Natural and anthropogenic sources of organic matter across Liao River delta: A combination of lipid biomarkers and isotope analyses

Journal Pre-proof Natural and anthropogenic sources of organic matter across Liao River delta: A combination of lipid biomarkers and isotope analyses Ben Liu, Yuxin He, Yanzhen Zhang, Yongge Sun, Yuntao Wang, Ding He PII:

S0272-7714(19)30618-3

DOI:

https://doi.org/10.1016/j.ecss.2020.106610

Reference:

YECSS 106610

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 25 June 2019 Revised Date:

29 November 2019

Accepted Date: 19 January 2020

Please cite this article as: Liu, B., He, Y., Zhang, Y., Sun, Y., Wang, Y., He, D., Natural and anthropogenic sources of organic matter across Liao River delta: A combination of lipid biomarkers and isotope analyses, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/ j.ecss.2020.106610. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Total lipid biomarker fingerprinting

1

Natural and anthropogenic sources of organic matter across Liao

2

River Delta: a combination of lipid biomarkers and isotope analyses

3

Ben Liu 1, 3, Yuxin He1, Yanzhen Zhang 1, Yongge Sun 1, Yuntao Wang2, Ding He 1, 2 *

4

1

5

China

6

2

7

Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

8

3

University of Chinese Academy of Sciences, Beijing, China.

9

*

Corresponding at [email protected] (D.H.)

Institute of Geology, School of Earth Sciences, Zhejiang University, Hangzhou,

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of

10 11

Abstract

12

Organic matter (OM) cycling between coastal wetlands and their connected rivers

13

is poorly constrained due to difficulties in assessing the composition of different

14

OM sources (natural vs. anthropogenic). Bulk characteristics and lipid biomarkers

15

were analyzed to distinguish different sources of OM in the sediments and soils

16

of Liao River Delta, Northeast China, including Liao River Wetland, its

17

connected Liao River, and nearby Daliao River. A similar range of stable carbon

18

isotopic values (δ13Corg) was observed in wetland soils (-27.8‰ to -22.6‰) and

19

river sediments (-26.0‰ to -23.3‰). In contrast, significantly higher stable

20

nitrogen isotopic values (δ15N) were observed in Daliao River sediments (5.8‰

21

to 7.7‰) than both Liao River and wetland soils. Lipid biomarkers, especially

22

n-alkyl lipids, phytosterols, triterpenoids, isoprenoids, monoalkyl glycerol ethers, 1

23

and monoacylglycerols, indicated that the natural OM input in Liao River Delta

24

was mainly of terrestrial origin, followed by in situ aquatic and microbial inputs.

25

In addition to natural OM, anthropogenic influences in the form of sewage and

26

petroleum inputs were evidenced by the detection of fecal sterols, plasticizers,

27

and petrogenic biomarkers. Biomarker distributions in samples from Liao River

28

and Liao River Wetland suggested similar OM sources or close interaction

29

between them, which may be caused by lateral transport considering the low

30

elevation delta exposed to strong tidal effects. In contrast, significantly higher

31

anthropogenic inputs were detected in Daliao River, with no connectivity to the

32

Liao River wetland. Taking advantage of isotopic and biomarker data, the

33

principal component analysis further suggests that both the natural wetland

34

distribution and anthropogenic activities may affect the OM sources and

35

distribution in coastal rivers, which serve as an important transit of OM to coastal

36

oceans.

37 38

Keywords: Coastal delta; organic matter; stable carbon and nitrogen isotopes;

39

lipid biomarkers; source apportionment

40 41

1.

Introduction

42

Coastal rivers and wetlands play an essential role in carbon and nutrient cycling

43

at both regional and global scales (e.g., Bianchi, 2007). They are regions where

44

organic matter (OM) from different sources mix and crucial zones for OM processing 2

45

(Middelburg and Herman, 2007). Wetlands stored 20~30% of earth soil carbon, thus

46

playing a significant role in both the carbon cycle and climate change (Roulet, 2000;

47

Bridgham et al., 2006). The exchange between coastal wetlands and rivers or estuaries

48

influences the diagenetic state and fate of OM in estuaries and coastal oceans (Canuel

49

and Hardison, 2016). Thus, the “wetland-river-estuary” system constitutes a crucial

50

part of the global carbon cycle (Bianchi, 2007).

51

Despite significant advances in understanding the character of OM transported

52

from land to coastal margins via rivers (McKee et al., 2004; Bianchi and Allison,

53

2009; Leithold et al., 2016), most studies usually focused on the bulk signatures of

54

OM, such as total organic carbon (TOC), total nitrogen (TN), TOC/TN, and isotopic

55

compositions (e.g., Chmura and Aharon, 1995; Ruttenberg and Goni, 1997; Ray et al.,

56

2015; Gawade et al., 2018). However, tracing the origin, transformation, and the fate

57

of OM in coastal environments is still challenging (Canuel and Hardison, 2016). For

58

instance, coastal rivers are usually connected with wetlands, which could be another

59

potential input of OM to rivers and thus complicate the sources and transformation of

60

estuarine OM (Maie et al., 2007; D. He et al., 2014; Tzortziou et al., 2015). Besides,

61

coastal rivers were usually subjected to different forms of anthropogenic influences,

62

such as oil pollution and sewage input (D. He et al., 2018b; Zhang et al., 2019).

63

Whether and how wetland distribution and anthropogenic activities would disturb the

64

OM cycling is challenging to address without understanding the OM source.

65

Therefore, the application of multiple biomarkers derived from both natural and

66

anthropogenic origins would help better understanding the OM source and transport 3

67

in coastal rivers.

