Source apportionment of sediment organic material in a semi-enclosed sea using Bayesian isotopic mixing model

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Source apportionment of sediment organic material in a semi-enclosed sea using Bayesian isotopic mixing model Jing Yua,b, Hua Zhanga,⁎ a Key Laboratory of Coastal Zone Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003,China b University of Chinese Academy of Sciences, Beijing 100049, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Stable isotopes MixSIR Carbon budget Atmospheric deposition Particulate organic carbon

To determine sources of organic material in semi-enclosed Bohai Sea, samples of marine surface sediments, suspended particulates in adjacent rivers and atmospheric deposition were collected and analyzed for grain size composition, total organic carbon(TOC and POC), total nitrogen (TN and PN), and stable isotopic composition (δ13C and δ15N). Measured bulk C/N ratio (5.50–12.28), δ13C (−23.59 ~ −19.54‰), and δ15N (2.80–8.07‰) values of surface sediment organic materials indicated a mixed source of marine and terrestrial contributions. Spatial distribution of organic C, N and their stable isotope composition indicated a land-sea gradient of organic material content and source combination. Using MixMIR model with dual isotopes, it was estimated that relative contributions of marine, riverine, and atmospheric sources to sediment mixture were 69.0%, 9.6%, and 21.4%, respectively. Our results demonstrated the advantage of Bayesian isotope mixing models over the conventional end-member mixing models for source apportionment in coastal seas with complex source origins.

1. Introduction Marine sediment is the largest global reservoir in the carbon cycle [e.g., (Ramaswamy et al., 2008; Walsh, 1991)]; 80–90% of global organic carbon sinks via water column and buries in continental margin, though the area accounts for only 10% of the world ocean (Goni et al., 1997; Winkelmann and Knies, 2005). As the main interfaces between continent and ocean, marginal seas store significant amounts of sediment organic material (OM) from autochthonous (e.g., marine phytoplankton) or allochthonous sources (e.g., organic material from river, and carbonaceous materials from atmospheric deposition) (Sarkar et al., 2015; Serna et al., 2010) due to high sedimentation rates and biological productivity (Yao et al., 2015). Allochthonous contribution of OM through river discharge and atmospheric deposition is particularly important in semi-enclosed marginal seas with substantial terrestrial influences (Goni et al., 1997; Sarma et al., 2014; Usui et al., 2006). Recently, atmospheric transport of carbonaceous materials derived from incomplete combustion of biomass and fossil fuels received increasing concern in marginal seas downstream of wind from mainland (Fang et al., 2015; Zong et al., 2015). However, contribution of atmospheric deposition to marine sediment organic material remained unknown and needed to be quantitatively evaluated (Mahowald, 2011).

⁎

Carbon and nitrogen stable isotopic compositions (δ13C and δ15N) and their elemental ratios (C/N) have been widely used as tracers to quantitatively elucidate the fate and origins of OM in the marine environments [e.g., (Liu et al., 2006; Thornton and McManus, 1994; Wada et al., 1987)]. Several studies using two or three end-member isotope mixing models to determine the distribution and sources of OM in estuaries, marginal seas, and continental shelves [e.g., (Cifuentes et al., 1996; Usui et al., 2006; Yu et al., 2015)]. However, conventional mixing models have critical limitations: 1) unable to solve the case where the number of sources exceeds the number of isotopes plus one; 2) unable to incorporate the variance of source isotopic composition; and 3) unreliable result with overlapping source isotopic composition (Davis et al., 2015; Phillips and Gregg, 2003). Mass balance mixing models such as IsoSource was developed to solve mixing of multiple sources but did not account uncertainty of source isotopic composition (Phillips and Gregg, 2003; Phillips et al., 2014). Recently, Bayesian models such as mixing models using sampling-importance-resampling (MixSIR) and Stable Isotope Analysis in R (SIAR) were developed to apportion the biogeochemical sources in mixed samples (Davis et al., 2015; Moore and Semmens, 2008; Phillips et al., 2014). Such model can apportion more than three sources when only two isotopes are used, and characterize the ranges and distributions of sources instead of using a single signature to represent one source. The most obvious advantage

Corresponding author. E-mail address: [email protected] (H. Zhang).

http://dx.doi.org/10.1016/j.marpolbul.2017.04.037 Received 26 January 2017; Received in revised form 17 April 2017; Accepted 21 April 2017 0025-326X/ © 2017 Published by Elsevier Ltd.

