plasticseurope.org/Document/plastics—the-facts-2016-15787.aspx?Page=
DOCUMENT&FolID=2.
Politikos, D.V., Ioakeimidis, C., Papatheodorou, G., Tsiaras, K., 2017. Modeling the fate
and distribution of floating litter particles in the Aegean Sea (E. Mediterranean).
Front. Mar. Sci. 4 (8). https://doi.org/10.3389/fmars.2017.00191.
Poulain, P.-M., 2001. Adriatic Sea surface circulation as derived from drifter data between
1990 and 1999. J. Mar. Syst. 29, 3–32. https://doi.org/10.1016/S0924-7963(01)
00007-0.
R Core Team, 2016. R: A Language and Environment for Statistical Computing. R
Foundation for Statistical Computing, Vienna, Austria.
Schmidt, L.K., Bochow, M., Imhof, H.K., Oswald, S.E., 2018. Multi-temporal surveys for
microplastic particles enabled by a novel and fast application of SWIR imaging
spectroscopy - study of an urban watercourse traversing the city of Berlin, Germany.
Environ. Pollut. 239, 579–589. https://doi.org/10.1016/j.envpol.2018.03.097.
Schulz, M., van Loon, W., Fleet, D.M., Baggelaar, P., van der Meulen, E., 2017. OSPAR
standard method and software for statistical analysis of beach litter data. Mar. Pollut.
Bull. 122, 166–175. https://doi.org/10.1016/j.marpolbul.2017.06.045.
Sheavly, S.B., Register, K.M., 2007. Marine debris & plastics: environmental concerns,
sources, impacts and solutions. J. Polym. Environ. 15, 301–305. https://doi.org/10.
1007/s10924-007-0074-3.
Simeoni, U., Corbau, C., 2009. A review of the Delta Po evolution (Italy) related to cli-
matic changes and human impacts. Geomorphology 107, 64–71. https://doi.org/10.
1016/j.geomorph.2008.11.004.
Steppeler, J., Doms, G., Schattler, U., Bitzer, H.W., Gassmann, A., Damrath, U., Gregoric,
G., 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorog.
Atmos. Phys. 82, 75–96.
Stroud, J.R., Lesht, B.M., Schwab, D.J., Beletsky, D., Stein, M.L., 2009. Assimilation of
satellite images into a sediment transport model of Lake Michigan. Water Resour.
Res. 45 (202). https://doi.org/10.1029/2007WR006747.
Suaria, G., Avio, C.G., Mineo, A., Lattin, G.L., Magaldi, M.G., Belmonte, G., Moore, C.J.,
Regoli, F., Aliani, S., 2016. The Mediterranean Plastic Soup: synthetic polymers in
Mediterranean surface waters. Sci. Rep. 6. https://doi.org/10.1038/srep37551.
Turra, A., Manzano, A.B., Dias, R.J.S., Mahiques, M.M., Barbosa, L., Balthazar-Silva, D.,
Moreira, F.T., 2014. Three-dimensional distribution of plastic pellets in sandy bea-
ches: shifting paradigms. Sci. Rep. 4 (4435). https://doi.org/10.1038/srep04435.
UNEP, 2016. Marine Plastic Debris and Microplastics – Global Lessons and Research to
Inspire Action and Guide Policy Change. United Nations Environment Programme,
Nairobi, Kenya (274 pp).
UNESCO, 1994. Protocols for the Joint Global Ocean Flux Study (JGOFS) Core
Measurements. UNESCO Publ. No 29. IOC Manuals and Guides, Paris, France. http://
unesdoc.unesco.org/images/0009/000997/099739eo.pdf.
Vianello, A., Boldrin, A., Guerriero, P., Moschino, V., Rella, R., Sturaro, A., Da Ros, L.,
2013. Microplastic particles in sediments of Lagoon of Venice, Italy: first observations
on occurrence, spatial patterns and identification. Estuar. Coast. Shelf Sci. 130,
54–61. https://doi.org/10.1016/j.ecss.2013.03.022.