68

With the development of analytical chemistry tools, the primary forms of

69

biochemicals (e.g., lipids, amino acids and hydrolysable sugars) as well as the bulk

70

OM signatures were analyzed simultaneously (Naraoka and Ishiwatari, 1999; Rushdi

71

et al., 2014, 2016), adding information on OM transport and cycling in coastal rivers

72

and wetlands. In particular, molecular biomarkers especially lipids (e.g., n-alkanes,

73

n-alkanoic acids, terpenoids, steroids) have been extensively used to determine

74

sources, transport and the diagenetic state of OM in coastal systems (e.g., Yunker et

75

al., 1993; Canuel, 2001; Jaffé et al., 2006; Medeiros et al., 2012). However, the total

76

lipids were usually separated into various sub-fractions (apolar and polar fractions) by

77

complicated operations, in which only a few compound classes were reported as

78

representative of the total fraction. For instance, the majority of studies only focused

79

on a few series of biomarker homologues such as saturated hydrocarbons, fatty acids

80

or sterols (Medeiros et al., 2008; Canuel et al., 2012; D. He et al., 2018a, b), providing

81

a partial understanding of OM cycling. In contrast, multi-biomarker studies have

82

shown promising insights for understanding both natural and anthropogenic OM

83

cycling in coastal environments with multiple OM sources (Medeiros et al., 2012;

84

Pisani et al., 2013; Rushdi et al., 2014).

85

The present study investigated the OM sources of sediments and soils in Liao

86

River delta (LRD), northeast China. The two main rivers in the delta were compared.

87

Liao River (LR) is well connected with natural reed wetlands, while Daliao River (DR)

88

is surrounded by rice paddy fields (Fig. 1). Anthropogenic contaminations (e.g., crude 4

89

oils, consumption of fossil fuels) were found ubiquitous (with different levels) in

90

LRD (Lin et al., 2013; Ma et al., 2014, 2017; Yuan et al., 2015, 2017). However,

91

previous studies only focused on apolar biomarkers such as aliphatic hydrocarbons

92

and PAHs (Lin et al., 2013; Ma et al., 2014), which usually consist <5% of total

93

organic solvent extractable compounds and could be extensively biased to

94

anthropogenic OM, such as petrogenic source due to potential oil contamination in

95

this case, and thus may have limited representativeness of whole (natural and

96

anthropogenic) OM source (Medeiros and Simoneit 2008; Medeiros et al., 2012). To

97

the best of our knowledge, most studies investigated coastal wetlands or rivers

98

separately, which limits the understanding of the OM cycling between coastal

99

wetlands and rivers or estuaries (e.g., Pisani et al., 2013).

100

In this study, multi-organic solvent-extractable lipid biomarkers (both apolar and

101

polar compounds) were analyzed in combination with bulk proxies (TOC, TN, carbon

102

and nitrogen isotopes) to better (i) identify the natural and anthropogenic sourced

103

biomarkers in LRD; (ii) compare the OM composition between Liao and Daliao River;

104

(iii) test if there is an association of OM exchange between Liao River Wetland and

105

Liao River. This study also aims to add information on OM sources and distribution

106

across a temperate “wetland-river-estuary” system.

107 108

2.

109

2.1. Study area description

110

Materials and methods

Liao River Delta locates in northeastern China near the Liaodong Bay of Bohai 5

111

sea, covering more than 3150 km2 (Ma et al., 2014). Liao River and Daliao River are

112

two main rivers flowing in LRD to Liaodong Bay. Liao River is the largest river in

113

Northeast China, the drainage of which is 2.29 million km2. LRD has a temperate

114

monsoon climate, whose annual precipitation ranges from ~350 to 1000 mm, and

115

70%–80% of total precipitation occurs between July and September. The mean

116

temperature is 8.4 ℃, with the highest temperature of 27.4℃ in September and the

117

lower temperature of -9.8 ℃ in January. The mean annual runoff of LRD is ~8.9

118

billion m3.

119

The reed and rice are the dominant plant species in the delta (Ye et al., 2016),

120

whereas corn has also covered a considerable area upstream of both Liao and Daliao

121

rivers (Ji and Zhou 2010; Fig. 1). Natural reed wetlands dominate in the middle and

122

lower reach of Liao River, while near the estuary, suaeda wetlands appear, and nature

123

reverse along the river has been set up to protect this natural wetland. In contrast, the

124

middle and lower reaches of Daliao River are covered by paddy fields. Besides, LRD

125

is the economic center of Northeast China, with an increasing population. Two main

126

cities in the delta, Panjin, and Yingkou, lie in the middle and lower reaches of the

127

Liao and Daliao rivers, respectively (Fig. 1), and various factories locate near the

128

estuary of Daliao River.

129

2.2. Sampling and preparation

130

Five surface soil samples (top 2 cm) from different locations of Liao River

131

Wetland and twelve surface sediment samples (top 2 cm) from Liao and Daliao River

132

were collected in winter, 2016 (marked by triangles, pentagrams, and diamonds, 6

133

respectively in Fig. 1). The river sediments were spatially distributed from midstream

134

to estuary in both rivers, whereas the soil samples were chosen to represent wetlands

135

with different dominant plant habitats such as reed (W-reed1 and W-reed2), suaeda

136

(W-suaeda1 and W-suaeda2) and rice (W-rice). All samples were wrapped with

137

pre-combusted alumina foils and placed in iceboxes during the transport back to the

138

lab. After freeze-dried, bulk roots or plant detritus were removed. Then each sample

139

was sieved to obtain fine particles (<0.125 mm) and stored -20℃ until further

140

analysis.

141

2.3. Analyses of bulk geochemical parameters

142

Samples were directly used for TN and stable nitrogen isotope (δ15N) analyses,

143

whereas samples for TOC and stable carbon isotope (δ13Corg) analyses were

144

pre-acidified with diluted HCl (6N, 60℃ for 24h) to remove inorganic carbon (Y. He

145

et al., 2015). TOC and TN were determined on an elemental analyzer (EA3000, Euro

146

Vector). The 1σ precision of replicate analysis was ±0.02% for TOC and ±0.006% for

147

TN. The carbon and nitrogen isotope compositions values were determined by isotope

148

ratio mass spectrometry (IRMS, MAT253, Thermo Fisher Scientific; D. He et al.,

149

2015a, 2016a, b). The results were provided in delta notation according to the

150

expression

151

[(15N/14N)s/(15N/14N)std - 1] ×1000, in ‰ where subscripts s and std refer to the sample

152

and C (or N) isotope standard, respectively. The 1σ precision of replicate analysis was

153

±0.2‰ for δ13Corg and ±0.1‰ for δ15N, respectively.