Please cite this article as: Yu, J., Marine Pollution Bulletin (2017), http://dx.doi.org/10.1016/j.marpolbul.2017.04.037

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Anhui and Jiangsu, have encountered the most severe air pollution in the last decade, due to heating in winter and straw burning (Fang et al., 2015; Zong et al., 2015). In addition, as the largest offshore crude oil production base in China, the Bohai Sea has numerous oil wells and drilling platforms under operation.

is that Bayesian can provide complete source proportional contributions, including the variance, mean value and quintiles of contributions. The Bayesian models were extensive applied to the investigation of trophic relationship in the food wed and recently been used in source apportionment of sediments (Cooper et al., 2015; Kubo and Kanda, 2017; Morris et al., 2015; Nosrati et al., 2014). Thus the main objectives of this study are 1) to evaluate the relative contribution of riverine discharge and atmospheric deposition to sediment organic material in a semi-enclosed sea; and 2) to test the applicability of isotopic mixing model (MixSIR) for source apportionment of sediment organic material. The study is conducted in the Bohai Sea in northeast China, which is a semi-enclosed shallow sea with tremendous anthropogenic influences.

2.2. Sample collection Surface marine sediment (top 3 cm depth) samples were collected using a stainless steel box corer sampler during the summer cruise in August 2014 at 99 locations in the Bohai Sea. These sample sites were distributed in the nearshore and offshore zones, covered most parts of the sea (Fig. 1). At each site, water temperature, salinity, and chlorophyll a fluorescence at different water depth was measured in situ using CTD (SBE 911plus, USA). Measured salinity values of marine sampling sites were between 28 and 31 psu. Sediment samples were collected in polyethylene bags, sealed and stored in a freezer until further analysis. Sediment samples for chemical analysis were freeze-dried, homogenized and ground to a fine powder with an agate pestle and mortar. The vegetation debris was removed before the grinding process. A portion of the homogenized fine powder samples was placed in glass tubes and acidized using 1 M hydrochloric acid to remove carbonate. After soaking for 12 h, hydrochloric acid was replaced for another 12 h until no air bubbles in tubes. The sample was then washed with deionized water to remove salts until becoming neutral and dried at 50 °C in a constant temperature oven. Water samples in 36 rivers around the Bohai Sea were collected using Niskin bottles in May, August, and December 2015. Based on measured salinity values (< 1 psu), all river samples are freshwater devoid of sea water influence. Total suspended particulate matter (TSM) were filtered (0.3–1.5 L) on precombusted (450 °C for 24 h), constant temperature (25 °C) and humidity (39%) for 24 h, and preweighed Whatman GF/F filters and then stored at −20 °C in refrigerator. After dried at 60 °C for 48 h, the samples were treated in the same condition of blank filters before being weighed on balance (Sartorius BSA224S, Germany) [e.g., (Sarma et al., 2014)]. A portion of dried samples were used for particulate nitrogen (PN) analysis. Filter samples for analysis of particulate organic carbon (POC) were dried at 50 °C for 24 h after removal of carbonated using HCl fumigation for 3 days (Hedges and Stern, 1984). Sampling of total suspended particulates (TSPs) in the air was conducted in Dalian (the station in Dalian Maritime University), Dongying (the Yellow River Delta Ecological Research Station of Coastal Wetland), and Longkou (the Longkou Environmental Monitoring Station of the State Ocean Administration of China) during 2013–2015 as shown in Fig. 1. TSP samples were collected on separate quartz filters using a high volume air sampler (Tisch Environmental, Inc., USA) at a flow rate of 0.3 m3/min.

