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Radium-traced nutrient outwelling from the Subei Shoal to the Yellow Sea: Fluxes and environmental implication
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Jian’an Liu1, Dongyan Liu1, Jinzhou Du1, *
Acta Oceanologica Sinica | 2022, 41(6) : 12 - 21
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Acta Oceanologica Sinica | 2022, 41(6): 12-21
• Dynamics of ecosystems and anthropogenic drivers in the Yellow Sea Large Marine Ecosystem •
Radium-traced nutrient outwelling from the Subei Shoal to the Yellow Sea: Fluxes and environmental implication
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Jian’an Liu1, Dongyan Liu1, Jinzhou Du1, *
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  • 1 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
Published: 2022-06-25 doi: 10.1007/s13131-021-1930-z
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The Subei Shoal is the largest sandy ridge in the southern Yellow Sea and is important source for nutrient loading to the sea. Here, the nutrient fluxes in the Subei Shoal associated with eddy diffusion and submarine groundwater discharge (SGD) were assessed to understand their impacts on the nutrient budget in the Yellow Sea. Based on the analysis of 223Ra and 224Ra in the field observation, the offshore eddy diffusivity mixing coefficient and SGD were estimated to be 2.3×108 cm2/s and 2.6×109 m3/d (16 cm/d), respectively, in the Subei Shoal. Combined the significant offshore decreasing gradients of nutrient in seawater of the Subei Shoal, the spatially integrated nutrient outwelling fluxes to the Yellow Sea were 262−1 465 μmol/(m2·d) for DIN, 5.2−21 μmol/(m2·d) for DIP and 711−913 μmol/(m2·d) for DSi. Compared to the riverine input, atmospheric deposition and mariculture, nutrient outwelling from the Subei Shoal might play an important role in nutrient budget of the Yellow Sea. These nutrient fluxes could provide 4.1%−23% N and 1.3%−5.3% P requirements for the primary productivity, and the deviated DIN/DIP ratios have the potential to affect the growth of phytoplankton in the marine ecosystem of the Yellow Sea.

nutrient outwelling  /  eddy diffusion  /  submarine groundwater discharge  /  radium isotopes  /  Subei Shoal  /  Yellow Sea
Jian’an Liu, Dongyan Liu, Jinzhou Du. Radium-traced nutrient outwelling from the Subei Shoal to the Yellow Sea: Fluxes and environmental implication[J]. Acta Oceanologica Sinica, 2022 , 41 (6) : 12 -21 . DOI: 10.1007/s13131-021-1930-z
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1 Introduction
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Nutrient concentrations and their ratios have been proved as one of the crucial indicators for health assessment of the ecological environment, especially in the coastal marine ecosystem. Coastal zone receives a large amount of nutrient from multiple sources, such as atmospheric deposition, riverine input and submarine groundwater discharge (SGD) (Anderson et al., 2002; Moore, 2010). Therefore, the coastal ecosystems are hotspots for nutrient accumulation, biological growth, and laterally outwelling nutrient to the open ocean (Andersen and Conley, 2009; Charette et al., 2007; Su et al., 2013b). This nutrient outwelling from coastal zone significantly elevates the oceanic nutrient level and changes the nutrient structure of the water ecosystems, which probably contribute to the occurrence of eutrophication, harmful algal blooms and hypoxia (Le Moal et al., 2019; Tang et al., 2004; Zhang et al., 2007a). For example, Kwon et al. (2019) suggested that nutrients transported horizontally from inner-shore waters were fueling the spread of dinoflagellate red tides along the coast of Korea.
