Science of the Total Environment 799 (2021) 149416

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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

Seasonal variations of nutrient concentrations and their ratios in thecentral Bohai Sea
Xiaokun Ding a,b, Xinyu Guo a,b,⁎, Huiwang Gao a,c, Jie Gao b, Jie Shi a,c, Xiaojie Yu a, Zhaosen Wu aaFrontiers Science Center for Deep Ocean Multispheres and Earth System, Key Laboratory of Marine Environment and Ecology, Ministry of Education of China, Ocean University of China, 238
Songling Road, Qingdao 266100, Chinab
Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japanc
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China

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• Apparent seasonal variations appearedin nutrient concentrations and the N/Pratio.
• A pelagic–benthic coupled model was
designed to quantify the inﬂuencemechanisms.
• Biological processes controlled the seasonal variation of nutrients.
• External inputs alleviated nutrient limitation in summer.
• Variation of N/P ratio of phytoplankton
uptakeaffected
thenitrateconcentration.

a r t i c l e

i n f oArticle history:
Received 5 April 2021
Received in revised form 17 July 2021
Accepted 29 July 2021
Available online 3 August 2021
Editor: Ouyang Wei

Keywords:
Seasonal variation
Nutrient ratio
External inputs
Biological process
Central Bohai Sea

a b s t r a c t
The Bohai Sea is a typical semi-enclosed sea in the northwest Paciﬁc, which is subject to serious eutrophication
due to human activities. Similar to hydrographic variables such as water temperature and salinity, the nutrient
concentrations and ratios in the Bohai Sea also exhibit seasonal variations. However, the effects of external inputs,
biological processes, and benthic processes on these seasonal variations have not been quantiﬁed to date. To address this issue, a physical–biological coupled model was developed to capture the seasonal nutrient cycling in
the central Bohai Sea. The simulation results revealed apparent seasonal variations in the concentrations of dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved silicate (DSi). The N/P ratio(as the molar ratio of DIN/DIP) also exhibited an apparent seasonal variation, with the maximum and minimum
values in surface water occurring in summer (>100) and winter (<30), respectively. The Si/N ratio (as the molar
ratio of DSi/DIN) was slightly higher in summer than in other seasons. The budget for three types of nutrient indicated that the biological processes determined the seasonal variations in nutrient concentrations and the N/Pratio. The external inputs of nutrients via river input, atmospheric deposition, and sediment release were probably important in summer when they could alleviate the reduced nutrient concentrations due to biological processes. To maintain a reasonable nitrogen budget, it was necessary to include benthic nitrogen loss, whichremoved a large amount of inorganic nitrogen in summer and autumn. In addition, the variation of N/P ratio of
phytoplankton uptake can reduce the ratio of DIN to DIP in surface water by ~20 in summer as compared tothe calculation with a ﬁxed N/P ratio (16:1) in phytoplankton uptake.
© 2021 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Laboratory of Marine Environment and Ecology, Ministry of Education of China,
Ocean University of China, 238 Songling Road, Qingdao 266100, China.
E-mail address: guoxinyu@sci.ehime-u.ac.jp (X. Guo).

https://doi.org/10.1016/j.scitotenv.2021.149416
0048-9697/© 2021 Elsevier B.V. All rights reserved.

X. Ding, X. Guo, H. Gao et al.

Science of the Total Environment 799 (2021) 149416

no literature addressing the quantitative impacts of external inputs
and local biological processes on the seasonal variations in nutrient concentrations and ratios in the Bohai Sea.
The purpose of this study is to quantitatively examine the impacts ofvarious biogeochemical processes on the seasonal variations in nutrient
concentrations and ratios in the central Bohai Sea (CBS) region. Accordingly, we developed a 1D physical–biological coupled model to describe
the seasonal nutrient cycling over the CBS. A series of numerical experiments were designed to analyze the impacts of multiple factors on theseasonal variations of nutrient concentrations and ratios.

1. Introduction
Nutrients including dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved silicate (DSi) are necessary for
phytoplankton growth, whose primary productivity is the basis of themarine ecosystem (Bristow et al., 2017). Increasing human activity increases the external input of nutrients into coastal regions and sometimes causes an over-enriched state of nutrients (i.e., eutrophication)(Kim et al., 2011; Song et al., 2016). Over-enriched nutrient conditions
lead to some environmental issues, such as the frequent occurrence ofharmful algal blooms and jellyﬁsh blooms, changes in plankton community structure, and ocean acidiﬁcation (Zhou et al., 2018; Wang et al.,2018a).
Similar to hydrographic variables such as water temperature and salinity, the nutrient concentrations in many coastal seas exhibit seasonalvariations, which have been widely reported to be associated with a variety of external factors and local biogeochemical processes. For example, the nutrient concentrations in the Bay of Bengal were higher
during the northeast monsoon season than those during other seasons
because of the large anthropogenic nutrient load (Achary et al., 2014).
The nitrate concentration was high in spring in the coastal waters of
Namibia due to strong Benguela upwelling (Louw et al., 2016). Similarly, the increase in nutrient concentrations off the northern Galician
coast in summer was found to be associated with upwelling events(Caseas et al., 1997). On the other hand, the uptake of nutrients by phytoplankton in spring and summer was also suggested to be the cause of
the lowest nutrient concentrations in the Seto Inland Sea (Yamaguchi
et al., 2020), southeastern Mediterranean Sea (Herut et al., 2000), andthe Baltic Sea (Lyngsgaard et al., 2017; Purina et al., 2018).
Nutrient ratios vary widely in nutrient-related processes. Comparedwith the ﬁxed Redﬁeld ratio (16:1) of nitrogen to phosphorus in theocean (Redﬁeld, 1934), there is excess nitrogen relative to phosphorus
in riverine input (Liu et al., 2009; Ounissi et al., 2018) and atmosphericdeposition (Shi et al., 2012). This excess nitrogen in coastal waters induces an N/P ratio (as the molar ratio of DIN/DIP) of >16 (Macias
et al., 2019). Naturally, with the seasonal variation in nutrient concentrations, nutrient ratios also show a seasonal variation (Fisher et al.,
1992; Fan and Song, 2014).
The Bohai Sea is a typical semi-enclosed shallow sea in the northwest Paciﬁc, which is subject to serious eutrophication (Song et al.,2016; Xin et al., 2019). Many ﬁeld surveys have documented seasonalvariations in the concentrations and ratios of nutrients in the Bohai
Sea (J.J. Wang et al., 2019; Zheng et al., 2020). In general, nutrient concentrations have exhibited maximum values in winter and minimum
values in summer in recent years (Sui et al., 2016; Zhou et al., 2018).
The highest N/P ratio is in summer, when its value exceeds the Redﬁeld
ratio of 16:1, while the Si/N ratio (as the molar ratio of DSi/DIN) isslightly higher in summer than in other seasons (Sui et al., 2016; J.J.
Wang et al., 2019). The extremely high N/P ratio in summer is probably
a cause of the drastic changes in the composition of the plankton community (Chen et al., 2016; Y.B. Wang et al., 2019), as well as the frequent
occurrence of harmful algal blooms (Song et al., 2016) and hypoxia(Zhai et al., 2019), which further induce a decline in the ﬁshery resources of the Bohai Sea (Zhang et al., 2012).
The Bohai Sea has been reported to have received >30% of mainland
China's total marine pollution, although it only constitutes <3% of thetotal marine area of China (Xin et al., 2019). Many studies have emphasized the important impact of external inputs on the nutrient budget of
the Bohai Sea, including river input (Liu, 2015; Ding et al., 2020), atmospheric deposition (Shou et al., 2018; Qi et al., 2020), nutrient releasefrom sediments (Liu et al., 2011; Mu et al., 2017), inputs from the Yellow
Sea (Zheng et al., 2020), and non-point sources (Liu et al., 2017; J.J.
Wang et al., 2019). In recent years, these excessive nutrient loads have
led to high phytoplankton biomass in the Bohai Sea, especially in summer (Ding et al., 2020). Naturally, phytoplankton-related biological processes also affect nutrient concentrations in seawater. To date, there is

