Article

Influences of Nutrient Sources on the Alternation of
Nutrient Limitations and Phytoplankton Community
in Jiaozhou Bay, Southern Yellow Sea of China
Jie Shi 1,2, Qian Leng 1, Junying Zhu 3, Huiwang Gao 1,2, Xinyu Guo 1,4 and Xinyan Mao 5,*
1

Key laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education,
238 Songling Road, Qingdao 266100, China; lengqian@stu.ouc.edu.cn (Q.L.); hwgao@ouc.edu.cn (H.G.)2 Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine
Science and Technology, Qingdao 266071, China; shijie@ouc.edu.cn
3 College of Marine Sciences, Hainan University, 58 Renmin Avenue, Haikou 570228, China;
zjyhn2020@163.com
4 Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-cho, Matsuyama 790-8577, Japan;
guoxinyu@sci.ehime-u.ac.jp
5 College of Oceanic and Atmospheric Sciences, Ocean University of China, 238 Songling Road,
Qingdao 266100, China* Correspondence: maoxinyan@ouc.edu.cn; Tel.: +86-532-6678-2269
Received: 29 January 2020; Accepted: 09 March 2020; Published: 12 March 2020

Abstract: A marine ecosystem box model was developed to reproduce the seasonal variations
nutrient concentrations and phytoplankton biomasses in Jiaozhou Bay (JZB) of China. Then, byremoving each of the external sources of nutrients (river input, aquaculture, wastewater discharge,
and atmospheric deposition) in the model calculation, we quantitatively estimated its influences onnutrient structure and the phytoplankton community. Removing the river input of nutrientsenhanced silicate (SIL) limitation to diatoms (DIA) and decreased the ratio of DIA to flagellates(FLA); removing the aquaculture input of nutrients decreased FLA biomass because it provided lessdissolved inorganic nitrogen (DIN) but more dissolved inorganic phosphate (DIP) as compared to
the Redfield ratio; removing the wastewater input of nutrients changed the DIN concentration
dramatically, but had a relatively weaker impact on the phytoplankton community than removing
the aquaculture input; removing atmospheric deposition had a negligible influence on the model
results. Based on these results, we suppose that the change in the external nutrients sources in thepast several decades can explain the long-term variations in nutrient structure and phytoplankton
community. Actually, the simulations for the 1960s, 1980s, and 2000s in JZB demonstrated the shiftof limiting nutrients from DIP to SIL. A reasonable scenario for this is the decrease in riverine SIL
and increase in DIP from aquaculture that has reduced DIA biomass, promoted the growth of FLA,
and led to the miniaturization of the phytoplankton.
Keywords: Jiaozhou Bay; nutrient limitation; phytoplankton community; nutrient sources;
ecosystem modeling

1. Introduction
Coastal areas are regions of transition from terrestrial to ocean environments. The primary
production is often high, and the transfer of this productivity through the food web could yieldprolific fisheries [1,2]. The growth of phytoplankton in coastal areas can be limited by several factors,
including light, temperature, and nutrients [3,4]. Among these, nutrients have attracted the mostattention because their input can be human-controlled to reverse eutrophication [5–7].
Sustainability 2020, 12, 2224; doi:10.3390/su12062224

www.mdpi.com/journal/sustainability

Sustainability 2020, 12, 2224

2 of 22

Nutrient elements such as nitrogen (N), phosphorus (P), and silicon (Si) have been reported tobe limitation factors for phytoplankton growth and their element ratio may affect community
composition [8–11]. Usually, there are several external sources of nutrients in coastal waters, such as
river inputs, exchanges with the marginal seas, and atmospheric deposition. The nutrient loads fromthese sources are different not only in the amount but also the element ratios, both of which havedramatically changed in past decades under the influences of anthropogenic activities and climatechanges [12]. The changes in external sources of nutrients naturally regulate phytoplankton growth,biomass, and species composition, causing fluctuations in the higher trophic species [13], which havebeen already observed in coastal seas [14,15].
Jiaozhou Bay (JZB) is a semi-enclosed and well-utilized bay in the southwestern Yellow Sea(Figure 1). It has an area of 390 km2 and an average water depth of approximately 7 m [16]. Manyobservations in JZB have documented the basic characteristics of the seasonal, interannual, and longterm variations in nutrient concentrations and phytoplankton biomasses [17–21]. It is suggested thatthe important factor controlling the phytoplankton growth in JZB is nutrients [22]. By comparing thenutrient loads and the standing stocks of nutrients in the bay, previous studies indicated that external
nutrient sources had decisive effects on the multi-time scale variations of nutrient structures[19,23,24]. However, most of the observations focused on the bay itself, with limited quantitative
information about the effects of different nutrient sources on the phytoplankton community
composition.
There are several external nutrient sources for JZB. JZB receives fresh water and nutrients from
more than ten rivers, among which Dagu River is the largest, accounting for 84% of the total
freshwater discharge into the bay [25]. Three of the sewage treatment plants in Qingdao City
discharge into JZB directly, i.e., the Haibohe, Licunhe, and Tuandao plants [23]. The fish bait and theexcretion of aquaculture species are sources of dissolved inorganic nitrogen (DIN) and dissolved
inorganic phosphate (DIP), which can accelerate the eutrophication [26]. Another source of nutrientsin JZB is the atmospheric deposition [27]. In addition, the nutrient concentrations are generally higher
in JZB than in the Yellow Sea (YS), and the water exchange with the YS removes nutrients from JZB[28]. Many studies calculated the loads of nutrients from these external sources and tried to estimate
their contributions to primary production and phytoplankton compositions [23,24]. However,assumption is always necessary in such discussion, because field observation essentially cannot
distinguish the nutrients from different sources.

Figure 1. The location and water depths (in meters) of Jiaozhou Bay. The estuary of Dagu River ismarked by a star. The observations, including concentrations of three nutrient elements andchlorophyll-a used to validate model results, are shown. Stations of monthly observations from June
2003 to May 2004 carried by [17] are marked by diamonds. Stations of the monthly observations fromJanuary 2003 to December 2003 carried by [18] are marked by triangles. Jiaozhou Bay Marine
Ecosystem Research Stations from 2004 to 2008 carried by [19] and from January 2011 to December
2011 carried by [29] are marked by dots.

Sustainability 2020, 12, 2224

3 of 22

Over the last half-century, the external sources of nutrients for JZB have changed under theanthropogenic perturbations [20,21,30]. The diminished river runoff due to the construction of water
conservancy projects, the increase in industrial and domestic wastewater discharge, the heavy
aquaculture activities, together, have had great impacts on the nutrient structures in JZB over the pastfew decades [31]. The unbalanced nutrient structure induced serious eutrophication and highfrequent occurrences of the harmful algal bloom in JZB [24]. In addition, more DIN and DIP loads
but less SIL load into JZB were suggested to decrease the biomass of large-size phytoplankton (DIA)
and increase the biomass of small-size phytoplankton (FLA) [19,32]. The miniaturization of thephytoplankton community subsequently hinders energy transport to the higher trophic levels. Some
toxic FLA have also caused the collapse of fisheries, along with the loss of several fish species [32,33].
To quantitatively understand the relationship between decadal shifts of nutrient loads of eachexternal source and the nutrient structures and phytoplankton community inside the bay, thecorrelation analysis used by previous studies is not sufficient, because we need more information tounderstand the mechanism behind such phenomena.
Numerical modeling is an effective way to reveal the mechanisms linking the external loads ofnutrients and the change in nutrient structure and phytoplankton community inside the bay. There
have been some related numerical studies [28,34,35]. However, they usually considered one group ofphytoplankton (DIA) and failed to reproduce the structure of phytoplankton community. Few
studies have considered the role of each external source of nutrients. Therefore, it is still unclear howeach external source of nutrients influences the variation in the phytoplankton community of JZB.
Considering the small size of the bay and the vertically homogeneous distribution of physical andbiogeochemical variables, we choose a box model in this study to quantify the influences of eachexternal source of nutrients on the nutrient concentration and phytoplankton community. The
understanding on the temporal changes over the entire bay is also helpful to understand theobservations over the bay in the past several decades.
In this study, based on the published estimation of the nutrient loads from various externalsources, we establish an ecosystem box model including three kinds of nutrients and two groups ofphytoplankton with different cell sizes to quantify the influences of each external source of nutrientson nutrient structure and variation in the phytoplankton community, indicated by the ratio of
DIA/FLA. The nutrient limitation conditions for three decades (the 1960s, the 1980s, and the 2000s)
are compared to explain the effects of anthropogenic activities on the unbalanced nutrient structure
and miniaturization of phytoplankton community.
2. Methods
2.1. Model Descriptions
The state variables in this box model included three kinds of nutrients (DIN, DIP, and SIL), twotypes of phytoplankton (DIA and FLA), and two types of detritus (DSi comprising Si and DNSi notcomprising Si). Zooplankton grazing on phytoplankton could be taken into account by increasing the
mortality rate of phytoplankton in the numerical model. The biogeochemical processes (Figure 2)
among state variables have been studied previously and used in many ecosystem models [36,37].
DIA absorbs DIN, DIP, and SIL for photosynthesis, and releases three types of nutrients throughrespiration. The mortality of DIA contributes to DSi, which returns to the three types of nutrients
through mineralization. The biogeochemical processes of FLA are similar to those of DIA. However,
FLA grows depending on DIN and DIP rather than SIL. The mineralization of DNSi releases DIN and
DIP. Sources and sinks for different state variables are provided in Appendix A. The parameters usedin this model were based on previous studies [28,37] and are given in Table 1. The external sourcesof nutrients included river input, wastewater discharge, aquaculture, and atmospheric deposition.
The exchange amounts of nutrients between the JZB and the YS depend on the differences inconcentrations and the average residence time (52 days) (A19). It has been reported that the fluxes
from sediments vary by nutrient, site, and redox conditions and directly observed benthic nutrient
fluxes are lower than diffusive fluxes calculated from pore waters, indicating that the nutrients

Sustainability 2020, 12, 2224

4 of 22

released from sediment pore waters are probably utilized in benthic production [27]. Therefore, thenutrients from benthic fluxes were not taken into consideration due to the limited observational data.

