Journal of Hazardous Materials 469 (2024) 134051

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Journal of Hazardous Materials
journal homepage: www.elsevier.com/locate/jhazmat

Effect of hydrodynamic and ecosystem conditions on persistent organic
pollutant temporal-spatial variations in the Yellow Sea
Aobo Wang a, Xinyu Guo b, *, Xiaokun Ding c, Jie Shi d, Jianhui Tang eaSchool of Hydraulic Engineering, Ludong University, Yantai 264025, China
Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japanc
School of Ocean, Yantai University, Yantai 264005, Chinad
Key Laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, 238 Songling Road, Qingdao 266100, Chinae
Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai
264003, Chinab

H I G H L I G H T S

G R A P H I C A L A B S T R A C T• POP transport in coastal seas is strongly
affected by physical and biologicalconditions.
• PCB-153 accumulates at sea bottom andBDE-209 accumulates at surface insummer.
• Different POP seasonal and spatial var­iations are caused by their H′ and BCF.
• Volatilization is significant for PCB-153
but not for BDE-209.
• Water-particle equilibrium time isaffected by particle change and sorption
rates.

A R T I C L E I N F O

A B S T R A C T

Keywords:
Coupled model
Persistent organic pollutants
Seasonal and spatial variations
BudgetBalanced mass concentration

Coastal seas are important pools of persistent organic pollutants (POPs) discharged from land. Considering thecomplex conditions in coastal seas and various biochemical features of POPs, special temporal-spatial variations
in POPs have been reported. To understand these variations, we developed a three-dimensional hydro­
dynamic–ecosystem–POP coupled model and applied it to the Yellow Sea. We selected two POP species (poly­chlorinated biphenyl congener 153 (PCB-153) and decabromodiphenyl ether (BDE-209)), which have different
biochemical properties, as target materials. The dissolved PCB-153 simulated concentration was high in latespring and low in autumn, whereas that of BDE-209 was high in summer and low in winter. Both PCB-153 andBDE-209 showed high particle-bound concentrations in early spring. In summer, dissolved PCB-153 accumulated
at the sea bottom, whereas dissolved BDE-209 accumulated at the sea surface. Seasonal and spatial variationdifferences in the two POPs are likely caused by greater Henry’s Law Constant (H′) and bioconcentration factor(BCF) of PCB-153 than that of BDE-209, which leads to higher volatilization and stronger absorption by theparticles for PCB-153 than BDE-209. As a component of such differences, the “biological pump” of PCB-153 in thecentral Yellow Sea is more apparent than that of BDE-209.

* Corresponding author.
E-mail address: guo.xinyu.mz@ehime-u.ac.jp (X. Guo).
https://doi.org/10.1016/j.jhazmat.2024.134051
Received 10 November 2023; Received in revised form 27 February 2024; Accepted 14 March 2024
Available online 15 March 2024
0304-3894/© 2024 Elsevier B.V. All rights reserved.

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Journal of Hazardous Materials 469 (2024) 134051

water mass in autumn, the high POP concentration in the bottom cold
water mass rose to the sea surface and formed a high concentration
patch. Therefore, the appearance, development, and disappearance ofbottom cold water masses can alter POP vertical distribution and sea­sonal variations in shelf seas.
Few studies have quantified the effects of hydrodynamic andecosystem conditions on POP behavior in seawater, although these ef­
fects are likely important for POP transportation, as mentioned above. In
this study, we chose the Yellow Sea, where there is a strong bottom cold
water mass called the Yellow Sea Bottom Water (YSBW), as an example
to investigate physical and biological environmental effects on POP
cycles using a three-dimensional hydrodynamic–ecosystem–POP
coupled model. In addition to the YSBW, tidal fronts, thermoclines, andspring phytoplankton blooms have also been reported in the Yellow Sea[26,23,49].
The Yellow Sea, which is surrounded by China and Korea, is heavily
polluted with different types of POPs [24,47]. The air–sea diffusive ex­change and sorption of POPs by phytoplankton are key processes of POP
behaviors in the shelf seas, which are affected by the Henry’s Law
Constant (H′) and bioconcentration factor (BCF) of POPs, respectively[12,13]. Some of PBDEs including the BDE-209 have been regulated but
their stock is still huge. The BDE-209 is the main component of PBDEs
and we found some field data of it in the south Yellow Sea which can beused to verify the model results [47]. The usage of PCB-153 was verylarge before the 1970s in China [48] and it is still detected in theenvironment now due to its long half-life time. In addition, some papers

1. Introduction
Persistent organic pollutants (POPs) are characterized by environ­mental persistence, long-range transport in the atmosphere and sea,high toxicity to organisms, and bioaccumulation along the food chain[30,6], and they are widely used in industry and agriculture since thelast century [3,45]. POPs generally originate from land and enter the sea
through the atmosphere and rivers [51], as a result, they are widely
detected in coastal seas and open oceans [20,50]. POPs in seawater canbe transported by ocean currents, absorbed by biogenetic particles, andsubsequently undergo biogeochemical cycles in marine ecosystems [1,34]. In addition, POP physical and biogeochemical processes in seawater
are related to water temperature, surface wind, and other factors [31,47]. Therefore, the concentrations of POPs present different temporaland spatial variations in seas with different physical and biologicalconditions [1,27,33,41].
As we reviewed reports on POPs observed in coastal seas, we foundsome interesting phenomena. Vertical distributions differ for POP spe­
cies observed in the same water [28,50]. For instance, hexa­
chlorobenzene presents a high concentration in the middle layer of thewater column while hexachlorocyclohexanes present high concentra­
tions at the sea surface in the Greenland Sea [28]; dimethyl phthalate
and diethyl phthalate show high concentrations at the sea surface,whereas dimethylglycol phthalate shows high concentrations at the sea
bottom in the Yellow Sea [50]. There are a high-concentration patches ofseveral POPs in both seawater and sediment in offshore areas far fromemission sources, such as in the Yellow Sea, Beibu Gulf, and the Arctic
Ocean [16,21,50]. In addition to the spatial distribution, seasonal var­iations in POPs in the same water also differ for different species, such as
decabromodiphenyl ethane (DBDPE) and decabromodiphenyl ether(BDE-209) in the Bohai Sea [25]. Notably, these phenomena generally
occur in seas where the water column is stratified and biogenic particles
are abundant.
Because POPs are lipophilic contaminants, they can easily enter
ocean ecosystems through absorption by biogenic particles [17,9].
Dissolved POPs at the sea surface can be absorbed by particles, such as
phytoplankton, and then transport to deep water along with sinking
particles [1,41]. As a result, surface concentrations of dissolved POPsdecrease, leading to more gaseous POPs entering seawater throughdiffusion at the air–sea interface [22]. The above effect of absorption tophytoplankton on air–sea POP diffusive exchange is known as the“biological pump” of POPs in the ocean [11,10]. Moreover, particulatePOPs can be transported from seawater to sediment because of particle
sinking, making the sediment an important sink of POPs [43,42].
The physical conditions are complex, especially in the marginal seaswith existing tidal currents, fronts, thermoclines, and other oceano­graphic phenomena [18,36,52]. POPs are transported in seawater via
advection and diffusion, and their concentrations are affected by com­plex physical conditions [32,33,39]. In the Yangtze River Estuary, pol­ychlorinated biphenyl (PCB) spatial variability is mainly related to
localized hydrological characteristics [6]. DBDPE seasonal variations in
the Bohai Sea are influenced by wind waves in winter and stratification
during summer [25]. A sharp concentration transition was observed forPOPs in the Atlantic Ocean due to the presence of a front, where the
PFOA concentration was > 100 pgL− 1 north of the front, but decreased
to < 50 pgL− 1 south of the front [27].
As a general feature, the summer shelf seas usually have a bottom
cold water mass, where nutrient concentrations are high [14]. Similar tonutrients, it can be hypothesized that the POPs absorbed by the bioge­
netic particles sink into the bottom cold water mass, and biogenetic
particle remineralization releases the particle-bound POPs to the dis­solved POPs within the bottom cold water mass. As the bottom cold
water mass is isolated from the surrounding water, dissolved POPs canaccumulate within the bottom cold water mass and form high concen­
trations in late summer. With the disappearance of the bottom cold

presented and compared H′ and BCF values of PCB-153 and BDE-209(Tables S2-S3), which are necessary to our modeling, and reported thegreat variation in the concentrations of these two POPs in the ocean ([2];[5,9,19,25,8]). Therefore, the PCB-153 and BDE-209 were selected as
the study targets.
2. Model description