2. Materials and methods 2.1. Study area Bohai Sea (37° 07′ ~ 41°0′ and 117° 35′ ~ 121° 10′) includes Laizhou Bay, Bohai Bay, Liaodong Bay, Bohai Strait and the Central Part. The shoreline of Bohai Sea has an overall length of 3780 km. The area of open water is about 7.7 × 104 km2. Bohai Sea is a typical semienclosed shallow marginal sea of western Pacific Ocean, surrounded by several industrialized cities. The Bohai Strait with 80.5 km wide on its eastern side is the only passage connection to the Yellow Sea. The average depth is about 18 m and the deepest site (80 m) is located in the Laotieshan Channel of the Bohai Strait (Fig. 1). Bohai Sea has irregular semi-diurnal tide, subjected to strong tidal currents. It is influenced by monsoon climate, prevailing northwest wind in winter and southeast wind in summer. The averaged annual precipitation in the Bohai Sea is 500–600 mm, the overwhelming high precipitation occurs in summer, with an average value of ~400 mm. Terrestrial contributions to the sea are mainly through large rivers with total runoff about 2.0 × 1010 m3/yr in year 2015. Among them, Yellow River contributes the highest runoff to the Bohai Sea with a discharge of 1.27 × 1010 m3, accounts for about 60% of total discharge. During the Chinese economy boom in the last three decades, the circum-Bohai region has become a hub of industrialization and urbanization. The study area has been increasingly affected by air pollution from largescale urbanization, industrialization and agriculture activities (Cao et al., 2006; He et al., 2015). Especially the North China Plain on the southwest of the Bohai Sea, which includes the two municipalities of Bejing and Tianjin and five provinces of Hebei, Henan, Shandong,

2.3. Chemical and stable isotope analysis The marine sediment, river TSM, and atmospheric TSP samples were analyzed for the total organic carbon (TOC) and total nitrogen (TN) via high temperature combustion on a CHNS analyzer (Elementar vario MACRO cube, Germany). Duplicate analyses of every ten samples were implemented. Replicate analysis gave a 1 σ precision of ± 0.4 wt % C and ± 1.3 wt% N. Standards used for carbon and nitrogen analysis include standard organic analytical standards (glutamate) and the national standard of soil (GBW 07434). The precision of this method based on replicate measurements (n = 6) of a reference standard is 0.8% for carbon and 1.3% for nitrogen. Sediment samples powder and a portion of the carbonate free samples were analyzed by an isotope ratio mass spectrometer (Finnigan DELTAplus XL, USA) for the measurement of the δ15N and δ13C values, respectively. The results are expressed as δ13C and δ15N in parts per thousand (permil; ‰), stable isotope ratios are reported in δ notation,

Fig. 1. Site locations of surface sediment samples (red dots), river particulate samples (purple triangles), atmospheric particulate samples (red squares) overlain on a bathymetric map of the Bohai Sea. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 1 Statistical description of geochemical properties of surface sediment samples from the Bohai Sea. Statistic

Sediment samples in Bohai Sea

Number of samples Minimum Maximum Mean Median First quartile Third quartile Standard deviation

TOC (mg/g)

TN (mg/g)

C/N

δ13C (‰)

δ15N (‰)

Clay (%)

Silt (%)

Sand (%)

Median grain size(μm)

99 0.62 8.76 4.00 3.63 2.57 5.54 1.85

99 0.10 1.32 0.59 0.54 0.35 0.81 0.29

99 5.50 12.28 8.21 7.98 7.03 9.11 1.52

98 −23.59 −19.54 −22.12 −22.12 −22.41 −21.80 0.60

99 2.80 8.07 5.94 6.02 5.58 6.34 0.72

93 0.26 31.30 17.80 17.68 12.00 24.53 7.78

93 1.00 77.56 54.23 60.67 42.37 69.33 18.70

93 0.35 98.75 27.97 18.32 8.21 46.84 24.73

93 7.17 339.37 48.68 24.27 10.31 57.94 60.69

δX:

δX = (R sample R standard − 1) × 10 3

the threshold acceptance values and 105 iterations for establishing the posterior probability of a set of resampled source contributions.

