River-ocean continuums are rich environments key to the transformations of organic matter and nutrients from many sources. Human impacts on these ecosystems can be local, upstream, or global. Particulate organic matter provides one tool for exploring these processes; inventories and carbon and nitrogen stable isotopes are useful indicators. This study conducted comprehensive field investigations from the upper reaches of the Yangtze River to the coastal waters to explore the distribution and spatial-temporal variation of particulate organic carbon (POC) and nitrogen (PON), and dissolved organic carbon (DOC) in surface waters from April–May (Spring) and October–November (Autumn) 2014. Carbon and nitrogen isotopes (δ13C and δ15 N) were used to assess organic matter sources. Numerous environmental factors were also measured. PON and POC exhibited similar spatial and temporal variations along the river mainstream. POC increased from the upper to lower reaches, similarly in both seasons; POC declined seaward from the river mouth. In contrast, PON showed greater seasonal and spatial variations along the continuum, with greater primary production in offshore areas. DOC along the continuum was higher in autumn, likely related to greater river discharge and warmer waters. The DOC/POC ratios by weight were >1 in most samples except for the ones collected from the turbidity maximum zone, suggesting that DOC contributed to a larger fraction of the total organic matter inventories. End-member mixing models revealed that the proportion of organic matter from upstream (allochthonous) sources was >70% in the mainstream during both seasons. In coastal waters, the proportion of allochthonous sources was only >57%. Compared with other major world rivers, DOC and allochthonous POC are especially high in the Yangtze River-ocean continuum. Human activities causing variations in terrestrial inputs are likely an important driving factor. In addition, the contribution of soil erosion caused by urbanization to riverine organic carbon cannot be ignored.

The river-ocean continuum, including the transition between freshwater and seawater, represents a prime zone of material transformation and removal from land to the sea (Bauer and Bianchi, 2011; Ward et al., 2017). Due to the large freshwater runoff from the river and the exchange of seawater here, the continuum is recognized as a dynamic interface where terrestrial, estuarine, and marine organic carbon is recycled (Bauer et al., 2013; Maciejewska and Pempkowiak, 2014). Rivers transport an estimated 20 Pg yr–1 of fluvial sediments to the costal margin and around 80% of organic carbon burial in sediments, and they contribute up to approximately 50% of the organic carbon supplied to the deep open ocean (Bianchi and Bauer, 2011; Moreira-Turcq et al., 2013). Thus, exploring organic matter transformation from the river to the ocean is key to understanding the larger global carbon cycle (Wu et al., 2007; Bauer et al., 2013).

Rivers are dynamic systems that are easily affected by climate change and human activities (Bauer et al., 2013; Regnier et al., 2013). During the past decade, the role of rivers in regional and global carbon budgets became increasingly popular with the researchers concerned (Bouillon et al., 2012). Understanding the fate of organic matter in these changing systems increased in value because the transport mode and sources of organic matter impact the regional carbon cycle over long timescales (Zhang et al., 2007; Bianchi and Bauer, 2011; Wu et al., 2013; Zhuang et al., 2018). The increased use of fertilizers and the construction of dams make these river and coastal ecosystems more vulnerable (Bianchi and Allison, 2009). Extreme climate events can alter precipitation and the frequency of floods, which lead to changes in river runoff and sediment load, affecting the flux and composition of materials from the river to the ocean (Gao and Wang, 2008; Dai et al., 2011). Examining the fate of organic matter in these vulnerable ecosystems can help illustrate the impacts of human activities and climate change and identify important new sources or sinks for global carbon (Hedges et al., 1997; Bauer et al., 2013).

Aquatic organic matter is commonly divided into particulate organic matter (POM) and dissolved organic matter (DOM; Maciejewska and Pempkowiak, 2014). Currently, global carbon budgets from riverine inputs to the ocean have been far more than 400 Tg C per year (Kwon et al., 2021). POM is typically measured and reported as particulate organic carbon (POC) and particulate organic nitrogen (PON), and DOM is most often represented by dissolved organic carbon (DOC). Riverine or estuarine POM and DOM can originate from a distance (allochthonous; e.g. soil organic matter, and aquatic emergent and submerged wetland vegetation), or from local (autochthonous; e.g. phytoplankton, benthic and epiphytic micro- and macro-algae), or anthropogenic sources (e.g., changes in land use, agricultural fertilizer, and industrial sewages), with each source exhibiting a spectrum of lability (Duan and Bianchi, 2006; Bauer and Bianchi, 2011; Moreira-Turcq et al., 2013; Canuel and Hardison, 2016). The sources and fate of terrestrial organic matter in rivers are largely affected by the river drainage basins (Wang et al., 2004; Liu et al., 2007; Bauer et al., 2013). Among all carbon transported by rivers, about 500 Tg is terrestrial, consisting of 60% DOC and 40% POC (Hedges et al., 1997; Moreira-Turcq et al., 2013).

Source is an important factor determining the composition, reactivity, fate, and environmental effects of POM in rivers and oceans (Schlünz and Schneider, 2000; Bauer et al., 2013; Medeiros et al., 2017; Rogers et al., 2019; Wang et al., 2020). C/N ratios, δ13C, and δ15 N are widely used to distinguish different sources of organic matter (Wu et al., 2007; Niemi and Michel, 2015; Naddafi et al., 2021). The C/N ratio of phytoplankton is generally between 4 and 10, whereas it is generally higher than 11–12 for terrestrial POM (Meyers, 1994; 1997; Wu et al., 2007) and sewage (Andrews et al., 1998; Kaiser et al., 2014). Terrestrial plants using the C3 photosynthetic pathway exhibit δ13C values ranging from –30 to –25‰ (average: –27‰), whereas plants using the C4 pathway range from –16 to –10‰ (Pancost and Boot, 2004). Marine organic matter typically has δ13C values ranging from –20 to –22‰ (Meyers, 1994). The δ15 N values of marine organic matter derived from phytoplankton, which generally use nitrate, typically range from 5‰ to 7‰ (average: 6‰; Lamb et al., 2006; Gireeshkumar et al., 2013). Synthetic fertilizers are more depleted than natural sources, resulting in δ15 N values between –4 and 4‰ (Kendall, 1998). By contrast, nitrogen from wastewater is less depleted, ranging from 10 to 20‰ (McKinney et al., 2001). When these anthropogenic nitrogen sources are incorporated by phytoplankton, they alter the δ15 N in POM accordingly (Cole et al., 2004; Wang et al., 2020).

The major rivers in the world, such as the Amazon River, the Mississippi River, the Congo River, and the Yangtze River, dominate biogeochemical processes and carbon cycling in marginal seas (Bianchi and Bauer, 2011; Bouillon et al., 2012; Wang et al., 2012; Medeiros et al., 2015; Ward et al., 2017). Under the background of global climate change, increasing human activities triggered tremendous changes in the sources and transport modes of global riverine sediments and POC, altered the role of the river-ocean continuum in the ecosystem and carbon cycle (Bauer et al., 2013; Gao et al., 2014a; Yang et al., 2015a; Seddon et al., 2016), and led to greater scientific interest (Tan et al., 1991; Bianchi et al., 2004; Wu et al., 2007; Medeiros et al., 2015). In the Yangtze River Basin and other large river systems around the world, human activities (such as pollutant release, dam construction) and extreme weather (such as floods) have modified estuarine systems over time, resulting in changes in the production, respiration, burial, and export of organic matter (Bauer and Bianchi, 2011; Canuel and Hardison, 2016). Recent research showed that the loss of carbon from the Lower Mississippi River was reduced by ≥40% or 1.1 Tg C per year due to river embankment, suggesting a significant human-caused shift in the quality and quantity of riverine organic matter exported to the ocean (Shen et al., 2021).

As the third-largest outflow river in the world, and the largest in Asia, the Yangtze River drains more than 1.8 million km2, discharging into one of the largest marginal seas in the world, the East China Sea (Gomes et al., 2018; Liang and Xian, 2018; Liu et al., 2020). The freshwater discharge and total suspended matter account for 90–95% of the total riverine contribution to the East China Sea (Zhang et al., 2007). In the Yangtze River basin, biogeochemical research has focused on sources, distributions, and fluxes of POC or DOC (Wu et al., 2007; Wang et al., 2008; Dai et al., 2011; Gao et al., 2012; Wu et al., 2015). Estuarine studies explored the distribution, migration, and transformation of POC or DOC from river to the ocean side (Zhang et al., 2007; Li et al., 2012; Li et al., 2014; Gomes et al., 2018; Gao et al., 2019). Under the complex hydrodynamic conditions in the Yangtze River estuary and its adjacent areas, currents such as the Changjiang Diluted Water and the Taiwan Warm Current also affect the migration and transformation of organic carbon input from the Yangtze River Basin (Yang et al., 2015a; Liu et al., 2018; Zhang et al., 2020). Continuum studies of POC and DOC from the Yangtze River basin to the coastal ocean are less common (Zhang et al., 2007; Li et al., 2015; Gao et al., 2019).

In recent decades, the construction and use of the Three Gorges Dam (TGD) have led to significant changes in the coastal ecosystem and associated with the phytoplankton community (Wu et al., 2007; Jiang et al., 2014; Yu et al., 2018; Gao et al., 2019). Algal blooms in the Yangtze River estuary and its adjacent areas occurred 126 times from 2000 to 2009 (Liang and Xian, 2018). The proportion of diatoms decreased, and phytoplankton biomass increased sharply with decreasing diatom-dinoflagellate ratio (Jiang et al., 2014). Although knowing organic matter composition along the continuum is crucial for evaluating these ecosystem changes (Fuzzi et al., 2015; Ward et al., 2017), monsoon-dominated variations in Asia’s large-river systems make gathering this information difficult (Yu et al., 2011; Ellis et al., 2012). Therefore, based on data collected from several surveys of the Yangtze River mainstream and the coastal waters, the purpose of this study was to determine: 1) the spatial-temporal distribution of organic matter in the Yangtze River-ocean continuum, 2) the main compositions of organic matter in the Yangtze River-ocean continuum, and 3) the relationships between organic matter and other environmental factors. Here, we show how monsoons and human activities change the concentrations and compositions of organic matter in this ecosystem.

