Effects of weathering process on the stable carbon isotope compositions of polycyclic aromatic hydrocarbons of fuel oils and crude oils

Marine Pollution Bulletin 133 (2018) 852–860

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Effects of weathering process on the stable carbon isotope compositions of polycyclic aromatic hydrocarbons of fuel oils and crude oils

T

⁎

Ying Lia, Yu Liub, , Dawei Jiangb, Jixiang Xub,c, Xinda Zhaob, Yongchao Houa a

College of Navigation, Dalian Maritime University, Dalian 116026, China College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, China c Maritime Safety Administration of the People's Republic of China, Beijing 100736, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Oil spills Weathering Polycyclic aromatic hydrocarbons Stable carbon isotope compositions

Two fuel oils and two crude oils were subjected to a 60-day weathering simulation experiment, and the effects of weathering on some common parameters for aromatics and aromatic δ13C values were studied. The results show that weathering of all oil samples affected little the DBT/P (dibenzothiophene/phenanthrene) ratio and methylphenanthrene distribution fraction. Four oil samples could be distinguished only by the DBT/P ratio. The effect of weathering on isotopes in polycyclic aromatic hydrocarbons was small. The results show that the types of four oil samples can be distinguished, while Kuwait and Russia crude oils cannot be discriminated from each other totally by double-coordinate two-dimensional maps for aromatic δ13C; all of the oil samples can be distinguished by principal component analysis of δ13C for aromatics, the relationship of DBT/P and PAHs δ13C values. Therefore, the δ13C value of aromatics can be used as an alternative index for the analysis of oil spills.

1. Introduction Oil spill accidents have occurred frequently in recent years. Much of the oil has leaked into the water and has undergone migration and transformation in the marine environment. The alkanes, polycyclic aromatic hydrocarbons (PAHs), and other organic pollutants in the oils are toxic to marine plankton, benthic organisms, mammals, and the coastal biological community (Beyer et al., 2016; Faksness et al., 2015; Yin et al., 2015). Coastal habitats are seriously polluted by oil; the marine ecological system consequently suffers widespread chronic damage (Peterson et al., 2003). Oil spill accidents also cause serious losses to the local society and economy (Guo, 2017; Zhu et al., 2014). Therefore, highly efficient identification of the types and sources of oil spills plays a critical role in pollution control of seas and investigations for accountability. The main chemical constituents of petroleum are saturated hydrocarbons, aromatic hydrocarbons, resin, and asphaltene. Saturated hydrocarbons and aromatic hydrocarbons are analyzed to identify common oil spills. PAHs in petroleum are mainly derived from precursors formed from microorganisms and terrestrial plants via geological synthesis. In organic geochemistry, the distribution of PAHs is used to indicate the source, thermal maturity, and sedimentary environment of crude oil and source rocks (Wilson and Jones, 1993). In comparison with precursors of PAHs in oil products, alkylated PAHs are

⁎

Corresponding author. E-mail address: [email protected] (Y. Liu).

https://doi.org/10.1016/j.marpolbul.2018.06.038 Received 4 January 2018; Received in revised form 6 June 2018; Accepted 11 June 2018 0025-326X/ © 2018 Elsevier Ltd. All rights reserved.

much more abundant and can therefore more accurately reflect the composition of PAHs in petroleum (Wang and Fingas, 1995). Aromatic hydrocarbons, which are the original molecules involved in the oil formation, have stronger resistance to weathering processes as compared with saturated hydrocarbons (Asif et al., 2011; Boehm et al., 1997; Zhou et al., 2015; Ebrahimi et al., 2007). Therefore, they are important indicators for oil sample identification. Different types and sources of oils have different compositions, distributions, and amounts of PAHs. Many studies using gas chromatography equipped with a flame ionization detector (GC-FID) and gas chromatography–mass spectrometry (GC–MS) have determined the compositions of PAHs in crude oils to analyze comprehensively the characteristics and sources of oils (Fernández-Varela et al., 2009; Yim et al., 2011). Previous research has shown that it is valid to utilize the distribution patterns of aromatic compounds and to determine the ratios of specific PAHs in order to differentiate among oil samples (Zhang et al., 2016; Yunker et al., 2002). For example, two kinds of fuel oils can be distinguished by using the characteristic ratios of dibenzothiophene and phenanthrene derivatives (Yim et al., 2011; Douglas et al., 1996), and the differences in the content of polycyclic aromatic sulfur heterocycles can be used to discriminate the different kinds of crude oils (Hegazi et al., 2004). However, the chemical composition of oil spills, such as composition of aromatic components, is well known to be affected by weathering processes. Different aromatic monomers reflect diverse effects of

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

A previous study by our group has demonstrated the variation of nalkane content and stable carbon isotope composition during short-term weathering of different types of oils. It proved that the stable carbon isotopic composition of n-alkanes can be effectively applied to oil fingerprint identification during short-term weathering of oil spills (Liu et al., 2017). The chemical components of oil spills including PAHs and n-alkanes are mainly consumed by evaporation, dissolution, disperation, photooxidation and biodegradation (Sebastião and Soares, 1995). However, there are great differences between PAHs and n-alkane components in oil products. The chemical properties of PAHs are more stable than those of n-alkanes, and PAHs and n-alkanes have different weathering characteristics. The rate of biodegradation of n-alkanes is much higher than that of photooxidation, while the main weathering process for PAHs is photooxidation. The weathering process of n-alkanes is more likely to be affected by dispersants, and PAHs are less affected by dispersants (Bacosa et al., 2015). In view of the above advantages, studying the role of weathering on the carbon stable isotopes of PAHs in oil samples it is of great significance to the identification and tracing of oil spills. Yanik et al. (2003) studied the effect of biodegradation on the stable carbon isotope composition of PAHs in crude oil and proved that the δ13C values of PAHs have a positive trend with the increase in biodegradation degree. There are no other studies on the regularity of weathering of the stable carbon isotopes of petroleum PAHs. In the present study, crude oils and fuel oils were used to conduct a weathering simulation experiment in order to assess the influence of weathering on the diagnostic ratios of PAHs and compositions of stable aromatic carbon isotopes. IRMS and diagnostic ratios of PAHs were used to differentiate all of the oils. Studies on changes in carbon stable isotope compositions of individual aromatic hydrocarbons during weathering processes are scarce at present. This study aims to determine the diagnostic ratios of PAHs and the isotopic profiles during the weathering process, as well as to provide an