(1)

2.5. Data analysis

where X is the C or N stable isotope, R is the ratio of heavy to light isotopies, and VPDB (Vienna Peedee Belemnite) and air are standards for δ 13C and δ 15N, respectively. Working standards were caffeine (IAEA 600) with heavy element (13C and 15N) of −27.77‰ and + 1.0‰, respectively. The analytical precisions based on replicate measurements of C and N isotopic analysis are ± 0.2‰ and ± 0.4‰.

Correlations between variables were based on Pearson's correlation coefficients. ANOVA test (one-way analysis of variance) was used to compare spatiotemporal changes on OC, TN, δ13C and δ15N in rivers and marine sediments. The interpolation patterns of parameter distribution were analyzed using Inverse Distance Weighting (IDW) geostatistical method for ArcGIS 10.2.

2.4. Sediment OM sources apportionment

3. Results and discussion

The MixSIR model provides a Bayesian framework to determine the probability distributions of proportional source contributions to isotope mixture (Moore and Semmens, 2008). Prior knowledge regarding proportional contribution of each source (fi) are defined using Dirichlet distributions, a multivariate generalization of the Beta distribution, which treats each source input as independent and requires the mean proportions for each OM source to sum to unity. The isotopic compositions of ith sources are treated as random variables characterized by normal distribution with mean and variance parameters defined for each isotope tracer j. User-specified fractionation distributions are used to adjust the means and variances of source isotopic parameters. Then means ( μ ) and variances (σ ) of isotope composition of proposed mixture are predicted from randomly drawn proportional source contributions (fi) using following equations.

3.1. Sediment C and N characterization Summary statistics of the geochemical properties of surface sediment samples in the Bohai Sea are given in Table 1. The TOC contents in marine sediments varied from 0.62 to 8.76 mg/g, averaging 4.00 ± 1.85 mg/g. The TN contents were in the range of 0.10 to 1.32 mg/g with a mean value of 0.59 ± 0.29 mg/g. Ranges of measured TOC and TN content were consistent with previous results reported in the Yellow River Estuary and the Central Bohai Sea (TOC: 0.8–9 mg/g and TN: 0.1–1.1 mg/g) (Liu et al., 2015). A significant positive correlation (p < 0.01) between contents of TOC and TN was observed (Table 2) with strong linear relationship (TOC = 6.11 × TN + 0.404, r2 = 0.918). This significant correlation indicated that N in sediment was predominantly bound to OM, despite that mineralization and nitrification- denitrification may also affect turnover of sediment nitrogen (Gaye et al., 2009; Thornton and McManus, 1994; Wu et al., 2003). Sediment C/N ratios ranged between 5.50 and 12.28 with mean value of 8.21 ± 1.52 (Table 1). The C/N ratio had been widely used as effective index to characterize predominant sources of OM in coastal and marginal sediments [e.g. (Lamb et al., 2006)]. Previous studies reported that C/N ratio of marine phytoplankton was close to 6.7 while that of terrestrial C4 plant was 28–46, and vascular plants had C/N ratios exceeding 12 (Cifuentes et al., 1996; Gao et al., 2005). C/N ratios of has a large variation of 0.81–8.3 (mean 2.4) in the aerosol samples from East Asia (Kawamura et al., 2004), and 0.7–9.5 (mean 2.8 ± 2.2) in suburban aerosol from Beijing area (He et al., 2015). Therefore, the C/N ratios indicated that the OM in Bohai Sea has mixed marine and terrestrial sources. In surface sediment of the Bohai Sea, values of δ13C ranged from − 23.59‰ to − 19.54‰ with mean of −22.12 ± 0.60‰, while δ15N values were between +2.80‰ and +8.07‰ with a mean of + 5.94 ± 0.72‰ (Fig. 2a). Similar isotopic compositions of C and N have been observed (δ13C: −24.6 to − 22.0‰ and δ15N: 3.9 to 6.4‰) in Pearl River estuary (Hu et al., 2006) and was previous reported (δ13C: −24.0‰ to − 20.8‰ and δ15N: 4.0 to 7.4‰) in central and southern regions of the Bohai Sea (Liu et al., 2015). There was no

n

μ j =

∑ [fi (msi j + mfi j ) ]

