Chemosphere 313 (2023) 137620

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Interannual variations in the nutrient cycle in the central Bohai Sea in
response to anthropogenic inputs
Xiaokun Ding a, b, Jie Shi a, c, *, Xinyu Guo d, Huiwang Gao a, c, Sumei Liu c, e, Wei Guo fa

Key Laboratory of Marine Environment and Ecology, Ministry of Education of China, Ocean University of China, 238 Songling Road, Qingdao, 266100, China
School of Ocean, Yantai University, Yantai, 264005, ChinacLaboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, ChinadCenter for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama, 790-8577, Japane
Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education of China, Ocean University of China, 238 Songling Road, Qingdao, 266100, Chinaf
Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, 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• An N- and P-based ecological model was
designed.
• Both concentration and conversion rates
of N increased from 1998 to 2017.
• Atmospheric deposition affects theinterannual variation of the N cycle.
• N concentration will decrease linearlywith declined nutrient input.
• The response of P cycle to anthropo­
genic inputs is weak.

A R T I C L E I N F O

A B S T R A C T

Handling Editor: Yongmei Li

In recent decades, there has been growing concern regarding the effects of human activities on the coastal
nutrient cycle. However, interannual variations in the coastal nutrient cycle in response to anthropogenic
nutrient input have rarely been quantified. In this study, a hydrodynamic-ecological model capable of describing
the nitrogen and phosphorus cycles was used to analyze interannual variations in the nutrient cycle in the central
Bohai Sea, a typical semi-enclosed sea in the Northwest Pacific. The results showed an increasing trend of dis­solved inorganic nitrogen and particulate nitrogen from 1998 to 2017, whereas different forms of phosphorus
showed no obvious interannual variations. The annual nutrient budgets were also quantitatively estimated from1998 to 2017. This indicates that atmospheric nitrogen deposition plays an important role in interannual vari­ations in the nitrogen cycle. A large amount of nitrogen from anthropogenic inputs was mainly removed by
sedimentation processes instead of increasing the standing stock of nitrogen in the sea. With the reduction ofanthropogenic inputs, the model showed that a variety of forms of nitrogen concentration decreased linearly,
whereas phosphorus concentration increased slightly. Therefore, although environmental governance caneffectively alleviate water eutrophication, it is necessary to avoid the situation where the dissolved inorganic

Keywords:
Nutrient cycle
Interannual variation
Anthropogenic inputs
Central Bohai Sea* Corresponding author. 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: shijie@ouc.edu.cn (J. Shi).
https://doi.org/10.1016/j.chemosphere.2022.137620
Received 14 September 2022; Received in revised form 15 December 2022; Accepted 19 December 2022
Available online 20 December 20220045-6535/© 2022 Elsevier Ltd. All rights reserved.

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nitrogen concentration in the sea becomes too low for phytoplankton to grow, which may determine the primary
productivity and eventually affect fishery resources.

1. Introduction

Governments worldwide often enact many marine environmental
policies to alleviate eutrophication in coastal waters. For example, aseries of measures, such as reducing the anthropogenic nutrient input
and increasing the pretreatment of pollution into the sea, have been
carried out to improve the ecological environment of Chesapeake Bay,
Baltic Sea, and Seto Inland Sea over the past few decades (Takeoka et al.,
2002; Prasad et al., 2010; Heiskanen et al., 2019). However, the obvious
differences appeared between the governance effects of different sea
areas. Evidence has shown that the Baltic Sea is still eutrophic owing to
insufficient governance (Heiskanen et al., 2019), while the Seto Inland
Sea is apparently in an oligotrophic condition caused by overregulation(Yamamoto, 2003).
In the last 20 years, the Chinese government has issued a series ofplans for controlling pollution in the BS (https://www.mee.gov.cn/),such as the Clean Bohai Sea Program (2001–2015), the Bohai Sea
Environmental Protection General Plan (2008–2020), and the Action
Plan for Launching the Tough Battle on the Integrated Rehabilitation of
the Bohai Sea (2018–2021). These policies have controlled external ni­
trogen inputs and effectively restrained the deterioration of water
quality in the BS (Tong et al., 2014; Zhou et al., 2021). At present, aseries of plans such as China’s 14th Five-Year Plan (2021–2025) and
Carbon Peaking (2030), and Carbon Neutrality Goals (2060) have been
proposed to improve the coastal water quality of China. Anthropogenic
nutrient inputs to the BS will further decrease under these government
plans. However, future variations in the nutrient cycle are unknown
under the current background of the continuous implementation ofemission reduction policies in the BS. Therefore, it is necessary to assess
the future variations in the nutrient cycle in the BS, which can provide agood reference for environmental protection and management
decision-makers.
Ocean numerical models have become indispensable tools for
exploring long-term variations in marine ecosystems (Capet et al.,2016). In this study, we used a vertical one-dimensional hydro­
dynamic-ecological coupled model developed in our previous study(Ding et al., 2021) to analyze interannual variations in the nutrient cycle
under the influence of anthropogenic external inputs in the central
Bohai Sea (CBS) from 1998 to 2017. A series of numerical experiments
were designed to predict future variations in the nutrient cycle under
different emission reduction scenarios.