Table 1 Data of four oils samples. Oil sample

Origins

Oil characteristics

Density (20 °C) kg/ m3

API gravity (°)

Viscosity (50 °C) mm2/s

KWT RUS 180# 380#

Kuwait Russia China China

Crude oil Crude oil Fuel oil Fuel oil

866.5 851.2 991.0 991.0

31.8 34.7 11.3 11.3

7.0 7.0 180.0 380.0

weathering resistance (John et al., 2016). In general, the degree of weathering of PAHs decreases with the increase in the number of aromatic compounds, and the weathering resistance becomes stronger with the increase in alkylation degree of aromatics (Wang and Fingas, 1995). The effectiveness of some characteristic parameters for aromatics also decreases because of weathering processes (Radovic et al., 2014). Identification of weathered oil samples using GC-FID and GC–MS may therefore be limited. Gas chromatography–isotope ratio mass spectrometry (GC–IRMS), another analytical technology, is an effective method for oil spill identification (Mansuy et al., 1997). Compound-specific isotope analysis, in particular, can reveal the source of the individual compounds on the molecular level and therefore has an advantage. Different sources of oil samples or even weathered samples can be effectively distinguished using GC–IRMS in order to determine the δ13C values of the n-alkanes in oils (Philp et al., 2002; Harvey et al., 2012; Peng et al., 2004; Sun et al., 2005; Liu et al., 2017). The stable carbon isotope composition of individual aromatic hydrocarbons is mainly used as an indicator of the source and age of oil and gas geochemicals (Maslen et al., 2011; Métayer et al., 2014). However, it has limited application in oil spill identification.

7 6

KWT

5 4

6 5

Value

Value

7

0d 3d 15d 30d 45d 60d

3

3 2

1

1 A B C D E F G H I

0

J K L M N

A B C D E F G H I

Diagnostic ratios

7 6

4

6

3

3 2

1

1

A B C D E F G H I

0d 3d 15d 30d 45d 60d

4

2

0

380#

5

Value

Value

5

J K L M N

Diagnostic ratios 7

0d 3d 15d 30d 45d 60d

180#

RUS

4

2

0

0d 3d 15d 30d 45d 60d

0

J K L M N

A B C D E F G H I

J K L M N

Diagnostic ratios

Diagnostic ratios Fig. 1. The changes of PAHs diagnostic ratios of oils in weathering process. The meanings of A–N are the same as in Table 2. 853

Marine Pollution Bulletin 133 (2018) 852–860

0.03 0.04 0.03 0.03 0.07 0.03 0.04 0.01 ± ± ± ± ± ± ± ± 380#

180#

Russia

Note: MPI1 = 1.5 × (3-MP + 2-MP)/(P + 9-MP + 1-MP); MPI2 = 3 × (2-MP)/(P + 9-MP + 1-MP); MPDF1 = (3-MP + 2-MP)/(1-MP + 2-MP + 3-MP + 9-MP); MPDF2 = 2 × (2-MP)/(1-MP + 2-MP + 3-MP + 9-MP).

0.08 0.06 0.07 0.06 0.18 0.16 0.14 0.15 0.04 0.05 0.06 0.05 0.03 0.07 0.08 0.05 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 3.08 2.75 2.65 2.45 5.03 5.33 4.93 4.00 1.61 1.91 2.06 1.86 1.63 2.12 2.14 1.73 0.47 0.52 0.51 0.51 0.53 0.54 0.56 0.55 0.60 0.64 0.64 0.61 0.61 0.67 0.64 0.69 0.25 0.26 0.24 0.26 0.40 0.40 0.40 0.41 0.50 0.53 0.50 0.53 0.61 0.62 0.59 0.60 5.14 3.41 2.15 6.34 0.95 1.51 3.04 2.74 2.27 1.93 1.60 1.42 2.36 2.73 2.67 2.25 Kuwait

0d 15 d 30 d 60 d 0d 15 d 30 d 60 d 0d 15 d 30 d 60 d 0d 15 d 30 d 60 d

1.34 0.79 0.79 0.62 1.15 0.56 0.44 0.00 1.86 1.21 1.17 1.06 1.50 1.53 1.27 1.26

± ± ± ± ± ± ±

0.07 0.04 0.02 0.01 0.04 0.02 0.01

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.12 0.07 0.06 0.13 0.04 0.01 0.02 0.03 0.02 0.01 0.01 0.04 0.02 0.02 0.03 0.05 3.50 3.35 4.44 4.89 1.30 1.31 1.88 2.56 1.24 1.42 0.73 1.07 1.68 1.75 1.46 1.69

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.11 0.15 0.10 0.04 0.03 0.01 0.14 0.06 0.05 0.01 0.01 0.05 0.07 0.03 0.05 0.02

1.02 0.22 0.19 0.22 0.51 0.40 0.38 0.36 0.83 0.82 0.50 0.66 0.37 0.65 0.51 0.45

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.01 0.01 0.03 0.01 0.02

0.72 0.48 0.47 0.38 0.68 0.76 0.53 0.39 0.79 0.99 0.82 0.78 0.75 0.82 0.66 0.68

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.02 0.02 0.03 0.02 0.02 0.01 0.02 0.03 0.03

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.03 0.01 0.01 0.01 0.04 0.01 0.01 0.01 0.05 0.03 0.01 0.02 0.01 0.02 0.02 0.54 0.69 0.71 0.49 1.73 1.25 0.60 0.43 0.77 1.12 1.20 0.88 1.12 0.95 0.94 0.92