### 2.1. Stations setting and sampling

Four expeditions to the Yangtze River and its coastal waters (up to approximately 150 km offshore) took place between April and November 2014. Two cruises were conducted along the >2000-km river mainstream on April 17–29 and October 12–24, 2014. Eleven stations were sampled (Figure 1), namely, Panzhihua, Yibin, Chongqing, Wanzhou, Badong, Yichang, Yueyang, Wuhan, Jiujiang, Datong, and Nanjing. Among them, Yichang and Jiujiang are the boundaries of the upper and middle, and the middle and lower reaches, respectively. All samples were collected upstream of the city centers, away from drainage to avoid interference from sewage. Three samples (left, center, right channel) were taken from each station, with the left and right channel samples located at one-third of the river width from the banks. The Yangtze River estuary and its adjacent waters (including inshore, turbidity maximum zone (TMZ), and offshore waters), were sampled May 6–15 (39 stations in total) and November 4–8 in 2014 (36 stations in total).

Figure 1.

Map of the Yangtze River basin and coastal waters, with the sampling stations marked. Our sampling stations are denoted by black circles in spring and autumn of the Yangtze River mainstream, purple circles in spring, and green triangles in autumn of the coastal waters. Red regions along the mainstream represent areas affected by the Three Gorges Project. The area surrounded by the blue dotted line at the estuary represents inshore waters and the area surrounded by the orange dotted line represents the turbidity maximum zone. DOI: https://doi.org/10.1525/elementa.2021.00034.f1

Figure 1.

Map of the Yangtze River basin and coastal waters, with the sampling stations marked. Our sampling stations are denoted by black circles in spring and autumn of the Yangtze River mainstream, purple circles in spring, and green triangles in autumn of the coastal waters. Red regions along the mainstream represent areas affected by the Three Gorges Project. The area surrounded by the blue dotted line at the estuary represents inshore waters and the area surrounded by the orange dotted line represents the turbidity maximum zone. DOI: https://doi.org/10.1525/elementa.2021.00034.f1

Close modal

Stations in the mainstream are Panzhihua (PZ), Yibin (YB), Chongqing (CQ), Wanzhou (WZ), Badong (BD), Yichang (YC), Yueyang (YY), Wuhan (WH), Jiujiang (JJ), Datong (DT), Nanjing (NJ), Shanghai (SH), Chongming (CM), and Qidong (QD).

For the mainstream, surface water (0.5 m) samples were obtained using a 5-L Niskin bottle, and then quickly filtered (80–300 mL) through duplicate 0.7-µm Whatman GF/F filters (pretreated at 450°C for 6 h; 25-mm diameter). The filters were wrapped in tinfoil and kept frozen (–20°C) for the determination of POC, PON, δ13C, and δ15 N; and the filtrates were collected in a 60-mL brown glass bottle (pretreated at 450°C for 6 h), to which 1 mL saturated mercuric chloride (HgCl2) solution was added, and then stored at 4°C for the determination of DOC. Water samples were also filtered through Whatman GF/F filters (pretreated at 450°C for 6 h; 47-mm diameter) to determine chlorophyll a (Chl a) and total suspended particulate matter (SPM) concentrations, and the filtrates were frozen at –20°C for the determination of nutrients. Besides, chemical oxygen demand (COD) samples were taken directly into a polyethylene storage bottle and frozen at –20°C until being analyzed in the laboratory. These bottles, previously acid-cleaned (10% HCl v/v), rinsed with ultrapure water (Millipore Milli-Q) and oven-dried at the laboratory, were rinsed three times with sample in the field and then overflowed.

For the coastal waters, surface water samples were collected using the same sampling methods. At each coastal site, hydrographic data such as depth (D), temperature (T), and practical salinity (S) were measured in situ using a conductivity-temperature-density recorder (CTD; Sea-Bird 25, USA). The pH was measured in situ using an INESA REX PHBJ-260F portable pH meter (Shanghai, China). The precision of pH was better than ±0.01 pH, and the accuracy of pH was better than ±0.01 pH.

The water temperature data for each station in the Yangtze River mainstream in 2014 were obtained from the Changjiang (Yangtze) Hydrological Yearbook (Changjiang Water Resources Commission of the Ministry of Water Resources, 2014a). Monthly water flow and sediment load at the Datong Hydrographic Station in 2014 (Figure S1) were obtained from the Yangtze River Sediment Bulletin (Changjiang Water Resources Commission of the Ministry of Water Resources, 2014b). The Datong Hydrographic Station is the last monitoring station in the lower reaches of the mainstream before it enters the East China Sea (Wang et al., 2012).

### 2.2. Bulk biochemical analyses

#### 2.2.1. Organic matter and isotopes

Filters (for POC, PON, δ13C, and δ15 N) were dried at 40°C, then exposed to the vapor of hydrochloric acid (HCl: 12 mol L–1) in an enclosed glass desiccator for 16 h to remove carbonate materials. After fumigation, they were folded in half and rinsed with ultrapure water until the pH was neutral, and then placed in a special acidification drying oven (60°C). After drying, the samples were packaged in numbered aluminum foil bags. The whole pre-treatment process was carried out in the fume hood.

The δ13C, δ15 N, and concentrations of POC and PON were measured using pre-treated samples. Analyses for samples were performed (Grassineau, 2006) on a Vario EL cube-IsoPrime 100 elemental analyzer-isotopic ratio mass spectrometer (EA-IRMS; Elementar, Germany) at the Analysis and Test Center, Third Institute of Oceanography, Ministry of Natural Resources, China. The prepared samples were oxidized at 950°C, followed by reduction over copper in a furnace at 600°C. The mass of POC and PON on the whole membrane was calculated, and the concentrations of POC and PON were calculated according to the volume of filtered water. Results of δ13C and δ15 N are reported in parts per thousand (‰) and calculated according to Equations 1 and 2:

1
2

where R (13C/12CPDB) is the carbon isotope abundance ratio of Pee Dee Belemnite (PDB), and R(15N/14Nair) is the nitrogen isotope abundance ratio of nitrogen in the air. Quality control consisted of regular assessments of accuracy, precision, and the analysis of blanks. Based on duplicate measurements, the standard deviation of the POC and PON was less than 0.6% and 0.5%, respectively. The precision of δ13C and δ15 N measurements between samples was better than ± 0.2‰ for δ13C and ± 0.3‰ for δ15 N.

In theory, the measurement of DOC in samples should use the measured total carbon minus inorganic carbon. However, inorganic carbon in seawater is the main component of total carbon, and its content is generally an order of magnitude higher than organic carbon, so the direct subtraction error would be large. Therefore, we used non-purgeable organic carbon as the measurement, i.e. the amount of carbon in organic matter contained in the water sample after acidification and purge/bubbling to remove inorganic carbon, which was measured by a high-temperature catalytic oxidation method using a TOC-VCPH total organic carbon analyzer (Shimadzu, Japan) at the Analysis and Test Center, Institute of Oceanology, Chinese Academy of Sciences. DOC samples were acidified with concentrated 2 M HCl until pH ≤ 2; inorganic carbon was converted to CO2 and then removed by aeration (purge/bubbling time, 100 s). Afterwards, the samples were injected into a high-temperature combustion tube (680°C), oxidized under the action of Pt catalyst, through the deprivation of brine, and then entered into the NDIR detector for the quantitative determination of total organic carbon. We used potassium hydrogen phthalate as a standard solution for the calibration curve. Analytical accuracy and precision were tested against the seawater reference sample (Hansell Mid Seawater Reference, 54–58 uM DOC). The limit of detection of the analytical method was 0.17 mg L–1, being 3x the standard deviation of the blank analyses. The concentration was calculated as an average of three replicates and the relative standard deviation of the determination was less than 2%.

#### 2.2.2. Determination of other environmental factors

For Chl a determination, one GF/F filter was extracted with 90% acetone and analyzed with a fluorometer (Turner Design; Parsons et al., 1984; Li et al., 2018), and the other was weighed to calculate SPM after being dried at 100°C for 24 h. After thawing, the COD samples were measured by the potassium iodide-alkaline potassium permanganate method according to China’s national standards (National Standards of People’s Republic of China, 2007), and the related standard deviation in the laboratory was 4%. Nutrients (including nitrate, nitrite, ammonia, phosphate, and silicate) were analyzed in the laboratory as described in detail in Liang and Xian (2018). Based on the analysis of selected samples and the reference materials, Chl a precision was better than ± 0.5%, the repetition accuracy of SPM was better than ± 1%, and the COD accuracy had a relative average error of 1.08%. Based on duplicate measurements, the standard deviations for nitrate, nitrite, and phosphate were < 0.3%, and for ammonia and silicate were < 0.4%.

### 2.3. Data analysis

We checked the normality of data for each group using Shapiro-Wilk tests ( p > 0.05; SPSS version 20.0; best for small sample sizes, n < 50, according to Mishra et al., 2019). Correlation analysis was used to determine relationships between organic matter and environmental parameters. ANOVA (the Kruskal-Wallis test) was used to establish the statistical significance of the differences in environmental parameters and organic matter between May and November. Both analyses were done with SPSS version 20.0 (IBM Corporation, Armonk, NY, USA). Principal component analysis (PCA) simplifies the data and characterization of a given poly-dimensional system with a small number of new variables (Loska and Wiechuła, 2003; Yang et al., 2015a) and was used to explore the relationship among measured parameters using RStudio (version 1.1.463). The contour figures were made by the software Surfer 11.0 (Golden Software Inc., Golden, CO, USA), and the line charts and triangular figures were done using the software Grapher 10 (Golden Software Inc., Golden, CO, USA).

To estimate the relative contributions of different organic matter sources in surface water of the mainstream and coastal waters, we used δ13C and δ15 N of particulate matter. The contribution of phytoplankton within the river system was ignored due to high SPM and light limitations (Wu et al., 2007). The main sources we considered in the mainstream were allochthonous and anthropogenic sources (using two end-members models), and the main sources in the Yangtze River estuary and its adjacent waters were the allochthonous sources, anthropogenic sources, and autochthonous sources (using three end-member models). The calculation model (Zhang et al., 2007) we used was as follows, where results are reported in parts per thousand (‰) and calculated according to Equation 3:

$[a11a12a13a21a22a23111]×[f1f2f3]=[b1b21]$
3

where fi is the percentage contribution from the jth source materials to the ith component aij, and bi accounts for the ith component for the samples in the river and estuary. Based on previous studies (Meyers, 1994; McKinney et al., 2001; Lamb et al., 2006; Wu et al., 2007), we chose the average of the end-member ranges from three sources (allochthonous, anthropogenic, and autochthonous organic materials) as the end-member value of this paper. Table S1 shows δ13C and δ15 N end-members and ranges of POC samples determined from three sources from the river and estuaries. If estuary samples (especially from the inshore water Stations 35, 36, 37, and 38) calculated with the three end-member model caused negative fractions, we recalculated using the two end–member model, which always used the allochthonous and anthropogenic sources. Sample results with errors less than 10% were retained, while those over 10% were eliminated (Liu et al., 2007).

