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Influence of sedimentary environment evolution on fingerprint characteristics of methane isotopes: A case study from Hangzhou Bay

Cite this dataset

Jiang, Wenqin (2023). Influence of sedimentary environment evolution on fingerprint characteristics of methane isotopes: A case study from Hangzhou Bay [Dataset]. Dryad. https://doi.org/10.5061/dryad.9w0vt4bjp

Abstract

To better understand the depositional constraints on the fingerprint characteristics of methane isotopes, we present a set of carbon/hydrogen isotopic data for CH4, CO2, pore water, carbonates, and total organic carbon (TOC) along a 70-m sedimentary core from Hangzhou Bay, China. The sedimentary facies (Units I, II, and III from upper to bottom) suggested depositional environments of the present estuary, shallow marine, and floodplain-estuary. The values of δDCH4 displayed similar trends with those of δDH2O and Cl- concentrations along the depth profiles. The values of δ13CCH4 generally synchronously changed with those of δ13CCO2. The variation trends of δ13CCH4 and δ13CCO2 were the same with δ13Ccarbonate from 10 m to 70 m depth but decoupled above 10 m. Calculations suggested that about 86% of methane was produced through the CO2 reduction pathway. In this pathway, the hydrogen in CH4 is from ambient water, while the carbon is from dissolved inorganic carbon. In our study, the low δDCH4 below 44.5 m corresponded to low δDH2O and low salinity during the cold and low-sea-level period. The values of δ13CCH4 in Units II and III were correlated with the δ13Ccarbonates, which is related to the sedimentary processes. But decoupling of low values of δ13CCH4 and δ13CCO2 from δ13Ccarbonates in Unit I may be related to preferential microbial consumption of labile compounds with light carbon isotopic compositions, such as lipids. In short, the variations of the stable carbon and hydrogen isotopic compositions of CH4 were largely related to sedimentary processes.

Methods

1. Gas content and isotopic composition analyses

Methane and carbon dioxide concentrations in the headspace of the glass vials containing 10-mL sediment were measured by a gas chromatograph coupled with thermal conductivity (GC-TCD, Thermo) and a Pora Plot Q column (30 m × 0.32 × 20 μm ). The temperatures of the column oven, the gasification chamber, and the thermal conductivity detector (TCD) were set at 60, 150, and 200°C, respectively. The filament voltage of TCD was 10 V, and the flow rate of the reference gas and the make-up gas (Helium) were 12 and 10 mL/min, respectively. Helium (UHP, 99.999%) was used as the carrier gas with a flow rate of 3 mL/min, with an injection volume of 50 μL and a split ratio of 10:1. The precision was less than 3%. Headspace levels are reported as % of headspace (v/v), and the gas content (mmol) per liter of sediment was calculated with the ideal gas law (Li et al., 2019).

The isotopic compositions (carbon and hydrogen of the CH4, carbon of the CO2) were measured on a MAT253 isotope-ratio mass spectrometer (IRMS) connected to a gas chromatograph (GC-IRMS, Thermo) with a HP-PLOT Q column (30 m × 0.32 mm inner diameter). The injection volume and split ratio were adjusted in real-time according to the concentrations of CH4 and CO2. CO2, CH4, and matrix gases were separated by GC. The inlet temperature of GC was 100°C. The initial column temperature was 50°C (holding for 3 min) and increased to 190°C (holding for 5 min) at 15°C /min. High purity He (99.999%) was used as carrier gas. The column flow rate was set at 1.5mL /min. δ13CCO2 was directly measured after separation by conveying the flow containing CO2 to the IRMS (Hou et al., 2012; Li et al., 2019). For δ13CCH4 analysis, The flow containing CH4 was conveyed to a combustion furnace with a nickel/nickel oxide catalyst at 1100°C, where CH4 was converted to CO2 and H2O. H2O was removed by passing a Nafion dryer and CO2 was loaded to IRMS for carbon isotopic measurement. For δDCH4 analysis, the flow containing CH4 was conveyed to a pyrolysis tube furnace (1420 oC) where CH4 was converted to carbon and H2. H2 was injected into IRMS for hydrogen isotopic measurement after passing a Nafion dryer. The ions (m/z) monitored by IRMS were 44, 45 and 46 for carbon isotopic analysis, and 2 and 3 for hydrogen isotopic analysis. The carbon isotope data (δ13C) are reported in per mil (‰) relative to the Pee Dee Belemnite (PDB) (Reeburgh, 2007), and the measurement accuracy was better than ± 0.2‰. The stable isotope ratios of hydrogen (δ13C)  are expressed as a per mil (‰) relative to the Standard Mean Ocean Water (VSMOW) (Whiticar, 1999; Li et al., 2019), and the precision was less than 2‰.