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.01 0.03 0.01 0.01

0.79 1.02 0.86 0.90 1.14 1.01 1.01 0.96 2.03 2.02 1.99 1.83 1.55 1.63 1.65 1.57

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.04 0.02 0.02 0.05 0.03 0.01 0.02 0.05 0.09 0.07 0.08 0.02 0.02 0.02 0.02

0.98 0.98 0.86 0.79 0.95 1.11 0.87 0.88 1.39 1.26 1.09 1.30 0.53 0.55 0.54 0.55

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.04 0.04 0.02 0.01 0.02 0.02 0.01 0.01 0.04 0.01 0.04 0.03 0.01 0.02 0.01 0.01

0.75 0.84 0.85 0.80 0.88 0.88 0.89 0.88 1.23 1.24 1.24 1.22 1.02 1.41 1.37 1.62

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.02 0.02 0.04 0.01 0.01 0.01 0.01 0.02 0.04 0.04 0.02 0.01 0.07 0.07 0.01

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.01 0.03 0.03 0.07 0.03 0.04 0.01 0.08 0.06 0.66 0.80 0.75 0.76 0.93 0.72 0.75 0.73 1.38 1.27 1.32 1.23 1.34 1.47 1.66 1.48

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.02 0.02 0.02 0.02 0.01 0.02 0.03 0.01 0.02 0.01 0.02 0.01 0.02 0.03 0.03 0.03

0.42 0.49 0.45 0.49 0.56 0.44 0.48 0.46 0.68 0.65 0.68 0.62 0.79 0.70 0.77 0.63

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.02 0.02 0.02 0.02 0.01 0.01 0.02 0.02 0.02 0.01 0.01 0.01 0.03 0.01 0.03 0.02

N M L K J I H G F E D C B A Ratios

Table 2 PAH diagnostic ratios of the four oils in the different weathering periods. A = 2-MN/1-MN; B = 1,6-DMN/1,2-DMN; C = 1,6-DMN/1,5-DMN; D = (2,3,6-TMN)/(1,4,6-TMN + 1,3,5-TMN); E = (1,3,7-TMN + 2,3,6TMN)/(1,3,5-TMN + 1,3,6-TMN + 1,4,6-TMN); F = 1,3,7-TMN/1,3,6-TMN; G = DBT/P; H = 2-MP/1-MP; I = 9 + 4-MP/1-MP; J = MPI1; K = MPI2; L = MPDF1; M = MPDF2; N = ∑P/∑D. Means are results of triplicate analyses.

Y. Li et al.

alternative method for the identification of oil spills. 2. Materials and methods 2.1. Weathering simulation experiment Two crude oils and two fuel oils were selected as research samples in this study (Table 1.). A 60-day weathering simulation experiment was conducted for these oil samples. The seawater was filtered by nylon cloth of 500-mesh to remove suspended substances. About 1800 ml of filtered seawater was transferred into every 2000 ml beaker. Approximately 8 g of each sample was taken and then its surface was uniformly covered with seawater. The average temperature was −1.18 ± 3.76 °C. 2.2. Sample preparation Neutral alumina and anhydrous sodium sulfate had to be activated for 4 h at 400 °C prior to use. About 0.2 g of each oil sample was dissolved in n-hexane (8 ml) and then subjected to ultrasonic concussion. The dissolved oil sample was transferred into a glass centrifuge tube, and 1 g of anhydrous sodium sulfate was added to it. The supernatant was collected for use after centrifugation at speed of 1500 r/min for 10 min. The column was packed with activated neutral alumina and washed with n-hexane before use. The supernatant was transferred to the column, and then the saturated hydrocarbons were eluted with nhexane (20 ml) (component A). Total aromatics (component B) were eluted with n-hexane–dichloromethane mixture (20 ml, 3:1, v/v) and were concentrated to 0.5 ml under a nitrogen stream prior to analysis using GC–MS. In order to better meet the conditions for GC–IRMS, component B had to be separated by column chromatography. Hexane–dichloromethane solution (20 ml, 9:1, v/v) was used to elute diaromatics (component C), and hexane–dichloromethane solution (20 ml, 2:1, v/v) was used to elute tricyclic aromatics (component D). Finally, the components were concentrated to 0.5 ml and then stored in a sample bottle at low temperature for GC–IRMS. 2.3. Instrumental analysis GC–MS analysis was performed on a Thermo Fisher (Thermo Fisher Scientific, USA) unit equipped with a DB-5MS quartz chromatography column (60 m × 0.25 mm × 0.25 μm). The carrier gas was He and its flow rate was 1–2 ml/min. Sampling was done in splitless mode. The inlet temperature was 280 °C, and the volume of injected sample was 1 μl. Heating started with a temperature of 60 °C, which was increased at a rate of 3 °C/min to 290 °C and then maintained for 10 min. The temperature of the ion source for the mass spectrometer was 230 °C, and temperature of the transmission line was 250 °C. The electron bombardment source for electron impact (EI) was 70 eV. GC–IRMS analysis was performed on a Delta V unit (Thermo Fisher Scientific, USA); chromatography conditions were the same as those for GC–MS. The stable carbon isotope ratio was calculated by using the standard, Vienna Pee Dee belemnite. The formula for the isotope ratio is as follows:

δ13C = [(Rsample / Rstandard ) − 1] × 103 where Rsample represents 13C/12C ratio of the sample; Rstandard is the 13 C/12C ratio of the international isotopic standard, Vienna Pee Dee Belemnite (VPDB). PAHs of crude oils were identified by matching retention times of 16 PAH standards. Alkyl aromatic hydrocarbon compounds were identified by comparison of mass spectra and retention times against published references (Emsbo-Mattingly and Litman, 2016; Nabbefeld et al., 2010). PAHs abbreviations are as follows: N – naphthalene; MN – methylnaphthalene; DMN – dimethylnaphthalene; TMN – 854

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

100

the average value of samples.