### 3.1. Environmental factors

River water temperature varied similarly in April and October (Changjiang Water Resources Commission of the Ministry of Water Resources, 2014a). Water temperature increased from the upper reaches to lower reaches in April (Zhang et al., 2019). The East Asian Monsoon climate (Li et al., 2018), causing the rainy season in the mainstream to be mainly from May to October, is the first control factor in the entire Yangtze River Basin. During this period, the amount of water flow and sediment load at the Hydrographic Station Datong was relatively high (Figure S1). SPM showed little temporal variation (except for an April increase midstream), but had a large spatial variation, increasing downstream in both April and October (Figure S2a). Chl a concentration was somewhat higher in April than in October, especially upstream. Both months had the same trend, with the middle reaches showing the highest concentrations, and upstream areas showing the lowest (Figure S2b). Nutrient concentrations in the Yangtze River mainstream have been reported previously (Liang and Xian, 2018); in the Yangtze River estuary and its adjacent waters, nutrients were provided by Mu et al. (2020), while spatial distributions of pH and COD are shown in this study (Figure S3).

Near the river mouth, seasonal temperature changes were also observed (Figure 2), with riverine waters warmer in May (approximately 21°C) than in November (approximately 19°C). A reverse trend was observed (P < 0.01) in sea surface temperature (SST) in the Yangtze River estuary and its adjacent waters, with cooler SST in May (average ± standard deviation (SD): 16.8 ± 2.0°C, n = 38) than in November (20.0 ± 0.7°C, n = 36). Thus, spatial gradients of temperature in May and November were very different (Figure 2a and b). In May, the temperature near the mouth of the river was warmer than in the offshore regions, while in November offshore waters were warmer. Sea surface salinity (SSS) in May showed a similar distribution as in November, with a gradually increasing salinity from the mouth to offshore (Figure 2c and d). The salinity of the surface water ranged from 0.16 to 32.2 (21.3 ± 10.9, n = 37) in May and from 0.15 to 32.8 (22.0 ± 11.0, n = 36) in November. No differences (p  >  0.05) were detected in average SSS between May and November.

Figure 2.

Spatial distributions of salinity (S) and temperature (T) in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are temperature contours in (a) spring and (b) autumn and salinity contours in (c) spring and (d) autumn. In the color scales on the right (upper for °C, lower for salinity), red indicates high values, and light purple indicates low values. DOI: https://doi.org/10.1525/elementa.2021.00034.f2

Figure 2.

Spatial distributions of salinity (S) and temperature (T) in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are temperature contours in (a) spring and (b) autumn and salinity contours in (c) spring and (d) autumn. In the color scales on the right (upper for °C, lower for salinity), red indicates high values, and light purple indicates low values. DOI: https://doi.org/10.1525/elementa.2021.00034.f2

Close modal

Chl a and SPM data have non-normal distributions because they have long right tails and cannot go below zero, so we report here their medians and interquartile ranges. The median Chl a concentration in May was 1.07 (0.64–1.81) µg L–1, which declined significantly (P < 0.01) to 0.12 (0.07–0.31) µg L–1 in November. The highest values appeared at offshore Stations 18 (3.53 µg L–1), 19 (6.00 µg L–1), and 20 (3.39 µg L–1) in May, and the peak in November was at Station 20 (1.79 µg L–1; Figure 3a and b). SPM in the surface water showed different distribution patterns compared with Chl a (Figure 3a–d). The median SPM was 7.30 (1.93–86.0) mg L–1 in May, and lower but more variable 4.83 (2.68–160) mg L–1 in November.

Figure 3.

Spatial distributions of chlorophyll a (Chl a) and suspended particulate matter (SPM) in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are contours of Chl a in (a) spring and (b) autumn and contours of SPM in (c) spring and (d) autumn. In the color scales on the right, red indicates high concentrations, and light purple indicates low concentrations. DOI: https://doi.org/10.1525/elementa.2021.00034.f3

Figure 3.

Spatial distributions of chlorophyll a (Chl a) and suspended particulate matter (SPM) in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are contours of Chl a in (a) spring and (b) autumn and contours of SPM in (c) spring and (d) autumn. In the color scales on the right, red indicates high concentrations, and light purple indicates low concentrations. DOI: https://doi.org/10.1525/elementa.2021.00034.f3

Close modal

### 3.2. Distribution and concentrations in the Yangtze River mainstream

#### 3.2.1. Organic matter

The distributions of organic matter concentration in the surface water in the Yangtze River mainstream in April and October are indicated in Figure 4a–c. The concentration of POC changed little between the two seasons, ranging from 0.5 to 2.2 (average ± SD: 1.2 ± 0.6, n = 33) mg C L–1 in April, and from 0.5 to 2.2 (1.1 ± 0.6, n = 33) mg C L–1 in October (Figure 4a). POC had similar distributions along the Yangtze River mainstream in both months. The lowest concentration stations for both sampling periods were observed in Yichang, the boundary point of the upper and middle reaches of the river. Downstream of Yichang, the POC concentration gradually increased. One difference was that the POC concentration peaked at Chongqing in April, but at Datong in October.

Figure 4.

Spatial variations of organic matter and isotopes along the Yangtze River mainstream. Blue dots represent samples collected in April, and magenta dots represent samples collected in October along the Yangtze River mainstream. Each value is the average of 3 samples, which were taken from each station. YC and JJ are the boundaries between the upper and middle, and middle and lower reaches. Error bars indicate standard deviation for each sample. Shown are spatial distributions of (a) POC in spring and autumn, (b) PON in spring and autumn, (c) DOC in spring and autumn, (d) δ13C in spring and autumn, and (e) δ15 N in spring and autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f4

Figure 4.

Spatial variations of organic matter and isotopes along the Yangtze River mainstream. Blue dots represent samples collected in April, and magenta dots represent samples collected in October along the Yangtze River mainstream. Each value is the average of 3 samples, which were taken from each station. YC and JJ are the boundaries between the upper and middle, and middle and lower reaches. Error bars indicate standard deviation for each sample. Shown are spatial distributions of (a) POC in spring and autumn, (b) PON in spring and autumn, (c) DOC in spring and autumn, (d) δ13C in spring and autumn, and (e) δ15 N in spring and autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f4

Close modal

The PON concentration in the Yangtze River mainstream also changed little between the two seasons, ranging from 0.02–0.16 (average ± SD: 0.09 ± 0.04, n = 33) mg N L–1 in April to 0.04–0.18 (0.11 ± 0.05, n = 33) mg N L–1 in October (Figure 4b). PON concentrations were slightly higher in October than in April except at station Panzhihua. From Chongqing to Yichang in the upper reaches, the concentrations of PON in both months gradually decreased, then increased, and finally reached the highest value at Nanjing. Similar to POC, the highest value of PON in April was observed at Chongqing, and the lowest value of PON in October occurred at Panzhihua.

DOC concentrations in the Yangtze River mainstream increased significantly (p < 0.01) from a range of 1.3–2.7 (average ± SD: 2.0 ± 0.4, n = 32) mg C L–1 in April to 3.7–5.5 (4.3 ± 0.7, n = 30) mg C L–1 in October (Figure 4c). DOC concentrations in the surface waters of the mainstream showed little difference from the upper reach to the lower reach, with the highest values in Wuhan (October) and Jiujiang (April).

#### 3.2.2. Carbon and nitrogen isotopes

The δ13C of organic matter in the Yangtze River mainstream changed little between the two seasons, ranging from –27.7 to –25.6‰ (average ± SD: –26.5 ± 0.7‰, n = 33) in April to –27.2 to –26.1‰ (–26.6 ± 0.4‰, n = 33) in October (Figure 4d). The distribution of δ13C, however, had greater temporal variation. Except for the river segment from Chongqing to Yueyang, the δ13C was more enriched in April than in October. The spatial variation trends of δ13C in April and October were similar, except at Chongqing, where the spatial distribution of δ13C from Panzhihua to Wanzhou presented a depletion trend and then showed an enrichment until Yueyang. The δ13C in Jiujiang was the lowest in the lower reaches, and from Jiujiang to Nanjing, the δ13C was gradually enriched in both April and October.

Stations in the mainstream on the x-axis are Panzhihua (PZ), Yibin (YB), Chongqing (CQ), Wanzhou (WZ), Badong (BD), Yichang (YC), Yueyang (YY), Wuhan (WH), Jiujiang (JJ), Datong (DT), and Nanjing (NJ). Three Gorges Dam (TGD) lies between BD and YC. In d), values for anthropogenic (Anth.) to allochthonous (Allo.) sources are indicated on the right; in e), values for anthropogenic sources from wastewater (w) and fertilizers.(f), and autochthonous sources (Auto.) are indicated on the right and with shaded color bars across the panel.

The δ15 N of organic matter in the Yangtze River mainstream also changed little between the two seasons, ranging from 1.6–11.8 (average ± SD: 5.3 ± 3.5, n = 33) ‰ in April to 2.0–10.2 (5.4 ± 2.6, n = 33) ‰ in October (Figure 4e). The δ15 N showed similar spatial trends in April and October. It was more enriched from Panzhihua to Badong, but more depleted from Badong to Nanjing. The most enriched values of δ15 N occurred at Badong, and the most depleted values were observed at Panzhihua. Moreover, the average δ15 N concentrations in the upper reaches were more enriched than in the middle and lower reaches.

### 3.3. Distribution and concentrations in coastal waters

#### 3.3.1. Organic matter

In the coastal waters, surface POC concentrations appeared to increase slightly, but not significantly, from 1.0–8.2 (average ± SD: 2.3 ± 2.0, n = 39) mg C L–1 in May to 0.9–8.1 (2.7 ± 2.2, n = 36) mg C L–1 in November (Figure 5a and b). For both months, the spatial patterns of POC were similar, with relatively high values in the nearshore and southwest waters, decreasing offshore. High POC concentrations in the southwest waters, however, were more spatially extensive in November than in May, and the average POC concentration in November was also higher than in May.