Figure 2. Schematic illustration of the ecosystem box model for Jiaozhou Bay. The state variables
inside rectangular boxes are three elements of nutrients (dissolved inorganic nitrogen (DIN),
dissolved inorganic phosphorus (DIP), and silicate (SIL)), two types of phytoplankton (diatoms (DIA)
and flagellates (FLA)), and two types of detritus (DSi comprising Si and DNSi not comprising Si). The
solid lines with arrows represent the biological processes, whose equations are given in Appendix A.
Table 1. Parameters used in the model.
Parameter
Pmax
Pmax
resp
resp
r
al_temp1
al_temp2
Iopt
Iopt
Kdin
Kdip
Ksil
Kdin
Kdip
Rmine
Rmine
N/P/Si

Value
1.3
0.3
0.138
0.05
0.12
0.055
0.054
120
70
1.4
0.12
2.4
1.2
0.1
0.1
0.15
16/1/22

Unit
day
day
day
℃
℃
Wm
Wm
μmolN LμmolP LμmolSi L
μmolN LμmolP Lday
day-

Description of Parameter
Maximum growth rate of DIA
Maximum growth rate of FLA
Percentage of basic respiration
Percentage of photorespiration
Phytoplankton mortality rate
Temperature-dependent growth rate
Temperature-dependent respiration rate
Optimum light intensity of DIA
Optimum light intensity of FLA
DIN half saturation constant for DIA
DIP half saturation constant for DIA
SIL half saturation constant for DIA
DIN half saturation constant for FLA
DIP half saturation constant for FLA
Mineralization rate of DIA detritus
Mineralization rate of FLA detritus
Atomic ratio of N, P, and Si

2.2. Model Configuration and Data Sources
2.2.1. CONTROL Run (the 2000s)
The simulation in the 2000s is named CONTROL. The external sources of nutrients were basedon observations in the 2000s (Table 2). The amounts of nutrients from rivers depended on both theriverine concentrations of nutrients and the discharge of fresh water (A18). In this model, only Dagu
River was taken into consideration, and the other small rivers were ignored. The river discharge (inL s ) of the Dagu River in the 2000s was derived from the model results of the Soil and Water
Assessment Tool (SWAT) [38]. The river discharge reached its peak in summer (July to September),
up to 9.0 × 104 L s , and remained low in other seasons, especially in winter (December to February),
with an annual total discharge of 3.0 × 1011 L. The riverine concentrations of DIN, DIP, and SIL werebased on observations in 2002 [23]. The annual amount of nutrients from wastewater was obtained
from investigations in 2002 [23]. The annual aquaculture input was estimated from aquaculture
conditions in 2008 [39]. Atmospheric depositions of nutrients, including wet and dry deposition, wereSustainability 2020, 12, 2224

5 of 22

based on rainwater and aerosol samples collected at the coastal site of JZB from April 2004 to March
2005 [27]. Nutrients from these three external sources (wastewater, aquaculture, and atmosphere)
were assumed to have little seasonal variations, and the annual amounts were averaged to each time
step for model calculation. The concentrations of nutrients in the YS were set to 6.0, 0.3, and 4.0
μmol L for DIN, DIP, and SIL, respectively, according to observations [18,40].
Table 2. The external sources of nutrients in the model.

Dagu River runoff (F)Riverine DIN concentration (C)
Riverine DIP concentration (C)
Riverine SIL concentration (C)
DIN from wastewater (L(DIN))
DIP from wastewater (L(DIP))
SIL from wastewater (L(SIL))
DIN from aquaculture (L(DIN) )DIP from aquaculture (L(DIP) )DIN from atmospheric deposition (L(DIN) )DIP from atmospheric deposition (L(DIP) )SIL from atmospheric deposition (L(SIL) )DIN concentration of YS (C)
DIP concentration of YS (C)
SIL concentration of YS (C
)

1960s
7.5
60
3
600
0
0
0
0
0
56
0.42
0.93
2.2
0.17
6.0

1980s
5.0
260
5.3
420
80
0.8
3.2
1.8
0.12
56
0.42
0.93
5.0
0.3
5.5

2000s
3.0
400
7.0
150
200
2.0
8.0
54
3.8
56
0.42
0.93
6.0
0.3
4.0

Unit
1011 L y-1
μmol Lμmol Lμmol L106 mol y
106 mol y
106 mol y
106 mol y
106 mol y
106 mol y
106 mol y
106 mol yμmol Lμmol Lμmol L

Reference[38,41][23,41][23,41][23,41][23,26][23,26][23,26][24,39][24,40]
[27]
[27]
[27]
[18,40][18,40][18,40]The physical forcing includes the annual cycles of sea surface light intensity (I0, in W m-2) andseawater temperature (Temp, in ℃), expressed by the following cosine functions of time (t in JulianDay) [42]:I = 240 − 140 × cos 2π ×

t + 10，365(1)

t − 40.
365(2)Temp = 13.5 − 11.0 × cos 2π ×

The average light intensity (I) in JZB is expressed as I = 0.4 × I0, considering the light attenuation
due to pure water and suspended particulate materials in the water column, which is derived inAppendix A.
The initial conditions of state variables were based on observed data from the winters of the
2000s [17,18]. The initial concentrations of DIN, DIP, and SIL were set to 13.0, 0.8, and 6.0 μmol L ,respectively. The initial biomasses of DIA and FLA were 0.72 and 0.08 μg L , respectively, in theform of chlorophyll-a (Chl-a) concentrations. After running the model for three years, the outputs
were close to the climatological state of the model. The outputs of the fifth year were used in thefollowing analysis.
The data used to validate the model results were the averaged values of several observations in
the 2000s (Figure 1) [17–19,29].
2.2.2. NORI, NOWA, NOAQ, and NOAT Runs
In order to examine the different effects of the external nutrient sources, four numericalexperiments were carried out and named NORI, NOWA, NOAQ, and NOAT, in which nutrientswere removed from river input, wastewater discharge, aquaculture activities, and atmosphericdeposition, respectively. Except for removing one external source of nutrients, the other conditions
used in these experiments were the same as CONTROL.
2.2.3. The 1960s and the 1980s Runs

Sustainability 2020, 12, 2224

6 of 22

As shown in Table 2, the runoff of the Dagu River decreased from 7.5 × 1011 L y in the 1960s
to 5.0 × 1011 L y in the 1980s [38,43]. The riverine concentration of SIL in the 1960s and the 1980s
was supposed to be proportional to the sediment loads of Dagu River, which were approximately
four and three times the amount of that in the 2000s, respectively [41,43]. The SIL concentration in
the river decreased from 600 μmol L in the 1960s to 420 μmol L in the 1980s (Table 2). The DIN(DIP) concentration in the river increased from 60 (3.0) μmol L in the 1960s to 260 (5.3) μmol L in
the 1980s [23,41]. It was reported by the Qingdao Statistical Yearbook in 2005 that the amount ofindustrial and domestic wastewater increased dramatically in the past 20 years. The amount ofwastewater in the 2000s was nearly twice that in the 1980s [19]. The nutrient loads in the 1980s were
estimated to be 40% of the values in the 2000s, proportional to the amount of wastewater (Table 2).
In the 1960s, industry activities were rarely thriving; therefore, the nutrients from wastewater
discharge can be neglected. The amounts of DIN and DIP from aquaculture activities in the 1980s
were estimated to be 1.8 × 106 mol y and 0.12 × 106 mol y due to the variation in aquacultureareas [24,44], while nutrients from aquaculture were ignored in the 1960s. Nutrients fromatmospheric deposition in the 1960s and 1980s were regarded to be the same as those in the 2000s
due to the data limitation.
Although the human-induced changes in the coastline position-configuration and nearshore
bathymetry have resulted in substantial changes to the residual current patterns, especially innortheastern JZB, the average residence time did not change apparently from 1966 to 2008 [45]. The
same residence time of 52 days was used to calculate the exchange with YS, but the nutrient
concentrations of YS changed a lot for different times (Table 2). The annual cycles of sea surface lightintensity and seawater temperature were treated the same as the CONTROL Run.
3. Results
3.1. Parameter Sensitivity Analysis
Model sensitivity experiments were conducted to examine how the predicted variables varywith the changes of the model parameters within a given range (Table 3). The predicted statevariables in these sensitivity experiments were the annual mean biomasses of DIA and FLA. Taking
the parameters listed in Table 1 as the control run, the value of any selected parameter was increased
and decreased by 50%. The model was run for three years in each sensitivity experiment, and theresults of the third year were used. The sensitivity of a predicted state variable to the selectedparameter was quantified as a factor s =|∆ ⁄ ||∆ ⁄ |, where A is the mean biomass of DIA or FLA and B

is the value of one model parameter. ∆A is the variation of A when the parameter varies by ∆B.
Table 3. Sensitivities of DIA and FLA annual mean biomass to model parameters.
Parameter
Pmax
Pmax
resp
resp
r
Iopt
Iopt
Kdin
Kdip
Ksil
Kdin
Kdip
Rmine
Rmine

Variation of
Parameter (%)+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50+50/−50

