Limnol. Oceanogr. 65, 2020, 1359–1379© 2020 Association for the Sciences of Limnology and Oceanography

doi: 10.1002/lno.11393

Dynamics of inorganic carbon and pH in a large subtropical continentalshelf system: Interaction between eutrophication, hypoxia, and oceanacidification

Yangyang Zhao ,1 Jing Liu,1 Khanittha Uthaipan,1 Xue Song,1 Yi Xu,1 Biyan He,2 Hongbin Liu ,3,4

Jianping Gan ,4,5,6 Minhan Dai 1*1State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China2College of Harbour and Environmental Engineering, Jimei University, Xiamen, China3Division of Life Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China4Department of Ocean Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China5Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China6Division of Environment and Sustainability, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR,China

AbstractWe examined the dynamics of dissolved inorganic carbon (DIC) and pH in the Pearl River Estuary (PRE) and

the adjacent northern South China Sea (NSCS) shelf in summer, aiming for a better understanding of the inter-action between eutrophication, hypoxia, and ocean acidification. Using a semi-analytical diagnostic approachbased on validated multiple end-member water mass mixing models, we showed a −191 � 54 μmol kg−1 deficitin DIC concentrations in an extensive surface plume bulge, corresponding to a significant pH increase of� 0.57 � 0.19 units relative to conservative mixing. In contrast, DIC additions in the bottom hypoxic zonereached � 139 � 21 μmol kg−1, accompanied by a decrease in pH of −0.30 � 0.04 units. In combination withstable carbon isotopic compositions, we found biological production and CO2 outgassing to be responsible forDIC deficits in surface waters, while degradation of organic matter (OM) accounted for DIC additions in bottomwaters. The PRE-NSCS plume system as a whole served as a net source of atmospheric CO2 from the perspectiveof Lagrangian observations, because strong CO2 outgassing in the inner estuary overwhelmed the CO2 uptakein the plume despite strong phytoplankton blooms. Using a two-layer box model, we further estimated that atleast � 45 � 13% of eutrophication-driven OM production in the surface plume accounted for 67 � 18% of theDIC addition and oxygen consumption in bottom waters. Eutrophication also buffered ocean acidification insurface waters while hypoxia enhanced it in bottom waters, but their effects on acid-base buffering capacitywere secondary to the amplification of coastal ocean acidification caused by freshwater inputs.

Coastal oceans are characterized by high productivity andintense physical-biogeochemical dynamics, and subject tointensifying anthropogenic perturbations (Bauer et al. 2013;Regnier et al. 2013). Elevated riverine loadings of man-madenutrients fuel rapid phytoplankton growth and massive algalproduction, a phenomenon known as eutrophication(e.g., Díaz and Rosenberg 2008; Gilbert et al. 2010), in theextensive river plumes, with attenuated turbidity on or beyondthe continental shelves (Wang 2006; Rabalais et al. 2009; Luet al. 2010; Huang et al. 2013). Under favorable winds, riverplumes may interact with CO2-laden, nutrient-rich upwelled

cold waters at the inshore edge of the plume (Gan et al. 2009b),which supports blooms of resident phytoplankton (Gan et al.2010; Han et al. 2012). As a result of eutrophication andfreshwater-input-derived stratification, oxidative degradationof sinking organic matter (OM) consumes oxygen and pro-duces dissolved inorganic carbon (DIC) in subsurface watersbelow the pycnocline, resulting in deoxygenation and eventhe development of hypoxia (Rabouille et al. 2008; Wang et al.2016; Su et al. 2017; Breitburg et al. 2018; Kralj et al. 2019) andenhanced acidification (Cai et al. 2011; Sunda and Cai 2012;Laurent et al. 2017) beyond that resulting from dissolution ofatmospheric CO2 in oceanic waters (Orr et al. 2005) or upwell-ing of cold, CO2-enriched subsurface waters (Feely et al. 2008).

DIC can serve as a tracer to examine the interactionbetween eutrophication, hypoxia, and ocean acidification. For*Correspondence: mdai@xmu.edu.cn

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a specific biological process, changes in DIC link well to thosein oxygen, nutrients, and produced/degraded OM, with a gen-erally constant stoichiometry (i.e., Redfield ratio; Redfieldet al. 1963), and to pH variations through the conversionsbetween carbonate system parameters (Zeebe and Wolf-Gladrow 2001). The sensitivity of pH to DIC changes, that is,the acid-base buffering capacity of the carbonate system, isalso affected by in situ biological metabolism, which can beexplicitly expressed by a buffer factor (βDIC = (∂ln[H+]/∂DIC)−1)(Egleston et al. 2010). When DIC approaches the value of totalalkalinity (TA), the buffering capacity reaches a minimum;inversely, the departure of DIC from TA increases the buffer-ing capacity to a different extent (Egleston et al. 2010). There-fore, pH variability in coastal waters can be considerablymodulated by eutrophication, hypoxia, and their resultantchanges to the buffering capacity of seawater.

However, a mechanistic understanding and quantitativediagnosis of DIC and pH dynamics in complex coastal envi-ronments is challenging. In contrast to coastal upwelling sys-tems (e.g., Gruber et al. 2011; Lachkar and Gruber 2011;Capone and Hutchins 2013; Frischknecht et al. 2018), thephysical transport and biological transformation of carbonand nutrients in coastal regions affected by large riverine dis-charges are more dynamic and complicated due to substantialanthropogenic disturbances (Doney 2010; Regnier et al. 2013;Wallace et al. 2014) and their spatial decoupling (Cai et al.2011; Rabalais et al. 2014; Zhang et al. 2018). A sufficientquantity of freshwater input facilitates the stabilization of thewater column and strengthens vertical stratification(MacCready et al. 2009; Lu and Gan 2015), leading to a prolif-eration in phytoplankton biomass in the thin, fast-flowingbuoyant plume flowing over onshore-entrained seawater(e.g., Yin et al. 2000; Dai et al. 2008; Zhou et al. 2008; Wonget al. 2015) but inhibiting the ventilation of subsurface water.The relatively slower turnover rate of the subsurface waterthus favors oxygen depletion and CO2 accumulation viamicrobial respiration of sinking particulate organic carbon(POC) (Lu et al. 2018), which prompts the occurrence of hyp-oxia and acidification. The change in redox state and the acid-base balance in turn modulates the transformations of carbonand nutrients therein (Hofmann and Schellnhuber 2009;Capone and Hutchins 2013). These processes add difficultiesfor process-based modeling and prediction of the evolution ofriver-dominated coastal systems.

The northern South China Sea (NSCS) shelf, an eastwardwidened shelf in the subtropics, features a strong river plumeoriginating from the Pearl River, the 17th largest river in theworld with a freshwater discharge of 3.26 × 1011 m3 yr−1,nearly 80% of which occurs during the wet season (April–September) (Dai et al. 2014). The Pearl River empties into theNSCS shelf via eight outlets through three subestuaries, theLingdingyang (LDY), Modaomen (MDM), and Huangmaohai(HMH) (Fig. 1), forming an extensive plume spreading widelyoffshore over the shelf (Gan et al. 2009b; Cao et al. 2011;

Chen et al. 2017) and even reaching far field into the TaiwanStrait under favorable winds (Bai et al. 2015) or into the SouthChina Sea basin via mesoscale eddies and filaments (He et al.2016). Coastal upwelling, driven by the prevailing southwestmonsoon and intensified by the widened shelf (Gan et al.2009a), interacts with the plume and accelerates the wind-driven current along its shoreward edge (Gan et al. 2009b),forcing the eastward-flowing buoyant plume to widen andthicken. The LDY Estuary, as the largest subestuary of thePearl River Estuary (PRE), receives about 50–60% of the totalriver discharge (Cai et al. 2004; Hu et al. 2011; Dai et al. 2014)and has been experiencing increasing loads of anthropogenicnutrients from domestic sewage, industrial wastewater, agri-cultural runoff, and aquaculture in the watershed (Huanget al. 2003; Qian et al. 2018). As such, eutrophication is anincreasingly recognized environmental issue in the PRE andadjacent shelf (Dai et al. 2008; Zhai et al. 2009; Gan et al.2010; Lu and Gan 2015), and is associated with the emergingseasonal hypoxia in bottom waters (Rabouille et al. 2008; Suet al. 2017; Lu et al. 2018; Qian et al. 2018).

