Chinese Journal of Oceanology and Limnology   2017, Vol. 35 issue(2): 367-382     PDF       
http://dx.doi.org/
Institute of Oceanology, Chinese Academy of Sciences
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Article Information

WANG Wentao(王文涛), YU Zhiming(俞志明), SONG Xiuxian(宋秀贤), WU Zaixing(吴在兴), YUAN Yongquan(袁涌铨), ZHOU Peng(周鹏), CAO Xihua(曹西华)
Characteristics of the δ15NNO3 distribution and its drivers in the Changjiang River estuary and adjacent waters
Chinese Journal of Oceanology and Limnology, 35(2): 367-382
http://dx.doi.org/

Article History

Received Oct. 6, 2015
accepted in principle Dec. 2, 2015
accepted for publication Feb. 2, 2016
Characteristics of the δ15NNO3 distribution and its drivers in the Changjiang River estuary and adjacent waters
WANG Wentao(王文涛)1,2,3, YU Zhiming(俞志明)1,2, SONG Xiuxian(宋秀贤)1,2, WU Zaixing(吴在兴)1,2, YUAN Yongquan(袁涌铨)1,2, ZHOU Peng(周鹏)1,2,3, CAO Xihua(曹西华)1,2        
1 Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;
2 Laboratory of Marine Ecology and Environmental Sciences, Qingdao National laboratory for Marine Science and Technology, Qingdao 266071, China;
3 University of Chinese Academy of Sciences, Beijing 100049, China
ABSTRACT: In this study, we conducted investigations in the Changjiang (Yangtze) River estuary and adjacent waters (CREAW) in June and November of 2014. We collected water samples from different depths to analyze the nitrogen isotopic compositions of nitrate, nutrient concentrations (including inorganic N, P, and Si), and other physical and biological parameters, along with the vertical distribution and seasonal variations of these parameters. The compositions of nitrogen isotope in nitrate were measured with the denitrifier method. Results show that the Changjiang River diluted water (CDW) was the main factor affecting the shallow waters (above 10 m) of the CREAW, and CDW tended to influence the northern areas in June and the southern areas in November. δ15NNO3 values in CDW ranged from 3.21‰-3.55‰. In contrast, the deep waters (below 30 m) were affected by the subsurface water of the Kuroshio Current, which intruded into the waters near 31°N in June. The δ15NNO3 values of these waters were 6.03‰-7.6‰, slightly higher than the values of the Kuroshio Current. Nitrate assimilation by phytoplankton in the shallow waters of the study area varied seasonally. Because of the favorable temperature and nutrient conditions in June, abundant phytoplankton growth resulted in harmful algae blooms (HABs). Therefore, nitrate assimilation was strong in June and weak in November. The δ15NNO3 fractionations caused by assimilation of phytoplankton were 4.57‰ and 4.41‰ in the shallow waters in June and November, respectively. These results are consistent with previous laboratory cultures and in situ investigations. Nitrification processes were observed in some deep waters of the study area, and they were more apparent in November than in June. The fractionation values of nitrification ranged from 24‰-25‰, which agrees with results for Nitrosospira tenuis reported by previous studies.
Key words: Changjiang River estuary     nitrate     nitrogen isotope     assimilation     nitrification    
1 INTRODUCTION

The distribution and variation of nitrogen directly affects marine productivity and sources as well as reflecting environmental pollution and ecological change (Edmond et al., 1985; Bao et al., 2006). However, the concentration of nitrogen is constantly increasing in most coastal areas due to the discharge of nutrients transported by rivers; these high concentrations can cause ecological disasters such as eutrophication, hypoxia, and harmful algae blooms (HABs) (Morrison et al., 1999; Alexander et al., 2000; Hagy et al., 2004; Middelburg and Levin, 2009).