In the past century, human activities have caused the nitrogen input
into rivers and atmospheric nitrogen emissions to increase from ~70 Tg
N yr− 1 and ~13 Tg N yr− 1 to ~150 Tg N yr− 1 and ~46 Tg N yr− 1,
respectively (Galloway et al., 2004; Dentener, 2006), which are even­tually transported to the marine environment. Coastal waters are tran­
sition regions from land to oceans and are regarded as vulnerable areas
under the strong influence of anthropogenic activities. In recent de­cades, the external inputs of anthropogenic pollutants have led toobvious interannual variations in coastal ecosystems, such as increasing
nutrient concentrations in the Mediterranean Sea (Moon et al., 2016)
and the succession of the plankton community in the Baltic Sea (Suik­kanen et al., 2007).
Nutrients such as dissolved inorganic nitrogen (DIN) and dissolved
inorganic phosphorus (DIP) form the basis of the marine biogeochemical
cycle (Bristow et al., 2017). Under the interannual increase of anthro­pogenic inputs, the nitrogen cycle and phosphorus cycle (termed the“nutrient cycle” henceforth) have also changed a lot in coastal seas inrecent decades (Kim et al., 2011; Yu et al., 2021), resulting in a series ofenvironmental problems such as eutrophication, harmful algal blooms,
hypoxia, and ocean acidification (Breitburg et al., 2018; Wang et al.,2018; Zhou et al., 2021). Understanding the interannual variation in the
nutrient cycle is essential for establishing water quality management
plans aimed at mitigating the negative impacts of anthropogenic inputs
in coastal seas. However, historical interannual variations in the coastal
nutrient cycle in response to anthropogenic external inputs have rarely
been quantified.
The Bohai Sea (BS), a semi-enclosed sea severely affected by humanactivities in the Northwest Pacific (Song et al., 2016; Zhou et al., 2021),
provides an ideal case for investigating how anthropogenic inputs affectthe nutrient cycle over time in coastal seas. In recent decades, nutrientsfrom river inputs (Zhang et al., 1994; Zhang et al., 1996; Wu et al.,2021), atmospheric deposition (Liu et al., 2008; Shou et al., 2018), andwastewater discharge (Wang et al., 2019a) have increased significantlywith rapid economic development. Although the BS only accounts for~1.6% of China’s coastal area, it receives ~36% of China’s land-based
wastewater and ~47% of solid pollutants (Wang and Wang, 2007; Xinet al., 2019). Many anthropogenic inputs have seriously affected thenutrient cycle in the BS (Ning et al., 2010; Wu et al., 2013; Wang et al.,2019a) resulting in increasing environmental problems. Evidence has
shown that the frequencies of harmful algal blooms and jellyfish blooms
have increased by several folds since 2000 in the BS (Dong et al., 2010;
Song et al., 2016).
Long-term field observations demonstrated that DIN concentrations
showed a significantly increasing trend in recent decades in the BS, while
DIP concentrations exhibited no obvious interannual variations, causinga shift in limiting nutrients for phytoplankton growth from nitrogen tophosphorus (Xu et al., 2011; Wang et al., 2019a; Xin et al., 2019).
Meanwhile, increasing plankton biomass and miniaturization of phyto­plankton have occurred as a result of human activities in recent years (Liet al., 2021; Zhai et al., 2021). However, although the changes in theconcentrations of these biogeochemical variables depend on the nutrient
cycle, most of the observations focused on the BS itself and barely studied
the interannual variations of various nutrient cycle processes, such as theassimilation of phytoplankton, excretion of plankton, and mineralizationof organic matter. In addition, it is difficult to distinguish the effects ofvarious external sources, such as river input and atmospheric deposition,on the interannual variations in the nutrient cycle processes in the BS
based only on field observations (Liu et al., 2017; Wang et al., 2019a; Xinet al., 2019; Ding et al., 2020; Zheng and Zhai, 2021).