δ13Corg

=

[(13C/12C)s/(13C/12C)std

7

-

1]

×1000

and

δ15N

=

154

2.4. Organic matter extraction and biomarker analysis

155

Each dried sample was ultrasonically extracted three times sequentially with

156

20ml of dichloromethane (DCM), 20ml of DCM, and 40ml of DCM and methanol

157

(3:1, v: v) mixture for a 15 min period each in a 150ml precombusted beaker. The

158

combined extract was filtered to remove sediment particles. The filtrate was

159

concentrated on a rotary evaporator to ca. 4ml. An aliquot (0.2-1ml) of each total

160

extract was dried under a flow of ultra-high pure nitrogen gas and then derivatized

161

with silylating reagent [100 µl N, O-bis(trimethylsilyl) trifluoroacetamide, BSTFA]

162

for 1.5 h at 70 °C (D. He et al., 2018b). Deuterated normal C36 alkane (C36D74) was

163

added to the silylated extract as an internal standard.

164

The

analyses

of

the

silylated

extracts

were

carried

out

by

gas

165

chromatography-mass spectrometry (GC-MS), using an Agilent 7980 GC coupled to a

166

5977 Mass Selective Detector with a DB-5 (Agilent) fused silica capillary column (30

167

m × 0.25 mm i.d., 0.25µm film thickness) and helium as the carrier gas. The GC

168

temperature program was set from the initial temperature of 60 °C (initial time 2 min)

169

to 300 °C (final holding time 30 min) at 3 °C/min. The MS detected ions with m/z

170

between 50 and 600, and was operated in the electron impact mode at 70 eV ion

171

source energy. The data were acquired and processed with Agilent ChemStation

172

software.

173

2.5. Identification, quantification, quality assurance and quality control

174 175

(QA/QC) The identification of each biomarker compound (including n-alkanes, n-alkanols, 8

176

n-alkanoic acids, steroids, stanols, triterpenoids, monoacylglycerol, MAGEs

177

(1-O-monoalkyl glycerol ethers), alkyl amides, plasticizers and UCM (unresolved

178

complex mixture)) was based primarily on GC retention times, their key ion patterns

179

and mass spectra (i.e., fragmentation patterns). The concentration of each identified

180

compound was semi-quantified by the internal standard, C36D74, assuming similar

181

response factors (D’Anjou et al., 2012; D. He et al., 2014, 2018b). The internal

182

calibration method based on six-point calibration curves was used for the

183

quantification of an individual compound. Surrogate was not spiked before extraction

184

for each sample, but mean recoveries of the compounds spiked into the typical

185

riverine and estuarine samples were >85 % for C36D74 (D. He et al., 2018b). Besides,

186

based on triplicate analyses of multiple typical rivers and estuarine sediment samples

187

with the same procedure, the standard deviations of most biomarker compound

188

concentrations were <15% (D. He et al., 2018b). The reported concentration of each

189

compound identified in this study was not recovery-corrected. The UCM

190

concentration was semi-quantitatively determined by integrating the total GC area

191

with a subtraction of the resolved peaks and using the average response factor of

192

C36D74 during the instrumental calibration. No background contamination from

193

laboratory processing was observed using procedural blanks.

194

2.6. Statistical analysis

195

The statistical analysis was carried out using the Statistical Product and Service

196

Solutions (SPSS) Statistics software (Version: 22.0, SPSS Inc., Chicago, Illinois).

197

Correlation analyses were performed and shown with the Pearson Correlation 9

198

Coefficient (r). Student’s t-test was used to compare means different sets of data,

199

before which, Kolmogorov–Smirnov test (K-S test) was carried out to ensure that the

200

data follows a normal distribution. Principal component analysis (PCA) was

201

performed combining various proxies (e.g., stable isotopic values, biomarker data) to

202

distinguish different OM distribution among LRD. Data of each index for PCA were

203

standardized by subtracting the mean value and dividing by the standard deviation,

204

while non-detectable values were replaced with concentration values of one half the

205

detection limits.

206 207

3.

Results and discussion

208

3.1. Bulk geochemical parameters

209

The TOC from most of the samples was between 0.2% and 0.7%, except for

210

W-reed1 (13±1%), W-reed2 (2.7±0.2%), and DR-2 (1.6±0.2%; Fig. 2a, Table S1),

211

which is similar to a previous study (Lin et al., 2013). No significant difference in

212

averaged TOC was found among samples from Liao River wetland, Liao and Daliao

213

River. No spatial trends were observed for Liao and Daliao River (p=0.867 and 0.098,

214

respectively). The TN from most samples ranged from 0.02% to 0.92%, except for

215

W-reed1 (0.38±0.01%), W-reed2 (0.35±0.01%), and DR-2 (0.16±0.01%). Similarly,

216

no significant difference and spatial trends were found among samples from different

217

places. There was a significantly positive correlation (R=0.707, p<0.05) between TOC

218

and TN. The TOC/TN ratios of W-suadea1, W-rice, and W-reed1 were >11, with the

219

highest value observed at W-reed1 (33.3±0.1), representing the dominant terrestrial 10

220

source (Meyers, 1997). However, for W-suaeda1, W-suaeda1, and all river sediments,

221

the TOC/TN values were between 4.8 and 7.9 (except for DR-2), indicating mixing

222

OM likely from both terrestrial and aquatic or anthropogenic sources with low

223

TOC/TN value (Meyers, 1997).