In this study, a model coupled with hydrodynamic, ecosystem, andPOP modules was developed to simulate the transport and biogeo­
chemical cycles of POPs in the Yellow Sea.
2.1. Hydrodynamic and ecosystem modules
Pollutants in seawater are transported via advection and diffusion by
ocean currents. The three-dimensional hydrodynamic module was basedon the Princeton Ocean Model [29] and had a 1/18◦ horizontal gridinterval [15,44]. The entire model domain (Fig. 1a) covers the Bohai,
Yellow, and East China Seas (117.5–131.5◦ E, 24–41◦ N), which was
divided into 254 grids in the zonal direction, 308 grids in the meridional
direction, and 21 sigma layers in the vertical direction (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, and − 1.000).
The module was driven by monthly sea surface temperature, surface
heat flux, evaporation, precipitation, and wind stress data in 2015,
downloaded from the official website of the National Oceanic and At­
mospheric Administration (https://psl.noaa.gov/cgi-bin/db_search/Se
archMenus.pl). We chose 2015 because some POP field observation
data were available for this year [47], which were very valuable toverify the model results. In addition, the physical environments andbiogeochemical processes of POPs in seawater in 2015 are not muchdifferent from those in the present and the relations between POP pro­
cesses and environmental conditions are objective laws which do notchange with years. This work mainly investigated the effects of bottom
cold water mass on the biogeochemical cycles and distribution of POPsin seawater, which always exist and are the same every year. Therefore,
the important conclusions drawn from the calculation for 2015 can beheld in other years including the present. The input conditions at the2

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Journal of Hazardous Materials 469 (2024) 134051

lateral boundary were set to those in the model used by Wang et al. [44],including the monthly seawater temperature, salinity, subtidal currents,
and tidal currents of the four major constituents (M2 , S2 , K1 , and O1 ).
The calculation was divided into internal and external modes with 360 s
and 6 s time steps, respectively.
The ecosystem module includes only two components. One is
phytoplankton, and the other is detritus. Phytoplankton concentration
was determined using the satellite-observed chlorophyll concentration.
Surface chlorophyll data were downloaded from the Ocean Color Web(https://oceancolor.gsfc.nasa.gov/), which provides multi-year averagedata for the Yellow Sea [35]. Chlorophyll surface concentrations wereexpanded into three-dimensional space by specifying different chloro­
phyll vertical profiles in different seasons (Fig. S1) as reported by Shi
et al. [35]. An equation was solved to determine the detritus concen­
tration. In addition to advection and diffusion due to water motion, we
included detritus sinking, phytoplankton mortality, and detritus
decomposition in this equation (Appendix A in the supplementarymaterial).

∂Cwdm
∂Cwdm
∂Cwdm
∂Cwdm
+u
+v
+ (w + ws )
∂t
∂x
∂y
∂z
(
)
∂
∂Cwdm
=
K
+ D(Cwdm ) + Bio(Cwdm )∂z h ∂z
∂Cwds
∂Cwds
∂Cwds
∂Cwds
+u
+v
+ (w + ws )
∂t
∂x
∂y
∂z
(
)
∂
∂Cwds
=
K
+ D(Cwds ) + Bio(Cwds )∂z h ∂z2.2.1. Transport equations of POPs
We considered three POP states in the POP module: dissolved (Cw ),phytoplankton-bound (Cwp ), and detritus-bound (Cwd ), where Cwp is thesum of the POPs in the phytoplankton matrix (Cwpm ) and those adsorbed
onto the phytoplankton surface (Cwps ). Similarly, Cwd is the sum of thePOP content in the detritus matrix (Cwdm ) and that adsorbed onto thedetritus surface (Cwds ). Particle-bound POPs (Cwpar ) are defined as POPsin all particles, that is, the sum of Cwpm , Cwps , Cwdm , and Cwds . The
equations used to calculate POPs in different states are as follows:
(
)
∂Cw
∂Cw
∂Cw
∂Cw ∂
∂Cw
+u
+v
+w
=
Kh
+ D(Cw ) + Bio(Cw )
(1)
∂z
∂t
∂x
∂y
∂z
∂z
)

(

)

(5)

In these equations, t is the time (s); x, y, and z are the zonal,
meridional, and vertical coordinates (m); u, v, and w are velocity com­ponents in three directions (ms− 1 ); ws is the detritus sinking velocity(ms− 1 ), and its value is given in Table S1 in the Supplementary Material;
Kh is the vertical mixing coefficient (m2 s− 1 ); Cw , Cwps , Cwpm , Cwdm , andCwds are PCB-153 or BDE-209 concentrations (μgm− 3 ); D(Cw ), D(Cwps ),D(Cwpm ), D(Cwds ), and D(Cwdm ) are horizontal diffusion terms; andBio(Cw ), Bio(Cwps ), Bio(Cwpm ), Bio(Cwds ), and Bio(Cwdm ) are biogeochem­
ical terms. The biogeochemical terms are described in Appendix A of the
Supplementary Material. Eqs. (1)–(5) were calculated in two steps: an
explicit scheme for horizontal and vertical advection, horizontal diffu­
sion, source, and biogeochemical terms, and an implicit scheme forvertical diffusion.

2.2. POP module((4)

2.2.2. Source and biogeochemical cycle of POPsA POP module schematic is shown in Fig. 2. At the air–sea interface,gaseous and aerosol-bound POPs enter seawater in a dissolved form
through diffusion and deposition, respectively [47,51]. Therefore, theair–sea diffusion flux and deposition flux from the atmosphere weretreated as surface boundary conditions for dissolved POPs, whose cal­culations were based on previously reported gaseous and aerosol-boundPOP concentrations (Appendix A). Some dissolved POPs decompose in
seawater owing to their reaction to light, and most of them are absorbed
by particles and experience biogeochemical cycles within these parti­
cles. POP sorption by particles is reversible and includes uptake anddepuration to the matrix, as well as their adsorption and desorption toparticle surfaces. In this system, POPs are removed from seawater bysinking to the sea floor with detritus. Some of the POPs (50 %) in thissinking detritus return to seawater through the detritus remineraliza­
tion, and the remaining POPs are buried in the sediments [37,4,40]. The
equations for calculating the biogeochemical processes are described in

)
(
)
(
∂Cwpm
∂Cwpm
∂Cwpm
∂Cwpm ∂
∂Cwpm
+u
+v
+w
=
K
+D Cwpm +Bio Cwpm
∂z h ∂z
∂t
∂x
∂y
∂z
(2)
)
(
)
(
∂Cwps
∂Cwps
∂Cwps
∂Cwps ∂
∂Cwps
+u
+v
+w
=
K
+ D Cwps + Bio Cwps
∂z h ∂z
∂t
∂x
∂y
∂z
(3)

Fig. 1. Bathymetric map of (a) model domain and (b) the Yellow Sea (shadings, unit: m). The dashed line in (b) denotes the section location (section 124o E) toshowing vertical distribution.
3

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Journal of Hazardous Materials 469 (2024) 134051

Fig. 2. Conceptual scheme of the hydrodynamic–ecosystem–POP coupled model. See the text for a description of the variables and related processes.

Appendix A.
The initial POP concentration in all the states was set to 0 pgm− 3 . The
coupled model was operated for three years, and the results from thefinal year were chosen for analysis to avoid the influence of initialconditions (Fig. S2).