Figure 5.

Spatial distributions of the organic matter content in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of (a) POC in spring, (b) POC in autumn, (c) PON in spring, (d) PON in autumn, (e) DOC in spring, and (f) DOC in autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f5

Figure 5.

Spatial distributions of the organic matter content in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of (a) POC in spring, (b) POC in autumn, (c) PON in spring, (d) PON in autumn, (e) DOC in spring, and (f) DOC in autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f5

Close modal

PON concentrations in the coastal waters increased significantly (0.01 < p < 0.05) from 0.02–0.29 (average ± SD: 0.14 ± 0.07, n = 39) mg N L–1 in May to 0.03–0.60 (0.21 ± 0.17, n = 36) mg N L–1 in November (Figure 5c and d). An increasing trend was observed from the inshore to offshore areas in May. In May, low PON was found near the estuary, and high PON areas were found in the northeast, east, and southeast of the survey area, with the highest value at Station 19 (0.29 mg N L–1). In contrast, PON distribution in November was higher inshore and lower offshore, similar to the distribution of POC.

Surface DOC in the coastal waters (Figure 5e) increased significantly (p < 0.01) from 1.2–3.2 (average ± SD: 2.1 ± 0.6, n = 26) mg C L–1 in May to 2.3–8.8 (4.3 ± 1.7, n = 36) mg C L–1 in November (Figure 5f). Note that offshore samples in May were missing. Compared with May, the DOC was patchier in November, and high concentrations were found in the estuary and to the south.

The DOC samples at the outermost stations in May (e) were contaminated due to sampling bottle breakage, and no data are available. In the color scales on the right, red indicates high concentrations, and light purple indicates low concentrations.

#### 3.3.2. Carbon and nitrogen isotopes

The δ13C values in the coastal waters changed little between the two seasons, ranging from –27.2 to –22.6‰ (average ± SD: –25.5 ± 1.1‰, n = 39) in May to –27.4 to –22.5‰ (–25.5 ± 1.1‰, n = 36) in November (Figure 6a and b). The regional average δ13C was similar in both months. The distributions of δ13C in May and November both showed lower values inshore. The less-depleted zone shifted northward in November, compared to May.

Figure 6.

Spatial distributions of the δ13C and δ15 N in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of (a) δ13C in spring, (b) δ13C in autumn, (c) δ15 N in spring, and (d) δ15 N in autumn. In the color scales on the right, red indicates high values, and light purple indicates low values. DOI: https://doi.org/10.1525/elementa.2021.00034.f6

Figure 6.

Spatial distributions of the δ13C and δ15 N in May and November. Black dots represent the sampling stations in May, and black triangles represent the sampling stations in November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of (a) δ13C in spring, (b) δ13C in autumn, (c) δ15 N in spring, and (d) δ15 N in autumn. In the color scales on the right, red indicates high values, and light purple indicates low values. DOI: https://doi.org/10.1525/elementa.2021.00034.f6

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The δ15 N in the coastal waters ranged from 1.8–13.1‰ (average ± SD: 6.8 ± 3.0‰, n = 39) in May and from 1.1–15.0‰ (6.7 ± 3.3‰, n = 36) in November (Figure 6c and d). Unlike the δ13C, the seasonal variation in δ15 N was large. In May, the δ15 N decreased with increasing salinity offshore; it also coincided with the flow of the Changjiang Diluted Water. In November, higher values of δ15 N appeared in the southwest and showed a decreasing trend toward the north, indicating that the δ15 N may be affected by the Taiwan Warm Current during this season.

### 3.4. Correlation analysis

Along the river (Table 1), POC and PON increased with SPM in both months (R = 0.78 and 0.91, respectively; p < 0.01). In April, POC increased with δ13C (R = 0.81; p < 0.01) but decreased with δ15 N (R = –0.68; p < 0.05). PON correlated positively with DOC (R = 0.77; p < 0.01) and COD (R = 0.92; p < 0.01). DOC also correlated positively with COD (R = 0.75; p < 0.01). In October, POC showed the strongest positive correlation with COD and DIP (R = 0.79 and 0.60, p < 0.01 and p < 0.05, respectively). PON also correlated positively with COD (R = 0.80; p < 0.01) and DIP (R = 0.71; p < 0.05). In October, DOC correlated with none of the environmental factors we measured.

Table 1.

Correlation coefficients (R) and significance (p < 0.05* or p < 0.01**) of the measured organic matter and environmental factors in surface water samples from the Yangtze River–ocean continuum. DOI: https://doi.org/10.1525/elementa.2021.00034.t1

SiteParameterPOCPONDOCδ13Cδ15NSSSSSTpHCODDIPDSiDINSPMChl a
Mainstream (April) POC 0.74** 0.38 0.81** –0.68* – – – 0.53 –0.29 0.39 –0.26 0.78** 0.16
PON – 0.77** 0.5 –0.35 – – – 0.92** 0.20 –0.01 0.33 0.91** 0.29
DOC – – 0.1 –0.11 – – – 0.75** 0.28 –0.14 0.50 0.59 0.24
δ13– – – –0.83** – – – 0.22 –0.60 0.61* –0.62* 0.66* 0.11
δ15– – – – – – – –0.05 0.72* –0.78** 0.57 –0.58 –0.54
Mainstream (October) POC 0.92** –0.05 0.29 –0.49 – – – 0.79** 0.60* 0.38 0.36 0.98** 0.50
PON – –0.17 0.19 –0.31 – – – 0.80** 0.71* 0.57 0.49 0.93** 0.56
DOC – – 0.04 –0.04 – – – 0.09 –0.07 0.28 0.42 –0.04 –0.02
δ13– – – –0.57 – – – 0.20 0.03 0.11 0.14 0.23 0.05
δ15– – – – – – – –0.24 –0.08 0.01 –0.10 –0.47 –0.43
Coastal waters (May) POC –0.01 0.54** 0.16 0.39* –0.70** 0.62** –0.57** 0.78** 0.65** 0.50** 0.64** 0.89** –0.20
PON – 0.38 0.56** –0.43** 0.30 –0.19 0.54** 0.22 –0.32 –0.29 –0.25 0.06 0.69**
DOC – – 0.02 0.55** –0.63** 0.62** –0.42* 0.61** 0.55** 0.56** 0.63** 0.46* 0.19
δ13– – – –0.23 0.26 –0.19 0.39* 0.31 –0.16 –0.32* –0.23 0.27 0.31
δ15– – – – –0.62** 0.49** –0.67** 0.24 0.53** 0.57** 0.55** 0.29 –0.58
Coastal waters (November) POC 0.85** 0.49** 0.08 0.10 –0.72** –0.75** –0.03 0.67** 0.73** 0.70** 0.64** 0.94** 0.16
PON – 0.41* 0.15 0.19 –0.60** –0.59** –0.17 0.51** 0.58** 0.56** 0.51** 0.78** 0.18
DOC – – –0.22 –0.05 –0.68** –0.46** –0.09 0.56** 0.66** 0.66** 0.66** 0.42* 0.10
δ13– – – 0.12 0.19 0.10 0.07 –0.03 –0.28 –0.23 –0.21 0.17 0.67**
δ15– – – – 0.27 –0.05 –0.61** 0.02 –0.20 –0.32 –0.38* 0.16 0.00
SiteParameterPOCPONDOCδ13Cδ15NSSSSSTpHCODDIPDSiDINSPMChl a
Mainstream (April) POC 0.74** 0.38 0.81** –0.68* – – – 0.53 –0.29 0.39 –0.26 0.78** 0.16
PON – 0.77** 0.5 –0.35 – – – 0.92** 0.20 –0.01 0.33 0.91** 0.29
DOC – – 0.1 –0.11 – – – 0.75** 0.28 –0.14 0.50 0.59 0.24
δ13– – – –0.83** – – – 0.22 –0.60 0.61* –0.62* 0.66* 0.11
δ15– – – – – – – –0.05 0.72* –0.78** 0.57 –0.58 –0.54
Mainstream (October) POC 0.92** –0.05 0.29 –0.49 – – – 0.79** 0.60* 0.38 0.36 0.98** 0.50
PON – –0.17 0.19 –0.31 – – – 0.80** 0.71* 0.57 0.49 0.93** 0.56
DOC – – 0.04 –0.04 – – – 0.09 –0.07 0.28 0.42 –0.04 –0.02
δ13– – – –0.57 – – – 0.20 0.03 0.11 0.14 0.23 0.05
δ15– – – – – – – –0.24 –0.08 0.01 –0.10 –0.47 –0.43
Coastal waters (May) POC –0.01 0.54** 0.16 0.39* –0.70** 0.62** –0.57** 0.78** 0.65** 0.50** 0.64** 0.89** –0.20
PON – 0.38 0.56** –0.43** 0.30 –0.19 0.54** 0.22 –0.32 –0.29 –0.25 0.06 0.69**
DOC – – 0.02 0.55** –0.63** 0.62** –0.42* 0.61** 0.55** 0.56** 0.63** 0.46* 0.19
δ13– – – –0.23 0.26 –0.19 0.39* 0.31 –0.16 –0.32* –0.23 0.27 0.31
δ15– – – – –0.62** 0.49** –0.67** 0.24 0.53** 0.57** 0.55** 0.29 –0.58
Coastal waters (November) POC 0.85** 0.49** 0.08 0.10 –0.72** –0.75** –0.03 0.67** 0.73** 0.70** 0.64** 0.94** 0.16
PON – 0.41* 0.15 0.19 –0.60** –0.59** –0.17 0.51** 0.58** 0.56** 0.51** 0.78** 0.18
DOC – – –0.22 –0.05 –0.68** –0.46** –0.09 0.56** 0.66** 0.66** 0.66** 0.42* 0.10
δ13– – – 0.12 0.19 0.10 0.07 –0.03 –0.28 –0.23 –0.21 0.17 0.67**
δ15– – – – 0.27 –0.05 –0.61** 0.02 –0.20 –0.32 –0.38* 0.16 0.00

In the coastal waters, POC, DOC, δ15 N, COD, DIP, DSi, DIN, and SPM were all positively correlated (Table 1; p < 0.01). POC correlated negatively with SSS and pH in May (R = 0.62 and –0.57, respectively; p < 0.01). PON had the strongest (p < 0.01) positive correlation with δ13C and pH, and Chl a, while PON had the greatest (p < 0.01) negative correlation with δ15 N in May. In November, the environmental factors that were the strongest (p < 0.01) related to POC were PON, DOC, SSS, SST, COD, DIP, DSi, DIN, and SPM. PON had the strongest (p < 0.01) correlations with SSS, SST, COD, DIP, DSi, DIN, and SPM. Also, DOC had important correlations with SSS, SST, COD, DIP, DSi, and DIN (p < 0.01).

