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Establishment and application of an intelligent treating method for oil spill identification
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Liju TAN1, Ruxiang ZHAO2, Xiaonan YIN2, Haijiang ZHANG2, *, Jiangtao WANG1, *
Acta Oceanologica Sinica | 2018, 37(11) : 116 - 122
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Acta Oceanologica Sinica | 2018, 37(11): 116-122
• Marine Oil Spill Response •
Establishment and application of an intelligent treating method for oil spill identification
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Liju TAN1, Ruxiang ZHAO2, Xiaonan YIN2, Haijiang ZHANG2, *, Jiangtao WANG1, *
Affiliations
  • 1 College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
  • 2 Yantai Oil Spill Response Technical Center of Yantai Maritime Safety Administration, Yantai 264000, China
Published: 2018-11-25 doi: 10.1007/s13131-018-1254-9
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In the identifying process of an oil spill accident, manual integral and artificial visual comparison are commonly used at present to determine the oil spill sources, these methods are time-consuming and easily affected by human factors. Therefore, it is difficult to achieve the purpose of rapid identification of an oil spill accident. In this paper, an intelligent method of automatic recognition, integration and calculation of diagnostic ratio of Gas Chromatography-Mass Spectrometer (GC/MS) spectrum are established. Firstly, four hundreds of samples collected around the world were analyzed using a standard method and Retention time locking technology (RTL) was applied to reduce the change of retention time of GC/MS spectrum. Secondly, the automatic identification, integration of n-alkanes, biomarker compounds, polycyclic aromatic hydrocarbons and calculation of the diagnostic ratios were realized by MATLAB software. Finally, a database of oil fingerprints were established and applied successfully in a spill oil accident. Based on the new method and database, we could acquire the diagnostic ratios of an oil sample and find out the suspected oil within a few minutes. This method and database can improve the efficiency in spilled oil identification.

oil fingerprint  /  automatic identification  /  diagnostic ratio  /  standardized database  /  oil spills sources
Liju TAN, Ruxiang ZHAO, Xiaonan YIN, Haijiang ZHANG, Jiangtao WANG. Establishment and application of an intelligent treating method for oil spill identification[J]. Acta Oceanologica Sinica, 2018 , 37 (11) : 116 -122 . DOI: 10.1007/s13131-018-1254-9
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1 Introduction
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Oil fingerprint identification is not only an important method to determine the source of spilled oil (Wang et al., 1995, 1999; Wang and Fingas, 1997; Wang, 2003; Stout et al., 2001), but also a crucial factor affecting the disposal efficiency in an oil spill accident (Sun et al., 2004, 2006). In the information of an oil fingerprint, the diagnostic ratio is a parameter with a large difference among various oil sources (Wang et al., 2004; Wang and Stout, 2007; Yang et al., 2008). Because this parameter is a ratio of typical characteristic components (oil fingerprint), the analytical error has little effect on the results (Daling et al., 2002; Christensen et al., 2005). Therefore, diagnostic ratios of oil fingerprints are widely applied in spilled oil identification (Wang et al., 2004; Wang and Stout, 2007; Yang et al, 2008; Bayona et al., 2015). For the oil spill accident involved ships, it is required to draw conclusions as soon as possible to release the irrelevant ship because of the huge economic losses of delays in shipment. However, it is difficult to achieve a rapid and accurate conclusion because it is drawn manually in most cases. In the widely used identification process of oil fingerprints, chromatograms are analyzed through visual comparison, chromatographic peaks are integrated artificially and diagnostic ratios are calculated manually, so the process is time-consuming and easily affected by human factors. For example, the European oil spill identification standard (CEN/TR 15522-2: 2012) (CEN/TR, 2012) recommends that more than 120 compounds should be identified for each oil sample, and China’s national standard Specifications for Identification System of Spilled Oils on the Sea (GB/T 21247–2007) recommends that 150 compounds should be identified for each oil sample. Due to the analyst’s habits on selection baseline, the starting and ending time are different, the integral results are also different. Thus, the classical identifying method is time-consuming and lack of accuracy.
