Estuarine, Coastal and Shelf Science 260 (2021) 107491

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Organic carbon production and nutrients consumption revealed by mixing
model in the energetic Changjiang Estuary
Xiaolu Wang a, Ying Wu a, *, Hui Wu a, Guosen Zhang a, Jie Jin a, Lijun Qi a, Xinyu Guo b,
Jinlong Dai a, Zhaoru Zhang c, Meng Zhou cabcState Key Laboratory of Coastal and Estuarine Research, East China Normal University, Shanghai, 200241, China
Center for Marine Environmental Studies, Ehime University, Matsuyama, 790-8577, Japan
School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China

A R T I C L E I N F O

A B S T R A C T

Keywords:
Water mass composition
Organic carbon production
Nutrient consumption
Phytoplankton community structure
China
Changjiang estuaryRiverine and oceanic nutrients co-influence the estuarine and coastal phytoplankton production and subsequentorganic carbon (OC) dynamics, which contribute significantly to the global carbon cycle. However, it is difficult
to quantify the contributions of nutrients from different sources. Based on in situ observations made in July 2017,a three end-member model was developed to estimate the nutrients consumed and OC produced by biologicalactivities in the Changjiang Estuary and its adjacent coastal region. In this way, the contributions of nutrientsfrom different sources to phytoplankton production were quantified. In onshore water with salinity less than 20,
the nutrients consumed and OC produced were mainly controlled by the mixing of various water masses andturbidity maximum zones. In offshore water with salinity larger than 20, the model results suggested that thebiogeochemical processes regulating nutrients and OC dynamics were mainly impacted by the Changjiang River
plume and Taiwan Warm Current deep water (TWCDW). In the stratified river plume region, nutrient uptake andOC production were controlled by the contributions of three end-members (Changjiang fresh water, Taiwan
Warm Current surface water and TWCDW), while in the upwelling-influenced region, OC production wasstrongly regulated by the nutrients supplied by the TWCDW. This study will also shed light on the influence ofmulti-sourced nutrient compositions and structures on the in situ biological OC production process in similar
dynamic estuarine regions.

1. Introduction
Organic carbon (OC) production and transformation in estuarine andcoastal regions is an important part of the global carbon cycle (Baueret al., 2013). Estuarine and coastal regions are among the most diverseand productive marine environments. They receive large amounts ofdissolved and particulate materials from rivers and are important zones
where terrigenous and marine materials mix (Bianchi et al., 2006;
Middelburg and Herman, 2007). These regions, especially many
river-dominated systems such as the Mississippi and Amazon river
plumes, are usually high-production zones with net OC produced byphytoplankton as related to the local nutrient structures (Benner andOpsahl, 2001; Carlson et al., 2000; Wang et al., 2004). The carbon cycles
in such energetic regions are tied to water mass mixing and nutrient
cycles. Within the euphotic zone, in addition to river inputs, phyto­plankton produce organic matter through primary production,

removing nutrients from waters, while upwelling supplies nutrients,
sustaining production (Chen et al., 2004; Hansell and Carlson, 2014).
Net OC production is always present during the bloom season due to thehigh primary production rate (Carlson et al., 2000). Moreover, nutrients
supplied by the river plume and upwelling enhance primary production.
However, the complex physical environment combined with biogeo­
chemical processes makes it difficult to quantify the net OC production
and related nutrient consumption.
Phytoplankton community structure can affect different processes ofextracellular release by phytoplankton, resulting in different OC dy­
namics. The quantity and quality of OC produced during phytoplankton
blooms varies considerably. For example, compared with blooms in theoligotrophic Sargasso Sea, one order of magnitude less dissolved organiccarbon (DOC) accumulated during a bloom in the eutrophic Ross Sea
despite four-to fivefold greater primary production in the Ross Sea(Carlson et al., 1998). Previous studies also showed that a large fraction* Corresponding author.
E-mail address: wuying@sklec.ecnu.edu.cn (Y. Wu).
https://doi.org/10.1016/j.ecss.2021.107491
Received 21 June 2020; Received in revised form 23 April 2021; Accepted 2 July 2021
Available online 7 July 2021
0272-7714/© 2021 Elsevier Ltd. All rights reserved.

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

of the accumulated total organic carbon (TOC) is partitioned into par­ticulate organic carbon (POC) in diatom-dominated blooms (Engel et al.,2002) and is partitioned into DOC in picoplankton-dominated blooms(Teira et al., 2001). Previous studies have indicated that hydrodynamics
and nutrient structures profoundly influence phytoplankton community
composition (Cloern and Dufford, 2005; Jiang et al., 2015; Li, 2002).
These results show that the dynamics of net OC production in phyto­plankton blooms are highly varied and complicated. Moreover, thenutrient status and phytoplankton community structure are importantfactors that influence these OC production processes.
In recent years, with economic development, the Changjiang Rivertransports vast amounts of terrestrial nutrients to coastal regions(Zhang, 1996), leading to high primary productivity and causing a seriesof environmental problems (Gao and Song, 2005; Ning et al., 1995; Zhu
et al., 2011). However, nutrient dynamics along the salinity gradient arecomplicated because of the presence of multiple water masses (Chen,
2009; Gong et al., 1996). Although riverine nutrient input tends to be adominant source, the contributions of nutrients transported by upwelled
nutrient-rich deep water also play a significant role. The
upwelling-derived nutrients could exceed river inputs from the Chang­
jiang River in the East China Sea (ECS) shelf system (Chen and Wang,
1999; Zhang et al., 2007). The nutrient structures provided by these twosources are significantly different. The Changjiang River inputs massive
nutrient-rich waters with high dissolved inorganic nitrogen:dissolved
inorganic phosphorus (DIN:DIP) ratios, leading to a large surface areawith excess nitrate and limited phosphorus (Chen, 2008).
Upwelling-derived water with a nutrient ratio close to the Redfield ratio
could alleviate the phosphorus deficiency in the surface area by sup­plying PO3−
4 (Chen et al., 2004). Meanwhile, due to the complex watermass and nutrient structure, the phytoplankton community composition
was spatially heterogeneous (Jiang et al., 2015). However, it is still
difficult to distinguish the biological response to different sources ofnutrients in such a complex system. Few studies have quantified thecontributions of river-derived and upwelling-derived nutrients tophytoplankton production and the relevant OC production.
To explore the contributions of Changjiang River inputs and up­
welling transport to nutrient consumption and OC production relevant
to phytoplankton production, a cruise was conducted in summer 2017 in
the Changjiang Estuary and its adjacent coastal region, which areaffected by both the river plume and upwelling. In this study, an endmember mixing model was used to examine the nutrients consump­tion and OC production due to biological activities. By adopting the endmember mixing model, this research aimed to semiquantitatively