In the coastal waters, nutrient cycles are mainly controlled by external supplements and internal biogeochemical cycles, in which external sources transport significant nutrient into the coastal zones and then involve in ecological and biogeochemical functions with the loss of nutrient (Falkowski et al. 1998; Walsh, 1991). Besides, the coastal waters also exchange numerous nutrients with the open ocean due to the water mixing, but attention cannot be paid to the processes. The primary mechanism behind nutrient is advection and diffusion movement from the coastal region to the open ocean, in which eddy diffusion as the main water mixing process could significantly contribute to the fate of nutrient (Pasquero, 2005; Su et al., 2013b). Previous studies revealed the importance of SGD for delivering nutrient into the coastal regions (Liu et al., 2018; Zhang et al., 2020a), whereas the contributes of nutrient outwelling through eddy diffusion processes from the coastal zone are still poorly understood (Su et al., 2013a; Su et al., 2013b). Especially in the river poorly-influenced coastal zone, eddy diffusion could act as the essential driver for nutrient moving from coastal waters to open ocean, however, very limited studies have demonstrated the significance of this process.
Due to the complex processes of water mixing, quantification of the eddy diffusion associated nutrient outwelling to the coastal ocean is challenging. In early stage, eddy diffusivity was determined by satellite remote sensing (Fischer, 1980) and numerical simulation (Werner et al., 1993), but it is difficult for wide calculation under the characteristic of small-scale temporal and spatial variability. Fortunately, radium isotopes have been shown to be useful tracers to access water movements, such as eddy diffusion and SGD, and fluxes of dissolved nutrient from the coastal zone to the ocean (Moore, 1996, 2000; Su et al., 2013a; Wang et al., 2018). Radium isotopes are produced by the decay of their parent U-Th series nuclides from sediment and/or soil in the rivers or the continental shelf and then transport to offshore seawater (Li et al., 1977; Swarzenski, 2007). There are four naturally occurring radium (Ra) isotopes, that is, 224Ra, 223Ra, 228Ra and 226Ra, are radiogenic with half-lives of 3.66 d, 11.6 d, 5.75 a and 1 600 a, respectively, which provide the information of water mixing processes on scales from days to years (Moore, 2000). Radium isotopes could be used to not only calculate advection and eddy diffusivity (Ku and Luo, 1994; Li and Cai, 2011), but also obtain SGD flux (Moore, 2010), and could also effectively avoid the influence of biological process (Sarmiento et al., 1990). Therefore, radium isotopes can give insights into the nutrient outwelling through eddy diffusion processes to the ocean.
Since 2007, the world’s largest green tides bloom occurred annually in the Yellow Sea (Liu et al., 2009a; Liu et al. 2016), and were recognized to be originated from the southwestern Yellow Sea near the coast of Jiangsu Province, which is called the Subei Shoal (Liu et al., 2013). As the essential biogenic element for phytoplankton growth in seawater, high nutrient, especially nitrogen levels, were considered as one of the most significant contributors to the development of green tides in the Subei Shoal (Li et al., 2015; Zhang et al., 2020b). Due to the unique features, which are broad tidal flats, elongated sand ridges and grooves and strong tidal forces, the Subei Shoal is a traditional aquaculture base for the Porphyra crops (Bao et al., 2015; Xiao et al., 2020). Meanwhile, the Subei Shoal is receiving a large amount of nutrient-rich SGD and it elevates the nutrient level in the seawater that may fuel green tides (Liu et al., 2017; Zhao et al., 2018). Therefore, the geochemical and ecological environment of the Subei Shoal is expected to influence or even regulate the ecosystem of the Yellow Sea (Xiao et al., 2020).
Previously, Men et al. (2006) used 228Ra to calculate horizontal eddy diffusion coefficient within 200 km offshore in the western Yellow Sea, and then Su et al. (2013b) in our laboratory estimated the horizontal transport of nutrient within 500 km offshore of the Yellow Sea combined 223Ra and 226Ra. However, later Moore (2015) indicated that it was inappropriate to model water mixing rates utilizing 226Ra and 228Ra in the coastal zone within 500 km offshore. Moreover, the previous study cannot evaluate the contribution of the Subei Shoal region. Therefore, this study, examined radium isotopes and nutrient in the Subei Shoal, aiming to quantify SGD-derived nutrient fluxes into the Subei Shoal, and the nutrient outwelling through eddy diffusion to the Yellow Sea, and evaluate the potential influence on harmful algal blooms in the Yellow Sea. Based on these results, this study would provide insights into nutrient transporting processes in the Subei Shoal and improve the knowledge of the biogeochemical process of water movement.