With the development of computer technology, various digitized handling methods began to be used to analyze the oil fingerprints so that spilled oil could be identified quickly and objectively (Qin, 2015). The German Hydrographic Bureau built a system to detect an oil spill named computerized oil spill identification (COSI) (Dahlman, 2016). Staniloae et al. (2001) built a pattern recognition system based on infrared spectroscopy and gas chromatography to compare spilled oil with suspected oil. Sun et al. (2012) developed a set of software for oil fingerprint identification which improved the investigation efficiency of the spilled oil. Al-Kaabi et al. (2017) identified some beached oil. Additionally, various measuring methods and oil fingerprint databases were built in some countries (Sun et al., 2006). Because of the different measuring methods of those database, they had no comparability among databases and lacked applicability for every country. Therefore, a standard method and database should be established to analyze the source of spilled oil automatically and accurately.
Based on the above analysis, this paper intends to solve three problems: (1) write a program that can recognize and integrate chromatogram peaks automatically, as well as calculate a large amount of oil diagnostic ratios automatically; (2) establish a database for the diagnostic ratios of known oils; and (3) verify the accuracy of the program and database through an oil spill accident.
2 Materials and methods
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2.1 Sample treatment
All oil samples were analyzed with a 7890A Gas Chromatography (GC)-5975C Mass Spectrometer(MS) (Agilent Technologies, USA) equipped with a HP-5MS fused silica capillary column (60 m, 0.25 mm i.d., 0.25 μm film thickness, J&W Scientific, USA) in the pulsed splitless mode. The GC oven temperature was programmed from 60 to 300°C at 6°C/min and maintained at 300°C for 30 min. Helium was used as the carrier gas with 1.0 mL/min constant flow. The temperature of the injector, transfer line and ion source was 290°C, 280°C and 230°C, respectively (Zhang et al., 2016).
Qualitative analysis of the oil samples was performed in selected ion monitoring (SIM) mode. All samples were analyzed twice in order to eliminate the effect of analytical variability and sample heterogeneity.
Approximately 0.15 g of pure oil sample was extracted with 10.0 mL of high purity n-hexane (Merck, HPLC, Germany)/dichloromethane (Merck, HPLC, Germany) (1:1, v/v), and approximately 1.0 g of anhydrous sodium sulfate was added to remove water. The mixture was vortexed for 30 s and centrifuged at 3 000 r/min. for 5 minutes. After that, 1.0 mL of the supernatant was transferred to a vial containing 1.0 g of silica gel which was activated at 180°C for 2 hours. Then, 5.0 mL of high purity n-hexane/dichloromethane (1:1, v/v) was added to the vial. The mixture was vortexed and centrifuged at 3 000 r/min. An aliquot of 1.0 mL of each extract was subjected to analysis (Zhang et al., 2016).
2.2 Retention time locking (RTL) technology
Retention time is a very important parameter for chromatographic instruments which affects the accuracy of separation and identification of interesting analytes from complex mixtures. However, the retention time is difficult to repeat and consistent due to the replacement, aging and cutting the chromatographic column, and the fluctuation of temperature and air pressure.
Retention time locking (RTL) technology is the feature of Agilent Chem Station software, which is developed to lock the retention time of the target analyte by adjusting the head pressure. In this work, RTL was applied to reduce the change of retention time at different time and on different GC/MS instruments.
2.3 Automatic identification and integration of GC/MS chromatogram peaks
Before automatic identification and integration by computer, manual identification and integration was needed to obtain relevant parameters of characteristic peaks, such as the ionic species, peak name and type (categorized into single peak, double peak and multi peak), retention time, and start time and end time of the peak. Among these parameters, the retention time could provide the positional information of the peak height, and the start time and end time could provide the integral range of peak area. The MATLAB software was used to establish a method of automatic identification and integration of chromatogram peaks on the basis that the retention time of the target compounds was basically unchanged by RTL technology. The specific steps to establish the method are as follows:
(1) Smoothing the chromatographic peaks
The chromatographic peaks need to be smoothed before identification because of the unavoidable noise, which may affect the identification of chromatographic peaks and cause misjudgment. It should be pointed out that the smoothed data were just used in the process of identification (found the appeared time of the peaks), and the raw data were still used in the integral operation for the precision.
(2) Automatic recognition of chromatographic peaks
According to the retention time, start time and end time obtained by manual integration, the peaks and valleys of chromatographic peaks were founded, and then chromatographic peaks were drew through our own program language.
(3) Automatic calculation of peaks’ areas
After the chromatographic peaks were identified automatically, the areas of the corresponding peaks were calculated by the trapezoid area formula with the determined starting position and end position and the corresponding horizontal baseline.