2. Methodology
2.1. Study area
The Bohai Sea (37°10′–41°10′ N, 117°35′–122°10′ E) has an average
depth of 18 m and a total area of 7.7 × 104 km2 (Fig. 1), and is connectedto the Yellow Sea through the Bohai Strait. Based on geographical location, the sea is usually divided into ﬁve areas: Liaodong Bay, Bohai Bay,
Laizhou Bay, Qinhuangdao coast, and the CBS (Zhang et al., 2017). Nutrients in coastal areas have been reported to be affected by non-point
emissions, such as sewage discharge and extensive aquaculture (J.J.
Wang et al., 2019; Xin et al., 2019). However, the necessary highresolution coastal non-point source emissions data over the entire
Bohai Sea is not available. Therefore, we have to select the CBS as ourstudy area, where coastal non-point source emissions affect little thenutrients concentration there (Li et al., 2015; J.J. Wang et al., 2019).
The CBS has an area of 2.9 × 104 km2 and an average depth of 22.5 m.
The phytoplankton biomass in the CBS accounts for nearly half of thatin the entire Bohai Sea (Ding et al., 2020). Therefore, clarifying the seasonal variations of nutrient concentrations in the CBS is helpful for understanding the nutrient cycle in the entire Bohai Sea.
2.2. Model description
A vertical 1D physical–biological coupled model was implemented
to analyze the nutrient dynamics in the CBS. The model was composedof three sub-models: a hydrodynamic model, pelagic biological model,
and benthic model. There were 40 layers in the vertical direction. The
hydrodynamic model was based on the 1D Princeton Ocean Model(Blumberg and Mellor, 1987), which provided the water temperatureand diffusivity coefﬁcient for the biological model. A detailed description and validation of the hydrodynamic model for the CBS are available
in Ding et al. (2020).
We developed a pelagic biological model describing the complex
processes of the planktonic ecosystem based on the North Paciﬁc Ecosystem Model for Understanding Regional Oceanography (NEMURO)(Kishi et al., 2007). As shown in Fig. 2, there are 14 state variables:+
two types of DIN (nitrate (NO−3 ) and ammonium (NH4 )), DIP, DSi,dinoﬂagellates (PS), diatoms (PL), two types of zooplankton(microzooplankton (ZS) and mesozooplankton (ZL)), bacteria, dissolved organic nitrogen (DON), dissolved organic phosphorus (DOP),
particulate organic nitrogen (PON), particulate organic phosphorus(POP), and biogenic silica (BSi). The governing equations and formulations of various biogeochemical processes are given in Text S1, and alist of the parameters used in the model is provided in Table S1.
The benthic model was based on the work of Capet et al. (2016), andincluded three types of organic matter (PON, POP, and BSi). It describes
the sedimentary accumulation, decomposition, and burial of organic
matter leaving the water column. All benthic variables were depthintegrated and expressed in mmol m−2. The model equations arepresented in Text S2, and the parameter values are listed in Table S2.
The pelagic model provided the input ﬂux of organic matter into thebenthic layer. After calculating the accumulation, decomposition, andburial of organic matter within the benthic layer, the benthic model2

X. Ding, X. Guo, H. Gao et al.

Science of the Total Environment 799 (2021) 149416

Fig. 1. (a) Location of the Bohai Sea, and (b) study area and bathymetry (contour lines, units in meters). The sea is divided into ﬁve subregions by white dashed lines: Liaodong Bay (LDB),
Qinhuangdao coast (QHD), Bohai Bay (BHB), Laizhou Bay (LZB), and the central Bohai Sea (CBS).

+
Fig. 2. Schematic of the pelagic–sediment coupled model. The pelagic model includes 14 state variables: nitrate (NO−3 ), ammonium (NH4 ), dissolved inorganic phosphorus (DIP),
dissolved silicate (DSi), dinoﬂagellates (PS), diatoms (PL), microzooplankton (ZS), mesozooplankton (ZL), bacteria (BAC), dissolved organic nitrogen (DON), dissolved organic
phosphorus (DOP), particulate organic nitrogen (PON), particulate organic phosphorus (POP), and biogenic silica (BSi). The benthic model includes six state variables: fast decaying
PON (CfPON), POP (CfPOP), and BSi (CfBSi), and slow decaying PON (CsPON), POP (CsPOP), and BSi (CsBSi). The colored solid lines represent different biogeochemical processes corresponding to
the words shown in the same color. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)3

X. Ding, X. Guo, H. Gao et al.

Science of the Total Environment 799 (2021) 149416

In addition, the nutrient ﬂuxes between the CBS and the Yellow Sea
were estimated to be much smaller than the riverine nutrient ﬂuxes;
hence, they were not considered in the model (see Text S3 for details).

subsequently provided the sediment–water ﬂuxes of nutrients, whichwere the bottom boundary conditions for nutrients in the pelagic
model.

2.3.2. Initial conditions and calculation
The observational data collected in winter over the CBS (Table S3)
were averaged to provide the initial vertical proﬁles of DIN, DIP, andDSi concentrations. Other pelagic biological variables, including phytoplankton, zooplankton, and other organic matter, were initially set toa homogeneous small value of 0.1 mmol m−3 (Fennel et al., 2006). In
addition, the benthic variables were initiated based on the simulated results for January over the Bohai Sea from a pelagic–benthic coupledmodel (Zhang et al., 2006). The initial values of inorganic nutrients inwater and particulate matters in sediment were listed in Table S4.
The physical and biological variables were simultaneously calculated
at a time step of 216 s. The model was integrated for ﬁve years with climatological surface forcing and monthly nutrient inputs. The ﬁrst four
years were for the model spin-up; thus, we chose the simulated resultsof the ﬁfth year for the analysis.

2.3. Model conﬁguration
The hydrodynamic model was initialized with the temperature andsalinity data in January and driven by climatological monthly atmospheric forcing, which was described in detail by Ding et al. (2020).
Here we only give the detailed conﬁgurations of the pelagic biologicalmodel and benthic model.
2.3.1. Nutrient input from boundaries
The Yellow River is the second-largest river in China, and accounts
for >75% of the total river discharge into the Bohai Sea (Liu and Yin,
2010). The nutrients carried by the diluted water of the Yellow Rivercan be transported into the CBS, especially in summer owing to thesoutheasterly wind (Wang et al., 2008), thereby playing an importantrole in primary production (Ding et al., 2020). The nutrient carried by
the small rivers into the sea usually stays along the coastal line because
its small river discharge cannot form a bulge that is a necessary condition for an offshore transport of river water (Yu et al., 2020). Consequently, the nutrients from the small rivers affect little the ecosystemof the central waters (Li et al., 2015; Ding et al., 2020). Thus, the nutrients from the Yellow River were considered to be the only riverine nutrient input in this study. The riverine nutrient input in the model
consisted of the long-term averaged (2008–2017) monthly ﬂuxes of
DIN, DIP, DSi, DOP, and particulate phosphorus (PP), which were calculated according to the regression relationships between nutrient ﬂuxes
and river discharge (Liu, 2015). As shown in Fig. S1a, the highest riverine nutrient ﬂuxes occurred in summer. Particulate nitrogen and silicon
were not considered in the model because their ﬂuxes were very low
compared to those of dissolved nitrogen and silicon (Liu et al., 2009;
Wang et al., 2012). The nutrients from the Yellow River exist mainly
in its diluted water that moves with the current. Therefore, the proportion of the nutrient ﬂux from the Yellow River into the CBS was assumed
to be the proportion of water ﬂux transported from the estuary into the
CBS without considering the consumption of nutrients during theirtransportation to the CBS (Ding et al., 2020). Moreover, the riverine PP
was assumed to be decomposed into DOP and DIP in the estuary,which were then transported into the CBS. The proportions of PP
decomposed into DOP (~4.8%) and DIP (~11.2%) were obtained fromHe et al. (2010). The calculated riverine ﬂuxes of nutrients into the
CBS are shown in Fig. S1b.
According to previous studies, atmospheric nitrogen deposition is
1.1–1.5 times higher than that from major rivers in the Bohai Sea, Yellow Sea, and East China Sea (Zhang et al., 2010; Shou et al., 2018; Qi
et al., 2020). In this study, the monthly atmospheric deposition ﬂuxes+
of NO−
3 and NH4 in the CBS were obtained from an atmospheric transport model (Guo, 2019; Fig. S1c). In this study, we did not consider theatmospheric phosphorus deposition since the phosphorus ﬂux from atmospheric deposition is extremely low in the Bohai Sea (Shi et al., 2012;
J.J. Wang et al., 2019).

2.4. Numerical experiment
To examine the impacts of various factors on the seasonal variations
in nutrients, we designed a series of numerical experiments after themodel spin-up period (Table 1). Case 0 was the realistic (control) casementioned in Section 2.3. Case 1 was used to investigate the effects ofbiological processes by removing all biological processes in seawater.
Cases 2 and 3 were used to examine the effects of river input and atmospheric deposition, respectively. Case 4 was used to investigate the effect of sediment nutrient release by closing the nutrient supply fromthe sediment–water interface. Case 5 was used to examine the effectof benthic nitrogen loss by setting the benthic nitrogen removal rate
to 0. Finally, Case 6 was used to investigate the effects of the variationof N/P ratio in phytoplankton uptake used in Case 0 by ﬁxing the N/Pratio in phytoplankton uptake to 16 (=the Redﬁeld ratio).
In the model, the temporal variation rate of the nutrient inventory
for a unit area was expressed by:
d
dt

Z 0
−h

C ðzÞdz ¼

Z 0−hBioðzÞdz þ Riv þ Atm þ Sed þ NL;

ð1Þ

where h is the water depth, C(z) denotes the nutrient concentration at
depth (z), Bio(z) represents the biological processes, Riv is the river input, Atm is the atmospheric deposition of DIN, Sed is the nutrient releasefrom sediments, and NL is the benthic nitrogen loss. To determine theeffects of each process on the nutrient inventory, we calculated the difference for each term on the right-hand side of Eq. (1) between Cases 1
to 5 and Case 0 (i.e., the control):"ΔNF ij ðt Þ ¼ NF 0i ðt Þ−NF ij ðt Þ;
ΔPF ij ðt Þ ¼ PF 0i ðt Þ−PF ij ðt Þ

ð2Þ

where NF and PF represent the terms in Eq. (1) for DIN and DIP, respectively; i (=I, II, III, IV, or V) is the index for the ﬁve terms on the righthand side of Eq. (1) and j (=1, 2, 3, 4, or 5) is the index for Cases 1–5.