2. Methodology
2.1. Study area
The BS has an average depth of 18 m and a total area of 7.7 × 104
km . It is connected to the Yellow Sea via the Bohai Strait (Fig. S1).
According to its geographical location, the BS can be divided into fivesub-regions (Zhang et al., 2017), of which the CBS is the largest with anarea of 2.9 × 104 km2 and an average depth of 22.5 m. Phytoplankton
biomass in the CBS accounts for nearly half of that in the entire BS (Dinget al., 2020). The two main sources of nutrients are input from the
Yellow River (point source pollution) and atmospheric deposition(non-point source pollution), both of which have a great influence on the
CBS ecosystem (Ding et al., 2021). The other terrestrial sources havelittle effect on the nutrient concentration in the CBS because of theextremely weak water exchange between the CBS and the area close to
the coastline (Cai, 2013; Li et al., 2015; Li, 2017). 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 themodel (Ding et al., 2021). Therefore, the interannual variations in the
nutrient cycle in the CBS were analyzed under the influence of river22

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Chemosphere 313 (2023) 137620

input and atmospheric deposition in this study, which could provide areference for understanding the nutrient cycle in the BS.

To evaluate variations in the nutrient cycle under different emission
reduction projects in the future, a series of numerical experiments were
designed based on the simulation of 1998–2017. The simulation results
at the end of 2017 for Case A0 were set as the initial conditions for thesimulation scenarios. Given the scenarios in 2017 as a reference, thenutrients from river input and atmospheric deposition were reduced by acluster of percentages from 0 to 100% at intervals of 10%. All 121 (11 ×
11) numerical simulations were run for 20 years after 2017. The results
were used to understand the variations in the nutrient cycle under
different external inputs.

2.2. Model description
The vertical one-dimensional physical-biological coupled model
developed in our previous study (Ding et al., 2021) was modified for thisstudy. As shown in Fig. S2, the model not only includes the nitrogen
cycle (Ding et al., 2021) but also describes the specific processes related
to phosphorus (P). There were 21 state variables in the model, including
nitrate (NO−3 ), ammonium (NH+4 ), dissolved inorganic phosphorus(DIP), dinoflagellates (PS–N, PS-P), diatoms (PL-N, PL-P), micro­
zooplankton (ZS-N, ZS-P), mesozooplankton (ZL-N, ZL-P), bacteria(BAC-N, BAC-P), dissolved organic nitrogen (DON), dissolved organic
phosphorus (DOP), particulate organic nitrogen (PON), particulateorganic phosphorus (POP) in seawater, fast-decaying PON (CfPON ) andPOP (CfPOP ), and slow-decaying PON (CsPON ) and POP (CsPOP ) in thesediment. A detailed description of the model and the values of themodel parameters for the CBS domain is available in Ding et al. (2021).
The modifications of the model are given in the Text S1 in Supplemen­tary materials.

2.4. Calculating fate of external nitrogen
To quantify the three main fates of external nitrogen (Section 4.2).
Annual changes in DIN and particulate N were evaluated by subtracting
the standing stock on the first day from the value on the last day of theyear. The annual net flux of nitrogen sedimentation (SF) was calculated
as follows:
SF = (SP365 − SP1 ) − (SR365 − SR1 )(1)where SP365 and SR365 are the net sinking flux of PON and the released
flux of DIN at the water-sediment interface on the last day of a specificyear, respectively, and SP1 and SR1 denote the corresponding fluxes on
the first day of that year.

2.3. Model configurations
2.3.1. Nutrient sources
River input and atmospheric deposition were considered the twomain external inputs of nutrients in the model. Other external nutrient
sources including sewage, aquaculture, submarine fresh groundwater
discharge, and Yellow Sea input, were not included in this study becauseof their weak impact on the CBS (see detailed descriptions in Text S2 in
Supplementary materials). The monthly nutrient input from the Yellow
River during 1998–2017 has been reported by Wu et al. (2021). Missing
monthly riverine nutrient flux data were calculated based on the
regression relationships between nutrient fluxes and river discharge(Liu, 2015). In this study, the monthly atmospheric deposition of DIN in2015 was obtained from an atmospheric transport model (Guo, 2019).
The time series of atmospheric N deposition flux during 1998–2017 wasestimated based on the atmospheric deposition data from 2015 and theinterannual trend of atmospheric nitrogen oxide emissions surrounding
the BS (Shangdong, Hebei, Beijing, Tianjin, and Liaoning) from 1998 to2017 (http://www.stats.gov.cn/tjsj/). In addition, the atmosphericdeposition of P was not considered because the phosphorus flux fromatmospheric deposition is extremely low in the BS (Shi et al., 2012;
Wang et al., 2019a).