(2)

i =1 n

σ j =

∑ { (fi )2 [ (ssi j )2 + (sfi j )2] }

(3)

i =1 th

where subscript i indicate i source and superscript j indicate jth isotope, n is the total number of sources, ms and ss are the mean and standard deviation of source isotopic compositions, respectively, while mf and sf are the mean and standard deviation of isotopic fractionation factor of sources, respectively. The likelihood of the data given the proposed sediment mixture is calculated based on μ and σ using equation, l

 (x | μ , σ ) =

m

∏∏ k =1 j =1

⎡ j j 2⎤ (x − μ ) − k ⎢ ⎥ 1 j 2  2 σ e ( ) ⎥ ⎢ j ⎢ σ × 2π ⎥ ⎣ ⎦

(4)

th

where subscript k indicate k sediment mixture, l is the total number of the sediment mixtures, m is the total number of isotope tracers, and x is the measured isotopic composition of sediment mixtures provided in the data file. The posterior probability distributions of source contributions are calculated based on numerical integration using sampling-importance-resampling (SIR) algorithm. The MixSIR model is implemented with 104 iterations to determine 3

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Table 2 Correlation coefficients (r) between measured geochemical parameters of surface sediment samples from the Bohai Sea.

TOC (mg/g) TN (mg/g) C/N δ13C (‰) δ15N (‰) Clay (%) Silt (%) ⁎⁎ ⁎

TN (mg/g)

C/N

δ13C (‰)

δ15N (‰)

Clay (%)

Silt (%)

Sand (%)

0.958⁎⁎

−0.143 −0.378⁎⁎

− 0.189⁎ − 0.143 -0.058

0.467⁎⁎ 0.533⁎⁎ − 0.341⁎⁎ 0.088

0.827⁎⁎ 0.859⁎⁎ −0.307⁎⁎ −0.190⁎ 0.489⁎⁎

0.585⁎⁎ 0.663⁎⁎ −0.567⁎⁎ −0.015 0.524⁎⁎ 0.683⁎⁎

-0.705⁎⁎ -0.774⁎⁎ − 0.526⁎⁎ 0.072 − 0.551⁎⁎ − 0.833⁎⁎ − 0.973⁎⁎

level of significance at p < 0.01. level of significance at p < 0.05.

