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Chemosphere 89 (2012) 389–397

Contents lists available at SciVerse ScienceDirect

Chemospherejournal homepage: www.elsevier.com/locate/chemosphere

A numerical study on the seasonal variability of polychlorinated biphenyls fromthe atmosphere in the East China Sea
Jun Ono a,b,⇑, Daisuke Takahashi c, Xinyu Guo d, Shin Takahashi d, Hidetaka Takeoka daGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
National Institute of Polar Research, Tachikawa, Japanc
Hokkaido National Fisheries Research Institute, Fisheries Research Agency, Kushiro, JapandCenter for Marine Environmental Studies, Ehime University, Matsuyama, Japanbh i g h l i g h t s" We developed a three-dimensional transport model for POPs.
" We applied it to PCB 153 in the East China Sea for the first time.
" PCB 153 concentration is high in winter and low in summer.
" The seasonal variation is mainly controlled by water temperature.
" Horizontal advection and vertical sinking are important for mass balance of PCB 153.

a r t i c l e

i n f oArticle history:
Received 21 November 2011
Received in revised form 14 April 2012
Accepted 19 May 2012
Available online 28 June 2012
Keywords:
Persistent organic pollutants
Three-dimensional transport model
East China Sea
Polychlorinated biphenyls
Seasonal variability
Henry’s law constanta b s t r a c t
A three-dimensional/high-resolution transport model for persistent organic pollutants (POPs) has beendeveloped for the East China Sea (ECS). The POPs model has four compartments (gaseous, dissolved, phytoplankton-bound, and detritus-bound phases) and includes processes for diffusive air–water exchange,
phytoplankton uptake/depuration to POPs, decomposition of dissolved phase, vertical sinking of phytoplankton, detritus production by phytoplankton mortality, and vertical sinking and decomposition of
detritus. The POPs model is coupled with an ocean circulation model that can reproduce the seasonal variation in physical variables to represent the advection and diffusion of POPs. We applied the POPs modelto the polychlorinated biphenyl congener 153 (PCB 153) from the atmosphere and examined the behaviorof PCB 153 in the ocean. The model showed a remarkable seasonal variability of PCB 153. Concentrationsin the dissolved and particulate phases are high in winter (January–March) and low in summer (July–September). In coastal regions, where chlorophyll a concentration is high, horizontal and vertical distributions in the dissolved and particulate PCB 153 concentrations are strongly affected by phytoplankton
uptake. The sensitivity experiments on the dynamics of PCB 153 suggested that a change of Henry’s
law constant associated with water temperature is the major factor controlling the seasonal variabilityof PCB 153. The model-based yearly mass balance of PCB 153 in the ECS indicated that most of the atmospheric input (35.5 kg year!1) is removed by the horizontal advection outside the ECS (19.0 kg year!1)
and accumulates to the sea bottom by vertical sinking (15.7 kg year!1). For comparison with PCB 153,
we also conducted simulations for PCB 52, 101, and 180. The seasonal variations are similar to that of
PCB 153. The mass balance of PCB 52 that has short half-life time and less hydrophobic property shows
the different results compared with PCB 101, 153, and 180.
! 2012 Elsevier Ltd. All rights reserved.

1. Introduction
Global contamination by persistent organic pollutants (POPs)such as hexachlorocyclohexanes (HCHs), dichloro diphenyl⇑ Corresponding author at: Graduate School of Frontier Sciences, The Universityof Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. Tel./fax: +81 4 7136
4122.
E-mail address: jo@1.k.u-tokyo.ac.jp (J. Ono).
0045-6535/$ - see front matter ! 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.chemosphere.2012.05.049

trichloroethanes (DDTs), and polychlorinated biphenyls (PCBs)
and their impacts on wildlife and ecosystems have been of concern
over the last decade. POPs are mainly characterized by their persistence, long-range transport in the atmosphere and the ocean, bioaccumulation, and toxicity. Thus, understanding and quantifying
the dynamics of POPs are important to assess their environmental
impact and final fate. In particular, the ocean is considered to be
the one of the global POPs reservoirs (Tanabe and Tatsukawa,
1983; Iwata et al., 1993), although it may not be as high as the soil

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J. Ono et al. / Chemosphere 89 (2012) 389–397

