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Ups and downs in the ocean: Effects of biofouling on the vertical transport of microplastics Merel Kooi, Egbert H Van Nes, Marten Scheffer, and Albert A. Koelmans Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 14 Jun 2017 Downloaded from http://pubs.acs.org on June 15, 2017

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Environmental Science & Technology

Ups and downs in the ocean: Effects of biofouling on vertical transport of microplastics

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Merel Kooi1,*, Egbert H. van Nes1, Marten Scheffer1, Albert A. Koelmans1,2

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Sciences, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The

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Netherlands

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Aquatic Ecology and Water Quality Management Group, Department of Environmental

Wageningen Marine Research, P.O. Box 68, 1970 AB IJmuiden, The Netherlands

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* Corresponding author: M. Kooi, Aquatic Ecology and Water Quality Management Group,

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Department of Environmental Sciences, Wageningen University & Research, P.O. Box 47, 6700

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AA Wageningen, The Netherlands – E-mail: [email protected]; Phone: +316 17640777

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ACS Paragon Plus Environment


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Keywords: microplastic, biofouling, settling, fate, vertical distribution, ocean models

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Abstract

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Recent studies suggest size-selective removal of small plastic particles from the ocean surface, an

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observation that remains unexplained. We studied one of the hypotheses regarding this size-

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selective removal: the formation of a biofilm on the microplastics (biofouling). We developed the

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first theoretical model that is capable of simulating the effect of biofouling on the fate of

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microplastic. The model is based on settling, biofilm growth and ocean depth profiles for light,

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water density, temperature, salinity and viscosity. Using realistic parameters, the model simulates

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the vertical transport of small microplastic particles over time, and predicts that the particles either

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float, sink to the ocean floor, or oscillate vertically, depending on the size and density of the

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particle. The predicted size-dependent vertical movement of microplastic particles results in a

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maximum concentration at intermediate depths. Consequently, relatively low abundances of small

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particles are predicted at the ocean surface, while at the same time these small particles may never

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reach the ocean floor. Our results hint at the fate of ‘lost’ plastic in the ocean, and provide a start

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for predicting risks of exposure to microplastics for potentially vulnerable species living at these

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depths.


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Abstract Art – TOC:

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Introduction

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Plastic waste entering the oceans was estimated between 4.8 and 12.7 million metric tons for

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2010, and these amounts are expected to increase one order of magnitude by 2025.1 This plastic

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debris will fragment to smaller particles due to photodegradation, physical erosion or

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biodegradation.2 Consequently, microplastics, defined as plastic particles < 5 mm, are numerically

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more abundant than larger plastic debris, both at the sea surface and in the water column.3–6

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Microplastics can, among others, be ingested by detritivores, deposit feeders, filter feeders and

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zooplankton.7–10 Ingestion may cause chemical leaching and blockage or damage of digestive

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tracks, resulting in satiation, starvation and physical deterioration of organisms.7

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Due to fragmentation of larger plastic, it is expected that the number of particles in the ocean

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increases exponentially with decreasing size.6 However, a size-dependent lack of small particles

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was observed at the ocean surface, suggesting that particles smaller than 1 mm are somehow

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‘lost’.6,11 While microplastic amounts have accumulated in the pelagic zone,12 microplastic fate

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below the ocean surface remains largely unknown.6,13 There are several hypotheses for the

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removal of these smaller particles, such as ingestion by marine organisms,6 vertical transport

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caused by wind mixing,14 the accumulation of organisms on plastics (biofouling) followed by

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settling,6,15 fast degradation to very small particles (‘nanofragmentation’)6 or a combination

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thereof. Here we study whether the biofouling hypothesis can explain the fate of the ‘lost’ plastics.

