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Fingerprinting and source identification of an oil spill in China Bohai Sea by gas chromatography-flame ionization detection and gas chromatography–mass spectrometry coupled with multi-statistical analyses
Journal of Chromatography A, 1216 (2009) 830–836
Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Fingerprinting and source identification of an oil spill in China Bohai Sea by gas chromatography-flame ionization detection and gas chromatography–mass spectrometry coupled with multi-statistical analyses Peiyan Sun a,b,∗ , Mutai Bao c , Guangmei Li a,b , Xinping Wang a,b , Yuhui Zhao a,b , Qing Zhou a,b , Lixin Cao a,b a
Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology, SOA, Qingdao 266033, China North China Sea Environmental Monitoring Center of State Oceanic Administration, Qingdao 266033, China c Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China b
a r t i c l e
i n f o
Article history: Received 23 April 2008 Received in revised form 18 November 2008 Accepted 26 November 2008 Available online 10 December 2008 Keywords: Oil spill identification Biomarkers Diagnostic ratios Correlation analysis
a b s t r a c t This paper describes a case study in which advanced chemical fingerprinting and data interpretation techniques were used to characterize the chemical composition and determine the source of an unknown spilled oil reported on the beach of China Bohai Sea in 2005. The spilled oil was suspected to be released from nearby platforms. In response to this specific site investigation need, a tiered analytical approach using gas chromatography–mass spectrometry (GC–MS) and gas chromatography-flame ionization detection (GC-FID) was applied. A variety of diagnostic ratios of “source-specific marker” compounds, in particular isomers of biomarkers, were determined and compared. Several statistical data correlation analysis methods were applied, including clustering analysis and Student’s t-test method. The comparison of the two methods was conducted. The comprehensive analysis results reveal the following: (1) The oil fingerprinting of three spilled oil samples (S1, S2 and S3) positively match each other; (2) The three spilled oil samples have suffered different weathering, dominated by evaporation with decrease of the low-molecular—mass n-alkanes at different degrees; (3) The oil fingerprinting profiles of the three spilled oil samples are positive match with that of the suspected source oil samples C41, C42, C43, C44 and C45; (4) There are significant differences in the oil fingerprinting profiles between the three spilled oil samples and the suspected source oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. © 2008 Elsevier B.V. All rights reserved.
1. Introduction As industrialization processes speed up worldwide, more and more petroleum energy sources are demanded. The exploitation of petroleum, especially benthic petroleum exploitation, develops rapidly. In China Bohai Sea, there are several hundreds of production oil wells and some drilling oil wells are under operation. According to incomplete statistics, the production of crude oil in Bohai Sea is increasing in recent years. Also, the imported amount of crude oil in China shows an increasing trend and it was 1.3 hundred million ton in 2004. The transportation on the Bohai Sea is very busy. The oil spill occurs every year in Bohai Sea [1–3]. It is a very difficult and complicated thing to do the spilled oil identification since there are so many suspected spilled oil sources (oil wells and ships). First, the ships, especially oil tankers may
∗ Corresponding author at: North China Sea Environmental Monitoring Center of State Oceanic Administration, Qingdao, Shandong 266033, China. Tel.: +86 532 85648756; fax: +86 532 85628344. E-mail address: [email protected] (P. Sun). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.100
transport and use different or similar oil. Second, even though in Bohai Sea, the crude oil is complicated. Third, the geological condition in Bohai Sea is complex. Crude oil can come from different geological formations, such as Dongying formation, Guangtao formation, Minghuazhen formation and Shahejie formation. In one oil platform, the crude oil from different oil wells may be different or may be very similar. Even in the same oil well, the crude oil collected at different depth may be different and the crude oil collected at the well mouth may be different since the different mixture ratios. Also, the crude oil from some oil platforms may be transported to the land by ship. So the spilled oil is often the mixture of several or more than ten oil wells on these oil platforms. Also, the sea condition, such as sea current in Bohai Sea is complex. All of these lead to the spilled oil identification difficult, as well as to the difficult to manage the spilled oil accident. This paper will give an example about a spilled oil accident and how to do a complex spilled oil identification which occurred on the beach of the China Bohai Sea. Flexible, tiered analytical approaches [4–6], which facilitate detailed chemical compositional analysis by GC–MS and GC-FID were used. These methods provide quantitative information of many individual petroleum hydrocar-
P. Sun et al. / J. Chromatogr. A 1216 (2009) 830–836 Table 1 Introduction of crude oil samples. Sample type
No.