11

224

The δ13Corg values ranged from -27.8‰ to -22.8‰, -23.7‰ to -23.3‰, and -26.0‰

225

to -23.6‰ for samples from Liao River Wetland, Liao and Daliao River, respectively

226

(Fig. 2b; Table S1). The δ13Corg values of the surface sediments of the Liao River had

227

little change (-23.7‰ to -23.3‰), in contrast to Daliao River (-26.0‰ to -23.6‰). No

228

apparent spatial trends were observed in both rivers. The averaged δ13Corg value of

229

samples in Liao River was significantly higher than those in Daliao River (p<0.05),

230

suggesting different OM sources between these two rivers. The δ13Corg values of the

231

five wetland soil samples covered a wide range of 5‰. Although the δ13Corg values at

232

W-reed1, W-rice, and W-reed2 were similar to typical wetland plants collected across

233

the sampling sites, such as reed (-30.0‰ to -25.1‰), suaeda (-31.1‰ to -25.0‰) and

234

rice (-29.6‰ to -27.5‰), the δ13Corg values at W-suaeda1 and W-suaeda2 were much

235

higher (-22.8‰ to -22.6‰), indicating allochthonous sources with higher δ13Corg

236

values (e.g., C4 plant-derived OM) might have contributed to these sites (Fig. 2b).

237

The δ15N values ranged from 1.4‰ to 5.8‰ (a range of 4.4‰), 4.8‰ to 5.9‰ (a

238

range of 1.1‰), and 6.0‰ to 7.7‰ (a range of 1.7‰) for samples from Liao River

239

Wetland, Liao and Daliao River, respectively (Fig. 2b; Table S1). The δ15N values of

240

Daliao River and Liao River showed no spatial trends. The averaged δ15N value of

241

Liao River sediments were significantly lower than that of Daliao River (p<0.01, Fig

242

2b; Table S1), probably due to relatively more anthropogenic OM in Daliao River

243

(Kendall et al., 2001; D. He et al., 2019).

244

Combining δ13Corg and δ15N, different types of samples were separated (Fig. 2b),

12

245

likely indicative of different OM sources between samples from different

246

environments. The samples at W-reed1, W-reed2, and W-rice were characterized by

247

low δ13Corg and δ15N values, representing the dominance of terrestrial input from

248

wetland plants such as rice, suaeda, and reed, which was further evidenced by high

249

TOC/TN at W-reed1 and W-rice. W-suaeda1 and W-suaeda2 were characterized by

250

low δ15N but high δ13Corg values. Since phytoplankton and C4 plant-derived OM can

251

have high δ13Corg values, both sources were possible (Meyers, 1997). A large area

252

outside Jinzhou city covered by corn (C4 plant) is connected to the reed wetland by

253

river channels (the largest one is shown in the Fig. 1), leading to the speculation that

254

C4 plant-derived OM is a potential reason lowering the

255

and W-suaeda2. Although the high δ15N values of samples in Liao and Daliao River

256

could be mainly explained by inputs from anthropogenic (i.e., fertilizer) or marine

257

OM, simultaneous lower δ13Corg values excluded the dominant input of marine OM in

258

Daliao River since marine OM should be characterized by higher δ13Corg values.

13

Corg values of W-suaeda1

259 260

3.2. Biomarker identification and distribution

261

The organic solvent extractable OM of samples mainly consists of various

262

homologue series, including n-alkanes, n-alkanols, n-alkanoic acids, steroids,

263

triterpenoids, isoprenoids, monoacylglycerols, MAGEs (1-O-monoalkyl glycerol

264

ethers), alkyl amides, plasticizers and hopanes (Fig. 3; Table S2).

265

3.2.1. n-Alkyl lipids

13

266

The n-alkanes were detected in all samples and ranged from C15 to C35 (the

267

subscripts refers to the carbon chain length, same for n-alkanols and n-alkanoic acids)

268

with a maximum concentration (Cmax) at C27, C29 or C31, and total concentrations from

269

0.5 to 14.6 µg/g dw. Long-chain (C>25) n-alkanes were dominant in all samples

270

(LMW/HMW<0.2, Fig. 4a). The CPI15-35 of n-alkanes ranged from 2.7 to 4.0, 2.0 to

271

4.3, and 3.2 to 4.5, for Daliao River, Liao River, and Liao River Wetland, respectively,

272

suggesting an overall predominant terrestrial input of these n-alkanes (Meyers, 1997;

273

Y. He et al., 2014).

274

The n-alkanols of all samples ranged from C14 to C32 with Cmax at C28 or C30, and

275

total concentrations from 1.7 to 53.0 µg/g dw. Overall, the distribution of n-alkanols at

276

all sites is similar, with long-chain n-alkanols dominated (LMW/HMW<0.1) (Fig. 4b),

277

and high CPI (CPIe/o(14~32)=5.8-25.8). This distribution indicates the dominant input of

278

vascular plant wax (Rushdi et al., 2006).

279

The n-alkanoic acids ranged from C12 to C32 with Cmax at C16, significant even to

280

odd carbon predominance, and total concentrations from 0.6 to 28.8 µg/g dw (except

281

for DR-2, 524 µg/g dw). There were two types of distribution patterns of n-alkanoic

282

acids. The first type, including DR-2, LR-1, and LR-3 to LR-6 and all soil samples

283

except W-rice, was dominated by short-chain homologues, with almost no longer

284

chain length (C>20) fatty acids detected (Table S2). The second type, including

285

W-rice, DR-3 to DR-6, and LR-2, was characterized by the presence of a high

286

concentration of C24-C30 fatty acids (Fig. 4c). The dominance of C16 n-alkanoic acids

14

287

was also observed in leaves and stems of reeds (Fig. S1), suggesting the potential

288

inputs from reed to LR, although aquatic macrophytes also had a similar distribution

289

of n-alkanoic acids (Volkman et al., 1981; Budge and Parrish, 1998; Rushdi et al.,

290

2018). The unsaturated fatty acids
291

Wetland, indicative of in situ fresh OM sources that were not subjected to extensive

292

biodegradation or oxidation (Haddad et al., 1992; Niggemann and Schubert, 2006).

293

The C15 and C17 fatty acids were consistently detected, suggesting the ubiquitous

294

source from microbes (mainly bacteria; e.g., Bianchi and Canuel, 2011).