The relationships between δ13C and the average POC/PON and δ15 N varied across each river and estuarine zone (Figure 7). The average values of δ13C in the three subregions of the mainstream varied significantly (p < 0.01) from the estuarine regions during spring and autumn. Meanwhile, the values of POC/PON and δ15 N from the upper reaches to the lower reaches were very similar, indicating that the sources of POM within the river basin were the same to a large extent. The average δ15 N of the inshore areas in spring was different from that of the TMZ and offshore, and the POC/PON in the TMZ was also significantly (p < 0.01) different from other regions. This pattern indicates that different sources of POM occurred in the subregions of the estuary in spring. In autumn, the inshore POC/PON, δ13C, and δ15 N were very similar to the river. Values within the TMZ and offshore regions were also very similar, showing that the sources of organic matter in each area were not very different.

Figure 7.

Scatter plots of average POC/PON and δ15 N against δ13C during spring and autumn of 2014. Purple circles represent samples collected in the spring, and green triangles represent samples collected in the autumn. All samples were collected in the Yangtze River-ocean continuum. Shown are the spatial relationships between (a) average POC/PON and δ13C and (b) average δ15 N and δ13C. The ovals indicate that most of the samples collected from the Yangtze River mainstream are concentrated in these areas. TMZ indicates turbidity maximum zone. DOI: https://doi.org/10.1525/elementa.2021.00034.f7

Figure 7.

Scatter plots of average POC/PON and δ15 N against δ13C during spring and autumn of 2014. Purple circles represent samples collected in the spring, and green triangles represent samples collected in the autumn. All samples were collected in the Yangtze River-ocean continuum. Shown are the spatial relationships between (a) average POC/PON and δ13C and (b) average δ15 N and δ13C. The ovals indicate that most of the samples collected from the Yangtze River mainstream are concentrated in these areas. TMZ indicates turbidity maximum zone. DOI: https://doi.org/10.1525/elementa.2021.00034.f7

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### 3.5. Sources of POM to the Yangtze River mainstream and coastal waters

Allochthonous sources of POM to the Yangtze River mainstream were large (Figure 8) and similar in both spring (average ± SD: 70 ± 15%, n = 11) and autumn (73 ± 8%, n = 11). In the coastal waters, the source of POM in spring was 61 ± 17% (n = 39) allochthonous, 17 ± 17% (n = 39) anthropogenic, and 22 ± 21% (n = 33) autochthonous. In autumn, allochthonous sources accounted for 57 ± 17% (n = 36), anthropogenic sources for 25 ± 17% (n = 36), and autochthonous sources for 18 ± 18% (n = 36). Inshore, allochthonous sources accounted for more than 66% in both seasons. In the TMZ, allochthonous inputs of the POM accounted for > 50% in autumn, which was higher than in spring (Figure 8). Autochthonous sources also contributed to the TMZ (Figure 8). In offshore waters, the proportion of allochthonous sources was still > 50%, but the proportion of autochthonous sources was also greater than in inshore waters and the TMZ. A total of 9 samples (Stations 5, 8, 10, and 11 in spring; Stations 28, 29, 30, 31, and 32 in autumn) were not included in the analysis due to results with errors over 10% (Liu et al., 2007).

Figure 8.

Estimates of the contribution of different sources to POM in the Yangtze River-ocean continuum. Red pluses indicate samples collected in the Yangtze River mainstream. Magenta rhombuses represent samples collected in the turbidity maximum zone (TMZ). Blue triangles represent samples collected in offshore waters. Black squares represent samples collected inshore waters. A sample falling inside the black triangles indicates that the organic matter comes from allochthonous, anthropogenic, and autochthonous sources. A sample falling on an axis indicates that the organic matter has two sources. Shown are contributions of different sources to POM in (a) spring and (b) autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f8

Figure 8.

Estimates of the contribution of different sources to POM in the Yangtze River-ocean continuum. Red pluses indicate samples collected in the Yangtze River mainstream. Magenta rhombuses represent samples collected in the turbidity maximum zone (TMZ). Blue triangles represent samples collected in offshore waters. Black squares represent samples collected inshore waters. A sample falling inside the black triangles indicates that the organic matter comes from allochthonous, anthropogenic, and autochthonous sources. A sample falling on an axis indicates that the organic matter has two sources. Shown are contributions of different sources to POM in (a) spring and (b) autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f8

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### 4.1. Composition of organic carbon in the Yangtze River-ocean continuum

As two basic forms of organic carbon, both DOC and POC can be transformed during the transportation process. Studies on several rivers have shown that DOC and POC can be transformed and interlinked at the river-ocean continuum through suspension and dissolution (Keil et al. 1997). The DOC/POC ratio by weight indicates the partition of organic carbon between dissolved and particulate fractions, and it can reflect the output characteristics of river organic carbon (Ran et al., 2013; Maciejewska and Pempkowiak, 2014). In this study, DOC/POC ratios by weight along the Yangtze River mainstream were larger than 1 both in spring (average ± SD: 1.9 ± 0.9, n = 11) and autumn (4.9 ± 2.9, n = 10), suggesting that DOC contributed to a larger fraction of the organic carbon than POC in freshwater. This finding indicates that the riverine organic carbon in the Yangtze River mainstream was transported primarily in the dissolved form (Alvarez-Cobelas et al., 2012). In the Yangtze River estuary and coastal waters, however, the DOC/POC ratios by weight observed at the TMZ were generally less than 1. The DOC/POC ratios by weight generally decreased from inshore to the TMZ and then increased from the TMZ to the offshore sites during both seasons (Figure 9), probably due to the sedimentation of SPM and the estuarine mixing of freshwater and seawater in the TMZ, which suggests that during seaward transporting from the TMZ, POC might have higher degradation rates than DOC, or that some POC molecules had been rapidly removed from the seawater owing to SPM depositing (Zhao and Gao, 2019).

Figure 9.

Spatial distributions of DOC/POC in May and November. DOC/POC refers to the weight ratio. Black dots represent the sampling stations in (a) May, and black triangles represent the sampling stations in (b) November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of DOC/POC in (a) spring and (b) autumn. In the color scale on the right, red indicates high ratios, and light purple indicates low ratios. DOI: https://doi.org/10.1525/elementa.2021.00034.f9

Figure 9.

Spatial distributions of DOC/POC in May and November. DOC/POC refers to the weight ratio. Black dots represent the sampling stations in (a) May, and black triangles represent the sampling stations in (b) November. All samples were collected in the Yangtze River estuary and its adjacent waters. Shown are spatial distributions of DOC/POC in (a) spring and (b) autumn. In the color scale on the right, red indicates high ratios, and light purple indicates low ratios. DOI: https://doi.org/10.1525/elementa.2021.00034.f9

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We also applied the method used by Abril et al. (2002) and Zhang et al. (2013) to our study in both seasons and found that ratios of DOC/POC by weight correlated negatively with SPM in the Yangtze River mainstream and coastal waters:

Here, when the DOC/POC ratio by weight equals 1, SPM would be 100 mg L–1 in April and 562 mg L–1 in October along the Yangtze River mainstream (Figure 10), and when SPM exceeded 100 mg L–1 in April or exceeded 562 mg L–1 in October, POC was the main form of transported organic carbon; otherwise, DOC became the dominant form. In fact, because most DOC/POC ratios by weight were greater than 1, DOC was the main form of organic carbon in the Yangtze River mainstream. In addition, the SPM critical value in the Yangtze River mainstream is very similar to that in European rivers (95 mg L–1; Abril et al., 2002) in spring. In the Yangtze River estuary and coastal waters, when SPM exceeded 53 mg L–1 in May, or 100 mg L–1 in November, POC was the main form of organic carbon, mainly in the TMZ. POC being the main form indicates that a series of complex biogeochemical changes occurred in the POC of the TMZ both in spring and autumn, which might include the adsorption-desorption of POC, the mutual conversion between POC and DOC, the flocculation of DOC, and the flocculation and sedimentation of SPM (Abril et al., 2002; Zhang et al., 2013; Xing et al., 2014). The TMZ is like a filter at the Yangtze River estuary and coastal waters. Although POC and DOC concentrations decrease from TMZ to the sea, the advantages of DOC gradually become prominent. The composition of organic matter has gradually changed from mainly POC in the TMZ to DOC in the offshore area. Therefore, in the Yangtze River-ocean continuum, the organic matter existed mainly in the form of particles in the TMZ, while mainly in dissolved form in other regions.

Figure 10.

Seasonal relationships between log10(DOC/POC) and log10(SPM) in the Yangtze River–ocean continuum. DOC/POC refers to the weight ratio. Black triangles represent the samples from the Yangtze River mainstream, and the black dotted line represents the relationship between DOC/POC and SPM in spring. Purple circles represent the samples from the Yangtze River estuary and its adjacent waters, and the purple dotted line represents the relationship between DOC/POC and SPM in spring. Red triangles represent the samples from the Yangtze River mainstream, and the red dotted line represents the relationship between DOC/POC and SPM in autumn. Blue squares represent the samples from the Yangtze River estuary and its adjacent waters, and the blue dashed line represents the relationship between DOC/POC and SPM in autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f10

Figure 10.