Vianello, A., Acri, F., Aubry, F.B., Boldrin, A., Camatti, E., Da Rosa, L., Marceta, T.,
Moschino, V., 2015. Occurrence and Distribution of Floating Microplastics in the
North Adriatic Sea: Preliminary Results. MICRO 2015 Seminar, Piran, Slovenia. May
2015.
Wagner, M., Scherer, C., Alvarez-Muñoz, D., Brennholt, N., Bourrain, X., Buchinger, S.,
Fries, E., Grosbois, C., Klasmeier, J., Marti, T., Rodriguez-Mozaz, S., Urbatzka, R.,
Vethaak, A.D., Winther-Nielsen, M., Reifferscheid, G., 2014. Microplastics in fresh-
water ecosystems: what we know and what we need to know. Environ. Sci. Eur. 26
(12). https://doi.org/10.1186/s12302-014-0012-7.
Warner, J.C., Sherwood, C.R., Signell, R.P., Harris, C.K., Arango, H.G., 2008.
Development of a three-dimensional, regionl coupled wave, current, and sediment-
transport model. Comput. Geosci. 34, 1284–1306.
Warner, J.C., Armstrong, B., He, R., Zambon, J.B., 2010. Development of a coupled
ocean–atmosphere–wave–sediment transport (COAWST) modeling system. Ocean
Model 35, 230–244.
Zbyszewski, M., Corcoran, P.L., 2011. Distribution and degradation of fresh water plastic
particles along the beaches of Lake Huron, Canada. Water Air Soil Pollut. 220,
365–372. https://doi.org/10.1007/s11270-011-0760-6.
Zhang, H., 2017. Transport of microplastics in coastal seas. Estuar. Coast. Shelf Sci. 199,
74–86. https://doi.org/10.1016/j.ecss.2017.09.032.
Zhang, P., Wai, O., Chen, X., Lu, J., Tian, L., 2014. Improving sediment transport pre-
diction by assimilating satellite images in a Tidal Bay model of Hong Kong. Water 6,
642–660. https://doi.org/10.3390/w6030642.
Zhang, W., Zhang, S., Wang, J., Wang, Y., Mu, J., Wang, P., Lin, X., Ma, D., 2017.
Microplastic pollution in the surface waters of the Bohai Sea, China. Environ. Pollut.
231, 541–548. https://doi.org/10.1016/j.envpol.2017.08.058.
E.C. Atwood et al. Marine Pollution Bulletin 138 (2019) 561–574
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... Moreover, plastics are subjected to physical, geochemical, biological and ecological processes at these dynamic locations, which in turn influences their fate in the environment [28][29][30] . These processes and impacts, especially on microplastics, have received relatively little attention [31][32][33] , yet they could be key to mitigating plastic pollution. For instance, the enhanced aggregation of surface drifters by fronts could potentially facilitate the cleanup of floating pollutants, as suggested by submesoscale fronts 34 . ...
... Microplastics are clearly enriched at estuary fronts compared to ambient waters ( fig. 1b). This fact is supported by modelling studies that demonstrate the occurrence of microplastics hotspots along salinity fronts 58,59 and studies that ascribe the observed distribution patterns of microplastics to the estuarine salinity fronts [31][32][33] . Indeed, the microplastics abundances at estuarine fronts (300-5,000 μm; 89-2,200 pieces per cubic metre, standardized using methods in the literature 60,61 ), are markedly higher than those observed in the open-ocean fronts (1.4-123.2 ...
... Data are taken from refs. [31][32][33]53,[55][56][57] . ...
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... Microplastics have been found in seawaters in several recent studies, and when compared using the same unit of measurement (items·m -2 ) and mesh size of the tools (330 µm), our findings are consistent with those of others, such as those found in Tuscany's coastal waters (0.069 ± 0.083 items·m -2 ; Baini et al. 2018 Consistently with previous studies (Atwood et al. 2019;Coll et al. 2012;Desforges et al. 2014;Pini et al. 2018), the concentration of MPs in the Ligurian and the Adriatic Seas decrease with distance from the coast. Sampling sites close to the coast show significantly higher MP concentrations than offshore waters (12-24 NM). ...