Sampling location
Suspected crude oil
A1 B1 B2 B3 B4 C1 C2 C3 C41 C42 C43 C44 C45 C5 C6
From a1 oil production platform of A oil field From b1 oil production platform of B oil field From b2 oil production platform of B oil field From b3 oil production platform of B oil field From b4 oil production platform of B oil field From c1 oil production well of C oil field From c2 oil production well of C oil field From c3 oil production well of C oil field From c4 drilling oil well of C oil field (formation 1) On the deck of c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 2) From c4 drilling oil well of C oil field (formation 3)
Spilled oil
S1 S2 S3
On the beach On the beach On the beach
bons and their relative distribution patterns, as well as a variety of specific diagnostic ratios. Correlation analysis methods were used to help defensibly identifying the spill source with high level of confidence. By comparing the results of these methods, it can be found that the cluster analysis based on the Aitchison metric is far more efficient than a Student’s t-test method only based on double-ratios, since it can consider all the analytical data on an equal footing and more efficient use of the data. The identification conclusion provides strong and scientific support for the management of spilled oil accident. As a technical supporting department for ocean management, North China Sea Environmental Monitoring Center of State Oceanic Administration does the spilled oil identification mainly in Bohai Sea every year, the results of this paper also provides some experience and an example for other spilled oil accidents identification in China Sea area. 2. Study area In August 2005, stranded oil was found on the beach of Bohai Sea. Considering the field sea condition at that time, some offshore crude oil production platforms and one drilling platform distributed nearby were suspected. 15 crude oil samples were collected, including five oil samples (A1, B1, B2, B3, B4) from five different oil platforms, three oil samples (C1, C2, C3) from three different oil wells from same oil field, six oil samples (C41, C43, C44, C45, C5, C6) were from a drilling platforms and one oil sample (C42) was collected on the deck of this drilling oil platform. Among them, the crude oil samples C41, C42, C43, C44, C45 comes from the same formation and C5, C6 comes from another two different formations. Three spilled oil samples (S1, S2, S3) were collected from three different spots on the beach. These oil samples were collected at two different times, including the first group of nine suspected crude oil samples (A1, B1, B2, B3, B4, C1, C2, C3, C41) and three spilled oil samples (S1, S2, S3); and the second group of six suspected crude oil samples (C42, C43, C44, C45, C5, C6). The details are shown in Table 1. 3. Methods 3.1. Sampling methods For the purpose of the oil spill investigation, three spilled oil samples were collected in different spots on the polluted beach, and 15 suspected crude oil samples were collected from the above-
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mentioned platforms, which were suspected to be the source of the released oil. 3.2. Sample storage and processing The oil samples were collected in an open 250 ml wide-mouth glass jar and transported to the laboratory, the analysis was conducted immediately. The remainders were stored at 4 ◦ C in the icebox. The samples were processed as followed: 0.8 g of crude oil samples was first dissolved in hexane and then centrifuged at 3000 r/min speed. A silica gel microcolumn (200 mm × 10.5 mm I.D.) with a PTFE stopcock was dry-packed with 3 g of activated silica gel and topped with about 0.5 cm high anhydrous granular sodium sulfate. Then the column was conditioned using 20 mL of hexane. Just prior to exposure of the sodium sulfate layer to air, 0.2 mL of the centrifuged oil solution was quantitatively transferred into the column using 3 mL hexane to complete the transfer. 12 mL Hexane was used to elute the saturated hydrocarbons and was used for analysis of an aliphatics, n-alkanes, and biomarker terpane and sterane compounds. Then it was concentrated under a stream of nitrogen to less than 1 mL, and then adjusted to accurate 1.0 mL for GC–MS and GC-FID analyses. 3.3. Materials The main chemicals and reagents used are anhydrous sodium sulfate (analytical-reagent grade), hexane (chromatographic grade), dichloromethane (DCM) (chromatographic grade), 5␣-androstane (internal standard, Sigma–Aldrich, St. Louis, MO, USA), and silica gel (particle diameter 75–150 m, pore size 150 Å). Prior to use, 200–300 g of silica gel was placed in a 900 mm × 41 mm I.D. chromatographic column and serially rinsed with approximately 3 × 250 ml acetone, n-hexane, and DCM. The silica gel was left in a fume hood overnight and completely dried at 40–50 ◦ C. The dried silica gel was then activated at 180 ◦ C for 20 h in a shallow tray that was loosely covered with aluminum foil. 3.4. Analyte selection Analyses for n-alkane distribution were performed on a Shimadzu (Kyoto, Japan) GC-2010 with the FID detector. Analyses for biomarker terpane and sterane compounds were performed on a Shimadzu GC–MS-QP2010. A DB-5 capillary chromatographic column (30 m × 0.32 mm I.D.) and a DB-5MS capillary column (30 m × 0.25 mm I.D.) were used, respectively for the GC-FID and GC–MS. System control and data acquisition was achieved with a GC solution and GC–MS solution software, respectively. For detailed chromatographic conditions and quality control, refer to Ref. [7]. 3.5. Data reduction and analysis method 3.5.1. Clustering analysis The n-alkanes and some biomarkers data were examined by cluster analysis. A dissimilarity matrix relating the hydrocarbon compositions of samples was constructed by calculating the Aitchison distance metric [8] between each pair of observations. The Aitchison distance is a quantitative multivariate metric of dissimilarity between two composition patterns u and U, calculated as
⎧ ⎫ 2 ⎬1/2 j−1 N ⎨ ui Ui dist (u, U) = ln − ln uj Uj ⎩ ⎭
(1)
j=2 i=1
where i and j are indexes of hydrocarbon analytes, and N is the total number of analytes considered. This metric has several advanta-
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Fig. 1. GC-FID chromatograms of the saturated hydrocarbon fractions (F1) of the first group samples (from up to down: A1, B1, B2, B3, B4, C1, C2, C3, C41, S1, S2, and S3).