The Changjiang River is the longest river emptying into the western Pacific Ocean, with an annual discharge of 9.21×1011 m3. The amount of nutrients containing nitrogen transported by the Changjiang River exceeds 7.5×1010 mol/a, which results in an imbalance in the nitrogen to phosphorus ratio in the coastal waters and has caused ecological disasters in the Changjiang River estuary and adjacent waters (CREAW) (Zhu et al., 2005; Zhou et al., 2008; Yu and Shen, 2011; Zhu et al., 2011). Therefore, it is important to study how the biogeochemical cycling of nitrogen affects the occurrence and decay of marine ecological disasters. However, considering the spatial complexity of the marine environment and the high sensitivity of the nitrogen cycle, it is difficult to identify accurately the sources and biogeochemical processes of nitrate in seawater by using traditional techniques to measure the concentrations of various nitrogen compounds.

Under natural conditions, nitrogen has two stable isotopes, 14N, and 15N. As a result, the isotopic composition of nitrate (δ15NNO3) varies depending on its source and is affected also by biogeochemical processes. Thus, the isotopic composition of nitrate can be used to analyze its sources and reactions in the marine environment (Sigman et al., 2009). A study by Liu and Kaplan (1989) found that the δ15NNO3 value in the seawater of southern California is similar to the value in the eastern part of the North Pacific; they concluded that the California waters could be affected by horizontal transportation of seawater from the North Pacific. According to the investigations in the estuaries of Tyne and Tweed, Ahad et al. (2006) measured δ15NNO3 values and nitrogen nutrients concentrations, noticing that high NO3 flux were associated with light δ15NNO3 values in winter. This result indicated minimal biological reaction of diffuse terrestrial sources under colder, wetter conditions. Another study found that the San Joaquin River was a source of nitrate to the San Francisco estuary by the nitrate isotopes along with stream flow variables. However, ammonium from the Sacramento River was possibly the sole source for supporting the toxic cyanobacteria bloom in this area, instead of nitrate (Lehman et al., 2015). Sugimoto et al. (2010) established a nitrogen cycle model for Ise Bay based on an investigation of stable isotopes in nitrogen compounds including δ15NNO3. The results of this model showed that regeneration of nitrate caused by significant nitrification was the dominant source of nitrogen in this bay rather than an external source. These studies demonstrate that techniques involving nitrogen stable isotopes are effective and can be used to investigate nitrate sources and cycling in the marine environment.

Recent studies have used the stable isotopic composition of nitrogen compounds to examine the sources of nitrate and its conversion processes in CREAW. For example, Liu et al. (2009) analyzed the distribution of δ15NNO3 values in this region, finding that the drivers of δ15NNO3, including physical, chemical, and biological processes, were distinct in various areas of the CREAW. Hsiao et al. (2014) calculated the nitrification rate, which may be one of the most significant causes of hypoxia in the bottom of the CREAW. They found that the nitrification rate could reach 4.6 μmol/L/day along the turbid Changjiang River plume, and it was controlled by biological and chemical processes. According to a freshwater-saltwater mixing model of nitrate and δ15NNO3 in the CREAW, internal regeneration was a considerable source of nitrate (Yu et al., 2015).

Above all, these researches have achieved impressive results in understanding the anthropogenic nitrate sources and biogeochemical progress in CREAW via δ15NNO3 analysis. However, there is also an ocean nitrate source carried by the Kuroshio Branch, which could spread into the deep waters of this region (Yang et al., 2012), but few studies discussed its affection on the δ15NNO3 distributions in this area. It would be significant helpful to hold integrated investigations on the affections of these two main sources. Therefore, based on former researches, we enlarged the observed area and analyzed spatial and temporal distributions of δ15NNO3 in spring and autumn, aiming to understand the influence and significance of crucial physical and biological processes that will regulate the ecological environment in the CREAW.

2 MATERIAL AND METHOD 2.1 Sample collection

Sample collection was carried out along three transects in the CREAW during cruises aboard the R/VsKexue No. 1 and Kexue No. 3 from June 8 to 11 and November 5 to 9, 2014. The first transect (CJ) extended from the runoff of the Changjiang River diluted water (CDW) into the Changjiang River estuary (CRE) to Cheju Island; the second transect (3100) extended along the latitude of 31°N from 122°E to 126°E; and the third transect (3000) extended along the latitude of 30°N (Fig. 1).