2.4 Establishment the database of oil diagnostic ratios
A total of 440 crude oil samples and fuel oil samples were collected worldwide, mainly collected from China, Kingdom of Saudi Arabia, Iraq, Kuwait, Venezuela, Korea, Singapore, etc, and then these samples were analyzed by GC/MS locked by RTL technology. Due to the use of RTL technique, the retention time of the target compounds in the oil samples varied little, which made it possible to automatically identify and integrate the spectrogram. Automatic recognition and integration of chromatographic peaks of oil samples were realized by programming language and MATLAB software. After that, the corresponding diagnostic ratios of oil samples were calculated, and then the oil fingerprint database was established based on these diagnostic ratios.
2.5 Automatic retrieval of diagnostic ratios for suspected oil samples
The correlation coefficient method was used to study the correlation between unknown spilled oil and oil samples in the database. A set of relative content ratios (x1, x2, ..., xn) of hydrocarbon was used to represent unknown spilled oil samples and another set of ratios (y1 , y2, …, yn) represented oil samples in the database. The Pearson correlation coefficient R between two samples was calculated by the formula below (Xu, 2012):
$R = \frac{{N\displaystyle\sum {{x_i}{y_i}} - \sum {{x_i}} \sum {{y_i}} }}{{\sqrt {N\displaystyle\sum {x_i^2} - {{\left({\sum {{x_i}} } \right)}^2}} \sqrt {N\displaystyle\sum {y_i^2} - {{\left({\sum {{y_i}} } \right)}^2}} }}.$
According to the results of R, we can distinguish the relationship between unknown spilled oil sample X and suspected oil Y in the database. The closer the R value approaches to 1, the greater the degree of correlation is between the two oils, while the closer the R value approaches to 0, the lower the correlation is between the two oils.
Generally, when the correlation coefficient R is greater than 0.95, the unknown oil can be regarded as the same kind of oil sample in the oil fingerprint database. In contrast, when the maximum correlation coefficient R is less than 0.8, it indicates that the same oil spill was not found in the oil fingerprint database. When the parameter R is between 0.8–0.95, there is a certain degree of similarity between unknown spilled oil sample and suspected oil sample.
3 Results
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3.1 Effect after using the RTL
Automatic recognition and automatic integration of chromatographic peaks by computer required highly accurate and repetitive retention time, therefore, RTL method was applied to reduce the change of retention time at different time and on different GC/MS instruments.
Before using RTL, the change of retention time of C17 on two different GC/MS instruments was 0.013 min (Fig. 1a), however, the corresponding time reduced to 0.003 min when using RTL (Fig. 1b). Thus, RTL technology could effectively reduce the change of retention time of the target compound.
3.2 Verification of the accuracy of automatic recognition and automatic integration of chromatographic peaks
To verify the accuracy of automatic recognition of chromatographic peaks, the peaks redrawn by computer and the original GC/MS spectrograms were compared. The verification of the accuracy of the automatic integral program was performed by comparing the diagnostic ratios calculated by computer and manual methods. Three kinds of typical oil fingerprints were analyzed and are listed below.
3.2.1 Automatic recognition of n-alkanes
The original GC/MS chromatogram of alkanes, pristane and phytane (m/z 85) and the chromatogram redrawn by computer according to the retention time, start time and other information are shown in Figs 2a and b.
It can be seen from Fig. 2 that the trend of n-alkanes redrawn by computer was completely consistent with the original chromatogram. This shows that the accuracy of automatic recognition of alkanes by computer was high and satisfactory.
3.2.2 Automatic integral of n-alkanes
The characteristic peaks of C17, pristane (Pr), C18, phytane (Ph) and their diagnostic ratios of C17/Pr, C18/Ph and Pr/Ph were commonly used in the semi-quantitative analysis of a spilled oil accident. Three kinds of crude oils and three kinds of fuel oils were selected to verify the accuracy of automatic integration. To ensure the integration accuracy of the featured peak, it is necessary to integrate the parallel sample data of oil samples, obtain their average, and then calculate the diagnostic ratios.
The comparison between the data of diagnostic ratios obtained by manual and automatic integration is shown in Table 1.
In Table 1, the data from different means were very close and the data of relative standard deviation (RSD%) were between 0.02 and 1.10 with an average of 0.47. All of the RSD% values were below 5%, so the automatic integration of a characteristic peak and the calculation of diagnostic ratios by computer can obtain accurate results.