Table 1
List of numerical experiments. “+” and “−” indicate that the process was included and excluded in the simulated case, respectively (see Section 2.4 for details).

Case 0Case 1Case 2
Case 3
Case 4
Case 5
Case 6

Biological processes

River input

Atmospheric deposition

Sediment nutrients release

Benthic N loss

Non-Redﬁeld ratio

+
−
+
+
+
+
+

+
+
−
+
+
+
+

+
+
+
−
+
+
+

+
+
+
+
−
+
+

+
+
+
+
+
−
+

+
+
+
+
+
+
−

4

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Science of the Total Environment 799 (2021) 149416

Accordingly, NF0i and PF0i are the DIN and DIP ﬂuxes of each process in
Eq. (1) for Case 0, while NFji and PFji are the DIN and DIP ﬂuxes of eachprocess in Eq. (1) for Cases 1–5.
We deﬁned the change in nutrient concentrations due to ΔNFji as
ΔDINji, and that due to ΔPFji as ΔDIPji:
2

Z
1 t
j
ΔNF ij ðt Þdt
6 ΔDINi ðt Þ ¼ h
0
6
Z
;
4
1 t
ΔPF ij ðt Þdt
ΔDIP ij ðt Þ ¼
h 0

ð3Þ

where h is the water depth.
To evaluate the contributions of ΔNFji or ΔPFji to the change in nutrient concentrations and ratios, we calculated the ratios of the nutrient
concentrations and N/P ratio change due to ΔNFji (or ΔPFji) to the corresponding values in Case 0, as expressed by:
2
6
6
6
6
4

RNij ðt Þ ¼ −ΔDINij ðt Þ=DIN0 ðt Þ
RP ij ðt Þ ¼ −ΔDIP ij ðt Þ=DIP 0 ðt Þ !
;DIN0 ðt Þ−ΔDINij ðt Þ DIN0 ðt Þ DIN0 ðt Þ−=
RNP ij ðt Þ ¼
DIP 0 ðt Þ
DIP 0 ðtÞDIP 0 ðt Þ−ΔDIP ij ðt Þ

ð4Þ

where i and j are the same as those described in Eq. (2), and DIN0 and
DIP0 are the simulated DIN and DIP concentrations in Case 0,
respectively.
2.5. Calculation of nutrients conversion time
Based on the nutrient inventory and ﬂuxes of different processes inseawater, the conversion time (γ) of nutrients for the different processes was calculated as follows:
R0γ¼−h Nut ðzÞdz

Fs

;

ð5Þ

where Nut represents the nutrient concentrations at depth z; Fs denotes
the nutrient ﬂuxes corresponding to one process; and h is the water
depth.
3. Results
Fig. 3. Monthly concentrations of DIN (a), DIP (b), and DSi (c) at different depths. Unit:
mmol m−3.

3.1. Seasonal variations in nutrient concentrations
The vertical proﬁles of the simulated nutrient concentrations in eachseason were generally within the range of observational data (Fig. S3),
indicating that the model could simulate the seasonal variations in thevertical distributions of nutrients in the CBS. Fig. 3 displayed the vertical
distributions of simulated DIN, DIP, and DSi in winter (January, February, and March), spring (April, May, and June), summer (July, August,and September), and autumn (October, November, and December).
The lowest and highest concentrations of these three dissolved nutrients were in summer and winter, respectively, while spring and autumn
were the transitional periods between these two extreme concentrations. The vertical mean concentrations of nutrients in winter, spring,summer, and autumn were 15.7 mmol m−3, 12.9 mmol m−3,
8.7 mmol m−3, and 12.4 mmol m−3 for DIN, respectively; 0.6 mmol
m−3, 0.4 mmol m−3, 0.2 mmol m−3, and 0.4 mmol m−3 for DIP, respectively; and 17.1 mmol m−3, 14.6 mmol m−3, 12.5 mmol m−3, and15.6 mmol m−3 for DSi, respectively. The summertime nutrient concentrations were signiﬁcantly lower in surface water (~8.5 mmol DIN m−3,<0.1 mmol DIP m−3, and ~10 mmol DSi m−3) than those in bottom
water (~9.5 mmol DIN m−3, >0.3 mmol DIP m−3, and ~15 mmol DSim−3), while almost no vertical variation was observed in the wintertime nutrient concentrations.

3.2. Seasonal variations of particulate concentration
Although this study focuses on the seasonal variations in nutrients,
nutrient cycling is closely related to changes in particulate materials
such as plankton and detritus. Here, we present the modeled seasonalvariations in these particulate materials. As shown in Fig. S4,
chlorophyll-a (Chl-a), an index of phytoplankton biomass, exhibited
an apparent seasonal variation in the CBS, which was consistent withthe observational data. The vertical mean Chl-a concentrations in winter, spring, summer, and autumn were 0.56 mg m−3, 1.1 mg m−3,
2.0 mg m−3, and 1.0 mg m−3, respectively. The Chl-a concentrations
were slightly higher in surface water than in bottom water in springand summer, whereas they exhibited a vertically homogenous distribution in autumn and winter.
The particulate materials peaked in summer with the highest concentration of ~1.2 mmol N m−3 for phytoplankton, ~1.8 mmol N m−3
for zooplankton, ~6.0 mmol N m−3 for PON, ~0.3 mmol P m−3 for POP,and ~6.0 mmol Si m−3 for BSi (Fig. S5). Although these particulate materials exhibited higher concentrations in surface water than in bottom
5

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Science of the Total Environment 799 (2021) 149416

m−2 d−1; DSi: 7.8 mmol m−2 d−1). The other biological processes(phytoplankton respiration, zooplankton excretion, and organic matter
mineralization) replenished the nutrients used by photosynthesis.
Based on the nutrient inventory and ﬂuxes of different processes inseawater, we calculated the conversion time (γ) of nutrients using
Eq. (5) for the different processes. The γ values of phytoplankton uptake
for DIN, DIP, and DSi were approximately 13 d, 6 d, and 36 d in summer,and 321 d, 171 d, and 481 d in winter. As the nutrient inventory in winter was only 1–3 times higher than that in summer (Fig. 4), the large
variation in γ of phytoplankton uptake likely depended on the nutrient
uptake ﬂux, whose seasonal variation would have led to rapid nutrient
cycling in summer.

water in summer, there was no obvious vertical difference between
them in other seasons. In addition, the peak zooplankton concentration
occurred approximately one month after that of phytoplankton(Fig. S5a, b). In summer, the increase in phytoplankton biomass provides a more adequate source of food for zooplankton, which in turnleads to an increase in zooplankton biomass, indicating a bottom-up
control in the plankton ecosystem. Compared to the limited observational data for particulate materials, our simulated values were of thesame order of magnitude as those reported in recent years (Xu et al.,2016; Wang et al., 2018b; Bu et al., 2019; Zhai et al., 2019).
3.3. Nutrient budget for each season
The model simulations provided a budget for all compartments in
the CBS (Fig. 4). The sources and sinks of nutrients include river input,
atmospheric nitrogen deposition, sediment release, benthic nitrogenloss, phytoplankton uptake and respiration, zooplankton excretion,and organic matter mineralization. For DIN, benthic nitrogen loss
(0.4–0.6 mmol m−2 d−1) was comparable to, but greater than, each external input (river input, 0.1–0.4 mmol m−2 d−1; atmospheric deposition, 0.2–0.4 mmol m−2 d−1; sediment release, 0.1–0.4 mmol m−2d−1). River inputs of DIP (0.6–4.4 μmol m−2 d−1) and DSi(0.1–0.2 mmol m−2 d−1) were slightly lower than their respective sediment release ﬂuxes (DIP: 5–16 μmol m−2 d−1; DSi: 0.1–0.5 mmol m−2d−1). It was noteworthy that the above external nutrient ﬂuxes wereconsiderably lower than those due to biological processes(e.g., summer values of 3.2–14.5 mmol m−2 d−1 for DIN, 163–732μmol m−2 d−1 for DIP, and 1.9–7.8 mmol m−2 d−1 for DSi). Phytoplankton uptake was the largest sink for nutrients in seawater, with thehighest values in summer (DIN: 14.5 mmol m−2 d−1; DIP: 0.73 mmol

3.4. Seasonal variations of nutrient ratios
As a result of the seasonal variations in nutrient concentrations, thenutrient molecular ratios also exhibited signiﬁcant seasonal variationsin the CBS (Fig. 5a). The N/P ratio was obviously higher in summer incomparison with that in other seasons. The summertime N/P ratio exhibited an obviously vertical difference with a higher value in surface
water (~100) than in bottom water (<30). However, there was almost
no vertical variation in the N/P ratio in winter, which was <30. The Si/
N ratio also presented some seasonal variations that were not as significant as those of the N/P ratio (Fig. 5b). The Si/N ratio was slightly higher
in summer than in other seasons, with the highest value of ~1.8 in bottom water. The Si/N ratio was vertically homogeneous in winter, with avalue of ~1. The slight seasonal variation in the Si/N ratio was mainly
caused by the competition between diatoms and dinoﬂagellates (seeText S5 in detail).