predict biological nutrient uptake and OC production and estimate thecontributions of nutrients from different sources to phytoplankton
production.
2. Materials and methods
2.1. Study area
The study area (Fig. 1) covers the Changjiang River Estuary andadjacent coastal region. The sampling stations mainly lie in waters
shallower than 50 m, and a deep submarine valley exists in the study
area. The submarine valley is situated off the Changjiang River Estuary
and extends from southeast to northwest.
The continental shelf circulation system (Fig. 1a) mainly comprises
two currents: the Changjiang River plume and the Taiwan Warm Current(TWC). The TWC flows steadily northward in summer, carrying waterwith deep ocean properties originating from the Kuroshio branch andTaiwan Strait. In summer, the TWC is enhanced by the southerly
monsoon. The main water masses in summer mainly include theChangjiang diluted water (CDW, freshwater from the Changjiang Riverthat debouches into the ECS and mixes with the ambient saline water,
generally with a salinity of less than 31; Mao et al., 1963), the Taiwan
Warm Current surface water (TWCSW) and the Taiwan Warm Current
deep water (TWCDW, saline waters brought from the south by the TWC;
Zhang et al., 2014). The Changjiang River discharge usually reaches its
peak value in summer. In the period of our survey, the discharge
increased from 55000 to 70000 m3/s, which resulted in a more extensive
water plume and progressively stronger stratification.
In boreal summer, the upwelling off the Changjiang Estuary andadjacent waters is confirmed to be a persistent feature on the northern
and western sides of the submarine valley (Lü et al., 2006). The up­
welling brings nutrient-rich water, especially PO3−
4 , to the surface layer(Chen et al., 2004). In the period of our survey, it mainly existed in theoffshore region of transects C2 and C3.
2.2. Sampling and methods
2.2.1. Sample collection
A cruise was undertaken onboard the ship Zheyuke during summer (1July to 8 July 2017). During the cruise, four transects extending from theriver channels of the southern branch to the outer edge of the Chang­
jiang River plume were surveyed. The sampling stations are shown in
Fig. 1b.

Fig. 1. (a) Schematic of the circulation in the East China Sea. The arrows indicate: ① the Changjiang Diluted Water (CDW), ② the Taiwan Warm Current (TWC), ③
the Yellow Sea Coastal Current (YSCC) and ④ Kuroshio. The flow patterns are based on Fig. 1 from Zhou et al. (2015). (b) Sampling stations in the Changjiang
Estuary. The station numbers follow the pattern of the first named station of each horizontal transect for brevity. (For interpretation of the references to color in thisfigure legend, the reader is referred to the Web version of this article.)2

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

Temperature, salinity, density, depth and fluorescence were
measured in situ at each station using a conductivity-temperature-depth
recorder (SBE 911, Sea-Bird Co.) and a fluorescence detector. Water
samples at different depths (2–4 layers, depending on the water depth)
were collected using 12 L Niskin bottles mounted on a rosette sampler.
Samples for DOC were filtered through 0.45 μm nylon filters (Rephile,
Shanghai, China). Samples for the nutrient analysis were filteredthrough a 0.45 μm polycarbonate membrane (Whatman), and then, asaturated HgCl2 solution was added to each sample for further analysis.
Samples for POC and total suspended particles (TSM) were filtered
immediately through precombusted (at 500 ◦ C for 5 h) GF/F filters (poresize: 0.7 μm, Whatman). The filters were used to determine the POC andTSM concentrations. Except for nutrient samples that were preserved atroom temperature in the dark, all samples were stored in a − 20 ◦ Cfreezer prior to analysis.

n∑where Ci is the characteristic nonconservative parameter concentrationof the n end-members.
The difference between the mixed and measured C concentrations
represents the biologically modulated C or ΔC. Positive values indicate
net C production and negative values suggest net C consumption. The
equation is described as follows:
ΔC = Cin situ − C0