3.2.3 Automatic recognition of biomarkers
Biomarkers can reflect the source properties and generate geological conditions of oil. Therefore, biomarker compounds such as terpane (m/z 191) and sterane (m/z 217 and m/z 218), and their corresponding diagnostic ratios, play a very important role on identifying spilled oil, especially for the analysis of weathered oil.
The original and redrawn GC/MS chromatograms of terpane were shown in Figs 3a and b.
It can be seen that the chromatogram redrawn by computer were completely consistent with the original one. This shows that the accuracy of the automatic identification of biomarkers by computer was high and satisfactory.
3.2.4 Automatic integration of biomarkers
The results of the diagnostic ratios of biomarkers obtained by manual and automatic integration are shown in Table 2.
Table 2 shows that the data obtained by different means are very close and most RSD% are below 5.00% and only one datum is larger than 5%. This demonstrates that the automatic integration for diagnostic ratios of biomarkers by computer are reliable.
3.2.5 Automatic recognition of polycyclic aromatic hydrocarbons (PAHs) and their homologues
PAHs and their homologues in oil include naphthalene, phenanthrene, dibenzothiophene, fluorine, chrysene and alkylated homologues. The composition of PAHs and their homologues in crude oil and petroleum products obtained by different refining processes are different. The relative molecular mass of PAHs and their homologues is large, their stability is high, and their weathering resistance is stronger than saturated hydrocarbons. Therefore, they are important indicators for the identification of spilled oil.
The diagnostic ratios in PAHs and their homologues such as 2-MP/1-MP (2- methyl phenanthrene/1- methyl phenanthrene, 192 ion), 4-MD/1-MD (4-methyl dibenzothiophene/1- methyl dibenzothiophene, 198 ion), C2-dbt/C2-phe (C2- dibenzothiophene/C2- phenanthrene, 212/206), C3-dbt/C3-phe (C3-dibenzothiophene/C3-phenanthrene, 226/220), and C3-dbt/C3-chr) (C3- dibenzothiophene/C3-chrysene, 226/270) were used commonly in oil spill identification. The original GC/MS spectrogram and the redrawn one by computer of the PAHs and their corresponding alkylated homologues are shown in Fig. 4.
3.2.6 Automatic integration of PAHs and their homologues
The comparison results of the diagnostic ratios for PAHs and their homologues obtained by manual and automatic integration are shown in Tables 3 and 4.
From the tables, we can see that in the crude oil and fuel oil samples, some diagnostic ratios such as 2-MP/1-MP, 4-MD/1-MD, C2-dbt/C2-phe, and C3-dbt/C3-chr have little differences between manual and automatic integration, and the RSD% values are almost lower than 5%. While the C3-dbt/C3-phe has slightly larger differences, the overall results are satisfactory.
Compared with manual integral and artificial visual comparison method, the automatic recognition and integration method significantly shortened the working time. However, the difficulty of the new method lied in how to accurately determine the starting and ending position of the chromatographic peaks, especially for low concentration chromatographic peaks.
3.3 Application of the diagnostic ratios database
In 2015, an unidentified oil spill was found in the sea area of Shandong Province, China. Samples of spilled oil were collected immediately and brought to the laboratory for analysis and identification.
3.3.1 Automatic calculation of the diagnostic ratios for unknown spilled oil
After the analysis was completed, the spectrogram of the unknown spilled oil was recognized and integrated automatically using the self-written program language. The diagnostic ratios were calculated according to the obtained peak area, and the data are shown in Table 5.
3.3.2 Automatic retrieval of the diagnostic ratios for unknown spilled oil
The diagnostic ratios of the unknown spilled oil were compared with the known samples in the oil fingerprint database using the self-written program language. The diagnostic ratios of the samples in the database were screened and sorted and 10 samples closest to the unknown one are shown in Table 6. According to the results of the automatic retrieval by computer, the fuel sample with the highest similarity to the oil fingerprint database was a sample collected in a certain engine room in 2007.
3.3.3 Comparison of spectral characteristics of unknown spilled oil and suspected oil
According to the automatic retrieval results from the oil fingerprint database, a known sample that had the highest similarity with the suspected oil was found in the database. This known sample was a fuel sample that was collected from a certain engine room in 2007. The spectrograms of n-alkanes, PAHs, biomarkers were extracted and analyzed further based on the experience of the analyst, and the detailed features of the spilled oil sample and the known one were achieved.
From the spectra, we can see that the distribution characteristics— spectrogram of alkanes, biomarker and PAHs of the two samples were very similar; therefore, the sample in our database and the unknown sample were likely from the same source. Their relationship was calculated and analyzed further.