2 Materials and methods
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2.1 Study area and sampling procedures
A field observation was carried out in the Subei Shoal in the southern Yellow Sea during March 23−31, 2017. The Subei Shoal is located off the coast of southeastern Jiangsu Province and is highly sensitive to a temperate monsoon climate. As the largest sandy ridge in the southern Yellow Sea, the water depth of the Subei Shoal is relatively shallow at approximately 12 m. Due to the strong tidal force, the Subei Shoal is well mixed and high turbid (Miao et al., 2020).
During this observation, radium isotopes and nutrient samples in seawater and coastal groundwater were collected, sampling stations were shown in Fig. 1. For radium isotopes, approximately 25 L seawater were collected using a submersible pump at 0.5−1 m depth, while coastal groundwater (~10 L) was obtained from a push-point piezometer using a peristaltic pump. The salinity was measured directly in situ using a pre-calibrated portable salinometer with multiple parameters (Germany, multi 350i). After collection, radium samples were filtrated to remove suspended particles and then allowed to pass through a cartridge that was filled with 20 g of MnO2-impregnated acrylic fiber to enrich radium isotopes (Moore, 1976).
Corresponding to each radium sample, ~60 mL water for nutrient were filtered with acid pre-cleaned 0.45 μm pore-size acetate cellulose filters into polyethylene bottles, and then the water samples were poisoned with saturated HgCl2 and stored in the dark.
2.2 Radium isotopes and nutrient analysis
After preparation, the cartridge with Mn-fiber was immediately placed in the shipboard radium delayed coincidence counter (RaDeCC) to measure 224Ra (Moore and Arnold, 1996). Each sample was again measured a week after collection to determine 223Ra, and 5 weeks after collection all the samples were analyzed for 228Th on the same instrument to correct for supported 224Ra (Moore, 2008). The uncertainties of 223Ra and 224Ra were estimated to be approximately 20% and 8%, respectively, using the equations reported in Garcia-Solsona et al. (2008).
Nutrient concentrations were quantified with an auto-analyzer (Model: Skalar SANplus). The concentration of dissolved inorganic nitrogen (DIN) is the sum of ${\rm{NO}} ^{-}_{3}$, ${\rm{NO}} ^{-}_{2}$and ${\rm{NH}} ^{+}_{4} $. dissolved inorganic phosphorus (DIP) and dissolved inorganic silicon (DSi) represent the concentrations of ${\rm{PO}} ^{3-}_{4} $ and ${\rm{SiO}} ^{2-}_{3} $, respectively. The analytical precisions of ${\rm{NO}} ^{-}_{2} $, ${\rm{NO}} ^{-}_{3} $, ${\rm{NH}} ^{+}_{4} $, ${\rm{PO}} ^{3-}_{4} $ and Si(OH)4 were all better than 5% and the detection limits were 0.01 μmol/L, 0.05 μmol/L, 0.05 μmol/L, 0.01 μmol/L and 0.1 μmol/L, respectively.