Figure 1 Sampling sites and the surface salinity distributions in the Changjiang River estuary and adjacent waters in June and November of 2014

Hydrographic parameters including temperature, salinity and density (sigma-t), were measured at each sampling site with a conductivity-temperature-depth (CTD) recorder. At each site, water samples were collected in 12 Niskin bottles at various depths throughout the water column (0, 10, 20, 30, 50, and 2 m above the bottom). After filtering 500 mL from each water sample through a GF/F filter membrane, the filtrate was poured into 50 mL sterile centrifuge tubes and 60 mL washed polyethylene bottles. Samples in the tubes were flash frozen with liquid nitrogen and then stored at -20℃ until nitrogen isotopic analysis. Approximately 0.1 mL of chloroform was added to the samples in the bottles, after which they were frozen at -20℃ until analyzed for nutrient concentrations. The filters were frozen in the dark until analysis for chlorophyll-a (chl-a). Water for dissolved oxygen (DO) analysis was adjusted on site according to the methods of Strickland and Parsons (1968).

2.2 Measurement of δ15N values in nitrate

The δ15NNO3 values of the samples were measured with the denitrifier method (Sigman et al., 2001; Coplen et al., 2012). In this method, P. aureofaciens, a denitrifier that lacks the enzyme to reduce N2O to N2, is used to convert NO3- and NO2- to N2O, which is subsequently purified by cryogenic trapping (Finnigan Precon; Thermo Fisher, USA) and separated chromatographically (GC Isolink). Next, the nitrogen isotopic composition of the N2O was analyzed using an isotope ratio mass spectrometer (Delta V Advantage). The δ15N values were expressed relative to atmospheric nitrogen according to the following equation:

δ15N (‰)=(Rsample-Rstandard)/Rstandard×1000,

where R=15N/14N. The instrument was calibrated using the USGS-34 and IAEA N3 isotopic standards. The analytical precision and reproducibility for δ15N values were generally better than±0.2‰ and±0.26‰, respectively. Because both nitrate and nitrite were reduced to N2O, the nitrate (NO3-) mentioned in this paper refers to NO3-+NO2-.

2.3 Nutrients, DO and chl-a analysis

NO3- was measured with the cadmium-copper reduction method (Barnes, 1959), NH4+ was measured with the indophenol blue method (Grasshoff, 1976), and PO43- was measured with phosphomolybdenum blue method (Strickland and Parsons, 1968). The concentrations of nutrients were measured using an SKALAR Flow Analyzer (Skalar Ltd., Netherland), and the detection limit was 0.14 μmol/L for NO3- and NH4+, and was 0.07 μmol/L for PO43-. The data quality was monitored by intercalibration (with a reproducibility better than 3%). DO concentrations were measured using the Winkler titration method (with a precision of 7×10-5 mg/L) (Strickland and Parsons, 1968). Chl-a was extracted using acetone and analyzed by fluorometric methods (with a detection limit of 0.01 mg/m) (Parsons et al., 1984).

The vertical distribution of each parameter was plotted in Surfer 12 (Golden Software, USA). The correlations between variables were plotted in Original 8 (Origin Lab Corporation, USA) and analyzed using Pearson correlations in the SPSS v19 software package (IBM Company, USA). The statistical significance was evaluated at P≤0.05.

3 RESULT 3.1 Hydrographic characteristics

In the shallow waters (above 10 m), stratified characteristics were observed in both the CJ and 3100 transects during June. Lower density and lower salinity water columns were observed at the nearshore stations, with minimum salinity and density values of 11.06 and 6.02, respectively. Salinity and density increased rapidly away from the coast, where salinity and density exceeded 30 and 20, respectively, in the shallow waters of the offshore stations of the CJ and 3100 transects (Fig. 2). The stratified characteristics were apparent in the temperature distribution above 10 m. Compared with the two northern transects, stratification was found throughout the 3000 transect, where both salinity and temperature were high in the shallow waters. The hydrographic characteristics of the three transects differed between November and June. During November, the shallow waters exhibited stratified hydrographic parameters at the near-shore stations, and the offshore stations showed vertical mixing.