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Biofouling is defined as the accumulation of organisms on submerged surfaces and affects the

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hydrophobicity and buoyancy of plastic.15–19 When the density of a particle with biofilm exceeds

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seawater density, it starts to settle.15,18 As seawater density gradually increases with depth, the

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particle can be expected to stay suspended at the depth were its density equals seawater density.6,15

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It has been hypothesized that the depths at which the particles are suspended might be equal to the

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pycno- and thermocline depth.15 It is also possible that particles sink deeper as several studies


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indicate the presence of microplastics on the ocean floor, although the transport mechanisms

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remain unknown.20–22 Biofouling occurs within days, and within weeks algal fouling communities

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are formed on the plastic surfaces.15,17,23 Plastics can start sinking within two weeks, the time

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depending on particle size, type, shape, roughness and environmental conditions.23 Ye and

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Andrady (1991) report sinking of plastics within seven weeks, but also indicate that defouling

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occurs quickly after particle submersion.15 Defouling can be the result of light limitation, grazing,

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or dissolution of carbonates in acid waters.6,15 Fouling also occurs under submerged conditions,

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but with different algae species and at a slower rate.15,17

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The development of dynamic models for biofouling and microplastic settling is essential in order

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to predict the vertical distribution of microplastics. The above shows that there are several

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empirical studies addressing biofouling, however, deterministic quantitative frameworks that are

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capable of simulating the effects of biofouling on the vertical distribution of microplastics are

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lacking. Modelling fouled plastic movement from a theoretical point of view is useful in order to

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evaluate the different factors and processes influencing microplastic biofouling and the associated

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sinking through scenario analysis. In the present study we provide the first theoretical model that

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simulates the effect of biofouling on the settling of microplastics. We modelled particles ranging

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between 10 mm and 0.1 µm in radius, and for convenience refer to all these particles as

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‘microplastics’. We present model simulations that show the effect of particle size, particle type

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(density), and oceanographic variables on the fouling of plastic by attachment of algae and

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subsequent settling. To explain the observed systems behaviour, selected additional calculations

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are performed. These calculations include assessment of the dependence of the model outcome to

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variability in some of the key parameters.

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Modelling the effect of biofouling on the vertical transport of microplastics



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The model simulates settling or rising of microplastic particles, dependent on density differences

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between the composite particles and seawater and uses well-established concepts and

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parameterizations for all of its components. The composite particle density was determined by

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algae attachment, growth, respiration and mortality. Settling of plastic particles over depth was

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modelled as a function of the settling velocity (m s-1).

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= (, )

(1)

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Microplastic particles come in many shapes. Hence, the settling velocity was calculated using a

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modification of the Stokes equation, provided by Dietrich (1982).24 This equation can be applied

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to both spherical and non-spherical particles by using an equivalent spherical diameter24 and was

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recently verified for microplastics, using settling data for spherical, pristine microplastic

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particles:25 ,

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(, ) = −

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in which is the density of the plastic particle with biofilm (kg m-3), , the sea water

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density at depth (kg m-3), , the kinematic viscosity of the seawater (m2 s-1), the

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gravitational acceleration (m s-2) and ∗ the dimensionless settling velocity.24 The dimensionless

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settling velocity ∗ is a function of the dimensionless particle diameter

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settling velocity was calculated as:24

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∗ = 1.74 ⋅ 10'

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log(∗ ) = −3.76715 + 1.92944 log

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0.00056(log

,

∗)

∗ ,

( ∗

'.3

(2)

for ∗

∗

∗.

The dimensionless

< 0.05

(3)

− 0.09815(log

for 0.05 ≥

∗

∗)

(.3

− 0.00575(log

∗)

4.3

+

≥ 5 ⋅ 105

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The dimensionless particle diameter was calculated from the equivalent spherical diameter

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(m):24


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∗

=

7 , 89:;

(4)

, 6{l6 ), differential settling (\N |H{69 ) and

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advective shear (\N k|l> ) collision frequencies (m3 s-1). These different encounter kernel rates

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were calculated as:36,38

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\N I>6{l6 = 4M(

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( \N |H{69 = MF

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\N k|l> = 1.3}(F + FN )4

GH

+

N )(F

+ FN )