Sensitivities of Annual DIA
Biomass
0.71/0.93
0/0
0.34/0.34
0.06/0.06
0.62/1.66
0.54/0.63
0/0
0/0
0/0
0.31/0.39
0/0
0/0
0.05/0.14
0/0

Sensitivities of Annual FLA
Biomass
0.13/0.12
0.99/0.99
0.22/0.22
0.06/0.06
0.65/1.91
0.11/0.12
0.31/0.17
0/0
0/0
0/0.01
0/0
0.17/0.20
0/0.020/0

The annual mean biomasses of DIA and FLA are sensitive to their own maximum growth rate
and mortality rate (s > 0.5). The sensitivity factors about the basic respiration were 0.34 and 0.22 forSustainability 2020, 12, 2224

7 of 22

DIA and FLA, respectively. The annual mean biomass of DIA was more sensitive to the half
saturation constant of SIL (s = 0.31−0.39) than those of DIN (s = 0) and DIP (s = 0), while that of FLA
was more sensitive to its half-saturation constant of DIP (s = 0.17−0.20) than that of DIN (s = 0). The
sensitivity of the half saturation constant of DIN was low, indicating that the growth of DIA and FLA
is not limited by DIN. The sensitivity analysis also quantified the competition between the two typesof phytoplankton. The annual mean biomass of FLA had a sensitivity to some parameters related to
the growth of DIA, such as the maximum growth rate of DIA (s = 0.12−0.13), the optimum lightintensity for DIA (s = 0.11−0.12), but not vice versa. It is suggested that DIA is more competitive than
FLA. On the other hand, the limiting nutrients for both DIA and FLA did not change when changing
the model parameters with a large range (i.e., 50%). Evidently, the model-based conclusions onlimiting nutrient and the phytoplankton community are robust.
3.2. Seasonal Variations of Nutrient Concentrations and Phytoplankton Biomasses
The simulated concentrations of the three types of nutrients in JZB exhibited apparent seasonalvariations (Figure 3). All three nutrient elements accumulated in winter, when the growth ofphytoplankton is limited by low water temperature and weak solar radiation. Then, theconcentrations of DIN, DIP, and SIL decreased to about half of their peak values in spring along withthe vigorous primary production. In summer, the concentrations increased gradually due to the large
river input, and the concentrations of DIN and SIL increased more significantly than DIP. In autumn,
the concentrations of DIP and SIL continuously increased in response to the reduction ofphytoplankton photosynthesis. However, the concentrations of DIN decreased slightly due to the
low river input.

Figure 3. Simulated and observed annual cycles of (a) DIN, (b) DIP, (c) SIL, and (d) DIA and FLA.
The solid circles, solid triangles, solid squares, and lines represent simulated results. The hollow
triangles, hollow squares, and asterisks represent the monthly average of observed values in Jiaozhou
Bay in the 2000s, published by [17–19,29] with observation information in Figure 1.

The biomasses of both DIA and FLA experienced an apparent bloom in summer and had theirlowest value in winter. The biomasses of DIA and FLA reached the peak values of 1.52 μg L and
0.52 μg L in August (Figure 3d), respectively, consistent with the maximum river runoff insummer. The biomass of DIA was higher than that of FLA throughout the year, especially in summer.
The annual mean ratio of DIA to FLA was 2.3, with the maximum value of 2.9 in August and theminimum value of 1.6 in January. Therefore, DIA is the dominant phytoplankton species in JZB,
which is consistent with the observations by [46], accounting for approximately 75% of the total
phytoplankton in August.
It has been reported that there is a significant correlation between Chl-a and phytoplankton
density when the dominant phytoplankton species are stable [47]. Since the biomasses of DIA andFLA represented by Chl-a concentrations could not be observed, the observed monthly density ofSustainability 2020, 12, 2224

8 of 22

DIA and FLA were used to validate the modeled seasonal variations of Chl-a concentrations. DIA
density was obviously higher than that of FLA all throughout the year, and they both experienced
similar seasonal variations with the peak in summer, which could also be captured by the modeled
Chl-a concentrations of DIA and FLA [29]. Observations in JZB showed two phytoplankton blooms
occurring in February and August [48,49]. Appearance of some cold-water species is suggested to be
responsible for the February bloom [18]. Because the two types of phytoplankton in our model did
not represent the cold-water species, we could not reproduce the bloom in February. Since the coldwater species of phytoplankton did not appear every February [19], the model was able to simulate
the main seasonal variations of nutrient concentrations and phytoplankton biomasses in JZB.
3.3. Seasonal Variations of Nutrient Limitations
The nutrient limitations on the growth of phytoplankton are given by the minimum value of thethree factors ( DIN = DIN⁄(DIN + K ) , DIP = DIP⁄(DIP + K ) , SIL = SIL⁄(SIL + K ) ),where K , K , K
are half-saturation constants (in Appendix A). The limiting nutrient in JZB wasdetermined through comparing the value of three factors. The simulated annual mean concentrationsof the DIN, DIP, and SIL were 19.8, 0.56, and 5.73 μmol L ), respectively. The ratio of DIN/DIP was
38/1, much larger than the Redfield ratio. Both the high concentration of DIN and the ratio of DIN/DIP
suggest that DIN was in surplus compared with DIP. As shown in Figure 4, the values of DIN
for
DIA and FLA were above 0.9 during the whole year, suggesting that DIN never served as the limiting
nutrient for the growth of phytoplankton. The limiting nutrient for DIA was SIL all year around (Figure4). The SILlim decreased from January to June, and the limitation effect was strongest in June, when its
value was 0.56. Then, the limitation effect gradually weakened due to the riverine SIL input in summer.
The growth of FLA was limited by DIP (Figure 4) all year round. The mean DIPlim for it was about 0.82,
which was always higher than the SILlim for DIA. Given that DIA is the dominant species in JZB, SILlim
appears favorable for controlling the ecological equilibrium of JZB.

Figure 4. Variations of nutrient limiting factors for the 1960s, 1980s, and 2000s ( DINlim =DIN⁄(DIN + KDIN ) , DIPlim = DIP⁄(DIP + KDIP) , SILlim = SIL⁄(SIL + KSIL ) ) for (a) DIA and (b) FLA,
whose definitions are given in Appendix A. The squares, circles, and triangles represent the DIN, DIP,
and SIL limitations, respectively. The dot lines, solid lines, and dash lines represent the nutrient
limiting factors for the 1960s, 1980s, and 2000s, respectively.

Sustainability 2020, 12, 2224

9 of 223.4. Annual Budgets of Nutrients
Figure 5 shows the annual budgets of DIN, DIP, and SIL. The four external sources of nutrients(river, wastewater, aquaculture, and atmosphere) were treated as forcing conditions in the model.
Their amounts are listed in Table 2. The budgets of biogeochemical processes represent the renewal
and cycle rate of nutrients in JZB. Phytoplankton plays an important role in the renewal of nutrients.
The net production of phytoplankton (photosynthesis minus respiration) is the main sink, with 316.89
× 106 mol y of DIN, 19.81 × 106 mol y of DIP and 382.37 × 106 mol y of SIL. The maximum net
production of DIA and FLA occurred in summer, consistent with their high biomasses. The
mineralization of detritus released 317.19 × 106 mol y of DIN, 19.83 × 106 mol y of DIP, and382.76 × 106 mol yof SIL, which are approximately 70% of the amount of photosynthesis
absorption. According to the model results, the annual mean concentrations of the three nutrients in
JZB are higher than those in YS. Annually, 432.09 × 106 mol of DIN, 8.36 × 106 mol of DIP, and 54.70 ×
106 mol of SIL are transported out from JZB to YS through water exchange. The nutrient budgets arein balance in the whole year, which indicates that the model followed the mass conservation.

Figure 5. Simulated nutrient budgets (×106 mol) during the whole year: (a) DIN; (b) DIP; (c) SIL.