Figure 2 Vertical distributions of temperature (℃), salinity and sigma-t in the Changjiang River estuary and adjacent waters in June and November of 2014

In the deep waters (below 30 m), vertical mixing of salinity and temperature were observed along the CJ and 3100 transects in both June and November, and the range of numerical variation was smaller relative to the shallow waters. The 3000 transect had stratified characteristics. During June in stations 3100-3 and 3000-3, the salinity was greater than 34, temperature < 18.4℃, and density below 30 m depth between 24.4-24.5 (Fig. 2). However, this high-salinity-anddensity water column was not observed in November.

3.2 Vertical distributions of δ15NNO3 values and nutrients

In June, δ15NNO3 values in the CJ, 3100, and 3000 transects were vertically stratified; there were low δ15NNO3 values near-shore and high levels off-shore in the shallow waters (Fig. 3). For instance, the δ15NNO3 value was 3.21‰ in the surface waters of the CJ-1 station, but this value increased to 21.39‰ in the surface waters of the CJ-5 station. The δ15NNO3 values at the surface and 5 m depth of station 3100-1 were 3.55‰ and 2.99‰, respectively, while the maximum δ15NNO3 value was 31.15‰ in the offshore surface waters of station 3100-8 (Fig. 3). Similarly, the maximum δ15NNO3 value in the 3000 transect was in the surface waters of station 3000-5 (30.73‰). In November the δ15NNO3 values at the surface and a 5 m depth in station CJ-1 were 6.92‰ and 7.02‰, respectively; these values were higher than in June. However, the November δ15NNO3 values at the surface and a 10 m depth of the offshore station CJ-6 were 8.94‰ and 9.06‰, respectively; these values were lower than in June (Fig. 3). The near-shore stations of the 3100 transect had stratified δ15NNO3 values. In this transect, the maximum δ15NNO3 value was 11.43‰ in November, which occurred at 10 m depth in station 3100-3. Vertical mixing was apparent offshore, and a minimum δ15NNO3 value of 2.52‰ occurred in the surface waters of station 3100-8 in November. The distribution of δ15NNO3 values along the 3000 transect in November was similar to the distribution in June; in both months, the waters were stratified with high values at shallow depths.

Figure 3 Vertical distributions of δ15NNO3 values (‰) in the Changjiang River estuary and adjacent waters in June and November of 2014

The δ15NNO3 values were stable in the deep waters of the CJ transect in June, with most values lower than 5‰ (Fig. 3). The minimum value was 0.14‰ (CJ-2, 20 m), and the maximum value was 5.45‰ (CJ-4, bottom). Most δ15NNO3 values were less than 10‰ in the deep waters, with the exception of a high value at a depth of 30 m at station 3100-8, where the δ15NNO3 value was 27.39‰. In contrast, the values of δ15NNO3 in the CJ and 3100 transects in November ranged from 2.25‰-11.43‰, which was narrower than in June (0.14‰-31.15‰). In the high salinity water column, δ15NNO3 values ranged from 6.07‰-7.65‰ in June and 7.84‰ in November.

In June, the concentrations of nutrients reached maximum values in the surface waters of near-shore stations in the CJ and 3100 transects. The concentrations of NO3- and PO43- decreased rapidly away from the coast. The 3000 transect had a stratified distribution of nutrients with high concentrations in the surface water (Fig. 4). Similarly to June, high nutrient levels were observed near-shore and low levels offshore during November in both the CJ and 3100 transects, and stratification was observed in the 3000 transect. However, the concentrations of nutrients in November were lower than in June, especially in near-shore area of the CJ transect.