KWT RUS 180# 380#

80

3. Results and discussion

RSD(%)

3.1. PAH diagnostic ratios

60 In this study, the aromatic parameters commonly used in geochemistry and in oil spill identification were selected to evaluate the effect of weathering processes on the diagnostic ratios of aromatic hydrocarbons. Here, diagnostic ratios were calculated from the chromatographic peak areas of aromatic components. The diagnostic ratios for aromatic hydrocarbons can reflect the maturity of crude oil and organic matter sources. For example, organic matter source and sedimentary environment affect 1,6-DMN and DBT/P, while MPI, MPDF, and other parameters mainly reflect the maturity of oil samples (Radke et al., 1986; Hughes et al., 1995; Aarssen et al., 1999; Kvalheim et al., 1987). They can be divided into two categories: the first six are the parameters of the naphthalene series, the others are the diagnostic ratios of the phenanthrene compounds (Table 2). The Fig. 1 shows the variation trend of PAHs diagnostic ratios of four oils in the weathering process. A ratio of four oils all showed declining trend. B ratio of Russia crude oil increased with increasing weathering degree and that of another oil has no change rules. C ratio of four oils changed acutely but showed no change rules. D ratio of two kinds of crude oils decreased with increasing weathering degree, while that of fuel oils displayed no regular trend. E and F ratios of all oil samples showed no change regular. From G to M ratios, they all showed narrow changing range. N ratio of Kuwait and Russia crude oils decreased with increasing weathering process, while that of fuel oils displayed unclear changing regular. Evidently, the diagnostic ratios of naphthalene series varied greatly in all of the oil samples, while the parameters of the phenanthrene series are relatively stable (Table 2, Fig. 2). This shows that the naphthalene series have weak resistance to weathering, while the phenanthrene series have strong resistance, which is consistent with previous conclusions. In addition, changes in the diagnostic ratio of aromatic hydrocarbons in the two crude oil samples were markedly larger than those in the diagnostic ratio of the two fuel oils (Table 2). The maximum and minimum changes of 2-MN/1-MN ratios of the four oil samples in the initial 60 days of weathering were those of Russia crude oil and 380# fuel oil, respectively. These suggest that the aromatic components of crude oil are more vulnerable to the weathering effect than is fuel oil. The relative standard deviation (RSD) is often used to evaluate the degree by which diagnostic ratios of oil spills are affected by the weathering process. According to the RSD of the oil samples' diagnostic ratio, the degree of the weathering effect on the diagnostic ratio for aromatics in descending order is Russia, Kuwait, 180#, and 380#. Naphthalene series parameters were enormously affected by weathering, and so they could not be applied in oil spill identification. The diagnostic index for the weathering resistance of the phenanthrene series was significantly greater (RSD < 12%). Generally, 5% is considered as a baseline for complete resistance to weathering (Wang et al., 2015; Stout et al., 2001; Li et al., 2009). Fig. 2 shows that the RSDs of DBT/P ratio and MPDF1 in all oil samples are < 5%, indicating that they are not affected by the weathering process. The aromatic parameters for strong resistance to weathering were used to distinguish the oil samples. DBT/P and MPDF1 ratios of the four oil samples were analyzed by one-way analysis of variance (ANOVA), and the results are shown in Tables 3 and 4. The results indicate that significance levels of DBT/P and MPDF1 ratios in the different oil samples were 0.000 and 0.005 respectively (P < 0.05, implying a significant difference). The ratios for the oil samples were then compared in pairs. There were significant differences in DBT/P ratio for every pair of oil samples, while the significance level for MPDF1 of the two fuel oils was 0.161 > 0.05, implying no significant difference. All of the oil samples could not be distinguished in pairs by MPDF1. Hence, the DBT/P ratio suggests strong weathering resistance and is useful for

40 20 0 A B C D E F G H I

J K L M N

Diagnostic ratios Fig. 2. RSDs of the different PAHs diagnostic ratios for the four oils in the weathering process. The meanings of A–N are the same as in Table 2. Table 3 Results of one-way analysis of variance (ANOVA) of DBT/P in different kinds of oils. ANOVA

DBT/P

Kuwait

Russia

180#

380#

0.000

Kuwait Russia 180# 380#

―

0.000 ―

0.000 0.000 ―

0.000 0.000 0.000 ―

Table 4 Results of ANOVA of MPDF1 for the different oils. ANOVA

MPDF1

Kuwait

Russia

180#

380#

0.005

Kuwait Russia 180# 380#

―

0.001 ―

0.000 0.000 ―

0.000 0.000 0.161 ―

trimethylnaphthalene; P – phenanthrene; MP – methylphenanthrene; DMP – dimethylphenanthrene; TMP – trimethylphenanthrene; F – fluorene; MF – methylfluorene; DBT – dibenzothiophene; MPI – methylphenanthrene index; MPDF – methylphenanthrene distribution fraction. The triplicates of all oil samples are carried out for GC–MS analysis in order to get reliable PAHs diagnostic ratios. The relative standard deviations for all triplicates are < 5%, which shows good stability of data. To obtain the stable carbon isotope data accurately, only aromatic hydrocarbons of the stable and flat baseline of chromatogram were selected, and aromatic substances of low concentration were removed from all data. Co-eluting compounds were usually ignored unless the total overflow included isomers (such as 2,6 + 2,7-DMN, 1,3 + 1,7DMN, and 1,3,5 + 1,4,6-DMN). In order to obtain reliable stable carbon isotope data, each sample was subjected to three parallel tests. The standard deviation for three parallel samples with aromatic δ13C of < 0.4‰ could be used in the data analysis; otherwise, they were removed. The formulas for the standard deviation (SD) and relative standard deviation (RSD) are as follows: n

SD =

∑i = 1 (x i − x )2 (n − 1)

RSD = [SD/ x ] × 100% where i stands for sample number; n represents sample size; x stands for 855

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

-20

-22

-24

-24

-26

-26

13C(‰)

13C(‰)

-22

-20

A

-28 -30

-34

-34 -36

P P N N N N N N N N N M M M M M M M M M M M 2- 1- -D -D -D -D 7-T 6-T 6-T 2- 17 2 6 5 , , , 1, 1, 1, 1, ,3 ,4 ,2 1 /1 1 3/ 5 1, 3, 1,