3. Results
3.1. Spatial distribution of salinity, TSM, fluorescence intensity, OC andnutrients
In the survey period, the surface salinity distribution showed that the
Changjiang River plume featured a typical northeastward summertime
extension, covering a large area outside the river mouth (Fig. 2a). The
salinity ranged from 0.12 to 34.57. The surface salinity ranged from 0.12
to 30.66, which meant that almost all the sampling stations in the sur­face layer featured CDW with a boundary of 31. The bottom layer
salinity ranged from 0.12 to 34.57, with most stations covered by saline
water and the CDW restricted to the river mouth (Fig. 2i). In the shallow
river channel and near the river mouth region, the water column was
well mixed. Stratification existed at a depth of approximately 20 m
beyond the shallow inner shelf at stations C2-7 and C3-4.
The TSM concentration of the sampled water ranged from 7.83 to
95.47 mg/L. In the river channel and near the river mouth region, theconcentration of TSM was high and decreased with increasing salinity.
In transect C2, stations C2-5 and C2-6 were in the turbidity maximum
zone (TMZ) with the highest TSM concentrations (57.09 and 70.70 mg/
L), and outside of these stations, the TSM concentration sharply
decreased. Similarly, station C3-2 (TSM concentration, 95.47 mg/L) wasin the TMZ in transect C3. The TSM concentration was much higher
nearshore than offshore. For the stations with salinities lower than 20,
the TSM concentration was high (50.55 ± 20.52 mg/L). With theextension of the Changjiang River plume, stratification appeared, andthe TSM concentration dropped to 20.22 ± 8.88 mg/L. Combined withthe high fluorescence intensity, this indicates the release of restrictions
on phytoplankton growth at stations with salinities greater than 20.
The fluorescence intensity of the sampled water ranged from 0 to
24.71 (arbitrary unit). In the river channel and near the river mouth
region, the fluorescence intensity was low and rapidly increased in theouter plume. The spatial distribution characteristics of POC (Fig. 2d andl) were similar to those of the fluorescence intensity in both the surface
and bottom layers (Fig. 2c and k). The surface water POC distribution
showed a very low value (1.12% ± 0.32%) in the river channel and near
the river mouth region with salinities less than 20 and a high value(4.31% ± 2.01%) in the offshore region (Fig. 2d). The bottom water POC
concentration in the nearshore region had a generally similar distribu­
tion and evolution pattern to that of the TSM, and the bottom POC value
was significantly lower than the surface value. In the offshore region,
especially transect C4, the POC concentration in the bottom water wasmuch higher than that at adjacent stations. Meanwhile, the good linear
relationship (R2 = 0.79, P < 0.01) between POC and fluorescence in­tensity showed that the POC concentration was mainly influenced byphytoplankton activities.
The relationship between POC (mg/L) and TSM (mg/L) showed that
there appeared to be two different populations, with different slopes(Fig. S1). The scatter of the data, particularly at lower TSM contents,n∑

Fi Ti1 = Tin1 situ

(1 − 2)

i=1
n
∑
i=1

……
n
∑

1
Fi Tin− 1 = Tinn− situ

(3 − 1)In this paper, we calculate the biologically produced OC (ΔOC) andnutrients consumed (ΔN) by using the temperature and salinity as
conservative tracers. The details are shown in section 3.4.

2.2.3. End-member mixing model
The end-member mixing model was based on mass conservation
equations for tracers, which were predicated on the assumptions that (1)the water is a mixture of source solutions (end-members) with fixedcompositions, (2) the mixing process is linear and relies completely onhydrodynamic mixing, (3) the solutes used as tracers are conservative,and (4) the source solutions have extreme concentrations (Barthold
et al., 2011).
The end-member mixing model consists of the following equations:（1 − 1）(2 − 1)i=12.2.2. Chemical analyses
The DOC samples underwent acidification to remove dissolved
inorganic carbon (DIC) and were then assessed using a TOC analyzer(Shimadzu TOC L-CPH, Japan) with the high-temperature catalyticoxidation method. Each sample was analyzed 3–5 times until the coef­
ficient of variation was less than 2%. We used sodium hydrogen phos­
phate to calibrate the analyzer with a linear regression model. In
addition, deep seawater (with a DOC concentration of 41–44 μmol, fromthe University of Miami) was analyzed for quality control.
The concentration of POC was determined by an elemental analyzer(Finnigan EA 1112, Thermo-Fisher, USA) connected to an isotope ratio
mass spectrometer (Finnigan Delta plus XP, Thermo-Fisher, USA) after
removing the carbonate fraction by acid fumigation with concentrated
HCl. Repeated analyses of the intra-laboratory standard samples
revealed a variation of <5% (Wu et al., 2003).
3−
2−
The concentrations of nutrients (NO−3 , NO−2 NH+
4 , PO4 and SiO3 )were determined by performing a continuous flow analysis (CFA) using
the Skalar Sanplus system in the laboratory with colorimetric methods
described by (Grasshoff et al., 1999). The analytical precisions
were 0.06 μM for NO−3 , 0.01 μM for NO−2 , 0.09 μM for NH+
4 , 0.03 μM for
2−
PO3−
4 , and 0.15 μM for SiO3 .

Fi = 1 (Fi ≥ 0)

Ci F

C0 =

(1 − n)i=1where n is the number of end-members. Fi is the proportion of each endmember, and Tin is the characteristic concentration of the chosen tracern
for each end-member. Tin
situ is the in situ measured concentration of thechosen tracer.
The mixing concentration of the nonconservative parameter (such as
OC and nutrients) C0 could be calculated as follows by mixing of the nend-members using the proportions obtained above:3

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

Fig. 2. Spatial distribution in surface and bottom: salinity (a), (i); TSM (b), (j); fluorescence intensity (c), (k); POC% (d), (l); DOC (e), (m); NO−3 (f), (n); SiO2−
3 (g), (o);PO3−4 (h), (p).