Figure 4 Vertical distributions of nitrate (NO3-), phosphate (PO43-) and ammonium (NH4+) (μmol/L) in the Changjiang River estuary and adjacent waters in June and November of 2014

Compared with the deep waters of other stations, there were high concentrations of NO3-and PO43-below a depth of 30 m at station 3100-3 during June; these were 11.97 μmol/L and 0.66 μmol/L, respectively, while the NH4+ concentrations were low (Fig. 4). In November, high concentrations of NO3- and PO43- appeared in the deep waters of station 3100-3, and NH4+ concentrations were higher than in June. The NH4+ and PO43- concentrations were high below a depth of 50 m, but the NO3-concentrations were low.

3.3 Chl-a and DO characteristics

Both chl-a and DO were high in shallow waters and low in deep waters (Fig. 5). The concentration of chl-a achieved a maximum value at a depth of 10 m at station CJ-3 (8.35 μg/L). In the 3100 transect, high concentrations of chl-a occurred at the surface of station 3100-1 (11.02 μg/L) and at a depth of 17 m at station 3100-5 (5.12 μg/L). The 3000 transect was stratified with respect to chl-a, with high values in the shallow waters of the near-shore stations (Fig. 5). The concentration of chl-a was lower in November than in June, and the maximum value occurred at a depth of 20 m at station 3100-3 (4.47 μg/L).

Figure 5 Vertical distributions of chl-a (μg/L) and DO (mg/L) in the Changjiang River estuary and adjacent waters in June and November of 2014

A low concentration of DO was observed in the deep waters of station 3100-3 in June (3.16-4.31 mg/L), which was the same in November when DO concentration ranged 2.85-3.08 mg/L. Another hypoxic area in this season was located below 50 m deep at station 3000-3, where DO concentration ranged 1.61-2.60 mg/L (Fig. 5).

4 DISCUSSION 4.1 Water column effects on the distribution of δ15NNO3 values

Our results show that the areas with low salinity and high nutrient concentrations are located in the near-shore shallow waters of the CJ and 3100 transects during June; during November, these areas shift to south, towards the 3100 and 3000 transects. In the coastal stations with low salinity that are affected by the CDW, δ15NNO3 values range 3.21‰-3.55‰. Previous research has shown that the δ15NNO3 values of freshwater in Changjiang River range 7.3‰-12.9‰ (Li et al., 2010). Because imported wastewater and chemical fertilizers have a high δ15N composition (Heaton, 1986; Kendall, 1998; Widory et al., 2005), δ15NNO3 valuesare higher inland. Previous studies have also reported that δ15NNO3 values range from 2.1‰ to 6.5‰ in the area of the CRE where salinity is below 32 (Liu et al., 2009) and from 0.5‰ to 22.3‰ in the CREAW (Chen et al., 2013). The δ15NNO3 composition of the CDW that we report in this paper is consistent with the CRE values.

Previous studies confirmed that the CDW moves northeast from the CRE towards Cheju Island during the spring; during the autumn, it turns to south under the influence of monsoon (Beardsley et al., 1985; Wang et al., 2003). We found significant negative correlations between NO3- and salinity in the shallow waters in June and November (Fig. 6a and d). Additionally, plots of δ15NNO3 versus salinity show conservative mixing trends in shallow waters in both June and November, which indicates that δ15NNO3 values increase with the degree of freshwatersaltwater mixing enhancing. Middelburg and Nieuwenhuize (2001) proposed an equation for calculating NO3- concentrations from conservative mixing based on a study of several estuaries in Europe. This equation has been applied to study the cycling of NO3- and δ15NNO3 in freshwater-saltwater mixing in the CRE (Liu et al., 2009; Yu et al., 2015). We do not use the equation directly, because most of our study area is far from the estuary and the turbidity maximum zone. However, we note that nitrate is negatively correlated with salinity, and plots of δ15NNO3 versus salinity demonstrate conservative mixing, as shown in previous surveys. Because of freshwater-saltwater mixing, NO3- decreases while δ15NNO3 values increase further from the coast. Therefore, CDW could be considered as the main source of terrigenous nitrate in the shallow waters (above 10 m) of the CREAW.