Carbonate chemistry dynamics in the PRE-NSCS shelf sys-tem have been extensively studied with respect to the partialpressure of CO2 (pCO2) and air–sea CO2 fluxes (Zhai et al.2005b; Guo et al. 2009), the inorganic carbon cycle and itsresponse to biological metabolism (Dai et al. 2008; Cao et al.2011), and the saturation state of carbonate minerals (Guoet al. 2008; Cao et al. 2011). Based on a three end-membermixing model and the mass balance of DIC and its isotopiccompositions, Su et al. (2017) quantified the relative contribu-tions of terrestrial- vs. marine-sourced OM to oxygen con-sumption in hypoxic bottom waters. In this study, we furtherexamine, semiquantitatively, how eutrophication in the sur-face water couples with hypoxia in the subsurface water, byextending our diagnosis of DIC variability to surface waterswhere eutrophication occurs in river plumes. Subsequently,we assess the contribution of eutrophication-produced OM tofueling the development of bottom water hypoxia using atwo-layer box model. Furthermore, we diagnose pH variationsmodulated by biological metabolism and changes in the buff-ering capacity of seawater under conditions of eutrophicationand hypoxia. Taken together, this study advances substan-tially our prior studies by examining the interplay betweeneutrophication, hypoxia, and acidification.

Materials and methodsCruise background

In the framework of the OCEAN-HK (Ocean Circulation,Ecosystem and HypoxiA arouNd Hong-Kong waters) project,in situ observations and sampling were conducted onboardthe R/V Haike 68 in the PRE and adjacent NSCS shelf waters.Eleven cross-shelf transects, F1 to M2 from west to east(Fig. 1a), were investigated from nearshore to the 50-misobath from 10 July 2017 to 21 July 2017. Additional stations

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examined in the nearshore area are also shown in Fig. 1a, mar-ked as F101A, F301A–F301D, and NF02.

As a consequence of abnormally heavy rainfall, the dis-charge of the West River recorded at the Wuzhou(IV) Hydrological Station peaked at � 40,000 m3 s−1 just1 week before the cruise (Fig. 2a), which is nearly four timesthe annual average and 150% larger than the average river dis-charge during wet seasons (Guo et al. 2008; Dai et al. 2014).At the Shijiao Hydrological Station, peak discharge(9,560 m3 s−1) was also observed for the North River (Fig. 2a).Even though the total discharge of the West River, the NorthRiver and the East River decreased to nearly 60% of the peak(maximum of � 34,190 m3 s−1) during the cruise, the develop-ment of the river plume flowing out of the estuary mouth waslikely affected by the peak river discharge, since it took almost7–10 d for the discharge signal at the Wuzhou (IV) or ShijiaoStations to travel downstream to the estuary mouth.

Additionally, prior to the cruise the southwest monsoonprevailed for nearly a month, favoring the eastward extensionof the river plume and the development of coastal upwellingnearshore (Gan et al. 2009a,b). However, the wind directionshifted to easterly during the cruise (Fig. 2b) and the eastwardspreading of the river plume was strongly inhibited, resultingin the generation of an anticyclonic recirculation bulge westoff the estuary mouth (Pan et al. 2014). In this scenario, thelong residence time of nutrient-laden freshwater allowed rapid

growth of phytoplankton blooms in the surface plume waters,and subsequently the development of hypoxia in bottomwaters (Lu et al. 2018).

Sampling, analysis, and data processingDuring the cruise, samples were collected from 5-liter free-

flow water samplers mounted onto a Rosette sampling assembly,equipped with a conductivity-temperature-depth recorder (Sea-Bird SBE917plus). Samples for DIC and TA analysis were storedin 250 mL PYREX® borosilicate glass bottles, and poisoned with250 μL of a HgCl2-saturated solution upon sample collection.Samples for pH were free of bubbles and placed in a 25 � 0.05�Cwater bath for about 0.5–4.0 h before their measurementsonboard. Samples for DIC stable carbon isotopes (δ13CDIC) andseawater stable oxygen isotopes (18OH2O) were collected withoutbubbles in 125 mL borosilicate glass bottles sealed with a rubberstopper and aluminum cap, and samples for δ13CDIC were alsopoisoned with 100 μL of the HgCl2-saturated solution.

The concentration of dissolved oxygen (DO) in discrete sam-ples was measured onboard within approximately 8 h using thespectrophotometric Winkler method (Dai et al. 2006), with aprecision of � 1 μmol kg−1. DIC was measured by acidifying0.3–0.7 mL of water samples and subsequently quantifyingreleased CO2 using an infrared CO2 detector (Apollo ASC-3)with a precision of � 2 μmol kg−1 (Cai et al. 2004). TA was deter-mined on 25 mL samples using an open-cell setting based on

Fig. 1. (a) Map of the study area in the PRE and adjacent waters, showing the cruise track, sampling transects, and bathymetry. The gray dotted linesare the depth contours at 5, 10, 20, 30, 40, and 50 m. The dark dashed lines show the cruise track and the dark arrows along the tracks show the direc-tion of the ship. The solid circles show the sampling stations. HM, HMH, MDM, and LDY denote the Humen outlet, Huangmaohai Estuary, ModaomenEstuary, and Lingdingyang Estuary, respectively. (b) A simplified illustration of the drainage system into the PRE. The width of the lines/arrows is propor-tional to the magnitude of freshwater discharge. The pentagrams show the locations of the major hydrological stations: Wuzhou (IV), Shijiao and Buoluo(II) on the West River, North River, and East River, respectively. (c) A schematic diagram showing water mass mixing for the surface and bottom layers inthe study area. The surface water in the study area is the mixture of West-river-derived freshwater (WF), composite freshwater (CF), and offshore surfacewater (SW), while the bottom water is a mixture of CF, SW, and upwelled subsurface water.

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the Gran titration technique (see details in Cai et al. 2010)with a Kloehn digital syringe pump. The analytical precisionwas � 2 μmol kg−1. Both DIC and TA were calibrated againstcertified reference material provided by Dr. A. G. Dickson atthe Scripps Institution of Oceanography, University of Cali-fornia, San Diego. pH was determined using an Orion Star®

A211 pH meter fitted with an Orion Ross® combination pHelectrode (8102BN). The pH system was calibrated againstthree NIST-traceable pH buffers (pH 4.01, 7.00, and 10.01 at25�C, Fisher Scientific). The precision and accuracy of the pHmeasurements was � 0.01 units. For δ13CDIC analysis, puregaseous CO2 was extracted from an � 20 mL DIC sample andsubsequently analyzed with an isotope ratio mass spectrome-ter (Finnigan MAT 252, Bremen, Germany), with a precisionbetter than 0.1‰. δ18OH2O was measured on an LGR® tripleisotope water analyzer (TIWA-45-EP), with a precision (1σ)better than � 0.1‰, against the VSMOW (Vienna StandardMean Ocean Water) reference. Chlorophyll a (Chl a) concen-trations were determined using a Turner Designs fluorometerafter extracting filters with 90% acetone at −20�C (He et al.2010b) and calibrating the instrument using a Sigma Chl astandard.

DIC and TA data were applied to the program CO2SYS-Excel (Lewis and Wallace 1998) to calculate pH. The dissocia-tion constants for carbonic acid (K1 and K2) were from Millero(2010) and KHSO4 was determined by Dickson (1990). pH cal-culated from DIC and TA at a constant 25�C has a maximumuncertainty of � 0.01 units (Orr et al. 2018). All pH datareported here are based on the total hydrogen scale (pHT)(Dickson 1993).

Acid-base buffer factor against changes in DIC (βDIC)To quantify the sensitivity of pH to changes in DIC, we

introduce the acid-base buffer factor βDIC as the inverse of thedifferential change in pH as a result of small changes in DIC(Egleston et al. 2010):

βDIC =∂ H+½ �

H+½ �∂DIC

� �−1

= ∂ln H+½ �=∂DICð Þ−1 ð1Þ

where [H+] is the concentration of hydrogen ions. As defined,βDIC has dimensions of micromoles per kilogram and can beexplicitly presented as a function of the concentration of

Fig. 2. Riverine discharge and winds from June to August in 2017. (a) Riverine discharge of the Pearl River’s main tributaries, that is, the West River(Wuzhou [IV]), North River (Shijiao), and East River (Boluo [II]). The gray shaded area indicates the cruise period. The gray solid and dashed lines denotethe wet-season mean and annual mean river discharge, respectively. (b) Wind speed and direction at Waglan Island was obtained from the Hong KongObservatory (http://www.hko.gov.hk/cis/climat_c.htm). The alternating dashed-dotted line divides the wind direction into four quadrants: northeast,southeast, southwest, and northwest.