... The concentration and distribution of MPs in the water surface are highly variable due to seasonal changes in river outflows, currents, mechanisms of degradation and fragmentation, changes in litter size, shape, buoyancy, and movement to and from other compartments (Atwood et al. 2019;Cózar et al. 2015;GESAMP 2019;Jambeck et al. 2015;Mansui et al. 2020). The abundance of MPs can be influenced by processes operating over hours, days, weeks, or months; including tidal conditions, short-term wind and rain events, and seasonal extremes (GESAMP 2019). ...
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A comprehensive understanding of the concentration of microplastics (MPs) in seawaters is essential to implement monitoring programs and understand the impacts on ecosystems, as required by the European legislation to protect the marine environment. In this context, the purpose of this study is to investigate the composition, quantity, and spatial distribution of microplastics from coastal to offshore areas in three Italian seawaters. In addition, the distribution of microplastics between surface and subsurface water layers was analyzed in order to better understand the dynamics of MPs in the upper layers of the water column. A total number of 6069 MPs (mean total concentration of 0.029 microplastics · m⁻²) were found to be heterogeneous in type, shape, and color. In general, MPs concentrations decrease with coastal distance, except when environmental forcings are predominant (such as sea currents). Moreover, the amount of surface MPs was almost four times that of subsurface microplastics, which consisted mostly of fibers. In light of these results, it becomes clear how critical it is to plan remediation actions and programs to minimize microplastic accumulations in the sea.
... As the longest river in Italy, the Po river drainage area (74,000 km 2 ) encompasses much of the northern region of the country, with >20 million inhabitants, and includes many large cities, as well as areas of intensive industrial and agricultural activities [55]. The Po river collects wastewater and rainwater from one of the most heavily industrialized areas of Europe, thus contributing to the anthropogenic pressure through large loadings of organic and inorganic chemicals, nutrients, and garbage, including those of a plastic nature [56,57]. ...
... The Po river collects wastewater and rainwater from one of the most heavily industrialized areas of Europe, thus contributing to the anthropogenic pressure through large loadings of organic and inorganic chemicals, nutrients, and garbage, including those of a plastic nature [56,57]. The river splits into many sub-rivers before flowing into the Adriatic Sea, the main recognized arms of which are the Po di Maistra, dellaPila, delle Tolle, di Gnocca (or dellaDonzella) and di Goro [55]. ...
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... Application of hydrodynamic and particle modeling may be able to fill this gap given its high (temporal) resolution in nature, for instance between revisit times of satellite orbits. Atwood et al. (2019) provided an example of integrating remote sensing and hydrodynamic modeling to assess microplastic pollution emitted by rivers in Italy. However, the application of this method in marine environments would be challenging. ...
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... In addition, due to environmental effects like wind and waves, buoyant materials likely have increased dispersion in their flow paths relative to the mean flux of water (Reed et al., 1994), which also suggests that changing environmental conditions may not affect the transport trajectories of all types of materials equally. Common types of positively buoyant materials in river deltas can include macroalgae (Messyasz et al., 2018;Thiel & Gutow, 2004), light large wood (Le Lay et al., 2013;Wohl, 2013), mineral and biogenic oils (Ayoub et al., 2018;Cathcart et al., 2020;De Laurentiis et al., 2020;Reed et al., 1994;Röhrs et al., 2018;Shaw et al., 2016), many juvenile organisms (e.g., seeds, larvae, and eggs) (Chambert & James, 2009;Sundby, 1983;Thiel & Gutow, 2005), frazil ice (Svensson & Omstedt, 1998), most plastics (Atwood et al., 2019;Lebreton et al., 2017;Van Sebille et al., 2020), and floating vascular vegetation (Thiel & Gutow, 2004). ...