2.3 SGD flux
This study would quantify the SGD flux based on 224Ra mass balance model that has been widely applied (Garcia-Orellana et al., 2014; Liu et al., 2018). Under steady state system, the input of 224Ra fluxes into the system equal to losses from the system (Moore, 1996). Therefore, by estimating all the sources and sinks of 224Ra except for SGD, 224Ra flux derived from SGD could be obtained. Subsequently, combined with 224Ra activity in coastal groundwater, this study could access the SGD flux. For these, the equation used for calculating SGD flux is expressed as
$\begin{split}& F_{\rm{SGD}}=\\&\dfrac{C_{\rm{SW}} \cdot V \cdot \text{λ} + \dfrac{\left({{C}}_{\rm{SW}} - {{C}}_{\rm{ocean}}\right) \cdot { V}}{\tau} - {{F}}_{\rm{riv}} \cdot {{C}}_{\rm{riv}} - {{F}}_{\rm{sed}} \cdot {A} - \dfrac{{{C}}_{\rm{des}} \cdot {{C}}_{\rm{SPM}} \cdot { V}}{{\tau}}}{{{C}}_{\rm{GW}}} ,\end{split} $
where FSGD refers to the flux of SGD; CSW is 224Ra activity in seawater of the study area; Cocean is 224Ra activity in seawater of ocean in the Yellow Sea; Criv is 224Ra activity in surrounding rivers; Cdes is 224Ra activity desorbed from suspended particulate matter (SPM); CGW is 224Ra activity in coastal groundwater; τ refers to water residence time in the study region; Friv refers to the freshwater discharge of rivers; Fsed refers to 224Ra diffusion rate from sediment; CSPM refers to the concentration of SPM in seawater; V refers to the water volume of the study area; and A refers to the area of the study region.
2.4 Offshore mixing
Considering the water residence time in the study area is on the scale of the week, this study would apply the short half-lives isotopes of 223Ra and 224Ra to estimate eddy diffusivity and advection rates (Moore, 2015; Zhao et al., 2018). Assuming the study area is in a steady state and there is no additional input of radium isotopes beyond the nearshore other than those constrained by benthic diffusion, and the water body in the study region is situated over the mean lifetimes of 223Ra and 224Ra, a fully-resolved 1 D advection–diffusion equation is built by combining 223Ra and 224Ra (Moore, 2000; Sippo et al., 2019; Su et al., 2013a), in which is shown as
$ \left.\begin{array}{c}{K}_{{\rm{h}}}{A}_{223}^{2}+{w}_{x}{A}_{223}={\text{λ} }_{223}\\ {K}_{{\rm{h}}}{A}_{224}^{2}+{w}_{x}{A}_{224}={\text{λ} }_{224}\end{array}\right\} , $
where x refers to the distance to the coastline; Kh and wx are offshore eddy diffusivity mixing coefficient and offshore advection rate; λ223 and λ224 are the decay constants of 223Ra and 224Ra, respectively; A223 and A224 are the index coefficients obtained by fitting the measured radium activities to a function with a form by
$ {C}_{x}={C}_{\mathrm{o}}\mathrm{exp}\left(-A\cdot x\right) , $
where Cx is radium activity at distance x; Co is initial radium activity along the coastline in coastal groundwater.
3 Results
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3.1 Radium
During the observation, salinity in surface seawater ranged from 27.4 to 32.1 in the Subei Shoal, in which the lowest salinity occurred near to shore and decreased moving offshore (Fig. 2a). In surface seawater, 224Ra and 223Ra activities ranged from 1.5 Bq/m3 to 32.0 Bq/m3 and 0.13 Bq/m3 to 1.40 Bq/m3, with the average activities of (11.0±7.5) Bq/m3 (n=24) and (0.50±0.31) Bq/m3 (n=24) , respectively. Similar to salinity, activities of 224Ra and 223Ra both showed significant decreasing trends from nearshore to offshore (Figs 2a−c ). In nearshore within 60 km from the coastline, the mean 224Ra and 223Ra activities were (16.0±7.0) Bq/m3 (n=12) and (0.71±0.30) Bq/m3 (n=12), which were 3.1- and 2.4-fold higher than those in offshore (60 km) water (224Ra: (5.2±1.8) Bq/m3, n=12; 223Ra: (0.29±0.11) Bq/m3, n=12), respectively. Combining all the radium data in the Subei Shoal, the log-linear gradients of 224Ra (n=24, R2=0.80, p＜0.001) and 223Ra (n=24, R2=0.76, p＜0.001) were observed with the decreasing distance from the shoreline (Fig. 3). All these suggested that the primary radium source came from the shoreline that would be SGD (Moore, 2010), and after leaving near shore, the decreasing trend of 224Ra and 223Ra indicated the influence of radioactive decay and water mixing of the Yellow Sea.