3. Results
3.1. Interannual variations of nutrient concentrations and ratios
As shown in Fig. 1, the simulated monthly nutrient concentrations
were generally consistent with the observations from 1998 to 2017. In
surface water, the simulated annual mean DIN concentrations exhibitedan obvious increasing trend with the minimum value of ~6.0 mmol m− 3
in 1998 and the maximum value of 14.8 mmol m− 3 in 2013, while theannual mean DIP had been around 0.3 mmol m− 3 with no obvious
interannual variations during 1998–2017. The lowest (troughs in Fig. 1)
and highest (peaks in Fig. 1) annual concentrations appeared in summer
and winter, respectively. The DIN concentrations in winter increased
from ~10 mmol m− 3 in 1998 to ~20 mmol m− 3 in 2013 and thendecreased slightly. The DIN concentrations in summer were always
lower than 5 mmol m− 3 during 1998–2017. The inputs of P-containing
nutrients from the external sources promote the photosynthesis ofphytoplankton in summer, resulting in a large amount of DIN con­sumption in seawater at the same time (Ding et al., 2021). During these
20 years, the DIP concentrations reached peak values (0.5 mmol m− 3) inwinter, twice the minimum value in summer. In addition, there was noapparent difference in DIN concentrations between the surface waterand bottom water. However, the DIP concentrations in the summer wereslightly higher in the bottom water than in the surface water. Fig. S3shows the simulated vertical distribution of monthly mean nutrient
concentrations from 1998 to 2017.
There were obvious interannual variations in the DIN to DIP ratio(DIN/DIP) from 1998 to 2017 as a result of the interannual variations in
DIN concentrations. The DIN/DIP ratio was highest in summer andlowest in winter (Fig. S3c). The DIN/DIP in the surface water in summer
increased from ~30 in 1998 to ~80 in 2013, and then decreased slightly
after 2013. The increasing trend of DIN/DIP was relatively weak in
winter, from ~20 in 1998 to ~35 in 2013. In addition, DIN/DIP
decreased with water depth in summer, but was almost homogenous in
winter.

2.3.2. Initial conditions and calculations
The hydrodynamic model was initialized using the temperature andsalinity data from January, and the biological model was initializedusing the observed nutrient concentrations in winter. For spin-up, themodel was integrated for 5 years as forced by daily surface forcing andmonthly nutrient inputs in 1998. The model was then integrated for 20
years from 1998 to 2017 using the daily surface forces and monthly
nutrient inputs of these 20 years. The description and data sources of theinitial conditions and surface forces have been described in detail in ourprevious studies (Ding et al., 2020, 2021). This simulation was named as
Case A0 (Table S1).
2.3.3. Numerical experiments
To examine the impact of river input and atmospheric deposition on
the interannual variation in the nutrient cycle, we designed two nu­
merical experiments. In Cases A1 and A2, the model was operated for 20
years (1998–2017) driven only by river input in 1998 or by atmosphericdeposition in 1998, respectively. Their results were compared with thoseof Case A0 to clarify the impacts of river input and atmosphericdeposition.

3.2. Interannual variations of particulate matter concentrations
The phytoplankton biomass, represented by the concentration ofchlorophyll-a (Chl-a), exhibited apparent interannual variations in the3

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Fig. 1. Comparison of the simulated DIN and DIP concentration and observational data from 1998 to 2017. (a, c): surface water; (b, d): bottom water.

Fig. 2. Comparison of the simulated monthly Chl-a concentration in surface water and satellite-derived data from 1998 to 2017.

CBS, which agreed well with the satellite-derived data (Fig. 2). The
simulated seasonal variations in Chl-a, maximum in summer but mini­
mum in winter, have been reported in our previous study (Ding et al.,2021). The surface concentrations of Chl-a in summer increased from~2 mg m− 3 in 1998 to ~4 mg m− 3 in 2013 but then decreased to ~3 mg
m− 3 in 2017. In contrast, the surface concentrations of Chl-a in winterexhibited no obvious variation from 1998 to 2017, and remained at a
low value of ~1 mg m− 3.
Figs. S4–S7 shows the simulated interannual variations of all par­ticulate matter concentrations from 1998 to 2017 and their N-to-P (N/P)
ratios. In general, the N concentrations of different particulate matter insummer showed an obvious increasing trend from 1998 to 2017,

whereas the P concentrations of particulate matter in summer decreasedslightly. These particulate matters remained at low concentrations in
winter, with little interannual variation. The N/P ratios in all particulatematter also showed an increasing trend, with the summertime ratios
increasing from ~16 in 1998 to ~30 in 2013 (Fig. S7). In addition, thesummertime concentrations of particulate P were higher in the bottom
water than in the surface water, whereas there was no apparent verticaldifference for particulate N, except for the increased PON with water
depth. The N/P ratios of particulate matter were higher in the surface
water (Fig. S7).
Diatoms were the dominant species because they have a higher
biomass than dinoflagellates (Fig. S4). It has also been reported that the4