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Understanding the way fluvially transported materials are partitioned in river deltas is essential for predicting their morphological change and the fate of environmental constituents and contaminants. Translating water‐based partitioning estimates into fluxes of nonwater materials is often difficult to constrain because most materials are not uniformly distributed in the water column and may have characteristic transport pathways that differ from the mean flow. Here, we present a novel reduced‐complexity modeling approach for simulating the patterns of transport of a diverse range of suspended fluvial inputs influenced by vertical stratification and topographic steering. We utilize a mixed Eulerian‐Lagrangian modeling approach to estimate the patterns of nourishment and connectivity in the Wax Lake and Atchafalaya Deltas in coastal Louisiana. Using the reduced‐complexity particle routing model dorado, in conjunction with a calibrated ANUGA hydrodynamic model, we quantify how transport patterns in each system change as a function of a material's Rouse number and environmental conditions. We find that even small changes to local topographic steering lead to emergent system‐scale changes in patterns of fluvial nourishment, with greater channel‐island connectivity for positively buoyant materials than negatively buoyant materials, hydraulically sorting different materials in space. We also find that the nourishment patterns of some materials are more sensitive than others to changes in discharge, tidal conditions, and anthropogenic dredging. Our results have important implications for understanding the eco‐geomorphic evolution of deltas, and our modeling framework could have interdisciplinary implications for studying the transport of materials in other systems, including sediments, nutrients, wood, plastics, and biotic materials.
... Paper [4] compares the results of modeling and collection of microplastics in the northern Italian part of the Adriatic Sea. In particular, the influence of the river Po is analyzed, which has a flow in the range of 100 to 11550 m 3 /s and carries with it significant amounts of microplastics. ...
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The progressive increase in the mass of microplastics in the ecosystem obliges us to urgently define measures to reduce its adverse effects, which primarily requires an understanding of the genesis of its presence and the dynamics of expansion through the biosphere. This paper aims to contribute to the understanding of the dynamics of microplastic particle motion, especially in the context of deposition rate with respect to microplastic material density, microplastic particle size and especially with respect to microplastic particle shape (which significantly affects shape resistance forces). For this purpose, an overview of existing works in the field of modeling the motion of microplastics is given, and a numerical model for modeling the transport of microplastic particles in an inhomogeneous fluid velocity field for laminar flow is formed. The proposed model is thus based on a system of two nonlinear ordinary differential equations.
... The common source of microplastics in freshwater is the dispersion of larger plastic particles dumped on land that eventually load into freshwater sources (Eerkes-Medrano et al., 2015). On the contrary, ~80 % of marine litter originates on land (Jambeck et al., 2015;Andrady, 2011), with half of it being released into oceans (Atwood et al., 2019). Between 1.15 and 2.41 million tons of plastic waste enters the global ocean annually through different river system. ...
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The Karnafullly River, which flows through Chattogram and falls into the Bay of Bengal, Bangladesh, is vulnerable to microplastic contamination. In this study, we looked at microplastics in the Karnafully River's surface water (5 sites), sediment (9 sites), and biota (4 species). Microplastic concentrations ranged from 0.57 ± 0.07 to 6.63 ± 0.52 items/L in surface water, 143.33 ± 3.33 to 1240 ± 5.77 items/kg dry weight in sediment, and 5.93 ± 0.62 to 13.17 ± 0.76 items/species in biota. A significant difference (P < 0.05) was found in the concentration of MPs in the Karnafully River's sediment, biota, and surface water. High percentage of fiber-shaped and small-sized MPs (<1 mm) were detected throughout the samples. Water and sediment MPs were often transparent/white and blue, whereas biota MPs were mostly black and red, indicating a color preference during biological uptake. The Bay of Bengal received 61.3 × 10⁹ microplastic items per day. The feeding zone of biota influenced the level of microplastics, with a trend of pelagic > demersal > benthic > benthopelagic. Polyethylene and polyethylene terephthalate were the most abundant polymer. Using the average fish intake rate in Bangladesh, we computed a possible consumption of 4015–7665 items of MPs/person/year.
... When combined with remote sensing techniques, (Atwood et al., 2019) demonstrated that satellite-derived images can be combined with particle tracking modelling to track microplastics in the Po River, Italy. In our study, examples investigated highlight the potential of particle tracking to interpolate between two cloud-free satellite overpasses. ...