Fig. S2. The respective linear relationships of NO−3 and SiO2−
3 to salinity
showed that the dominant source was the Changjiang River. The
2−
decrease in NO−3 , PO3−
4 and SiO3 concentrations in some samples withsalinity greater than 20 indicated consumption in the surface layer.
Jiang et al. (2021) reported with results from the same cruise that theenrichment in δ15N–NO-3 and δ18O–NO-3 indicated biological NO−3 con­sumption by phytoplankton. In contrast to NO−3 , the accumulation ofNH+
4 was observed in water with salinity greater than 20, which was
related to nitrification (Jiang et al., 2021). Hence, NO−3 was an impor­tant nitrogen source for phytoplankton in our research. The PO3−
4 con­
centration in the bottom layer showed an obvious increase at salinity
greater than 30, which may be influenced by the TWC. This character­istic could also be seen in transect C1 (Figure 2p).

probably indicates a mixture of the two populations. The samples with
POC% values greater than 2 mainly exhibited high fluorescence in­tensity values, which indicates high phytoplankton biomass, while thosewith POC% values less than 2 were usually associated with low fluo­
rescence intensity values. In more detail, the detrital source, repre­
senting river particles or resuspended sediment, gives a mode withvalues below 2%, while the planktonic source appears in a mode withvalues over 2%. The scatter of the data also shows that the mixing ofthese two different populations is not linear but depends on water cir­
culation and the extent of the river plume (Cauwet and Mackenzie,1993). Our results also show that there were two regimes of particle
sources.
Both surface and bottom DOC concentrations decreased from theriver mouth to the offshore waters (Fig. 2e and m), which indicates that
relatively abundant DOC was transported from the Changjiang River. In
addition, high concentrations of DOC were found in the surface layer inthe stratified offshore region. DOC did not exhibit conservative behavior
in this estuarine system in our survey period. In the TMZ, the DOC values
were very high, which suggested remobilization of DOC from the nearsurface or bottom sediments.
The dissolved NO−3 , PO3−and SiO2−
concentrations showed a4
3
decreasing trend from the river mouth to the offshore water (Fig. 2). The
2−
dissolved NO−3 , PO3−
4 and SiO3 concentrations at river channel station
C2-1 with a salinity of approximately zero were ca. 110 μМ, 1.60 μМ,
and 110 μМ, respectively. These concentrations decreased to ca. 5 μМ,
0.5 μМ, and 15 μМ, respectively, at offshore station C2-10 (salinity wasapproximately 29). High NO−3 and PO3−
4 concentrations were observedin the TMZ zone at station C2-6. The distributions of all nutrients in thesurface layer exhibited a tongue-like shape, similar to the salinity dis­tribution. The bottom nutrient distribution exhibited a pattern very
similar to that at the surface, except for PO3−
4 . The relationships between
nutrients and salinity in the surface and bottom layers are shown in

3.2. Vertical distribution of salinity, TSM, fluorescence intensity, OC andnutrients
The distribution of the major parameters along transects C3 and C4 isshown in Fig. 3. In transect C3, the low-temperature, high-salinity bot­
tom water climbed along the bottom slope near the station C3-4 andformed salinity fronts with a high-salinity gradient. These characteristics
indicated that this transect was influenced by upwelling during thesurvey period. To the west of the front, the water was well mixed withlittle difference between the surface and bottom layers. To the east of thefront, there was obvious stratification at a depth of approximately 20 m.
High primary production was indicated by the high fluorescence in­tensity, with high OC concentrations and low nutrient concentrations.
Transect C2 exhibited a very similar phenomenon to transect C3(Fig. S3).
In transect C4, the distribution of salinity showed strong stratifica­tion. Near station C4-6, relatively high primary production was alsocharacterized by high fluorescence intensity, with high OC
4

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

3−
Fig. 3. The vertical profiles of salinity (a), (i); TSM (b), (j); fluorescence intensity (c), (k); POC% (d), (l); DOC (e), (m); NO−3 (f), (n); SiO2−
3 (g), (o); PO4 (h), (p) alongtransects C3 and C4.

concentrations and low nutrient concentrations. This location was on
the main pathway of the Changjiang River plume. Transect C1 has very
similar features to transect C4 (Fig. S4).

samples. In general, two factors (Factors 1 and 2) were responsible for90.6% of the variance in the data, which was enough to explain thevariation tendencies of the parameters (Fig. 4a). Factor 1 accounted for
79.8% of the total variance in the data set and showed high positive
loadings for salinity and negative loadings for TSM. Factor 2 accounted
for only 10.8% of the total variance. The variable with the highest
positive Factor 2 loading was POC, while DOC and dissolved oxygen(DO) were also associated with Factor 2, but their values were lower

3.3. Principal component analysis (PCA) results
For the PCA analysis, the final data set contained a total of 7 vari­ables (salinity, temperature, TSM, DO, DOC, POC and δ13C) and 73
5

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

Fig. 4. Plots of (a) variable loadings and (b) sample scores of Factor1 and Factor2 from PCA of all samples from study area.