Sea, especially during summer (Fig. 4 a3) when the YSCWM was at themost stable state and material exchange between YSCWM and sur­rounding water was weak ([52]). The particle-bound PCB-153 concen­
tration increased with water depth in all seasons, except spring. During
spring, the particle-bound PCB-153 concentration was higher in theupper layer than in the lower layer due to phytoplankton blooms in theupper seawater.
To understand the particle mass effect on the particle-bound PCB153 concentration, we calculated the particle-bound PCB-153 concen­
tration per unit mass of particles, which was defined as the mass con­centration. As shown in Fig. S4, the particle-bound PCB-153 massconcentration at the sea surface was higher in winter and spring than in
the other seasons. In addition, the PCB-153 mass concentration wasvertically homogenous in winter but varied significantly in other seasons(Figs. S4b1–b4). This mass concentration increased gradually from thesurface to bottom layers in summer, while it increased in the following
order: middle, surface, and bottom layers in spring and autumn.
Fig. S5 presents the time series of the spatial mean of PCB-153
concentrations in the central Yellow Sea, the domain of which isshown in Fig. 3 a1. These spatial mean concentrations are averaged
vertically and horizontally. The dissolved PCB-153 mean concentration
peaked in June and was the lowest in November, whereas that of theparticle-bound form was the highest in March and the lowest in
September. The dissolved and particle-bound concentration ranges were13.1–22.8 pgm− 3 and 2.6–8.9 pgm− 3 , respectively. The mass concen­
tration of PCB-153 was highest in May and lowest in October and therange was 0.051–0.105 ngg− 1 , which was affected by the dissolved PCB153 concentration and water temperature, which will be further dis­cussed in Section 4.3.

2.2.3. Verification of POP module
Field data for BDE-209 in the Yellow Sea were used to verify themodel results because there were no observational data for PCB-153
concentrations in the study area. The sum of the surface dissolved andparticle-bound concentrations for BDE-209 was approximately 4000
pgm− 3 south of the Yellow Sea [47], whereas that of the simulation re­sults obtained in this study was ~1800 pgm− 3 . Therefore, the model
results were of the same magnitude as the field data, however, theirvalues were minimal, which may be due to the absence of an influx ofPOPs from the Yangtze River and the resuspended particles in this study
model.
3. Results
3.1. Temporal and spatial variations of PCB-153
There were obvious seasonal variations in the horizontal and vertical
distributions of PCB-153 concentrations (Figs. 3 and 4). Considering theeffects of phytoplankton blooms on POP concentrations in April, we
selected this month to represent spring. February, August, andNovember were selected to represent winter, summer, and autumn,
respectively. As shown in Fig. 3 a1–a4, the dissolved PCB-153 concen­
tration at the sea surface was higher in winter and spring than in summer
and autumn. In addition, there were some high-concentration patches ofdissolved PCB-153 in the central Yellow Sea in spring, which were likely
caused by low particle mass in the same location (Figs. S1a1–a4).
Particle-bound PCB-153 had the highest surface concentration in spring
and the lowest surface concentration in summer. The latter was related
to the weak sorption of dissolved PCB-153 by particles in summer, which
was caused by the high water temperature (Fig. 3 b1–b4).
We illustrated the vertical distribution of PCB-153 concentrations
along a section at 124º E (dashed line in Fig. 1b) to examine its rela­tionship with the Yellow Sea Cold Water Mass (YSCWM). The dissolved
PCB-153 concentration showed little vertical change in winter butchanged significantly in other seasons (Fig. 4 a1–a4). From spring toautumn, the concentration was lower in the middle layer than at the sea
surface and bottom (Fig. 4 a2–a4). The presence of a high phytoplankton
concentration in the subsurface during this period increased dissolved
PCB-153 adsorption onto biological particles (Fig. S1b2–b4). Moreover,dissolved PCB-153 accumulated at the sea bottom in the central Yellow

3.2. Temporal and spatial variations of BDE-209
BDE-209 also showed clear seasonal and spatial variations. At the sea
surface, the dissolved BDE-209 concentration was higher in summer andautumn than in winter and spring (Fig. 5 a1–a4), indicating a feature
different from its atmospheric input flux. During winter, there were noevident high-concentration patches in the central Yellow Sea. The sur­face particle-bound BDE-209 concentration changed slightly fromautumn to spring and was the lowest in summer.
The vertical patterns of dissolved BDE-209 concentration changed
significantly in different seasons; the concentration was vertically ho­
mogenous in winter and increased in the order of the middle layer, sea
surface, and sea bottom in spring whereas in the order of middle layer,
sea bottom, and sea surface in summer and autumn (Fig. 6 a1–a4).
Notably, dissolved BDE-209 showed the highest concentration at the sea
surface during summer. The particle-bound BDE-209 vertical
4

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Journal of Hazardous Materials 469 (2024) 134051

Fig. 3. Horizontal distributions of (a1–a4) surface dissolved and (b1–b4) surface particle-bound PCB-153 concentrations. The dashed square in (a1) denotes thecentral Yellow Sea area used in this study.

distribution changed slightly from autumn to spring, was approximately
the same at all depths in winter and was the highest in the middle layerin summer (Fig. 6 b1–b4).
As shown in Fig. S6, the surface BDE-209 mass concentration in theparticles was higher in winter than in the other seasons. The BDE-209
mass concentration was vertically homogenous in winter. From spring

to autumn, it was higher at the sea bottom than at other depths. More­over, there was almost no seasonal variation in the mass concentrationof BDE-209 in the bottom water.
Regarding spatial mean BDE-209 concentrations in the central Yel­low Sea, dissolved BDE-209 was the highest in July and lowest in March,and particle-bound BDE-209 was the highest in winter and lowest in5

A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 4. Vertical distributions of (a1–a4) dissolved and (b1–b4) particle-bound PCB-153 concentrations along section 124o E.

summer (Fig. S7). The dissolved and particle-bound concentration
ranges were 590–755 pgm− 3 and 102–288 pgm− 3 , respectively. The
spatial mean mass concentration of BDE-209 showed minimal seasonalvariation (Fig. S7) because of the inverse seasonal patterns of the watertemperature and dissolved BDE-209 concentration. The BDE-209 massconcentration decreased with water temperature, but increased with its
dissolved concentration, which is further discussed in Section 4.3.

are analyzed for dissolved and particle-bound phases respectively, to get
the respective reasons for seasonal variations of dissolved and particlebound concentrations. Notably, although the adsorption and reminer­
alization are the same for both dissolved and particle-bound phases, theeffects of these two processes on dissolved and particle-bound concen­
tration are inverse.
At the air–sea interface, the wet and dry deposition fluxes weregreater than the air–sea diffusive flux and became the main source ofdissolved PCB-153 (Fig. 7a). The air–sea diffusive flux presented the
opposite sign in summer and winter, indicating a minimal contribution
to net PCB-153 input into the sea over a one-year timescale. In addition
to external sources, biogeochemical processes in seawater can affectdissolved PCB-153 concentrations. Detritus decomposition acted as thesource for dissolved PCB-153, leading to an increase in the dissolved
concentration. Adsorption by phytoplankton and detritus are the sinkterms of dissolved PCB-153 and lead to a decrease in the dissolved
concentration. As shown in Fig. 7a, the absolute values of the PCB-153
biogeochemical fluxes have similar seasonal patterns and are generally
higher in spring than in other seasons.
In the central Yellow Sea, the source terms of particle-bound PCB153 are the adsorption of dissolved PCB-153 by phytoplankton anddetritus, whereas the decomposition of detritus and the sinking of