-22

C

-26 -28

-20 -22 -24 -26

D

0d 3d 15d 30d 45d 60d

-28

-30

-30

-32

-32

-34

-34

-36

-36

2M 2, N 6/ 12, M 7- N 1, DM 6- N 1, DM 5- N 1, DM 2 N 1, -D 3, M N 1, 1, 7-T 3, 3, M 5/ 6- N 1, T 4, M 6 N 2, -T 3, M 6 N 1, -T 2, M 6- N TM N 2M 1- P M P

2M N 2, 6/ 1-M 2, 1, 7-D N 3/ 1, MN 7D 1, MN 6D 1, MN 21, DM 3, 7- N 1, 1,3 TM 3, N 5/ ,6-T 1, 4, MN 6TM N 2M P 1M P

13C(‰)

-24

0d 3d 15d 30d 45d 60d

13C(‰)

-20

0d 3d 15d 30d 45d 60d

-32

2M 2, N 6/ 12, M 1, 7- N 3/ D 1, M 7- N 1, DM 6- N 1, DM 5- N 1, DM 2- N 1, D 3, M N 1, 1, 7-T 3, 3, M 5/ 6- N 1, T 4, M 6 N 1, -T 2, M 6- N TM N 2M 1- P M P

-36

-28 -30

0d 3d 15d 30d 45d 60d

-32

B

Fig. 3. δ13C values of alkylnaphthalene and methyl phenanthrene isomers in the four oil samples from different weathering periods. (A) Kuwait crude oil; (B) Russia crude oil; (C) 180# fuel oil; (D) 380# fuel oil.

compounds in each oil sample at different weathering stages. We took the average δ13C values of all isomers of the series (Fig. 4). Because of the weak resistance to weathering ability of naphthalene, it was completely consumed at 15 d, so the corresponding δ13C value could not be obtained. We can see that the δ13C value of each aromatic series decreases roughly with the increase in aromatic ring number and alkylation degree. The maximum δ13C value of all aromatic series in Kuwait crude oil is −22.876‰, and the minimum value is −30.983‰. The largest δ13C value of the series of aromatic hydrocarbons in Russia crude oil is −25.574‰, while the lowest value is −31.502‰. The largest and lowest δ13C values of the aromatic series in 180# fuel oil are −28.084‰ and −32.862‰, respectively. The least negative and negative δ13C values of the aromatic series in 380# fuel oil are −28.167‰, −33.868‰, respectively. In general, carbon isotope features markedly differed among the crude oils and fuel oils. The two crude oils differed while the two fuel oils were similar in terms of the stable carbon isotope composition. The effect of weathering on the stable carbon isotope composition of PAHs is depicted in Figs. 3 and 4. Weathering affected naphthalene in the two crude oil samples at 15 d, which thereafter completely disappeared; fuel oil which was less affected. This indicates that naphthalene in crude oils has no resistance to weathering. After 60 days of weathering of oil samples, almost all the values of PAHs showed a slightly positive trend. The maximum standard deviation of δ13C of the same substance in Kuwait crude oil (1-MN) is 1.131‰ during weathering. In Russia crude oil, the substance affected most by weathering is 1,3,7-TMN, and the standard deviation of stable carbon isotope in the

distinguishing the different oil samples. 3.2. Stable carbon isotope compositions of individual PAHs 3.2.1. Effects of weathering on the stable carbon isotope compositions of individual PAHs The stable carbon isotope compositions of alkylnaphthalene and methyl phenanthrene isomers in the four oil samples subjected to different weathering periods are shown in Fig. 3. The standard deviation of parallel samples of each oil sample is < 0.398‰. δ13C values of the individual DMN and TMN isomers in some oil samples are missing because of low concentration, such as 2,3,6-TMN in Kuwait crude oil, 2,6/2,7-DMN and 1,3,6-TMN in Russia crude oil, 1,5-DMN 1,2,6-TMN in 180# fuel oil, and 1,3/1,7-DMN in 380# fuel oil. δ13C values of naphthalene and phenanthrene isomers for Kuwait crude oil, Russia crude oil, 180# fuel oil, and 380# fuel oil respectively ranged from −29.259 to −23.177‰, from −32.566 to −24.653‰, from −33.757 to −28.449‰, and from −33.723 to −28.307‰ (Fig. 3). The stable carbon isotope compositions of the two crude oils are less negative, as are those of the two fuel oils, albeit lower. The δ13C value generally declined with the increase in the ring number in the four oil samples (i.e., from methyl naphthalene isomers to methyl phenanthrene isomers). The carbon isotope curves of Kuwait crude oil and 180# fuel oil are relatively smooth, whereas those of Russia crude oil and 380# fuel oil fluctuated greatly. The different features of the four oils suggest diverse stable carbon isotope compositions. We also measured the δ13C values of each series of aromatic 856

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

-20

-22

-24

-24

-26

-26

13C(‰)

13C(‰)

-22

-20

A

-28 -30

0d 3d 15d 30d 45d 60d

-32 -34 -36

B

-28 -30

0d 3d 15d 30d 45d 60d

-32 -34

N MN DMNTMN F

MF DBT P

-36

MP DMPTMP

N MN DMNTMN F

AHs

-22

C

-24

13C(‰)

-20

0d 3d 15d 30d 45d 60d

-26

-22

-28

-32

-32

-34

-34

-36 MF DBT

P

0d 3d 15d 30d 45d 60d

-28 -30

MN DMN TMN F

D

-26

-30

N

MP DMPTMP

-24

13C(‰)

-20

MF DBT P

AHs

-36

MP DMP

N MN DMNTMN F

AHs

MF DBT P

MP DMPTMP

AHs

Fig. 4. δ13C values of each series of aromatic compounds of each oil sample at different weathering stages. (A) Kuwait crude oil; (B) Russia crude oil; (C) 180# fuel oil; (D) 380# fuel oil.