than those of POC.
The scores of PCA Factors 1 and 2 of all 73 samples are plotted inFig. 4b. The figure shows a marked difference between onshore stations
with salinities less than 20 and offshore stations with salinities greaterthan 20, which indicated that the samples could be divided into twogroups. The scores of these two groups of samples were relatively clus­
tered, with only a few scattered points. The difference between these
two groups was predominantly influenced by Factor 1. This result
indicated that along Factor 1, the samples from the river channel andnear the river mouth region were resolved from samples collected fromthe offshore region by the physical environment, such as the salinity andTSM. The scattered points in the offshore region samples were mainly

separated by Factor 2, reflecting the different biogeochemical processes
between the surface and bottom layers.
3.4. End-member determination for the mixing model (selection of endmembers and model validation)
The potential temperature-salinity diagram illustrates a three endmember mixing scheme in our study area (Fig. 5) according to themethod given by (Renner, 1989). The method proposed that the esti­
mated mixture is represented by the vertex of the cross-polytope of thesmallest diagonal from the observed sample point to the space spannedby the specified end-members. All the data are scattered in the interior of

Fig. 5. Potential temperature (◦ C) vs salinity scheme in the study area based on the data collected in the survey in July 2017. The gray dots are CTD profile data in all
stations. Also shown are the end-members for Changjiang River Fresh Water (CFW), Taiwan Warm Current Deep Water (TWCDW), and Taiwan Warm Current Surface
Water (TWCSW, Zhang et al., 2014).
6

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

a planar triangular space which illustrates that there should be a three
end-member mixing scheme in our study area (Renner, 1993). We
defined the three end-members as Changjiang fresh water (CFW, indi­cated by the fuchsia star), TWCDW (indicated by the green star), and
TWCSW (indicated by the brown star) according to the potential tem­
perature and salinity. This classification of end-members is consistent
with that analyzed in Zhang et al. (2014) under conditions comparable
to those involved herein. Two tracers of water masses – potential tem­
perature and salinity – were chosen as conservative tracers to calculate
the physical process induced changes in OC and nutrients in the study
area (Cao and Dai, 2011; Han et al., 2012; Wang et al., 2016).
The CFW and TWCDW end-members were obtained by averaging
potential temperatures, salinity, OC and nutrient concentrations at the
sampling stations considering their water mass characteristics. The
TWCSW end-member was obtained from the literature with conditions
similar to our observations because we did not obtain typical samples inour study area. The summary of the end-member values adopted in thisstudy is given in Table 1.
Based on the three end-member model, we derived fractional con­tributions for samples in the region to predict the conservative mixing
values of OC and nutrients. We assume that the three major endmembers in our study area are CFW, TWCDW, and TWCSW. Accord­
ingly, our estimates of the mixing ratio of water masses indicate thecontribution of each end-member to the local water mass composition,and their fractions are f1, f2, and f3 for CFW, TWCDW, and TWCSW,
respectively (Fig. 6).
To better quantify the net variation in biogeochemical parameters inthe study area, the uncertainties derived from the three end-member
mixing model were further constrained. Based on the calculation
equations, the sources of uncertainty in the derivation of net variation
were associated with the uncertainties in tracer values of the three endmembers. The uncertainties in the biogeochemical parameters could be
estimated using error propagation formulas (Taylor, 1982). In thisstudy, the DOC deviation uncertainties (δ(ΔDOC)) varied from 3.08 to
9.29, δ(ΔPOC) varied from 0.79 to 9.31, δ(ΔNO-3) varied from 1.39 to2−
7.93, δ(ΔPO3−4 ) varied from 0.08 to 0.21, and δ(ΔSiO3 ) varied from0.67 to 2.87.

low, which suggested that DOC was not produced by biological activ­
ities. The high concentrations found in the bottom samples, where
resuspension of sediment is evident, suggest that remobilization of DOC
from interstitial waters is an important process that contributes DOC tooverlying estuarine waters. In the offshore region, the ΔDOC and ΔPOC
were 10.16 ± 10.08 μM and 24.61 ± 33.78 μM, which were approxi­
mately 11% and 51% of the measured values, respectively. The high net
OC production indicates OC sources in addition to physical processes,
most likely from biological production. Hence, the OC dynamics in the
low-salinity onshore region were mainly controlled by physical mixing
and processes in the TMZ, and were only weakly influenced by biolog­
ical activities. The OC dynamics in offshore regions are highly related tobiological activities, especially primary production.
The ΔDOC and ΔPOC values in the upwelling-influenced transect C3
ranged from − 6.49 μM to 26.21 μM and from − 4.12 μM to 84.57 μM,
respectively. The ΔDOC and ΔPOC values in the stratified transect C4
ranged from − 1.31 μM to 31.49 μM and from − 2.01 μM to 75.56 μM,
respectively. The majority of ΔOC values were positive, suggesting
significant biological production of OC in the offshore region. The pos­
itive ΔOC values in transect C3 occurred at stations beyond the salinity
front (station C3-5, Fig. 7a and c). In transect C4, positive ΔOC values
occurred in the surface layer (Fig. 7b and d). However, there were nomarked differences in ΔOC values between the upwelling influenced
transect C3 and the stratified transect C4.
The distribution of ΔN in transects C3 and C4 is also shown in
Fig. 7e–j. In transect C3, the ΔN values at station C3-4 where the bottom
water climbed along the bottom slope, were nearly zero, while themeasured nutrient values, especially PO3−
4 , were higher. This finding
may suggest that upwelling was a mechanism providing nutrients,
especially PO3−
4 , to the surface layer from the bottom water. Beyond
station C3-4, the nutrients in the surface layer were significantly
consumed, especially at high OC production stations. In transect C4,
above the pycnocline over the surface layer, the positive ΔOC values andnegative ΔN values showed the OC accumulation and nutrient con­sumption. These results implied that OC production was mainly caused
by primary production during the survey period.
Based on the model-derived consumption of individual nutrients,
nutrient uptake ratios were estimated. The uptake ratios of ΔNO-3:ΔPO3−
4
in the offshore region were 26.31 in transects C2 and C3 and 22.44 intransects C1 and C4 (represented by the slope of ΔNO-3 versus ΔPO3−
4 ,Fig. S5), which exceed the classic Redfield stoichiometry. One of thepossible reasons may be the phytoplankton physiology leading to theirdifferent demands for NO−3 over PO3−
4 (Klausmeier et al., 2004). Klaus­
meier et al. (2004) used a stoichiometrically explicit model of phyto­plankton physiology and resource competition to derive from firstprinciples the optimal phytoplankton stoichiometry under diverse
ecological scenarios. The researchers found that under phosphate limi­
tation, the optimal strategy yielded a structural DIN:DIP ratio of 45.0,
much higher than the Redfield ratio of 16. The Changjiang Estuary andadjacent coastal region is a typical phosphate limitation area (Cao andWang, 2012; Han et al., 2003). Another possible reason may be the