4. Discussion
4.1. Reasons for different seasonal variations of two POPs
Based on the results described in Sections 3.1 and 3.2, we identified
two major differences in PCB-153 and BDE-209 spatiotemporal varia­
tions. First, the seasonal variations in the dissolved concentrations
differed, whereas the seasonal variations in the gaseous and aerosolbound PCB-153 and BDE-209 concentrations in the atmosphere weresimilar. Second, the vertical distributions of their dissolved concentra­
tions in summer also differed.
To understand the seasonal variations in each form of PCB-153 andBDE-209, we integrated their fluxes over the entire central Yellow Sea
for source and sink terms. In this subsection, the biogeochemical fluxes
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Journal of Hazardous Materials 469 (2024) 134051

Fig. 5. Horizontal distributions of (a1–a4) surface dissolved and (b1–b4) surface particle-bound BDE-209 concentrations.

detritus to the sea bottom reduce the particle-bound PCB-153 concen­
tration, and therefore are its sink terms (Fig. 7b).
The seasonal variation of dissolved PCB-153 was mainly affected by
the dry/wet deposition fluxes from the atmosphere and sorption by
particles, which was related to the aerosol-bound PCB-153 concentra­tion [Eqs. (S10)-(S11)] and the particle mass in seawater as well as watertemperature [Eq. (7); Eqs. (S18)-(S21)], respectively. The dry and wet

deposition fluxes were greater than the transfer flux between dissolved
and particle-bound PCB-153. As a result, the net flux (the difference
between source and fate fluxes) of dissolved PCB-153 was positive from
November to June (Fig. 7a), which led to the peak dissolved PCB-153
concentration in June (Fig. 7a). The seasonal variation of PCB-153
particle-bound concentration was influenced by the sorption of phyto­plankton. The transfer flux between the dissolved and particle-bound7

A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 6. Vertical distributions of (a1–a4) dissolved and (b1–b4) particle-bound BDE-209 concentrations along section 124o E.

PCB-153 was greater in winter and spring (Fig. 7b) owing to the low
water temperature and high phytoplankton biomass [Eq. (7); Eqs. (S18)(S21)], respectively, which resulted in the appearance of a peak particlebound PCB-153 concentration in spring (Fig. 7b). The PCB-153 transport
fluxes through the lateral boundaries were minimal compared with the
PCB-153 input and biogeochemical fluxes (Fig. 9); therefore, they were
not included in net flux calculations.
Regarding BDE-209, the air–sea diffusive flux remained positive
throughout the year and became the main source of dissolved BDE-209(Fig. 8a). It was only in February and March that the BDE-209 dry andwet deposition fluxes were greater than the air–sea diffusive flux
(Fig. 8a). Considering the source and sink terms of dissolved andparticle-bound BDE-209 were the same as those of PCB-153, the sea­sonal variation in BDE-209 biogeochemical fluxes showed little differ­ence from those of PCB-153. The net flux of dissolved BDE-209 was
positive from April to July (Fig. 8a) owing to the large air–sea exchange
flux and minimal particle adsorption flux during this period, which led
to the maximum and minimum dissolved BDE-209 concentrations inJuly and March, respectively (Fig. 8a). The particle-bound BDE-209
concentration reached its peak in spring (Fig. 8b) because the transfer
flux between dissolved and particle-bound BDE-209 was greater inwinter and spring (Fig. 8b), similar to that of PCB-153.

4.2. Reasons for different spatial patterns of two POPs
As mentioned in Section 4.1, the vertical distributions of dissolved
PCB-153 and BDE-209 concentrations were different in summer. To
investigate the reasons for the different vertical patterns, we separated
the water column into two layers and calculated the budgets of POPs forthe upper and lower layers in the study area (central Yellow Sea) fromMarch to August. The boundary between the upper and lower layers was
set at half the water column depth. We selected the period from March toAugust because the dissolved PCB-153 and BDE-209 concentrations
were all vertically homogenous in March, but gradually presented ver­tical variations from March to August.
The PCB-153 and BDE-209 budgets in the upper and lower layers∫dwere integrated using the following equation: SF= dse ∭ F(x, y,z)dxdydzdt, where ds and de denote the first day of March and the last dayof August, respectively; F(gm− 3 day− 1 ) denotes the flux of one
biogeochemical process; dx, dy, and dz are the unit distances (m) inthree directions; and dt is the unit time interval (day). To compare theimportance of the biogeochemical cycles of PCB-153 and BDE-209, theflux of each process was normalized by dividing the increase in PCB-153
and BDE-209 masses in the lower layer between those of the first day ofMarch and the last day of August.

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Journal of Hazardous Materials 469 (2024) 134051

Fig. 7. Fluxes in figure are the integral in whole space of study area and in one day. Concentrations in figure is the mean concentration in the whole space of study
area and during one day. (a) Time series of source and fate fluxes and dissolved PCB-153 concentrations in the seawater. The diffusive air–sea and deposition fluxes
are represented by the red and blue solid lines, respectively, with a scale shown on left axis. The biogeochemical fluxes are represented by the dashed lines including
phytoplankton adsorption (blue), detritus adsorption (green), and detritus remineralization (red), with a scale shown on the right axis. The net flux of dissolved PCB153 is represented by the solid black line, with a scale shown on the left axis. The spatially averaged concentration of dissolved PCB-153 is represented by the dotdashed line, with a scale shown on the right axis. Positive fluxes are defined to increase the dissolved PCB-153. The concentration shown in the figure is one tenth ofthe dissolved PCB-153 concentration to match the left axis scale and the unit is pgm− 3 . (b) Time series of source and fate fluxes and particle-bound PCB-153 con­centrations in the seawater. The biochemical fluxes are represented by the dashed lines including phytoplankton adsorption (blue), detritus adsorption (green),
detritus remineralization, and detritus sinking (pink), with a scale on the right axis. The net flux of particle-bound PCB-153 is represented by the solid black line, witha scale shown on the left axis. Positive fluxes defined to increase the particle-bound PCB-153. “PHY” denotes phytoplankton; “DET” denotes detritus. The spatiallyaveraged concentration of particle-bound PCB-153 is represented by the dot-dashed line, with a scale shown on the right axis. The concentration shown in the figure
is one tenth of the particle-bound PCB-153 concentration to match the right axis scale and the unit is pgm− 3 .

From March to August, the mass of dissolved PCB-153 decreased in
the upper layer and increased in the lower layer (Fig. 9a), indicating theaccumulation of the dissolved PCB-153 in the lower layer (Fig. 4). As
shown in Fig. 9a, almost all PCB-153 from the atmosphere was absorbedby phytoplankton, whose mortality converted phytoplankton-boundPCB-153 into detritus-bound PCB-153. The sinking of detritus-boundPCB-153 transported PCB-153 into the lower layer, where detritus
decomposition returned the particle-bound PCB-153 to the dissolved
form. To understand the transfer of dissolved PCB-153 to the particles in
the upper layer, the transformation rate (Rtrans) was defined using Eq.
(6).
Rtrans =Ncwp + Ncwd − Nmin
Nas + Ndep + Ndif + Nadvof the dissolved PCB-153 entering the upper layer from the boundaries
and even background dissolved PCB-153 at the start time of integration
will be transferred to the particle PCB-153 and then transported from theupper layer to the lower layer by particle sinking. If Rtrans is < 1.0, theresidual dissolved PCB-153 flux from the boundaries will increase thedissolved PCB-153 mass in the upper layer. Using the values given in
Fig. 9a for the upper layer, Rtrans was calculated to be > 1.0 for PCB-153,
indicating that almost all the dissolved PCB which entered the upper
layer through the boundaries from March to August finally dropped into
the lower layer by sinking with detritus.
From March to August, the BDE-209 mass increased in both theupper and lower layers, and increased more in the upper layer than in
the lower layer (Fig. 9b), which is consistent with the results shown in
Fig. 6. Compared to PCB-153, less dissolved BDE-209 was adsorbed onto
the particles, and its transformation rate from the upper to the lower
layer was 0.76, resulting in dissolved BDE-209 accumulation at the sea
surface.
The differences between the PCB-153 and BDE-209 fluxes weremainly caused by their different biochemical properties, including theirfugacity gradient from the atmosphere to seawater and the portion be­
tween water and particles. These biochemical properties were affectedby two parameters, H′ and bioconcentration factors (BCFs), respectively.
The BCF was defined as the ratio of POP mass concentration in particles
to its dissolved concentration at equilibrium ([8]). The BCF is related towater temperature and is calculated by Eq. (7).
The H′ value of PCB-153 was significantly greater than that of BDE-