P. Sun et al. / J. Chromatogr. A 1216 (2009) 830–836
833
Fig. 1. (Continued)
geous statistical properties, including scale invariance, symmetry (i.e. dist(u,U) = dist(U,u)), non-negativity and the property that dist(u,u) = 0. These latter three properties meet the requirements for a dissimilarity metric for statistical cluster analysis. The hierarchical structure of the resulting matrix was determined from agglomerative and divisive clustering methods using the Cluster Procedure in SAS version 6. Agglomerative clustering employed the “average” method, while the divisive approach employed the “complete linkage” method. The hierarchical structure was visualized by constructing dendrograms, which were inspected for evidence of spatial coherence among the clusters. 3.5.2. Student’s t-test and comparison of diagnostic ratios between oil samples As a most commonly used statistical significance tests, Student’s t-test is used to compare the means of two samples. In
recent years, the Student’s t-test was used to compare the diagnostic ratios between oil samples. Here a brief introduction was given. The Student’s t-distribution can be described by two parame¯ which is the center of the distribution, ters: the mean value, M, and the standard deviation, s, which is the spreading of the individual observations around the mean. Given those two parameters, the shape of the distribution further depends of the number of degrees of freedom, n − 1, where n is the number of analyses. The confidence interval is an expression stating that the true mean, , is likely to lie within a certain distance from the measured ¯ The confidence interval of is given by mean, M. ts ¯ ±√ M = M n
(2)
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where s is the measured standard deviation between triplicate samples, n is the number of analyses and t is the Student’s t. The degrees of freedom are defined as n − 1. Student’s t-test provides comparison results within a predefined confidence level % (CL%). Confidence levels commonly used are 90%, 95% and 99%, with most usual (at least in the field of chemical analysis) the 95%. If triplicate samples are analyzed (n = 3), the degree of freedom is 2, and a 95% confidence level gives t = 4.303. Formula can then be simplified to formula: ¯ ± 2.484s M = M
(3)
If the spilled oil sample is shown as x and the suspected oil sam¯ ple is shown as y, then the mean value of diagnostic ratios (¯x and y) of x and y, and the standard deviation sx and sy can be calculated. Based on the formula (3), their confidence interval of x and y can be obtained. Based on x¯ and y¯ with the corresponding positive and negative error, some scattered points can be shown in plane figure. If the error bars of all diagnostic ratios are almost overlapping the straight-line (x = y) under the confidence interval 95%, the spilled oil sample displays a positive match to the source oil. Of course, only those indices that can be measured precisely should be evaluated for comparing candidate sources to a spilled oil. To accommodate both for the limitation in analytical precision and for the impact of eventual samples inhomogeneity, a protocol has been suggested by which candidate diagnostic ratios are evaluated in order to identify those that are most useful for further correlation analysis. More details are shown in Ref. [6]. 4. Results and discussion 4.1. GC-FID screening In order to characterize the suspected oil samples as soon as possible, the GC-FID was used to analyze the saturated hydrocarbons of the first group samples, which included nine suspected oil samples and three spilled oil samples. The corresponding GCFID chromatograms of the suspected source oil samples A1, B3 and C3 (shown in Fig. 1) are significantly different from that of three spilled oil samples (S1, S2 and S3) and the other suspected oil samples, with lower abundances of n-alkanes and more defined unresolved complex mixture (UCM) “humps” than the other oil samples, suggesting that they may be suffered from biological degradation under the ground. Therefore, these three suspected oil
Fig. 3. Clustering results based on relative peak area of n-alkanes with carbon number greater than 17.
samples (A1, B3 and C3) can be eliminated from the list of suspected sources. Fig. 2 depicts graphically the n-alkanes distributions including pristane and phytane (peak area relative to n-C25 ) of the three spilled oil samples. The spilled oil samples S1, S2 and S3 show nearly identical GC chromatographic profiles of n-alkane distribution, except for different abundances of low-molecular-mass n-alkanes, which means they suffered different weathering. This indicated that they might come from the same source. In order to further investigate the relationship between these samples, the clustering analysis method was used for n-alkanes with the carbon number greater than n-C17 on which the weathering effects were less. The clustering results are shown in Fig. 3. It can be seen that the suspected oil samples B1, B3, B4 on the B oil field and C1 and C2 which come from the C oil field are further away from the three spilled oil samples S1, S2 and S3. They have more difference in n-alkane distribution with the spilled oil samples. But a closer relationship between the three spilled oil samples (S1, S2, and S3) and the suspected oil sample C41 exists. In fact, this clustering result also indicates the comparison of the ratios difference of n-C17 /pristane, n-C18 /phytane and pristane/phytane. Based upon above-mentioned information, the suspected oil samples A1, B1, B2, B3, B4, C1, C2 and C3 do not come from the samples source with the spilled oil samples. The three spilled oil samples (S1, S2, and S3) probably come closer with the suspected crude oil sample C41, which comes from the drilling oil platform C. 4.2. Analysis of GC–MS data of biomarker compounds Based on the GC-FID screening results described above, the second group of oil samples were collected from the same platform as
Fig. 2. n-Alkane (including pristine and phytane) distribution of the spill samples and some of the three spilled oil samples S1, S2, and S3.
P. Sun et al. / J. Chromatogr. A 1216 (2009) 830–836
Fig. 4. Clustering results based on biomarker compounds relative peak areas.