Fig. 4. Seasonal budgets for nutrients in the central Bohai Sea. The blue numbers are the vertical mean concentrations (mmol/m3) of variables, including phytoplankton nitrogen content(Phy-N), phytoplankton phosphorus content (Phy-P), phytoplankton silicon content (Phy-Si), zooplankton nitrogen content (Zoo-N), zooplankton phosphorus content (Zoo-P),
zooplankton silicon content (Zoo-Si), dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), dissolved silicate (DSi), organic nitrogen (ON), organic phosphorus(OP), and biogenic silica (BSi). The black numbers denote the vertically integrated nutrients ﬂuxes (DIN and DSi: mmol m−2 d−1; DIP: 10−2 mmol m−2 d−1). (a, e, i) winter; (b, f,j) spring; (c, g, k) summer; (d, h, l) autumn. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)6

X. Ding, X. Guo, H. Gao et al.

Science of the Total Environment 799 (2021) 149416

Fig. 5. Monthly N/P ratio (a) and Si/N ratio (b) at different depths.

4. Discussion
4.1. Effects of multiple processes on seasonal variations of nutrients
Fig. 6. Mean monthly vertical DIN (a), DIP (b), and N/P ratio (c) in different numericalexperiments. In subﬁgure (b), the line of Case 0 overlaps the lines of Case 3 and Case 5.

Many studies have found that the nutrients in the Bohai Sea were
supplied by riverine inputs (Liu et al., 2009), atmospheric deposition(Shou et al., 2018; Qi et al., 2020), and sediment release (Liu et al.,2011). Benthic nitrogen loss at the water–sediment interface played
an important role in removing nitrogen from seawater (Zhang et al.,2018; Zheng et al., 2020). Seasonal differences in the above processes
at their boundaries naturally affect the seasonal variations of nutrient
concentrations in seawater. Moreover, nutrient budgets have indicatedthe important role of biological processes in promoting the rapid conversion of nutrients in seawater, especially in summer (Fig. 4). Therefore, we designed a series of numerical experiments to examine theeffects of these processes on the seasonal variations of nutrient in the
CBS (see Section 2.4 for details of the conﬁguration).
Fig. 6 showed the vertical mean values of the simulated DIN, DIP, andN/P ratio of seawater in different numerical experiments. The seasonalvariations of DIN, DIP, and the N/P ratio in the control group (Case0) changed completely when biological processes were excluded inCase 1, indicating the critical role of biological processes in the seasonalvariations of nutrients in the CBS. In comparison to Case 0 (after one
year of integration), the simulated DIN concentration in Case 2 (river
input excluded), Case 3 (atmospheric deposition excluded), and Case
4 (sediment nutrient release excluded) decreased by 3 mmol m−3,
4 mmol m−3, and 3 mmol m−3, respectively, while those for DIP decreased by 0.05 mmol m−3, 0 mmol m−3, and 0.16 mmol m−3, respectively. Because of the different N/P ratios in external inputs compared toseawater, the vertical mean value of the N/P ratio decreased by ~6 inCase 2 and ~10 in Case 3, but increased by ~3 in Case 4, in comparison
to Case 0 in summer. When benthic nitrogen loss was excluded (Case5), the DIN concentration increased by 9.1 mmol m−3 after one yearof integration compared with Case 0, which increased the N/P ratio by~12. Overall, the comparison of results in different numericalexperiments exhibited that biological processes played the most important role in the seasonal variation of nutrients, although the other processes such as external input and output could also affect theconcentration of nutrients. It must be noted that although the physicalfactors such as light intensity and water temperature have no direct effects on the nutrient budget, they actually affects the biologicalprocesses.
As described in Section 2.4, we deﬁned ﬁve variables to evaluate thecontribution of each process to the seasonal variation: (1) ΔNFji and(2) ΔPFji to determine the change in the DIN and DIP ﬂuxes between
Case 0 and Cases 1–5; (3) RNji, (4) RPji, and (5) RNPji to determine the percentages of the DIN concentration, DIP concentration, and N/P ratio inCase 0 changed by the process excluded in the other ﬁve cases.

4.1.1. Biological processes
Fig. 7a and b indicated that biological processes led to the highest
nutrient removal rates (DIN: 1.9 mmol m−2 d−1; DIP: 0.08 mmol m−2d−1) in summer, while they replenished a large fraction of nutrientsin autumn. The inclusion of biological processes also resulted in a slightincrease of sediment release (DIN: <0.3 mmol m−2 d−1; DIP:
<0.01 mmol m−2 d−1) and a decrease of benthic nitrogen loss (DIN:<0.4 mmol m−2 d−1) during summer and autumn. Fig. 8a and b showedthat the DIN and DIP concentrations in Case 0 increased by ~100% and~200% in summer when biological processes were excluded in Case 1.
Because of the difference in each ﬂux (Fig. 7a–b), the increases in DIN
and DIP in summer in Case 1 relative to Case 0 were mainly inducedby the direct effect of biological processes within seawater rather than7

X. Ding, X. Guo, H. Gao et al.

Science of the Total Environment 799 (2021) 149416

Fig. 7. Changed ﬂuxes of various nutrient-related processes for DIN (a, c, e, g) and DIP (b, d, f, h) using the results of Case 0 minus those in the numerical experiments (Cases 1 to 4). The
colored solid lines represent different processes corresponding to the words shown in the same color. Positive values correspond to increasing nutrient concentrations after adding theprocess (see Eq. (2) for calculating ΔNF and ΔPF). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)their uptake. Cultivation experiments revealed that the N/P ratio in phytoplankton uptake (3–160) was always lower than the seawater N/P
ratio (4–330) under different nutrient levels in the Bohai Sea (Zouet al., 2001). Consequently, the seawater N/P ratio increased signiﬁcantly with enhanced phytoplankton photosynthesis in summer. In addition, most of the organic matter sequestered in marine sediments
comes from the deposition of organic matter biosynthesized by marineorganisms inhabiting surface waters (Wakeham and Lee, 1993). Although the organic matter in sediment could be mineralized into inorganic nutrients and then returned to the upper water column, a time
lag of the order of several months exists between the deposition of particulate material and its mineralization in sediment (Rudnick andOviatt, 1986). This was demonstrated by the increased nutrient releasefrom sediment after strong biological processes in summer in the present study (Fig. 7a, b). Strong biological processes in summer also reduced the ﬂux of benthic nitrogen loss (Fig. 7a) by absorbing DIN,
which is an important substrate for benthic nitrogen loss (Fennel,2017).

by changes in the release of nutrients from sediment and/or benthic nitrogen loss.
Because of the lower N/P ratio involved in biological processes compared with ambient seawater, the N/P ratio of seawater in Case 0 decreased by ~25% in summer when the direct effect of biologicalprocesses was excluded (Fig. 8c). Although this had little effect on the
nutrient concentrations, the changes in release of nutrients from sediments and benthic nitrogen loss in Case 1 led to decreases of ~5% and~20% in the seawater N/P ratio in Case 0 during summer and autumn(Fig. 8c). These reductions related to the lower N/P ratio associatedwith sediment release and the higher N/P ratio associated with benthic
nitrogen loss compared with seawater. Therefore, the seawater N/P
ratio of Case 0 reduced by >40% during summer when biological processes were excluded (Fig. 8c).
The Bohai Sea is an important ﬁshing ground in China, and its maximum primary productivity can reach 1500 mg C m−2 d−1 in summer(COOA, 2016). Strong phytoplankton photosynthesis in summer (Dinget al., 2020) results in low nutrient concentrations in seawater following
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Science of the Total Environment 799 (2021) 149416

Fig. 8. Changed percentages of DIN (a, d, g, j), DIP (b, e, h, k), and the N/P ratio (c, f, i, l) due to the changed processes in the numerical experiments (Cases 1 to 4) relative to the results of thecontrol group (Case 0). The colored lines represent different processes corresponding to the words shown in the same color. Positive values correspond to increasing nutrient
concentrations or the N/P ratio in Case 0 if excluding the process (see Eqs. (3) and (4) for calculating RN, RP, and RNP). (For interpretation of the references to color in this ﬁgurelegend, the reader is referred to the web version of this article.)CBS. Nevertheless, as the N/P ratio of river water (>300) is much higher
than that of seawater (Liu et al., 2012), the large amount of nutrientstransported by river discharge into the sea led to an increase in the seawater N/P ratio (Fig. 6).