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hydrogen ions and the carbonate and borate species in solu-tion (Egleston et al. 2010).

βDIC =DICTAc

× HCO−3

� �+4 CO2−

3

� �+

H+½ � B OHð Þ−4� �

Khb + H+½ � + H+½ �+ OH−½ �� �

–TAc

ð2Þ

where TAc is carbonate alkalinity ( = HCO−3

� �+2 CO2−

3

� �); Khb is

the acidity constant for boric acid (Dickson 1990); B OHð Þ−4� �

isthe concentration of boron oxyhydroxide in seawater which

varies with salinity (Uppström 1974); and HCO−3

� �, CO2−

3

� �,

and [OH−] are concentrations of bicarbonate, carbonate, andhydroxide ions, respectively. Since βDIC quantifies the abilityof pH values to resist changes in DIC, higher βDIC values indi-cate a stronger buffering capacity of the system.

Multi-endmember mixing modelsThree-endmember mixing models are used to construct the

conservative mixing schemes among different water massesfor the surface and bottom layers, respectively, as shown inFig. 1c (Cao et al. 2011; Han et al. 2012; Su et al. 2017). Asfreshwater discharge into the coastal area occurs mainlythrough the MDM and LDY estuaries (Fig. 1b), two freshwater

end-members are defined: West-River-derived Freshwater(WF) and Composite Freshwater (CF). WF, with high DIC andTA concentrations, flows out from the MDM and HMH estuar-ies into the surface layer, but hardly sinks below thepycnocline due to shallow topography around the estuarymouths, while CF, composed of freshwater from the WestRiver, North River and East River, flows into the LDY Estuaryaround Humen (HM) outlet, finding its way into the bottomlayer through two deep channels in the LDY Estuary (Hu et al.2011). Offshore subsurface water, containing significant con-centrations of nutrients below the mixed layer, extends freelyonto the coast along the shelf (Wong et al. 2015), and here isdefined as Upwelled Subsurface Water (SUB), differing fromthe Offshore Surface Water (SW) in the mixed layer (Cao et al.2011; Han et al. 2012; Su et al. 2017). The mixing models aredefined as follows:

f EM1 + f EM2 + f EM3 = 1 ð3ÞP1EM1 × f EM1 + P1EM2 × f EM2 + P1EM3 × f EM3 = P1

meas ð4ÞP2EM1 × f EM1 + P2EM2 × f EM2 + P2EM3 × f EM3 = P2

meas ð5Þ

where the subscripts EM1, EM2, and EM3 denote the threewater masses (CF, WF, and SW for the surface layer and CF,SW, and SUB for the bottom layer, respectively), and thesuperscript meas denotes measured values; P1 and P2 are con-servative parameters, salinity (S) and TA for the surface layerand salinity and potential temperature (θ) for the bottomlayer, respectively, since θ might show nonconservativebehavior in the surface layer due to exchange of latent andsensible heat between seawater and the atmosphere (Cayan1992). TA was assumed to be quasi-conservative in the surfacelayer as biological alterations in OM production/degradationcould be neglected and biogenic CaCO3 production/dissolu-tion could be ruled out due to the conservative behavior ofsurface calcium concentrations with respect to salinity (Caoet al. 2011). f represents the fraction that each end-membercontributes to the in situ samples. These fractions wereapplied to predict conservative concentrations of DIC(DICcons) and its isotopic composition (δ13CDIC

cons) resultingsolely from conservative mixing.

DICcons =DICEM1 × f EM1 +DICEM2 × f EM2 +DICEM3 × f EM3 ð6Þ

Conservative pHT,25 (pHT,25cons) was calculated via CO2SYS-

Excel by DICcons and TA (Lewis and Wallace 1998). The differ-ence (Δ) between measured and conservative DIC values(ΔDIC = DICmeas − DICcons) represents the magnitude of thebiological alteration of DIC and/or air–sea exchange of CO2

(Wang et al. 2016; Su et al. 2017), whereas the difference in pH(ΔpH = pHT,25

meas − pHT,25cons) additionally represents buffer-

ing capacity changes of the carbonate system (Cai et al. 2011).

Semi-analytical diagnostic method based on DIC and δ13CDIC

The stable carbon isotopic composition of DIC in seawateris progressively fractionated with changes in DIC concentra-tions, which could be used to identify the processes that con-trol DIC dynamics (Alling et al. 2012; Samanta et al. 2015).Equations 4, 5 define the variations in DIC concentrations andδ13CDIC values across a mixing region where there are no othersources or sinks of DIC. By comparing each measured δ13CDIC

value and DIC concentration to that expected from conserva-tive mixing, the deviations from mixing can be defined as

Δδ13CDIC = δ13CDICmeas−δ13CDIC

cons ð8Þ

δ13CDICcons =δ13CEM1 ×DICEM1 × f EM1 + δ

13CEM2 ×DICEM2 × f EM2 + δ13CEM3 ×DICEM3 × f EM3

DICcons ð7Þ

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RDIC =DICmeas−DICcons

DICcons =ΔDICDICcons ð9Þ

Primary production incorporates isotopically fractionatedseawater DIC into organisms, and thus progressively changesthe δ13C composition of the residual seawater DIC throughRayleigh distillation (Alling et al. 2012). The equilibrium frac-tionation factor εPP is defined as

εPP = δ13Cproduct−δ13Csubstrate ð10Þ

and the difference in δ13CDIC could be expressed as a functionof the amount of DIC consumed by primary production:

Δδ13CDIC = 103 αPP−1ð Þln DIC f=DICi� �

≈RDIC αPP−1ð Þ103 ð11Þ

where DICf and DICi are the DIC concentrations at the final andinitial state of the system, respectively; and αPP can be derivedfrom εPP using the approximation ε ≈ 103lnα ≈ 103(α−1)(Emerson and Hedges 2008).

Similarly, during CO2 outgassing, the 13C/12C ratio in theresidual waters is also fractionated between HCO−

3 and CO2

(aqueous) by Rayleigh distillation. The fractionation factorαCO2 is temperature-dependent and can be estimated usingthe following equation (Rau et al. 1996):

δ13CCO2 = δ13CDIC + 23:644−9701:5=T ð12Þ

where T is the temperature in Kelvin and δ13CDIC is approxi-mately equal to that of HCO−

3 . Using the equilibrium fraction-ation factor εCO2 (= δ13CCO2 − δ13CDIC), the difference ofδ13CDIC could be expressed as a function of the amount ofDIC loss by CO2 outgassing, similar to Eq. 11:

Δδ13CDIC = 103 αCO2−1ð Þln DIC f=DICi� �

≈RDIC αCO2−1ð Þ103

ð13Þ

Degradation of OM normally produces DIC without any sub-stantial isotopic fractionation relative to the total organic carbon(OC) pool (Norrman et al. 1995; Hullar et al. 1996; Meyers1997), and has negligible effects on the isotopic value of the sub-strate compared to the uncertainties in the value of the degradedOC (Breteler et al. 2002; Waite et al. 2005). This process willtherefore add DIC to the system with a relatively constant δ13Cvalue, so that the mass balance of isotopic DIC composition(Wang et al. 2016; Su et al. 2017) can be expressed as

δ13CDICmeas ×DICmeas = δ13CDIC

cons ×DICcons + δ13COC ×DICOC

ð14Þ

where DICOC and δ13COC are the DIC concentration and itsisotopic composition released from OC degradation, thusDICOC = DICmeas − DICcons. Equation 14 can then be trans-formed to

δ13CDICmeas −δ13CDICcons =DICmeas−DICcons

DICmeas δ13COC−δ13CDICcons�

ð15Þ

When ΔDIC is far less than DICcons, combining withEqs. 8, 9, it yields

Δδ13CDIC =RDIC × δ13COC−δ13CDICcons� ð16Þ

In all the above cases, Δδ13CDIC and RDIC show a linear rela-tionship, with a slope constrained by the fractionation factor(αPP or αCO2) or the difference in δ13C between degraded OCand that of DIC established by conservative mixing before anyCO2 additions, which could indicate the controlling processesacting on DIC variability (Alling et al. 2012; Samantaet al. 2015).