4. Discussion
4.1. Net OC production and nutrient uptake derived from the three endmember mixing model
Based on the model, the net OC production (ΔDOC and ΔPOC for
DOC and POC, respectively) was derived. In the onshore region, the
ΔDOC and ΔPOC values were 2.15 ± 22.83 μM and 0.60 ± 8.84 μM,
which were approximately 9% and 11% of the measured values,
respectively. The low net OC production value represents low primary
production, suggesting that OC distributions were mainly controlled byphysical mixing of the end-members. The high ΔDOC value in this re­gion mainly appeared in the TMZ, but the ΔPOC value of this area was

Table 1
Summary of end-member values and their uncertainties used in the three end-member mixing model.
water mass

salinity

temperature (◦ C)

density (kg/m3)

DOC (μM)

POC (μM)

NO−3
(μM)

SiO2−
3 (μM)PO3−
4 (μM)

CFW

0.13 ± 0.01

26.41 ± 0.27

TWCDW

34.52
±0.04
33.72a19.52 ± 0.30

996.83±0.07
1024.71±0.06
1020.80

124.48
±9.29
75.99
±3.88
80±6b

57.21
±9.31
7.19
±2.05
8.75c

118.67
±7.93
11.16
±1.74
6.07
±2.34d111.24
±2.07
16.08
±0.72
9.73
±3.99d

1.70
±0.21
0.76
±0.10
0.40
±0.17dTWCSW

27.12aaZhang et al. (2014).
Hung et al. (2003).
c
Zhu et al. (2006).
dWang (2007).
b

7

X. Wang et al.

Estuarine, Coastal and Shelf Science 260 (2021) 107491

Fig. 6. Three end-member composite fractions (f1: CFW (a), (b); f2: TWDW (c), (d); f3: TWSW (e), (f)) distribution in transects C3 and C4.

Fig. 7. OC and nutrients deviation ΔDOC (a), (b); ΔPOC (c), (d); ΔNO-3 (e), (f); ΔPO4 3- (g), (h); andΔSiO3 2- (i), (j) distribution derived from the end-member mixing
model along transects C3 and C4.

8

X. Wang et al.

Estuarine, Coastal and Shelf Science 260 (2021) 107491

for ΔSiO2−
3 in transects C2 and C3 (Table 3), suggesting that CFW wasless important in regulating the dynamics of nutrients and OC produc­tion in our study area. However, SiO2−
3 was mainly from terrestrial
sources (i.e., soil) and was transported by rivers (Struyf et al., 2009),
which also further confirmed the reliability of our model. ΔPO3−
4 wascorrelated only with TWCDW, which further stressed the importance ofPO3−
4 supplementation from deep water. The upwelling center is usually
located west of the algal bloom (Zhao et al., 2001), which is consistent
with our findings. The upwelling water that reached the surface wastransported offshore and delivered nutrients to the site of the algal
blooms, which were primarily limited by phosphate availability due tothe influence of the plume with a high DIN:DIP ratio. In support of thisinference, the DIN:DIP ratio in the surface water in transects C2 and C3
was 76.13, much lower than 119.18 in transects C1 and C4. The phos­
phate supplied from the TWCDW likely played a very important role inpromoting phytoplankton growth. These results agree with those ofprevious studies; compared to the Changjiang discharge, upwelled wa­
ters are relatively rich in phosphorus (Chen et al., 2004), andupwelling-driven mixing prevents the development of phosphorus stressin microphytoplankton (Liu et al., 2013). In the nutrient-rich river
plume region, although the plume water contained more nitrogen thanphosphorus, the microphytoplankton did not experience phosphorus
stress except in the plume edge region (Liu et al., 2013). Hence, in such aphosphorus-limited study area, the stratified and upwelling regions had
different ways to prevent phosphorus stress.