reservoir (Jurado et al., 2004; Lohmann et al., 2007). From observations in the open ocean, Tanabe and Tatsukawa (1983) showed thatthe vertical distribution of PCBs and DDTs are almost homogeneous and suggested that PCBs and DDTs are rapidly and abundantly transported by sinking particles. Lohmann et al. (2006)
reported that PCBs in the surface ocean are transported to the deep
ocean in association with deep water formation. These studies imply that the deep ocean can be a candidate for the ultimate fate ofmany POPs.
POPs emitted into the atmosphere can be transported to remoteareas and be deposited to the ocean by diffusive air–water exchange flux, dry deposition, or wet deposition in rain or snow.
The dissolved POPs are absorbed by the phytoplankton in the surface ocean, and then are transported to the deep ocean by verticalsinking of the particle-bound POPs, and finally accumulated to thesea bottom. POPs in the sediment may be brought back into thewater column by resuspension. The behaviors of POPs in the ocean
are complex as mentioned above. To describe and understand thecomplex three-dimensional dynamics of POPs in the ocean andto predict fate of POPs in the ocean, a numerical model is a usefultool. Numerical modeling studies suggest that not only horizontal
advection and diffusion by current and turbulence but also verticaltransport of POPs absorbed into phytoplankton are important processes (Wania et al., 1998; Dachs et al., 1999).
So far, most numerical modeling of POPs in the ocean has been
based on a box model (Dachs et al., 1999, 2000, 2002). Using thebox model for air–water–phytoplankton exchanges, Dachs et al.
(1999) showed that air–water exchange is influenced by phytoplankton biomass and growth rate. Wania et al. (2001) and Breivik
and Wania (2002) evaluated mass balance for PCBs and HCH in theBaltic Sea, respectively, using a multimedia mass balance model.
Jurado et al. (2007) developed a one-dimensional dynamic coupledhydrodynamic-contaminant model and applied it to a Mediterranean continental shelf environment. In addition, Meijer et al.
(2006, 2009) developed a dynamic flux model describing POPs fateand cycling in lakes and assessed the role of internal biogeochemical cycling with respect to accumulation of four different PCBs.
On the other hand, numerical studies of POPs using a threedimensional model including advection and diffusion processes
have been also reported. Schenker et al. (2008) evaluated a global
environmental fate model for DDTs, which are representativehydrophobic POPs, by a comparison with the field measurements.
They documented that good agreement between the model andmeasurements in the soil and air compartment but not in ocean
water, and suggested high variability of DDTs concentrations andvarious reactions in the ocean including the removal process todeep water as possible factors for such discrepancy between themodel and measurements. Recently, Guglielmo et al. (2009) developed a global three-dimensional atmosphere–ocean general circulation model with a marine carbon cycle model (MPI-MCTM) and
Kawai et al. (2009) developed a Finely-Advanced Transboundary
Environmental model. Stemmler and Lammel (2009, 2010) examined the global dynamics for c-HCH, DDT, PCBs, and PFOA using
MPI-MCTM. Their studies paid attention to POPs dynamics in the
global scale and showed very interesting and important results.
Furthermore, Guglielmo et al. (2012) studied the global fate andtransport of c-HCH and DDT using MPI-MCTM with and without
the sea ice dynamics.
With respect to the regional POPs modeling, Ilyina et al. (2006,2008) has developed a three-dimensional transport model for POPsand investigated the fate of c-HCH, a-HCH, and PCB 153 in the
North Sea. However, the POPs modeling has not yet been studied
in the East Asia, where the environmental contamination by POPs
still continues even now. In particular, the East China Sea (ECS)(Fig. 1a), including the Yellow and Bohai Seas, is surrounded byhighly industrialized areas of the East Asian countries, which canbe potential sources of POPs. In fact, monitoring studies of POPs
using fishes have shown that POPs concentration in the ECS washigh (Ueno et al., 2003). However, the detailed dynamics of POPsin the ECS has not yet been understood well.
In this study, we developed a three-dimensional POPs modeland applied it to the ECS for the first time. The objective in thisstudy is to investigate the key process affecting POPs dynamics
in the ECS, particularly focusing on the seasonal variability in
PCB 153 from the atmosphere as a first step toward the realistic
numerical simulation of POPs. The rest of this paper is as follows.
Section 2 describes a three-dimensional/high-resolution transportmodel for POPs in the ECS. In Section 3, we present results fromnumerical simulations applied to PCB 153 and its mass balance,
and then discuss the results of a series of numerical experiments
designed to investigate the effects of water temperature, windspeed, chlorophyll a, seasonal variation in the gaseous POPs, sinking velocity of particles, and mortality rate on the behavior of thePOPs in the ECS. Section 4 makes some concluding remarks.
2. Model description
2.1. POPs transport model
To incorporate the effects of advection and diffusion of POPs bycurrents and turbulence, we used a three-dimensional ocean circulation model in the ECS (Guo et al., 2003), which is based on the
Princeton Ocean Model (POM) (Blumberg and Mellor, 1987; Mellor, 2003). This model can well reproduce seasonal variation in
the physical fields of the ECS. The model domain and bathymetry,
covering the Bohai Sea, Yellow Sea, and ECS, are shown in Fig. 1a.
The grid in this model has a resolution of 1/18" ("5 km on average)
horizontally and 21 sigma (a vertical coordinate scaled by thewater column depth with 0 at sea surface and !1 at sea bottom)
levels vertically (0.000, !0.002, !0.004, !0.010, !0.020, !0.040,!0.060, !0.080, !0.100, !0.120, !0.140, !0.170, !0.200, !0.300,!0.400, !0.500, !0.650, !0.800, !0.900, !0.950, !1.000).
Our POPs transport model includes four compartments: gaseousPOPs (CA), dissolved POPs (CW), phytoplankton-bound POPs (CWP),
and detritus-bound POPs (CWD). As for CWP, we considered it as
the sum of the concentrations in the phytoplankton surface (CWPS)
and matrix (CWPM), according to Dachs et al. (1999). The threedimensional equations describing the transport of POPs for eachcompartment in the ocean are given by following equations

!
"
@C W
@C W
@C W
@C W
@
@C W
þ F C ðC W Þ þ BI ðC W Þ;
Kh
þu
þv
þw
¼
@z
@t
@x
@y
@z
@z

ð1Þ

@C WPS@C WPS@C WPS@C WPSþuþvþ ðw þ wsp Þ
@t
@x
@y
@z
!
"
@
@C WPS
þ F C ðC WPS Þ þ BI ðC WPS Þ;
¼
Kh
@z
@z
@C WPM@C WPM@C WPM@C WPMþuþvþ ðw þ wsp Þ
@t
@x
@y
@z
!
"
@
@C WPMKh¼
þ F C ðC WPM Þ þ BI ðC WPM Þ;
@z
@z
@C WD
@C WD
@C WD
@C WD
þu
þv
þ ðw þ wsd Þ
@t
@x
@y
@z
!
"
@
@C WD
Kh
þ F C ðC WD Þ þ BI ðC WD Þ
¼
@z
@z

ð2Þ

ð3Þ

ð4Þ

Here CW, CWPS, CWPM, and CWD are concentrations (pg m!3); t is time(s); x, y, and z are the zonal, meridional and vertical coordinates

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J. Ono et al. / Chemosphere 89 (2012) 389–397

a

391

b

Fig. 1. (a) Model domain and bathymetry. Monthly averaged surface current in August is superimposed by vectors. The 20, 50, and 100 m isobaths are also superimposed bysolid lines. A dashed line denotes the section along which vertical structures of PCB 153 are presented in Figs. 5 and 6. SP and KP denote the Shandong and Korean Peninsulas.
(b) Schematics of physical–biochemical processes for POPs. The POPs model includes four compartments: gaseous POPs (CA), dissolved POPs (CW), phytoplankton-bound POPs
(CWP), and detritus-bound POPs (CWD). See text for detailed explanations on the physical–biochemical processes.

(m); u, v, and w are their velocity components (m s!1); wsp and wsd
represent the sinking velocity (m s!1) of phytoplankton or detritus(see Appendix A in Supplementary material); Kh is the vertical eddy
diffusivity coefficient (m2 s!1) calculated by a 2.5 Mellor–Yamada
turbulence closure scheme (Mellor and Yamada, 1982); FC(CW),
FC(CWPS), FC(CWPM), and FC(CWD) are the horizontal diffusion terms
parameterized according to the Smagorinsky diffusion scheme(Smagorinsky, 1963); and BI(CW), BI(CWPS), BI(CWPM), and BI(CWD)
are the biochemical processes for each compartment and will be described in Appendix A in Supplementary material. The model
numerical scheme is similar to that used for solving variables suchas salinity and temperature in POM. The variables at z-levels areconverted to those at sigma coordinates in the model. The integration of Eqs. (1)–(4) is carried out by two steps. The first step is to
explicitly integrate CW, CWPS, CWPM, and CWD for advection and horizontal diffusion. The second step is to implicitly solve CW, CWPS,CWPM, and CWD for vertical diffusion. A first-order upstream scheme(Smolarkiewicz, 1984) was used for advection in Eqs. (1)–(4).
2.2. Biochemical processes for POPs
Fig. 1b shows the schematics of physical–biochemical processes
for POPs. In the ocean, a proportion of the dissolved POPs is absorbed by the surface and matrix of phytoplankton while someare decomposed. Phytoplankton-bound POPs are affected by three
processes: uptake/depulation, natural mortality, and vertical sinking of phytoplankton. The natural mortality of phytoplankton
transforms phytoplankton-bound POPs into detritus-bound POPs.
Some detritus-bound POPs are transported to the deep ocean byvertical sinking of detritus, and others are transformed into dissolved POPs by decomposition of detritus. In the present POPsmodeling, we did not include processes on the dissolved organic
matter, zooplankton, river discharge, sediments and so on,
although these processes are important for POPs dynamics (e.g.,
Guglielmo et al., 2009; Stemmler and Lammel, 2009; Meijer
et al., 2009; Berrojalbiz et al., 2009). These processes have not been
well understood in the ECS due to the lack of data. Therefore, as afirst step of our POPs modeling, we focus on the physical–biochemical processes shown in Fig. 1b and leave these processes as future