-22

A

-24

-24

-26

13C of F(‰)

13C of F(‰)

-22

KWT RUS 180# 380#

-28 -30

-26 -28 -30

-32

-32

-34

-34

-34

-32

-30

-28

-26

-24

-22

KWT RUS 180# 380#

B

-34

-32

-30

-28

-26

-24

-22

13C of DMP(‰)

13C of P(‰)

Fig. 5. Plots of δ13C values of two aromatic compounds in the oil samples for discriminating the different oils. (A) δ13C of P versus δ13C of F;(B) δ13C of DMP versus δ13C of F.

the oil samples provide information about the oil spills.

weathering period is 0.870‰. In 180# and 380# fuel oils, the substances with the greatest changes in δ13C due to weathering are methylfluorene and 1-MP, with standard deviations of 1.124‰ and 1.054‰, respectively. The standard deviation of δ13C of every PAH in the four oil samples during the 60 d weathering process is < 1.131‰, which implies small changes in the PAHs' carbon stable isotopes during the process. Hence, the stable carbon isotope compositions of PAHs in

3.2.2. Discrimination of oils using the δ13C values of individual aromatic hydrocarbons The δ13C values of all aromatic hydrocarbons of the four oil samples at different weathering stages were measured, and the δ13C values of fluorene, phenanthrene, and dimethyl phenanthrene were used in 857

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

that they can reflect the stable carbon isotope composition of these aromatic hydrocarbons. The principal components of the two-coordinate scatter diagram are shown in Fig. 6. The chart for the first principal component suggests that δ13C of Kuwait crude oil had the least negative value, 180# and 380# fuel oils had the most negative values. According to the second principal component, Russia crude oil was most enriched in 13C between the two crude oils, and Kuwait crude oil had the lowest δ13C value between the two fuel oils. We can see that the four weathered oil samples can be distinguished by the stable carbon isotope composition of aromatic hydrocarbons (Fig. 6).

Table 5 The initial factor load matrix. PAHs

Component

2-MN 1-MN 1,6-DMN 1.2-DMN 1,3,7-TMN 1,3,5/1,4,6-TMN F MF DBT P 2-MP 1-MP DMP

1

2

0.948 0.893 0.892 0.746 0.587 0.863 0.929 0.655 0.844 0.867 0.895 0.819 0.611

−0.051 −0.242 −0.232 −0.434 −0.708 0.321 0.144 0.509 −0.329 −0.300 0.211 0.443 0.738

2.0

Maslen et al. (2011) discriminated the sources of crude oil in Western Australia by using the plots of P/DBT ratio versus δ13C of aromatic isomers. Hughes et al. (1995) identified the depositional environment of the source rocks by using scatter plots of DBT/P ratio versus Pristane/ Phytane (Pr/Ph). In the preceding part of this study, the effectiveness of DBT/P ratio in identifying oil samples and the strong weathering resistance were proven by data analysis. Therefore, the DBT/P ratios and δ13C values of phenanthrene, 1-methyl phenanthrene, dimethylphenanthrene, and fluorene of the four oil samples in the six weathering stages were used to construct the corresponding scatter plots (Fig. 7). The DBT/P ratio of Kuwait crude oil was the minimum, while that of 380# fuel oil was the maximum. The DBT/P ratios of Russia crude oil and 180# fuel oil were intermediate of these values; Kuwait crude oil and 380# fuel respectively had the least and most negative δ13C values of phenanthrene. 1-MP of Russia crude oil was enriched in 13C, and both fuel oils were depleted of 13C; δ13C of dimethylphenanthrene in Russia crude oil was the largest, while of dimethyl phenanthrene in 180# fuel oil the lowest. Kuwait crude oil and 180# fuel respectively had the least and most negative δ13C values of fluorene. The four oils had original characteristics, which can be used to distinguish them using plots of DBT/P ratio versus δ13C of aromatic isomers.

KWT RUS 180# 380#

1.5 1.0

PC2(16.71%)

3.3. Distinguishing the different oils using aromatic ratios and stable isotope compositions of the aromatic hydrocarbons

0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.0 -1.5 -1.0 -0.5 0.0 0.5 PC1(67.24%)

1.0

1.5

2.0

Fig. 6. PCA results of δ13C values of aromatic hydrocarbons.

4. Conclusions coordinate plots (Fig. 5). Fluorene and phenanthrene in Kuwait crude oil had the least negative δ13C value, followed by that of Russia crude oil. δ13C of fluorene in 180# fuel oil was the most negative, and that of dimethyl phenanthrene in 380# fuel oil was the most negative (Fig. 5A). In addition, δ13C of dimethyl phenanthrene in Russia crude oil was the least negative, followed by those of Kuwait and 380# fuel oil. The δ13C value of dimethyl phenanthrene in 180# fuel oil was the most negative (Fig. 5B). In conclusion, the types of four oil samples could be distinguished, while Kuwait and Russia crude oils couldn't be discriminated from each other totally by the stable carbon isotopic composition of the different aromatic hydrocarbons.

The diagnostic ratios of various weathered oils indicate that weathering markedly affects parameters for the naphthalene series, and that the extent of weathering of crude oil is much greater than that of fuel oil. The change in diagnostic ratios of the phenanthrene series was markedly smaller than that of the naphthalene series, especially DBT/P and MPDF1, and their RSD was < 5%. Hence, they were unaffected by weathering. One-way ANOVA showed that the DBT/P ratio could be used to distinguish the four oil samples in pairs, but MPDF1 in the two fuel oils could be not distinguished. The weathering process had some effect on δ13C of the aromatic hydrocarbons, and the weathering process led to depletion of naphthalene and some alkyl aromatics; hence, the corresponding δ13C values were missing. However, the largest standard deviation of δ13C of the same aromatic hydrocarbons (1.131‰) was attained in different weathering stages. This reveals that δ13C of the aromatic hydrocarbons is relatively stable. Two scatter diagrams for aromatic δ13C were used to distinguish the different types of oil samples, but this method was limited by discriminating Kuwait and Russia crude oils. PCA of aromatic δ13C could also distinguish the oil samples. The best method of distinguishing combines the characteristic DBT/P ratio of aromatic hydrocarbon with the δ13C of aromatics. This study provides reference methods for the identification of oil products.