multiple phosphate sources for phytoplankton growth in addition toPO3−
4 . To adapt to low phosphate concentrations, some species of algae
may utilize dissolved organic phosphate (DOP) to survive (Huang et al.,2005; Smayda, 1997). Under conditions of DIP depletion, DOP may be
stimulated to become bioavailable by phytoplankton metabolizedalkaline phosphatase enzyme activity (APA) (Nicholson et al., 2006).
Huang et al. (2005) observed that the mean value of DOP accounted for46.3 ± 24.7% of the total dissolved phosphorus for dinoflagellates in thespring of 2005 in the ECS, which shows that DOP was a potentiallysignificant phosphorus source for algae growth.
The uptake ratios of ΔNO-3:ΔSiO2−
3 in the offshore region were 0.61
in transects C2 and C3 and 0.71 in transects C1 and C4, which are below
the classic Redfield stoichiometry. Part of the reason this ratio occurred
was the mixture of different algae species. Diatoms and dinoflagellates
dominated the community in the Changjiang Estuary (Jiang et al., 2015;
Luan et al., 2006). One of the reasons for the lower NO−3 consumption
was that ΔNO-3 is not the only nitrogen source for algae growth. As
previously reported, the dominant species in the ECS were diatoms anddinoflagellates in summer (Guo et al., 2014). In contrast to di­noflagellates, only diatoms need significant amounts of silicon. How­ever, the demands for silicon and nitrogen change due to thephysiological changes in diatoms under stress during photosynthesis (e.
g., nutrient stresses). The Si/N ratio of the diatom’s chemical composi­tion under higher Si/N ratio stress is higher than that under stress-free
conditions by a factor of 2–3 (Claquin et al., 2002; Kudo et al., 2000).
Specifically, cells that are grown with adequate nutrients and light tend
to have a Si/N ratio on the order of 1:1 (Brzezinski, 1985), whereas thosegrown under nutrient-limited conditions have Si/N ratios that can be
many times higher than this value (Claquin et al., 2002; Kudo et al.,2000). During the survey period, the measured NO−3 :SiO2−
3 ratio wasapproximately 0.79 ± 0.69 in transects C2 and C3 and 0.78 ± 0.37 intransects C1 and C4 (without the very high value at station C4-7), whichmeans a relative lack of NO−3 . These combined factors caused lower
ΔNO-3:ΔSiO2−
3 ratios. Although the physical environments of transects
C2 and C3 differed from those of transects C1 and C4, the levels of
biological nutrient consumption did not show significant differences.

4.3. Biogeochemical implications
With the abundant nutrients carried by the Changjiang River andupwelled TWCDW, the Changjiang Estuary and its adjacent coastal re­gions form a high production region that has significant phytoplankton
blooms and OC production in the summer. However, due to the different
contributions of these two nutrient sources, the biological processes aredifferent. Guo et al. (2011) observed algae species during summer
phytoplankton blooms. The observation report stated that in the
upwelling-affected area, the dominant algae species were diatoms,
whereas in the northeastern Changjiang Estuary, a high dinoflagellate
abundance was observed. Mapping these two observation areas onto ourstudy area, the former corresponds to transects C2 and C3, whereas thelatter corresponds to transect C4 in the stratified region. This difference
in algae species may be related to the differences in the water masscompositions and nutrient structure. During the survey period, themeasured Si/N ratios were approximately 0.79 ± 0.34 in transects C1
and C4 and 1.02 ± 0.55 in transects C2 and C3. Since silicate is indis­
pensable for diatoms, the conditions in transects C2 and C3 were moresuitable for diatoms. Moreover, the different tolerances of algae species
to phosphorus are limited. Nutrients from the CFW had a high DIN:DIP
ratio of 71.10 ± 14.97, while the TWCDW provided the nutrients with a
DIN:DIP ratio of 21.21 ± 5.42, which is close to the Redfield ratio. These
results are consistent with previous observations (Chen et al., 2004;
Chen, 2008). Previous studies have shown that diatoms can easily
become dominant when growing in nonlimited conditions (Smayda,
1997), whereas when nutrients are limited, diatoms are more easily
affected than dinoflagellates, especially under phosphorus limitation

4.2. Net nutrients consumed and OC production related to different watermass compositions
The vertical water mass compositions in transects C3 and C4 areshown in Fig. 6. For the CFW distribution, transects C3 and C4 had very
similar patterns with diluted water floating on the surface. However,differently, the distribution of the three end-members in transect C4 wasin the horizontal strip. In transect C3, the TWCDW climbed along thebottom and cropped out at the surface, indicating supplementation ofnutrients from the bottom water.
The good correlation between nutrients and the three end-members
showed the combined action of CFW, TWCSW and TWCDW in regulating
the dynamics of nutrients and OC production in transects C1 and C4(Table 2). Additionally, the good correlation between OC production
and nutrient consumption (the scatter diagram shown in Fig. S6) further
indicated that OC production was mainly related to phytoplankton
growth. In contrast, CFW did not correlate with other variables except

Table 2
Correlation coefficient matrix of end-member fractions, organic production, and nutrient uptake in transect C1 and C4 in the offshore region.

ΔDOC
ΔPOC
ΔNO-3
ΔPO3−
4
ΔSiO2−
3

f1

f2

f3

ΔDOC

ΔPOC

ΔNO-3

ΔPO34

0.489**
0.580**− 0.610**− 0.628**− 0.787**− 0.677**− 0.736**0.765**
0.630**
0.800**

0.661**
0.693**− 0.718**− 0.533**− 0.683**

0.642**− 0.436**− 0.403*− 0.520**− 0.580**− 0.336*− 0.433**

0.681**
0.876**

0.717****. Significant correlation at level of P < 0.01.
*. Significant correlation at level of P < 0.05.
9

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

Table 3
Correlation coefficient matrix of end-member fractions, organic production, and nutrient uptake in transect C2 and C3 in offshore region.

ΔDOC
ΔPOC
ΔNO-3
ΔPO3−
4
ΔSiO2−
3

f1

f2

f3

ΔDOC

ΔPOC

ΔNO-3

ΔPO34

− 0.079
0.298− 0.077− 0.415− 0.633**− 0.535*− 0.538*
0.526*
0.471*
0.792**

0.735**
0.487*− 0.617**− 0.316− 0.583**0.145− 0.567**− 0.171− 0.446*− 0.604**− 0.218− 0.4120.591**
0.706**

0.680****. Significant correlation at level of P < 0.01.
*. Significant correlation at level of P <0.05.