works. The biochemical processes BI in Eqs. (1)–(4) for CW, CWPS,CWPM, and CWD are given in Appendix A in Supplementary material.
2.3. Model setup
In the present study, gaseous POPs concentration (CA) is supplied to the ocean via the diffusive air–water exchange flux. We
did not take into account dry and wet particle deposition because
few observations are available for these two processes in the ECS.
In addition, Jurado et al. (2005) estimated the dominant deposition
process for the global oceans and showed that PCBs are primarily
introduced into the ocean by the diffusive air–waver exchange flux
almost everywhere. The surface boundary condition for dissolved
POPs concentration (CW) is therefore expressed as

Kh

@C W
¼ F AW ;@zð5Þ

where FAW (pg m!2 s!1) is the net flux of POPs between air andwater. Following Bidleman and McConnell (1995), FAW is given by!
"CA
F AW ¼ kAW C W ! 0 ;H

ð6Þ

where H0 is the dimensionless Henry’s law constant corrected forwater temperature (Bamford et al., 2000, 2002), and kAW (m s!1)is the mass transfer velocity between air and water. To calculate
kAW, we used wind speed data from the European Remote-Sensing
satellites ERS–1 and ERS–2. Since the net flux of POPs between air
and water is affected by wind speed and water temperature, their
spatial distributions in February and August, as examples, are given
in Appendix B.1 in Supplementary material. The other specificparameters and equations used in the air–water exchange process
are summarized in Supplementary Table 3.
The target compound of POPs is polychlorinated biphenyls congener 153 (PCB 153) with the more hydrophobic characteristic. The
gaseous PCB 153 concentration was assumed to be horizontally
uniform throughout the year and set to 2.08 pg m!3, which was
calculated on the basis of data from Jaward et al. (2005). In theinitial and open boundary conditions, the dissolved PCB 153 concentration, the phytoplankton-bound PCB 153 concentration, andAuthor's personal copy392

J. Ono et al. / Chemosphere 89 (2012) 389–397

the detritus-bound PCB 153 concentration were set to 0 pg m!3.
Consequently, the PCB 153 in our results has a single source, i.e.,from the atmosphere. The model was integrated for 5 years andthe last year was analyzed. This result will be referred to hereinafter as the control-run.
For comparison with other PCB congener, we also conductedsimulations for PCB 52, 101, and 180.
3. Results and discussion
The monthly means of dissolved PCB 153 concentration (CW)
and particulate PCB 153 concentration (CWP + CWD), both averaged
over the whole volume of the model domain, that of surface flux
(FAW in Eq. (6)) averaged over the whole surface area of the model
domain, and that of sinking flux ([w + wsp]CWP + [w + wsd]CWD in the
deepest sigma layer.) averaged over the whole bottom area of themodel domain present clear seasonal variability (Fig. 2). Concentrations in the dissolved and particulate PCB 153 and surface andsinking fluxes are all high in winter (January–March) and low insummer (July–September). There is a one month time lag between
monthly surface flux and monthly dissolved PCB 153 concentration. The surface flux reaches a maximum (minimum) in January(August), while the dissolved PCB 153 concentration becomesmaximum (minimum) one month later. The similar seasonal variation can also be seen in the results of PCB 52, 101, and 180, while
their amplites are different (see Appendix B.4 of Supplementarymaterial for detailed explanations).
3.1. Horizontal and vertical structures of PCB 153
To examine the horizontal structures of PCB 153 in summer
and winter, horizontal distributions of surface flux and surface

concentrations in the dissolved and particulate PCB 153 in February and August are shown in Fig. 3. The surface flux by diffusive
air–water exchange is higher in February than in August (Fig. 3aand b). The highest surface flux area in February is mainly formedin coastal regions from China to the Korean Peninsula (Fig. 3a).
Associated with the high surface flux, surface concentrations of
dissolved and particulate PCB 153 were also higher in February(Fig. 3c and e) than in August (Fig. 3d and f). Interestingly, in coastal regions, where the surface chlorophyll a concentration is highest(see Supplementary Fig. 1a), the surface dissolved PCB 153 concentration is lower there than in offshore regions (Fig. 3c). In contrast,
the surface particulate PCB 153 concentration is higher in coastal
regions than in offshore regions (Fig. 3e). In coastal regions, the dissolved PCB 153 is absorbed by phytoplankton and converted toparticulate PCB 153. On the other hand, the surface flux in Augustis small (Fig. 3b) and the dissolved and particulate PCB 153 concentrations are also low (Fig. 3d and f). In the central Yellow Sea, thesurface flux becomes negative, indicating possible release of
PCB153 from the sea to the air. The negative surface flux in summer is caused by the high surface water temperature in the central
Yellow Sea that induces the volatilization of PCB 153 from the surface ocean (Fig. 3b).
Fig. 4 shows bottom concentrations in dissolved and particulate
PCB 153 and the sinking flux in February and August. In February,
the bottom dissolved (Fig. 4a) and bottom particulate (Fig. 4c) PCB
153 concentrations are high near the coast. In particular, the bottom dissolved PCB 153 concentration is high around the southernpart of the Shandong Peninsula, along the coast from 25 to 30 "N in
China, and in the Yellow Sea. As a result, the sinking flux of particulate PCB 153 also becomes large in these coastal regions (Fig. 4e).
The bottom dissolved PCB 153 concentration is high in the Yellow
Sea in August (Fig. 4b), and even higher in February. With theevolution of the mixed layer in winter (January–March), a largeabFig. 2. Time series of monthly averaged (a) dissolved (solid line) and particulate(dashed line) PCB 153 concentrations, and (b) the surface flux of PCB 153 from theatmosphere to the ocean (solid line) and the sinking flux of PCB 153 from the watercolumn to the sea bottom (dashed line), averaged over the whole area of the East
China Sea.

a

c

e

b

d

f

Fig. 3. Horizontal distributions of simulated results in February and August. (a andb) Surface flux of PCB 153 from the atmosphere to the ocean, (c and d) dissolved PCB
153 concentrations at the surface, and (e and f) particulate PCB 153 concentrations
at the surface.