295

3.2.2. Triterpenoids and steroids

296

Pentacyclic triterpenoids, including β- and α-amyrins and 3-keto-urs-12-ene,

297

were detected in most of the samples, with the total concentrations from below

298

detection limit (LD) to 0.88 µg/g dw, suggesting the input of higher vascular plants

299

especially from the angiosperms (Diefendorf et al., 2012). Different sterols are

300

derived from animals, plants and algae (specific for green, red, blue-green, and brown

301

algae), and their distributions have potential reflecting microbial activity in the soil,

302

and cooking in urban areas (Rogge et al., 1991; Volkman et al., 1998; Volkman, 2005).

303

Therefore, they are widely used to identify the source and fate of OM in the natural

304

environment (e.g., Rushdi et al., 2014). The phytosterols, mainly campesterol,

305

stigmasterol, and sitosterol, in the samples are mainly derived from vegetation (e.g.,

306

Hartmann, 1998). The low ratios (all <0.8) of cholesterol-to-(campesterol +

307

stigmasterol + sitosterol) likely indicates a relatively low contribution of microbial

15

308

inputs to the sterols (Volkman, 2005; Fig. 4d; Table S2). Stanols such as coprostanol,

309

epi-coprostanol, 24-ehtylcoprostanol, cholestanol, campestanol, and stigmastanol

310

were also detected, indicative overall reducing conditions at these sites (Rushdi et al.,

311

2014). The coprostanol, epi-coprostanol, and 24-ehtylcoprostanol are fecal sterols,

312

which are typical indicators of anthropogenic sewage input (D. He et al., 2018b).

313

Epi-coprostanol is commonly converted from coprostanol by intensive microbial

314

activities and is usually detected in digested sludge samples (McCalley et al., 1981).

315

Therefore, the presence of epi-coprostanol in most of the river sediments suggested

316

that the sewage has been microbially reworked or partially digested (Bull et al., 2002),

317

which is reasonable considering that there are numerous wastewater treatment

318

systems in cities (e.g., Panjin and Yingkou) along both rivers.

319

The concentration of coprostanol ranged from BD (below detection limit) to 0.85

320

µg/g dw, which is lower than that observed in Xiaoqing River, an extensively

321

eutrophic river connected with Laizhou Bay of Bohai Sea (D. He et al., 2018b). The

322

concentration of coprostanol has been used to indicate the level of sewage

323

contamination (e.g., Grimalt et al., 1990; Rada et al., 2016; D. He et al., 2018b). For

324

example, Grimalt et al., (1990) suggested that coprostanol concentrations >0.1 µg/g

325

dw were indicative of sewage contamination, whereas ‘significant’ sewage

326

contamination was defined as the level >0.5 µg/g dw (González-Oreja and

327

Saiz-Salinas, 1998). Rada et al. (2016) used 0.7 µg/g dw as the threshold of sewage

328

contamination. Based on this criterion, only DR-3 had concentration >0.7 µg/g,

16

329

suggesting visible sewage contamination at this site (Fig. 4d). The concentrations of

330

coprostanol and epi-coprostanol in Daliao River were all higher than in Liao River

331

(p<0.05), indicating higher anthropogenic sewage input in Daliao River.

332

3.2.3. Isoprenoids

333

Phytol, derived from the chlorophyll in all photosynthesizing plants, is likely

334

among the most abundant acyclic isoprenoid compound in the biosphere (Rontani and

335

Volkman, 2003). Its degradation products have been widely used as biogeochemical

336

tracers in aquatic environments (Rontani et al., 1990). Phytol and phytanic acid (the

337

acid form of phytol) were detected in all samples with concentration ranged from 0.1

338

to 1.5 µg/g dw, and 0.02 to 0.40 µg/g dw, respectively (Table S2, Fig. 5a). Both phytol

339

and phytanic acid showed higher averaged concentration in sediments of Daliao River

340

than Liao River (p<0.05), suggesting higher in situ primary productivity from

341

phytoplankton in Daliao River, in agreement with higher water column chlorophyll

342

concentration observed previously in Daliao River than Liao River (D. He et al.,

343

2019).

344

3.2.4. Monoacylglycerols

345

Monoacylglycerols, derived from the active phospholipid pool of cell wall

346

components, are labile and short-lived in the environment due to rapid chemical and

347

enzymatic hydrolysis (Volkman et al., 1998). The monoacylglycerols occurred in all

348

samples except DR-6, with concentrations ranged from 0.1 to 2.8 µg/g dw. The

349

monoacylglycerols had acyl chain length between C14 and C16, with C16

17

350

(1-O-hexadecanoyl glycerol) as the most abundant homologue (Table S2). Since

351

monoacylglycerols

352

decomposition and should not survive under long term degradation process, their wide

353

detection further suggested either dominant fresh OM input or limited alteration of

354

OM in most of the samples.

are

labile

intermediate

compounds

during

early

OM

355

3.2.5. MAGEs

356

MAGEs have been reported in bacteria favoring high temperature or anoxic

357

habitats, especially sulfate-reducing bacteria (SRB) (Jahnke et al., 2001; Rütters et al.,

358

2001). They have also been reported in a range of geothermal sediments (Pancost et

359

al., 2006), terrestrial, and lake environments (Yang et al., 2015). A recent study

360

suggests they likely originate from some unknown aerobic bacterial sources other

361

than SRB in estuarine and marine environments (Wang and Xu, 2016).

362

In contrast with most previous studies where saponification or acid hydrolysis

363

were applied to samples before the analysis of MAGEs (e.g., Yang et al., 2015; Wang

364

and Xu, 2016), none of these pretreatments were performed in this study. Therefore,

365

all the identified MAGEs must be a free form. MAGEs were detected in all samples,

366

with total concentrations ranged from 0.1 to 2.3 µg/g dw (Fig. 5b). The chain length

367

of alkyl groups was usually between 14 and 18, whereas trace amounts of MAGEs

368

with the C19 side chain were found in a few samples (LR-2, LR-3). The most

369

abundant homologues were i-C15:0 (i denotes iso-) or n-C16:0. A similar distribution of

370

MAGEs was also observed in the sediments of Yangtze River (D. He et al., 2017).