Figure 6 Correlations between salinity, chl-a (μg/L), nitrate (μmol/L) and δ15NNO3 values (‰) in the shallow waters (above 10 m) of the CREAW in June (a, b, c) and November (d, e, f) of 2014

The correlations between salinity and NO3- in the deep waters (below 30 m) are not as obvious as in the shallow waters because CDW does not affect the deep waters in this area. In June, the deep waters of stations 3100-3 and 3000-3 exhibit a water column with salinity and density exceeding 34 and 24.2 (Fig. 2). High concentrations of nitrate and phosphate are observed in these water columns, and δ15NNO3 values range from 6.03‰ to 7.6‰ (Figs. 3 and 4). We speculate that this water column may originate from a branch of the Kuroshio Current.

This speculation is based on several lines of evidence. First, the shallow waters are affected by CDW and display stratified characteristics, which prevents vertical mixing in the deep waters of stations 3100-3 and 3000-3. In addition, the physical characteristics of this water column, which are similar to those in open ocean waters, differ from CDW and the coastal currents. Secondly, it has already been demonstrated that there is an intrusion of Kuroshio subsurface water in the bottom of the ECS, and the Near-shore Kuroshio Branch Current (NKBC) can reach 30.5°N (Yang et al., 2011, 2012). The intrusion of the Kuroshio Current is considered an important source of phosphate, leading to high PO43- concentrations (Chen, 1996; Chen and Wang, 1999; Fang, 2004; Liu et al., 2000). According to investigations that occurred during the same period, the density and salinity in the deep waters of stations 3100-3 and 3000-3 are slightly lower than in the NKBC and in the Kuroshio subsurface water. However, the PO43- concentrations are similar to the values in the NKBC and the Kuroshio subsurface water (Wang, unpublished data). Furthermore, δ15NNO3 values in this water column range 6.07‰-8.62‰, which is slightly higher than that of the Kuroshio subsurface water (δ15NNO3=5.5‰-6.1‰, Liu et al., 1996). The higher δ15NNO3 values in this water column may result from biogeochemical processes during transport of the NKBC. All above factors suggest that the deep waters below 30 m of stations 3100-3 and 3000-3 were influenced by the NKBC during June.

In parts of the deep waters in the CREAW during November, there is a positive correlation between NO3- and salinity and a negative correlation between δ15NNO3 values and salinity (Fig. 7d, f). The water column in the 3000 transect has salinity greater than 34 as well as high PO43- concentrations and δ15NNO3 values similar to June. However, the density of this water column is lower than 24. Therefore, we infer that the intrusion of Kuroshio subsurface water was not strong enough to reach the CRE in November during our investigation.

Figure 7 Correlations between salinity, DO (mg/L), nitrate (μmol/L) and δ15NNO3 values (‰) in the deep waters (below 30 m) of the CREAW in June (a, b, c) and November (d, e, f) of 2014 The dashed area in Fig.7b stands for the stations which probably had the correlations between nitrate and DO.
4.2 Autotrophic assimilation of nitrate

Our results show that there is a remarkable positive correlation between NO3- and chl-a (P < 0.01) in the shallow waters during June, with an R2 of 0.326 6 (Fig. 6b). However, there is no significant correlation between NO3-and chl-a in November. In addition, the NO3-concentration decreased while the δ15NNO3 values increased away from the coast in the shallow waters of the CREAW both in June and November during our investigations. The correlation between NO3- and δ15NNO3 is exponential (Fig. 8). It suggests that assimilation by phytoplankton might be another significant control on nitrate, in addition to the effect of currents.