Seasonal relationships between log10(DOC/POC) and log10(SPM) in the Yangtze River–ocean continuum. DOC/POC refers to the weight ratio. Black triangles represent the samples from the Yangtze River mainstream, and the black dotted line represents the relationship between DOC/POC and SPM in spring. Purple circles represent the samples from the Yangtze River estuary and its adjacent waters, and the purple dotted line represents the relationship between DOC/POC and SPM in spring. Red triangles represent the samples from the Yangtze River mainstream, and the red dotted line represents the relationship between DOC/POC and SPM in autumn. Blue squares represent the samples from the Yangtze River estuary and its adjacent waters, and the blue dashed line represents the relationship between DOC/POC and SPM in autumn. DOI: https://doi.org/10.1525/elementa.2021.00034.f10

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### 4.2. Variations and influential factors of POM

#### 4.2.1. Yangtze River mainstream

Variations in the amount and sources of POM in the Yangtze River’s mainstream are influenced by hydroelectric infrastructure, human activities, and climate change (Xiao et al., 2018). POM concentrations in subtropical rivers and estuaries usually show strong seasonal variability (Duan and Bianchi, 2006; Duan et al., 2014), but in our study, little change in POM was observed between April and October (Table 2; Figure 4). Almost invariably, in both seasons, allochthonous sources (> 70%) dominated the contribution in the mainstream (Figure 8) and were less affected by seasonal variations than coastal waters (Figure 8; Meyers, 1994; Kendall, 1998; Pancost and Boot, 2004; Kaiser et al., 2014). As POC, PON, and SPM in the Yangtze River mainstream all had a significant (p < 0.01) positive correlation with each other (Table 1), POM was greatly affected by the SPM concentrations. Also, because there was no significant (p > 0.05) difference in the water and sediment discharge of the Yangtze River in April and October, the SPM variations in April and October were more consistent (Figure S2). Therefore, seasonal changes in POM were not obvious. Furthermore, spatial variations did occur, consistent with Wu et al. (2007), reflecting the high input of anthropogenic matter (reaching 20–50%) in the sampling sites close to big cities (such as Chongqing, Wuhan, and Nanjing).

Table 2.

Biogeochemical parameters (average ± standard deviation, or range) of the Yangtze River-ocean continuum.

SitesSPM (mg L–1)POC (mg C L–1)PON (mg N L–1)C/Nδ13C (‰)δ15 N (‰)n valuesSampling time (year.month)Reference
Mainstreama 38.1 ± 30.5 1.2 ± 0.6 0.09 ± 0.04 17.6 ± 10.4 –26.5 ± 0.7 5.3 ± 3.5 11 2014.04 This study
Mainstreama 32.1 ± 30.4 1.1 ± 0.6 0.11 ± 0.05 10.3 ± 3.2 –26.6 ± 0.4 5.4 ± 2.6 11 2014.10 This study
Upper reach 70.7 ± 40.7 0.9 ± 0.5 – 14.9 ± 4.0 –25.5 ± 1.2 4.2 ± 1.0 – 1997.04–05 Wu et al., 2007
Upper reach 14.2 ± 16.2 1.0 ± 0.7 0.06 ± 0.03 21.0 ± 13.7 –26.8 ± 0.9 6.6 ± 4.3 2014.04 This study
Upper reach 10.1 ± 8.7 0.7 ± 0.2 0.07 ± 0.02 10.3 ± 3.9 –26.7 ± 0.6 6.5 ± 3.2 2014.10 This study
Middle reach 115 ± 35.6 1.5 ± 0.5 – 15.8 ± 9.0 –25.4 ± 0.7 4.8 ± 2.0 – 1997.04–05 Wu et al., 2007
Middle reach 93.9 ± 23.9 0.9 ± 0.1 – – –24.8 ± 0.1 – 16 2003.04–05 Yu et al., 2011
Middle reach 14.6 ± 8.0 0.3 ± 0.5 – – –24.8 ± 0.4 – 16 2006.10–11 (drought year) Yu et al., 2011
Middle reach 60.3 ± 5.3 1.3 ± 0.2 0.11 ± 0.02 13.5 ± 0.4 –26.3 ± 0.4 2.9 ± 0.3 2014.04 This study
Middle reach 41.8 ± 10.1 1.2 ± 0.2 0.12 ± 0.02 9.8 ± 2.3 –26.7 ± 0.6 3.9 ± 1.1 2014.10 This study
Lower reach 68.9 ± 19.1 0.7 ± 1.0 – 13.5 ± 10.0 –25.7 ± 0.1 4.3 ± 1.0  1997.04–05 Wu et al., 2007
Lower reach 82.2 ± 29.7 0.8 ± 0.1 – – –24.9 ± 0.1 – 16 2003.04–05 Yu et al., 2011
Lower reach 53.9 ± 6.6 0.9 ± 0.2 – – –24.9 ± 0.1 – 16 2006.10–11 (drought year) Yu et al., 2011
Lower reach 117 1.3 ± 0.1 0.14 ± 0.03 (PN)b 10.9 – – 30 2009.04 Wang et al., 2012
Lower reach 125 1.6 ± 0.1 0.26 ± 0.03 (PN) b 10.2 –25.5 – 30 2009.10 Wang et al., 2012
Lower reach 76.7 ± 3.8 1.8 ± 0.1 0.16 ± 0.01 13.4 ± 1.3 –26.1 ± 0.0 5.1 ± 0.1 2014.04 This study
Lower reach 83.9 ± 10.1 2.0 ± 0.3 0.18 ± 0.0 11.1 ± 1.53 –26.4 ± 0.2 4.5 ± 0.1 2014.10 This study
Coastal waters 275 (8.3–963) 3.8 (0.3–19.1) 0.36 (0.04–2.16) 13.6 (7.4–39.2) – – – 2004.11–12 Song et al., 2007
Coastal waters – 3.7 (0.7–34.8) 0.57 (0.03–9.13) (PN) b 11.4 (4.0–29.6) – – – 2012.08 Xing et al., 2014
Coastal waters 100 ± 199 2.3 ± 1.98 0.14 ± 0.07 22.4 ± 23.0 –25.5 ± 1.13 6.8 ± 3.0 39 2014.05 This study
Coastal waters 120 ± 193 2.7 ± 2.2 0.21 ± 0.17 16.4 ± 8.1 –25.5 ± 1.1 6.7 ± 3.3 36 2014.11 This study
TMZ 298 ± 223 0.9 ± 0.2 – 10.7 ± 0.8 –24.4 ± 0.4 – 2011.06 Pan et al., 2015
TMZ 422 ± 319 6.0 ± 2.3 0.15 ± 0.05 55.8 ± 35.5 –25.0 ± 1.0 8.3 ± 2.1 2014.05 This study
TMZ 371 ± 198 5.4 ± 1.6 0.29 ± 0.13 23.5 ± 7.3 –25.2 ± 0.7 6.9 ± 1.0 2014.11 This study
SitesSPM (mg L–1)POC (mg C L–1)PON (mg N L–1)C/Nδ13C (‰)δ15 N (‰)n valuesSampling time (year.month)Reference
Mainstreama 38.1 ± 30.5 1.2 ± 0.6 0.09 ± 0.04 17.6 ± 10.4 –26.5 ± 0.7 5.3 ± 3.5 11 2014.04 This study
Mainstreama 32.1 ± 30.4 1.1 ± 0.6 0.11 ± 0.05 10.3 ± 3.2 –26.6 ± 0.4 5.4 ± 2.6 11 2014.10 This study
Upper reach 70.7 ± 40.7 0.9 ± 0.5 – 14.9 ± 4.0 –25.5 ± 1.2 4.2 ± 1.0 – 1997.04–05 Wu et al., 2007
Upper reach 14.2 ± 16.2 1.0 ± 0.7 0.06 ± 0.03 21.0 ± 13.7 –26.8 ± 0.9 6.6 ± 4.3 2014.04 This study
Upper reach 10.1 ± 8.7 0.7 ± 0.2 0.07 ± 0.02 10.3 ± 3.9 –26.7 ± 0.6 6.5 ± 3.2 2014.10 This study
Middle reach 115 ± 35.6 1.5 ± 0.5 – 15.8 ± 9.0 –25.4 ± 0.7 4.8 ± 2.0 – 1997.04–05 Wu et al., 2007
Middle reach 93.9 ± 23.9 0.9 ± 0.1 – – –24.8 ± 0.1 – 16 2003.04–05 Yu et al., 2011
Middle reach 14.6 ± 8.0 0.3 ± 0.5 – – –24.8 ± 0.4 – 16 2006.10–11 (drought year) Yu et al., 2011
Middle reach 60.3 ± 5.3 1.3 ± 0.2 0.11 ± 0.02 13.5 ± 0.4 –26.3 ± 0.4 2.9 ± 0.3 2014.04 This study
Middle reach 41.8 ± 10.1 1.2 ± 0.2 0.12 ± 0.02 9.8 ± 2.3 –26.7 ± 0.6 3.9 ± 1.1 2014.10 This study
Lower reach 68.9 ± 19.1 0.7 ± 1.0 – 13.5 ± 10.0 –25.7 ± 0.1 4.3 ± 1.0  1997.04–05 Wu et al., 2007
Lower reach 82.2 ± 29.7 0.8 ± 0.1 – – –24.9 ± 0.1 – 16 2003.04–05 Yu et al., 2011
Lower reach 53.9 ± 6.6 0.9 ± 0.2 – – –24.9 ± 0.1 – 16 2006.10–11 (drought year) Yu et al., 2011
Lower reach 117 1.3 ± 0.1 0.14 ± 0.03 (PN)b 10.9 – – 30 2009.04 Wang et al., 2012
Lower reach 125 1.6 ± 0.1 0.26 ± 0.03 (PN) b 10.2 –25.5 – 30 2009.10 Wang et al., 2012
Lower reach 76.7 ± 3.8 1.8 ± 0.1 0.16 ± 0.01 13.4 ± 1.3 –26.1 ± 0.0 5.1 ± 0.1 2014.04 This study
Lower reach 83.9 ± 10.1 2.0 ± 0.3 0.18 ± 0.0 11.1 ± 1.53 –26.4 ± 0.2 4.5 ± 0.1 2014.10 This study
Coastal waters 275 (8.3–963) 3.8 (0.3–19.1) 0.36 (0.04–2.16) 13.6 (7.4–39.2) – – – 2004.11–12 Song et al., 2007
Coastal waters – 3.7 (0.7–34.8) 0.57 (0.03–9.13) (PN) b 11.4 (4.0–29.6) – – – 2012.08 Xing et al., 2014
Coastal waters 100 ± 199 2.3 ± 1.98 0.14 ± 0.07 22.4 ± 23.0 –25.5 ± 1.13 6.8 ± 3.0 39 2014.05 This study
Coastal waters 120 ± 193 2.7 ± 2.2 0.21 ± 0.17 16.4 ± 8.1 –25.5 ± 1.1 6.7 ± 3.3 36 2014.11 This study
TMZ 298 ± 223 0.9 ± 0.2 – 10.7 ± 0.8 –24.4 ± 0.4 – 2011.06 Pan et al., 2015
TMZ 422 ± 319 6.0 ± 2.3 0.15 ± 0.05 55.8 ± 35.5 –25.0 ± 1.0 8.3 ± 2.1 2014.05 This study
TMZ 371 ± 198 5.4 ± 1.6 0.29 ± 0.13 23.5 ± 7.3 –25.2 ± 0.7 6.9 ± 1.0 2014.11 This study

aMainstream values are averages for the entire river.

bPN indicates particulate total nitrogen.