18

371

The wide occurrence of free-formed MAGEs suggests they could be produced from a

372

variety of microbes in both terrestrial and estuarine environments. Higher

373

concentrations of MAGEs were detected in Liao River Wetland than Liao River

374

(p<0.05), suggesting the potential stronger microbial activities in wetland soils than

375

the river sediments.

376

3.2.6. Alkyl amides and plasticizers

377

Alkyl amides are proposed to mainly originate from (i) directly reactions

378

between fatty acids and ammonia occurring naturally in biomass burning; or (ii) early

379

diagenesis of OM in soils and sediments (Abas and Simoneit 1996; Simoneit et al.,

380

2003; McKee and Hatcher, 2010). In this study, four alkyl amides, including C16

381

hexadecanamide, 9-octadecenamide, C18 octadecanamide, and C22 erucylamide, were

382

detected, with the total concentrations from 0.1 to 7.1 µg/g dw (Fig. 5b).

383

9-octadecenamide and erucylamide were the most dominant ones, occupying about 80%

384

of the total amide contents. The higher concentrations of alkyl amides detected in

385

Daliao River seem to suggest higher biomass burning due to, more often burning of

386

rice straw to introduce nutrients to the surrounded rice paddies for the following year

387

planting (Fig. 1). Alkyl nitriles were not detected, suggesting the dehydration of the

388

alkyl amides is likely to be a slowing process during early diagenesis (Wang et al.,

389

2017).

390

Four plasticizer compounds, including diethyl-, dibutyl-, dioctyl-phthalates, and

391

dioctyl adipate, were detected, with total concentrations ranging from 0.1 to 3.5 µg/g

19

392

dw. Since no plasticizer compounds were detected in any of our operational blank

393

treatments, their presence suggests OM input from plastic litter and detritus (e.g.,

394

plastic bags) in the samples (Wormuth et al., 2006). The averaged concentration of

395

plasticizers was lower in Liao River (p<0.05), suggesting less pollution from plastic

396

litter or detritus in this river (Fig. 5c).

397

3.2.7. Unresolved complex mixture (UCM) and hopanes

398

The UCM concentrations showed a broad range from 5.1 to 170 µg/g dw. The

399

hopanes with the 17α(H),21β(H)-series in most samples had concentrations ranged

400

from 0.04 to 1.23 µg/g dw. The occurrence of hopanes and UCM confirmed the

401

ubiquitous oil pollution in LRD, as suggested by previous studies (Lin et al., 2013;

402

Ma et al., 2014; Yuan et al., 2015). Daliao River had a higher average concentration of

403

UCMs than that in Liao River (p<0.05), suggesting either stronger biodegradation or

404

higher petrogenic input in Daliao River (Fig. 5c, Aboulkassim and Simoneit, 1995;

405

Rushdi et al., 2018). Since boat transportation is not prohibited in the Daliao River,

406

and a small harbor is located close to DR-6 (Fig. 1), higher petrogenic input in Daliao

407

River should be the dominant reason. In contrast, with waterway control at both the

408

east and west part of Liao River (especially LR-5 and LR-6) due to the presence of

409

nearby provincial and national conservation areas, limited boat transportation is

410

allowed; thus lower oil pollution in the form of boat transportation is likely expected.

411

Also, the dilution of natural OM from Liao River Wetland could also be another factor

412

lowering the concentration of petrogenic biomarkers in Liao River.

20

413 414

3.3. Summarized assessment and apportionment of OM sources in LRD

415

Multiple biomarkers presented in section 3.2 were used to distinguish different

416

OM sources, including terrestrial, aquatic and anthropogenic sources (Rushdi et al.,

417

2016). We have to note that most of the biomarkers are not unique to each specific

418

source, instead they were predominantly derived from a specific source (e.g.,

419

terrestrial, aquatic or anthropogenic in this study) and thus be widely utilized to trace

420

their distributions and relative changes across both spatial and temporal scales,

421

especially in settings with complex source of OM (e.g., Bianchi and Canuel, 2011;

422

Canuel et al., 2012; Canuel and Hardison, 2016).

423

3.3.1. Terrestrial/higher plant source

424

The distribution of n-alkanes and n-alkanols (high CPI value and low value of

425

LMW/HMW) indicates that terrestrial input is likely the primary source of all samples

426

(Meyers and Ishiwatari, 1993) (Fig. 4a, b). This is consistent with the distribution of

427

sterols with an overall low value of cholesterol/phytosterols (including campesterol,

428

stigmasterol, and sitosterol, representing the input of higher plants; Moreau et al.,

429

2002, Fig. 4d). With terrestrial input predominant in all samples, a high proportion of

430

short-carbon-chain n-alkanoic acids (C16 and C18) in Liao River and most wetland

431

samples could mainly be explained by the input from wetland plants, especially reeds,

432

in agreement with the distribution of n-alkanoic acids in reed leaf and stem (Fig. S1).

433

Considering the dominant input of terrestrial OM and lower contribution from

21

434

aquatic inputs (described later), the much higher values of δ13Corg of river sediments

435

and wetland soils including W-suaeda1 and W-suaeda2 were likely to be caused by

436

the input of C4 plants, such as grass in wetlands or corns in the upper reaches of the

437

river basins (Fig. 1). Furthermore, higher δ13Corg values in Liao River and W-suaeda

438

than Daliao River might also because of the input of C4 plants rather than aquatic OM

439

since lower aquatic input was evidenced (described later) from our biomarker

440

fingerprinting.

441

3.3.2 Aquatic and microbial source

442

Although the input of terrestrial OM was predominant in all samples, the aquatic

443

and microbial inputs existed, as shown by minor short-chain n-alkanes, n-alkanols,

444

and n-alkanoic acids (e.g., C15 and C17 n-alkanoic acids; Fig. 4a, b), and MAGEs.