(6)

Here, Ncwp and Ncwd are the POP adsorption fluxes by phytoplankton
and detritus, respectively; Nmin is the POP flux due to detritus decom­
position; Nas is the air–sea diffusive flux, which is positive when the flux
flowed from the atmosphere to the sea; Ndep is the POP deposition flux
from the atmosphere to the sea; and Ndif and Nadv are the dissolved POP
fluxes through vertical diffusion and vertical advection respectively at
the interface between the upper and lower layers, whose values are
positive for upward flux. The fluxes in Eq. (6), were integrated fromMarch to August in the upper layer.
The transformation rate represents the relative ratio of dissolved
PCB-153 owing to absorption by the particles to dissolved PCB-153
supplied from the boundaries in the upper layer. If Rtrans is > 1.0, all
9A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 8. The same as Fig. 7 but for decabromodiphenyl ether (BDE-209).

209, resulting in a smaller PCB-153 fugacity gradient from the atmo­
sphere to the sea. The air–sea exchange of POPs included volatilizationfrom sea to atmosphere and gaseous deposition from atmosphere to sea.
The fluxes of these two processes were shown in Fig. 10. The magnitudesof the volatilization flux and gaseous deposition flux of PCB-153 wereclose, and the volatilization flux was even larger than the gaseousdeposition flux in summer, indicating the high volatilization of PCB-153.
However, the volatilization flux of BDE-209 was much smaller than thegaseous deposition flux all year round. As a result, the BDE-209 accu­
mulated in the surface layer when the seawater was stratified in sum­mer, but the PCB-153 was not due to the high volatilization. On the other
hand, the BCF of PCB-153 was 1.24 times greater than that of BDE-209,
leading to greater PCB-153 fugacity gradient from seawater to the par­ticles; thus, PCB-153 was more easily transported from the upper to thelower layer along with these particles.
Diffusive air–sea exchange and dry/wet deposition were the sourcesof PCB-153 and BDE-209. Regarding PCB-153, the diffusive air–sea ex­surface due to weak material exchange in YSCWM, large transport fromthe upper layer to the lower layer, and high volatilization; dissolved
BDE-209 accumulated at the sea surface during summer owing to water
stratification, high gaseous deposition, and weak transport from the sea
surface to the sea bottom.
BCF T = BCF 298 × exp[

ΔHs
1
1
)]
×( −
T 298
R

(7)

Here, BCFT (m3 kg− 1 ) is the BCF value at certain water temperature;
BCF298 (m3 kg− 1 ) is the BCF value at 298 K; ΔHs (kJmol− 1 ) is theenthalpy of sorption by phytoplankton; R(kJmol− 1 K− 1 ) is the gas
constant; T(K) is water temperature ([8]). The values of BCF298 , ΔHs ,and R are listed in Table S1.
According to the model results, the concentrations of POPs were
spatially heterogeneous and the spatial distributions varied largely withseasons. Therefore, the multi-box models were unable to simulate thespatial variations of POPs in the complex marine environment, although
they were usually used to calculate the exchange fluxes of POPs between
different mediums [38]. In addition, the model with spatial resolution
can also be used to calculate the exchange fluxes of POPs at the air–sea
interface by treating the sea as a box, which was the integral of POP flux
at each grid point.

change was weaker due to a large H′, and its dissolved concentration was
mainly related to the wet/dry deposition flux from the atmosphere. The
PCB-153 atmospheric deposition flux was less than the PCB-153 sinking
flux from the upper to the lower layers during spring and summer(Fig. 9a), indicating that the dissolved PCB-153 concentration decreasedin the upper layer. The dissolved PCB-153 concentration increased
during spring and summer in the lower layer (Fig. 9a); thus, dissolved
PCB-153 accumulation at the sea bottom occurred during summer.
Regarding BDE-209, atmospheric input through the diffusive exchange

4.3. POP mass concentrations in particles
POP absorption by particles is a reversible process that includes POP
uptake and depuration in the particle matrix, as well as their adsorption
and desorption on the particle surface. The reversible process can reacha balance, when the uptake flux equals the depuration flux and theadsorption flux equals the desorption flux, that is, when the Fcwpm , Fcwps ,Fcwdm , and Fcwds values in Equations (S18)–(S21) are equal to zero. Ac­
cording to the definition of BCF in Section 4.2, the balanced mass con­centration of POPs in particles is the product of dissolved concentration
and BCF.
The mass concentration of POPs in the particles increased with theconcentration of dissolved POPs and their BCFs. The POP BCFs were

was greater due to its small H′ value, and the diffusive exchange flux was
higher than the dry/wet deposition flux (Fig. 9b). The BDE-209 input
flux from the atmosphere was the highest in summer, which was related
to the seasonal patterns of its gaseous concentration (Fig. S3) anddiffusive air–sea flux (Fig. 8a and Fig. 10b). In addition, the BDE-209
fugacity gradient from seawater to particles was relatively weakcompared to that of PCB-153, which led to weak transport of dissolved
BDE-209 from the upper layer to the lower layer. Therefore, dissolved
PCB-153 concentration was much higher at the sea bottom than at the10

A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 9. Normalized budget of (a) PCB-153 and (b) BDE-209 for March to August in the upper and lower layers of central Yellow Sea (square area shown in
Fig. 3a1).ΔCw , ΔCwp , and ΔCwd are the normalized variations of dissolved, phytoplankton-bound, and detritus-bound POP concentrations, respectively. Each arrow
represents the normalized flux of one biogeochemical process, and the horizontal arrows on the right side of the box denote the normalized advection and diffusion
fluxes through lateral boundaries; “YSCWM” denotes Yellow Sea Cold Water Mass.

11

A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 10. (a) Time series of volatilization (black line) and gaseous deposition (blue line) fluxes of PCB-153. (b) Time series of volatilization (black line with a scale on
the left axis) and gaseous deposition (blue line with a scale on the right axis) fluxes of BDE-209.

affected by water temperature, as shown in Figs. S8 and S9, low watertemperatures induced greater BCFs ([8]). Consequently, the mass con­centration of POPs was also affected by the water temperature. Based on
their dissolved concentrations and BCFs, the balanced mass concentra­
tions of PCB-153 and BDE-209 were first determined, and the ratio of
their mass concentrations to the balanced mass concentrations was thencalculated. This ratio indicates the proximity of the particle-bound POPs
to the absorption balance (Figs. 11 and 12).