Sustainability 2020, 12, 2224

10 of 223.5. Estimation of the Contributions of Nutrient External Sources on Nutrient Limitations andPhytoplankton Community
The responses of the nutrients and phytoplankton structures in JZB to each external nutrient
source were examined by comparing the results of NORI, NOWA, NOAQ, and NOAT with those of
CONTROL (Figures 6 and 7). The nutrients flux of atmospheric deposition was the smallest among
the four external sources. The DIA and FLA biomasses decreased slightly by 0.2% and 0.5% in NOAT,
respectively. Removing atmospheric deposition had a negligible influence on the model results ofboth phytoplankton biomasses and nutrient concentrations. The results of the other threeexperiments are discussed in detail bellow.
3.5.1. The Effects of River Input
The Dagu River inputs 45.79 × 106 mol SIL into JZB per year, contributing up to 80% of the annual
amount from the four external sources. Meanwhile, it is a large source of DIN (122.10 × 106 mol y-1),
just less than that from wastewater discharge. The amount of DIP from river input is 2.14 × 106 mol
y-1, which is similar as that from wastewater but much less than that from aquaculture.
Comparing the results of NORI with those of CONTROL, the annual mean concentrations of
DIN, DIP, and SIL decreased by 19%, 11%, and 25%, respectively. The decreases are apparent in allthese three nutrient elements (Figure 6). Moreover, the nutrient supply from the river exhibited an
obvious seasonal variation due to the significantly different runoff during the wet and dry seasons.
As a result, nutrient concentrations hardly changed in the dry seasons and changed a lot in the wet
seasons. In summer, the peak of DIN disappeared in NORI, and the concentration of DIN increased
from July, and then began to decline after reaching the peak in November (Figure 6a). The
concentrations of DIP and SIL decreased continuously in summer in NORI rather than increase in
CONTROL (Figure 6b, c). After reaching the trough in July, the DIP and SIL concentrations in NORI
increased from August, and then began to decrease after reaching the peak in November. The peaktime was one month later than other numerical experiments. The maximum decrease of SIL and DIP
also occurred in summer, when the values were only half those in CONTROL.
The values of SIL and DIP limiting factors in NORI decreased evidently in summer, enhancing
the SIL limitation for DIA and DIP limitation for FLA (Figure 7). The SIL limiting factor in spring andwinter changed relatively slightly. SIL still acted as the limiting nutrient for the growth of DIA all
year round, but SIL limitation was strengthened in NORI (Figure 7).
The annual biomass of DIA decreased by 17%, while the decreasing percentage of FLA was only
3%. The biomasses of DIA and FLA changed the most in summer, which were reduced by 30% and6%, respectively. The annual mean ratio of DIA to FLA decreased from 2.3 to 2.0, indicating that theriver input of nutrients has stronger influences on DIA than on FLA. The enhanced SIL limitation
leads to a phytoplankton structure favored to FLA.
3.5.2. The Effects of Aquaculture Activities
Aquaculture provides 54 × 106 mol y-1 DIN to JZB, which is similar to the amount fromatmospheric deposition, but only a quarter of that from wastewater discharge. However, it is themain source of DIP, with an annual amount nearly twice that from wastewater or river. The ratio of
DIN to DIP from aquaculture is 14, indicating that aquaculture is the only source with a lower ratioof DIN/DIP than the Redfield ratio.
DIN concentration in NOAQ exhibited similar seasonal variations as that in CONTROL, but thevalue was about 2.0 μmol L less than that in CONTROL (Figure 6a). Among the results of
CONTROL and four experiments, the DIP concentration in NOAQ almost maintained the lowest
throughout the year (Figure 6b), only slightly larger than that in NORI in summer. No SIL comesfrom aquaculture, so the SIL concentration did not change in NOAQ.
The DIP limiting factor for FLA decreased by 0.05 compared with that in CONTROL (Figure 7).
The FLA biomass decreased by 5% annually and changed the most in June, which was decreased by
8%, while the DIA biomass had nearly no change all year round without this source. The ratio of DIA

Sustainability 2020, 12, 2224

11 of 22

to FLA increased from 2.3 in CONTROL to 2.4 in NOAQ. Therefore, FLA is more sensitive to thissource than DIA, and aquaculture activities have effects on the miniaturization of phytoplankton size.
3.5.3. The Effects of Wastewater Discharge
Wastewater is the largest source of DIN, with an annual amount of 200 × 106 mol y-1, which isnearly twice that from rivers. However, it only contributes half of the amount of DIP fromaquaculture and one sixth of the amount of SIL from rivers. The ratio of DIN/DIP/SIL isapproximately 100/1/4 in wastewater (Figure 5), which suggests that wastewater brings more DIN
into JZB compared with DIP and SIL.
The seasonal variations of nutrient concentrations, nutrient limiting factors, and phytoplankton
biomasses in NOWA were similar to those in CONTROL (Figure 6). However, the values weredifferent, especially for DIN concentration. DIN concentration in NOWA remained the lowest among
the four experiments nearly all year round, and only larger than that in NORI in summer (Figure 6a).
DIP concentration in NOWA was also lower than that in CONTROL all through the year, however
there was even no considerable change in SIL concentration. Wastewater discharge brings more
excessive DIN into JZB, therefore, the limiting factors of SIL for DIA and DIP for FLA in NOWA
became smaller than those in CONTROL, indicating a strengthened SIL limitation for DIA and DIPlimitation for FLA.
FLA biomass decreased by 3%, while DIA biomass decreased by 2% annually in NOWA.
Seasonally, the DIA and FLA biomasses decreased more in winter and spring than in summer andautumn, because the river input relieved the unbalance of nutrient structures caused by thewastewater discharge in summer. In general, the ratio of DIA to FLA increased slightly annually.

Figure 6. The simulated annual cycles of nutrient concentrations and percent change ofphytoplankton biomasses in numerical experiment NORI (red line), NOWA (blue line), NOAQ
(orange line), and NOAT (green line): (a) DIN; (b) DIP; (c) SIL; (d) DIA; (e) FLA. The simulations in
the 2000s (CONTROL) are shown with black lines. NORI, NOWA, NOAQ, and NOAT are cases of
the nutrients from river, wastewater, aquaculture, and atmosphere, respectively.

Sustainability 2020, 12, 2224

12 of 22

Figure 7. Variations of nutrient limiting factors (SIL
for DIA and DIP
for FLA) in numericalexperiments NORI (red line), NOWA (blue line), NOAQ (orange line), and NOAT (green line). The
simulations in the 2000s (CONTROL) are shown with black lines. The solid and hollow circles
represent DIP limiting factor for FLA and SIL limiting factor for DIA, respectively.

3.5.4. The Effects of External Sources on the Cycling Rate of Nutrients in JZB and the Exchange with
the YS
Besides the changes in the nutrient concentrations and phytoplankton biomasses in JZB whenremoving each external source of nutrients, the cycling rates of nutrients among different statevariables exhibited even more apparent variations, which could not be revealed directly by in-site
observations. Comparing the annual budgets of SIL in NORI and CONTROL (Table 4), the annual
photosynthesis of DIA was 366.15 × 106 mol SIL when removing river input, which is about 33% lessthan that in CONTROL. Meanwhile, the respiration of DIA and the mineralization of DSi decreased
by about 37%. The decrease in each biochemical process of SIL cycle is even more apparent than thatin the standing stocks of SIL in JZB when removing river input. Similar situations occurred in the DIP
cycle when removing nutrients from aquaculture. The annual amount of DIP related process (thephotosynthesis and respiration of FLA, the mineralization of DNSi) decreased up to 10% whenremoving aquaculture, while the decrease in annual FLA biomass was 5%. These variations in
NOWA are not as much as those in NORI and NOAQ. That is because wastewater discharges mainly
DIN to JZB, which is not the limiting nutrient. Therefore, it can be concluded that removing externalsources of limiting nutrients obviously slows down the cycling rate of nutrients in JZB.
In addition, there were obvious decreases in the exchanging amounts of nutrients with the YS inthese four numerical experiments. When removing river input, the transporting amount of SIL fromJZB to the YS decreased from 54.70 × 106 mol y-1 to 8.92 × 106 mol y-1. Wastewater discharge has lessinfluence on the phytoplankton biomasses than river input and aquaculture activities. However, theexchanging amount of DIN in NOWA decreased to only half of that in CONTROL. So, besides theeffects on JZB itself, the external sources of nutrients have more remote influences on the adjacentseas through water exchange controlled by complicated hydrodynamic processes.

Sustainability 2020, 12, 2224

13 of 22

Table 4. Simulated nutrient budgets (Unit: × 106 mol) during the whole year in CONTROL, NORI, NOWA, NOAQ, and NOAT.

CONTROL

NORI

NOWA

NOAQ

NOAT

DIN
DIP
SIL
DIN
DIP
SIL
DIN
DIP
SIL
DIN
DIP
SIL
DIN
DIP
SIL

DIA Photosynthesis

DIA Respiration

FLA Photosynthesis

FLA Respiration−395.83−24.74−544.27−266.29−16.64−366.15−384.07−24.00−528.10−395.83−24.74−544.27−394.49−24.66−542.42+117.75+7.36+161.90+91.83+5.74+126.25+115.40+7.21+158.69+117.75+7.36+161.90+117.48+7.34+161.54−49.91−3.12
0−46.50−2.90
0−47.81−2.98
0−45.40−2.84
0−49.49−3.09
0+11.10+0.69
0+10.61+0.66
0+10.79+0.67
0+10.44+0.65
0+11.04+0.69
0DSi
Mineralization+278.37+17.40+382.76+174.62+10.92+240.10+268.96+16.81+369.82+278.37+17.40+382.76+277.29+17.33+381.29

DNSi
Mineralization+38.82+2.43
0+35.90+2.24
0+37.03+2.31
0+35.00+2.19
0+38.47+2.40
0Exchange with YS−432.09−8.36−54.70−309.99−6.22−8.92−232.09−6.36−46.70−378.09−4.56−54.70−376.09−7.94−53.77