Article
Propelled by the rapid development of equipment, technology and computational power, the monitoring and simulation of the hydrodynamics in lakes have steadily advanced. In contrast, water quality simulations are more difficult to implement, due to the difficulty in obtaining large-scale, spatially resolved field observations for model validation and the number of interacting processes to be parameterized. Here we demonstrate that remote sensing data can be used to inform Lagrangian particle tracking in a large lake, and vice versa. We used total suspended matter (TSM) as a parameter that can be both estimated from the backscattering in satellite images and modelled in terms of particle abundance. Specifically, we compared TSM concentrations in Lake Geneva deduced from images taken by Sentinel-2 and Sentinel-3 satellites to those estimated from Delft3D hydrodynamic and particle tracking models. TSM concentrations obtained from both methods were compared over a time span of up to 5 days in several scenario studies, including instantaneous and continuous point sources and large-scale TSM simulations. The results demonstrate that remote sensing images can serve to calibrate and validate particle tracking models with independent observations. The model was able to capture both the position of a TSM cloud arising 5 days after an instantaneous point source release, and the direction of particle transport and TSM plume size resulting from a continuous source. Even when simulating the whole lake domain, model results closely approximated the satellite-derived TSM concentrations along lake transects within 9%. In return, the particle tracking model was able to complete partially impaired satellite images, and fill in a four-day image gaps between satellite revisits. The synergy of remote sensing techniques and particle tracking modelling allows a rapid, continuous and more accurate analysis on solute transport in lakes.
Article
Microplastics pollution is an emerging environmental concern. However, there are almost no MPs numerical simulation studies in the Yangtze Estuary which is considered as the largest plastic export in the world and quantitative simulation is not carried out in the existing models. Therefore, completing quantitative simulation and exploring different patterns of MPs transport are the main objectives of this study. In addition, the concentration distribution and risk of MPs are also analyzed. Mass-Number method is proposed to quantitatively simulate microplastics concentration in Feb. and May with errors of less than 18%. Compared with sediment flocculation and settling transport, independent floating transport is more susceptible to surface currents resulting in increased beaching and more inhomogeneous concentration distribution. Meanwhile, under the influence of current, local topography and salt wedge, the MPs perform linear motion and clockwise spiral motion inside and outside the estuary and rapidly form a "hot spot" on the southeastern part of Chongming Island and 57% to 90% of MPs are beached or settled inside the estuary, especially on the north shore. Therefore, MPs risk in some sensitive targets should be concerned according to risk assessment results. Our results break the space-time limit and explore the fate of MPs in the Yangtze Estuary and provide new idea and concern of MPs numerical simulation.
Article
Understanding the water circulation in oceans and coastal seas is among the key topics of oceanographic and climate research. Hydrodynamic studies form the basis for many oceanographic subjects, whether sediment transport, morphology, water quality, ecological and climate changes are being investigated. Hydrodynamic modelling of oceans and coastal seas has become a fundamental tool for describing the dynamics of marine environments, revealing the human impact on the sea and promoting sustainable development of marine resources. By complementing - through data assimilation - more and more diffuse and integrated global and regional observing systems (composed of coastal gauges, moorings, buoys, satellites, drifters), hydrodynamic models provide a deterministic 4D view of the ocean state. In this context, the semi-enclosed Adriatic Sea represents a natural long-standing laboratory for hydrodynamic modelling. The peculiar historical, morphological and oceanographic characteristics of this basin and its complex coastline stimulated over decades the development and application of several ocean and coastal models. In this work, we review different aspects of hydrodynamic modelling covered by the literature, highlighting the wide variety of model applications carried out in the Adriatic Sea which could serve as examples for semi-enclosed, marginal and coastal seas worldwide. Within the scope of the review, we find that although significant progress has been made over the last few decades, most of the modelling studies underrate the importance of a detailed representation of the land-coastal-sea fluxes. We list a set of recommendations that can be used as guidelines for model implementation to broaden the applicability of hydrodynamic models in future studies. Finally, we discuss the remaining questions that still need to be further explored.