Although there were some spatial variations in the ratio of the massconcentration to the balanced mass concentration of PCB-153, forexample, close to 1 in the subsurface layer in April (Fig. 11), this ratio
was approximately 0.85 and varied little among the seasons. This meansthat the particle-bound PCB-153 was still far from the absorption bal­ance, and consequently, its concentration could increase with the dis­solved concentration. In other words, the dissolved PCB-153
concentration increased at the sea surface and sea bottom because of

Fig. 11. (a1–a4) Horizontal and (b1–b4) vertical distributions for the ratio of simulated PCB-153 mass concentration to its balanced mass concentration.
12

A. Wang et al.

Journal of Hazardous Materials 469 (2024) 134051

Fig. 12. (a1–a4) Horizontal and (b1–b4) vertical distributions for the ratio of simulated BDE-209 mass concentration to its balanced mass concentration.

input from the atmosphere and detritus remineralization, respectively,
and the mass concentration could also increase in these regions.
On the other hand, although the seasonal and spatial variations of
the aforementioned ratio for BDE-209 were similar to those for PCB-153,
the value for BDE-209 was greater than that of PCB-153, indicating thatthe mass concentration of BDE-209 in the particles was closer to theabsorption balance than that of PCB-153.
The sorption of POPs by particles includes uptake to the matrix andadsorption on the surface of particles. Therefore, the mass concentrationis composed of mass concentration in the matrix and on the surface andthese two mass concentrations satisfied Eqs. (8)–(9).
dCpm= ku × Cw − kd × Cpm − kPC × Cpm
dt

(8)

dCps
= ksu × Cw − ksd × Cps − kPC × Cps
dt(9)

temporal change rate of particles in the model is much smaller than the
depuration (0.8 day− 1 for PCB-153; 1.49 day− 1 for BDE-209) anddesorption (7.8 day− 1 for PCB-153; 8.1 day− 1 for BDE-209) rates and thetime for water-particle equilibrium depends mainly on the depurationand desorption rates. This was the cause that the quasi-constant ratio of
simulated mass concentration to the balanced mass concentration ap­
pears in time and space (Fig. 11). In addition, based on the Eqs. (10)(11), the time to get water-particle equilibrium will be decreased if thedepuration and desorption rates are increased. So the mass concentra­tion of BDE-209 in the particles was closer to the balanced state than thatof PCB-153.
As shown by the Eqs. (10)-(11), the sorption of POPs by particles inthe seawater was dynamic and nonequilibrium, and was affected by thesorption rates and variation in the particle mass. The equilibrium par­titioning was assumed in some contaminant fate models, which focusedon the steady-state mass balance of POPs in the environment [46,7].
Compared the approach assuming equilibrium partitioning, the model
used in the paper was more accurate to simulate the dynamic transform
processes of POPs in seawater.

Here t (s) denotes time; Cpm and Cps (ngg− 1 ) are the mass concentra­tion of POPs in particle matrix and surface, respectively; kPC (day− 1 ) is thechange rate of particles (positive for particle increase and negative forparticle decrease), and the ku , kd , ksu , and ksd definitions are listed inTable S1.
Assuming that the dissolved POP concentrations are constant andusing an initial condition of zero for the mass concentration, the solu­
tions of Eqs. (8)–(9) are as follows:
Cpm =ku × Cw(1 − e− (kd +kPC )×t )
kd

(10)

Cps =

ksu × Cw
(1 − e− (ksd +kPC )×t )
ksd

(11)

5. Conclusions
In this study, we selected the central Yellow Sea as the target area toinvestigate the effects of physical and biological conditions in seawateron POP behavior. Notably, PCB-153 and BDE-209 were chosen as thetarget contaminants for a comparative analysis of POPs with different
properties. The results indicated that the spatial mean concentration ofdissolved PCB-153 was the highest in June and lowest in October,whereas that of particle-bound PCB-153 was higher in spring than in theother seasons, which was affected by the particle biomass and wet/dry
deposition flux from the atmosphere. Dissolved PCB-153 accumulated at
the sea bottom during summer because the sinking flux of PCB-153
owing to the particles was almost the same as the wet/dry deposition
flux from the atmosphere. Regarding BDE-209, its spatial mean dis­solved concentration was the highest in July and lowest in March,whereas that of the particle-bound form was higher in spring, affected byparticle biomass and the gaseous deposition flux. However, dissolved
BDE-209 accumulated at the sea surface during summer because thesinking process of BDE-209 was weaker than the gaseous deposition

Based on the functions of Cpm and Cps , it can be found that the time to
get water-particle equilibrium is related to the sum of depuration andparticle change rates [Eq. (10)] or the sum of desorption and particle
change rates [Eq. (11)]. In the coupled model, the monthly mean sat­
ellite data of chlorophyll was used to represent the seasonal variation ofphytoplankton and such monthly data cannot represent the real rate ofspring bloom in the phytoplankton. The absolute change rate of particles
was no more than 0.05 day− 1 according to Fig. S1. Therefore, the13

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Journal of Hazardous Materials 469 (2024) 134051

Appendix A. Supporting information

during this period.
The different vertical patterns of PCB-153 and BDE-209 were causedby the larger H′ values and BCFs of PCB-153, which led to higher vola­
tilization and larger sorption by particles of PCB-153, respectively. As aresult, the dissolved PCB-153 accumulated at the sea bottom while thedissolved BDE-209 accumulated at the sea surface. The time to getwater-particle equilibrium was related to the sorption rates of POPs byparticles and the particle change rate. The effect of sorption by particles
on the air–sea diffusive exchange was more obvious for PCB-153 thanBDE-209, due to the higher volatilization of PCB-153.
The ocean is an important pool of POPs originating from land and thebottom cold water in the ocean can capture POPs for weak material
exchange with the surrounding water. In addition, some bottom cold
waters in ocean are the farms for cold-water fish. The quantified relation
between capture by cold water and POP biochemical features wasindicated in this work, which is significant to select POPs insulated frombottom cold water as an industrial substitute to protect the fish farm.
The surface phytoplankton concentrations used to drive the POP module
were the monthly mean satellite data, which were not able to simulate
the biological dilution of POP mass concentration in particles caused by
the short-term spring bloom. Therefore, a dynamic ecosystem module
should be coupled to the calculation of the POP module shortly.

Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.jhazmat.2024.134051.
References[1] Alekseenko, E., Thouvenin, B., Tronczyński, J., Carlotti, F., Garreau, P., Tixier, C.,et al., 2018. Modeling of PCB trophic transfer in the Gulf of Lions; 3D coupledmodel application. Mar Pollut Bull 128, 140–155. https://doi.org/10.1016/j.
marpolbul.2018.01.008.
[2] Bamford, H.A., Poster, D.L., Baker, J.E., 2000. Henry’s law constants ofpolychlorinated biphenyl congeners and their variation with temperature. J Chem
Eng Data 45, 1069–1074. https://doi.org/10.1021/je0000266.
[3] Berrojalbiz, N., Dachs, J., Del Vento, S., Ojeda, M.J., Valle, M.C., CastroJiménez, J., et al., 2011. Persistent organic pollutants in Mediterranean seawaterand processes affecting their accumulation in plankton. Environ Sci Technol 45,
4315–4322. https://doi.org/10.1021/es103742w.
[4] Burdige, D.J., 2007. Preservation of organic matter in marine sediments: controls,mechanisms, and an imbalance in sediment organic carbon budgets? Chem Rev
107, 467–485. https://doi.org/10.1021/cr050347q.
[5] Cetin, B., Odabasi, M., 2005. Measurement of Henry’s law constants of seven
polybrominated diphenyl ether (PBDE) congeners as a function of temperature.
Atmos Environ 39, 5273–5280. https://doi.org/10.1016/j.atmosenv.2005.05.029.
[6] Chen, L., Yang, Y., Chen, J., Gao, S., Qi, S., Sun, C., et al., 2017. Spatial-temporalvariability and transportation mechanism of polychlorinated biphenyls in the
Yangtze River Estuary. Sci Total Environ 598, 12–20. https://doi.org/10.1016/j.
scitotenv.2017.04.069.
[7] Choi, S.D., Wania, F., 2011. On the reversibility of environmental contamination
with persistent organic pollutants. Environ Sci Technol 45 (20), 8834–8841.
https://doi.org/10.1021/es2017544.
[8] Del Vento, S., Dachs, J., 2002. Prediction of uptake dynamics of persistent organic
pollutants by bacteria and phytoplankton. Environ Toxicol Chem 21, 2099–2107.
https://doi.org/10.1002/etc.5620211013.
[9] Frouin, H., Dangerfield, N., Macdonald, R.W., Galbraith, M., Crewe, N., Shaw, P.,
et al., 2013. Partitioning and bioaccumulation of pcbs and pbdes in marine
plankton from the strait of Georgia, British Columbia, Canada. Prog Oceanogr 115,
65–75. https://doi.org/10.1016/j.pocean.2013.05.023.
[10] Galbán-Malagón, C., Berrojalbiz, N., Ojeda, M.J., Dachs, J., 2012. The oceanic
biological pump modulates the atmospheric transport of persistent organic
pollutants to the Arctic. Nat Commun 3, 862–869. https://doi.org/10.1038/
ncomms1858.
[11] Galbán-Malagón, C.J., Berrojalbiz, N., Gioia, R., Dachs, J., 2013. The “degradative”
and “biological” pumps controls on the atmospheric deposition and sequestrationof hexachlorocyclohexanes and hexachlorobenzene in the North Atlantic andArctic Oceans. Environ Sci Technol 47, 7195–7203. https://doi.org/10.1021/
es4011256.
[12] González-Gaya, B., Casal, P., Jurado, E., Dachs, J., Jiménez, B., 2019. Verticaltransport and sinks of perfluoroalkyl substances in the global open ocean. Environ
Sci: Process Impacts 21 (11), 1957–1969. https://doi.org/10.1039/c9em00266a.
[13] González-Gaya, B., Fernández-Pinos, M.C., Morales, L., Méjanelle, L., Abad, E.,
Piña, B., et al., 2016. High atmosphere-ocean exchange of semivolatile aromatichydrocarbons. Nat Geosci 9 (6), 438–442. https://doi.org/10.1038/ngeo2714.
[14] Guo, J., Yuan, H., Song, J., Li, X., Duan, L., 2020. Hypoxia, acidification andnutrient accumulation in the Yellow Sea Cold Water of the South Yellow Sea. Sci
Total Environ 745, 141050. https://doi.org/10.1016/j.scitotenv.2020.141050.
[15] Guo, X., Hukuda, H., Miyazawa, Y., Yamagata, T., 2003. A triply nested ocean
model for simulating the Kuroshio - Roles of horizontal resolution on JEBAR.
J Phys Oceanogr 33, 146–169. https://doi.org/10.1175/1520-0485.
[16] Hao, Z., Xu, H., Feng, Z., Zhang, C., Zhou, X., Wang, Z., et al., 2021. Spatial
distribution, deposition flux, and environmental impact of typical persistentorganic pollutants in surficial sediments in the Eastern China Marginal Seas
(ECMSs). J Hazard Mater 407, 124343. https://doi.org/10.1016/j.
jhazmat.2020.124343.
[17] Hoondert, R.P.J., Van Den Brink, N.W., Van Den Heuvel-Greve, M.J., Ragas, A.M.
J., Jan Hendriks, A., 2020. Implications of trophic variability for modelingbiomagnification of POPs in marine food webs in the svalbard archipelago. Environ
Sci Technol 54, 4026–4035. https://doi.org/10.1021/acs.est.9b06666.
[18] Hsu, P.C., Lee, H.J., Zheng, Q., Lai, J.W., Su, F.C., Ho, C.R., 2020. Tide-induced
periodic sea surface temperature drops in the coral reef area of Nanwan Bay,Southern Taiwan. J Geophys Res: Oceans. https://doi.org/10.1029/
2019JC015226.
[19] Jiang, S., Wan, M., Lin, K., Chen, Y., Wang, R., Tan, L., et al., 2024. Spatiotemporal
distribution, source analysis and ecological risk assessment of polychlorinatedbiphenyls (PCBs) in the Bohai Bay, China. Mar Pollut Bull 198. https://doi.org/
10.1016/j.marpolbul.2023.115780.
[20] Ju, T., Ge, W., Jiang, T., Chai, C., 2016. Polybrominated diphenyl ethers indissolved and suspended phases of seawater and in surface sediment from Jiaozhou
Bay, North China. Sci Total Environ 557–558, 571–578. https://doi.org/10.1016/j.
scitotenv.2016.03.013.
[21] Kaiser, D., Hand, I., Unger, D., Schulz-Bull, D.E., Waniek, J.J., 2015. Organic
pollutants in the central and coastal Beibu Gulf, South China Sea. Mar Pollut Bull
101, 972–985. https://doi.org/10.1016/j.marpolbul.2015.10.023.

Environmental Implication
Persistent organic pollutants (POPs) are easy to bioconcentrate andthreaten human health due to their high toxicity. The ocean is important
pool of POPs originating from land and the bottom cold water in ocean
can capture POPs for weak material exchange between cold water andsurrounding water. The capture process is related to POP biochemical
features. Some POPs accumulate in the bottom cold water but some
don’t, and the prediction for POP vertical patterns by a model is useful tochoose the latter POPs as industrial substitute of the former, for thebottom cold water is sometimes the fish farm.
CRediT authorship contribution statement
Jianhui TANG: Data curation, Formal analysis. Jie SHI: Data cura­tion, Methodology. Xiaokun DING: Data curation, Investigation,
Methodology. Xinyu GUO: Conceptualization, Funding acquisition,
Investigation, Supervision, Validation, Writing – review & editing. Aobo
WANG: Conceptualization, Data curation, Formal analysis, Investiga­
tion, Methodology, Software, Visualization, Writing – original draft,
Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The authors do not have permission to share data.
Acknowledgements
This study is funded by the National Nature Science Foundation ofChina (41976013), a Grant-in-Aid for Scientific Research (MEXT
KAKENHI, Grant 20KK0239), and Sasakawa fund of the Japan Science
Society (No. 2021-2028). This work is also supported by the Ministry of
Education, Culture, Sports, Science and Technology, Japan (MEXT) to a
project on Joint Usage/Research Center–Leading Academia in Marine
and Environment Pollution Research (LaMer).

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A. Wang et al.