Sustainability 2020, 12, 2224

14 of 223.6. Long-Term Variations of the Nutrient Limitation and Phytoplankton Community
As shown in Section 3.5, the external sources of nutrients have various influences on the nutrient
structure and phytoplankton community in the JZB. Since the nutrient loads from the externalsources have dramatically changed by human activities in the past several decades, it may result inchanges in nutrient limitation and then the phytoplankton community in JZB. Considering different
amounts of nutrients from the four external sources in different decades (Table 2), the model
reproduced the nutrient structure and phytoplankton community in the 1960s and the 1980s.
The annual cycles of nutrient concentrations and phytoplankton biomasses in these two decades
showed similar seasonal patterns to the results in the 2000s. The simulated concentrations of DIN and
DIP in the 1960s were in good agreement with the observations (Table 5). The concentration of SIL in
the 1960s could not be evaluated due to there being too few observations. The simulated
concentrations of DIN, DIP, and SIL in the 1980s almost fell into the ranges of observations in thisdecade (Table 5). The concentrations of DIN and DIP in the 2000s were about six times and triple
those in the 1960s, respectively. However, the SIL concentration in the 2000s was only half of that in
the 1980s and one quarter of that in the 1960s.
The increase in DIN was mainly caused by the river input and wastewater discharge. The river
input was determined by both the runoff and the riverine concentrations. Although the annual river
runoff decreased from 7.5 × 1011 L in the 1960s to 3.0 × 1011 L in the 2000s, the annual amount of DIN
from the river still increased from 45 × 106 mol in the 1960s to 120 × 106 mol in the 2000s due to thedramatic increase of the riverine DIN concentration. The continuously prosperous industry brought
about more and more wastewater, which made wastewater become the biggest source of DIN until
the 2000s (Table 2). Consequently, the annual DIN input from the four external sources increased
from 101 × 106 mol in the 1960s to 268 × 106 mol in the 1980s, and then reached 430 × 106 mol in the
2000s. The expansion of aquaculture activities was mainly responsible for the rising input of DIP to
JZB. The total DIP input increased from 2.67 × 106 mol in the 1960s to 3.99 × 106 mol in the 1980s, then
to 8.32 × 106 mol in the 2000s. Overall, the DIN concentration in JZB experienced a faster increase thanthat of DIP. On the contrary, the concentration of SIL showed an apparently decreasing trend fromthe 1960s to the 2000s (Table 5), which was definitely caused by the decreases in both the river runoff
and the riverine SIL concentration.
The long-term variations in nutrient concentrations changed the nutrient limitation situations
and the phytoplankton compositions in JZB. In the 1960s, the limiting nutrient for both DIA and FLA
was DIP (Figure 4). The DIP limiting factor for DIA was smaller than that for FLA due to the larger
DIP half-saturation constant for DIA, indicating the stronger limitation for DIA. However, thedominant species was still DIA, due to its larger maximum growth rate. The annual mean ratio of
DIA to FLA was approximately 2.7. The nutrient limitation in the 1980s changed dramaticallycompared with the situation in the 1960s. DIP was still the limiting nutrient for FLA. However, DIP
and SIL alternatively limited the growth of DIA in the 1980s (Figure 4a). From January to June, thelimiting factor of SIL (SIL ) was smaller than that of DIP (), indicating the concentration of
SIL controlling the growth of DIA. From July to November (the flood season), the limiting nutrient
shifted from SIL to DIP. Although the concentration of SIL decreased from the 1960s to the 1980s, theincreasing wastewater discharge and aquaculture activities brought a large amount of DIP andrelieved the DIP limitation, making nutrient limitation in the 1980s weaker than the situation in the
1960s. As a result, the annual mean biomass of DIA increased from the 1960s (0.64 μg L ) to the 1980s(0.80 μg L ). The annual ratio of DIA to FLA in the 1980s was approximately 2.9, which was larger
than that in the 1960s. The ecosystem in JZB changed from DIP limitation to SIL limitation under the
influence of reduced SIL input, and relatively increased DIP input from the 1980s to the 2000s. The
enhanced SIL limitation from the 1980s to the 2000s (Figure 4a) led to a more apparent increase in
FLA biomass and a comparatively insignificant increase of DIA biomass. The annual ratio of DIA to
FLA greatly reduced from 2.9 to 2.3, which resulted in the miniaturization of the phytoplankton
community.

Sustainability 2020, 12, 2224

15 of 22

Table 5. The simulated and observed nutrient concentrations (μmol L ) in the 1960s, 1980s, and 2000s[17,18,31].

DIN
DIP
SIL

1960s
Simulation Observation
0.98–5.43
1.00–4.00
0.18–0.46
0.10–0.22
8.38–62.83-

1980s
Simulation Observation
12.05–23.65
2.55–19.50
0.08–0.52
0.20–0.72
5.59–27.66
0.20–11.50

2000s
Simulation Observation
17.24–37.74
3.60–44.21
0.09–1.37
0.09–2.00
2.44–12.51
1.10–20.39

4. Implications and Limitations
The nutrient limitation may be caused by low concentrations or alternations due to changing
ratios. The more obvious changes in phytoplankton are not in the biomass, but in the community
composition in JZB. This shift in phytoplankton species was mainly caused by the change in nutrient
ratios. From the 1980s to the 2000s, the DIN/DIP/SIL changed from 31/1/26 to 35/1/10, according to
the model results. The increases in DIN and DIP concentrations (especially DIN) and the decrease in
SIL concentration resulted in a more unbalanced nutrient structure [50]. DIP limitation for DIA
growth has almost been eliminated. Meanwhile, strengthened SIL limitation has led to the decreaseof DIA, and the smaller species have taken over the niche vacated by the larger species [20].
River input is the main source of SIL, and therefore becomes the only source to support a diatom
dominant phytoplankton community in JZB. Damming usually decreases SIL load, which makes SIL
a potential limiting nutrient in coastal waters [51,52], such as Guadiana estuaries in Mediterranean[53], Sweden, Finland [54], and the Baltic Sea [14]. The sharp decrease of SIL flux caused higher
frequency of occurrence of harmful algal bloom [55,56]. In addition, low SIL concentration limited
the silicification ability of DIA, and then reduced their absorption of Cd in the seawater, which finallyresulted in heavy metal pollution [57] and toxin enrichment through the aquatic food chain [58]. In
the North Sea, more reduction in riverine P loads than N loads caused large offshore gradient of
DIN/DIP ratio in spring, from 375:1 nearshore toward 1:1 in the central North Sea and thephytoplankton growth was limited by P and Si nearshore, co-limited by N and P in a transitional
region, and limited by N in the outer-shore waters [59].
Nutrients from aquaculture, with a lower DIN/DIP ratio compared with those from othernutrient sources, led to the more obvious growth of FLA than that of DIA in JZB. It was reported thatthe phytoplankton community experienced a shift from DIA-dominated to FLA-dominated based on
observations in an offshore shellfish-cultured area located in the northern Yellow Sea of China [60].
There was also a worldwide phenomenon that excessive discharge of DIP increased the risks ofeutrophication and threatened the environmental regulations in the aquaculture waters [61]. So, theutilization efficiency of nutrients was regarded to be an important indicator, whether the aquaculture
activities were sustainable or not [60]. In addition, it has been reported that aluminum pillaredbentonite can decrease the P loads from aquaculture, and it can be used for the treatment ofaquaculture discharge water before releasing into the coastal seas [62].
The effects of wastewater vary due to its various components in different countries. A previous
study [63] reported that the low N/P ratio of wastewater inputs resulted in a DIN-limited and DIPsaturated system in the estuaries of Chesapeake Bay, which is totally different from the situation in
JZB. Reduced loads of nutrients from the Luggage Point sewage treatment plants into the Moreton
Bay resulted in a decrease in DIN concentration and phytoplankton biomass [64]. In the Scheldt
estuary of the Netherlands, upgrading the wastewater treatment plant dramatically reduced DIN
and DIP delivery to the coastal seas. In the absence of decreases in SIL export, the reduction of DIN
and DIP fluxes and the improvement of the DIN/DIP ratio compared to SIL favored the growth of
DIA [65]. After decreases in N and P inputs through a wastewater-improvement project, the trophic
status shifted from hyper-eutrophic to eutrophic-mesotrophic in Boston Harbor. As a result, the N
limitation relative to P limitation increased and the primary production and phytoplankton biomass
decreased [66]. Nitrogen reduction reduced the winter spring diatom bloom and winter Chl-a levels
were lower significantly after nutrient reduction in Narragansett Bay [67].

Sustainability 2020, 12, 2224

16 of 22

The model results showed that the atmospheric deposition had little influence on the variations
in phytoplankton biomass and composition in JZB. The atmospheric deposition provided a similar
amount of DIN to JZB compared with the aquaculture. However, it brought less DIP and SIL, which
acted as the limitation nutrients in JZB in the 2000s. It was widely reported that atmosphericdeposition was an important source of nutrients and promoted primary production in open oceans[68,69]. The atmospheric deposition of nitrogen contributed about 10% of the primary production in
the western Pacific Ocean [70]. Even in regional seas, the atmospheric deposition is also an importantsource of nutrients. Taking YS for example, the estimated annual amount of nitrogen fromatmospheric deposition was about five times that from the rivers. However, their contributions to theannual primary production of the whole Yellow Sea were not very high, because there was alreadysurplus DIN in the seawaters. Only in certain areas and certain periods can their contributions not be
neglected. The nitrogen from atmospheric deposition promoted the spring phytoplankton bloom inthe central part of the Yellow Sea when DIN was used up by the fast growth of phytoplankton [71].
During heavy-polluting weathers, such as dust or haze events, the amount of nitrogen fromatmospheric deposition increased to twice as much as that in normal days [72], which aggravated the
DIP and SIL limitation situation and then affected the composition of the phytoplankton community[73].
It was also indicated by the nutrient budgets that the external sources of nutrients havesubsequent effects on the adjacent seas besides the bay itself. When considering the effects of climatechange, the remote influences of the oceans on the marginal seas always gain more attraction.
However, it cannot be neglected that anthropogenic activities can also affect the remote oceans
through coastal waters and marginal seas. For example, the material exchange between the East
China Sea shelf (marginal sea) and Kuroshio affects not only the ecosystem of the East China Sea but
also that of the Northwestern Pacific Ocean [37,74,75].
It should be noted that the influence discussed above primarily focuses on the entire JZB. The
box ecosystem model was effective when focusing on the temporal variation of nutrient compositionand succession of the phytoplankton community of the entire JZB under the influence ofanthropogenic activities. However, it could not differentiate the spatial variations in JZB due toinhomogeneity of the nutrient sources, e.g., the aquaculture activities and wastewater have moresignificant impacts on shore areas. In addition, the nutrients from benthic fluxes were not taken into
consideration due to the limited observational data. This is, however, probably important to the shore
area because of presence of large tidal flat in JZB. Therefore, a fully three-dimensional model with amoving boundary at shore area is probably necessary in the future to further understand the spatial
variation in the bay.
5. Conclusions
The simulated annual cycles of nutrient concentrations and phytoplankton biomasses in the
2000s suggest that the growth of DIA is limited by SIL, while that of FLA is limited by DIP. Changes
in the external nutrient sources lead to long-term variations (the 1960s, the 1980s, and the 2000s) innutrient limitation and the phytoplankton community. Removing the nutrients from the river input
enhances SIL limitation for DIA and decreases the ratio of DIA to FLA from 2.3 to 2.0. Aquaculture
activities are the main source of DIP; therefore, the FLA biomass decreases when removing thisexternal source. The wastewater has a relatively weaker impact on the change of phytoplankton
communities than aquaculture, and the influence of atmospheric deposition is likely negligible.
Author Contributions: Conceptualization, J.S., H.G. and X.G.; methodology, Q.L. and X.M.; validation, Q.L. andJ.Z.; formal analysis, Q.L.; writing—original draft preparation, J.S.; writing—review and editing, Q.L., J.S. and
X.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Youth Talent Support Program of the Laboratory for Marine Ecology andEnvironmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), grant number
LMEES-YTSP-2018-02-03; the National Natural Science Foundation of China-Shandong Joint Fund for Marine
Ecology and Environmental Sciences, grant number U1606404.