als, which had lower δ13C and higher δ15N than autochthonous sources. Similarly, TN showed significant correlation with δ15N (r = 0.533, p < 0.01). A significant negative relationship was observed between C/N ratios and δ15N (r = − 0.341, p < 0.01), which indicate the influence of terrestrial sources dominated by C3 plants, which has higher C/N ratio (> 12) and lower δ15N (0.4 ± 0.9‰) than marine primary production (Cifuentes et al., 1996). In the Bohai Sea, significant positive correlation existed between content of fine sediment (clay and silt) and TOC (p < 0.01) as well as TN (p < 0.01) (Table 2). It was not surprising since fine sediments were known to have a large surface area which can absorb and preserver organic matter (Keil et al., 1994). In addition, C/N ratios were found to be negatively correlated with clay (r = − 0.307, p < 0.01) and silt (r = − 0.567, p < 0.01) contents and significant positive correlations were observed between δ15N and clay (r = 0.489, p < 0.01) as well as silt (r = 0.524, p < 0.01). The significant correlations between fine sediment with C/N ratio as well as δ15N suggest ocean circulation might carry a substantial fraction of organic matter and redistribute them in the Bohai Sea (Ramaswamy et al., 2008; Zhu and Chang, 2000). Fig. 3 illustrated the spatial distributions of TOC and TN in the surface sediment of Bohai Sea. There was a gradual decrease trend of TOC and TN content from west coast to Bohai Strait in the east (Fig. 3a and b). This spatial distribution trend of OM obtained in the Bohai Sea continental shelf was comparable to that in other continental shelves, which had similar trend of higher concentration in the inner shelf (Hu et al., 2006; Ramaswamy et al., 2008). There was a general gradient showing an increase in C/N ratios from southwest to northeast in the Bohai Sea (Fig. 3c). High C/N ratios appeared near the mouth of three major rivers (Liao River, Luan River, and Yellow River) with values close to that of terrestrial soil ecosystem and riverine input. However, sediments of Bohai Bay and Laizhou Bay, two coastal bays heavily impacted by anthropogenic activities, has relatively low mean C/N ratios of 8.1 and 7.0, respectively. An explanation was that high loading of dissolved nitrogen from rivers stimulated the growth of plankton and resulted in the subsequent deposition of organic material in marine sediments (Yao et al., 2015). In addition, the increasing load of nitrogen from atmospheric deposition might also contribute to the low C/N ratio in these two coastal bays. Relatively lower δ13C values were observed in Yellow River Estuary (− 22.47 ± 0.57) and Bohai Bay (− 22.38 ± 0.53), which suggest influences of sediment input from the Yellow River (Fig. 2b, Fig. 3d). Our results agreed with the sediment transport studies that demonstrated the majority of sediment load from Yellow River were deposited in the estuary and coastal Bohai Bay (Sündermann and Feng, 2004). The highest δ15N values in surface sediments appeared at the top of three bays (Bohai Bay, Laizhou Bay, Liaodong Bay) and the δ15N values decreased toward the Bohai Strait. The spatial pattern of the δ15N was consistent with the fact that suspended particles in atmospheric deposition had higher δ15N value than marine sources. Both δ13C and δ115N values suggested the substantial contribution of riverine and atmospheric sources to OM in

Fig. 2. Carbon and nitrogen stable isotope composition of organic material (OM) in surface sediment samples of the Bohai Sea. a) Isotopic comparison of sediment with potential sources. Red open circle are measured δ13C and δ15N of sediment OM. Solid points and error bars represent, respectively, the mean and standard deviation of isotope signatures of three sources. b) Comparison between different geographic regions. Colored solid points and error bars represent, respectively, the mean and standard deviation of isotope signatures of different geographic regions (YRE: Yellow River Estuary; BHB: Bohai Bay; BHS: Bohai Strait; CBS: Central Bohai Sea; LDB: Liaodong Bay; LZB: Laizhou Bay). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

significant correlation between values of δ13C and δ15N in this study (r = 0.088, Table 2). This lack of correlation suggested that sediment organic material had high degree of heterogeneity due to complex source of origins. Results of correlation analysis in Table 2 showed significant correlations among geochemical parameters. TOC was significantly correlated with both δ13C (r = − 0.189, p < 0.05) and δ15N (r = 0.467, p < 0.01), confirming that areas with the high TOC content in Bohai Sea accumulated more allochthonous organic materi4

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Fig. 3. Contour graph showing the spatial variation of a) Total organic carbon (TOC), b) Total nitrogen (TN), c) C/N ratio, d) δ13C, e) δ15N, and f) Chlorophyll a in surface sediments of the Bohai Sea. The blue columns indicate mean parameter values measured in suspended particle samples from rivers. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