ResultsVertical distribution

The vertical distribution of various parameters alongsection A from the HM outlet to the 50-m isobath is shown inFig. 3. The freshwater mixed with seawater to form a 10–15 mthick buoyant plume (salinity < 33), detaching from the bot-tom near Sta. A10 and flowing over the onshore-intruded coldsubsurface seawater (Fig. 3a,b). The DIC distribution generallyfollowed the salinity pattern, ranging from � 1450 μmol kg−1

at a salinity of � 0 near the HM outlet to � 1920 μmol kg−1 ata salinity of 33.7 in the offshore surface layer (Fig. 3c). Excep-tions were observed at Sta. A10–A14, where strong stratifica-tion occurred at the interface of the surface plume and bottomseawater, and the DIC concentrations were as low as1476–1670 μmol kg−1 in the surface plume while the bottomlayer was nearly 10–70 μmol kg−1 higher than offshore subsur-face water (Fig. 3c). These exceptions are likely attributed toprimary production in the surface plume, reflected by a maxi-mum DO value of � 294 μmol kg−1, and degradation of OMin the bottom hypoxic zone (DO < 63 μmol kg−1), which hada minimum DO of � 42 μmol kg−1. These DO concentrationsin the surface and subsurface waters amount to � 35% overand � 75% below the DO saturation level, respectively(Fig. 3e). A patch of high Chl a concentrations (6.4–12.9 μg L−1) was also observed in the surface plume, peakingat Sta. A11 (Fig. 3f).

The distribution of δ13CDIC also mirrored the salinity pat-tern, varying from −11.5‰ in freshwater to � 0.4–0.8‰ inoffshore seawater (Fig. 3d). However, the pH distribution wasmuch like a composite of salinity and DO, showing anincreasing trend with salinity offshore, but interrupted by apositive correlation with DO in the surface plume (Fig. 3e,g).The maximum pH value (pHT,25 = 8.47) was observed in thesurface plume at Sta. A13 along with a high DO concentration(� 277 μmol kg−1), suggesting a significant impact by biologi-cal productivity. The buffer factor βDIC was generally lower

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than 150 μmol kg−1 upstream of Sta. A8, except in the surfacelayers of Sta. A1 and A5. Moving offshore, βDIC increased toover 240 μmol kg−1, with high values in particular in surfacewaters (Fig. 3h). It is interesting to note that pH and βDIC

values in the bottom hypoxic zone were not the lowestobserved despite excessive DIC additions by microbial respira-tion (Fig. 3c,g), implying a substantial role of water massmixing relative to biological metabolism in controlling the pHvariability.

Isosurface distributionPast the estuary mouth, a two-layer structure was evident

in response to both river discharge and wind-driven currents.The abnormally large discharge of riverine freshwater beforethe cruise (Fig. 2a) flowed downstream to the estuary, leadingto the extensive surface plume observed on the adjacent shelfduring the cruise (Fig. 4a). Inhibited by east winds during the

cruise (Fig. 2b), it deflected to the west off the estuary mouthand flowed over the onshore-intruded cold bottom seawater(Fig. 4e,f), which was mainly driven by persistent southwestwinds before the cruise (Fig. 2b). However, the shifting ofwinds to easterly favored accumulation of plume waters near-shore and lengthened the residence time of bottom waters,facilitating oxygen consumption and the development of hyp-oxia (Lu et al. 2018).

Correspondingly, the distributions of DIC and pHshowed distinct patterns in surface and bottom layers. DICwas significantly depleted in the surface plume off theMDM and HMH estuaries to concentrations lower thanambient seawater and in the freshwater discharged from theHM outlet (Fig. 4c). In contrast, DIC was elevated in bottomwaters approximately along the 20-m isobath to concentra-tions � 30–150 μmol kg−1 higher than in offshore subsur-face waters (Fig. 4g). These DIC concentration patterns were

Fig. 3. Distribution of (a) salinity, (b) temperature (�C), (c) DIC (μmol kg−1), (d) stable carbon isotopes of DIC (δ13CDIC, ‰), (e) DO (μmol kg−1), (f)Chl a concentrations (μg L−1), (g) pHT at 25�C (pHT,25), and (h) the acid-base buffering factor (βDIC, μmol kg−1) along Section A from the Humen outletto the 50-m isobath in the PRE and adjacent shelf waters. The red box in the inserted map in (a) shows the location of the transect. The red contour in(a) shows the surface plume (salinity < 33). The white and magenta contours in (e) show the hypoxic (DO < 63 μmol kg−1) and low-oxygen(DO < 94 μmol kg−1) zones.

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also similar to DO distributions, which were � 40–70% overthe saturation level (DO > 300 μmol kg−1) in the surfaceplume and less than � 45% of the saturation level(DO < 94 μmol kg−1) in bottom waters (Fig. 4i,m). These dis-tributions of DIC and DO could be attributed to the strongphytoplankton blooms in the surface plume reflected by thehigh Chl a concentrations, which were larger than� 12 μg L−1 and showed a maximum of 26.8 μg L−1 atSta. F201 (Fig. 4j). Intense community productivity wouldfurther fuel the microbial respiration below the pycnocline,resulting in hypoxia (DO < 63 μmol kg−1) and excessive DICproduction (Fig. 4g,m).

The distribution of δ13CDIC seemed to follow the salinitypattern; values were higher in the bottom layer compared tothe surface plume, with the exception of the highest value(> 1.0‰) in the offshore bottom waters between the 30-mand 40-m isobath (Fig. 4d,h), corresponding to the relativelyhigh temperature and low DIC (Fig. 4f,g).

Except in the LDY Estuary, pH distributions in both surfaceand bottom layers varied inversely with DIC (Fig. 4c,g,k,o),suggesting that similar processes controlled the pH and DICchanges. The high pH values offshore of the MDM Estuary(pHT,25 > 8.40) in the surface plume corresponded to lowerDIC concentrations driven by biological productivity, withthe maximum pH value (pHT,25 = 8.81) coinciding with theminimum DIC value of � 1317 μmol kg−1. In bottom waters,the low pH values were mainly measured nearshore in thehypoxic zone, along with relatively high DIC concentrations.However, pH values were low in both surface and bottomlayers in the LDY Estuary, and seemed to be decoupled fromthe DIC variations. On the other hand, we found that in theLDY Estuary βDIC was also much lower than on the shelf out-side of the estuary mouth (Fig. 4l,p), where βDIC likely showeda negative relationship with pH in the surface layer but a posi-tive relationship with pH in the bottom layer. In this case,both the mixing of acidic, low buffering capacity freshwater

Fig. 4. Distributions of salinity, temperature (�C), DIC (μmol kg−1), stable carbon isotope of DIC (δ13CDIC, ‰), DO (μmol kg−1), Chl a concentrations(μg L−1), pHT at 25�C (pHT,25), and the acid-base buffering factor (βDIC, μmol kg−1) in the surface (a–d, i–l) and bottom (e–h, m–p) layers of the PREand adjacent shelf waters. The white and magenta contours in (m) show the hypoxic (DO < 63 μmol kg−1) and low-oxygen (DO < 94 μmol kg−1) zones.

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and in situ biological activity may have controlled the spatialpH variability.

The qualitative relationship between DIC, pH, and DODIC concentrations and pH varied differently with respect

to DO in the surface plume and bottom layer, respectively(Fig. 5). In bottom waters, DIC concentrations increased withthe level consumption of DO (Fig. 5a), suggesting microbialrespiration of OM contributed to the production of DIC. Incontrast, the relationship between DIC concentrations andDO in surface waters was more variable, although theyremained negatively correlated. Coinciding with the produc-tion of DO by photosynthesis, DIC concentrations decreasedat a higher rate in surface waters than observed in bottomwaters, suggesting that other processes than biological metab-olism may have played a role in surface waters. These pro-cesses include water mass mixing and/or air–sea gas exchange.Samples from the LDY Estuary (Fig. 5a) further supported that

the mixing between riverine freshwater and seawater mightdominate the DIC variability in the surface plume.