4.1.2. River input
As shown in Fig. 7c and d, river inputs supplied a large amount of nutrients to the CBS, with the highest ﬂuxes of ~0.6 mmol m−2 d−1 for DIN
and ~0.008 mmol m−2 d−1 for DIP in summer. These riverine nutrient
ﬂuxes resulted in a higher nutrient consumption by biological processes
in summer (DIN: ~0.2 mmol m−2 d−1; DIP: ~0.002 mmol m−2 d−1). The
additional organic matter in summer replenished nutrients throughremineralization in autumn. Moreover, benthic nitrogen loss also increased after summer because the bottom DIN concentration increased,with the highest value of ~0.18 mmol m−2 d−1 (Fig. 7c). A slightly increased nutrient ﬂux from sediment release was also conﬁrmed in autumn when river input was included in the simulation (Fig. 7c, d).
Fig. 8d and e showed that the DIN concentration, DIP concentration,
and N/P ratio in Case 0 decreased by >30%, >10%, and >20% in summer
when the direct effect of river input was excluded. The changes in theeffects of biological processes, benthic nitrogen loss, and sediment nutrient release on the nutrient concentrations and N/P ratio were limited
to <10% when river input was included in the simulation (Fig. 8d–f).
Although many studies have emphasized the importance of riverine
inputs in supplying seawater nutrients in summer in the coastal watersof the Bohai Sea (Wang et al., 2017), the nutrient ﬂuxes of river input
were still much lower than the nutrient loss in biological processes(Fig. 4). Consequently, the river inputs cannot replenish the loss of nutrients due to the strong biological processes in summer. This leads tothe occurrence of the lowest nutrient concentrations in summer in the

4.1.3. Atmospheric deposition
Fig. 7e showed that the DIN ﬂux of atmospheric deposition wasslightly higher in summer and autumn (0.2–0.6 mmol m−2 d−1) thanin winter and spring (0.1–0.4 mmol m−2 d−1). With the inclusion of atmospheric deposition, the benthic nitrogen loss increased by ~0.2 mmol
m−2 d−1 for DIN after summer (Fig. 7e). Atmospheric deposition of DIN
did not affect biological processes because the limiting nutrient in the
CBS is phosphate (Wang et al., 2009; Xu et al., 2011), which was not provided via atmospheric deposition. Fig. 8g and i reveal that both the DIN
and N/P ratio in Case 0 decrease by >30% in summer when the direct effect of atmospheric deposition was excluded. The change in the benthic
nitrogen loss caused by atmospheric deposition led to a <12% increasein both the DIN concentration and N/P ratio in Case 0 (Fig. 8g, i). The
DIN ﬂux of atmospheric deposition was comparable to that of river inputs into the CBS (Fig. 4), which has been emphasized in recent studies(Shou et al., 2018; J.J. Wang et al., 2019; Zhang et al., 2020). However,the limiting nutrient for phytoplankton growth in the Bohai Sea haschanged to phosphorus owing to excessive nitrogen input (Wanget al., 2009; Xu et al., 2011). Thus, it is necessary to rethink the role ofatmospheric deposition in primary production because the previous
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Science of the Total Environment 799 (2021) 149416

results of sensitivity experiments suggested an S value of 0.21, indicating that the DIN concentration in our model was not very sensitive tothe value of n0. Thus, the benthic nitrogen loss given by the model canbe considered a robust estimation.
Field observational data and experimental data indicate that someenvironmental factors, such as the bottom oxygen concentration, thequantity and quality of organic matter, and microbial functional traits,
may also affect the rate of benthic nitrogen loss (Zhang et al., 2018;
Song et al., 2020). However, little is known about the speciﬁc functional
relationship between the removal rate and these environmental factors(Fennel, 2017). With a better understanding of these relationships in
the future, a more comprehensive model of benthic nitrogen loss is
expected.
Fig. 9 summarized the summertime effects of the interactions
among nutrient-related processes on the nutrient concentrations and
N/P ratio in the CBS. Overall, biological processes play a critical role indecreasing the nutrient concentrations and increasing the N/P ratio
through the direct effect of phytoplankton uptake. The external inputs
of nutrients through boundaries such as river input, atmospheric deposition, and sediment release are important sources of nutrients in seawater, which can also affect nutrient concentrations and N/P ratio byaffecting other nutrient-related processes. Benthic nitrogen loss is essential for maintaining the nitrogen balance in the sea by directly removing a large amount of inorganic nitrogen.

calculation based on the atmospheric deposition of nitrogen is likely anoverestimation.
4.1.4. Sediment nutrient release−2 −1
Our simulated nutrient ﬂuxes (NH+d ; DIP:
4 : <0.6 mmol m<0.02 mmol m−2 d−1) released from sediments in the CBS were consistent with the observational data (see Text S4 for details). Fig. 7g and hshowed that the nutrient ﬂux from sediment was higher in summer
and autumn than in winter and spring. Because of the large amount oforganic matter accumulated in the summer (Fig. S5) and the highest
bottom water temperature in September (Fig. S8), the mineralization
rate of organic matter in sediment was highest in September, which
led to the highest sediment nutrient ﬂux. The DIN and DOP removedby biological processes increased by ~0.2 mmol m−2 d−1 and~0.01 mmol m−2 d−1, respectively, in summer due to sediment release(Fig. 7g, h). These nutrients were replenished in autumn by the mineralization of organic matter. In addition, the DIN ﬂux associated withbenthic nitrogen loss increased by <0.2 mmol m−2 d−1 when sedimentnutrient release was included in the simulation (Fig. 7g). Fig. 8j and k
showed that the DIN and DIP concentrations in Case 0 decreased by~30% and ~45%, respectively, in summer and autumn when the direct effect of sediment nutrient release was excluded. The change in biologicalprocesses caused by sediment nutrient release led to a ~7% increase in
DIN and ~25% increase in DIP in summer with respect to Case 0, while
the change in benthic nitrogen loss led to a <8% increase in DIN in summer and autumn. Because the N/P ratio in the sediment was lower thanthat in seawater, the seawater N/P ratio of Case 0 increased by >20% insummer when sediment nutrient release was excluded (Fig. 8l). The N/Pratio of Case 0 for summer and autumn decreased by <10% due to biological processes and increased by <7% due to benthic nitrogen loss
when sediment nutrient release was excluded. Overall, sediment release is an important source of nutrients in the Bohai Sea (Liu et al.,2011; Mu et al., 2017), and hinders the decrease in nutrient concentrations while increasing the N/P ratio in summer.

4.2. Effect of nitrogen-to-phosphorus stoichiometry in phytoplankton
uptakeThe Redﬁeld ratio (N:P = 16:1) is widely used to link marine nitrogen and phosphorus cycles in many biogeochemical models (Anderson
and Sarmiento, 1994; Laurent et al., 2012). However, empirical and theoretical studies have shown that the ratio of nitrogen to phosphorus inphytoplankton uptake varies with growth conditions (Mills and Arrigo,
2010; Garcia et al., 2018). In the Bohai Sea, incubation experiments havealso shown that the N/P ratio of phytoplankton uptake depends on the
nutrient ratio in ambient seawater (Zou et al., 2001). Therefore, theuse of a ﬁxed ratio in modelling biogeochemical processes in the CBS
is not a good choice.
Galbraith and Martiny (2015) found that the N/P ratio in planktonchanged largely across the typical range of DIP concentrations in theocean, while it changes only slightly at very low DIN concentrations. Accordingly, Macias et al. (2019) provided a parameterized solution describing the variation in the N/P ratio of phytoplankton uptake as afunction of the DIP concentration in seawater (Eq. (S26)), which had
been applied in biogeochemical simulations of marginal seas with excessive nitrogen. In this study, we used the scheme provided by
Macias et al. (2019) to include the variation of the N/P ratio in phytoplankton uptake.
To estimate the effect of the variation of the N/P ratio in phytoplankton uptake on the seasonal variation of nutrient concentrations, we conducted another numerical experiment (Case 6) in which the N/P ratio in
phytoplankton uptake followed the ﬁxed Redﬁeld stoichiometry.
As shown in Fig. S9a–c, the DIN concentration decreased obviously
from June to October in Case 0 compared with Case 6, with the highest
reduction of >2.5 mmol m−3 in surface water. There was no differencein the DIP concentration between the two cases (Fig. S9d–f). The Bohai
Sea experiences serious phosphorous limitation, with an N/P ratio muchhigher than 16, especially in summer (Wang et al., 2009; Xu et al.,2011). The low DIP concentration led to a stable DIP ﬂux of phytoplankton uptake in the two cases. Because the N/P ratio in phytoplankton uptake increased in summer due to the decreased DIP concentration inCase 0, more DIN was removed in Case 0 than in Case 6. Consequently,
the N/P ratio of seawater decreased more (>18) in Case 0 than in Case
6 in summer (Fig. S9g–i). In cold months, the relatively high DIP concentration allowed the N/P ratio of phytoplankton uptake similar to the

4.1.5. Importance of benthic nitrogen loss
Benthic nitrogen loss via denitriﬁcation and anaerobic ammonium
oxidation plays a critical role in removing nitrogen from coastal waters(Fennel, 2017). The limited observational data indicate that nitrogenloss at the sediment–water interface is an important sink of DIN in the
Bohai Sea (Zhang et al., 2018; Zheng et al., 2020). However, the speciﬁc
regulatory mechanisms of benthic nitrogen loss have large uncertainties. In this study, we represented the benthic nitrogen loss rate asa function of the bottom temperature and nitrate concentration(Eq. (S69)), according to Yoshikawa et al., 2005 and Tan et al. (2020).
The calculated annually averaged ﬂux of benthic nitrogen loss was
0.49 mmol m−2 d−1, which is close to an observed value of 0.37 mmol
m−2 d−1 (Zhang et al., 2018; Zheng et al., 2020). The ﬂuxes of benthic
nitrogen loss in each season exceeded 50% of the DIN inputs from rivers
and the atmosphere in the CBS (Fig. 4a–d). Fig. 6a indicated that the DIN
concentration decreased by >5 mmol m−3 in summer when benthic nitrogen loss was included in the simulation. Consequently, the increasein the N/P ratio in seawater was greatly suppressed by the benthic nitrogen loss, which prevented the seawater N/P ratio from rising by >20 insummer (Fig. 6c). Overall, benthic nitrogen loss is of great importance tothe seasonal variations of the DIN concentration and N/P ratio in theCBS, and is second to the inﬂuence of biological process.
In this study, we also examined the extent to which the calculated
DIN concentration varied with the rate of benthic nitrogen loss (n0 in
Eq. (S69)). For this purpose, we increased and decreased the value ofn0 by 50% and ran the model for a year in each of the sensitivity experiments. The sensitivity of the DIN concentration to n0 was quantiﬁed by
ΔC=C
j (Pan et al., 2017), where C is the annual mean DIN
a factor of S ¼ j Δα=αconcentration, α is the value of n0 used in the model, and ΔC is the variation in C corresponding to the change (Δα) of the parameter n0. The
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Science of the Total Environment 799 (2021) 149416