W

(14)

(



GH

N

are the diffusivity of the plastic particle and the algae cells (m2 s-1), and } is

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in which

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the shear rate (s-1). For the Brownian encounter kernel rate, the diffusivity of the plastic and algae

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was calculated as:36

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GH

=

and

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~(dA(4.W) L >

and

N

=

~(dA(4.W) L >Q

(15)

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where is the Boltzmann constant (m2 kg s-2 K-1), R the dynamic water viscosity (kg m-1 s-1)

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and where the radius of the total particle, F = FGH + IJ , and the radius of the algae, FN , were

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calculated assuming a spherical particle or algae shape (m).

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Algae growth (term 2 in Equation 11) was modelled as

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R(S , T ) = RG (T )Φ(S )

for Sr{6 < S < Srls

(16)

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where RG (T ) is the growth rate under optimal temperature conditions for a certain light intensity

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T , and Φ(S) is the temperature influence on the growth rate.39

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RG (T ) = Rrls ⋅

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Φ(S ) =

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In the above equations, T is the light intensity at depth (µE m-2 s-1), TG is the optimal light

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intensity for algae growth (µE m-2 s-1), Rrls is the maximum growth rate under optimal conditions

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(s-1), is the initial slope (s-1) and Srls , Sr{6 and SG are the maximum, minimum and optimal

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temperature to sustain algae growth respectively (ºC).39 For S < Sr{6 and S > Srls , Rrls = 0

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and therefore R(S , T ) is zero.39 The light intensity at depth was calculated according to the law

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of Lambert-Beer:

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T = T3 b

g A unv W g?

(17)

=

(d dunv )(d du; )=

7d? du; 8⋅t7d? du; 87d d? 87d? dunv 87d? Adu; (d 8x

(18)

(19)


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in which T3 is the light intensity at the surface and is the extinction coefficient (m-1). Light

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availability at the sea surface was calculated using a sinusoidal function:40,41

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T3 = Tr sin(2M)

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in which Tr is the light intensity at noon (µE m-2 s-1). When the sinus function of Equation 20

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becomes negative, a value of 0 µE m-2 s-1 was assumed. The light extinction, needed for Equation

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19, is assumed to be dominated by water and algae induced extinctions, and can be calculated as:40

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= + G ]ℎ_ `

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with and G the extinction coefficients for water and chlorophyll respectively.

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Algae mortality and respiration, i.e. terms 3 and 4 of Equation 11 respectively, where modelled

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using a constant mortality and respiration rate. A temperature dependency of respiration was

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realised with a VW3 coefficient. The VW3coefficient indicates how much the respiration rate

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increases when the temperature increases 10 ºC.42,43 As algae growth (Eq 10, term 2) and algae

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respiration (Eq 10, term 4) are temperature dependent, a seawater temperature profile over depth

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was needed. Seawater temperature was empirically approximated using a Hill function,44 which

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adequately captured the characteristic shape of a thermocline:

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S = S>J + 7SI − S>J 8 ? A ?

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in which S is the water temperature at depth (°C), S>J the water temperature at the surface

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(°C), SI the water temperature at the sea bottom (°C), the thermocline depth (m) and a

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parameter defining the steepness of the thermocline.

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As mentioned above, the density difference between the particle and seawater is the main driver of

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the modelled transport. The above Equations 5 – 22 detailed the calculation of the total density

(20)

(21)

?

(22)



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( in Equation 2). Hereafter we focus on the calculation of the seawater density (, in

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Equation 2). Seawater density is determined by temperature and salinity ( ), according to:45

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, = 7`W + `( S + `4 S ( + `' S 4 + `c S ' 8 + 7W + ( S + 4 S ( + ' S 4 +

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c ( S ( 8

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The temperature profile was calculated with Equation 22. A salinity depth profile ( , g kg-1) was

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calculated using a fifth order polynomial function.