sample C41. These suspected source oil samples were numbered as C42, C43, C44, C45, C5 and C6. Among them, C41, C42, C43, C44, C45 crude oil samples come from the same formation 1. C5 and C6 crude oil samples come from two different formations (formation 2 and formation 3). All the oil samples, including the first group of oil samples, were further analyzed by GC–MS. Peak area ratios relative to 17␣(H), 21(H)-hopane (at m/z 191 and m/z 217) were determined for selected highly degradation-resistant biomarker terpane and sterane compounds and some diagnostic ratios of target biomarkers of these oil samples were obtained. These biomarker compounds and diagnostic ratios have been increasingly used in recent years for the purposes of source identification and differentiation of oils [9–12]. The clustering analysis was used based on the relative peak area of the biomarker compounds above-mentioned. The results are shown in Fig. 4. It can be seen that all the oil samples are classi-
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fied into two groups, the first group includes oil samples C41, C42, C43, C44, C45, S1, S2 and S3; and the other group includes oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. This indicates there is a clear difference for their sources, especially their biological and geological environment. In fact, the suspected oil samples C41, C42, C43, C44, C45 comes from the same geological formation. This can also be confirmed in terpane (at m/z 191) and sterane (at m/z 217) mass chromatogram. The terpane (at m/z 191) abundances of oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6 are slightly higher than those of oil samples C41, C42, C43, C44, C45, S1, S2 and S3. But the sterane (at m/z 217) abundances of oil samples C41, C42, C43, C44, C45, S1, S2 and S3 are much higher than those of oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6, The oil samples C41, C42, C43, C44, C45, S1, S2 and S3 also show similar biomarker distribution profiles. Based on above-mentioned information, it can be confirmed furtherly that the suspected oil samples C41, C42, C43, C44, C45 comes the same source with the spilled oil samples S1, S2, S3. There is a clear difference between the suspected oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6 and the spilled oil samples S1, S2 and S3. 4.3. Correlation analysis of diagnostic ratios by Student’s t-test Diagnostic ratios need to be compared in an unbiased manner. Several methods of increasing sophistication are available for this correlation analysis [13–16], such as double-ratio cross-plots for comparing a large number of samples simultaneously while examining only two ratios at a time, linear correlation of diagnostic ratios derived from the “Student’s t” statistical tool for comparing two samples but relying on some evaluated ratios at a time.
Fig. 5. Correlation between two oil samples using at 95% confidence limit.
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P. Sun et al. / J. Chromatogr. A 1216 (2009) 830–836
The Student’s t-test distribution method was often used to obtain the correlation analysis result of two oil samples. The principle and process of Student’s t-test method used in oil diagnostic ratios was introduced in improved Nordtest for spill oil identification in detail. In this work only correlation analysis was performed and the results were given in Fig. 5. Eleven diagnostic ratios including C23 /C24 , C28 ␣/ (␣ + ␣␣␣), C29 ␣␣␣ (S)/(S + R), C28 ␣/C27 –C29 ␣, C29 ␣/C27 –C29 ␣, C29 ␣/(C29 ␣ + C30 ␣), C31 ␣(S)/(S + R)), C32 ␣(S)/(S + R), C33 ␣(S)/(S + R), C34 ␣(S)/(S + R) and C35 ␣(S)/(S + R)) were determined according to the evaluation criterion in the Student’s t-test for diagnostic ratios. From Fig. 5, it can be seen that for all the oil pair plots, the error bars of the diagnostic ratios almost fall on the linear regression (x − y) at 95% confidence level, which indicates a positive match between two oil samples. Comparing the results of the clustering analysis and Student’s t-test method, it can be found that the results were based on all the ratios of biomarkers by the clustering analysis and the same cluster can be gathered fast, the Student’s t-test method must evaluate and determine some diagnostic ratios and the results can be obtained only on two samples, of course the consistent of two samples can be found more clearly. 5. Conclusions In this paper, a step-by-step tiered approach was used for identifying a spilled oil. The saturated hydrocarbon including n-alkanes and biomarkers (terpanes and sterances) were analyzed by GC-FID and GC–MS techniques. By comparing the distribution of saturated hydrocarbon, the value of diagnostic ratios, and the correlation analysis on target compounds and diagnostic ratios by using the clustering method, Student’s t-test method, the following identification results were obtained: (1) The oil fingerprints of three spilled oil samples (S1, S2 and S3) are positive match each other; (2) the three spilled oil samples have undergone variable weathering, dominated by evaporation, with a resultant decrease in low-molecule-mass n-alkanes;
(3) the oil fingerprinting of the three spilled oil samples is identical with that of the suspected oil samples C41, C42, C43, C44 and C45; (4) there are significant differences in oil fingerprints between the three spilled oil samples and the suspected oil samples S1, A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. Acknowledgement The presentation of this paper was funded by The National Natural Science Foundation of China (50604013). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2008.11.100. References [1] Y.H. Deng, China Offshore Oil Gas 18 (2006) 361. [2] Z.G. Qu, Z.L. Wang, J.L. Ding, China Offshore Oil Gas 19 (2007) 353. [3] P.Y. Sun, M.T. Bao, Z.H. Gao, M. Li, Y.H. Zhao, X.P. Wang, Q. Zhou, X.L. Wang, Acta Oceanol. Sin. 5 (2006) 55. [4] Z.D. Wang, S.A. Stout, Oil Spill Environmental Forensics, Elsevier, New York, 2007. [5] P.S. Daling, L.-G. Faksness, A.B. Hansen, A. Scott, Stout. Environ. Forensics 3 (2002) 263. [6] CEN, European Committee for Standardization, Oil spill identification, TC/BT TF 120 WI CSS27003, 2006. [7] Z.D. Wang, M. Fingas, K. Li, J. Chromatogr. Sci. 32 (1994) 36. [8] J. Aitchison, Math. Geology 24 (1992) 365. [9] Z.D. Wang, M. Fingas, M. Landriault, L. Sigouin, S. Grenon, D. Zhang, Environ. Technol. 20 (1998) 851. [10] Z.D. Wang, M. Fingas, P. Lambert, G. Zeng, C. Yang, B. Hollebone, J. Chromatogr. A 1038 (2004) 201. [11] B. Paul, D.S. Gregory, B.A. William, Mar. Pollut. Bull. 34 (1997) 599. [12] J.X. Gong, Fujian Environ. (Chin) 19 (2002) 53. [13] K. Lavine, Anal. Chem. 67 (1995) 3846. [14] J.H. Christensen, G. Tomasl, A.B. Hansen, Environ. Sci. Technol. 39 (2005) 255. [15] W.A. Burns, P.J. Mankiewicz, A.E. Bence, D.S. Page, K.R. Parker, Toxicol. Chem. 16 (1997) 1119. [16] J.H. Christensen, A.B. Hansen, U. Karlson, J. Mortensen, O. Andersen, J. Chromatogr. A 1090 (2005) 133.
Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Fingerprinting and source identification of an oil spill in China Bohai Sea by gas chromatography-flame ionization detection and gas chromatography–mass spectrometry coupled with multi-statistical analyses Peiyan Sun a,b,∗ , Mutai Bao c , Guangmei Li a,b , Xinping Wang a,b , Yuhui Zhao a,b , Qing Zhou a,b , Lixin Cao a,b a
Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology, SOA, Qingdao 266033, China North China Sea Environmental Monitoring Center of State Oceanic Administration, Qingdao 266033, China c Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China b
a r t i c l e
i n f o
Article history: Received 23 April 2008 Received in revised form 18 November 2008 Accepted 26 November 2008 Available online 10 December 2008 Keywords: Oil spill identification Biomarkers Diagnostic ratios Correlation analysis
a b s t r a c t This paper describes a case study in which advanced chemical fingerprinting and data interpretation techniques were used to characterize the chemical composition and determine the source of an unknown spilled oil reported on the beach of China Bohai Sea in 2005. The spilled oil was suspected to be released from nearby platforms. In response to this specific site investigation need, a tiered analytical approach using gas chromatography–mass spectrometry (GC–MS) and gas chromatography-flame ionization detection (GC-FID) was applied. A variety of diagnostic ratios of “source-specific marker” compounds, in particular isomers of biomarkers, were determined and compared. Several statistical data correlation analysis methods were applied, including clustering analysis and Student’s t-test method. The comparison of the two methods was conducted. The comprehensive analysis results reveal the following: (1) The oil fingerprinting of three spilled oil samples (S1, S2 and S3) positively match each other; (2) The three spilled oil samples have suffered different weathering, dominated by evaporation with decrease of the low-molecular—mass n-alkanes at different degrees; (3) The oil fingerprinting profiles of the three spilled oil samples are positive match with that of the suspected source oil samples C41, C42, C43, C44 and C45; (4) There are significant differences in the oil fingerprinting profiles between the three spilled oil samples and the suspected source oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. © 2008 Elsevier B.V. All rights reserved.
1. Introduction As industrialization processes speed up worldwide, more and more petroleum energy sources are demanded. The exploitation of petroleum, especially benthic petroleum exploitation, develops rapidly. In China Bohai Sea, there are several hundreds of production oil wells and some drilling oil wells are under operation. According to incomplete statistics, the production of crude oil in Bohai Sea is increasing in recent years. Also, the imported amount of crude oil in China shows an increasing trend and it was 1.3 hundred million ton in 2004. The transportation on the Bohai Sea is very busy. The oil spill occurs every year in Bohai Sea [1–3]. It is a very difficult and complicated thing to do the spilled oil identification since there are so many suspected spilled oil sources (oil wells and ships). First, the ships, especially oil tankers may
∗ Corresponding author at: North China Sea Environmental Monitoring Center of State Oceanic Administration, Qingdao, Shandong 266033, China. Tel.: +86 532 85648756; fax: +86 532 85628344. E-mail address: [email protected] (P. Sun). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.100
transport and use different or similar oil. Second, even though in Bohai Sea, the crude oil is complicated. Third, the geological condition in Bohai Sea is complex. Crude oil can come from different geological formations, such as Dongying formation, Guangtao formation, Minghuazhen formation and Shahejie formation. In one oil platform, the crude oil from different oil wells may be different or may be very similar. Even in the same oil well, the crude oil collected at different depth may be different and the crude oil collected at the well mouth may be different since the different mixture ratios. Also, the crude oil from some oil platforms may be transported to the land by ship. So the spilled oil is often the mixture of several or more than ten oil wells on these oil platforms. Also, the sea condition, such as sea current in Bohai Sea is complex. All of these lead to the spilled oil identification difficult, as well as to the difficult to manage the spilled oil accident. This paper will give an example about a spilled oil accident and how to do a complex spilled oil identification which occurred on the beach of the China Bohai Sea. Flexible, tiered analytical approaches [4–6], which facilitate detailed chemical compositional analysis by GC–MS and GC-FID were used. These methods provide quantitative information of many individual petroleum hydrocar-
P. Sun et al. / J. Chromatogr. A 1216 (2009) 830–836 Table 1 Introduction of crude oil samples. Sample type
No.