Fig. 9. Changed percentages of DIN, DIP, and the N/P ratio in summer due to the interactions among nutrient-related processes if one speciﬁc factor is excluded. I, II, III, IV, and V represent
biological processes, river input, atmospheric deposition, sediment release, and benthic nitrogen loss, respectively.

seasonal variations in other coastal waters. However, there are many
control mechanisms for the seasonal variation of nutrients in various
coastal waters. For example, phytoplankton growth is the dominant factor modifying the seasonal variation of nutrients in the Baltic Sea, BlackSea, and Seto Inland Sea, which is similar to the results for the CBS. Terrestrial inputs play a critical role in the seasonal variation of nutrients in
the Yangtze River Estuary, northern Beibu Gulf, and Chesapeake Bay.
Anthropogenic runoff increases nutrient concentrations during thenortheast monsoon in the Bay of Bengal. The transportation of nutrientsby seasonal ocean currents leads to a signiﬁcant seasonal variation ofnutrients in the coastal waters of Namibia and the northern Galician
coast. The seasonal variation of nutrients in the coastal Yellow Sea andRedﬁeld ratio. Consequently, the DIN concentration and N/P ratio in seawater were almost the same in the two cases in cold months.
4.3. Implications for seasonal variations of nutrients in global coastal
watersTo deepen our understanding of the seasonal variation of nutrientsin coastal waters, we present a comparison of our results in the CBS
with the reported results in other coastal waters worldwide. As shownin Table 2, the nutrient concentrations in the CBS varied within therange of those reported for other coastal waters. Similar to the CBS, nutrient concentrations and the N/P ratio also exhibited signiﬁcantTable 2Comparison of the seasonal variations in nutrient concentrations of coastal waters worldwide.
Nutrient concentration (mmol m−3)

Area
DIN
Central Bohai Sea
Coastal Yellow Sea
Yangtze River Estuary
Northern Beibu Gulf
Bay of Bengal
Baltic Sea
Black Sea
Eastern Seto Inland Sea

DIP

6–18
0–0.8
4.5–15 0.1–0.6

N/P ratio

DSi

Seasonal variation

Value

Seasonal variation

8–18
3–12

Lowest in summer
Lowest in March, May, and June

20–12023–77

Peak in summer
Highest in spring

0–40
0–1
0–80
0–65
0–2.5 0–112
0–21
0–7
6–28
0.4–21 0–1.1
2–26
0–3
–
0–3
0.5–8 0.1–0.9 1–30

Highest in summer
Peak in summer
Peak during the northeast monsoon
Lowest in summer
Lowest in summer
Lowest in spring–summer

Chesapeake Bay

0–125

0.1–1.3 0–100

Eastern Gulf of Mexico(Florida Bay)
Coastal waters off
Namibia
Northern Galician coast

0–40

0–0.2

–

4–30

–

0–40

Highest DIN and DSi in winter–spring;highest DIP in summer
Lowest DIN in summer; no obvious
seasonality for DIP
Peak in spring

0–4

Highest in summer

0.5–10 0.1–0.8

10–400 Highest in summer
10–40
Peak in summer
0.5–84 Peak in December
5–132 Lowest in summer––
0–10
Highest in
spring–summer
1–1000Highest in
winter–spring
15–500 Lowest in summer

Dominant factor

References

Biological processes
Terrestrial input andphytoplankton growthRiver input
River input
Anthropogenic runoff
Phytoplankton growth
Phytoplankton growth
Phytoplankton growth

[1]
[2]
[3]
[4]
[5]
[6,7]
[8]
[9]

Terrestrial input

[10]
[11]

[13]

–

–

Water exchange andphytoplankton growth
Benguela upwelling

3–18

Highest in summer

Coastal upwelling[12][1] This study; [2] Shi et al. (2015); [3] Fan and Song (2014); [4] Lai et al. (2014); [5] Achary et al. (2014); [6] Lyngsgaard et al. (2017); [7] Purina et al. (2018); [8] Grégoire et al. (2008); [9]Yamaguchi et al. (2020); [10] Malone et al. (1996); [11] Pennock et al. (1999); [12] Louw et al. (2016); [13] Caseas et al. (1997).
11

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Science of the Total Environment 799 (2021) 149416

References

Florida Bay is caused by several factors (Table 2). Overall, our quantitative analysis of the effects of multiple processes on seasonal variations of
nutrient in the CBS provides an important reference for similar studies
in other coastal waters.

Achary, M.S., Panigrahi, S., Satpathy, K.K., Sahu, G., Mohanty, A.K., Selvanayagam, M.,
Panigrahy, R.C., 2014. Nutrient dynamics and seasonal variation of phytoplankton assemblages in the coastal waters of southwest bay of Bengal. Environ. Monit. Assess.
186 (9), 5681–5695.
Anderson, L.A., Sarmiento, J.L., 1994. Redﬁeld ratios of remineralization determined bynutrient data analysis. Glob. Biogeochem. Cycles 8 (1), 65–80.
Blumberg, A.F., Mellor, G.L., 1987. A description of a three-dimensional coastal ocean circulation model. In: Heaps, N.S. (Ed.), Three-Dimensional Coastal Ocean Models. AGU,
Washington, D. C, pp. 1–16.
Bristow, L.A., Mohr, W., Ahmerkamp, S., Kuypers, M.M.M., 2017. Nutrients that limit
growth in the ocean. Curr. Biol. 27 (11), R474–R478.
Bu, Y.Q., Zhu, L.Y., Cheng, X., Dong, H.H., Sui, Y., Wang, C., 2019. Community structure ofzooplankton and its relationship with environmental factors in the Bohai and thenorth Huanghai Sea in summer and winter. Period. Ocean Univ. China 49 (02),59–66 (In Chinese with English abstract).
Capet, A., Meysman, F.J.R., Akoumianaki, I., Soetaert, K., Grégoire, M., 2016. Integrating
sediment biogeochemistry into 3D oceanic models: a study of benthic-pelagic coupling in the Black Sea. Ocean Model 101, 83–100.
Caseas, B., Varela, M., Canle, N., Gonzalez, N., Bode, A., 1997. Seasonal variations of nutrients, seston and phytoplankton, and upwelling intensity off La coruna (NW Spain).
Estuar. Coast. Shelf Sci. 6 (44), 767–778.
Chen, Y., Gao, Y., Chen, C., Liang, J., Sun, L., Zhen, Y., Qiao, L., 2016. Seasonal variations ofphytoplankton assemblages and its relation to environmental variables in a scallop
culture sea area of Bohai Bay China. Mar. Pollut. Bull. 113 (1–2), 362–370.
COOA, 2016. China Offshore Ocean Atlas—Marine Life and Ecology. Ocean Press, Beijing,
China (In Chinese).
Ding, X.K., Guo, X.Y., Zhang, C., Yao, X.H., Liu, S.M., Shi, J., Luo, C.X., Yu, X.J., Yu, Y., Gao,
H.W., 2020. Water conservancy project on the Yellow River modiﬁes the seasonalvariation of chlorophyll-a in the Bohai Sea. Chemosphere 254, 126846.
Fan, W., Song, J.B., 2014. A numerical study of the seasonal variations of nutrients in the
Changjiang River estuary and its adjacent sea area. Ecol. Model. 291, 69–81.
Fennel, K., 2017. Ocean hotspots of nitrogen loss. Nature 551 (7680), 305–306.
Fennel, K., Wilkin, J., Levin, J., Moisan, J., O'Reilly, J., Haidvogel, D., 2006. Nitrogen cycling in
the middle Atlantic bight: results from a three-dimensional model and implications
for the North Atlantic nitrogen budget. Glob. Biogeochem. Cycles 20 (3).
Fisher, T.R., Peele, E.R., Ammerman, J.W., Harding, L.W., 1992. Nutrient limitation of phytoplankton in Chesapeake Bay. Mar. Ecol. Prog. Ser. 82 (1), 51–63.
Galbraith, E.D., Martiny, A.C., 2015. A simple nutrient-dependence mechanism forpredicting the stoichiometry of marine ecosystems. Proc. Natl. Acad. Sci. 112 (27),8199–8204.
Garcia, N.S., Sexton, J., Riggins, T., Brown, J., Lomas, M.W., Martiny, A.C., 2018. High variability in cellular stoichiometry of carbon, nitrogen, and phosphorus within classesof marine eukaryotic phytoplankton under sufﬁcient nutrient conditions. Front.
Microbiol. 9, 543.
Grégoire, M., Raick, C., Soetaert, K., 2008. Numerical modeling of the Central Black Sea
ecosystem functioning during the eutrophication phase. Prog. Oceanogr. 76 (3),286–333.
Guo, W., 2019. Modelling the Effects of the Atmospheric Nitrogen Deposition on the Primary Production Process in the Southern Yellow Sea. Master thesis. Ocean Universityof China, Qingdao (In Chinese with English abstract).
He, H.J., Yu, Z.G., Yao, Q.Z., Chen, H.T., Mi, T.Z., 2010. The hydrological regime and particulate size control phosphorus form in the suspended solid fraction in the dammed
huanghe (Yellow River). Hydrobiologia 638 (1), 203–211.
Herut, B., Almogi-Labin, A., Jannink, N., Gertman, I., 2000. The seasonal dynamics of nutrient and chlorophyll a concentrations on the SE Mediterranean shelf-slope. Oceanol.
Acta 23 (7), 771–782.
Kim, T., Lee, K., Najjar, R.G., Jeong, H., Jeong, H.J., 2011. Increasing N abundance in thenorthwestern Paciﬁc Ocean due to atmospheric nitrogen deposition. Science 334(6055), 505–509.
Kishi, M.J., Kashiwai, M., Ware, D.M., Megrey, B.A., Eslinger, D.L., Werner, F.E., NoguchiAita, M., Azumaya, T., Fujii, M., Hashimoto, S., Huang, D., Iizumi, H., Ishida, Y., Kang,
S., Kantakov, G.A., Kim, H., Komatsu, K., Navrotsky, V.V., Smith, S.L., Tadokoro, K.,
Tsuda, A., Yamamura, O., Yamanaka, Y., Yokouchi, K., Yoshie, N., Zhang, J., Zuenko,
Y.I., Zvalinsky, V.I., 2007. NEMURO—a lower trophic level model for the North Paciﬁcmarine ecosystem. Ecol. Model. 202 (1–2), 12–25.
Lai, J.X., Jiang, F.J., Ke, K., Xu, M.B., Lei, F., Chen, B., 2014. Nutrients distribution and trophic
status assessment in the northern Beibu Gulf, China. Chin. J. Oceanol. Limnol. 32 (5),1128–1144.
Laurent, A., Fennel, K., Hu, J., Hetland, R., 2012. Simulating the effects of phosphorus limitation in the Mississippi and Atchafalaya River plumes. Biogeosciences 9 (11),4707–4723.
Li, Y.F., Wolanski, E., Zhang, H., 2015. What processes control the net currents throughshallow straits? A review with application to the Bohai Strait, China. Estuar. Coast.
Shelf Sci. 158, 1–11.
Liu, S.M., 2015. Response of nutrient transports to water–sediment regulation events in
the huanghe basin and its impact on the biogeochemistry of the bohai. J. Mar. Syst.
141, 59–70.
Liu, H., Yin, B.S., 2010. Numerical investigation of nutrient limitations in the Bohai Sea.
Mar. Environ. Res. 70 (3–4), 308–317.
Liu, S.M., Hong, G.H., Zhang, J., Ye, X.W., Jiang, X.L., 2009. Nutrient budgets for large chinese estuaries. Biogeosciences 6 (10), 2245–2263.
Liu, S.M., Li, L.W., Zhang, Z., 2011. Inventory of nutrients in the bohai. Cont. Shelf Res. 31(16), 1790–1797.