Over the past two decades, POC concentrations in the Yangtze River mainstream have been changing (Table 2; Figure S4a), especially in the middle reaches, likely related to both climate variability and the completion of the TGD in 2006. In the upper reach, POC was pretty much the same between 1997 and 2014 (Table 2; Figure S4a). However, compared with 2007, SPM and δ13C in 2014 decreased remarkably, likely related to the fact that the dam construction caused upstream particulate matter to settle in the reservoir area (Figure S4b; Zhang et al., 2007). In the middle reach, near the dam, SPM declined sharply (Table 2; Figure S4b); POC experienced a sharp decline from 1997 to 2006 and then increased (Table 2; Figure S4a). These changes were directly related to the construction of the TGD (Wu et al., 2015). Coarse terrestrial particles were trapped by the TGD, reducing SPM in the middle reach (Table 2; Figure S4b). In May 2006, the entire TGD was completed, the water level in the reservoir was raised 156 m (Mao et al., 2011), and the POC in the middle reach declined. At the same time, unusually high air temperatures occurred, along with droughts in the Yangtze River basin. Less precipitation would lead to lower inputs of organic-rich soil (Yu et al., 2011). These changes in POC may have been affected by both climate change and the second impoundment phase of the TGD (Zou and Gao, 2007; Dai et al., 2011).

Downstream, POC has steadily increased since 1997. Riverine POC has potential sources from soil organic matter, terrestrial plant debris, plankton, and other aquatic plants (Kendall, 1998; Duan and Bianchi, 2006; Moreira-Turcq et al., 2013). Given the high SPM (Table 2; Figure S4b) and light limitation in the river system, the contribution of autochthonous sources within the river system was ignored (Wu et al., 2007). The construction of the TGD intensifies the scouring effect of the downstream channel (Huang et al., 2019). The δ13C is similar to that of riverbank surface soil (–29 to –24‰) from 1997, showing that downstream POC mainly comes from topsoil running off (Pancost and Boot, 2004; Wu et al., 2007), due to the rapid urbanization in the Yangtze River Economic Belt and the aggravation of soil erosion caused by human activities (Cui et al., 2021). The degree of urbanization in the middle and lower reaches of the Yangtze River Basin is much higher than that in the upper reaches. Especially in the lower reaches, in the process of accelerating urbanization, the degree of soil erosion caused by human activities is also higher than that in the upper reaches. POC concentrations downstream have been increasing over the two decades examined (Table 2). According to the rapidly expanding economy, POC in the lower reaches of the Yangtze River may continue to increase in the foreseeable future. In addition, frequent flooding is another factor (Gao and Wang, 2008; Gao et al., 2012). Due to large changes in hydrology, the impacts from human activities such as dams and more extreme events from climate change can be seen in these long-term variations in organic matter.

#### 4.2.2. Coastal waters

Spatiotemporal variation of POM in the Yangtze River estuary is influenced by environment factors (e.g., temperature, SSS), terrestrial inputs, human activities, and primary production (Zhang et al., 2007; Gao et al., 2014b). COD, DOC, dissolved nutrients (DIP, DSi, and DIN), and SPM all contributed positively to the accumulation of POM in the estuary in both seasons (Table 1), and the large contribution of terrestrial inputs to the estuary was confirmed (Li et al., 2014). POC changed very little with the season (Figure 5a and b; Table 2) while PON had high seasonal variation (Figure 5c and d; Table 2). Compared with previous studies (Song et al., 2007; Xing et al., 2014; Table 2), POC in the coastal waters has decreased over the past decade.

In the TMZ, POC concentrations were higher than previously reported (Pan et al., 2015). Our δ13C, δ15 N, and C/N results indicate large human input as well as terrestrial sources to the TMZ (see also Zhang et al., 2007). For inshore waters, POM was higher in autumn than in spring, because the proportion of anthropogenic sources in autumn was higher than in spring (Figure 8) and δ15 N was lower in autumn. According to the sources of nitrogen isotopes (Kendall, 1998), POM in inshore waters might be greatly affected by agricultural fertilizer inputs.

Offshore, the springtime rise in PON was supported by other indications of high primary production: high δ13C, higher pH (phytoplankton photosynthetic rate), and higher Chl a (phytoplankton biomass), especially around Stations 19 and 20, confirming previous algal bloom observations in this area (Zhou et al., 2008; Liu et al., 2017). In the last few decades, eutrophication in the Yangtze River estuary and the adjacent East China Sea has been seriously exacerbated by the overloading of nutrients from human activities such as industrial sewage and agricultural fertilizer waters (Chai et al., 2006; Song et al., 2017). Excessive nutrients lead to the blooming of phytoplankton and harmful algae, resulting in the accumulation of organic matter in the water (Zhou et al., 2008; Shen, 2020). For the healthier development of estuarine ecosystems, effective control of agricultural and wastewater sources of nutrients should be carried out in the future.

### 4.3. Impacts of human activities and climate change on organic matter

Compared with other major world rivers (Table 3), the DOC concentrations of the Yangtze River mainstream are at the middle level in general. In the lower reaches, especially in the flood season (autumn), the DOC concentrations of the Yangtze River are similar to those in the lower Amazon River (Seidel et al., 2015) and the Mississippi River (Wang et al., 2004). They are lower than in the Congo River (Spencer et al., 2012) and the Yenisei River (Herrault et al., 2016), however, and higher than in the Yellow River (Xia and Zhang, 2011; Wang et al., 2012), Pearl River (Duan and Bianchi, 2006), Columbia River (Dahm et al., 1981) and the Nile River (Ludwig et al., 1996). In the estuary, the DOC in autumn is higher than that of the Amazon River Plume (Seidel et al., 2015) and the Mississippi River Plume (Wang et al., 2004). In the dry season (spring), DOC is similar in amount to the Pearl River (Chen et al., 2004). These results may reflect that the physical dilution by ocean water is an important driver of DOC variability in river plumes (Bianchi et al., 2004; Bauer and Bianchi, 2011; Medeiros et al., 2015; Seidel et al., 2015). DOC in river systems is also reactive; once the terrigenous DOC enters the coastal ocean, it will quickly become remineralized (Hedges et al., 1997; Bianchi et al., 2004). Thus, the degree to which DOC in rivers is diluted and remineralized depends on coastal ocean circulation, which leads to differences among estuaries. Compared with the Amazon River and Mississippi River estuaries, the Yangtze River estuary is also located at a higher latitude, where water is cooler and light more limiting. Photo- and biotransformation processes are important factors affecting DOM composition along the plume (Medeiros et al., 2015; Seidel et al., 2015). Furthermore, higher water temperatures in the plume likely enhance microbial activities (Pomeroy and Wiebe, 2001), which may further stimulate bacterial decomposition of terrigenous DOC.

Table 3.

Comparison of organic carbon and isotopes in the Yangtze River–estuary continuum with other major world rivers and estuaries. DOI: https://doi.org/10.1525/elementa.2021.00034.t3

River (lower reaches)Estuary (or plume)
LocationPOC (mg C L–1)DOC (mg C L–1)δ13C (‰)POC (mg C L–1)DOC (mg C L–1)δ13C (‰)References
Nile River (Egypt) 3.9 3.0 – – – – Ludwig et al., 1996
Amazon River (Brazil) 2.4–4.6 3.5–5.0 –29.9 to –26.6 (–28.0)a 0.02–0.6 (0.2) a 0.9–3.9 –25.6 to –19.9 (–23.7) a Druffel et al., 2005; Moreira-Turcq et al., 2013; Seidel et al., 2015
Mississippi river (United States) 0.7–3.5 (1.9) a 3.9–6.2 –23.9 to –25.0 1.3–4.0 0.7–1.4 –18.7 to –22.5 Trefry et al., 1994; Wang et al., 2004; Duan and Bianchi, 2006; Cai et al., 2015
Congo River (DR Congo) 1.3–1.8 (1.5) 10.5–10.7 –28.8 to –27.6 (–28.2) – – – Spencer et al., 2012
Columbia River (United States) 0.4–0.7 2.8–3.0 –25.5‰ 0.04–50 – – Dahm et al., 1981
Yenisei River (Russia) – 5.0–15.6 (8.3) – – – – Herrault et al., 2016
Mekong River (Vietnam) 0.4–9.0 (2.3) 1.4–6.4 (2.7) – – – – Noh et al., 2013
Yangtze River (China) 1.5–1.8 (1.7); 1.2–2.2 (1.7) 2.1–2.7 (2.4); 3.7–5.1 (4.2) –26.4 to –26.1 (–26.2); –27.2 to –26.3 (–26.7) 1.0–8.2 (2.3); 0.9–8.1 (2.7) 1.2–3.2 (2.1) a; 2.3–8.8 (4.3) –27.2 to –22.6 (–25.5); –27.4 to –22.5 (–25.5) This study (spring in 2014; autumn in 2014)
Yellow River (China) 1.6–3.0 2.6–3.9 –25.6 to –23.4 (–24.2) – 2.6−2.7 –25.7− –24.9 Gu et al., 2009; Xia and Zhang, 2011; Wang et al., 2012; 2018
Pearl River (China) 0.6–3.2 (1.7) 1.0–3.8 –26.6 to –23.4 0.3–2.7 (1.0) 1.2–3.5 –30.1 to –22.0 (–26.7) Chen et al., 2004; Duan and Bianchi, 2006; Zhang et al., 2009; Guo et al., 2015
River (lower reaches)Estuary (or plume)
LocationPOC (mg C L–1)DOC (mg C L–1)δ13C (‰)POC (mg C L–1)DOC (mg C L–1)δ13C (‰)References
Nile River (Egypt) 3.9 3.0 – – – – Ludwig et al., 1996
Amazon River (Brazil) 2.4–4.6 3.5–5.0 –29.9 to –26.6 (–28.0)a 0.02–0.6 (0.2) a 0.9–3.9 –25.6 to –19.9 (–23.7) a Druffel et al., 2005; Moreira-Turcq et al., 2013; Seidel et al., 2015
Mississippi river (United States) 0.7–3.5 (1.9) a 3.9–6.2 –23.9 to –25.0 1.3–4.0 0.7–1.4 –18.7 to –22.5 Trefry et al., 1994; Wang et al., 2004; Duan and Bianchi, 2006; Cai et al., 2015
Congo River (DR Congo) 1.3–1.8 (1.5) 10.5–10.7 –28.8 to –27.6 (–28.2) – – – Spencer et al., 2012
Columbia River (United States) 0.4–0.7 2.8–3.0 –25.5‰ 0.04–50 – – Dahm et al., 1981
Yenisei River (Russia) – 5.0–15.6 (8.3) – – – – Herrault et al., 2016
Mekong River (Vietnam) 0.4–9.0 (2.3) 1.4–6.4 (2.7) – – – – Noh et al., 2013
Yangtze River (China) 1.5–1.8 (1.7); 1.2–2.2 (1.7) 2.1–2.7 (2.4); 3.7–5.1 (4.2) –26.4 to –26.1 (–26.2); –27.2 to –26.3 (–26.7) 1.0–8.2 (2.3); 0.9–8.1 (2.7) 1.2–3.2 (2.1) a; 2.3–8.8 (4.3) –27.2 to –22.6 (–25.5); –27.4 to –22.5 (–25.5) This study (spring in 2014; autumn in 2014)
Yellow River (China) 1.6–3.0 2.6–3.9 –25.6 to –23.4 (–24.2) – 2.6−2.7 –25.7− –24.9 Gu et al., 2009; Xia and Zhang, 2011; Wang et al., 2012; 2018
Pearl River (China) 0.6–3.2 (1.7) 1.0–3.8 –26.6 to –23.4 0.3–2.7 (1.0) 1.2–3.5 –30.1 to –22.0 (–26.7) Chen et al., 2004; Duan and Bianchi, 2006; Zhang et al., 2009; Guo et al., 2015