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The aim of this study is to develop standard statistical methods and software for the analysis of beach litter data. The optimal ensemble of statistical methods comprises the Mann-Kendall trend test, the Theil-Sen slope estimation, the Wilcoxon step trend test and basic descriptive statistics. The application of Litter Analyst, a tailor-made software for analysing the results of beach litter surveys, to OSPAR beach litter data from seven beaches bordering on the south-eastern North Sea, revealed 23 significant trends in the abundances of beach litter types for the period 2009–2014. Litter Analyst revealed a large variation in the abundance of litter types between beaches. To reduce the effects of spatial variation, trend analysis of beach litter data can most effectively be performed at the beach or national level. Spatial aggregation of beach litter data within a region is possible, but resulted in a considerable reduction in the number of significant trends.
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A circulation model is coupled to a Lagrangian particle-tracking model to simulate the transport floating litter particles in the Aegean Sea, Greece (Eastern Mediterranean). Considering different source regions and release dates, simulations were carried out to explore the fate and distribution of floating litter over 1990–2009, taking into account the seasonal and interannual variability of surface circulation. Model results depicted recurrently high concentrations of floating litter particles in the North Aegean plateau, the Saronikos Gulf, and along Evia and Crete islands. Modeled transport pathways of floating litter demonstrated that source regions are interconnected, with Saronikos Gulf being a main receptor of litter from other sources. Notably higher percent of litter exit (∼35%) than enter the model domain (∼7%) signified that Aegean Sea seems to act as a source rather than receptor of floating litter pollution in the Eastern Mediterranean Sea. Beached litter was found around 10%, mostly located in the western part of the Aegean Sea. This is the first modeling study to explore the transport of floating marine litter in Greek waters.
Article
Following the widespread assumption that a majority of ubiquitous marine microplastic particles originate from land-based sources, recent studies identify rivers as important pathways for microplastic particles (MPP) to the oceans. Yet a detailed understanding of the underlying processes and dominant sources is difficult to obtain with the existing accurate but extremely time-consuming methods available for the identification of MPP. Thus in the presented study, a novel approach applying shortwave infrared imaging spectroscopy for the quick and semi-automated identification of MPP is applied in combination with a multitemporal survey concept. Volume-reduced surface water samples were taken from transects at ten points along a major watercourse running through the South of Berlin, Germany, on six dates. After laboratory treatment , the samples were filtered onto glass fiber filters, scanned with an imaging spectrometer and analyzed by image processing. The presented method allows to count MPP, classify the plastic types and determine particle sizes. At the present stage of development particles larger than 450 mm in diameter can be identified and a visual validation showed that the results are reliable after a subsequent visual final check of certain typical error types. Therefore, the method has the potential to accelerate microplastic identification by complementing FTIR and Raman microspectroscopy. Technical advancements (e.g. new lens) will allow lower detection limits and a higher grade of automatization in the near future. The resulting microplastic concentrations in the water samples are discussed in a spatio-temporal context with respect to the influence (i) of urban areas, (ii) of effluents of three major Berlin waste-water treatment plants discharging into the canal and (iii) of precipitation events. Microplastic concentrations were higher downstream of the urban area and after precipitation. An increase in microplastic concentrations was discernible for the wastewater treatment plant located furthest upstream though not for the other two.
Article
Micro-Fourier transform infrared (micro-FTIR) spectroscopy and Raman spectroscopy enable the reliable identification and quantification of microplastics (MPs) in the lower micron range. Since concentrations of MPs in the environment are usually low, the large sample volumes required for these techniques lead to an excess of co-enriched organic or inorganic materials. While inorganic materials can be separated from MPs using density separation, the organic fraction impedes the ability to conduct reliable analyses. Hence, the purification of MPs from organic materials is crucial prior to conducting an identification via spectroscopic techniques. Strong acidic or alkaline treatments bear the danger of degrading sensitive synthetic polymers. We suggest an alternative method, which uses a series of technical grade enzymes for purifying MPs in environmental samples. A basic enzymatic purification protocol (BEPP) proved to be efficient while reducing 98.3 ± 0.1 % of the sample matrix in surface water samples. After showing a high recovery rate (84.5 ± 3.3 %), the BEPP was successfully applied to environmental samples from the North Sea where MPs numbers range from 0.05 to 4.42 items m−³. Experiences with different environmental sample matrices were considered in an improved and universally applicable version of the BEPP, which is suitable for Focal plane array detector (FPA)-based micro-FTIR analyses of water, wastewater, sediment, biota and food samples.