3.3.4 Comparison of the diagnostic ratios of spilled oil and suspected oil with the repeatability limit
The comparison method of the repeatability limit was recommended by the European Oil Identification System (PD CEN/TR) and had been verified by researchers (Sun et al., 2011).
(1) Brief introduction to the repeatability limit
The basic principle and comparison method of the repeatability limit were described in the literature (Sun et al., 2011; Qin, 2015). The repeatability limit means that the probability of the difference between two methods for the same sample should be less than the repeatability limit r, which is 95% (General Administration of quality supervision, inspection and Quarantine of the People’s Republic of China, 2007). The formula of repeatability limit r95% is shown as below:
$r_{95\%} = 2\sqrt 2 r_s = 2.8 r_s ,$
where rs is the relative standard deviation.
If the absolute deviation of two diagnostic ratios does not exceed the repeatability limit, the two diagnostic ratios are consistent. Generally, RSD% of diagnostic ratios used for spilled oil identification should not be more than 5%; therefore, we used this value for subsequent calculation. The sample mean was used in the formula to replace the population mean, and formula 3 was changed into
$r_{95\%} = 2.8 \times \overline x \times 5\% = \overline x \times 14\% ,$
where $\overline {{x}} $ is the sample mean.
For spilled oil identification, if the absolute deviation of two diagnostic ratios is less than r95%, then the two samples can be considered to be the same.
(2) Screening of diagnostic ratios
Parallel samples were analyzed for all oil samples using GC/MS, then all spectrograms were integrated and the diagnostic ratios were calculated. The absolute deviations of all pairs of parallel diagnostic ratios were compared with the repeatability limit r95%. Those diagnostic ratios whose absolute deviations were larger than repeatability limit r95% were rejected, while those diagnostic ratios whose absolute deviations were smaller than r95% were used to identify the unknown spilled oil.
(3) Comparison of the diagnostic ratios
The screened diagnostic ratios were compared. If the absolute deviation between samples was larger than repeatability limit r95%, the oil fingerprints of two samples were different. If all absolute deviations of the diagnostic ratios were smaller than r95%, the oil fingerprints of two samples were the same.
According to the rule of repeatability limit, the diagnostic ratios of the unknown spilled oil and the fuel sample in our database were analyzed and are shown in Table 7.
By comparing the absolute deviation with r95% of each diagnostic ratio, we can intuitively judge the consistency of the unknown spilled oil with the fuel oil sample in our database. It can be seen from Table 7 that all of the absolute deviations of the diagnostic ratios after screening were less than the corresponding r95%. Therefore, the oil fingerprint characteristics of the unknown spilled oil were consistent with that of the suspected oil samples. Using this comparison method, we can obtain more precise results that are consistent with the literature (Sun et al., 2011).
Through this database of diagnostic ratios of different kinds of oil, we can determine the unknown oil rapidly and accurately. It will be a very important application in the identification of spilled oil sources.
4 Conclusions
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Based on the results and discussion, the main conclusions are listed as follows:
(1) The program is written with MATLAB software to realize the automatic recognition and integral of chromatographic peaks and calculate the diagnostic ratios automatically. The characteristic chromatographic peaks can be n-alkanes, biomarker compounds, PAHs, etc.
(2) Hundreds of oil samples collected worldwide were analyzed by GC/MS and diagnostic ratios were obtained. Using these diagnostic ratios, a database of oil fingerprints was established and could be used to determine the watercrafts causing the oil spill accident.
(3) The methods of automatic recognition and integration and the database were tested in a sudden marine oil spillage. The suspected sample in the oil fingerprint database was retrieved rapidly and accurately; therefore, this intelligent treating method could improve the efficiency of oil spill identification in routine enforcement.
Funding
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  • The National Key Research and Development Program under contract No. 2016YFC1402101; the Project of Maritime Safety Administration.
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Appendix
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Year 2018 volume 37 Issue 11
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Article Info
doi: 10.1007/s13131-018-1254-9
  • Receive Date：2017-12-21
  • Online Date：2026-04-14
  • Published：2018-11-25
Article Data
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History
  • Received：2017-12-21
  • Accepted：2018-04-02
Funding
The National Key Research and Development Program under contract No. 2016YFC1402101; the Project of Maritime Safety Administration.
Affiliations
    1 College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
    2 Yantai Oil Spill Response Technical Center of Yantai Maritime Safety Administration, Yantai 264000, China

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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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