Figure 8 Relationships between δ15NNO3 values and ln[NO3] in the shallow waters of the CREAW in June (a) and November (b) of 2014

High concentrations of NO3-imported into the CRE by CDW lead to an imbalance in the N/P ratio, so that extra NO3- is available in coastal water, easily causing dinoflagellate blooms (Shi et al., 2003; Zhou et al., 2003; Chai et al., 2006; Li et al., 2009). Furthermore, a Prorocentrum donghaiense bloom is reported in the CRE in June during our investigations (China Oceanic Information Network, 2014). This can explain the remarkable correlation between NO3- and chl-a. However, there is no evidence that HABs appeared in the CREAW in November. Besides, the fact that NH4+ concentrations are higher in November than in June indicates that the process of NO3- assimilation by phytoplankton is limited. Hence, the correlation between NO3- and chl-a is not obvious in November.

Phytoplankton assimilation of NO3- preferentially utilizes the lighter nitrogen isotope (14N), causing enrichment of the heavier nitrogen isotope (15N) in seawater. This isotopic fractionation is a distinguishing factor of this biogeochemical process. The isotopic fractionation (ε) can be calculated via a Rayleigh model, and the results can be used to analyze the process and the biological factors that lead to the consumption of nitrate (Pennock et al., 1996). In a closed system with the progress of phytoplankton assimilating nitrate and accumulating the product over time, the Rayleigh equations describe the evolution of isotope ratio in the nitrate substrate. Thus, the fractionation for assimilation (εass) of nitrate can be calculated as follows:

δ15NNO3=initialδ15NNO3-εass×lnf,

in which f is the fraction of nitrate in the seawater. The ln[NO3] is an approximation for lnf (Altabet, 2006), so the slope of the plots is the isotopic fractionation.

In this study, the εass is estimated 4.57‰ and 4.41‰ in June and November, respectively. Although these two results are approximate, we have noted that nitrate assimilation by phytoplankton is not evident in November. Furthermore, the Rayleigh model shows that the plots are mostly dispersive in June and concentrated in November (Fig. 8). Therefore, we suggest that the εass value for November is representative of assimilation at some stations rather than the entire area.

The isotopic fractionation for nitrate assimilation that we report is consistent with the data from laboratory cultures and is similar to the results of previous studies in the ocean. Nonetheless, the εass values reported in this paper are higher than observations in a similar region (Table 1). Considering that previous studies were conducted in areas that are closer to the turbidity maximum zone, where high concentrations of nutrients are supplied by CDW and the rate of nutrient release is higher than in the adjacent open sea, we propose that mixing effects tend to mute the assimilation effect. Thus, isotopic fractionation is lower than in the CREAW. The lower fractionations for nitrate assimilation in the ECS were observed (Table 1, Umezawa et al., 2014), which may be attributed to inhibition by accelerated release of ammonium during the summer (Kanda et al., 2003).

Table 1 Estimates of nitrate isotopic fractionation for assimilation (εass)
4.3 Characteristics of the nitrification process

Because of the importation of CDW, stratified characteristics of vertical distributions are observed around the CREAW, which isolates the bottom of the water column. It is logical that the internal biogeochemical processes throughout the column would follow this distribution. We observed the negative correlations between NO3- and DO in the deep waters (below 30 m) of some stations in June. These stations were located in the nearshore regions and affected by the CDW in the shallow waters (Fig. 7b, the dashed area). The correlation is more obvious in November than in June, when the R2 exceeds 0.4 (Fig. 7e). Furthermore, the areas with low DO concentrations ( < 4 mg/L) during June and the hypoxia area in November are also observed significant correlations between NO3- and DO. It is possible that microbial nitrification occurs in deep waters, consuming DO and resulting in an oxygen deficiency. Yu et al. (2015) also inferred that intensive nitrification existed in this area based on their freshwater-saltwater mixing model.