Article
Microplastic pollution of the marine environment has received increasing attention from scientists, the public, and policy makers over the last few years. Marine microplastics predominantly originate near the coast and can remain in the nearshore zone for some time. However, at present, there is little understanding of the fate and transport of microplastics in coastal regions. This paper provides a comprehensive overview of the physical processes involved in the movement of microplastics from estuaries to the continental shelf. The trajectory and speed of microplastics are controlled by their physical characteristics (density, size, and shape) and ocean dynamic conditions (wind, waves, tides, thermohaline gradients, and the influence of benthic sediments). Microplastic particles can be subjected to beaching, surface drifting, vertical mixing, and biofouling, as well as bed-load and suspended load transport processes, until reaching terminal deposition on beaches, in coastal marshes, in benthic sediments or until they are carried by ocean currents to subtropical convergence zones. The dynamic interaction of released microplastics with the shoreline is regulated by onshore/offshore transport, which is impacted by the source location as well as the geometry, vegetation, tidal regime, and wave direction. Wind and wave conditions dominate surface drifting of buoyant particles through Ekman drift, windage, and Stokes drift mechanisms. Neustic microplastic particles travel in the subsurface because of vertical mixing through wind-driven Langmuir circulation and heat cycling. Increasing accumulation of microplastics in benthic sediments needs to be quantitatively explored in terms of biofouling, deposition, entrainment, and transport dynamics. Further studies are required to understand the following: 1) the primary parameters (e.g., windage, terminal velocity, diffusivity, critical shear stress) that determine microplastic transport in different pathways; 2) dynamic distribution of microplastics in various coastal landscapes (e.g., wetlands, beaches, estuaries, lagoons, barrier islands, depocenters) regulated by hydrodynamic conditions; and 3) interactions between the physical transport processes and biochemical reactions (degradation, flocculation, biofouling, ingestions).
Article
Plastic particles in marine and freshwater environments span from macroscopic to microscopic size classes. Each may have a different impact on individuals, populations and ecosystems, but still the wide variety of methods used in beach sediment sampling inhibit comparisons among studies and therefore hampers a risk assessment. A large portion of the uncertainties is due to differing sampling strategies. By quantifying the alongshore distribution of macro- and microplastic particles within five beaches of Lake Garda, we aim to shed light on the accumulation behavior of microplastic particles at an exemplary lake which might give indications for potential sampling zones. The identification of plastic at the single particle level with a spatial resolution down to 1μm was performed by Raman microspectroscopy. Given the time consuming approach we reduced the number of samples in the field but increased the spatial area where a single sample was taken, by utilizing a transect approach in combination with sediment cores (5cm depth). The study revealed that, in comparison to the water line and the high-water line, the drift line of all five beaches always contained plastic particles. Since the drift line accumulate particulate matter on a relatively distinct zone, it will enable a comparable sampling of microplastic particles. The applied sampling approach provided a representative method for quantifying microplastic down to 1μm on a shore consisting of pebbles and sand. Hence, as first step towards a harmonization of beach sediment sampling we suggest to perform sampling at the drift line, although further methodological improvements are still necessary.
Article
The ubiquitous presence and persistency of microplastics in aquatic environments is of particular concern because these pollutants represent an increasing threat to marine organisms and ecosystems. An identification of the patterns of microplastic distribution will help to understand the scale of their potential effect on the environment and on organisms. In this study, the occurrence and distribution of microplastics in the Bohai Sea are reported for the first time. We sampled floating microplastics at 11 stations in the Bohai Sea using a 330 μm trawling net in August 2016. The abundance, composition, size, shape and color of collected debris samples were analyzed after pretreatment. The average microplastic concentration was 0.33 ± 0.34 particles/m(3). Micro-Fourier transform infrared spectroscopy analysis showed that the main types of microplastics were polyethylene, polypropylene, and polystyrene. As the size of the plastics decreased, the percentage of polypropylene increased, whereas the percentages of polyethylene and polystyrene decreased. Plastic fragments, lines, and films accounted for most of the collected samples. Accumulation at some stations could be associated with transport and retention mechanisms that are linked to wind and the dynamics of the rim current, as well as different sources of the plastics.