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J. Ono et al. / Chemosphere 89 (2012) 389–397

a

c

e

b

d

f

393

in regions, where surface chlorophyll a concentration is high(Fig. 5a and d), and then it is changed into particulate PCB 153(Fig. 5d and e). As a consequence, low dissolved PCB 153 corresponds to high particulate PCB 153 (Fig. 5d and e) and the sinking
flux by vertical sinking of particulate PCB 153 is also high in theseregions (Fig. 5b).
In August, the wind becomes weak (Fig. 6a) and the sea surface
water temperature becomes high (Fig. 6c) but the surface chlorophyll a concentration changes little (Fig. 6a). The dissolved PCB
153 concentration is locally high in the middle and bottom layersof the Yellow Sea (Fig. 6d), while that in the surface layer is low
due to the small surface flux (Fig. 6b). The high concentration area
is consistent with regions of low temperature, i.e., the Yellow Sea
Cold Water. This suggests that the bottom regions of the Yellow
Sea become the reservoir of POPs such as PCBs. The particulate
PCB 153 concentration is also high in the middle layer but is only
about half that in February. As a result, the sinking flux also
becomes low (Fig. 6b).
3.2. Mass balance of PCB 153 in the East China Sea
To elucidate the important processes affecting the behavior of
PCB 153 in the ocean, a mass balance of PCB 153 in the ECS was calculated using the simulated result for the control-run (Fig. 7). The
yearly averaged biomass of PCB 153 integrated over the whole volume of the model domain was calculated as follows:

Fig. 4. Horizontal distributions of simulated results in February and August. (a andb) Dissolved PCB 153 concentration at the bottom, (c and d) particulate PCB 153
concentrations at the bottom, and (e and f) sinking flux of PCB 153 to the bottom.

amount of PCB 153 must be transported to the bottom layer. However, this process cannot induce a higher bottom concentration ofdissolved PCB 153 in summer than in winter. Another importantfactor is the development of strong stratification from spring tosummer in the central Yellow Sea. The presence of strong stratification in summer prevents not only the vertical transfer of heatfrom the sea surface to the sea bottom that induces the formationof cold bottom water, i.e., the well-known Yellow Sea Cold Water(e.g., Park et al., 2011), but also the vertical transfer of dissolved
PCB 153. The combination of the latter and the decomposition ofparticulate PCB 153 attached to phytoplankton and detritus whose
sinking is not affected by the stratification induces the accumulation of dissolved PCB 153 in the bottom layer of the central Yellow
Sea. The low concentration of bottom particulate PCB 153 (Fig. 4d)and low sinking flux (Fig. 4f) also supports the decomposition ofparticulate PCB 153 in the bottom layer.
To examine the vertical structure of PCB 153 along a section
from the Yellow Sea to the East China Sea, the vertical distributionof dissolved and particulate PCB 153 concentrations, together withsome physical and biological variables, at 124"E in February andAugust are presented in Figs. 5 and 6, respectively. In February,
the horizontal variation in surface flux of PCB 153 along the section(Fig. 5b) generally corresponds to that of chlorophyll a (Fig. 5a),which is high in regions offshore of the Changjiang River mouth
and in coastal regions of the Yellow Sea. Corresponding to the surface flux, the dissolved PCB 153 concentration is also high. It has astrong vertical variation, being generally high at the surface but
low at the bottom, which is different from the vertically homogeneous water temperature. Such vertical variation in the dissolved
concentration is formed by the high surface flux. On the other
hand, there is also an apparent horizontal variation in the dissolved
PCB 153 (Fig. 5d). The behavior of dissolved PCB 153 is thus affected not only by physical processes but also biological processes.
The dissolved PCB 153 is absorbed by the abundant phytoplankton

R 365 RRR
1Cðx; y; z; tÞdx dy dz dt
;
R 365
dt
1

ð7Þ

where C is PCB 153 concentration (kg m!3) that is function of space(x, y, z) and time (t); dx, dy, dz are unit distance (m) in three directions of space; dt is a unit time (day). The value was estimated tobe 7.1, 0.9, and 0.1 kg, for dissolved PCB 153, phytoplankton-bound
PCB 153, and detritus-bound PCB 153, respectively. Most PCB 153 in
the ocean has been found to be dissolved PCB 153 ("87%). The
yearly integrated PCB 153 from the atmosphere into the ocean isestimated to be 35.5 kg year!1. Of the dissolved PCB 153,
18.1 kg year!1 is removed by horizontal advection outside the ECS
and 18.3 kg year!1 is absorbed by phytoplankton, while
0.5 kg year!1 is decomposed. 7.0 kg year!1 of phytoplankton-bound
PCB 153 is transformed into detritus-bound PCB 153 through themortality process. Most of the particulate PCB 153 (CWP + CWD) isdeposited to the sea bottom by vertical sinking (15.7 kg year!1),
while advection (0.9 kg year!1) of particulate PCB 153 and decomposition (1.7 kg year!1) of detritus-bound PCB 153 are not significant processes for the mass balance.
Our results for the mass balance of PCB 153 in the whole ECS issummarized as follows: the atmospheric input is 35.5 kg year!1,
among which 19 kg (54%) is removed by horizontal advection ofdissolved and particulate PCB 153 outside the ECS and 15.7 kg(45%) is accumulated to the sea bottom by vertical sinking of particulate PCB 153. In the cases of PCB 101 and 180, most of theatmospheric input is also removed by horizontal advection andsinking flux. However, in the case of PCB 52, decomposition of
the dissolved phase and advection are larger and sinking flux issmaller than those in the PCB 101, 153, and 180 (see the Appendix
B.4 of Supplementary material for detailed explanations).
3.3. Key process responsible for seasonal variability in PCB 153
To clarify the key process responsible for the seasonal variability in PCB 153, we performed three sensitivity experiments, a chlorophyll-run, wind-run, and temperature-run, as described inAppendix B.2 in Supplementary material and compared the calculated monthly volume-averaged dissolved and particulate PCB 153