deposition processes from river and atmosphere to sea bottom does not involve isotopic fractionation, the mean and variance of fractionation factors (mf and sf) for all sources were set to be zero in this research (Nosrati et al., 2014). In the first model run, measured δ13C and δ15N values of all sediment samples were used as input mixture data to evaluate the source contribution to the entire Bohai Sea and the model estimated contributions of the three OM sources were shown in Fig. 4. The MixSIR model estimated that marine primary production contributed between 65.8 and 72.2% (best estimate 69.0%), riverine input contributed between 6.6 and 12.5% (best estimate 9.6%), and atmospheric deposition contributed between 17.4 and 25.7 (best estimate 21.4%). The dominance of marine organic sources is expected in coastal seas (Cifuentes et al., 1996; Kubo and Kanda, 2017; Morris et al., 2015; Usui et al., 2006). Marine primary production undergoes burial and sedimentation processes and ultimately forms a major fraction of OM in surface sediment (Duarte et al., 2005). Using terrestrial and marine end-

coastal sediments (Fig. 2a).

3.2. Source apportionment Our analysis showed that three dominant sources, i.e., marine phytoplankton, riverine source, and atmospheric deposition, contribute to sediments OM of Bohai Sea. Previous studies applied two endmember mixing model based on fixed δ13C values of marine organic carbon (− 20.5‰) and terrestrial organic carbon (− 27.0‰) to estimate the autochthonous and allochthonous contribution to sediment OM of Bohai Sea (Gao et al., 2012; Liu et al., 2015). This overly simplified mixing model was inadequate for the source apportionment with more than two sources and incapable of incorporating variance of source isotopic composition. A more critical limitation was that the isotopic composition of atmospheric deposition substantially overlapped with the riverine and marine sources (Fig. 2a), which made it indistinguishable in simple mixing model (Davis et al., 2015). Here, a Bayesian stable isotope mixing model (MixSIR) with dual isotope (13C and 15N) was employed to statistically estimate the posterior probability distributions of source contributions to sediment mixture (Moore and Semmens, 2008). Values of δ13C and δ15N for three sources, i.e., marine, riverine, and atmospheric in the MixSIR model were derived from measured and literature values (Fig. 2a). The δ13C (− 27.37 ± 3.71‰) and δ15N (6.77 ± 6.05‰) for riverine input were calculated from measured values of suspended particles in 36 rivers around the Bohai Sea. The mean δ13C indicated that C3 plant pathway was the main source of riverine organic carbon and large variation of δ15N indicated different source materials (Gaye et al., 2009). Mean δ13C value (− 24.75 ± 0.62‰) and mean δ15N value (9.24 ± 4.64‰) of the atmospheric deposition were calculated by analyzing suspended particulates samples of three coastal cities (see Fig. 1), which was similar to the values reported for aerosols from vicinity of Beijing (He et al., 2015) and East Asia (Kawamura et al., 2004). Input data of marine sources were based on reported δ13C (− 20.28 ± 1.33‰) and δ15N (4.67 ± 0.54‰) values of phytoplankton sampled from Bohai Sea (Wan et al., 2005). Since sediment

Fig. 4. Posterior estimates of proportional contributions of sources to organic material in the surface sediments of Bohai Sea based on MixSIR model.

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4. Conclusions

Table 3 MixSIR model estimates of the mean percentile contribution (with 95% confidence intervals) of different sources to the organic material in the surface sediment mixtures from different geographic regions of the Bohai Sea.a Regions

Primary production

Riverine sources

Atmospheric deposition

Bohai Sea Bohai Bay Yellow River Estuary Laizhou Bay Liaodong Bay Central Bohai Bohai Strait

69.0(65.8–72.2) 64.1(58.1–69.7) 67.6(50.9–82.4)

9.6(6.6–12.5) 11.0(5.8–16.2) 12.6(1.9–25.2)

21.4(17.4–25.7) 24.9(17.2–33.6) 19.8(3.7–41.3)

70.6(63.7–77.0) 70.7(63.2–77.6) 71.5(62.1–79.8) 70.7(58.6–81.4)

8.2(1.0–17.2) 7.5(1.3–14.4) 8.1(1.2–16.1) 10.0(1.3–20.3)