Nitrification reactions can be divided into two steps. First, NH4+ is oxidized to NO2- by nitrifying bacteria. Second, NO2- is oxidized to NO3- in a reaction that is catalyzed by nitrite oxidase. The first step is similar to assimilation by phytoplankton because 14N is preferentially used when nitrifying bacteria oxidize NH4+ to NO2-. The resulting nitrate is enriched with respect to 14N, which lowers the δ15N value. Thus, the fractionation for nitrification (εnit) can be calculated using a Rayleigh model to determine the drivers of this process. Based on the Rayleigh model, the isotopic fractionation for nitrification (εnit) of ammonium can be calculated as follows:

δ15NNO315NNH4+εnit×fnit/(1-fnit)×lnfnit,

where the fnit is the fractionation of remaining substrate (Altabet, 2006). In the nitrification reaction, fnit can be calculated as:

fnit=[NH4+]/[DIN],

in which DIN stands for the total dissolved nitrogen. According to the correlations between NO3- and DO, we calculate the εnit in the related stations, which presented significant correlations in June and November. Our results show that in June and November, the εnit values are 25‰ and 24‰, respectively (Fig. 9). This result is consistent with the study conducted in Ise Bay (Sugimoto et al., 2008).

Figure 9 Relationships between δ15NNO3 values and the fractionation from nitrification in the CREAW deep waters in June (a) and November (b) of 2014

In addition to physical and chemical factors such as salinity, temperature, and NH4+concentration (Ward, 1996), diverse microorganisms affect this process, especially ammonia-oxidizing archaea (AOA) and bacteria (AOB), which control the first step of the nitrification process. Based on laboratory cultures of AOB, Casciotti et al. (2003) showed that the isotopic fractionation of Nitrosospira tenuis (a β-Proteobacterium) is (24.6±1.4)‰. Our results show a similar fractionation.

The ammonia monooxygenase (amoA) gene can be used to analyze the existence and abundance of nitrifying bacteria in situ investigations. For instance, the correlation between amoA and rate of ammonia oxidation was found to be significantly positive in the Gulf Bay (R2=0.9, P < 0.001, Beman et al., 2008). In the CREAW, a large amount of amoA from β-Proteobacteria was observed attached to sediments; amoA distribution was correlated with salinity and NO3- concentrations as well as to the area of hypoxia and low NH4+ concentrations (Chen et al., 2014). Moreover, because of the vertical transportations and resuspension of sediments, dramatic nitrification was indicated in this area. Similarly, an investigation by Hsiao et al. (2014) showed that the abundance of β-Proteobacteria was one of the crucial factors that controlled the rate of nitrification in the CREAW.

We cannot firmly conclude that N. tenuis is the dominant nitrifier in the study area because microbial data at the species level is absent. Thus, further research is necessary. However, based on our data and combined with previous researches, it still can be speculated that nitrification in the CREAW is mainly controlled by N. tenuis in both June and November.

5 CONCLUSION

Surveys in the CREAW in June and November of 2014 indicate that CDW is the dominant source of terrestrial material in the shallow waters, and the constant influx of CDW gives rise to vertical stratification. The characteristic values of δ15NNO3 in CDW range 3.21‰-3.55‰. In the deep waters of the 3100 and 3000 transects, the water column has high salinity and high concentrations of NO3- and PO43- during June, which may be derived from the NKBC. The δ15NNO3 values are 6.03‰-7.6‰, which are higher than the values in the Kuroshio subsurface waters. However, a distinct water column is not observed in November. The isotopic fractionation of assimilation is similar in June and November (4.57‰ and 4.41‰). These results are consistent with laboratory cultures and several in situ investigations. However, we conjecture that the increase in δ15NNO3 values is only controlled by phytoplankton assimilation at some stations during November because there is no significant correlation between chl-a and NO3-. Additionally, the correlation between DO and NO3-in the deep waters is more significant in November. We estimate that the isotopic fractionation for nitrifications ranges 24‰-25‰. Our results match the εnit of Nitrosospira tenuis measured in laboratory cultures. Therefore, we speculate that nitrification in the deep waters of the CREAW is regulated by ammonia-oxidizing bacteria of which N. tenuis is probably the dominant species.

6 ACKNOWLEDGEMENT

We thank HE Liyan, LIU Yang and XU Xin for great assistance of the field work and sample analysis. We are grateful to the reviewers for their constructive comments and suggestions.

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