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a

b

c

d

e

Fig. 5. Distributions of (a) wind speed (black) and surface chlorophyll a concentration (purple), (b) surface flux of PCB 153 from the atmosphere to the ocean (red) and sinking
flux of PCB 153 (blue), (c) water temperature, and (d) dissolved and (e) particulate PCB 153 concentrations along the section shown in Fig. 1a, in February. Contour intervals
are 2.0 "C for water temperature, 40 pg m!3 for dissolved PCB 153, and 12 pg m!3 for particulate PCB 153. (For interpretation of the references to colour in this figure legend,
the reader is referred to the web version of this article.)with those from the control-run (Supplementary Fig. 2). The
seasonal cycle in PCB 153 in the chlorophyll-run is almost the sameas that in the control-run. However, the dissolved PCB 153 concentration increases somewhat in February–April, while the particulate PCB 153 concentration decreases. This may be partly because
the yearly averaged chlorophyll a concentration used in the chlorophyll-run is lower than the chlorophyll a concentration in February–April used in the control-run. The seasonal cycle in PCB 153
is also present in the wind-run but the maximum value in winterbecomes smaller than that in the control-run. In the temperature-run, the seasonal cycle in PCB 153 is significantly different
from that in the control-run and the maximum value in winterbecomes smallest of all sensitivity experiments, indicating thatwater temperature likely play the most important role in determining the maximum values of dissolved and particulate PCB
153 in winter. On the other hand, the minimum values of dissolved
and particulate PCB 153 are not sensitive to changes in wind speed,
water temperature, and chlorophyll a concentration.
Here we discuss these the mechanisms in more detail. In thepresent study, we have taken account only gaseous PCB 153 fromthe atmosphere through the diffusive air–water exchange flux. As
shown in Eq. (6), the net flux of PCB 153 between air and water
depends on the mass transfer velocity (kAW), dissolved PCB 153
concentration (CW), gaseous PCB 153 concentration (CA), andAuthor's personal copy

J. Ono et al. / Chemosphere 89 (2012) 389–397

395

a

b

c

d

e

Fig. 6. Similar to Fig. 5, but for August. Contour intervals are 2.5 "C for water temperature, 30 pg m!3 for dissolved PCB 153, and 3 pg m!3 for particulate PCB 153.

Henry’s law constant (H0 ). As shown in Supplementary Table 3,
Henry’s law constant depends closely on water temperature, i.e.,low (high) temperature results in a low (high) value of Henry’s
law constant. Since we use a constant concentration of gaseous
PCB 153 throughout the year, the CA/H0 term in Eq. (6) increases
in winter and decreases in summer. Consequently, the surface flux
from the atmosphere to the ocean becomes large in winter andsmall in summer. This explains how the seasonal variability in
PCB 153 is caused by that in water temperature through Henry’s
law constant. Since the mass transfer velocities between air andwater (kAW) depend on wind speed (Supplementary Table 3), seasonal variability in PCB 153 may be also influenced by that in windspeed. However, the change in Henry’s law constant with watertemperature is more significant for the seasonal variability of
PCB 153 than that in the mass transfer velocities with wind speed.

Consequently, the water temperature is the first contributor to theseasonal variability of PCB 153 and the wind speed is secondary,while that of chlorophyll a is not so significant.
4. Concluding remarks
On the basis of previous numerical studies (Dachs et al., 1999,
2000, 2002), we newly developed a three-dimensional/high-resolution transport model for POPs and applied it to the ECS for the
first time. To investigate the dynamics of PCB 153 from the atmosphere in the ocean, the model was coupled with an ocean circulation model that can well reproduce the velocity fields in the ECS.
The model revealed that concentrations of dissolved and particulate PCB 153 exhibit clear seasonal variations with higher values
in winter (January–March) and lower values in summer (July–

Author's personal copy396

J. Ono et al. / Chemosphere 89 (2012) 389–397

Fig. 7. Yearly integrated mass balances (kg year!1) calculated by the model. Eacharrow denotes a physical–biochemical process on PCB 153. The values given above
CW, CWP, and CWD indicate the biomass in each phase.

September). Sensitivity experiments indicate that the seasonalvariability of PCB 153 is mainly caused by that of Henry’s law constant, which depends closely on water temperature. From the massbalance of PCB 153, most of the atmospheric input (35.5 kg year!1)
by diffusive air–water exchange is removed by horizontal advection (19.0 kg year!1) and accumulated to the sea bottom by verticalsinking (15.7 kg year!1).
The present study has focused on the seasonal variability of PCB
153 from the atmosphere by the diffusive air–water flux, as a firststep toward realistic simulation of POPs transport. In our study
area, however, the Changjiang and Yellow Rivers also carry large
amounts of suspended particulate matter (SPM) and freshwater,which can also be sources of POPs. In addition, resuspension of particulate PCB 153 from sediments may be an important process forthe behavior of POPs in shallow coastal regions, where wind waves
and tidal currents are dominant. Therefore, to adequately represent the behaviors of POPs, our future model should take accountof the following processes: (1) river discharge and SPM, (2) resuspension at the sea bottom, (3) temporal and spatial variations ofPOPs concentration in the atmosphere, (4) POPs concentrations
at the open boundaries, and (5) dry and wet deposition. At thesame time, continuous POPs monitoring or observations are alsoneeded to quantify POPs impact and to validate the model results.
Acknowledgements
We are grateful to Gan Zhang and Jordi Dachs for their useful
discussion and comments. We would like to thank Joji Ishizaka of
Nagoya University for providing satellite Chlorophyll a data. This
study was supported by a Grant-in-Aid for the Global COE Programof Ehime University (Leader: Prof. Shinsuke Tanabe) from theJapanese Ministry of Education, Culture, Sports, Science andTechnology.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.chemosphere.
2012.05.049.
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Supplementary material

12Appendix A
On the basis of the biochemical processes in Fig. 1b, BI in Eqs. (1) to (4) for CW , CW P S ,CW P M , and CW D can be formulated as follows:−1
BI (CW ) = −FW P S − FW P M + ϕD CW D − t1/2
CW ,

(A.1)

BI (CW P S ) = FW P S − ϕM CW P S ,(A.2)

BI (CW P M ) = FW P M − ϕM CW P M ,(A.3)

BI (CW D ) = ϕM CW P M + ϕM CW P S − ϕD CW D

(A.4)

Here, FW P S (pg m−3 s−1 ) is uptake between the water and the phytoplankton surface; FW P M(pg m−3 s−1 ) is uptake between the water and the phytoplankton matrix; ϕD (s−1 ) is thedecomposition rate of detritus-bound POPs; ϕM (s−1 ) is the mortality rate of phytoplanktonbound POPs; and t1/2 (s) is the half-life time of dissolved POPs.
We incorporated the water-phytoplankton (surface and matrix) exchange process following
Dachs et al. (1999). The phytoplankton uptake in this study can be described as follows:
FW P S = (ksu BP CW − ksd CW P S ) ×

1
,
86400

FW P M = (ku BP CW − kd CW P M ) ×

1
86400

(A.5)

where BP (kg m−3 ) is phytoplankton biomass, ksu (m3 kg−1 day−1 ) is the uptake constant forthe phytoplankton surface, ku (m3 kg−1 day−1 ) is the uptake constant for the phytoplankton
matrix, ksd (day−1 ) is the depuration constant for the phytoplankton surface, and kd (day−1 )is the depuration constant for the phytoplankton matrix. Equations and parameters in the
uptake/depuration processes are summarized in Supplementary Tables 1 and 2.

