Forensic Investigations of Diesel Oil Spills in the Environment Using

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Energy and the Environment

Forensic Investigations of Diesel Oil Spills in the Environment using Comprehensive Two-Dimensional Gas Chromatography – High Resolution Mass Spectrometry and Chemometrics: New perspectives in the Absence of Recalcitrant Biomarkers Guilherme Lionello Alexandrino, Giorgio Tomasi, Paul Kienhuis, Fabio Augusto, and Jan H. Christensen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05238 • Publication Date (Web): 05 Dec 2018 Downloaded from http://pubs.acs.org on December 6, 2018

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Environmental Science & Technology

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Forensic Investigations of Diesel Oil Spills in the Environment using

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Comprehensive Two-Dimensional Gas Chromatography – High

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Resolution Mass Spectrometry and Chemometrics: New perspectives in

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the Absence of Recalcitrant Biomarkers

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Guilherme L. Alexandrino*1,2, Giorgio Tomasi2, Paul G. M. Kienhuis3, Fabio Augusto1 and Jan. H.

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Christensen2

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1

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970, Campinas – SP, Brazil.

Institute of Chemistry, State University of Campinas, Cidade Universitária Zeferino Vaz, 13083-

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Thorvaldsensvej 40, DK-1871, Frederiksberg C, Denmark.

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2, Lelystad, The Netherlands

Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen,

Ministry of Infrastructure and Water Management, Rijkswaterstaat laboratory, Zuiderwagenplein

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*Corresponding author:

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Guilherme L. Alexandrino, Ph.D.

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Analytical Chemistry, Department of Plant and Environment Sciences

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Faculty of Sciences, University of Copenhagen

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Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark

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Phone: +45 50 27 23 80

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E-mail: [email protected]

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1 ACS Paragon Plus Environment


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Abstract

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Forensic investigations of oil spills aim to find the responsible source(s) of the spill. Oil

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weathering processes changes the chemical composition of the spilled oil and makes the matching of

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oil spill samples to potential sources difficult. Diesel oil spill cases are more challenging, because

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biomarkers recalcitrant to long-term weathering are absent. We developed and tested a new method

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for the analysis and matching of diesel oil spills using two-dimensional gas chromatography – high

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resolution mass spectrometry (GC×GC – HRMS) and 2D-CHEMSIC (2-Dimensional CHEMometric

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analysis of Selected Ion Chromatograms), an extension of the CHEMSIC method to GC×GC data.

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The 2D-CHEMSIC performs pixel-based analysis using chemometrics on concatenated sections of

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2D extracted ions chromatograms to assess the overall chemical variability of the samples, with

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potential applications for matching spill-source pairs in forensic investigations. The method was

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tested on samples from a number of diesel oil spill cases, i) distinguishing chemically similar source

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diesels, ii) investigating weathering effects on spill samples to determine type and degree of

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weathering, and iii) to improve matching of diesel oil spill affected by weathering. Positive matches

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for spill-source pairs were identified after excluding the signals from the hydrocarbons most

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susceptible to evaporation, and photo-oxidized spills were also matched due to the presence of

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unaffected hydrocarbons. Forensic diagnostics obtained by the 2D-CHEMSIC were validated by the

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conventional CEN-Tr method.

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Keywords: quadrupole – time of flight mass spectrometry, diesel oil spills, pixel-based analysis,

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weathering, chemometrics

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1. Introduction

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Oil spill investigations demand large effort worldwide to identify the source of the pollution, to 1,2.