Article
This is the first survey to investigate the occurrence and extent of microplastic contamination in sediments collected along a coast-open sea 140 km-long transect in the Central Adriatic Sea. Plastic debris extracted from 64 samples of sediments were counted, weighted and identified by Fourier-transform infrared spectroscopy (FT-IR). Several types of plastic particles were observed in 100% of the stations. Plastic particles ranged from 1 to 30 mm in length. The primary shape types by number were filaments (69.3%), followed by fragments (16.4%), and film (14.3%). Microplastics (1–5 mm) accounted for 65.1% of debris, mesoplastics (5–20 mm) made up 30.3% of total amount, while macro debris (> 20 mm) accounted for 4.6% of total plastics collected. Identification through FT-IR spectroscopy evidenced the presence of 6 polymer types: the majority of plastic debris were nylon, polyethylene and ethylene vinyl alcohol copolymer. Our data are a baseline for microplastic research in the Adriatic Sea.
Article
Most plastic pollution originates on land. As such, freshwater bodies serve as conduits for the transport of plastic litter to the ocean. Understanding the concentrations and fluxes of plastic litter in freshwater ecosystems is critical to our understanding of the global plastic litter budget and underpins the success of future management strategies. We conducted a replicated field survey of surface plastic concentrations in four lakes in the North American Great Lakes system, the largest contiguous freshwater system on the planet. We then modeled plastic transport to resolve spatial and temporal variability of plastic distribution in one of the Great Lakes, Lake Erie. Triplicate surface samples were collected at 38 stations in mid-summer of 2014. Plastic particles >106 μm in size were quantified. Concentrations were highest near populated urban areas and their water infrastructure. In the highest concentration trawl, nearly 2 million fragments km−2 were found in the Detroit River—dwarfing previous reports of Great Lakes plastic abundances by over 4-fold. Yet, the accuracy of single trawl counts was challenged: within-station plastic abundances varied 0- to 3-fold between replicate trawls. In the smallest size class (106–1,000 μm), false positive rates of 12–24% were determined analytically for plastic vs. non-plastic, while false negative rates averaged ~18%. Though predicted to form in summer by the existing Lake Erie circulation model, our transport model did not predict a permanent surface “Lake Erie Garbage Patch” in its central basin—a trend supported by field survey data. Rather, general eastward transport with recirculation in the major basins was predicted. Further, modeled plastic residence times were drastically influenced by plastic buoyancy. Neutrally buoyant plastics—those with the same density as the ambient water—were flushed several times slower than plastics floating at the water's surface and exceeded the hydraulic residence time of the lake. It is likely that the ecosystem impacts of plastic litter persist in the Great Lakes longer than assumed based on lake flushing rates. This study furthers our understanding of plastic pollution in the Great Lakes, a model freshwater system to study the movement of plastic from anthropogenic sources to environmental sinks.
Article
Residence times of microplastics were estimated based on the dependence of meso- and macrolitter residence times on their upward terminal velocities (UTVs) in the ocean obtained by one- and two-year mark-recapture experiments conducted on Wadahama Beach, Nii-jima Island, Japan. A significant linear relationship between the residence time and UTV was found in the velocity range of about 0.3–0.9 ms− 1, while there was no significant difference between the residence times obtained in the velocity range of about 0.9–1.4 ms− 1. This dependence on the UTV would reflect the uprush-backwash response of the target items to swash waves on the beach. By extrapolating the linear relationship down to the velocity range of microplastics, the residence times of microplastics and the 1D onshore-offshore diffusion coefficients were inferred, and are one to two orders of magnitude greater than the coefficients of the macroplastics.