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cell abundance of diatoms accounted for >70% of phytoplankton in the
BS (COOA, 2016; Zhang, 2016; Wang et al., 2019b). The simulated
zooplankton concentrations varied from 0 to 2.3 mmol N m− 3 (Fig. 1aand c), which agreed well with the observations (0–2.5 mmol N m− 3)
over the CBS (Du et al., 2013; Xu et al., 2016; Bu et al., 2019) deduced
from the relationship between the zooplankton wet weight and its ni­
trogen content (Moriarty et al., 2012). It has been reported that PON,
DON, and DOP in summer were around 4.8, 14.5, and 0.3 mmol m− 3,
respectively (Liu et al., 2011; Zhai et al., 2019), which also matched ourmodel results. In addition, the simulated concentrations of BAC-N inwinter, spring, summer, and autumn from 2006 to 2007 were 0.2, 0.3,
1.1, and 0.8 mmol N m− 3, respectively, which corresponded well withthe observed seasonal variation in bacteria concentration (Sun, 2012).
Overall, our model largely reproduced seasonal and interannual varia­
tions in particulate matter concentrations in the CBS.

trend over the past 20 years. Biological processes are the main sinks of
DIN. The loss rate of DIN caused by biological processes increased from
~0.4 mmol N m− 2 d− 1 in 1998 to ~1.1 mmol N m− 2 d− 1 in 2013 andthen decreased to 0.7 mmol N m− 2 d− 1 in 2017 (Fig. 3a).
These biological processes include phytoplankton uptake andrespiration, zooplankton excretion, and the mineralization of organic
matter. Fig. 3b shows the variations in each biological process related
to DIN from 1998 to 2017. The uptake by phytoplankton was thelargest sink of DIN, which changed DIN to the particulate form of ni­
trogen. Phytoplankton uptake by photosynthesis increased from ~8mmol N m− 2 d− 1 in 1998 to ~10.5 mmol N m− 2 d− 1 in 2012 and thenslightly decreased. Other biological sources, including phytoplankton
respiration, zooplankton excretion, and mineralization of organic
matter, work together to compensate for the consumption of phyto­plankton growth.
For DIP (Fig. 4), the biological processes and the sediment release
acted as the most important sink and source, respectively, and both ofthem increased from ~0.02 mmol P m− 2 d− 1 in 1998 to ~0.04 mmol P
m− 2 d− 1 in 2017. Comparing the two processes, the river input remained
at a low value of 0.01 mmol P m− 2 d− 1 during these 20 years, except fora slight increase around 2002 and a decrease around 2013. Among all
the biological processes, phytoplankton uptake was the main sink of
DIP, whereas phytoplankton respiration, zooplankton excretion, andmineralization acted as the source. The DIP flux of each specific bio­
logical process was approximately one order of magnitude larger than
the net value of all biological processes, which indicates that plankton
activity accelerated the DIP cycle.

3.3. Nutrient budgets
Nutrient budgets per unit area of the water column were calculated
from 1998 to 2017 (Figs. 3 and 4) based on the model results. Nutrient
sources and sinks include river input, atmospheric deposition, sediment
release, and biological processes. The river input, atmospheric deposi­tion, and sediment release acted together as sources of DIN with thesame order of magnitude. Their values varied within the range of
0.1–0.5 mmol N m− 2 d− 1 (Fig. 3a). The river input increased after 2002
but decreased after 2013. Atmospheric deposition showed a significant
increasing trend from 1998 to 2017, although there was a slightdecrease after 2011. The sediment release also showed an increasing

Fig. 3. Simulated vertically integrated fluxes of the DIN source-sink processes from 1998 to 2017. Biological processes include phytoplankton uptake and respiration,
zooplankton excretion, and the mineralization of organic matter. Net DIN reprent the change rate in DIN of seawater considering all processes. Positive values
correspond to increasing nutrient concentrations after adding the process.
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Fig. 4. Simulated vertically integrated fluxes of the DIP source-sink processes from 1998 to 2017. Biological processes include phytoplankton uptake and respiration,
zooplankton excretion, and the mineralization of organic matter. Net DIP reprent the change rate in DIP of seawater considering all processes. Positive values
correspond to increasing nutrient concentrations after adding the process.