Sustainability 2020, 12, 2224

17 of 22

Acknowledgments: We would like to thank Dr. Pu Xiang who provided the discharge of Dagu River and Prof.
Liu Sumei who provided the data of nutrient sources in the 2000s.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.

Appendix A
The state variables in this ecosystem box model included three elements of nutrients (DIN, DIP,
and SIL), two types of phytoplankton (DIA and FLA), and two types of detritus (DSi comprising Si
and DNSi not comprising Si). The state variables obey the following equations:
∂DIA
= DIA
∂t

− DIA

− DIA

,

(A1)

∂FLA
= FLA
∂t

− FLA

− FLA

,

(A2)

∂DIN
= DIA
∂t

− DIA

+ FLA

− FLA

+ DSi

2N + DNSi

2N + ϕ(DIN),

(A3)

∂DIP
= DIA
∂t

− DIA

+ FLA

− FLA

+ DSi

2P + DNSi

2P + ϕ(DIP),

(A4)

= DIA

− DIA

+ DSi

2Si + ϕ(SIL),

∂DSi
= DIA
∂t

− DSi

= FLA

− DNSi

(A5)
(A6)

,
.

(A7)

The governing equations of biological processes are as follows. The photosynthesis of DIA andFLA (DIA
and FLA) is expressed by:
DIA
FLA= Pmax× temp= Pmax× rad

× temp× min( DIN

× rad

, DIP

× min(DIN

, SIL

, DIP

) × DIA,) × FLA,

(A8)
(A9)

where Pmax
and Pmax are the maximum growth rates of DIA and FLA, respectively; andtemp , rad, DIN, DIP , and SIL
are the limiting factors of temperature, light intensity,and nutrients for DIA, and temp , rad, DIN, and DIP
are those for FLA.
The nutrient limitation follows the Michaelis-Menten kinetics: N=, where N denotes one

nutrient element concentration (DIN, DIP, or SIL) and K is the half saturation constant of N for DIA
or FLA.
×()
The temperature limitation follows the Q10 Rule: temp = e _, where al_temp1
is the temperature-dependent growth rate.
The light limitation is expressed by rad

=

(

e

)

, where I

is the optimum lightintensity, and I is the average value of the actual light intensity in the water column related to thelight intensity at sea surface (I ) and the light attenuation caused by pure water and suspended
particulate materials. I is the actual light intensity at a water depth z, expressed as a function of I
and the attenuation caused by water: I = I e , where k is the attenuation rate.
The coefficient k should be larger in the shallow region. Therefore, k = 0.7 − 0.04 × D = 0.42
(D = 7 m, the average depth of JZB), which was used to calculate the exponential attenuation rate of

Sustainability 2020, 12, 2224

18 of 22

light in JZB [23]. In the box model, the average vertical attenuation rate (0.4) was used to calculate theaverage light intensity in the water column:
I = 0.4 × I .

(A10)The respiration is expressed as:
DIA

FLA

= Pmax= Pmax× resp× resp+ resp× rad
× min( DIN
resp_temp × DIA,
+ resp× rad

× min(DIN

, DIP

, DIP

× FLA,, SIL) × resp

) ∗

(A11)

(A12)

where resp , resp , and resp_temp
are the percentage of basic respiration, photorespiration,and temperature-dependent respiration rates.
The mortality of phytoplankton was assumed to be proportional to the biomass:
DIA=r

× DIA,

(A13)

FLA

=r

× FLA,(A14)

where ris the mortality rate.
The mineralization of detritus is expressed as:DSi=r

× DSi,

(A15)

DNSi

=r

× DNSi,(A16)

where ris the mineralization rate of detritus.
ϕ(N) (in μmol L s ) represents the changing rate of nutrient concentrations from the externalsources of nutrients, where N denotes one nutrient element (DIN, DIP, or SIL), including river input,
wastewater discharge, aquaculture contribution, atmospheric deposition, and exchange with YS.
The annual loads of nutrients from wastewater, aquaculture, and atmosphere are given in Table
2, which are averaged to the concentration increase at each time step. Therefore, these three sourcesof nutrients do not have seasonal variations. It can be expressed as:
ϕ(N) =

L(N),24h 60m 60s
365d ×
×
×
×V
d
h
m

(A17)

where L(N) (I = 1–3) are the annual loads of nutrients from wastewater discharge, aquaculture
contribution, and atmospheric deposition (Table 2; in mol y ), respectively; and V is the water
volume of JZB (V = 2.7 × 10 L).
Nutrients from river input can be calculated as:

ϕ(N)

F
×C
24h 60m365d ××
× 60s/m
d
h
=
V

,

(A18)

where F is the freshwater discharge (in L y ); C
is the riverine concentrations of three nutrient
elements; and V is the water volume of JZB. The seasonal variation trends of freshwater discharge
are based on the model result of Soil and Water Assessment Tool (SWAT) with a daily resolution [32].
Nutrients exchange with YS can be expressed as:Sustainability 2020, 12, 2224

19 of 22

ϕ(N)

=

×(

)

,

(A19)

where ART is the average residence time of JZB (ART = 52 d); C is the concentrations of threenutrient elements in YS (Table 2); C
is the concentrations of three nutrient elements in JZB that arecalculated step-by-step by the model; and V is the water volume of JZB.
References
1.

2.

3.
4.

5.
6.

7.
8.
9.
10.
11.
12.
13.

14.
15.

16.

17.

18.

Gobler, C.J.; Cullison, L.A.; Koch, F.; Harder, T.M.; Krause, J.W. Influence of freshwater flow, ocean
exchange, and seasonal cycles on phytoplankton–nutrient dynamics in a temporarily open estuary. Estuar.
Coast. Shelf Sci. 2005, 65, 275–288, doi:10.1016/j.ecss.2005.05.016.
Gong, G.C.; Liu, K.K.; Chiang, K.P.; Hsiung, T.M.; Chang, J.; Chen, C.C.; Hung, C.C.; Chou, W.C.; Chung,
C.C.; Chen, H.Y.; et al. Yangtze River floods enhance coastal ocean phytoplankton biomass and potential
fish production. Geophys. Res. Lett. 2011, 38, L13603:1–L13603:6, doi:10.1029/2011GL047519.
Downing, J.A. Marine nitrogen: Phosphorus stoichiometry and the global N:P cycle. Biogeochemistry 1997,37, 237–252, doi:10.1023/A:1005712322036.
Moore, C.M.; Mills, M.M.; Arrigo, K.R.; Berman–Frank, I.; Bopp, L.; Boyd, P.W.; Galbraith, E.D.; Geider,
R.J.; Guieu, C.; Jaccard, S.L.; et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 2013, 6,
701–710, doi:10.1038/ngeo1765.
Wu, J.T.; Chou, T.L. Silicate as the limiting nutrient for phytoplankton in a subtropical eutrophic estuary of
Taiwan. Estuar. Coast. Shelf Sci. 2003, 58, 155–162, doi:10.1016/S0272-7714(03)00070-2.
Desmit, X.; Thieu, V.; Billen, G.; Campuzano, F.; Dulière, V.; Garnier, J.; Lassaletta, L.; Ménesguen, A.;
Neves, R.; Pinto, L.; et al. Reducing marine eutrophication may require a paradigmatic change. Sci. Total
Environ. 2018, 635, 1444–1466, doi:10.1016/j.scitotenv.2018.04.181.
Ibáñez, C.; Peñuelas, J. Changing nutrients, changing rivers. Science 2019, 365, 637–638,
doi:10.1126/science.aay2723.
Redfield, A.C.; Ketchum, B.H.; Richards, F.A. The influence of organisms on the composition of seawater.
In The Sea; Hill, M.N., Ed.; Interscience Publishers: New York, NY, USA, 1963; Volume 2, pp. 26–77.
Ryther, J.H.; Dunstan, W.M. Nitrogen, phosphorus, and eutrophication in the coastal marine environment.
Science 1971, 171, 1008–1031, doi:10.1126/science.171.3975.1008.
Rhee, G.Y. Effect of N:P atomic ratios and nitrate limitation on algal growth, cell composition and nitrate
uptake. Limnol. Oceanogr. 1978, 23, 10–25, doi:10.4319/lo.1978.23.1.0010.
Tyrerell, T. The relative influences of nitrogen and phosphorus on ocean primary production. Nature 1999,
400, 525–531, doi:10.1038/22941.
Glibert, P.M.; Maranger, R.; Sobota, D.J.; Bouwman, L. The Haber Bosch–harmful algal bloom (HB–HAB)
link. Environ. Res. Lett. 2014, 9, 105001, doi:10.1088/1748-9326/9/10105001.
Wan, Z.; Bi, H.; She, J.; Maar, M.; Jonasson, L. Model study on horizontal variability of nutrient N/P ratio
in the Baltic Sea and its impacts on primary production, nitrogen fixation and nutrient limitation. Ocean
Sci. Discuss. 2012, 9, 385–419, doi:10.5194/osd-9-385-2012.
Danielsson, Å.; Papush, L.; Rahm, L. Alterations in nutrient limitations–Scenarios of a changing Baltic Sea.
J. Mar. Syst. 2008, 73, 263–283.
Wu, M.L.; Wang, Y.S.; Wang, Y.T.; Yin, J.P.; Dong, J.D.; Jiang, Z.Y.; Sun, F.L. Scenarios of nutrient alterations
and responses of phytoplankton in a changing Daya Bay, South China Sea. J. Mar. Syst. 2017, 165, 1–12,
doi:10.1016/j.jmarsys.2016.09.004.
Yao, P.; Yu, Z.G.; Deng, C.M.; Liu, S.X.; Zhen, Y. Spatial–temporal distribution of phytoplankton pigments
in relation to nutrient status in Jiaozhou Bay, China. Estuar. Coast. Shelf Sci. 2010, 89, 234–244,
doi:10.1016/j.ecss.2010.07.003.
Li, C.L.; Zhang, F.; Yang, X.; Yang, B.; Shen, Z.L.; Sun, S. Concentration, distribution and annual fluctuationof chlorophyll–a in the Jiaozhou Bay. Oceanol. Limnol. Sin. 2005, 36, 499–506. (In Chinese with English
abstract)
Li, Y.; Li, R.X.; Wang, Z.L.; Zhu, M.Y.; Sun, P.X.; Xia, B. A preliminary study on phytoplankton community
structure and its change in the Jiaozhou Bay. Adv. Mar. Sci. 2005, 23, 320–334. (In Chinese with English
abstract)