In the coastal groundwater, the mean activities of 224Ra and 223Ra were (34±15) Bq/m3 (n=5) and (1.40±0.42) (n=5) Bq/m3, which were 3.2- and 2.7-fold of the activities in surface seawater of the Subei Shoal, indicating that coastal groundwater could be a dominated source of radium isotopes in surface seawater.
3.2 Nutrients
The nutrient analysis of the surface seawater revealed that DIN concentration ranged from 17 μmol/L to 59 μmol/L with an average of (30±10) μmol/L (n=24), DIP concentration ranged from 0.42 μmol/L to 1.2 μmol/L with an average of (0.70±0.20) μmol/L (n=24), and DSi concentration ranged from 6.5 μmol/L to 30 μmol/L with an average of (16±6.3) μmol/L (n=24). Nutrient concentrations in surface seawater both showed clear decreasing trends from nearshore water to offshore oceanic water (Fig. 4), suggesting that nutrients have similar sources with radium isotopes in the Subei Shoal. Within 60 km from the shoreline, the mean concentrations of DIN, DIP and DSi were (37±9.4) μmol/L, (0.81±0.18) μmol/L and (20±5.3) μmol/L, while concentrations in offshore (＞60 km) water were (23±3.9) μmol/L, (0.59±0.14) μmol/L and (12±3.4) μmol/L, respectively. When plotting the nutrient concentrations in all samples versus distance from the shoreline, distinct linear relationships were observed for DIN (n=24, R2=0.67, p＜0.001), DIP (n=24, R2=0.38, p=0.001 3) and DSi (n=24, R2=0.69, p＜0.001) (Fig. 5), also implied that coast would be the main source for nutrient in the Subei Shoal. Nutrient concentrations in coastal groundwater were 2.3, 2.0 and 9.0 times higher for DIN, DIP and DSi, respectively, which averaged of (68±50) μmol/L, (1.4±1.2) μmol/L and (142±102) μmol/L.
3.3 SGD flux
Based on Eq. (1), a mass balance model based on 224Ra was established to calculate SGD flux in the Subei Shoal. As shown in Table 1, this study obtained each parameter, and the SGD flux was estimated to be 2.6×109 m3/d. Normalized the SGD flux by area of the study region, the SGD rate could convert to be 16 cm/d.
3.4 Eddy diffusion calculations
Based on Eq. (2), this study used all the 224Ra and 223Ra data in the Subei Shoal to calculate offshore eddy diffusivity mixing coefficient and advection. With parameters shown in Table 2, this study estimated Kh and wx in the study region to be 2.3×108 cm2/s and −0.31 m/s. Additionally, this study divided all the radium stations into five transects, namely, transect SF to SB from north to south. Similar to all data, significant log-linear gradients of 224Ra and 223Ra activities were both observed from the five individual transects (Table 2). Therefore, using the same equation, this study obtained eddy diffusivity Kh ranged from 9.8 × 107 cm2/s to 3.9 × 108 cm2/s with an average value of 2.3 × 108 cm2/s, advection wx ranged from −0.048 m/s to −0.69 m/s with an average value of −0.33 m/s. The negative values of advection indicated onshore advection.