4. Discussion

Overall, comparisons of the three cases suggested that atmospheric N
deposition played a critical role in the interannual increases in DIN
concentrations and DIN/DIP during 1998–2017. However, river input
slightly affected the interannual variations of nutrient concentrations in
the CBS. This phenomenon is not limited to the BS. In recent years,~90% of nitrogen and ~60% of phosphorus in the northeastern Medi­terranean Sea have originated from atmospheric deposition (Koçak
et al., 2010). Atmospheric deposition accounts for >60% of the DIN
sources in the Yellow Sea and BS (Zhao et al., 2016). The increased
nutrient flux of atmospheric deposition significantly affected the inter­annual variations in nutrients in these seas.

4.1. Effects of river input and atmospheric deposition on the interannual
variations of nutrients
Under the influence of river input and atmospheric deposition, theDIN and DIP concentrations in the CBS exhibited obvious interannual
variations, which were identified not only in our study but also by ob­
servations (Wang et al., 2019a; Xin et al., 2019). The effects of river
input and atmospheric deposition on interannual variations were
examined by comparing the results of Cases A1 and A2 with those of
Case A0 (Fig. 5).
In Case A1 (Fig. 5a), removing the interannual variations in river
input did not change the increasing trend of DIN concentrations. How­ever, without the inclusion of the interannual variation in atmosphericdeposition in Case A2, the DIN concentrations showed no obvious
interannual differences between 1998 and 2017 (Fig. 5a). The DIN con­centrations in Case A2 were reduced to ~10 mmol m− 3, which was nearly
half of that in Case A0 around 2013. Compared to DIN concentrations,
there were no obvious differences in DIP concentrations among the threecases except that the DIP was slightly lower in Case A1 than in Case A0
after 2011 due to the less riverine P input in Case A1 (Fig. 5b). Conse­
quently, the interannual variations in DIN/DIP were mainly determinedby variations in DIN concentrations. The DIN/DIP ratio was higher inCase A1 than in Case A0 after 2013 because of the slightly decreased DIP;
however, the interannual variations were similar in Cases A0 and A1. The
DIN/DIP in Case A2 did not present an interannual increase, as in CaseA0, but decreased to approximately 16 in 2017 (Fig. 5c).

4.2. Fate of external nitrogen after entering the CBS
The contribution of Yellow River to the CBS is 0.21 mmol N m− 2 d− 1,
and the amount from the atmospheric depositions is 0.20 mmol N m− 2
d− 1. The DIN concentration would increase by ~133 mmol N m− 3 (=(0.21 + 0.20) mmol N m− 2 d− 1 × 365 d/22.5 m × 20 years) in the CBS
from 1998 to 2017 if we assume that they were not involved in thebiochemical processes and water exchange with the adjacent sea.
Because the DIN concentration increased by ~10 mmol N m− 3 in ourmodel results (Fig. 1), there are also other important fates for DIN fromthe river and atmosphere.
DIN transforms into particulate N after entering seawater, which canbe indicated by both regular seasonal blooms of plankton and unex­
pected events such as outbreaks of harmful algal blooms. It has beenreported that both the occurrence frequency and area of harmful algal
blooms have increased significantly in recent decades over the BS (Song6

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Fig. 5. Monthly variations in DIN concentration (a), DIP concentration (b), and DIN/DIP (c) from 1998 to 2017 simulated in different numerical experiments.

et al., 2016; Wang et al., 2019a), suggesting that more DIN from externalsources transforms into particulate N. Then, part of the particulate N in
the seawater enters the sediment in the form of debris through net
deposition. Zheng et al. (2020) and Tian et al. (2020) evaluated thenitrogen budget in the BS and showed that the sedimentation of par­ticulate nitrogen is an important sink of nitrogen in seawater.
Other processes, such as marine fish catch and exchange with the
Yellow Sea, may have little effect on the reduction of nitrogen inseawater. The catch of fish in the entire BS was ~0.6 Tg year− 1 in recent
years (CFSY, 2020). According to the proportion of nitrogen in fish
(Ramseyer, 2002), the calculation showed that the N-removal ratecaused by marine fishing was ~0.04 mmol N m− 2 d− 1 in the BS, which
was much lower than the rates of river input and atmospheric deposition(Fig. 3a). Since the water exchange is very weak (water residence time:
1.1–5.2 years) in the BS (Cai, 2013; Li et al., 2015; Li, 2017), the ni­
trogen cycle of the CBS may also be less affected by horizontal con­vection exchange. Therefore, the three main fates of external nitrogen
increase seawater DIN, increase seawater particulate nitrogen, and enter