1

To include the degradation process of dissolved POPs by photolysis and biodegradation, weused the half-life time of POPs in Eq. (A.1). To represent the natural mortality process (ϕMs−1 ) of phytoplankton-bound POPs, we used a formulation and parameters from Sohma et al.
(2004):
ϕM = α1 exp(β1 T ) ×

1
86400

(A.6)

where α1 (= 0.00042 day−1 ) is the phytoplankton mortality rate at 0 o C, β1 (= 0.0693 o C−1 )is the temperature coeﬃcient for phytoplankton mortality, and T is temperature (o C). A proportion of the detritus-bound POPs is transformed into POPs in water by decomposition. The
decomposition process (ϕD , s−1 ) of detritus-bound POPs was assumed to be dependent on temperature following Kawamiya et al. (1995):

ϕD = α2 exp(β2 T ) ×

1
86400

(A.7)

where α2 (= 0.03 day−1 ) is the detritus decomposition rate at 0 o C, and β2 (= 0.0693 o C−1 ) is
the temperature coeﬃcient for detritus decomposition, and T is temperature (o C). The sinking
velocity (wsp m s−1 ) of phytoplankton-bound POPs was assumed to be constant vertically, andwas set to be 1/86400 m s−1 (1 m day−1 ). Following Kawamiya et al. (1995), the sinking
velocity (wsd m s−1 ) of detritus-bound POPs was set to be 10/86400 m s−1 (10 m day−1 ) in
the upper 100 m layer, and 100/86400 m s−1 (100 m day−1 ) in the region deeper than 170 m.
Between 100 m and 170 m, a linear relation between sinking velocity and depth was assumed.
We used a half-life time of PCB 153, t1/2 = 4.32×108 (s), given by Sinkkonen and Paasivirta(2000) for the degradation process of dissolved PCB 153.
In order to include the phytoplankton uptake process, the vertical distributions of phytoplankton biomass (BP kg m−3 in Eq. (A.5)) are calculated by a constant weight ratio (Riemann2

et al., 1989):
BP =

100
× Chl × 10−6 ,
3.03

(A.8)

3

where Chl (mg m−3 ) is the vertical distribution of chlorophyll a concentration calculated by the4Gaussian model (Siswanto et al., 2006) from monthly averaged observed data of chlorophyll a5

concentration at the surface from the Sea-viewing Wide Field-of-view Sensor (Sea WiFS). It must6

be noted that the weight ratio used in Eq. (A.8) has been suggested to vary seasonally (Riemann7

et al., 1989) and spatially (Chang et al., 2000). The usage of Eq. (A.8) excludes such temporal8

and spatial variations in the weight ratio but makes model results easily understood. As an

9example of monthly phytoplankton biomass, we describe the features of the surface chlorophyll

10a in winter and summer in Appendix B.1.

11

Appendix B

12

B.1 Physical and biological environments in the East China Sea

13

As an example of monthly phytoplankton biomass, we here describe the features of the surface

14

chlorophyll a in winter and summer. In February, the surface chlorophyll a concentration is high15

along the coasts from 30 to 35o N on the Chinese side of the ECS and in the Bohai Sea, but is low

16

in the Yellow Sea (Supplementary Fig. 1a). In August, the distribution of surface chlorophyll a17

is similar to that in February, but its concentration becomes low (Supplementary Fig. 1b).

18

To calculate kAW (Supplementary Table 3), we used wind speed data from the European

19

Remote-Sensing satellites ERS−1 and ERS−2. Since the net flux of POPs between air and20

water is aﬀected by wind speed and water temperature, their spatial distributions in February

21

and August, as examples, are shown in Supplementary Figs. 1c to 1f. In February, the whole

22

area of the ECS is dominated by northerly winds with speeds larger than 5 m s−1 and is covered3

23

by water with temperatures lower than 16 o C. In particular, the water temperature is very low

24in the Bohai and Yellow Seas. In August, southeasterly winds are dominant over the ECS, but

25

the wind speeds are smaller than 4 m s−1 and water temperature is high over the whole ECS.

26

B.2 Key process responsible for seasonal variability in PCB 153

27

To elucidate the most important factor controlling the seasonal variability in PCB 153, we

28

designed three sensitivity experiments in each of which the temporal variation of one parameter29

was removed while that of the others was kept. Specifically, we removed the temporal variation30

in chlorophyll a concentration (chlorophyll-run), wind speed (wind-run), and water temperature31(temperature-run), respectively. The calculated monthly volume-averaged dissolved and partic-

32

ulate PCB 153 in each sensitivity experiments are shown in Supplementary Fig. 2. The detailed

33

explanations are mentioned in text.

34

We next examined impacts of the seasonal variation in chlorophyll a, wind speed, and water35

temperature on the mass budget of PCB 153 (Supplementary Fig. 3). The removal of seasonal36

variation in water temperature and in wind speed reduces the annual surface flux by 45% and37

22%, respectively, compared with the control-run. As a result of the decrease in surface flux,

38

the mass flow associated with the other processes also decreases. For example, the reduction in

39

phytoplankton uptake in the temperature-run is ∼40%. Thus, phytoplankton uptake-induced

40

depletion of the dissolved PCB 153 partly contributes to the decrease of surface flux. In contrast,

41

the mass balance in the chlorophyll-run is similar to that in the control-run, as suggested in Sup-42

plementary Fig. 2. This is because the supply of gaseous PCB 153 by the diﬀusive air-water43

exchange flux is controlled primarily by water temperature and secondly by wind speed.

44

B.3 Other processes aﬀecting seasonal variability in PCB 1534

45

In our simulation, the gaseous PCB 153 concentration was assumed to be constant through-46

out the year, meaning that gaseous PCB 153 concentration is not influenced by physical and47

biochemical processes in the ocean. However, the real concentration of PCB 153 in the at-48

mosphere changes temporally. To examine impacts of gaseous PCB 153 concentration on the49

behavior of PCB 153 in the ocean, we conducted two additional numerical experiments. In

50

the first numerical experiment, we carried out four simulations in which the gaseous PCB 153

51

concentration (2.08 pg m−3 ) used in the control-run was multiplied by constants of 0.5, 2.0, 5.0,

52

and 10.0, respectively. In the second numerical experiment, we also performed four simulations

53

in which the gaseous PCB 153 concentration varies sinusoidally with a maximum value (3.08 pg

54m−3 ) in February and a minimum value (1.08 pg m−3 ) in August (winter-run), with a maximum

55

in May and a minimum in November (spring-run), with a maximum in August and a minimum

56

in February (summer-run), and with a maximum in November and a minimum in May (fall-run),57

while the yearly averaged concentration is the same as that (2.08 pg m−3 ) in the control-run. In

58

all simulations, the other boundary and initial conditions as well as the model integration period

59

were the same as in the control-run. We compared the seasonal variability and biomass for the60

dissolved and particulate PCB 153 in the additional simulations with those in the control-run.