4 Discussion
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4.1 Eddy diffusivity
As shown in Eq. (2), the 1 D advection–diffusion model was developed based on the assumptions of there is no additional radium input for the surface radium activity. Thus, the influence of benthic radium flux on eddy diffusivity should be negligible. In order to determine the potential impacts of benthic radium flux, this study used a simple method introduced in Su et al. (2013a), which deduced a parameter of β (d−1), and obtained by
$ \beta =\frac{{F}_{\rm{sed}}\cdot A}{V\cdot {C}_{\rm{SW}}} . $
In Eq. (4), the sediment diffusion fluxes of 223Ra and 224Ra were 0.24 Bq/(m2·d) and 20 Bq/(m2·d) (Gu, 2015; Liu et al., 2018). Therefore, this study calculated β223 and β224 to be 0.040 d−1 and 0.156 d−1, respectively. It should be noted that the parameter β is in the same dimension with decay constant of radium isotopes (Su et al., 2013a). The results revealed that there were β223 and β224 both lower than the decay constant of 223Ra (0.059 7 d−1) and 224Ra (0.189 d−1), indicating that benthic radium flux played relatively fewer contributions to the radium activity in surface seawater compared to decay and water mixing (Moore, 2000). Furthermore, this study conducted an estimation based on β223 and β224, considering the influence of benthic radium flux. By applying Eq. (2), this study obtained eddy diffusivity Kh of 2.1 × 108 cm2/s and wx advection of −0.29 m/s, which was comparable to the results under the assumption of no benthic radium flux, suggesting the availability of using Eq. (2) to calculate the eddy diffusivity in the Subei Shoal of this study. Additionally, this study calculated offshore eddy diffusivity Kh exceeded advection by several orders of magnitude, which the average Kh : wx ratio of 100 km, suggesting that the 223Ra and 224Ra activities in surface seawater were mainly controlled by decay and dilution rather than advection (Moore, 2000; Sippo et al., 2019).
Due to the different hydrological environments, eddy diffusivity Kh varies considerably in different places, and covers a wide range (Su, 2013). In previous study, Men et al. (2006) estimated horizontal eddy diffusion coefficient at 2.9 × 107 cm2/s based on 228Ra within 200 km offshore in the western Yellow Sea, north of the study area. Then Su et al. (2013b) combined 223Ra and 226Ra, and obtained offshore Kh to be 3.7 × 107 cm2/s within 500 km offshore in the Yellow Sea. Compared to these results, the estimated Kh (2.3 × 108 cm2/s) was almost an order of magnitude higher. In the Subei Shoal, the surrounding irrigation canals and rivers deliver freshwater into the study area, although the discharge flux is relatively small (Ma et al., 2010) and not even reflected in the distribution of salinity (Fig. 2a), it may contribute to offshore transport of water mixing and elevate eddy diffusivity (Zhu and Wu, 2018). Additionally, a high tidal range of average at 4 m occurs in the Subei Shoal, which could also influence eddy diffusivity (Ding et al., 2014; Sippo et al., 2019). Here, this study summarized the eddy diffusivities estimated by 223Ra and 224Ra from different tidal ranges and scales (distance offshore), the results showed that eddy diffusion was higher when the study regions in higher tidal ranges and greater scales (Fig. 6), and similar relationship between eddy diffusion and distance offshore were also examined in previous studies (Dulaiova et al., 2009; Gómez-Álvarez et al., 2019; Su, 2013). Therefore, the eddy diffusivity Kh estimated in this study was reasonable and could be applied to calculate the nutrient fluxes in the following discussion.
4.2 Nutrient outwelling fluxes
In general, SGD and eddy diffusion are not only simple water movements, more importantly, they also carry a large amount of dissolved chemicals transporting (Sippo et al., 2019; Su et al., 2013a; Zhang et al., 2020a). The widely used method for determining nutrient flux transported from SGD is to multiple the SGD flux by the difference of nutrient concentrations between coastal groundwater and nearshore seawater (Liu et al., 2019; Wang et al., 2015). This study chose the average nutrient concentrations of seawater within 60 km from the shoreline as the nearshore seawater endmember, resulting in nutrient concentrations in SGD endmember were 31 μmol/L, 0.61 μmol/L and 104 μmol/L for DIN, DIP and DSi, respectively. Therefore, the nutrient fluxes transported from SGD were obtained to be 8.1×107 mol/d for DIN, 1.6×106 mol/d for DIP and 2.2×108 mol/d for DSi, which were comparable with the results reported in Zhao et al. (2018) of the Subei Shoal. Given that such fluxes of nutrient outwelling from the Subei Shoal could be distributed evenly over the Yellow Sea, which covers an area of 3.09 × 1011 m2, the normalized nutrient fluxes via SGD would be 262 μmol/(m2·d), 5.2 μmol/(m2·d) and 711 μmol/(m2·d) for DIN, DIP and DSi, respectively (Fig. 7).