Sustainability 2020, 12, 2224

19.
20.
21.
22.
23.
24.

25.
26.
27.
28.

29.
30.
31.
32.
33.
34.

35.

36.

37.
38.
39.
40.

41.
42.

20 of 22

Sun, X.X.; Sun, S.; Zhang, Y.S.; Zhang, F. Long term changes of chlorophyll–a concentration and primary
productivity in the Jiaozhou Bay. Oceanol. Limnol. Sin. 2011, 42, 654–661. (In Chinese with English abstract)Zhao, S.J.; Jiao, N.Z.; Shen, Z.L.; Wu, Y.L. Causes and consequences of changes in Nutrient Structure in the
Jiaozhou Bay. J. Integr. Plant Biol. 2005, 47, 396–410, doi:10.1111/j.1744-7909.2005.00005.x.
Wang, B.D.; Wang, Z.L. Long–term variations in chlorophyll–a and primary productivity in Jiaozhou Bay,
China. J. Mar. Biol. 2011, doi:10.1155/2011/594684.
Yang, D.F.; Gao, Z.H.; Sun, P.Y.; Li, M.; Qu, Y.F. Mechanism of nutrient silicon and water temperature
influences on phytoplankton growth. Mar. Sci. Bull. 2006, 8, 49–59.
Liu, S.M.; Zhang, J.; Chen, H.T.; Zhang, G.S. Factors influencing nutrient dynamics in the eutrophic
Jiaozhou Bay, North China. Prog. Oceanogr. 2005, 66, 66–85, doi:10.1016/j.pocean.2005.03.009.
Wu, Y.L.; Sun, S.; Zhang, Y.S. Long–term change of environmental and its influence on phytoplankton
community structure in Jiaozhou Bay. Oceanol. Limnol. Sin. 2005, 36, 487–498. (In Chinese with English
abstract)
Liu, Z.; Wei, H.; Liu, G.L.; Zhang, J. Simulation of water exchange in Jiaozhou Bay by average residence
time approach. Estuar. Coast. Shelf Sci. 2004, 61, 5–35, doi:10.1016/j.ecss.2004.04.009.
Song, X.X.; Yu, Z.M. Nutrient effect on phytoplankton in typical mariculture waters in summer in thenortheast of the Jiaozhou Bay. Oceanol. Limnol. Sin. 2007, 38, 446–452. (In Chinese with English abstract)
Liu, S.M.; Li, X.N.; Zhang, J.; Wei, H.; Ren, J.L.; Zhang, G.L.; Zhang, G.L. Nutrient dynamics in Jiaozhou
Bay. Water Air Soil Pollut. 2007, 7, 625–643, doi:10.1007/s11267-007-9125-y.
Liu, Z.; Wei, H.; Bai, J.; Zhang, J.; Liu, D.Y.; Liu, S.M. Nutrients seasonal variation and budget in Jiaozhou
Bay, China: A 3–dimensional physical–biological coupled model study, Water Air Soil Pollut. 2007, 7, 607–
623, doi:10.1007/s11267-007-9128-8.
Luo, X.; Sun, X.X.; Zheng, S.; Zhao, Y.F. On phytoplankton community structure and causative factors in
the Jiaozhou Bay in 2011. Oceanol. Limnol. Sin. 2016, 47, 915–923, doi:10.11693/hyhz20160300050.
Liu, H.; Yin, B.S. Numerical investigation of nutrient limitations in the Bohai Sea. Mar. Environ. Res. 2010,
70, 308–317, doi:10.1016/j.marenvres.2010.06.003.
Shen, Z.L. Historical changes in nutrient structure and its influences on phytoplankton composition in
Jiaozhou Bay. Estuar. Coast. Shelf Sci. 2001, 52, 211–224, doi:10.1006/ecss.2000.0736.
Liu, D.; Sun, J.; Zhang, J.; Liu, G. Response of the diatom flora in Jiaozhou Bay, China to environmental
changes during the last century. Mar. Micropaleontol. 2008, 66, 279–290, doi:10.1016/j.marmicro.2007.10.007.
Liu, D.; Sun, J.; Zou, J.; Zhang, J. Phytoplankton succession during a red tide of Skeletonema costatum in
Jiaozhou Bay of China. Mar. Pollut. Bull. 2005, 50, 91–94.
Chen, C.S.; Ji, R.B.; Zheng, L.Y.; Zhu, M.Y.; Rawson, M. Influences of physical processes on the ecosystem
in Jiaozhou Bay: A coupled physical and biological model experiment. J. Geophys. Res.-Oceans. 1999, 104,
29925–29949, doi:10.1029/1999JC900203.
Shi, H.H.; Shen, C.C.; Li, F.; Wang, Y.Z.; Parameter sensitivity analysis of a coupled biological-physicalmodel in Jiaozhou Bay. Acta Ecologica Sinica, 2014, 34, 41–49, doi:10.5846/stxb201304280854. (In Chinesewith English abstract).
Skogen, M.D.; Svendsen, E.; Berntsen, J.; Aksenes, D.; Ulvestad, K.B. Modeling the primary production in
the North Sea using a coupled three–dimensional physical–chemical–biological ocean model. Estuar. Coast.
Shelf Sci. 1995, 41, 545–565, doi:10.1016/0272-7714(95)90026–8.
Zhao, L.; Guo, X.Y. Influence of cross–shelf water transport on nutrients and phytoplankton in the East
China Sea: A model study. Ocean Sci. 2011, 7, 27–43, doi:10.5194/osd-7-1405-2010.
Pu, X. Shanghai Nuclear Engineering Research & Design Institute Co, LTD., Shanghai, China. Personalcommunication, 2011.
Yang, W.H.; Gao, H.W. Research on numerical model and environmental impact of scallop cultivation in
Jiaozhou Bay. Mar. Environ. Sci. 2010, 29, 559–563. (In Chinese with English abstract)
Ren. L.; Zhang, M.P.; Sun, J.; Li, T.; Zhu, C.J. Analysis of some key ecological parameters in the seawater ofJiaozhou Bay. J. Ocean Univ. 2003, 33, 557–564, doi:10.3969/j.issn.1672-5174.2003.04.010. (In Chinese withEnglish abstract).
Zhang, Z.; Wang, J.T. Overviews on the nutrients of Jiaozhou Bay. Ocean Sci. 2009, 33, 90–94. (In Chinesewith English abstract)Ren, L.; Zhang, M.P.; Brockmann, U.H.; Feng, S.Z. Pelagic nitrogen cycling in Jiaozhou Bay, a model study
Ⅰ: The conceptual model. Chin. J. Oceanol. Limnol. 2003, 21, 358–367.

Sustainability 2020, 12, 2224

43.
44.
45.
46.

47.

48.
49.
50.
51.

52.
53.

54.
55.
56.

57.
58.
59.

60.

61.
62.

63.

64.