445

Considering n-C27, C29, and C31 as terrestrial alkanes (Ter-alkanes), and n-C15, C17,

446

and C19 represent aquatic alkanes (Aqu-alkanes). We also checked the n-alkane

447

distribution in both leaf and stem of reed and no short-chain n-alkanes (n-C15, C17, and

448

C19) were detected (data not shown). The averaged aquatic input of n-alkanes in LRD

449

was ~10%. MAGEs suggested higher microbial activities in wetland soils than river

450

sediments from both rivers (p<0.05). Based on the distribution of MAGEs (without a

451

high concentration of m-C17:0), they may derive from microbial organisms other than

452

sulfate-reducing bacteria (Wang and Xu, 2016). Samples in Daliao River had more

453

aquatic OM than those in Liao River (Fig. 5a, b), which is suggested by the high

454

concentration of phytol representing the in situ primary production (Rontani et al.,

22

455

1990). This is expected since Daliao River is characterized by a higher eutrophication

456

level, enhancing the primary production (D. He et al., 2019).

457

3.3.3 Anthropogenic source

458

The anthropogenic input is widely present by the detection of petrogenic

459

biomarkers (pristane and phytane, hopanes with geochemical configuration),

460

plasticizer compounds, and fecal sterols. The low CPI of n-alkanes in some samples

461

with no significant aquatic input (e.g., 1.76 at LR-3) and detection of UCM with

462

branched and cyclic hydrocarbons (Aboul-Kassim and Simoneit, 1995, 1996) indicate

463

the presence of petroleum hydrocarbons in this delta system. This was further

464

confirmed by the occurrence of pristane, phytane, and the 17α,21β-hopane homologue

465

series (Table S2), which have been reported as proxies for petroleum contamination in

466

environmental samples (Ekpo et al., 2012). The oil pollution derived compounds are

467

much higher (1.6 to 4 times) in reed wetlands than in paddy fields and suaeda

468

wetlands (Table S2). There are also oil pollution sources in the surface sediments of

469

rivers, and the petroleum pollution in Daliao River is more severe than other locations.

470

Based on the distribution of UCM in Daliao River, the primary source of crude oil

471

pollution may be in the middle or upper reaches of the river, and due to the dilution

472

effect, the UCM content tends to decrease towards the estuary.

473

The coprostanol showed that the sewage input is also one of the main sources of

474

anthropogenic OM (Leeming 1996; D. He et al., 2018b). The coprostanol

475

concentration showed that the wetland (except paddy field W-rice) was less polluted

23

476

by sewage, which is probably because of the wetland protection policies and much

477

lower human population density. The higher concentration of coprostanol in

478

sediments of Daliao River suggests stronger sewage pollution. As epi-coprostanol is a

479

degradation product of coprostanol, the ratio of coprostanol/epi-coprostanol could

480

reflect the degree of degradation (Fattore, 1996). The lowest values of

481

coprostanol/epi-coprostanol accompanied with high concentrations coprostanol were

482

observed at LR-1 and DR-4, which are near to Panjin and Yingkou cities (Figs. 1, 4d).

483

That means these two cities could be possible input of sewage pollution in Liao and

484

Daliao River, respectively. The distribution of alkyl amides showed those are from

485

human activity (Simoneit et al., 2003; Wang et al., 2017). The higher contents of alkyl

486

amides and coprostanol in Daliao River indicate that it has relatively higher urban

487

pollution, in agreement with the relatively high δ15N values detected that might also

488

indicate the presence of sewage pollution (Savage, 2005).

489

3.3.4 Integration and semi-quantifications

490

Although quantification of each OM source is difficult to accomplish, the

491

relative percentage of each OM source could be estimated by considering the

492

combined concentrations of a series of biomarkers to provide a rough numerical

493

indication of the three OM sources, in order to obtain the distribution characteristics

494

of both natural and anthropogenic organic matter (Medeiros and Simoneit, 2008;

495

Rushdi et al., 2014, 2016, 2018; Poerschmann et al., 2017). These combinations

496

include: (i) terrestrial/higher plant biomarkers including alkanes (C≥23), alkanols

24

497

(C≥23), phytosterol as well as triterpenoids; (ii) aquatic biomarkers including alkanes

498

(C≤20), alkanols (C≤20), microbial-derived sterols and MAGEs; (iii) anthropogenic

499

biomarkers including fecal sterols, alkyl amides, plasticizers, hopanes with the

500

17α(H),21β(H)-series and UCM (Fig. 5d). Based on this simple calculation, the

501

percentage of terrestrial biomarkers encompassed ca. 76% (72% to 80%), 66% (59%

502

to 80%), 81% (77% to 91%) in samples from Liao River, Daliao River, and Liao

503

River Wetland, respectively. The averaged relative percentages of aquatic biomarkers

504

were 14% (9% to 19%), 17% (12% to 20%), and 14% (8% to 20%) in Liao River,

505

Daliao River, and Liao River Wetland, respectively. The relative percentages of

506

anthropogenic biomarkers were highest in Daliao River (15% to 22%), followed by

507

Liao River (7% to 15%) and Liao River Wetland (1% to 7%). Although the results

508

were only based on total lipid biomarkers that could be analyzed by GC-MS and thus

509

must have limitations and bias for referring the whole OM pool, they did show a

510

reasonably promising and meaningful spatial distribution scheme of OM in LRD,

511

especially when compared with the bulk proxies (e.g., stable isotopes or C/N ratios)

512

and just a few homologues (e.g., only n-alkanes).

513 514

3.4. Factors affecting the OM distribution in LRD and further implications

515

In order to further assess the major factors that lead to the OM distribution

516

difference in samples from LRD, PCA was performed on the datasets containing both

517

bulk metrics including TOC, TN, δ13Corg and δ15N, and molecular biomarkers

25

518

including n-alkanes (L/H-ane for short of low/high molecular weight n-alkanes),

519

n-alkanols (L/H-ol), n-alkanoic acids (L/H-acid), steroids (P-sterol for phytosterol and

520

O-sterol for others), fecal stanol (F-ol), MAGEs, alkyl amides (amide), plasticizers

521

(PAES), hopanes, phytols, phytanic acid (P-acid), monoacylglycerols (Mono) and

522

UCM. Five principal components (PCs, with eigenvalues >1) were responsible for 90%

523

of the total variance, suggesting complex OM sources in LRD (the loading plot was

524

shown in Fig. 5e). PC1 accounting for 50% of the variance had positive correlations

525

with H-ol, P-sterol, L-acid, L-ane, MAGE, Mono, etc. All of the proxies except PAEs,

526

suggest biogenic input, including wetland plants and aquatic organisms. PC2, which

527

positively correlated with F-stanol and amides, explained 17% of the total variance.