The nutrient outwelling derived by eddy diffusion was calculated by multiplying the linear gradients of DIN, DIP and DSi by the eddy diffusivity mixing coefficient, which has been widely applied in previous studies (Moore, 2000; Santos et al., 2008; Sippo et al., 2019). From Fig. 5, significant offshore nutrient gradients were observed in the Subei Shoal, and those were 0.248 3 μmol/(L·km) for DIN, 0.003 6 μmol/(L·km) for DIP and 0.154 8 μmol/(L·km) for DSi. Here, this study used the average mixing coefficient, calculated from all data and combined five individual transects, of 2.3×108 cm2/s. Besides, considering the coastline covered of 183 km and the mean well-mixed layer depth of 5 m in the study area of the Subei Shoal (Cheng et al., 2017; Zhou et al., 2008), DIN, DIP and DSi outwelling into the Yellow Sea were estimated to be 1 465 μmol/(m2·d), 21 μmol/(m2·d) and 913 μmol/(m2·d), respectively (Fig. 7).
As a result, combined these two pathways of SGD and eddy diffusion, the nutrient outwelling fluxes from the Subei Shoal to the Yellow Sea were determined to be 262−1 465 μmol/(m2·d), 5.2−21.0 μmol/(m2·d) and 711−913 μmol/(m2·d) for DIN, DIP and DSi, respectively (Fig. 7). In previous studies, the traditional external sources for nutrient in the Yellow Sea were known from surrounding rivers and atmospheric deposition (Liu et al., 2003), in which rivers could provide 212 μmol/(m2·d) DIN, 3.3 μmol/(m2·d) DIP and 142 μmol/(m2·d) DSi (Liu et al., 2009b), and atmospheric deposition supplied 99 μmol/(m2·d) DIN, 1.6 μmol/(m2·d) DIP and 3.8 μmol/(m2·d) DSi (Zhang et al., 2007b). This comparison suggested that nutrient outwelling fluxes from the Subei Shoal were approximately 1−7 times higher than the riverine inputs and atmospheric deposition. In addition, the discharge of mariculture was also recognized as a considerable external source of nutrient to the Yellow Sea, which was reported to be 5.8 μmol/(m2·d) and 0.10 μmol/(m2·d) for DIN and DSi, respectively (Li et al., 2015). Therefore, this study could conclude that the nutrient outwelling from the Subei Shoal was the dominant nutrient source (Fig. 8), and have the potential to affect the biogeochemical cycles in the Yellow Sea. It should be noted that the obtained nutrient outwelling fluxes to the Yellow Sea were overestimated, because this study did the calculations without considering the influence of biological uptake and scavenge on nutrient cycles in the seawater (Charette et al., 2007; Su et al., 2013b). Nevertheless, the results could also shed light on potential environmental impacts of nutrient outwelling via SGD and eddy diffusion from coastal waters.