In general, pH showed a positive relationship with DO(Fig. 5b). In the bottom layer, pH decreased by up to� 0.3 units with the consumption of DO and the regenerationof DIC from OM degradation, implying that with deoxygen-ation or even the development of hypoxia, coastal ocean acid-ification was largely enhanced compared with nonhypoxicconditions. Conversely, pH was greatly elevated along withthe increase in DO in the surface layer on the shelf, with therelationship having a higher slope and more scatter thanobserved for bottom waters. In the LDY Estuary, pH wasalmost as low as in the shelf plume but increased at a higherrate with DO despite an increase in the DIC concentration,implying changes in the buffering capacity driven by riverinefreshwater mixing and/or biological metabolism contributedto the pH variability.

DiscussionControlling processes for DIC and pH variabilitySelection of end-members and model validation

The TA–S and θ–S plots displayed three-endmember mixingschemes for the surface and bottom layers over the PRE andadjacent shelf waters (Fig. 6a,b), consisting of CF, WF, and SWfor the surface layer and CF, SW, and SUB for the bottomlayer. By choosing S = 33.7 as the SW salinity endmember, weassumed a DIC value of � 1922 � 5 μmol kg−1 and a TA valueof � 2226 � 5 μmol kg−1 as representative values for SW (Caoet al. 2011; Guo and Wong 2015; Su et al. 2017). A DIC con-centration of � 2022 � 3 μmol kg−1 and a TA concentration of� 2273 � 5 μmol kg−1 observed at S = 34.5 represent the SUBend-member (Guo and Wong 2015; Su et al. 2017). For river-ine freshwater endmembers, CF flowed out from the innerLDY Estuary with a DIC concentration of 1392 � 35 μmol kg−1

and a TA concentration of 1241 � 34 μmol kg−1, while WFflowed out from the MDM (and HMH) Estuary with a DICconcentration of � 1600 μmol kg−1 and a TA concentration of� 1650 μmol kg−1, which are close to the characteristic DICand TA values of the West River in summer (Guo et al. 2008).The DIC and TA values of the CF end-member were lowercompared to those used by Su et al. (2017), which may beexplained by dilution from abnormally high river discharge.In addition, we calculated the DIC and TA values of the CFend-member based on the proportions of discharges from thethree main tributaries flowing into the LDY Estuary (Cai et al.2004; Dai et al. 2014), which resulted in values similar to ourselection here.

The δ13CDIC value was 0.8 � 0.0‰ in the offshore surface

water at S = 33.7, where nutrient (NO−3 +NO−

2 and PO3−4 , not

shown) concentrations were close to their detection limits andDO was nearly saturated, indicating little biological activity.δ13CDIC values of −11.5‰ for CF and−9.8‰ for WF are repre-sentative of the freshwater sources; the discrepancy between

Fig. 5. (a) DIC and (b) pHT,25 vs. DO in the surface and bottom layers ofthe PRE and adjacent shelf waters. The triangles and circles represent sam-ples collected from the surface and bottom layers on the shelf, while thesquares represent samples collected from the LDY Estuary. The solid line,short dashed line, dotted-dashed line, and long dashed line show themodeled DIC concentrations or pH values that vary with DO due to pri-mary production or degradation of OM based upon the following watermasses: Upwelled subsurface water (SUB), offshore surface water (SW),composite freshwater (CF), and West River-derived freshwater (WF),respectively.

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these two sources may be attributed to discharge-dependentflushing from their watersheds (Guo et al. 2015; Zhong et al.2018), and differing levels of biological alteration in theirupstream reaches (Zhai et al. 2005b; Dai et al. 2006; He et al.2014). A summary of the end-member values used in thisstudy is listed in Table 1.

We calculated the fraction of each water mass based on TAand salinity for the surface layer and potential temperatureand salinity for the subsurface layer (Eq. 3–5), so as to predictDIC (DICcons) and its isotopic composition (δ13CDIC

cons) solelyfrom conservative mixing (Eqs. 6, 7). We chose the stable oxy-gen isotope composition of seawater (δ18OH2O) as a conserva-tive tracer to validate our model prediction, assuming thatevaporation/precipitation was not significant. This assump-tion is supported by the short residence time of freshwater inthe LDY Estuary (< 3 d; Wong and Cheung 2000). Our model-

derived values were in good agreement with the field observa-tions (Fig. 6c), which strongly supported our modelpredictions.

DIC and pH changes beyond physical mixingWater mass mixing between riverine freshwater and seawa-

ter, and biological metabolism together controlled the vari-ability of DIC and pH in the PRE and adjacent shelf waters,especially within the surface plume. As shown in Fig. 5, inbottom waters, the microbial respiration of OM appearsmainly responsible for the high DIC concentrations and lowpH, as indicated by their variations with DO along themodeled lines based on the SUB endmember. However, DICand pH variations with respect to DO in the surface plumedeviated from all modeled lines no matter which endmemberswere chosen. Therefore, it is essential to use multi-endmember

Fig. 6. (a) Potential temperature θ (�C) vs. salinity, (b) TA vs. salinity, and (c) predicted δ18OH2O (δ18OH2Opre) vs. measured δ18OH2O (δ18OH2O

meas) inthe PRE and adjacent shelf waters. Solid gray, orange, and dark green circles denote whole water column, surface, and bottom samples, respectively. Theblue, red, yellow, and light green triangles represent the endmember values of composite freshwater (CF), west-river-derived freshwater (WF), offshoresurface water (SW), and upwelled subsurface water (SUB), respectively. The predicted δ18OH2O values were calculated based on the three-endmembermixing schemes for the surface and bottom layer, respectively.

Table 1. Summary of end-member values adopted in the three-endmember mixing models.

Water mass θ (�C) Salinity DIC (μmol kg−1) TA (μmol kg−1) δ13CDIC (‰) δ18OH2O (‰)

Composite freshwater 28.4 0.1 1392�35* 1241�34* −11.5 −7.0West-river-derived freshwater 29.1 3.3 1599 1652 −9.8 −6.4Offshore surface water 29.3 � 0.1 33.7 � 0.1 1922 � 5 2226 � 5 0.8 � 0.0 0.0

Upwelled subsurface water 22.5 � 0.1 34.5 � 0.0 2022 � 3 2273 � 5 0.4 � 0.1 0.5

*The uncertainties of the composite freshwater end-member were derived from samples collected in the river channel just upstream of the Humen outletbefore the cruise.

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mixing models to predict conservative values and decomposethe DIC and pH variability into components driven by physi-cal mixing and biological activity (and air–sea CO2 exchange)for better understanding the DIC and pH dynamics under theinteractions of eutrophication, hypoxia, and acidification.

Comparing measured DIC concentrations and pHT,25 withconservative values predicted by mixing models, we foundDIC was largely removed in the surface layer with maximumremoval at a salinity of � 10, while most added DIC was inthe bottom layer (Fig. 7a); correspondingly, pHT,25 increasedto a large extent in surface waters but decreased in bottomwaters (Fig. 7b). The distributions of ΔDIC and ΔpHT,25 weregenerally in line with the spatial patterns of the surface plumeand bottom hypoxic zone (Fig. 8). In the surface plume, ΔDICwas as large as −100 to −300 μmol kg−1 corresponding withsalinities of 5–25 (Fig. 7a), yielding an average value of−191 � 54 μmol kg−1 in the extensive freshwater bulge(Fig. 8a). This DIC removal is nearly six times larger thanreported by Cao et al. (2011) in the offshore plume bulge tothe far east of the PRE mouth, but these values could be com-parable when zoomed into the same salinities of 25–33, asΔDIC displayed a decreasing tend with increasing salinitywhen salinity values were > 10 (Fig. 7a). δ13CDIC also showed aremarkable increase with DIC removal in the surface plume(Fig. 7c). The corresponding ΔpHT,25 was generally greaterthan 0 (Fig. 7b), reaching � 0.57 � 0.19 units in the plumebulge centered off the MDM Estuary (Fig. 8b). The buffer fac-tor βDIC also rose by 50–120 μmol kg−1 in the surface plume

(Fig. 7d), suggesting a large increase in the buffering capacityof seawater.