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= W c + ( ' + 4 4 + ' ( + c +

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Below J{s , the depth at which the salinity profile becomes constant, a constant value J{s is

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assumed. Last, the kinematic viscosity, , (m2 s-1), as was used in Equation 2, was calculated as

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, =

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based on the seawater density and the dynamic viscosity, R, (kg m-1 s-1). The dynamic

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viscosity of seawater was calculated using empirical equations.45 Viscosity was derived from the

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temperature and salinity profile, by first calculating the water dynamic viscosity R, , followed by

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calculation of the seawater dynamic viscosity: 45

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R, = 4.2844 ⋅ 10c + 3.Wc(d A'.554)=5W.(5

(26)

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R, = R, (1 + O + ( )

(27)

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in which

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O = 1.541 + 1.998 ⋅ 10( S − 9.52 ⋅ 10c S (

(28)

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= 7.974 − 7.561 ⋅ 10( S + 4.724 ⋅ 10' S (

(29)

(23)

for < J{s

,

(24)

(25)

,

W


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Parameters and numerical model implementation. Model parameters were estimated based on

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literature data, and are summarized in Table S1 of the supporting information (SI). Here some key

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choices are briefly discussed. In this model, no specific algae species is modelled. Instead, we

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modelled an average marine species, with the parameter characteristics defined by the mean or

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median values reported for “marine algae” or “marine phytoplankton”. By default, the biofilm

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density (IJ , Equation 5) was set at 1388 kg m-3, which is the median of the measured values of

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Fisher et al. (1983). This median value is of the same order of magnitude as the 1250 kg m-3 used

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in the modelling study by Besseling et al. (2017).46,47 An algae cell volume (N , Equation 10) of

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2.0⋅10-16 m3 was used, which represents the median value reported by Lopéz-Sandoval et al.

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(2014).48 Algae biomass was lost through respiration and mortality (Equation 11). In this study a

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(grazing) mortality of 0.39 d-1 was used, a mean value for oceanic habitats.49 A respiration rate of

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0.1 d-1 was used in combination with a respiration VW3value of 2, which is a commonly used

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value.43,48,50–52 The maximum growth rate (Rrls , Equation 16) was set at 1.85 d-1, the value

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reported for Nannochloropsis oceanica by Bernard and Rémond (2012).39 A light intensity at noon

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of 1.2⋅108 µEm-2d-1 was used, together with an optimal light intensity of 1.8⋅1013 µEm-2d-1 (Tr

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and TG in Equation 20 and 17, respectively).39,53 A light : dark duration of 12 hours each was

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assumed. The conversion from carbon to algae cells is highly variable, ranging between 35339 to

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47.76 pg carbon cell-1.32 We choose the median value, 2726⋅10-9 mg carbon cell-1.

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Parameter values for the depth profiles of temperature and salinity were obtained from the NASA,

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choosing the North Pacific near Hawaii as a default location.54,55 For temperature, Equation 22

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was fitted through the data points obtained from the surface to 4000 m depth, with a resulting R2

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of 0.99. The salinity profile was fitted as a 5th order polynomial function (R2 = 0.74) from the

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surface to 1000 m depth, followed by a constant value from 1000 m to 4000 m depth. Parameters

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of these fits are also provided in Table S1.



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The model was solved using MATLAB R2012a with GRIND for MATLAB (http://www.sparcs-

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center.org/grind.html). Because the modelled system appeared to be a stiff system, the

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MATLAB’s ode23s solver was used.

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Model simulations. Different analyses were done with the model described above, focussing on

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the effect of particle density and size on vertical transport in the water column. The effect of

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particle density was studied by simulating the most common polymer types: high density

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polyethylene (HDPE), low density polyethylene (LDPE), polypropylene (PP), polyvinylchloride

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(PVC) and polystyrene (PS). HDPE, LDPE and PP are initially buoyant plastics (density plastic

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