[37] Su, B., Pahlow, M., Oschlies, A., 2016. Box-modelling of the impacts of atmosphericnitrogen deposition and benthic remineralisation on the nitrogen cycle of theeastern tropical South Pacific. Biogeosciences 13, 4985–5001. https://doi.org/
10.5194/bg-13-4985-2016.
[38] Sun, X., Ng, C.A., Small, M.J., 2018. Modeling the impact of biota on
polychlorinated biphenyls (PCBs) fate and transport in Lake Ontario using a
population-based multi-compartment fugacity approach. Environ Pollut 241,
720–729. https://doi.org/10.1016/j.envpol.2018.05.068.
[39] Wagner, C.C., Amos, H.M., Thackray, C.P., Zhang, Y., Lundgren, E.W., Forget, G.,
et al., 2019. A global 3-D ocean model for PCBs: benchmark compounds forunderstanding the impacts of global change on neutral persistent organic
pollutants. Glob Biogeochem Cycles 33, 469–481. https://doi.org/10.1029/
2018GB006018.
[40] Wallmann, K., 2010. Phosphorus imbalance in the global ocean? Glob Biogeochem
Cycles 24, 1–12. https://doi.org/10.1029/2009GB003643.
[41] Wang, A., Guo, X., Shi, J., Luo, C., Gao, H., 2019. A simulation of the seasonalvariation of decabromodiphenyl ether in a bay adjacent to the Yellow Sea. Sci Total
Environ 664, 522–535. https://doi.org/10.1016/j.scitotenv.2019.01.385.
[42] Wang, G., Peng, J., Yang, D., Zhang, D., Li, X., 2015. Current levels, composition
profiles, source identification and potentially ecological risks of polychlorinatedbiphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) in the surface
sediments from Bohai Sea. Mar Pollut Bull 101, 834–844. https://doi.org/
10.1016/j.marpolbul.2015.11.028.
[43] Wang, G., Peng, J., Xu, X., Zhang, D., Li, X., 2016. Polybrominated diphenyl ethers
in sediments from the Southern Yellow Sea: concentration, composition profile,source identification and mass inventory. Chemosphere 144, 2097–2105. https://doi.org/10.1016/j.chemosphere.2015.10.088.
[44] Wang, Q., Guo, X., Takeoka, H., 2008. Seasonal variations of the Yellow River
plume in the Bohai Sea: a model study. J Geophys Res Ocean 113, 1–14. https://doi.org/10.1029/2007JC004555.
[45] Wang, Q., Tsui, M.M.P., Ruan, Y., Lin, H., Zhao, Z., Ku, J.P.H., et al., 2019.
Occurrence and distribution of per- and polyfluoroalkyl substances (PFASs) in theseawater and sediment of the South China sea coastal region. Chemosphere 231,
468–477. https://doi.org/10.1016/j.chemosphere.2019.05.162.
[46] Wania, F., Breivik, K., Persson, N.J., McLachlan, M.S., 2006. CoZMo-POP 2 - A
fugacity-based dynamic multi-compartmental mass balance model of the fate ofpersistent organic pollutants. Environ Model Softw 21 (6), 868–884. https://doi.
org/10.1016/j.envsoft.2005.04.003.
[47] Wu, Z., Lin, T., Guo, T., Li, Y., Li, Z., Guo, Z., 2020. Occurrence, air-sea exchange,and gas-particle partitioning of atmospheric polybrominated diphenyl ethers from
East Asia to the Northwest Pacific Ocean. Chemosphere 240, 124933. https://doi.
org/10.1016/j.chemosphere.2019.124933.
[48] Xu, Y., Tian, C., Wang, X., Ma, J., Tang, J., Chen, Y., et al., 2018. An improved
inventory of polychlorinated biphenyls in China: a case study on PCB-153. Atmos
Environ 183, 40–48. https://doi.org/10.1016/j.atmosenv.2018.04.013.
[49] Yang, H.W., Cho, Y.K., Seo, G.H., You, S.H., Seo, J.W., 2014. Interannual variationof the southern limit in the Yellow Sea Bottom Cold Water and its causes. J Mar
Syst 139, 119–127. https://doi.org/10.1016/j.jmarsys.2014.05.007.
[50] Zhang, Z.M., Zhang, H.H., Zou, Y.W., Yang, G.P., 2018. Distribution andecotoxicological state of phthalate esters in the sea-surface microlayer, seawaterand sediment of the Bohai Sea and the Yellow Sea. Environ Pollut 240, 235–247.
https://doi.org/10.1016/j.envpol.2018.04.056.
[51] Zhen, X., Li, Y., Tang, J., Wang, X., Liu, L., Zhong, M., et al., 2021.
Decabromodiphenyl ether versus decabromodiphenyl ethane: source, fate, andinfluencing factors in a coastal sea nearing source region. Environ Sci Technol.
https://doi.org/10.1021/acs.est.0c08528.
[52] Zhong, Y., Qiao, L., Song, D., Ding, Y., Xu, J., Xue, W., et al., 2020. Impact of cold
water mass on suspended sediment transport in the South Yellow Sea. Mar Geol
428, 106244. https://doi.org/10.1016/j.margeo.2020.106244.

[22] Lammel, G., Meixner, F.X., Vrana, B., Efstathiou, C.I., Kohoutek, J., Kukučka, P.,
et al., 2016. Bidirectional air-sea exchange and accumulation of POPs (PAHs, PCBs,
OCPs and PBDEs) in the nocturnal marine boundary layer. Atmos Chem Phys 16(10), 6381–6393. https://doi.org/10.5194/acp-16-6381-2016.
[23] Lee, J., Pang, I.-C., Moon, J.-H., 2016. Contribution of the Yellow Sea bottom cold
water to the abnormal cooling of sea surface temperature in the summer of 2011.
J Geophys Res Ocean 121, 3777–3789.
[24] Li, Z., Fan, X., Mu, Y., Wang, L., Liang, J., Deng, L., 2020. Distribution
characteristics and risk assessment of polyhalogenated carbazoles in seawater of
the Yellow Sea. Mar Pollut Bull 161, 111656. https://doi.org/10.1016/j.
marpolbul.2020.111656.
[25] Liu, L., Zhen, X., Wang, X., Li, Y., Sun, X., Tang, J., 2020. Legacy and novel
halogenated flame retardants in seawater and atmosphere of the Bohai Sea: Spatial
trends, seasonal variations, and influencing factors. Water Res 184, 116117.
https://doi.org/10.1016/j.watres.2020.116117.
[26] Liu, S.M., Altabet, M.A., Zhao, L., Larkum, J., Song, G.D., Zhang, G.L., et al., 2017.
Tracing nitrogen biogeochemistry during the beginning of a spring phytoplankton
bloom in the Yellow Sea using coupled nitrate nitrogen and oxygen isotope ratios.
J Geophys Res Biogeosciences 122, 2490–2508. https://doi.org/10.1002/
2016JG003752.
[27] Lohmann, R., Belkin, I.M., 2014. Organic pollutants and ocean fronts across the
Atlantic Ocean: a review. Prog Oceanogr 128, 172–184. https://doi.org/10.1016/j.
pocean.2014.08.013.
[28] Ma, Y., Adelman, D.A., Bauerfeind, E., Cabrerizo, A., McDonough, C.A., Muir, D.,et al., 2018. Concentrations and water mass transport of legacy POPs in the Arctic
Ocean. Geophys Res Lett 45. https://doi.org/10.1029/2018GL078759, 12,97212,981.
[29] Mellor, G.L., Häkkinen, S.M., Ezer, T., Patchen, R.C., 2002. A generalization of asigma coordinate ocean model and an intercomparison of model vertical grids. In:
Pinardi, N., Woods, J. (Eds.), Ocean Forecasting: Conceptual Basis andApplications. Springer, Berlin, pp. 55–72. https://doi.org/10.1007/978-3-66222648-3_4.
[30] Meng, J., Hong, S., Wang, T., Li, Q., Yoon, S.J., Lu, Y., et al., 2017. Traditional and
new POPs in environments along the Bohai and Yellow Seas: an overview of China
and South Korea. Chemosphere 169, 503–515. https://doi.org/10.1016/j.
chemosphere.2016.11.108.
[31] Nizzetto, L., Gioia, R., Li, J., Borgå, K., Pomati, F., Bettinetti, R., et al., 2012.
Biological pump control of the fate and distribution of hydrophobic organic
pollutants in water and plankton. Environ Sci Technol 46 (6), 3204–3211. https://doi.org/10.1021/es204176q.
[32] O’Driscoll, K., Ilyina, T., Pohlmann, T., Mayer, B., Damm, P., 2011. Modelling thefate of persistent organic pollutants (POPs) in the North Sea system. Procedia
Environ Sci 6, 169–179. https://doi.org/10.1016/j.proenv.2011.05.018.
[33] O’Driscoll, Kieran, Mayer, B., Ilyina, T., Pohlmann, T., 2013. Modelling the cyclingof persistent organic pollutants (POPs) in the North Sea system: fluxes, loading,seasonality, trends. J Mar Syst 111–112, 69–82. https://doi.org/10.1016/j.
jmarsys.2012.09.011.
[34] Ono, J., Takahashi, D., Guo, X., Takahashi, S., Takeoka, H., 2012. A numerical
study on the seasonal variability of polychlorinated biphenyls from the atmospherein the East China Sea. Chemosphere 89, 389–397. https://doi.org/10.1016/j.
chemosphere.2012.05.049.
[35] Shi, J., Liu, Y., Mao, X., Guo, X., Wei, H., Gao, H., 2017. Interannual variation ofspring phytoplankton bloom and response to turbulent energy generated byatmospheric forcing in the central Southern Yellow Sea of China: satellite
observations and numerical model study. Cont Shelf Res 143, 257–270. https://doi.org/10.1016/j.csr.2016.06.008.
[36] Styles, R., Snedden, G.A., Jarrell Smith, S., Bryant, D.B., Boyd, B.M., Gailani, J.Z.,
et al., 2021. Seasonal controls on sediment delivery and hydrodynamics in avegetated tidally influenced interdistributary island. J Geophys Res Ocean. https://doi.org/10.1029/2020jc016146.

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