the sediment by sedimentation.
Considering the changes in nutrients in the CBS throughout the year,
as shown in Fig. 6, the amount of external nitrogen input from both theriver input and atmospheric deposition increased from ∼ 2 ×
109 mol year− 1 in 1998 to ∼ 8 × 109 mol year− 1 in 2012, then decreasedto ∼ 4 × 109 mol year− 1 until 2017. Nitrogen sedimentation exhibited asimilar interannual variation as the external nitrogen input. The change
in DIN standing stock in the seawater exhibited positive values during
2002–2013 due to the increase in external input, but became negative
since 2013 as the external input decreased. However, the rate of change
was much smaller than that of sedimentation. The annual mean standing
stock of particulate nitrogen in seawater showed little change from 1998
to 2017.
The increase in PON concentration in the sediments (Fig. S8) also
confirmed that the sedimentation of particulate nitrogen increases in
response to an increase in external nitrogen. The observed data also
showed that the concentrations of organic nitrogen in sediments haveshown an increasing trend in recent decades (Li et al., 2021). The
Fig. 6. Fate of external nitrogen after entering the sea
from 1998 to 2017. External input include riverine
input and atmospheric deposition. The three mainfates of external nitrogen increase seawater DIN, in­crease seawater particulate nitrogen, and enter the
sediment by sedimentation. Annual changes in DIN
and particulate N were evaluated by subtracting thestanding stock on the first day from the value on thelast day of the year. The annual net flux of nitrogen
sedimentation was calculated using Eq. (1).

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increase in particulate nitrogen concentration in sediments intensifies
aerobic mineralization, which further leads to hypoxia in the bottom
seawater and sediment (Song et al., 2016; Zhai et al., 2019; Chen et al.,2022). Anaerobic ammonia oxidation and denitrification can convert
DIN into molecular nitrogen, which is an effective way to relieve theaccumulation of nitrogen in sediment (Fennel, 2017; Zhang et al., 2018).
The dissolved nitrogen released from the buried organic matter insediment was estimated to have a low value and a small variation range(Burdige, 2005; Zhang et al., 2014), which indicate its small effect on theinterannual variations of nutrient budgets in the central Bohai Sea.
Overall, sedimentation acted as a reservoir and played an essential role
in removing external nitrogen from seawater.

deposition. In contrast, DIP concentrations showed no obvious responseto changes in river input and atmospheric deposition until the flux ofatmospheric deposition was reduced by less than 80%. When atmo­
spheric deposition is reduced by more than 80%, linear increases in DIP
concentrations occur with a decrease in river input or atmosphericdeposition. The low DIN concentrations induced by more than 80%
decrease in atmospheric deposition limit the growth of phytoplankton.
In this situation, the reduction in DIN input can induce an increase in
DIP concentration because DIP becomes a residual variable compared tothe cases where the flux of atmospheric deposition is reduced by lessthan 80%.
The government of China is continuing to implement integrated
land-sea pollution prevention and control measures to improve coastal
water quality. According to China’s 13th Five-Year Plan for Energy
Conservation & Emission Reduction (2015–2020) and 14th Five-Year
Plan (2021–2025) (http://www.gov.cn/zhengce/index.htm), the emis­
sion loads of atmospheric nitrogen oxide and riverine nitrogen are ex­pected to decrease by > 25% and >18% from 2015 to 2025,
respectively. Assuming that the atmospheric deposition and river input
are reduced by the expected proportions, the simulations exhibited thatthe annual mean DIN concentrations will decrease to ~10.1 mmol m− 3
in 2025 with the maximum value of 17.2 mmol m− 3 appearing in winter.
The annual mean DIP concentration will be low at ~0.3 mmol m− 3.
According to previous research, the predicted DIN concentrations in
2025 are still classified as eutrophic waters (Wang et al., 2018; Zheng
et al., 2020). In another long-term plan to reduce anthropogenic pollu­
tion, China aims to peak carbon emissions by 2030 and reduce CO2 by
60–65% compared with 2005 (Gao, 2016; Fang et al., 2022). Assumingthat the external nutrient inputs decrease at this rate, the model results
show that the mean DIN concentration will decrease to ~5.6 mmol m− 3
in 2030, whereas the DIP concentration will remain at a low value of~0.3 mmol m− 3. The simulated annual maximum value of DIN con­centration will be ~8.2 mmol m− 3 in the winter of 2030, which con­forms to the Class I water (14.3 mmol m− 3) according to Marine Water
Quality Standard in China (Wu et al., 2010). This means that eutro­
phication will be greatly relieved by 2030 in the CBS with management
policies for the Chinese water environment.