In contrast, in the bottom layer within the 30-m isobath, DICadditions occurred with an average of � 112 � 30 μmol kg−1 and� 139 � 21 μmol kg−1 in the low-oxygen (DO < 94 μmol kg−1)and hypoxic waters, respectively (Fig. 8c). These DIC additionswere accompanied by a decrease in δ13CDIC, which reached−0.4 � 0.1‰ in bottom hypoxic zone (Fig. 7c). ΔpHT,25 in thebottom layer varied from −0.37 to 0.03 units, with an average of−0.24 � 0.07 and −0.30 � 0.04 units in the low-oxygen andhypoxic zone, respectively (Figs. 7c, 8d). The buffering capacityof bottom waters was greatly weakened, reflected by a decrease ofβDIC of up to � 60 μmol kg−1 (Fig. 7d).

Processes affecting DIC and pH other than physical mixingWhile physical mixing between riverine freshwater and sea-

water dominated the distributions of DIC concentrations andpH, a number of other processes are clearly important as well,and may explain deviations from conservative mixing. Usingthe semi-analytical diagnostic method based on changes inDIC concentrations and δ13CDIC, we discuss the effects of pri-mary production, degradation of OM, and CO2 exchangebetween seawater and the atmosphere on DIC alone, since pro-cesses that alter DIC would modulate pH as well due to theredistribution of inorganic carbon species in seawater (Zeebeand Wolf-Gladrow 2001). The additional effect of changes inbuffering capacity on pH dynamics will be discussed afterward.

Fig. 7. (a) DIC concentrations (μmol kg−1), (b) pHT,25, (c) δ13CDIC values (‰), and (d) βDIC (μmol kg−1) plotted against salinity in the surface (triangle)and bottom (circle) layers. Conservative values calculated based on the three-endmember mixing schemes are shown in pink, while measured values areshown in blue.

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The theoretical derivation of Eqs. 11, 13, and 16 shows alinear relationship between Δδ13CDIC and RDIC with a slopeconstrained by αPP, αCO2, or (δ13COC − δ13CDIC

cons). CO2 isfractionated relative to HCO−

3 ions (�88�4% of total inor-ganic carbon species in the study region) by a fractionationfactor that is temperature-dependent (Eq. 12) and variesbetween −8.3 and −8.6, resulting in αCO2 values of0.991–0.992 for this study area. In this case, there is an almostlinear relationship between Δδ13CDIC and RDIC with a slope ofapproximately −8.5 for CO2 outgassing (Line 2 in Fig. 9).Assuming that marine phytoplankton preferentially useCO2[aq] as a carbon source (Roberts et al. 2007) with an aver-age atmospheric δ13CCO2 value of −8‰ (Gruber et al. 1999), afractionation effect of −11‰ to −15‰ is needed to reach thewell-documented marine produced δ13C range of −19‰ to−23‰ (e.g., Peterson and Fry 1987). Li et al. (2018) reportedthat diatoms with δ13CPOC values of −21.0�3.3‰ were thedominant phytoplankton species in the lower reaches of thePRE, the same region as in this study. These values are consis-tent with previous observations (−19.4�0.8‰ in significantphytoplankton blooms from stations with salinity > 26, Suet al. 2017; −20.9‰ in a tow-net phytoplankton sample fromthe outer LDY Estuary, Chen et al. 2008; −20.8�0.4‰ inphytoplankton collected from the NSCS, He et al. 2010a). Lab-oratory experiments with diatoms also demonstrated a frac-tionation close to these values (Burkhardt et al. 1999).

Therefore, we used a value for fractionation of −13.0‰between δ13CCO2 values and δ13CPOC of primary production.Assuming that equilibrium between CO2[aq] and HCO−

3 isalways faster than losses of CO2 by primary production, thecombination of these two fractionation processes gives a totalεPP between primary production and DIC of nearly −21‰.Using the approximation εPP≈103(αPP−1), an αPP value of0.979 is obtained for the removal of CO2 during primary pro-duction, resulting in a slope of −21 (Line 1 in Fig. 9). For deg-radation of OM, the slope of the line is determined byδ13COC = −21‰ for marine-produced OC (Line 3 in Fig. 9)(Peterson and Fry 1987; Su et al. 2017; Li et al. 2018) and byδ13COC = −28.6‰ for terrestrial OC (Line 4 in Fig. 9)(−28.7‰, Yu et al. 2010; −28.3�0.7‰, Su et al. 2017;−28.8�0.2‰, Li et al. 2018), and the δ13CDIC value for con-servative mixing in each bottom water sample is between0.0‰ and+0.5‰.

The Δδ13CDIC and RDIC values calculated for samples areplotted in Fig. 9, where the origin represents the values calcu-lated for conservative mixing. Surface layer samples generallyfall in the upper left quadrant of the plot, where elevatedδ13CDIC values (Δδ13CDIC > 0) are associated with decreasedDIC concentrations (RDIC < 0), which may be explained by theremoval of DIC through outgassing of CO2 and by carbon fix-ation through primary productivity. As seen in Fig. 9, nearlyall surface layer data are located to the left of Line 1, which is

Fig. 8. DIC and pH changes deviated from conservative mixing in the surface layer (a, b) and in the bottom layer (c, d). The blue shaded area indicatesa deficit of DIC in the surface layer or a lower pH in the bottom layer compared to conservative values, whereas the red shaded area indicates excessiveDIC in the bottom layer or a higher pH in the surface layer.

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indicative of primary production, implying additional CO2

losses by outgassing from seawater. CO2 loss to the atmo-sphere seems to contradict the fact that primary productionleads to undersaturation of pCO2 (as low as < 150 μatm withrespect to equilibrium with the atmosphere, which is� 400 μatm), especially in waters at salinities of 5–25 that con-tain significant phytoplankton blooms (Fig. 4). Blooms resultin the uptake of CO2 from the atmosphere and elevated DICconcentrations. However, before traveling downstream to theplume bulge, the surface water showed a substantial over-saturation of pCO2 from the HM outlet (� 5000 μatm) to nearthe estuary mouth (> 600 μatm), predominantly due to exten-sive remineralization of terrestrial OM in the upper tributaries(Cai et al. 2004; Zhai et al. 2005b; Dai et al. 2006; Guo et al.2009). Therefore, from the perspective of Lagrangian observa-tion, the CO2-oversaturated surface water first experiencesCO2 outgassing and a sharp decrease in pCO2 in the LDY Estu-ary due to the loss of DIC content in the water mass and theincreasing buffering capacity of the carbonate system whenmixing with seawater (Cai et al. 2014). When flowing out ofthe estuary mouth, the attenuated turbidity and available lightconditions favored the rapid growth of phytoplankton (Yin

et al. 2000; Dai et al. 2008; Zhou et al. 2008), and resulted in aseawater pCO2 drawdown to below the atmospheric level.Integrating the CO2 flux between seawater and the atmo-sphere over the timescale of water mass transport from theHM outlet to the shelf suggests that it would be positive andthe net effect of gas exchange would be CO2 outgassing fromseawater to the atmosphere, provided the transit times in theLDY Estuary and on the shelf were 2 and 10 d (Wong andCheung 2000; Lu et al. 2018), respectively, and wind condi-tions were similar. Guo et al. (2009) estimated that the annualCO2 emission from the PRE system to be � 0.36 Tg C on thebasis of seasonal and zonal distributions of pCO2. Zhai et al.(2005a) also suggested that the NSCS shelf served as a sourceof atmospheric CO2. Even though their studies covered only apart of the estuary-shelf system or captured a snapshot intime, both concluded that the PRE-NSCS shelf plume systemmight be a net source of atmospheric CO2, even with strongphytoplankton production in the extensive plume waters.These findings help to demonstrate the complex the source/sink mechanisms of coastal estuary-shelf systems.

In contrast, most samples from the bottom layer fall in thelower right quadrant, where depleted δ13CDIC (Δδ13CDIC < 0)values are associated with increased DIC concentrations(RDIC > 0), which may be explained by the addition of DICfrom the degradation of OM (Fig. 9). These data points frombottom waters are mostly located between lines indicative ofmarine-produced and terrestrial OC degradation (Line 3 andLine 4, respectively, in Fig. 9), suggesting marine- andterrestrial-sourced OM together contributed to the addition ofDIC. Marine-sourced OM could be derived from the in situphytoplankton production in the surface blooms (Fig. 4j),while the terrestrial-sourced OM is what survived degradationduring transport from the estuaries. Even though terrestrialOM transported onto the shelf might experience intenseremineralization by microbial respiration in the LDY Estuary(Guo et al. 2009), some may still be available to cause oxygenconsumption in the shelf bottom waters due to abnormallylarge river discharge and a short transport time through theLDY Estuary. On the other hand, the MDM and HMH Estuar-ies directly discharge riverine freshwater onto the shelf(Fig. 1), bringing abundant terrestrial POC to fuel the micro-bial respiration after settling through the pycnocline.