61

The first four simulations showed that the yearly averaged dissolved and particulate PCB

62

153 concentrations linearly changed in proportion to the gaseous PCB 153 concentration (Sup-63

plementary Fig. 4). To investigate the impact of the change in the supply of gaseous PCB 153 on

64

the seasonal variation of dissolved and particulate PCB 153, we calculated a ratio of diﬀerence

65

between the two extreme values of monthly mean to annual mean as a seasonal index of PCB

66

153. The ratios were 1.1 and 1.8 for the dissolved and particulate phases in the control-run,5

67

respectively, and these two values did not change among the four simulations. These results

68

indicate that a diﬀerence in the supply of gaseous PCB 153 is not important for the seasonal

69

cycle in dissolved and particulate PCB 153 concentrations as long as the concentration is a70

constant value throughout the year.

71

From the second four simulations, the dissolved and particulate PCB 153 concentrations

72

were high in winter and low in summer to fall (Supplementary Fig. 5a), regardless of the sea-73

sonal variation pattern in gaseous PCB 153 concentration. These are similar to results from74

the control-run. However, there is also an apparent diﬀerence in their seasonal variations. Con-75

centration diﬀerences between the winter- or summer-runs and the control-run become large in76

January−June. This is partly because the winter-run in which gaseous PCB 153 concentration77

reaches a maximum in February increases the concentration gradient between the gaseous phase78

and the dissolved phase, resulting in increased surface flux (Supplementary Fig. 5b), while the79summer-run, in which gaseous PCB 153 concentration reaches a minimum in February, decreases

80

the concentration gradient between the gaseous phase and the dissolved phase, resulting in de-81

creased surface flux (Supplementary Fig. 5b). From ratios of diﬀerence between the two extreme

82

values of monthly mean to annual mean in each simulations (Supplementary Table 4), ratios in83

the spring- and fall-runs are found to be similar to those in the control-run. However, ratios in84

the winter-run are 1.3 and 2.1 for the dissolved and particulate phases, respectively, indicating

85

larger seasonal variation, while those in the summer-run are 0.8 and 1.4, representing smaller86

seasonal variation. Such seasonal variations would be caused by the diﬀusive air-water exchange

87

flux associated with the that in gaseous PCB 153. Thus a future model should incorporate the88

temporal variations in gaseous PCB 153 and its interaction with the PCB 153 in the ocean.

89

B.4 Simulations for diﬀerent congeners of PCBs

6

90

For comparison with PCB 153, we conducted simulations for PCB 52, 101, and 180. Pa-91

rameters used in simulations are shown in the Supplementary Table 5, but all other conditions

92

are the same with those for PCB 153. The dissolved and particulate concentrations of PCB 52,

93101, and 180 were high in winter and low in summer to fall (Supplementary Fig. 6), which are

94

similar to the seasonal variation of PCB 153. However, there is a prominent diﬀerence in their95

amplitudes. This is due to the diﬀerence in the supply of gaseous concentration (Supplementary

96

Table 5). It is also noted that the diﬀerence between PCB 52 and PCB 101 is considerably large

97

in the dissolved concentration but small in the particulate concentration (Supplementary Figs.

98

6a and 6b). From the results of mass balance, the yearly integrated PCB 52, 101, and 180 from99

the atmosphere into the ocean are estimated to be 140.0, 74.7, and 41.0 kg year−1 , respectively

100(Supplementary Table 6). In the cases of PCB 101 and 180, the input from the atmosphere is101

removed by horizontal advection (57% and 55%) and sinking flux (39% and 44%), which are102

roughly the same as results for PCB 153. In the case of PCB 52, however, decomposition (8%)

103of the dissolved phase and advection (63%) are larger and sinking flux (28%) is smaller than104

those in the PCB 101 and 180. This is because PCB 52 has the shorter half-life time and has

105

less hydrophobic property, compared with PCB 101 and 180.

7

106

References used in Supplementary material

107

Chang, J., Shiah, F.-K., Gong, G.-C., Chiang, K.P., 2003. Cross-shelf variation in carbon-to-108

chlorophyll a ratios in the East China Sea, summer 1998. Deep-Sea Res. II 50, 1237-1247.

109

Del Vento, S., Dachs, J., 2002. Prediction of uptake dynamics of persistent organic pollutants

110

by bacteria and phytoplankton. Environ. Toxicol. Chem. 21, 941-953.

111

Jaward, F.M., Zhang, G., Nam, J.J., Sweetman, A.J., Obbard, J.P., Kobara, Y., Jones, K.C.,

112

2005. Passive air sampling of polychlorinated bipheneyls, organochlorine compounds, and113

polybrominated diphenyl ethers across Asia. Environ. Sci. Technol. 39, 8638-8645.

114

Kawamiya, M., Kishi, J.M., Yamanaka, Y., Suginohara, N., 1995. An ecological-physical

115

116

117

118

119

120

121

coupled model applied to Station Papa. J. Oceanogr. 51, 635-664.
Riemann, B., Simonsen, P., Stensgaard, L., 1989. The carbon and chlorophyll content ofphytoplankton from various nutrient regimes. J. Plankton Res. 11, 1037-1045.
Sinkkonen, S., Paasivirta, J., 2000. Degradation half-life times of PCDDs, PCDFs and PCBs
for environmental fate modeling. Chemosphere 40, 943-949.
Siswanto, E., Ishizaka, J., Yokouchi, K., 2006. Optimal primary production model and parameterization in the eastern East China Sea. J. Oceanogr. 62, 361-372.

122

Sohma, A., Sekiguchi, Y., Nakata, K., 2004. Modeling and evaluating the ecosystem of sea-

123

grass beds, shallow waters without sea-grass, and an oxygen-depleted oﬀshore area. J.

124

Mar. Sys. 45, 105-142.

89Depuration constant of phytoplankton surface at T K for POPs
Uptake constant of phytoplankton matrix at T K for POPs
Depuration constant of phytoplankton surface at T K for POPs
Uptake constant of phytoplankton surface at 298 K for PCB 153

110039
287.6
2474
1.08

d−1
m3 kg−1 d−1
d−1
m3 kg−1 d−1
d−1
m3 kg−1 d−1
d−1
K

T BCF (298)ksd (T ) = ηηHH(298)(T ) 298 BCF (T ) ksd (298)

T BCF (T )ku (T ) = ηηPP(298)(T ) 298 BCF (298) ku (298)T
kd (T ) = ηηPP(298)(T ) 298 kd (298)ksu (298)

ksd (298)ku (298)kd (298)

T

Water temperature

Depuration constant of phytoplankton matrix at 298 K for PCB 153

Uptake constant of phytoplankton matrix at 298 K for PCB 153

Depuration constant of phytoplankton surface at 298 K for PCB 153

Uptake constant of phytoplankton surface at T K for POPs

m3 kg−1 d−1

DescriptionTksu (T ) = ηηHH(298)(T ) 298 ksu (298)

Value

Unit

Equations or parameters

Supplementary Table 2 for water viscosity (ηH m2 s−1 ), octanol viscosity (ηP cP), and bioconcentration factors (BCF m3 kg−1 ).