aValues in parentheses are the averages.

POC concentration in the lower reaches of the Yangtze River is moderate compared to other large river systems (Table 3). It is similar to the Mississippi River, the Congo River, the Yellow River, and the Pearl River (Trefry et al., 1994; Duan and Bianchi, 2006; Spencer et al., 2012; Wang et al., 2012; Guo et al., 2015), but higher than the Columbia River (Dahm et al., 1981), and lower than the Nile River, Amazon River, or Mekong River (Ludwig et al., 1996; Moreira-Turcq et al., 2013; Noh et al., 2013). In the Yangtze River estuary, the POC is relatively high, similar to that in the Mississippi River (Trefry et al., 1994), but higher than in the Pearl River (Guo et al., 2015). It is much lower than in the Amazon River (Druffel et al., 2005), however.

Explanations for differences in POC among global rivers require additional information from isotopes. Isotope information reveals that allochthonous organic matter in the Yangtze River is high in the mainstream, like other large rivers. The range of δ13C of POC in the mainstream is –27.2 to –26.1‰ (Table 3), more depleted than the Pearl River (Zhang et al., 2009), Mississippi River (Wang et al., 2004), and Columbia River (Dahm et al., 1981), but less depleted than the Amazon (Moreira-Turcq et al., 2013) and Congo River (Spencer et al., 2012). The more negative δ13C in the Yangtze River mainstream indicates a large source of terrestrial material (Ceburnis et al., 2016), which becomes depleted as the water flows to the coastal ocean (–27.4 to –22.5‰ in the Yangtze River estuary). In the estuary, the δ13C was less depleted in the Yangtze River estuary than in the Amazon River plume (Druffel et al., 2005) or the Mississippi estuary (Wang et al., 2004), indicating a greater contribution of terrestrial material to the Yangtze River estuary. The Pearl River, where terrestrial inputs are also large (Guo et al., 2015), is similar to the Yangtze River estuary.

Combined, these continuum characteristics reflect the large freshwater discharge and human impacts in the Yangtze system (Wang et al., 2004; Zhang et al., 2007). First, the physical environment of the Yangtze River continuum is characterized by high discharge (27,856 m–3 s), large terrestrial input, subtropical temperatures, and light, modest phytoplankton production in the estuary, with little influence of offshore currents. The discharge is greater than the Mississippi River (16,792 m–3 s) or the Pearl River (22,000 m–3 s; Ou et al., 2009; Bianchi and Bauer, 2011; Goes et al., 2014; Xu et al., 2018), but less than the Amazon River (120,000 m–3 s). The Amazon River, Mississippi River, and the Pearl River are located in the lower latitudes (Duan and Bianchi, 2006), so the waters are warmer and light may be less limiting. Primary production of the Amazon River Plume is greater than the Yangtze River estuary (Zhang et al., 2007; Gomes et al., 2018; Shen, 2020). Due to the suitable temperature and light in the Mississippi River Plume, primary production contributes large amounts of POC during most of the year (spring, summer, and autumn; Breed et al., 2004; Green et al., 2006). In contrast, the POC of the Pearl River estuary and the Yangtze River estuary mainly comes from terrestrial inputs. Offshore currents at the equatorial Amazon plume are especially influential on the fate of that plume (Cole et al. 2013).

Human impacts are also high in the Yangtze River estuary due to heavily urbanized shorelines and high nutrient inputs. These conditions are also found in the Pearl River and the Mississippi River continuums (Duan and Bianchi, 2006; Cai et al., 2015; Wu et al., 2015). High water discharge is linked with large anthropogenic inputs to these estuaries, particularly from agricultural fertilizers from the watershed, and urban sewage effluents from major cities (Goolsby et al., 2000; Duan and Bianchi, 2006; Li et al., 2014). In contrast, the Amazon River continuum is generally less affected by human activities (Gomes et al., 2018). The eutrophication from these human activities can be seen through the changes of POC production in the Yangtze River estuary.

As climate generally alters the hydrology by affecting precipitation in recent years, riverine organic matter also changes with the variations of hydrology. Naturally, in seasons or years with high precipitation in the Yangtze River Basin, the river has more water discharge, carrying more terrestrial material into the estuaries, and vice versa (Gao et al., 2012; Gao et al., 2014b). Meanwhile, large-scale human activities, especially dam construction and increased eutrophication, have increased the disturbance to river–estuary ecosystem. Construction of the TGD especially altered the downstream hydrological regime along the Yangtze River: water discharge of the first decade after the closing of the TGD (2003–2012) was 67 km3 yr–1 (approximately 7%) lower than that of the previous 50 years (1950–2002; Yang et al., 2015b). These changes have affected the terrestrial organic matter transport and estuary environment, and human development (Yang et al., 2015b; Zhang et al., 2016). Eutrophication in the Yangtze River estuary has accelerated since the 1990s, and the increased nutrient inputs have contributed to the amount of anthropogenic organic matter into the estuary. This increased input is mainly due to the economic development along the Yangtze River Basin and coastal areas, including increased fertilizer use and enhanced discharge of sewage wastewater (Chai et al., 2006; Cheng et al., 2012). In addition, due to the process of urbanization, soil erosion caused by human activities has an impact on river POC, which cannot be ignored.

Here we establish that the physical-chemical environment can control the distribution of organic matter in the river-ocean continuum. During periods of global change, the spatial-temporal distribution of organic matter in the river–ocean system is affected by climate change and anthropogenic disturbances. Determining the distribution and sources of riverine and estuarine organic matter is crucial for understanding carbon cycling in the river-ocean continuum and is an important tool for assessing climate change and anthropogenic impacts. This study presented spatial-temporal variations and stable isotopic signature distributions of POM and DOC in the surface water of the Yangtze river-ocean continuum. Significant seasonal and spatial variations of organic matter were observed, which indicated that monsoon and human activities changed the concentrations and compositions of organic matter in this system. These changes have a significant impact on the regional carbon cycle and coastal ecosystems. Except for the turbidity maximum zone, the organic matter of the Yangtze River-ocean continuum mainly existed in the form of dissolved organic carbon. Increased human activities will have exacerbated the impact on the Yangtze River continuum, including increasing dam construction, additional eutrophication, and accelerating soil erosion by rapid urbanization. This study provides good background data for further work and has revealed significant human impacts on the Yangtze River-ocean continuum. A more balanced approach to economic and ecological development will be required to return to a healthy ecosystem in the future.

Data deposition: All of the data used in this article are publicly available. Please see Table S2 in the Supplementary Materials.

Figure S1. Monthly water flow (*108 m3 month–1) and sediment load (*107 kg month–1) were measured at the Datong Hydrographic Station in 2014.

Figure S2. Spatial variations of chlorophyll a and suspended particulate matter along the Yangtze River main stream.

Figure S3. Spatial distributions of pH in the Yangtze River estuary in May and November.

Figure S4. Regional variations of POC, SPM and δ13C in the Yangtze River mainstream in the past 20 years.

Table S1. δ13C and δ15 N end–members and ranges of POC samples from different sources.

Table S2. Original data sets (separate Excel file).

Z-SS acknowledges China Scholarship Council (CSC) for support. We are grateful to the scientists and crew members aboard “Zhe Shengyu 10201” for their help in sampling. We acknowledge Xijie Yin for great assistance in carbon and nitrogen isotopes analyses using EA-IRMS. We thank Mingjiang Cai for refining the figures, and we also thank Linquan Mu and Leslie Cyrene Townsell in our Lab for their comments and suggestions on our manuscript.

This work was supported by grants from the National Natural Science Foundation of China (31872568 to X-WW) and the Key Deployment Project of Center for Ocean Mega-Science, Chinese Academy of Sciences (COMS2019Q14 to X-WW).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• Contributed to conception and design: Z-SS, X-WW, PLY.

• Contributed to acquisition of data: Z-SS, LC, S-ZL, X-WW.

• Contributed to analysis and interpretation of data: Z-SS, PLY, LC, S-ZL, X-WW.

• Drafted and or revised this article: Z-SS, PLY, LC, S-ZL, X-WW.

• Approved the submitted version for publication: Z-SS, PLY, X-WW, LC, S-ZL.

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How to cite this article: Zhang, S, Yager, PL, Liang, C, Shen, Z, Xian, W. 2022. Distribution and spatial-temporal variation of organic matter along the Yangtze River-ocean continuum. Elementa: Science of the Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.00034

Domain Editor-in-Chief: Jody W. Deming, University of Washington, Seattle, WA, USA

Associate Editor: Lisa A. Miller, Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC, Canada

Knowledge Domain: Ocean Science

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