Bottom water outliers falling outside of Line 3 and Line4, together with data points falling in the lower left quadrant(Fig. 9), likely reflect the combined effects of degradation ofOC and CO2 outgassing. As discussed above, the water massmight experience microbial degradation of terrestrial OM andthen CO2 outgassing in the estuary before being dischargedonto the shelf and accumulating in the bulge or mixing intothe bottom layer. In this case, the signal of OC degradationand CO2 outgassing would be imprinted in the water massproperties and could be revealed by chemical tracers. Othersample values falling in the upper right quadrant (Fig. 9) arefrom the LDY Estuary, characterized by a high concentration

Fig. 9. Deviations in δ13CDIC (Δδ13CDIC, ‰) and DIC concentrations (RDIC)from conservative estuarine mixing in the PRE and adjacent shelf waters. Thetriangles and circles represent samples collected from the surface and bottomlayers on the shelf, while the squares represent samples collected from theLingdingyang Estuary (LDY). Also shown are the calculated vectors for theeffects of the most likely processes affecting DIC. Two vectors are shown forthe degradation of OC, as its effect on DIC will depend upon whether theOC is of marine (δ13COC = −21.0‰, Line 3) or terrestrial (δ13COC = −28.6‰,Line 4) origin. Losses of DIC by primary production (Line 1) alone cannotexplain the data and thus additional losses of DIC with less isotopic fraction-ation by outgassing of CO2 (Line 2) is required. The surface samples locatedin the upper right quadrant were characterized by high suspended particulatematerial concentrations in the LDY Estuary, which might be associated withCaCO3 dissolution. See the text for more details.

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of suspended particulate matter, possibly associated with thedissolution of CaCO3 (Alling et al. 2012).

Contribution of eutrophication in bottom hypoxiadevelopment

In the bottom layer, the seawater was isolated from theatmosphere by strong stratification, thus gas exchangebetween seawater and the atmosphere is negligible. As notedabove, the isotopic composition of biologically released DICshould be identical to the δ13COC. We can derive the δ13C ofOC that degraded to produce DIC and consume DO, using theDIC isotopic mass balance expressed as Eq. 14 (Wang et al.2016; Su et al. 2017),

δ13COC =δ13Cmeas

DIC ×DICmeas−δ13CconsDIC ×DICcons

DICmeas−DICcons ð17Þ

which can be rearranged into

Δ δ13CDIC ×DIC�

= δ13COC ×ΔDIC ð18Þ

The slope of the linear regression shown in Fig. 10 repre-sents δ13COC, reflecting the contribution of the original δ13Csignature of the degraded OC added to the observed DIC.Here, the δ13COC is equal to −23.2 � 1.3‰, almost the sameas that calculated by Su et al. (2017) in the PRE. Althoughselective diagenesis of isotopically heavy constituents, forexample, proteins and carbohydrates, would leave residualOM that is isotopically lighter than the original OM, the effectappears to be small, usually less than 2‰ (Meyers 1997 andreference therein). In fact, the δ13C of residual bulk POC inbottom waters varies between −24‰ and −25‰ (F. Mengpers. comm.). Yu et al. (2010) also reported that the

sedimentary δ13CPOC ranged between −24.0‰ in the freshwa-ter areas and −25.4‰ in brackish water areas.

Based on the carbon isotopic mass balance of OM, the con-tributions of marine sources to oxygen-consuming OM wereestimated based on the following equation (Hu et al. 2006):

fmar %ð Þ= δ13Coc−δ13Cterr�

= δ13Cmar−δ13Cterr�

×100 ð19Þ

where δ13Cmar and δ13Cterr are representative values of marineand terrestrial POC, respectively, approximately −20.5‰(Chen et al. 2008; He et al. 2010a) and −28.6‰ (Yu et al.2010; Su et al. 2017; Li et al. 2018) in this study area. Wefound that marine-sourced OC contributed 67 � 18% of theobserved DIC addition to bottom waters, and terrestrial OMcontributed the other 33 � 18%, supporting the results of Suet al. (2017).

Marine-sourced OC is thought to be produced due to eutro-phication in the surface plume, thus the contribution ofeutrophication-produced OM to the development of bottomhypoxia may be estimated based on the DIC budgets using atwo-layer box model. Here, we define the thickness of the sur-face plume layer and the bottom layer by the static stabilityE = −1/ρ (∂ρ/∂z) (Pond and Pickard 1983), where ρ is density ofseawater (kg m−3) and z is the depth (m), because eutrophica-tion mainly happened in the relatively unstable surfaceplume, and derive the thickness (D) of the box at each station.Integrating the nonconservative DIC variations over the studyarea in each box, we estimate the fraction of eutrophication-produced OM that degraded in the bottom box to fuel thedevelopment of hypoxia:

f eu %ð Þ= fmar

XΔDICbot ×Dbotð Þ=

XΔDICsurf ×Dsurfð Þ ð20Þ

where subscripts surf and bot denote the surface plume sam-ples and bottom samples, respectively. The box model integra-tion indicates that nearly 45 � 13% of eutrophication-produced OM fueled the oxygen consumption, and thus thedevelopment of hypoxia, in the bottom layer. This fraction isconsidered to be the lower limit since CO2 outgassing to theatmosphere also contributed to the observed DIC deficit inthe surface plume waters. In this sense, the other half of theDIC deficit in the surface plume might be lost to the atmo-sphere, transformed into POC sinking down to the bottomsediments, or contribute to the accumulation of dissolvedorganic carbon (DOC). Assuming water mass transports fromHM outlet to the plume bulge � 3 d (Wong and Cheung2000) with the surface pCO2 varying from � 5000 μatm to� 150 μatm, we roughly estimate that net CO2 outgassingalong the pathway could be responsible for � 15–20% of theobserved DIC deficit in the surface plume. In addition, thepartitioning ratio of the DIC uptake by phytoplankton intoDOC accumulation ranged from � 7% estimated by Li et al.(2018) to 24–27% reported by Wu et al. (2017) for the sameplume area. The residual DIC loss in the plume could likely be

Fig. 10. Deviations in δ13CDIC × DIC (Δ(δ13CDIC × DIC)) and DIC con-centrations (ΔDIC) from conservative estuarine mixing in the PRE andadjacent shelf waters. Samples were collected from bottom water(> 12 m). Also shown are data collected in 2014 and reported by Su et al.(2017). The black solid and red dashed lines indicate linear regressionlines of data collected in 2017 and 2014, respectively.

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attributed to suspended/sinking POC in the water column andPOC burial in the bottom sediments.

Interplay between eutrophication, hypoxia, and oceanacidification

Eutrophication not only transfers the DIC in the surface layerdown to the bottom layer through primary production and OCdegradation, but also modulates the vertical structure of DOconcentrations in the water column. Changes in DIC beyondphysical mixing (ΔDIC) were closely related to the DO

variations (ΔDO) (Fig. 11a) calculated based on the endmembermixing models. In the bottom shelf waters, ΔDIC and ΔDOwere related by an integrated ratio of −0.89 � 0.02 (Fig. 11a),smaller than that derived from the Redfield stoichiometry formarine-sourced OM (C:O = −106:138 ≈ −0.77). Even taking intoaccount one third of oxygen-consuming OM sourced from land(C:N > 20, Meyers 1997), the theoretical ratio of ΔDIC to ΔDOdiffers from the estimated values in the bottom layer, suggestingthat low-nutrient OM, such as diatom bloom-produced OMunder nitrate depletion, might contribute to the oxygen

Fig. 11. (a) ΔDIC vs. ΔDO, (b) ΔpHT,25 vs. ΔDIC, (c) ΔpHT,25 vs. ΔDO, (d) conservative βDIC (βDICcons) vs. ΔDIC, (e) ΔβDIC vs. ΔDO, and (f) βDIC

cons

vs. ΔDO in the PRE and adjacent shelf waters. The triangles and circles represent samples collected from the surface and bottom layers on the shelf, whilethe squares represent samples collected from the Lingdingyang Estuary (LDY). The blue, black, and red solid lines in (a) denote the slopes of ΔDIC plottedagainst ΔDO in the surface and bottom shelf waters and LDY Estuary waters derived from the type II regression. The black and red solid lines in (b)denote the slopes of ΔpHT,25 plotted against ΔDIC in the bottom shelf waters and LDY Estuary waters derived from the type II regression, while the bluedashed lines in (b) denote the range of changes in pHT,25 against ΔDIC in the surface shelf waters.