Supplementary Table 1: Physicochemical parameters in the water-phytoplankton exchange process (Vento and Dachs, 2002). See

10

(

2

7.21

Octanol viscosity at 298 K

cP

Water viscosity at 293 K

Water viscosity for 293 < T < 373

Water viscosity for 273 < T < 293

Ideal gas constantηP (298)

2.56 × 10−5

8.31

Octanol viscosity at T K

m2 s−1

Pa m3 mol−1 K−1

Enthalpy of sorption for POPs

BCFs for the surface (matrix) at 298 K for PCB 153

Bioconcentration factors (BCFs)

Description

ηP−0.2661 (T ) = ηP−0.2661 (298) + T −298233

ηH (293)

−293)
(T )
log10 ηηHH(293)= 1.3272(293−T )−0.001053(TT −168-3.30233

1301
log10 ηH (T ) = 998.333+8.1855(T −293)+0.00585(T−293)2

R

35

kJ mol−1

∆HS

Value

396 (1934)

Unit

m3 kg−1

1
1
T − 298)]

BCF (298)

∆HS
BCF
R
BCF (298) = exp[

Equations or parameters

is water temperature.

Supplementary Table 2: Physicochemical parameters in the water-phytoplankton exchange process (Vento and Dachs, 2002). T (K)

11

′

2

)−0.5

2780

66.1
0.19

kJ mol−1
kJ mol−1 K−1

∆HH

∆SH

lnH = −∆HH /RT + ∆SH /R′

2.56×10−5

m2 s−1

DH O, a2

5.2×10−6

Wind speed at 10 m heightm s−1

Entropy of the phase change for PCB 153

Enthalpy of phase change for PCB 153

Henry’s law constant

Molecular diﬀusivity for H2 O

Molecular diﬀusivity for PCB 153

Schmidt number for PCB 153

Mass transfer coeﬃcient in air

m s−1

Mass transfer coeﬃcient in waterm s−1

Description
Mass transfer coeﬃcient between air and waterm2 s−1

)0.61

Value

m s−1

Unit

DP OP s, a

ScP OP s

U10

kA = (0.2U10 + 0.3) DP OP s, a /DH O, a(

kW = 0.45U101.64 ScP OP s /600(

1/kAW = 1/kW + 1/kA H

Equations or parameters

water temperature. R (Pa m3 mol−1 K−1 ) is the ideal gas constant given in Supplementary Table 2.

Supplementary Table 3: Physicochemical parameters in the air-water exchange process (Bidleman and McConnell, 1995). T (K) isSupplementary Table 4: Ratios of diﬀerence between the maximum and minimum value to theyearly-mean concentration in the dissolved (CW ) and particulate (CW P + CW D ) PCB 153. The
ctl denotes the control-run. For the definition of winter-, spring-, summer-, and fall-runs, referto text.

ctl

winter-run

spring-run

summer-run

fall-run

Ratio for the dissolved PCB 153

1.1

1.3

1.1

0.8

1.2

Ratio for the particulate PCB 153

1.8

2.1

1.9

1.4

2.0

12

Supplementary Table 5: Parameters used in simulations for PCB 52, 101, and 180. CA (pgm−3 ) is gaseous concentration (Jaward et al., 2006), t1/2 (×108 s) is half-life time (Sinkkonen
and Paasivirta, 2000), ksu (m3 kg−1 day−1 ) is the uptake constant for the phytoplankton surface,ku (m3 kg−1 day−1 ) is the uptake constant for the phytoplankton matrix, ksd (day−1 ) is thedepuration constant for the phytoplankton surface, and kd (day−1 ) is the depuration constantfor the phytoplankton matrix (Vento and Dachs, 2002).

CA

t1/2

ksu

ku

ksd

kd

PCB52

10.10

1.08

82608

287.6

402.5

0.89

PCB1014.68

2.16121044287.6

1559.01.15

PCB180

2.46

8.64

82000

287.6

1300.00.53

13

Supplementary Table 6: Yearly-integrated mass (kg year−1 ) balances calculated by the model.

PCB 52

PCB 101

PCB 180

Surface FLux

140.0

74.7

41.0

Sinkgin Flux

38.5

29.4

17.8

Decomposition of CW

11.3

2.5

0.3

Decomposition of CW D

2.9

2.9

2.0

Advection/Diﬀusion of CW

87.4

40.9

21.5

Advection/Diﬀusion of CW P

1.1

1.2

0.9

Advection/Diﬀusion of CW D

0.2

0.3

0.2

Uptake-Depuration

42.7

33.6

20.9

CW

38.8

1.7

0.1

CW P

16.8

1.5

0.1

CW D

8.4

1.0

0.1

14

Supplementary Fig. 1: Horizontal distributions of monthly-averaged (a and b) surface chlorophyll a concentration, (c and d) wind speed and direction at 10 m height, and surface watertemperature, in February and August, respectively.

15

Supplementary Fig. 2: Time series of (a) monthly-averaged concentration in the dissolved
and particulate PCB 153 and (b) surface and sinking fluxes, from control-run (black lines),
chlorophyll-run (red lines), wind-run (green lines), and temperature-run (blue lines), respectively.

16

Supplementary Fig. 3: Yearly-integrated mass (kg year−1 ) balances calculated by the modelfor the control-run (plus), chlorophyll-run (light-gray bar), wind-run (heavy-gray bar), andtemperature-run (black), respectively. SF is yearly-integrated mass by surface flux of PCB 153
from the atmosphere to the ocean, VS is that by vertical sinking of PCB 153 to the bottom, DD
is that by decomposition of dissolved PCB 153, DP is that by decomposition of phytoplanktonbound PCB 153, AD is that by advection/diﬀusion of dissolved and particulate PCB 153, UP isthat by phytoplankton uptake, and MT is that by mortality of phytoplankton-bound PCB 153.

17

Supplementary Fig. 4: Ratio of the yearly-integrated concentrations in the dissolved (opencircles) and particulate (black circles) PCB 153 in four sensitivity experiments to those in thecontrol-run. Factor is a coeﬃcient multiplied by the gaseous concentration (2.08 pg m−3 ) usedin the control-run.

18

Supplementary Fig. 5:

Similar to Supplementary Fig 4, but for the control-run (blacklines),February-run (blue lines), May-run (green lines), August-run (red lines), and Novemberrun (sky-blue lines), respectively.

19

Supplementary Fig. 6: Time series of monthly-averaged (a) dissolved and (b) particulate concentrations and (c) surface and (d) sinking fluxes, for PCB 153 (black lines), 52 (red lines), 101(green lines), and 180 (blue lines), respectively.

20