3.2.3. Principal component analysis of the stable carbon isotope compositions of aromatic compounds The δ13C values of the 13 aromatic hydrocarbons, which can be measured in all four oil samples, and their weathering resistance in 60 d were studied. The hydrocarbons were 2-MN, 1-MN, 1,6-DMN, 1,2-DMN, 1,3,7-TMN, 1,3,5/1,4,6-TMN, F, MF, DBT, P, 2-MP, 1-MP, and DMP. In order to simplify these numerous indicators, principal component analysis (PCA) was applied to the data for the stable carbon isotope composition of the hydrocarbons. The initial factor load matrix is shown in Table 5. The absolute values can be used to measure the contribution of every PAH's data to two principal components. It can be seen that 2-MN, 1-MN, 1,5-DMN, 1,3,5/1,4,6-TMN, F, DBT, P, 2-MP and 1-MP have high factor load on the first principal, and 1,3,7-TMN, DMP have high factor load on the second principal. The first principal components accounted for 67.24% of the variance, the second principal components accounted for 16.71% of the variance. The two principal components accounted for 83.95% of the variance overall, indicating

Acknowledgments This study was supported by the National Science & Technology Pillar Program (2015BAD17B05) and the Maritime Science & Technology Project (2014LNMSA001). 858

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

B

KWT RUS 180# 380#

-24

-26 -28 -30

-26 -28 -30

13

13C of P(‰)

-24

-32

-32

-34

-34

0.1

-22

0.2

0.3

0.4

0.5

0.6

0.7

0.1

0.2

0.3

DBT/P

C

C of F(‰)

-28

13

-30

-34

0.5

0.6

KWT RUS 180# 380#

D

-30

-34 0.4

0.7

-28

-32

0.3

0.6

-26

-32

0.2

0.5

-24

-26

0.1

0.4

DBT/P -22

KWT RUS 180# 380#

-24

13C of DMP(‰)

-22

KWT RUS 180# 380#

A

C of 1-MP(‰)

-22

0.7

DBT/P

0.1

0.2

0.3

0.4

0.5

0.6

0.7

DBT/P

Fig. 7. Plots DBT/P ratio versus δ C of aromatic hydrocarbons of P (A), 1-MP (B), DMP (C), and F (D). 13

References

chromatography (GC) isotope ratio mass spectrometry (IRMS). Talanta 99 (18), 262–269. Hegazi, A.H., Andersson, J.T., El-Gayar, M.S., 2004. Application of gas chromatography with atomic emission detection to the geochemical investigation of polycyclic aromatic sulfur heterocycles in Egyptian crude oils. Fuel Process. Technol. 85 (1), 1–19. Hughes, W.B., Holba, A.G., Dzou, L.I.P., 1995. The ratios of dibenzothiophene to phenanthrene and pristane to phytane as indicators of depositional environment and lithology of petroleum source rocks. Geochim. Cosmochim. Acta 59, 3581–3598. John, Gerald F., Han, Yuling, Prabhakar Clement, T., 2016. Weathering patterns of polycyclic aromatic hydrocarbons contained in submerged Deepwater Horizon oil spill residues when re-exposed to sunlight. Sci. Total Environ. 573, 189–202. Kvalheim, O.M., Christy, A.A., Telnæs, N., Bjørseth, A., 1987. Maturity determination of organic matter in coals using the methylphenanthrene distribution. Geochim. Cosmochim. Acta 51 (7), 1883–1888. Li, Y., Xiong, Y., Yang, W., Xie, Y., Li, S., Sun, Y., 2009. Compound-specific stable carbon isotopic composition of petroleum hydrocarbons as a tool for tracing the source of oil spills. Mar. Pollut. Bull. 58 (1), 114–117. Liu, Y., Xu, J., Chen, W., Li, Y., 2017. Effects of short-term weathering on the stable carbon isotope compositions of crude oils and fuel oils. Mar. Pollut. Bull. 15 (1), 238–244. Mansuy, L., Philp, R.P., Allen, J., 1997. Source identification of oil spills based on the isotopic composition of individual components in weathered oil samples. Environ. Sci. Technol. 31 (12), 3417–3425. Maslen, E., Grice, K., Dawson, D., Le Metayer, P., Edwards, D., 2011. Stable carbon isotopic compositions of individual aromatic hydrocarbons as source and age indicators in oil from Western Australian Basins. Org. Geochem. 41, 387–398. Métayer, P.L., Grice, K., Chow, C.N., Caccetta, L., Maslen, E., Dawson, D., et al., 2014. The effect of origin and genetic processes of low molecular weight aromatic hydrocarbons in petroleum on their stable carbon isotopic compositions. Org. Geochem. 72, 23–33. Nabbefeld, B., Grice, K., Summons, R.E., Hays, L.E., Cao, C.Q., 2010. Significance of polycyclic aromatic hydrocarbons (PAHs) in Permian/Triassic boundary sections. Appl. Geochem. 25 (9), 1374–1382. Peng, X., Zhang, G., Chen, F., Liu, G., 2004. Stable carbon and hydrogen isotopic fractionations of alkane compounds and crude oil during aerobically microbial degradation. Chin. Sci. Bull. 49 (24), 2620–2626. Peterson, C.H., Rice, S.D., Short, J.W., Esler, D., Bodkin, J.L., Ballachey, B.E., et al., 2003. Long-term ecosystem response to the Exxon Valdez oil spill. Science 302 (5653), 2082–2086.