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consumption (Wetz and Wheeler 2003). Indeed, the diatomSkeletonema costatum formed the dominant phytoplankton com-munity in the PRE during wet seasons when salinity was lessthan 30 (Huang et al. 2004; Qiu et al. 2010). Studies of hypoxiaoff Changjiang Estuary also suggest that diatom dominates thesinking POC that decomposes and ultimately consumes DO inthe subsurface water (Wang et al. 2017). In the surface plumewaters, the ΔDIC/ΔDO ratio was −1.46 � 0.18, exhibiting highvariability and nearly doubling the Redfield ratio for primaryproduction (Fig. 11a). The variability may be attributed to therelatively faster gas exchange of oxygen than CO2 at the surfaceseawater–atmosphere interface (Zeebe and Wolf-Gladrow 2001),and the rapid turnover of photosynthetically produced OM inthe surface plume waters (Hopkinson et al. 2002). In contrast,ΔDIC was out of phase with the DO variations in the LDY Estu-ary according to the Redfield ratio, which may be due to thegreater difference in pCO2 than the partial pressure of oxygenbetween seawater and the atmosphere and/or their oppositedirections of gas exchange. Thus eutrophication and hypoxia,combined with the upward transport of DIC and nutrients tothe surface layer through diapycnal mixing or upwelling, gener-ate a loop for DO release from bottom waters to the atmosphere(Zhai et al. 2009).

Eutrophication also modulates the pH variability and cor-relates with ocean acidification indirectly through DIC varia-tions. As shown in Figs. 7, 11b, removal of DIC by primaryproduction and CO2 outgassing increase pH up to 0.8 unitsin surface waters, while addition of DIC by degradation ofOM in bottom waters results in a pH decrease by � 0.4 units.In bottom waters, pH decreased with increasing DIC at a rateof (−1.86 � 0.04) × 10−3 unit per μmol kg−1 of DIC change,which was nearly one-third of the rate in the LDY Estuary([−5.97 � 1.14] × 10−3 unit per μmol kg−1 of DIC change;Fig. 11b). Since the pH variability in bottom waters wasmainly controlled by degradation of OM, the higher rate ofpH change in the LDY Estuary was likely affected by otherfactors, such as the buffering capacity of seawater. We foundthat the acid-base buffer factor βDIC could explain the differ-ence in the rate of pH change in bottom waters and the LDYEstuary, as the conservative βDIC (βDIC

cons) in bottom waters(� 230 � 4 μmol kg−1) was approximately three times higherthan in the LDY Estuary (Fig. 11d), consistent with the pHvariations compared with changes in DIC. In the surfaceplume waters, the rate of pH increase with DIC decrease wassimilar to the range in bottom waters and in the LDY Estuary(Fig. 11b), indicating the combined effects of biologicalmetabolism and water mass mixing with low-buffer-capacityriverine waters flowing out from the LDY Estuary (Fig. 11d).In this sense, eutrophication buffers ocean acidification insurface waters, but the resulting DIC addition enhancescoastal acidification in bottom waters (Cai et al. 2011; Wal-lace et al. 2014; Laurent et al. 2017), more so than acidifica-tion induced by physical mixing of the riverine water andseawater alone.

Ocean acidification in coastal waters is also greatly ampli-fied with the development of bottom hypoxia (Cai et al. 2011;Feely et al. 2018). As shown in Fig. 11c, the slope of ΔpHT,25

against ΔDO increased with the decreasing DO in bottomwaters, suggesting an increasing rate of pH change with oxy-gen consumption. Cai et al. (2011) attributed the accelerationof pH decrease with the oxygen consumption in subsurfacewaters to a weaker seawater buffering capacity, since thesimultaneous changes in DIC concentration would redistrib-ute the proportions of different species of inorganic carbon(Zeebe and Wolf-Gladrow 2001) and result in a nonlinear vari-ability in pH. This was reflected by changes in the acid-basebuffer factor βDIC which decreased linearly by up to� 64 � 8 μmol kg−1 in hypoxic waters (Fig. 11e). In this sense,ocean acidification in coastal waters was mainly driven by OMdegradation and simultaneously amplified by the reducedbuffering capacity of seawater. In contrast, the increase in DOby primary production was accompanied by a higher rate ofpH change in the surface layer than in bottom waters(Fig. 11c), even though there was a significant rise in acid-basebuffering capacity of surface waters (Fig. 11e). The apparentcontradiction between the higher increase in both pH andβDIC in surface waters could be explained by the conservativebuffering capacity of seawater before changes by biologicalmetabolism and/or air–sea gas exchange, which was muchlower than in bottom waters (Fig. 11f). Therefore, the effect ofchanges in the acid-base buffering capacity induced by biolog-ical metabolism is secondary to that caused by freshwaterinputs.

ConclusionsThe spatial variability of DIC and pH in the PRE and the

adjacent NSCS shelf waters were mainly controlled by thewater mass mixing, between the riverine freshwater and sea-water, and the biological metabolism of primary producers inthe surface layer and microbial respiration in the bottom layer.Even though strong phytoplankton blooms were responsiblefor a substantial DIC deficit in the surface plume waters, thewater mass carried the signal of CO2 outgassing as it traveledthrough the inner estuary, that is, LDY Estuary. From a per-spective of Lagrangian observations, the estuary-shelf plumesystem as a whole served as a source of atmospheric CO2.

In bottom waters, 67 � 18% of DIC addition was attribut-able to the degradation of marine-sourced OM, supporting theresults of Su et al. (2017) that suggest terrestrial OM accountsfor nearly 35% of oxygen-consuming OM. At least � 45 �13% of the eutrophication-driven production of OM in thesurface plume was exported downwards into bottom watersfor DIC regeneration, fueling oxygen consumption, and eventhe development of hypoxia, while the other half of the DICdeficit in the surface plume might be lost to the atmosphere,transformed into POC sinking down to the bottom sedimentsor contribute to the accumulation of DOC. However, the

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partition of the residual DIC deficit needs further quantifica-tion in combination of high-resolution air–sea CO2 fluxes andstable carbon isotopic composition of POC and DOC.

Eutrophication buffered ocean acidification in the surfaceplume waters by spurring primary production and DIC con-sumption, but subsequent OC export and the resultant DICregeneration enhanced coastal acidification in bottom waters,relative to that induced by the physical mixing of the riverinewater and seawater alone. Ocean acidification in coastal waterwas further greatly aggravated with the development of bot-tom water hypoxia due to a reduction in the acid-base buffer-ing capacity of seawater. However, the effect of changes in theacid-base buffering capacity caused by freshwater inputs andthe amplification of coastal ocean acidification ranked first-order compared to that induced by biological metabolism.

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AcknowledgmentsWe thank the captain and the crew of the R/V Haike 68 for their coop-

eration during the cruise; Zhiqiang Liu, Xiaozheng Zhao, and ZhongmingLu for providing the conductivity-temperature-depth data; Liguo Guo,Yan Li, and Pengfei Liu for their assistance in sample analysis; and RichardSmith and Stella Woodard of Global Aquatic Research for assistance inEnglish. We express our gratitude to the two anonymous reviewers fortheir insightful comments and recommendations that help substantiallyimprove clarity of the paper. This research was funded by the Hong KongResearch Grants Council under the Theme-based Research Scheme (TRS)through grant T21-602/16-R and the Ministry of Science and Technology

of China under the National Key Scientific Research Project through grant2015CB954000.

Conflict of InterestNone declared.

Submitted 17 April 2019

Revised 23 September 2019

Accepted 16 November 2019

Associate editor: Lauren Juranek

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