Sampling location
Suspected crude oil
A1 B1 B2 B3 B4 C1 C2 C3 C41 C42 C43 C44 C45 C5 C6
From a1 oil production platform of A oil field From b1 oil production platform of B oil field From b2 oil production platform of B oil field From b3 oil production platform of B oil field From b4 oil production platform of B oil field From c1 oil production well of C oil field From c2 oil production well of C oil field From c3 oil production well of C oil field From c4 drilling oil well of C oil field (formation 1) On the deck of c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 1) From c4 drilling oil well of C oil field (formation 2) From c4 drilling oil well of C oil field (formation 3)
Spilled oil
S1 S2 S3
On the beach On the beach On the beach
bons and their relative distribution patterns, as well as a variety of specific diagnostic ratios. Correlation analysis methods were used to help defensibly identifying the spill source with high level of confidence. By comparing the results of these methods, it can be found that the cluster analysis based on the Aitchison metric is far more efficient than a Student’s t-test method only based on double-ratios, since it can consider all the analytical data on an equal footing and more efficient use of the data. The identification conclusion provides strong and scientific support for the management of spilled oil accident. As a technical supporting department for ocean management, North China Sea Environmental Monitoring Center of State Oceanic Administration does the spilled oil identification mainly in Bohai Sea every year, the results of this paper also provides some experience and an example for other spilled oil accidents identification in China Sea area. 2. Study area In August 2005, stranded oil was found on the beach of Bohai Sea. Considering the field sea condition at that time, some offshore crude oil production platforms and one drilling platform distributed nearby were suspected. 15 crude oil samples were collected, including five oil samples (A1, B1, B2, B3, B4) from five different oil platforms, three oil samples (C1, C2, C3) from three different oil wells from same oil field, six oil samples (C41, C43, C44, C45, C5, C6) were from a drilling platforms and one oil sample (C42) was collected on the deck of this drilling oil platform. Among them, the crude oil samples C41, C42, C43, C44, C45 comes from the same formation and C5, C6 comes from another two different formations. Three spilled oil samples (S1, S2, S3) were collected from three different spots on the beach. These oil samples were collected at two different times, including the first group of nine suspected crude oil samples (A1, B1, B2, B3, B4, C1, C2, C3, C41) and three spilled oil samples (S1, S2, S3); and the second group of six suspected crude oil samples (C42, C43, C44, C45, C5, C6). The details are shown in Table 1. 3. Methods 3.1. Sampling methods For the purpose of the oil spill investigation, three spilled oil samples were collected in different spots on the polluted beach, and 15 suspected crude oil samples were collected from the above-
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mentioned platforms, which were suspected to be the source of the released oil. 3.2. Sample storage and processing The oil samples were collected in an open 250 ml wide-mouth glass jar and transported to the laboratory, the analysis was conducted immediately. The remainders were stored at 4 ◦ C in the icebox. The samples were processed as followed: 0.8 g of crude oil samples was first dissolved in hexane and then centrifuged at 3000 r/min speed. A silica gel microcolumn (200 mm × 10.5 mm I.D.) with a PTFE stopcock was dry-packed with 3 g of activated silica gel and topped with about 0.5 cm high anhydrous granular sodium sulfate. Then the column was conditioned using 20 mL of hexane. Just prior to exposure of the sodium sulfate layer to air, 0.2 mL of the centrifuged oil solution was quantitatively transferred into the column using 3 mL hexane to complete the transfer. 12 mL Hexane was used to elute the saturated hydrocarbons and was used for analysis of an aliphatics, n-alkanes, and biomarker terpane and sterane compounds. Then it was concentrated under a stream of nitrogen to less than 1 mL, and then adjusted to accurate 1.0 mL for GC–MS and GC-FID analyses. 3.3. Materials The main chemicals and reagents used are anhydrous sodium sulfate (analytical-reagent grade), hexane (chromatographic grade), dichloromethane (DCM) (chromatographic grade), 5␣-androstane (internal standard, Sigma–Aldrich, St. Louis, MO, USA), and silica gel (particle diameter 75–150 m, pore size 150 Å). Prior to use, 200–300 g of silica gel was placed in a 900 mm × 41 mm I.D. chromatographic column and serially rinsed with approximately 3 × 250 ml acetone, n-hexane, and DCM. The silica gel was left in a fume hood overnight and completely dried at 40–50 ◦ C. The dried silica gel was then activated at 180 ◦ C for 20 h in a shallow tray that was loosely covered with aluminum foil. 3.4. Analyte selection Analyses for n-alkane distribution were performed on a Shimadzu (Kyoto, Japan) GC-2010 with the FID detector. Analyses for biomarker terpane and sterane compounds were performed on a Shimadzu GC–MS-QP2010. A DB-5 capillary chromatographic column (30 m × 0.32 mm I.D.) and a DB-5MS capillary column (30 m × 0.25 mm I.D.) were used, respectively for the GC-FID and GC–MS. System control and data acquisition was achieved with a GC solution and GC–MS solution software, respectively. For detailed chromatographic conditions and quality control, refer to Ref. [7]. 3.5. Data reduction and analysis method 3.5.1. Clustering analysis The n-alkanes and some biomarkers data were examined by cluster analysis. A dissimilarity matrix relating the hydrocarbon compositions of samples was constructed by calculating the Aitchison distance metric [8] between each pair of observations. The Aitchison distance is a quantitative multivariate metric of dissimilarity between two composition patterns u and U, calculated as
⎧ ⎫ 2 ⎬1/2 j−1 N ⎨ ui Ui dist (u, U) = ln − ln uj Uj ⎩ ⎭
(1)
j=2 i=1
where i and j are indexes of hydrocarbon analytes, and N is the total number of analytes considered. This metric has several advanta-
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Fig. 1. GC-FID chromatograms of the saturated hydrocarbon fractions (F1) of the first group samples (from up to down: A1, B1, B2, B3, B4, C1, C2, C3, C41, S1, S2, and S3).