4.3 Environmental implications
With the rapid development of mariculture and significant terrestrial materials input, the Subei Shoal was recognized as the hot spot for nutrient enrichment, and the high concentrations of nutrient often occur in the nearshore water of the Subei Shoal (Li et al., 2015; Shi et al., 2015). In such a case, the high nutrient supply, especially DIN and DIP, supported the Subei Shoal became the originating area of green tides bloom (Li et al., 2017; Liu et al., 2013). Meanwhile, along with the water movement, nutrient in the Subei Shoal may transport to the Yellow Sea basin and then influence its ecological environment. It was reported that the assimilated nutrient fluxes for supporting phytoplankton growth in the Yellow Sea were 6 350 μmol/(m2·d) for N and 400 μmol/(m2·d) for P on average (Fig. 8) (Jin et al., 2013). Assuming that nutrient outwelling fluxes from the Subei Shoal were bioavailable and assimilated by phytoplankton, the nutrient fluxes could contribute to 4.1%−23% N and 1.3%−5.3% P for the primary productivity in the Yellow Sea, revealing that the nutrient outwelling played an important role in maintaining phytoplankton primary production in the Yellow Sea. Meanwhile, it is clearly observed that the recognized DIN and DIP fluxes could not meet the nutrient demand for phytoplankton growth in the Yellow Sea, suggesting that there should be other nutrient sources to balance the requirements, such as benthic fluxes and regeneration and recycling of nutrient (Liu et al., 2003; Ziegler and Benner, 1999). For instance, Ulva prolifera, known as green tides, could also assimilate dissolved organic nitrogen (DON) and phosphorus (DOP) directly (Shi et al., 2015), and that SGD and eddy diffusion transport DON and DOP from the Subei Shoal are known now, further confirming the significance of the nutrient outwelling.
More importantly, this study found that nutrient outwelling from the Subei Shoal not only could contribute to nutrient budgets in the Yellow Sea, but also may alter the nutrient structures. In the seawater of the Yellow Sea, the stoichiometric ratios of DIN/DIP and DSi/DIP were observed to be 17−40 and 27−47, respectively (Jin et al., 2013; Shi et al., 2015), which deviated from the Redfield ratio of 16, indicating a P limitation condition in the Yellow Sea. This study noticed that DIN/DIP ratios in nutrient outwelling were 50−69 and much higher than the Redfield ratio by a factor of 3−4. Therefore, large nutrient fluxes with deviated DIN/DIP ratio outwelling from the Subei Shoal were bound to aggravate the P limitation and probably affect the eco-environment of the Yellow Sea.
5 Conclusions
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Because of the application limitations of 226Ra and 228Ra in the coastal waters, 223Ra and 224Ra are more suitable for calculating the water mixing processes in the Subei Shoal. This study demonstrates the applications of 223Ra and 224Ra in obtaining the offshore eddy diffusivity mixing coefficient in the Subei Shoal, as well as SGD. The results reveal that the nutrient outwelling fluxes from the Subei Shoal not only contribute to the nutrient budgets and phytoplankton primary production, but also affect the nutrient structures in the Yellow Sea. These large amounts of nutrient outwelling fluxes from the Subei Shoal may contribute to the development of the green tides bloom in the Yellow Sea, however, more robust and in-depth research in the future are warranted.
Acknowledgements
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We thank Captain Lin Wei and the crew on R/V SURUYUYUN-288 for their assistance in the sample collection. We thank Wanli Xing and Qian Ma from SKLEC/ECNU for their help in field observations.
Funding
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  • The National Science and Technology Major Project of the Ministry of Science and Technology of China under contract No. 2016YFC1402106; the National Natural Science Foundation of China under contract Nos 41376089, 41576083, 41976040, 41876127 and 42030402; the China Postdoctoral Science Foundation under contract No. 2020M671048.
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doi: 10.1007/s13131-021-1930-z
  • Receive Date：2021-01-26
  • Online Date：2025-11-21
  • Published：2022-06-25
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  • Received：2021-01-26
  • Accepted：2021-04-19
Funding
The National Science and Technology Major Project of the Ministry of Science and Technology of China under contract No. 2016YFC1402106; the National Natural Science Foundation of China under contract Nos 41376089, 41576083, 41976040, 41876127 and 42030402; the China Postdoctoral Science Foundation under contract No. 2020M671048.
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    1 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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