4.3. Variations of nutrient cycle under different emission reduction
scenarios
A group of numerical experiments was carried out to examine thevariations of the nutrient concentration and associated biogeochemical
fluxes in the CBS by reducing the river input and atmospheric deposition
respectively (see Section 2.3.3 for details of the configuration). Fig. S9shows the variations in DIN and DIP concentrations when the river input
and atmospheric deposition were reduced by 20%, 40%, 60%, 80%, and100%, respectively. The DIN concentrations decreased when river input
and atmospheric deposition were reduced (Fig. S9a). When the river
input and atmospheric deposition were completely closed for 20 years,
the DIN concentrations in the seawater were reduced from ~11 mmol
m− 3 to ~1.5 mmol m− 3. After a rapid decrease in DIN concentrations in
the first few years, DIN concentrations remained in a comparably steadystate. On the other hand, DIP concentrations do not exhibit obvious
decreases when river input and atmospheric deposition are reduced.
However, it showed a persistent increase when river input and atmo­
spheric deposition were completely closed (Fig. S9b). Under the scenario
of no external input, the low DIN concentration in seawater limited thegrowth of phytoplankton and led to the accumulation of DIP in seawater.
Fig. 7 summarizes the nutrient concentrations of all 121 numericalexperiments in the 20th simulation year. The DIN concentrations
decreased linearly with the reduction in river input or atmosphericdeposition. DIN concentrations decrease to ~3.9 mmol m− 3 when weremove the atmospheric deposition and decrease to ~9.5 mmol m− 3
when we remove the river input. The nitrogen flux of atmosphericdeposition was higher than that of river input in 2017 (Fig. 3a), which
led to a greater reduction in DIN with the removal of atmospheric

5. Conclusions
In this study, a vertical one-dimensional hydrodynamic-ecologicalcoupled model was used to analyze the interannual variation in nutrient

Fig. 7. Simulated annual mean DIN (a) and DIP concentrations (b) in the 20th year after 2017 under the scenarios where river input and atmospheric deposition are
reduced by different percentages, respectively. The equations in the lower right corner of the sub-figures denote the multiple linear fitting equations of different
variables. Riv represents the percentage reduction in river input, and Atm represents the percentage reduction in atmospheric deposition.
8

Chemosphere 313 (2023) 137620

X. Ding et al.

concentrations and their related cycle fluxes in the CBS under the in­fluences of atmospheric deposition and river input. The model results
showed an increasing trend for the concentrations of dissolved inorganic
nitrogen and different forms of particulate nitrogen from 1998 to 2017,
whereas the concentrations of phosphorus in different forms showed no
obvious interannual variation. Annual nutrient budgets were estimatedfrom 1998 to 2017, which indicated that increasing biological process
fluxes were an important sink for additional nutrients from rivers andthe atmosphere. Numerical experiments suggest that atmospheric ni­
trogen deposition plays an important role in the interannual variation of
the nitrogen cycle, but has little effect on the interannual phosphorus
cycle. The calculations proved that a large amount of nitrogen from theexternal input was mainly removed by sedimentation instead ofincreasing nitrogen storage in seawater. We designed a series of nu­
merical experiments to understand the future variations in nutrient
concentrations under different emission reduction plans. The results
showed that concentrations of different forms of nitrogen decreased
linearly with decreased flux of external input in the future, whereas
concentrations of phosphorus increased with declining nutrient input.
Our CBS findings provide an important guide on how to regulate
anthropogenic nutrient inputs into a coastal sea.

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Author contributions statement
Xiaokun Ding: Writing – original draft. Jie Shi: Review & writing.
Xinyu Guo: Writing – review & editing. Huiwang Gao: Methodology.
Sumei Liu: Data curation. Wei Guo: Resources.
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.
Acknowledgments
This study was funded by the National Natural Science Foundation of
the China-Shandong Joint Fund for Marine Science Research Centers(U1806211, U1906215) and NSFC (41876125, 41976013). X. Ding
thanks the China Scholarship Council (CSC) and the Ministry of Edu­
cation, Culture, Sports, Science and Technology, Japan (MEXT) for a
project on Joint Usage/Research Center, Leading Academia in Marine
and Environmental Research (LaMer) for supporting his study in Japan.
X. Guo thanks the support from the Environment Research and Tech­
nology Development Fund (JPMEERF20212001) of the Environmental
Restoration and Conservation Agency of Japan.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.chemosphere.2022.137620.
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