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Fig. 1. (Continued)
geous statistical properties, including scale invariance, symmetry (i.e. dist(u,U) = dist(U,u)), non-negativity and the property that dist(u,u) = 0. These latter three properties meet the requirements for a dissimilarity metric for statistical cluster analysis. The hierarchical structure of the resulting matrix was determined from agglomerative and divisive clustering methods using the Cluster Procedure in SAS version 6. Agglomerative clustering employed the “average” method, while the divisive approach employed the “complete linkage” method. The hierarchical structure was visualized by constructing dendrograms, which were inspected for evidence of spatial coherence among the clusters. 3.5.2. Student’s t-test and comparison of diagnostic ratios between oil samples As a most commonly used statistical significance tests, Student’s t-test is used to compare the means of two samples. In
recent years, the Student’s t-test was used to compare the diagnostic ratios between oil samples. Here a brief introduction was given. The Student’s t-distribution can be described by two parame¯ which is the center of the distribution, ters: the mean value, M, and the standard deviation, s, which is the spreading of the individual observations around the mean. Given those two parameters, the shape of the distribution further depends of the number of degrees of freedom, n − 1, where n is the number of analyses. The confidence interval is an expression stating that the true mean, , is likely to lie within a certain distance from the measured ¯ The confidence interval of is given by mean, M. ts ¯ ±√ M = M n
(2)
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where s is the measured standard deviation between triplicate samples, n is the number of analyses and t is the Student’s t. The degrees of freedom are defined as n − 1. Student’s t-test provides comparison results within a predefined confidence level % (CL%). Confidence levels commonly used are 90%, 95% and 99%, with most usual (at least in the field of chemical analysis) the 95%. If triplicate samples are analyzed (n = 3), the degree of freedom is 2, and a 95% confidence level gives t = 4.303. Formula can then be simplified to formula: ¯ ± 2.484s M = M
(3)
If the spilled oil sample is shown as x and the suspected oil sam¯ ple is shown as y, then the mean value of diagnostic ratios (¯x and y) of x and y, and the standard deviation sx and sy can be calculated. Based on the formula (3), their confidence interval of x and y can be obtained. Based on x¯ and y¯ with the corresponding positive and negative error, some scattered points can be shown in plane figure. If the error bars of all diagnostic ratios are almost overlapping the straight-line (x = y) under the confidence interval 95%, the spilled oil sample displays a positive match to the source oil. Of course, only those indices that can be measured precisely should be evaluated for comparing candidate sources to a spilled oil. To accommodate both for the limitation in analytical precision and for the impact of eventual samples inhomogeneity, a protocol has been suggested by which candidate diagnostic ratios are evaluated in order to identify those that are most useful for further correlation analysis. More details are shown in Ref. [6]. 4. Results and discussion 4.1. GC-FID screening In order to characterize the suspected oil samples as soon as possible, the GC-FID was used to analyze the saturated hydrocarbons of the first group samples, which included nine suspected oil samples and three spilled oil samples. The corresponding GCFID chromatograms of the suspected source oil samples A1, B3 and C3 (shown in Fig. 1) are significantly different from that of three spilled oil samples (S1, S2 and S3) and the other suspected oil samples, with lower abundances of n-alkanes and more defined unresolved complex mixture (UCM) “humps” than the other oil samples, suggesting that they may be suffered from biological degradation under the ground. Therefore, these three suspected oil
Fig. 3. Clustering results based on relative peak area of n-alkanes with carbon number greater than 17.
samples (A1, B3 and C3) can be eliminated from the list of suspected sources. Fig. 2 depicts graphically the n-alkanes distributions including pristane and phytane (peak area relative to n-C25 ) of the three spilled oil samples. The spilled oil samples S1, S2 and S3 show nearly identical GC chromatographic profiles of n-alkane distribution, except for different abundances of low-molecular-mass n-alkanes, which means they suffered different weathering. This indicated that they might come from the same source. In order to further investigate the relationship between these samples, the clustering analysis method was used for n-alkanes with the carbon number greater than n-C17 on which the weathering effects were less. The clustering results are shown in Fig. 3. It can be seen that the suspected oil samples B1, B3, B4 on the B oil field and C1 and C2 which come from the C oil field are further away from the three spilled oil samples S1, S2 and S3. They have more difference in n-alkane distribution with the spilled oil samples. But a closer relationship between the three spilled oil samples (S1, S2, and S3) and the suspected oil sample C41 exists. In fact, this clustering result also indicates the comparison of the ratios difference of n-C17 /pristane, n-C18 /phytane and pristane/phytane. Based upon above-mentioned information, the suspected oil samples A1, B1, B2, B3, B4, C1, C2 and C3 do not come from the samples source with the spilled oil samples. The three spilled oil samples (S1, S2, and S3) probably come closer with the suspected crude oil sample C41, which comes from the drilling oil platform C. 4.2. Analysis of GC–MS data of biomarker compounds Based on the GC-FID screening results described above, the second group of oil samples were collected from the same platform as
Fig. 2. n-Alkane (including pristine and phytane) distribution of the spill samples and some of the three spilled oil samples S1, S2, and S3.
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Fig. 4. Clustering results based on biomarker compounds relative peak areas.
sample C41. These suspected source oil samples were numbered as C42, C43, C44, C45, C5 and C6. Among them, C41, C42, C43, C44, C45 crude oil samples come from the same formation 1. C5 and C6 crude oil samples come from two different formations (formation 2 and formation 3). All the oil samples, including the first group of oil samples, were further analyzed by GC–MS. Peak area ratios relative to 17␣(H), 21(H)-hopane (at m/z 191 and m/z 217) were determined for selected highly degradation-resistant biomarker terpane and sterane compounds and some diagnostic ratios of target biomarkers of these oil samples were obtained. These biomarker compounds and diagnostic ratios have been increasingly used in recent years for the purposes of source identification and differentiation of oils [9–12]. The clustering analysis was used based on the relative peak area of the biomarker compounds above-mentioned. The results are shown in Fig. 4. It can be seen that all the oil samples are classi-
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fied into two groups, the first group includes oil samples C41, C42, C43, C44, C45, S1, S2 and S3; and the other group includes oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. This indicates there is a clear difference for their sources, especially their biological and geological environment. In fact, the suspected oil samples C41, C42, C43, C44, C45 comes from the same geological formation. This can also be confirmed in terpane (at m/z 191) and sterane (at m/z 217) mass chromatogram. The terpane (at m/z 191) abundances of oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6 are slightly higher than those of oil samples C41, C42, C43, C44, C45, S1, S2 and S3. But the sterane (at m/z 217) abundances of oil samples C41, C42, C43, C44, C45, S1, S2 and S3 are much higher than those of oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6, The oil samples C41, C42, C43, C44, C45, S1, S2 and S3 also show similar biomarker distribution profiles. Based on above-mentioned information, it can be confirmed furtherly that the suspected oil samples C41, C42, C43, C44, C45 comes the same source with the spilled oil samples S1, S2, S3. There is a clear difference between the suspected oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6 and the spilled oil samples S1, S2 and S3. 4.3. Correlation analysis of diagnostic ratios by Student’s t-test Diagnostic ratios need to be compared in an unbiased manner. Several methods of increasing sophistication are available for this correlation analysis [13–16], such as double-ratio cross-plots for comparing a large number of samples simultaneously while examining only two ratios at a time, linear correlation of diagnostic ratios derived from the “Student’s t” statistical tool for comparing two samples but relying on some evaluated ratios at a time.