21 of 22

Editorial Board of Annals of Bays in China. Annals of Bays in China (Series 4); Wang, J.L., Ed; China Ocean
Press: Beijing, China, 1993; pp. 157–169, ISBN 7-5027-2563-6. (In Chinese)
Zhang, J. Watershed nutrient loss and eutrophication of marine recipients: A case study of the Jiaozhou
Bay. Water Air Soil Pollut. 2007, 7, 583–592, doi:10.1007/s11267-007-9130-1.
Shi, J.H.; Li, G.X.; Wang, P. Anthropogenic influence on the tidal prism and water exchanges in Jiaozhou
Bay, Qingdao, China. J. Coast. Res. 2011, 27, 57–72, doi:10.2112/JCOASTRES-D-09-00011.1.
Yao, P.; Yu, Z.G.; Deng, C.M.; Liu, S.X.; Zhen, Y. Functional types of phytoplankton in Jiaozhou Bay using
diagnostic pigment analysis (DPA). Oceanol. Limnol. Sin. 2013, 44, 866–876 (in Chinese with English
abstract)
Du, S.; Huang, S.; Zang, C.; Wu, M.; Gao, F.; Lin, C.; Guo, Y.; Luo, Y. Correlation research between theindicators of phytoplankton standing stock Ⅱ: Chlorophyll a and algal density. J. Water Resour. Water Eng.
2011, 22, 44–49. (In Chinese with English abstract)
Sun, S.; Liu, G.M.; Zhang, Y.S. Community composition and distribution character of phytoplankton in the
Jiaozhou Bay in the 1990s. Oceanol. Limnol. Sin. 2002, 33, 37–44. (in Chinese with English abstract)
Wu, Y.L.; Sun, S.; Zhang, Y.S.; Zhang, F. Quantitative study on long–term variation of phytoplankton in
Jiaozhou Bay. Oceanol. Limnol. Sin. 2004, 35, 518–523. (In Chinese with English abstract)Zhao, S.J.; Jiao, N.Z.; Wu, C.W.; Liang, B.; Zhang, S.Y. Evolution of nutrient structure and phytoplankton
composition in the Jiaozhou Bay ecosystem. J. Environ. Sci. 2005, 17, 95–102.
Li, M.; Xu, K.; Watanabe, M.; Chen, Z. Long–term variations in dissolved silicate, nitrogen, and phosphorus
flux from the Yangtze river into the east China sea and impacts on estuarine ecosystem. Estuar. Coast. Shelf
Sci. 2007, 71, 3–12, doi:10.1016/j.ecss.2006.08.013.
Liu, M.; Zhi, C.Y.; Li, K. Mechanism and ecological effect of river–transported silicon flux reduction. Ecol.
Sci. 2010, 29, 171–175, doi:10.3969/j.issn.1008-8873.2010.03.014. (in Chinese with English abstract)
Domingues, R.B.; Barbosa, A.B.; Galvão, H.M. River damming leads to decreased phytoplankton biomass
and disappearance of cyanobacteria blooms. Estuar. Coast. Shelf Sci. 2014, 136, 129–138,
doi:10.1016/j.ecss.2013.11.012.
Conley, D.; Stålnacke, P.; Pitkänen, H.; Wilander, A. The transport and retention of dissolved silicate byrivers in Sweden and Finland. Limnol. Oceanogr. 2000, 45, 1850–1853, doi:10.4319/lo.2000.45.8.1850.
Wu, G.J.; Cao, W.Z.; Wang, F.F.; Su, X.L.; Yan, Y.Y.; Guan, Q.S. Riverine nutrient fluxes and environmental
effects on China’s estuaries. Sci. Total Environ. 2019, 661, 130–137, doi:10.1016/j.scitotenv.2019.01.120.
Garnier, J.; Beusen, A.; Thieu, V.; Billen, G.; Bouwman, L. N:P:Si nutrient export ratios and ecologicalconsequences in coastal seas evaluated by the icep approach. Glob. Biogeochem. Cycles 2010, 24,
doi:10.1029/2009GB003583.
Ma, J.; Zhou, B.B.; Duan, D.D.; Wei, Y.; Pan, K. Silicon limitation reduced the adsorption of cadmium inmarine diatoms. Aquat. Toxicol. 2018, 202, 136–144, doi:10.1016/j.aquatox.2018.07.011.
Wang, W.X.; Dei, R.C.H. Metal uptake in a coastal diatom influenced by major nutrients (N, P, and Si).
Water Res. 2001, 35, 315–321, doi:10.1016/S0043-1354(00)00256–6.
Burson, A.; Stomp, M.; Akil, L.; Brussaard, C.P.D.; Huisman, J. Unbalanced reduction of nutrient loads hascreated an offshore gradient from phosphorus to nitrogen limitation in the North Sea. Limnol. Oceanogr.
2016, 61, 869–888, doi:10.1002/lno.10257.
Liang, Y.; Zhang, G.T.; Wan, A.Y.; Zhao.; Z.X.; Wang, S.W.; Liu, Q. Nutrient–limitation induced diatom–
dinoflagellate shift of spring phytoplankton community in an offshore shellfish farming area. Mar. Pollut.
Bull. 2019, 141, 1–8, doi:10.1016/j.marpolbul.2019.02.009.
Sugiura, S.H. Phosphorus, Aquaculture, and the Environment. Rev. Fish. Sci. Aquac. 2018, 26, 515–521,
doi:10.1080/23308249.2018.1471040.
Kumararaja, P.; Suvana, S.; Saraswathy, R.; Lalitha, N.; Muralidhar, M. Mitigation of eutrophication
through phosphate removal by aluminium T pillared bentonite from aquaculture discharge water. Ocean
Coast. Manage. 2019, 182, 104951, doi:10.1016/j.ocecoaman.2019.104951.
Fisher, T.R.; Hagy, J.D.; Boynton, W.R.; Williams, M.R. Cultural eutrophication in the Choptank andPatuxent
estuariesofChesapeakBay.
Limnol.
Oceanogr.
2006,
5,
435–447,
doi:10.4319/lo.2006.51.1_part_2.0435.
Saeck, E.A.; O’Brien, K.R.; Weber, T.R.; Burford, M.A. Changes to chronic nitrogen loading from sewage
discharges modify standing stocks of coastal phytoplankton. Mar. Pollut. Bull. 2013, 71, 159–167,
doi:10.1016/j.marpolbul.2013.03.020.

Sustainability 2020, 12, 2224

65.

66.

67.

68.

69.

70.

71.

72.

73.

74.
75.

22 of 22

Gypens, N.; Delhez, E.; Brunier, A.V.; Vanhoutte–Brunier, A.; Burton, S.; Thieu,V.; Passy, P.; Liu, Y.;
Callens, J.; Rousseau, V.; Lancelot, C. Modelling phytoplankton succession and nutrient transfer along theScheldt
estuary(Belgium,
The
Netherlands).
J.
Mar.
Syst.
2012,
128,
89–105,
doi:10.1016/j.jmarsys.2012.10.006.
Taylor, D.I.; Candace, C.A.; Giblin, A.E.; Tucker, J.; Diaz, R.J.; Keay, K. Wastewater input reductions reverse
historic hypereutrophication of Boston Harbor, USA. Ambio 2020, 49, 187–196, doi:10.1007/s13280-01901174-1.
Oviatt, C.; Smith, L.; Krumholz, J.; Coupland, C.; Stoffel, H.; Keller, A.; McManus, M.C.; Reed, L. Managed
nutrient reduction impacts on nutrient concentrations, water clarity, primary production, and hypoxia ina north temperate estuary. Estuar. Coast. Shelf Sci. 2017, 199, 25–34, doi:10.1016/j.ecss.2017.09.026.
Zhang, G.S.; Zhang, J.; Liu, S.M. Characterization of nutrients in the atmospheric wet and dry deposition
observed at the two monitoring sites over yellow sea and East China Sea. J. Atmos. Chem. 2007, 57, 41–57,
doi:10.1007/s10874-007-9060-3.
Duce, R.A.; Laroche, J.; Altieri, K.; Arrigo, K.R.; Baker, A.R.; Cornell, S.; Dentener, F.; Galloway, J.;
Ganeshram, R.S.; Geider, R.J.; et al. Impacts of Atmospheric Anthropogenic Nitrogen on the Open Ocean.
Science 2008, 320, 893–897, doi:10.1126/science.1150369.
Martino, M.; Hamilton, D.; Baker, A.R.; Jickells, T.D.; Bromley, T.; Nojiri, Y.; Quack, B.; Boyd, P.W. Western
pacific atmospheric nutrient deposition fluxes, their impact on surface ocean productivity. Glob. Biogeochem.
Cycles 2014, 28, 712–728, doi:10.1002/2013GB004794.
Guo. W.; Shi, J.; Zhu, J.Y.; Zhao, L. Modelling the Effects of the Atmospheric Nitrogen Deposition on thePrimary Production Process in the Southern Yellow Sea. J. Ocean Univ. 2020, 50, 115–125,
doi:10.16441/j.cnki.hdxb.20190028. (In Chinese with English abstract)
Qi, J.H.; Liu, X.H.; Yao, X.H.; Zhang, R.F.; Chen, X.J.; Lin, X.H.; Gao, H.W.; Liu, R.H. The concentration,
source and deposition flux of ammonium and nitrate in atmospheric particles during dust events at acoastal site in northern China. Atmos. Chem. Phys. 2018, 18, 571–586, doi:10.5194/acp-18-571-2018.
Xing, J.W.; Song, J.M.; Yuan, H.M.; Li, X.G.; Li, N.; Duan, L.Q.; Kang, X.M.; Wang, Q.D. Fluxes, seasonalpatterns and sources of various nutrient species (nitrogen, phosphorus and silicon) in atmospheric wet
deposition and their ecological effects on Jiaozhou Bay, North China. Sci. Total Environ. 2017, 576, 617–627,
doi:10.1016/j.scitotenv.2016.10.134.
Guo, X.; Zhu, X.H.; Wu, Q.S.; Huang, D.J. The Kuroshio nutrient stream and its temporal variation in the
East China Sea. J. Geophys. Res.Oceans. 2012, 117, C01026:1–C01026:17, doi:10.1029/2011JC007292.
Guo, X.; Zhu, X.H.; Yu, L.; Huang, D.J. Spatial variations in the Kuroshio nutrient transport from the East
China Sea to south of Japan. Biogeosciences 2013, 10, 6403–6417.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).