5. Summary
In this study, a physical–biological coupled model was developed
to analyze the impact of external nutrient inputs and biological processes on the seasonal variations of nutrients in the CBS. The model
results showed signiﬁcant seasonal variations in nutrient concentrations and the N/P ratio. In summer, a large amount of nutrients was
consumed during phytoplankton photosynthesis, in which thenitrogen-to-phosphorus stoichiometry in phytoplankton uptake
was lower than the N/P ratio of seawater. This process was the
major cause of the lowest nutrient concentrations and the highest
N/P ratio in summer. River inputs, atmospheric deposition, and sediment nutrient release replenished nutrients, which, to some extent,
alleviated the decrease in nutrient concentrations in summer. Benthic nitrogen loss was essential for maintaining the nitrogen balancein the CBS by removing a large amount of inorganic nitrogen in summer and autumn. Additionally, we demonstrated that the variationin nitrogen-to-phosphorus stoichiometry of phytoplankton uptake
with the N/P ratio of ambient seawater is also an important factor affecting the nutrient ratio in the CBS. Therefore, such variation should
be included in future studies to obtain a better understanding of thenutrient dynamics in coastal waters. This work is a reference for establishing water quality management plans aimed at mitigating thenegative impacts of nutrient pollution (i.e., eutrophication) in global
coastal seas.
CRediT authorship contribution statement
Xiaokun Ding: Writing – original draft. Xinyu Guo: Writing –review & editing. Huiwang Gao: Writing – review & editing. Jie Gao:
Data curation. Jie Shi: Methodology. Xiaojie Yu: Resources. Zhaosen
Wu: Software.
Declaration of competing interest
The authors whose names are listed immediately below certify thatthey have no afﬁliations with or involvement in any organization or entity with any ﬁnancial interest (such as honoraria; educational grants;
participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony
or patent-licensing arrangements), or non-ﬁnancial interest (such as
personal or professional relationships, afﬁliations, knowledge or beliefs)
in the subject matter or materials discussed in this manuscript.
Acknowledgements
This study is funded by the National Natural Science Foundation ofChina-Shandong Joint Fund for Marine Science Research Centers(U1906215, U1806211) and NSFC (41876125). X. Ding thanks the
China Scholarship Council (CSC) for supporting his stay in Japan and
the Ministry of Education, Culture, Sports, Science and Technology,
Japan (MEXT) to a project on Joint Usage/Research Center, Leading Academia in Marine and Environmental Research (LaMer) for supporting
his study in Japan. We also thank Dr. Naoki Yoshie for providing thesource code of NEMURO.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scitotenv.2021.149416.
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Science of the Total Environment 799 (2021) 149416
Wang, X.L., Cui, Z.G., Guo, Q., Han, X.R., Wang, J.T., 2009. Distribution of nutrients and eutrophication assessment in the Bohai Sea of China. Chin. J. Oceanol. Limnol. 27 (1),
177–183.
Wang, X.C., Ma, H.Q., Li, R.H., Song, Z.S., Wu, J.P., 2012. Seasonal ﬂuxes and source variation of organic carbon transported by two major Chinese Rivers: the Yellow Riverand Changjiang (Yangtze) river. Glob. Biogeochem. Cycles 26 (2).
Wang, X.J., Jin, C.J., Wang, L.S., Zhang, C.S., 2018. Distribution characteristics and inﬂuencing factors of particulate organic carbon in the Yellow Sea and the Bohai Sea in summer of 2016. Haiyang Xuebao 40 (10), 200–208 (In Chinese with English abstract).
Wang, Y.J., Liu, D.Y., Lee, K., Dong, Z.J., Di, B.P., Wang, Y.Q., Zhang, J.J., 2017. Impact ofwater-sediment regulation scheme on seasonal and spatial variations of biogeochemical factors in the Yellow River estuary. Estuar. Coast. Shelf Sci. 198, 92–105.
Wang, B.D., Xin, M., Wei, Q.S., Xie, L.P., 2018. A historical overview of coastal eutrophication in the China seas. Mar. Pollut. Bull. 136, 394–400.
Wang, J.J., Yu, Z.G., Wei, Q.S., Yao, Q.Z., 2019. Long-term nutrient variations in the Bohai
Sea over the past 40 years. J. Geophys. Res. Oceans 124 (1), 703–722.
Wang, Y.B., Sun, Y.Y., Wang, C.X., Chen, W.W., Hu, X.K., 2019. Net-phytoplankton community structure and its environmental correlations in central Bohai Sea and the Bohai
Strait. Aquat. Ecosyst. Health Manag. 22 (4), 481–493.
Xin, M., Wang, B.D., Xie, L.P., Sun, X., Wei, Q.S., Liang, S.K., Chen, K., 2019. Long-termchanges in nutrient regimes and their ecological effects in the Bohai Sea, China.
Mar. Pollut. Bull. 146, 562–573.
Xu, S.S., Song, J.M., Yuan, H.M., Li, X.G., Li, N., Duan, L.Q., Yu, Y., 2011. Petroleum hydrocarbons and their effects on ﬁshery species in the Bohai Sea, North China. J. Environ. Sci.
23 (4), 553–559.
Xu, D.H., Sun, X.M., Chen, B.J., Xia, B., Cui, Z.G., Zhao, J., Jiang, T., Xia, L.C., Qu, K.M., 2016.
The ecological characteristics of zooplankton in the central Bohai Sea. Prog. Fish.
Sci. 37 (04), 7–18.
Yamaguchi, H., Koga, N., Ichimi, K., Tada, K., 2020. Seasonal variations in phytoplankton
productivity in a shallow cove in the eastern Seto Inland Sea, Japan. Fish. Sci. 86(6), 1067–1078.
Yoshikawa, C., Yamanaka, Y., Nakatsuka, T., 2005. An ecosystem model including nitrogenisotopes: perspectives on a study of the marine nitrogen cycle. J. Oceanogr. 61 (5),921–942.
Yu, X.J., Guo, X.Y., Gao, H.W., 2020. Detachment of low-salinity water from the yellow
river plume in summer. J. Geophys. Res. Oceans 125 (10).
Zhai, W.D., Zhao, H.D., Su, J.L., Liu, P.F., Li, Y.W., Zheng, N., 2019. Emergence of Summertime Hypoxia and Concurrent Carbonate Mineral Suppression in the Central Bohai
Sea, China. Journal of Geophysical Research: Biogeosciences 124 (9), 2768–2785.
Zhang, X.L., Wu, Z.M., Li, J., Yu, G.Y., Zhang, Z.N., Gao, S.H., 2006. Modeling study of seasonal variation of the pelagic-benthic ecosystem characteristics of the Bohai Sea.
J. Ocean Univ. China 5 (1), 21–28.
Zhang, Y., Yu, Q., Ma, W.C., Chen, L.M., 2010. Atmospheric deposition of inorganic nitrogento the eastern China seas and its implications to marine biogeochemistry. J. Geophys.
Res. 115 (D7).
Zhang, B., Li, Z.Y., Jin, X.S., 2012. Functional groups of ﬁsh assemblages and their major
species in the Bohai Sea. J. Fish. China 01, 64–72.
Zhang, H.L., Qiu, Z.F., Sun, D.Y., Wang, S.Q., He, Y.J., 2017. Seasonal and interannual variability of satellite-derived chlorophyll-a (2000–2012) in the Bohai Sea, China. Remote
Sens. 9 (6), 582.
Zhang, X.L., Zhang, Q.Q., Yang, A.J., Hou, L.J., Zheng, Y.L., Zhai, W.D., Gong, J., 2018. Incorporation of microbial functional traits in biogeochemistry models provides better estimations of benthic denitriﬁcation and anammox rates in coastal oceans. J. Geophys.
Res. Biogeosci. 123 (10), 3331–3352.
Zhang, C., He, J.Y., Yao, X.H., Mu, Y.C., Guo, X.Y., Ding, X.K., Yu, Y., Shi, J.H., Gao, H.W., 2020.
Dynamics of phytoplankton and nutrient uptake following dust additions in the
Northwest Paciﬁc. Sci. Total Environ. 739, 139999.
Zheng, L.W., Zhai, W.D., Wang, L.F., Huang, T., 2020. Improving the understanding of central Bohai Sea eutrophication based on wintertime dissolved inorganic nutrient budgets: roles of North Yellow Sea water intrusion and atmospheric nitrogen deposition.
Environ. Pollut. 267, 115626.
Zou, L., Zhang, J., Pan, W.X., Zhan, Y.P., 2001. In situ nutrient enrichment experiment in thebohai and Yellow Sea. J. Plankton Res. 23 (10), 1111–1119.
Zhou, L.M., Sun, Y., Zhang, H.H., Yang, G.P., 2018. Distribution and characteristics of inorganic nutrients in the surface microlayer and subsurface water of the bohai and yellow seas. Cont. Shelf Res. 168, 1–10.