Fig. 5. Correlation between two oil samples using at 95% confidence limit.
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The Student’s t-test distribution method was often used to obtain the correlation analysis result of two oil samples. The principle and process of Student’s t-test method used in oil diagnostic ratios was introduced in improved Nordtest for spill oil identification in detail. In this work only correlation analysis was performed and the results were given in Fig. 5. Eleven diagnostic ratios including C23 /C24 , C28 ␣/ (␣ + ␣␣␣), C29 ␣␣␣ (S)/(S + R), C28 ␣/C27 –C29 ␣, C29 ␣/C27 –C29 ␣, C29 ␣/(C29 ␣ + C30 ␣), C31 ␣(S)/(S + R)), C32 ␣(S)/(S + R), C33 ␣(S)/(S + R), C34 ␣(S)/(S + R) and C35 ␣(S)/(S + R)) were determined according to the evaluation criterion in the Student’s t-test for diagnostic ratios. From Fig. 5, it can be seen that for all the oil pair plots, the error bars of the diagnostic ratios almost fall on the linear regression (x − y) at 95% confidence level, which indicates a positive match between two oil samples. Comparing the results of the clustering analysis and Student’s t-test method, it can be found that the results were based on all the ratios of biomarkers by the clustering analysis and the same cluster can be gathered fast, the Student’s t-test method must evaluate and determine some diagnostic ratios and the results can be obtained only on two samples, of course the consistent of two samples can be found more clearly. 5. Conclusions In this paper, a step-by-step tiered approach was used for identifying a spilled oil. The saturated hydrocarbon including n-alkanes and biomarkers (terpanes and sterances) were analyzed by GC-FID and GC–MS techniques. By comparing the distribution of saturated hydrocarbon, the value of diagnostic ratios, and the correlation analysis on target compounds and diagnostic ratios by using the clustering method, Student’s t-test method, the following identification results were obtained: (1) The oil fingerprints of three spilled oil samples (S1, S2 and S3) are positive match each other; (2) the three spilled oil samples have undergone variable weathering, dominated by evaporation, with a resultant decrease in low-molecule-mass n-alkanes;
(3) the oil fingerprinting of the three spilled oil samples is identical with that of the suspected oil samples C41, C42, C43, C44 and C45; (4) there are significant differences in oil fingerprints between the three spilled oil samples and the suspected oil samples S1, A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6. Acknowledgement The presentation of this paper was funded by The National Natural Science Foundation of China (50604013). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2008.11.100. References [1] Y.H. Deng, China Offshore Oil Gas 18 (2006) 361. [2] Z.G. Qu, Z.L. Wang, J.L. Ding, China Offshore Oil Gas 19 (2007) 353. [3] P.Y. Sun, M.T. Bao, Z.H. Gao, M. Li, Y.H. Zhao, X.P. Wang, Q. Zhou, X.L. Wang, Acta Oceanol. Sin. 5 (2006) 55. [4] Z.D. Wang, S.A. Stout, Oil Spill Environmental Forensics, Elsevier, New York, 2007. [5] P.S. Daling, L.-G. Faksness, A.B. Hansen, A. Scott, Stout. Environ. Forensics 3 (2002) 263. [6] CEN, European Committee for Standardization, Oil spill identification, TC/BT TF 120 WI CSS27003, 2006. [7] Z.D. Wang, M. Fingas, K. Li, J. Chromatogr. Sci. 32 (1994) 36. [8] J. Aitchison, Math. Geology 24 (1992) 365. [9] Z.D. Wang, M. Fingas, M. Landriault, L. Sigouin, S. Grenon, D. Zhang, Environ. Technol. 20 (1998) 851. [10] Z.D. Wang, M. Fingas, P. Lambert, G. Zeng, C. Yang, B. Hollebone, J. Chromatogr. A 1038 (2004) 201. [11] B. Paul, D.S. Gregory, B.A. William, Mar. Pollut. Bull. 34 (1997) 599. [12] J.X. Gong, Fujian Environ. (Chin) 19 (2002) 53. [13] K. Lavine, Anal. Chem. 67 (1995) 3846. [14] J.H. Christensen, G. Tomasl, A.B. Hansen, Environ. Sci. Technol. 39 (2005) 255. [15] W.A. Burns, P.J. Mankiewicz, A.E. Bence, D.S. Page, K.R. Parker, Toxicol. Chem. 16 (1997) 1119. [16] J.H. Christensen, A.B. Hansen, U. Karlson, J. Mortensen, O. Andersen, J. Chromatogr. A 1090 (2005) 133.