2. Analyses of hydrogen isotopic composition and anion concentration of pore water

Pore water in sediment was obtained by centrifugation. Hydrogen isotopic compositions (D/H) of the pore water were analyzed using the high-temperature pyrolysis method (Mazzini et al., 2018), and an elemental analyzer coupled with an Isotope Ratio Mass Spectrometer (HT-IRMS) at the Qingdao Institute of Marine Geology, China. The results are reported relative to Vienna Standard Mean Ocean Water (VSMOW) with a precision of less than 2‰. Anion concentrations in pore water were determined via ion chromatography with a Dionex ICS-600 chromatograph (AS23 column, with 10 mM Na2CO3/NaHCO3 as an eluent, Dionex, USA) (Zeng et al., 2019; Wang et al., 2020).

3. Contents and carbon isotopic compositions of sedimental TOC and carbonate

The sediment samples were freeze-dried in a vacuum at -80°C and ground in an agate mortar. The contents and carbon-stable isotopic compositions of TOC (δ13CTOC) were analyzed after decalcification with 10% HCl. The mass lost after acidification was considered as carbonate. The homogenized and decalcified sediment samples were loaded on a Vario Macrocube elemental analyzer (Elementar, Germany) and a MAT253 Elemental analyzer- Isotope ratio mass spectrometer (Thermo Fisher, USA) for contents and stable carbon isotopic composition measurements and the precision was less than 0.2‰ (Schmidt et al., 2017; Zhu et al., 2021). Carbonate in freeze-dried sediment was converted to CO2 with pure H3PO4 at 70°C for δ13C measurements (Zhu et al., 2021). The stable carbon isotopic compositions of CO2 from carbonate were measured on a MAT253 isotope-ratio mass spectrometer connected to a gas chromatograph (Gas Bench II -IRMS, Thermo) using a Pora Plot Q column (30 m × 0.32 mm×20 μm, Agilent). The precision was less than 0.1‰.

4. Microbial composition analysis

DNA was extracted from 25 sedimentary subsamples using the FastDNA® Spin Kit for Soil (MP Biomedicals, USA). The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). The hypervariable V4 region of the bacterial and archaeal 16S rRNA gene was amplified with primer pairs 515FmodF (5'-GTGYCAGCMGCCGCGGTAA-3') and 806RmodR (5'-GGACTACNVGGGTWTCTAAT-3') by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA), which were barcoded for different samples (Walters et al., 2016). The PCR amplification of the 16S rRNA gene was performed as follows: initial denaturation at 95°C for 3 min, followed by 29 cycles of denaturing at 95°C for 30 s, annealing at 55°C for 30 s and extension at 72°C for 45 s, and single extension at 72°C for 10 min, and end at 10°C. The PCR mixtures contained 5 × TransStart FastPfu buffer 4 μL, 2.5 mM dNTPs 2 μL, forward primer (5 μM) 0.8 μL, reverse primer (5 μM) 0.8 μL, TransStart FastPfu DNA Polymerase 0.4 μL, BSA 0.2 μL, template DNA 10 ng, and finally ddH2O up to 20 μL. PCR reactions were performed in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Illumina MiSeq PE300 platform (Illumina, San Diego, USA) paired-end sequencing was performed on purified amplicons pooled in equimolar amounts (Wang et al., 2021). All the raw reads were deposited in the Genome Sequence Archive in the BIG Data Center at the Beijing Institute of Genomics (BIG; Chinese Academy of Sciences) under accession number CRA006673.

The raw 16S rRNA gene sequencing reads for sediment were demultiplexed, quality-filtered by fastp version 0.20.0 (Chen et al., 2018) and merged by FLASH version 1.2.7 (Magoč et al., 2011) with the criteria in the previous literature (Li et al., 2020). After removing the low-quality and chimeric sequences, operational taxonomic units (OTUs) were clustered at a 97% sequence similarity level using the UPARSE algorithm (Edgar, 2013). Taxonomy was assigned using the ribosome database project (RDP) classifier algorithm (Cole et al., 2014) against Silva reference databases with a confidence threshold of 0.7.

Funding

China Geological Survey, Award: DD20190237

China Geological Survey, Award: ZD20220602

National Natural Science Foundation of China, Award: 42176091

National Natural Science Foundation of China, Award: 91851106