Liu, S.M., Li, L.W., Zhang, G.L., Liu, Z., Yu, Z., Ren, J.L., 2012. Impacts of human activities onnutrient transports in the huanghe (Yellow River) estuary. J. Hydrol. 430–431,
103–110.
Liu, J.N., Du, J.Z., Yi, L.X., 2017. Ra tracer-based study of submarine groundwater discharge
and associated nutrient ﬂuxes into the Bohai Sea, China: a highly human-affectedmarginal sea. J. Geophys. Res. Oceans 122 (11), 8646–8660.
Louw, D.C., van der Plas, A.K., Mohrholz, V., Wasmund, N., Junker, T., Eggert, A., 2016. Seasonal and interannual phytoplankton dynamics and forcing mechanisms in thenorthern Benguela upwelling system. J. Mar. Syst. 157, 124–134.
Lyngsgaard, M.M., Markager, S., Richardson, K., Møller, E.F., Jakobsen, H.H., 2017. How
well does chlorophyll explain the seasonal variation in phytoplankton Activity?
Estuar. Coasts 40 (5), 1263–1275.
Macias, D., Huertas, I.E., Garcia-Gorriz, E., Stips, A., 2019. Non-redﬁeldian dynamics driven
by phytoplankton phosphate frugality explain nutrient and chlorophyll patterns inmodel simulations for the Mediterranean Sea. Prog. Oceanogr. 173, 37–50.
Malone, T.C., Conley, D.J., Fisher, T.R., Glibert, P.M., Harding, L.W., Sellner, K.G., 1996. Scalesof nutrient-limited phytoplankton productivity in Chesapeake Bay. Estuaries 19 (2),371–385.
Mills, M.M., Arrigo, K.R., 2010. Magnitude of oceanic nitrogen ﬁxation inﬂuenced by thenutrient uptake ratio of phytoplankton. Nat. Geosci. 3 (6), 412–416.
Mu, D., Yuan, D., Feng, H., Xing, F., Teo, F.Y., Li, S., 2017. Nutrient ﬂuxes across sedimentwater interface in Bohai Bay Coastal Zone, China. Mar. Pollut. Bull. 114 (2), 705–714.
Ounissi, M., Amira, A.B., Dulac, F., 2018. Riverine and wet atmospheric inputs of materials
to a North Africa coastal site (Annaba Bay, Algeria). Prog. Oceanogr. 165, 19–34.
Pan, S.S., Shi, J., Gao, H.W., Guo, X.Y., Yao, X.H., Gong, X., 2017. Contributions of physicaland biogeochemical processes to phytoplankton biomass enhancement in the surface
and subsurface layers during the passage of Typhoon Damrey. Jo. Geophys. Res.
Biogeosci. 122 (1), 212–229.
Pennock, J.R., Boyer, J.N., Herrera-Silveira, J.A., Iverson, R.L., Whitledge, T.E., Mortazavi, B.,
Comin, F.A., 1999. Nutrient behavior and phytoplankton production in Gulf of Mexico
estuaries. Biogeochem. Gulf Mexico Estuar. 109–162.
Purina, I., Labucis, A., Barda, I., Jurgensone, I., Aigars, J., 2018. Primary productivity in the
Gulf of Riga (Baltic Sea) in relation to phytoplankton species and nutrient variability.
Oceanologia 60 (4), 544–552.
Qi, J.H., Yu, Y., Yao, X.H., Gang, Y., Gao, H.W., 2020. Dry deposition ﬂuxes of inorganic nitrogen and phosphorus in atmospheric aerosols over the marginal seas and Northwest Paciﬁc. Atmos. Res. 245, 105076.
Redﬁeld, A.C., 1934. On the proportions of organic derivations in sea water and their relation to the composition of plankton. In: Daniel, R.J. (Ed.), James Johnstone Memorial
Volume. University Press of Liverpool, pp. 177–192.
Rudnick, D.T., Oviatt, C.A., 1986. Seasonal lags between organic carbon deposition andmineralization in marine sediments. J. Mar. Res. 4 (44), 815–837.
Shi, J.H., Gao, H.W., Zhang, J., Tan, S.C., Ren, J.L., Liu, C.G., Liu, Y., Yao, X.H., 2012. Examination of causative link between a spring bloom and dry/wet deposition of Asian dust in
the Yellow Sea, China. J. Geophys. Res. Atmos. 117 (D17).
Shi, X.Y., Qi, M.Y., Tang, H.J., Han, X.R., 2015. Spatial and temporal nutrient variations in
the Yellow Sea and their effects on Ulva prolifera blooms. Estuar. Coast. Shelf Sci.
163, 36–43.
Shou, W.W., Zong, H.B., Ding, P.X., Hou, L.J., 2018. A modelling approach to assess the effects of atmospheric nitrogen deposition on the marine ecosystem in the Bohai Sea,
China. Estuar. Coas. Shelf Sci. 208, 36–48.
Song, N., Wang, N., Lu, Y., Zhang, J., 2016. Temporal and spatial characteristics of harmful
algal blooms in the Bohai Sea during 1952–2014. Cont. Shelf Res. 122, 77–84.
Song, G.D., Liu, S.M., Zhang, J., Zhu, Z.Y., Zhang, G.L., Marchant, H.K., Kuypers, M.M.M.,
Lavik, G., 2020. Response of benthic nitrogen cycling to estuarine hypoxia. Limnol.
Oceanogr. 66 (3), 652–666.
Sui, Q., Xia, B., Xie, H.B., Cui, Y., Chen, B.J., Cui, Z.G., Ding, D.S., 2016. Study on temporal andspatial variation of nutrients and evaluation on eutrophication in the seawater of theBohai Sea in Winter and Spring of 2014. Prog. Fish. Sci. (02), 10–15 (In Chinese withEnglish abstract).
Tan, E., Zou, W.B., Zheng, Z.Z., Yan, X.L., Du, M.G., Hsu, T.C., Tian, L., Middelburg, J.J., Trull,
T.W., Kao, S., 2020. Nat. Clim. Change 10 (4), 349–355.
Wakeham, S.G., Lee, C., 1993. Production, transport, and alteration of particulate organic
matter in the marine water column. In: Engel, M.H., Macko, S.A. (Eds.), Organic Geochemistry: Principles and Applications. Springer US, Boston, MA, pp. 145–169.
Wang, Q., Guo, X.Y., Takeoka, H., 2008. Seasonal variations of the Yellow River plume in
the Bohai Sea: a model study. J. Geophys. Res. Oceans 113 (C8).

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