Aarssen, B.G.K.V., Bastow, T.P., Alexander, R., Kagi, R.I., 1999. Distributions of methylated naphthalenes in crude oils: indicators of maturity, biodegradation and mixing. Org. Geochem. 30 (10), 1213–1227. Asif, M., Nazir, N., Fazeelat, T., Grice, K., Nasir, S., Saleem, A., 2011. Applications of polycyclic aromatic hydrocarbons to assess the source and thermal maturity of the crude oils from Lower Indus Basin, Pakistan. Pet. Sci. Technol. 29, 2234–2246. Bacosa, Hernando P., Erdner, Deana L., Liu, Zhanfei, 2015. Differentiating the roles of photooxidation and biodegradation in the weathering of Light Louisiana Sweet crude oil in surface water from the Deepwater Horizon site. Mar. Pollut. Bull. 95, 265–272. Beyer, J., Trannum, H.C., Bakke, T., Hodson, P.V., Collier, T.K., 2016. Environmental effects of the Deepwater Horizon oil spill: a review. Mar. Pollut. Bull. 110 (1), 28–51. Boehm, P.D., Douglas, G.S., Burns, W.A., Mankiewicz, P.J., Page, D.S., Bence, A.E., 1997. Application of petroleum hydrocarbon chemical fingerprinting and allocation techniques after the Exxon Valdez, oil spill. Mar. Pollut. Bull. 34 (8), 599–613. Douglas, G.S., Bence, A.E., Prince, R.C., Mcmillen, S.J., Butler, E.L., 1996. Environmental stability of selected petroleum hydrocarbon source and weathering ratios. Environ. Sci. Technol. 30 (7), 2332–2339. Ebrahimi, D., Li, J., Hibbert, D.B., 2007. Classification of weathered petroleum oils by multi-way analysis of gas chromatography–mass spectrometry data using PARAFAC2 parallel factor analysis. J. Chromatogr. A 1166 (1), 163–170. Emsbo-Mattingly, S.D., Litman, E., 2016. 5–Polycyclic aromatic hydrocarbon homolog and isomer fingerprinting. In: Standard Handbook Oil Spill Environmental Forensics, pp. 255–312. Faksness, L.G., Altin, D., Nordtug, T., Daling, P.S., Hansen, B.H., 2015. Chemical comparison and acute toxicity of water accommodated fraction (WAF) of source and field collected macondo oils from the Deepwater Horizon spill. Mar. Pollut. Bull. 91 (1), 222–229. Fernández-Varela, R., Andrade, J.M., Muniategui, S., Prada, D., Ramírez-Villalobos, F., 2009. The comparison of two heavy fuel oils in composition and weathering pattern, based on IR, GC-FID and GC–MS analyses: application to the Prestige wreckage. Water Res. 43 (4), 1015–1026. Guo, W., 2017. Development of a statistical oil spill model for risk assessment. Environ. Pollut. 230, 945–953. Harvey, S.D., Jarman, K.H., Moran, J.J., Sorensen, C.M., Wright, B.W., 2012. Characterization of diesel fuel by chemical separation combined with capillary gas

859

Marine Pollution Bulletin 133 (2018) 852–860

Y. Li et al.

Wilson, S.C., Jones, K.C., 1993. Bioremediation of soil contaminated with polynuclear aromatic hydrocarbons (PAHs): a review. Environ. Pollut. 81 (3), 229–249. Yanik, P.J., O'Donnell, T.H., Macko, S.A., Qian, Y., Ii, M.C.K., 2003. The isotopic compositions of selected crude oil pahs during biodegradation. Org. Geochem. 34 (2), 291–304. Yim, U.H., Ha, S.Y., An, J.G., Won, J.H., Han, G.M., Hong, S.H., et al., 2011. Fingerprint and weathering characteristics of stranded oils after the Hebei Spirit oil spill. J. Hazard. Mater. 197 (24), 60–69. Yin, F., John, G.F., Hayworth, J.S., Clement, T.P., 2015. Long-term monitoring data to describe the fate of polycyclic aromatic hydrocarbons in Deepwater Horizon oil submerged off Alabama's beaches. Sci. Total Environ. 508 (1), 46–56. Yunker, et al., 2002. PAHs in the Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and composition. Org. Geochem. 33, 489–515. Zhang, H., Wang, C., Zhao, R., Yin, X., Zhou, H., Tan, L., et al., 2016. New diagnostic ratios based on phenanthrenes and anthracenes for effective distinguishing heavy fuel oils from crude oils. Mar. Pollut. Bull. 106 (1–2), 58–61. Zhou, P., Chen, C., Ye, J., Shen, W., Xiong, X., Hu, P., et al., 2015. Combining molecular fingerprints with multidimensional scaling analyses to identify the source of spilled oil from highly similar suspected oils. Mar. Pollut. Bull. 93 (1–2), 121–129. Zhu, H., Lin, P., Pan, Q., 2014. A CFD (computational fluid dynamic) simulation for oil leakage from damaged submarine pipeline. Energy 64 (1), 887–899.

Philp, R.P., Allen, J., Kuder, T., 2002. The use of the isotopic composition of individual compounds for correlating spilled oils and refined products in the environment with suspected sources. Environ. Forensic 3 (3–4), 341–348. Radke, M., Welte, D.H., Willsch, H., 1986. Maturity parameters based on aromatic hydrocarbons: influence of the organic matter type. Org. Geochem. 10 (1), 51–63. Radovic, Jagoš R., Aeppli, Christoph, Nelson, Robert K., et al., 2014. Assessment of photochemical processes in marine oil spill fingerprinting. Mar. Pollut. Bull. 79, 268–277. Sebastião, P., Soares, C.G., 1995. Modeling the fate of oil spills at sea. Spill Science & Technology Bulletin. 2 (2–3), 121–131. Stout, S.A., Uhler, A.D., McCarthy, K.J., 2001. A strategy and methodology for defensibly correlating spilled oil to source candidates. Environ. Forensic 2, 87–98. Sun, Y., Chen, Z., Xu, S., Cai, P., 2005. Stable carbon and hydrogen isotopic fractionation of individual n-alkanes accompanying biodegradation: evidence from a group of progressively biodegraded oils. Org. Geochem. 36 (2), 225–238. Wang, M., Wang, C., He, S., 2015. Source identification of oil spills using compoundspecific carbon isotope analysis based on “7–16” oil spill in Dalian, China. Aquatic Procedia. 3, 197–202. Wang, Z., Fingas, M., 1995. Differentiation of the source of spilled oil and monitoring of the oil weathering process using gas chromatography–mass spectrometry. J. Chromatogr. A 712 (2), 321–343.

860