(Egge, 1998; Wang et al., 2013). The upwelled TWCDW can relieve thephosphorus deficiency in the upwelling affected transects C2 and C3.
Another influencing factor may be the physical environment. Numerous
studies have revealed that the water turbulence level has a direct effecton the composition of the phytoplankton community. Dinoflagellates
are favored in regions with established stratification or upwelling
relaxation periods (Blasco, 1977; Juhl et al., 2000; Latz et al., 2009;
Pollingher and Zemel, 1981; Sharples et al., 2001; Thomas and Gibson,
1990), while diatoms are often found to dominate in more turbulent
environments and transient blooms associated with upwelling events(David and Maura, 2002; Tilstone et al., 2000). The vertical migration
capability of dinoflagellates enables them to access nutrients in stratified
water (Cullen, 1985; Cullen and Horrigan, 1981; Ross and Sharples,
2007; Smayda, 1997). In contrast, vertical mixing in the environment
tends to benefit diatom survival and negatively buoyant nonmotile cells
are dependent on turbulence to be resuspended in the photic zone
(Huisman and Sommeijer, 2002). As evidence in this study, the nutrientswere consumed in almost all sections of stratified C4 but only in theupper layer in transect C3 (Fig. 7). Observations have also shown thatphytoplankton community composition exhibits complex spatial het­
erogeneity (Jiang et al., 2015; Luan et al., 2006). The dominant species
and community distribution were predominantly controlled by thephysicochemical properties of the CDW and TWC (e.g., salinity, tem­
perature, nutrients, and turbidity; Jiang et al., 2015).
Differences in compositions may cause differences in the community
structure (Varkitzi et al., 2010) and even influence OC cycling, since thedifferent forms of OC are transformed by different algae species. Since
phytoplankton are a major source of organic matter (Hill and Wheeler,
2002), OC dynamics are closely related to phytoplankton bloom species.
Previous field and laboratory experimental studies showed that a large
fraction of OC produced during phytoplankton blooms is partitioned
into the particulate pool when diatoms dominate the bloom (Engel et al.,
2002; Hasegawa et al., 2010; Malej et al., 2003; Wetz and Wheeler,
2003) and is partitioned into DOC when picoplankton dominate thebiomass (Carlson et al., 1998; Teira et al., 2001). Malinsky-Rushansky
and Legrand (1996) proposed that small phytoplankton released more
DOC than larger phytoplankton. However, Conan et al. (2007) suggested
that a diatom-dominated phytoplankton assemblage produced more
POC and DOC than that dominated by other species, but the relative
partitioning of POC and DOC did not depend on the composition ofphytoplankton. Overall, these studies showed that the algae species may
have a great influence on the form and dynamics of OC. Hence,
exploring the different OC dynamics with different nutrient structures
under different water mass mixtures may be necessary.

dynamics. In onshore water with salinities smaller than 20, nutrient
consumption and OC production are mainly controlled by the watermass mixture and modified by the TMZ. In offshore water with salinities
greater than 20, the model results suggested that the different processes
regulating nutrients and OC dynamics in typical transects were impacted
by the Changjiang River plume and TWCDW due to biological activities.
In the stratified river plume region, the consumed nutrients were
controlled by a combination of three end-members, while in the
upwelling-affected region, they were regulated by the supply of nutri­ents from the TWCDW. Such different activities have important impli­
cations for carbon cycling in this study area. The different sources ofnutrients with different nutrient structures will induce spatial hetero­geneous phytoplankton community compositions. The relative parti­
tioning of POC and DOC is influenced by the composition ofphytoplankton. Due to these facts, the CDW and TWC play an importantrole in regulating the dynamics of OC production in Changjiang Estuary.
It should be noted that since the time limited observation in summer
may affect the temporal upscaling of our work, results in other seasons
maybe vary due to the different environmental factors.
Author statement
Xiaolu Wang: Conceptualization, Methodology, Formal analysis,
Investigation, Data curation, Writing-Original Draft, Visualization.
Ying Wu: Conceptualization, Methodology, Resources, WritingReview & Editing, Supervision.
Hui Wu: Writing-Review & Editing.
Guosen Zhang: Investigation.
Jie Jin: Investigation.
Lijun Qi: Investigation.
Xinyu Guo: Writing-Review & Editing.
Jinlong Dai: Investigation.
Zhaoru Zhang: Investigation.
Meng Zhou: Funding acquisition.
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.
Acknowledgements
Analytical measurements were carried out at the State Key Labora­
tory of Estuarine and Coastal Research under grants from the Natural
Science Foundation of China (grant numbers 41530960). Technicalsupport by the Zheyuke crew during the cruise is acknowledged. The
assistance of all members of the organic geochemical group in samplingand daily experiments is appreciated.

5. Conclusion
In the Changjiang Estuary and adjacent coastal regions, the Chang­
jiang River and TWC were two important pathways of nutrients, andthey both contributed to phytoplankton production. Our three endmember mixing model was used to estimate the nutrient consumption
and OC production due to biological activities and provided insights for
identifying the relative contributions of nutrients from different water
masses to phytoplankton production and subsequent organic carbon

Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecss.2021.107491.
10

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Estuarine, Coastal and Shelf Science 260 (2021) 107491

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