528

Thus, PC2 might mainly represent OM of anthropogenic input. UCM showed positive

529

correlations with PC1 and PC2, indicating its multiple sources.

530

From a general perspective, samples from wetland soils were separated from

531

river sediments by PC1, whereas Daliao River sediments were separated from Liao

532

River sediments by PC2 (Fig. 5f). Liao River Wetland is an essential biogenic OM

533

source considering the high TOC% and concentrations of biogenic biomarkers, which

534

are revealed by more positive PC1 values. Although Daliao River showed higher

535

biogenic input than Liao River probably due to its higher primary productivity, it is

536

characterized by a higher concentration of anthropogenic biomarkers and higher δ15N

537

values, leading to more positive PC2 values. Since Liao River is connected to Liao

538

River Wetland, the lower anthropogenic inputs in Liao River than the Daliao River

26

539

could also likely be caused by the dilution of lateral transport of natural OM from

540

Liao River Wetland. In fact, coastal wetlands have been recognized as a critical OM

541

source to nearby rivers and estuaries through various ways, such as pore water

542

exchange and tidal exchange (Goni et al., 2006; Tzortziou et al., 2011, 2015; He et al.,

543

2014; Osburn et al., 2019; Sadat-Noori and Glamore, 2019). Considering the low

544

elevation of Liao River Wetland and Liao River (c.a. 0 to 5 m) and high amplitudes of

545

tides up to c.a. 4 m, the tidal exchange of OM should be likely (Wang et al., 2013).

546

However, whether wetlands mainly serve as processors or reservoirs of OM to the

547

coastal rivers remains poorly known due to limited spatiotemporal investigations in

548

this study.

549

To better constrain the regional and global estuarine OM cycle, it is critical to

550

understand the sources and distribution of sedimentary OM in different types of

551

estuaries. Large rivers and estuaries have attracted extensive attention due to their

552

massive runoff, and therefore, large transport of OM. In contrast, fewer studies have

553

focused on intermediate coastal rivers (D. He et al., 2019 and references therein),

554

although they have been realized to also play important roles in OM cycling (e.g.,

555

Goñi et al., 2013). This study further suggests that the sources and composition of

556

sedimentary OM in intermediate coastal rivers are likely affected by both the

557

connected coastal wetlands and regional anthropogenic activities. Considering the

558

worldwide occurrence of coastal wetlands and projected increasing anthropogenic

559

influences (Spivak et al., 2019), further studies are needed to perform in coastal delta

27

560

environments with different degree of anthropogenic perturbance in order to better

561

constrain the sources and distribution of natural and anthropogenic OM in coastal

562

rivers at both regional and global scales (Canuel et al., 2012; Canuel and Hardison,

563

2016).

564 565

4.

Conclusion

566

Three main ideas can be concluded as follows: i) the significant controlling

567

factors for the distribution of river and estuary sedimentary OM in LRD: the wetland

568

input, the upstream riverine input, and anthropogenic influence; ii) significant input in

569

Liao River from nearby reed and suaeda wetlands suggested wetlands was likely an

570

important OM source to connected rivers; iii) human activities are important factors

571

resulting both the high anthropogenic and accompanied high aquatic OM input in

572

Daliao River. Although the low spatial and temporal sampling resolution limits the

573

determination of OM dynamics across the LRD, this study demonstrates the benefits

574

of combining bulk metrics, multi-lipid biomarkers and further principal component

575

analysis to assess OM sources and distributions across a river-wetland-estuary system

576

with complex OM sources. Both coastal wetlands and different forms of

577

anthropogenic activities (e.g., oil pollution, plastic detritus, and sewage) are likely

578

important factors affecting the OM sources and distributions of nearby coastal rivers

579

and estuaries. Further spatiotemporal studies are needed to better constrain the OM

580

cycling across typical wetland-river-estuary systems.

28

581 582

Acknowledgment

583

D. He designed this study. All other authors joined in the data interpretation and

584

discussion. We appreciate the help of Q. Lu, H. Wang, H. Yuan, X. Ding, and L. Pei

585

during the field sampling. This work was supported by National Science Foundation

586

of China [41973070 and 41773098 to D. He; 41503090 and 41877332 to Y. He] and

587

the hundred talent program of Zhejiang University [188020*194231701/008 to D.

588

He].

589 590

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Figure captions Fig. 1. Map showing sample sites and locations of samples Fig. 2. Bulk geochemical parameters of samples from Liao River Delta: (a) TOC/TN vs. TOC (%); (b) δ15N (‰) vs. δ13Corg (‰). Fig. 3. GC-MS total ion current traces of total extracts of the samples from (a) Liao River Wetland, (b) Daliao River and (c) Liao River. Fig. 4. Distributions of (a) n-alkanes, (b) n-alkanols, (c) n-alkanoic acids and (d) sterols and stanols in the samples from Liao River Delta Fig. 5. Distribution of (a) phytol and phytanic acid, (b) MAGEs and alkyl amides, (c) PAEs and UCM in the samples from Liao River Delta, different letters (a, b and c) in each panel represent significant differences in samples among Liao River Wetland (LRW), Daliao River (DR), and Liao River (LR) (p<0.05); (d) the estimated relative percentage of terrestrial, aquatic, and anthropogenic OM calculated by considering the combined concentrations of a suit of biomarkers; (e, f) plots of loadings and scores from PC1 and PC2 from PCA in Liao River Delta.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

1

Highlights

2

• Few studies investigated the organic matter source in a temperate coastal river

3

and wetland system.

4

• The terrestrial source was dominant followed by anthropogenic and microbial

5

inputs.

6

• Anthropogenic inputs were mainly derived from sewage and petroleum

7

pollution.

8

• Wetland and anthropogenic inputs affect the organic matter composition in river

9

sediments.

1

Declaration of competing interest None.

Author statement None