21.2(10.6–31.1) 21.8(13.0–31.8) 20.4(10.1–32.3) 19.3(6.0–34.6)

a

Combination of dual isotope analysis (δ13C and δ15N) and the Bayesian model (MixSIR) offered a quantitative approach for evaluating the probability distributions of latent source contributions by incorporating source variance and prior information. Our results demonstrated that organic matter in sediment of Bohai Sea mainly derived from the marine autogenous source, which accounted for approximately 69.0%, whereas the riverine inputs contributed around 9.6% and atmospheric deposition contributed around 21.4%. Furthermore, high proportion contribution of atmospheric deposition appeared in Bohai Bay, which clearly demonstrated the influence of air pollutants carried from North China Plain. Overall, the findings from this research indicated atmospheric deposition has become the major pathway of transporting terrestrial organic material to the sediment of semi-enclosed Bohai Sea. The Bayesian isotope mixing models demonstrated substantial advantage over the conventional end-member mixing models for source apportionment in coastal seas with multiple sources.

Values inside parentheses show estimation uncertainty at 95% confidence interval.

member mixing models with measured δ13C values, it was estimated the marine algae contributed an average 69% of the organic carbon to the Bohai Sea (Liu et al., 2015). Similar study found the surface sediments in Bohai Bay were dominated by marine derived organic carbon, with a range of 51–87% (Gao et al., 2012). The estimated relative contributions showed a larger contribution from atmospheric deposition than riverine input, which contradicted the conventional belief that river discharge was the primary pathway of terrestrial input (Goni et al., 1997; Hu et al., 2006; Sarma et al., 2014; Yao et al., 2015). However, recent studies in the Bohai Sea have reported that coastal cities around the Bohai Sea were subjected to serious air pollution problem and the high concentrations of atmospheric aerosols contained high amount of particulate carbon and nitrogen (Cao et al., 2006; He et al., 2015). The incomplete combustion of carbon-contained material in the Bohai Economic Rim resulted in high concentrations of atmospheric PM2.5 and high carbon content in the suspended particles (Wang et al., 2014; Zong et al., 2015). Largescale urban heating in winter resulted in more carbonaceous materials suspended in atmosphere. The dominant East Asian Monsoon carries the carbonaceous material through air and a fraction of the suspended particles would sink to marine sediment in the form of black carbon (BC). It was reported that BC constituted 27–41% of sediment organic carbon in the Bohai Sea, which is substantially higher than other coastal seas (Kang et al., 2009). The calculated budget of black carbon demonstrated that atmospheric deposition and riverine sources contributed ~93Gg/yr and ~85Gg/yr, respectively, to the Bohai Sea (Fang et al., 2015). Furthermore, due to the rapid population increase and economic growth in the region, the majority of the rivers discharging to the Bohai Sea were dammed to conserve freshwater resources, which has resulted dramatic decrease of sediment load and carbon flux to the Bohai Sea (Wang et al., 2006). Therefore, the estimated proportional source contribution reflected the present state in Bohai Sea where atmospheric deposition surpassed river discharge as the dominant pathway of terrestrial organic material input to marine sediment. To further examining the influences from rivers and atmospheric deposition in different areas of Bohai Sea, MixSIR model was applied to subset of samples from six different geographic regions (see Fig. 1). The estimated proportional source contributions are given in Table 3. The best estimate of marine primary production contribution varied from 64.1% in the Bohai Bay to 71.5% in the Central Bohai Sea. The highest proportional riverine contribution was in the Yellow River Estuary (best estimate 12.6%), which is not surprising consider the substantial amount of sediment load from the Yellow River. Similarly, the highest atmospheric contribution was in the Bohai Bay (24.9%), which located to the leeward of the North China Plain and was the primary sink of atmospheric pollutant transported from two megacities Beijing and Tianjin (Zong et al., 2015). In general, our results showed Bayesian MixSIR model using δ13C and δ15N values was a reliable method to quantify source contribution to marine sediment organic material and is preferable to non-Bayesian models.

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