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assess environmental risks and to mitigate potential damages

Weathering effects such as

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evaporation, dissolution, photo-oxidation and microbial degradation affect the hydrocarbons profile

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in spilled fuels, while the most weathering-resistant petroleum biomarkers steranes and hopanes,

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which are contained exclusively in the heavier fractions, can persist

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identifications of petroleum mid-distillates, such as diesel fuels, are more challenging compared to

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cases involving heavy fuels or crude oils as the spill-source pairs matching can only be based on

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lighter compounds that are more susceptible to weathering processes. Bicyclic sesquiterpanes is one

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group of compounds that have been used to this end 4,5,6 as their isomer composition is unaffected by

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short-term weathering effects 7,8. However, more diagnostic compound groups are sought for oil spill

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identification of mid-distillates affected for both short-term but especially for long-term weathering.

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Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful tool for the

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separation of highly complex samples such as oil products 9. GC×GC uses two capillary columns

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with orthogonal separation properties (e.g.; non-polar × polar or vice versa) connected through a

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modulator, which periodically concentrates a fraction of the eluate coming from the first column (first

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dimension, 1D) and next reinjects this fraction as a narrower band into the head of the shorter second

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column (second dimension, 2D). The analytical advantages of GC×GC have been demonstrated for

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the analysis of crude oils and petroleum products and the more detailed chemical fingerprints of

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different classes of hydrocarbons have already been investigated in crude oils for geochemistry

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purposes 10,11,12, for the characterization of petroleum distillates 13,14,15 and for oil spill investigations

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16.

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HRMS), has been shown to provide a more accurate target analysis of key compounds and less

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ambiguous identifications of the chromatographic peaks 17.

1,3.

Forensic oil spill

More recently, the coupling of GC×GC with high resolution mass spectrometry (GC×GC –



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In oil spills forensics using conventional 1D GC-FID and GC-MS, the golden standard is a tiered

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approach used for data analysis which encompasses the visual comparison of chromatograms

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targeting the n-alkanes distribution and the calculation of diagnostic ratios from the relative peak

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areas or concentrations of PAHs and saturated petroleum biomarkers 18. However, the conventional

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approach is less suited for handling long-term weathered mid-distillates due to the absence of the

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recalcitrant biomarkers that could provide successful oil-source correlations in forensic investigations

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about oil spills. Pixel-based analysis of sections of chromatograms is an especially interesting

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alternative to peak integration for forensic mid-distillate spill investigations, because the search for

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spill-source matching is done using all the potential chromatographic pixels, instead of only few

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selected compounds susceptible to weathering. Christensen et al. have already introduced the so-

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called CHEMSIC method (CHEMometric analysis of Selected Ion Chromatograms) for forensic

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investigations about crude oil environmental spills using 1D GC-MS

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important advantages compared to the conventional approach, such as i) a more comprehensive

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diagnostics as a larger fraction of the chromatographic data is analyzed instead of only pre-selected

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target compounds, and ii) higher flexibility in forensic investigations as tens of different SICs can be

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combined for the analysis 20.

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CHEMSIC provides

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In this study, we developed and tested a new method for analysis and matching of diesel oil spills

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using GC×GC – HRMS and 2D-CHEMSIC, an extension of the CHEMSIC method to GC×GC data.

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In conventional 1D GC-MS, the forensic investigations are focused to the analysis of the compound

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groups included in the CEN-guidelines

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resolution MS detection. Conversely, GC×GC – HRMS provides enhanced chromatographic

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separation and the HRMS detection provides accurate mass extracted ions chromatograms (EICs) for

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less ambiguous identification of the group of compounds 25. This more powerful instrumentation is a

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key advantage when analyzing highly complex mixture of naphthenes in mid-distillates that could

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due to coelution of compounds group and the low-


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contain a great diagnostic power for forensic investigations, because they are relatively recalcitrant

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toward biodegradation, dissolution and photo-oxidation 8,26. The method was tested on samples from

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a number of diesel oil spill cases. The sample set was split into; i) diesels spills containing the initial

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suspected source(s) (Group A), and ii) diesel spills with no initial suspected source (Group B). The

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samples were analyzed using a reversed phase columns set for the GC×GC, i.e. polar × non-polar,

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because this column configuration has provided better chromatographic resolution between

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linear/branched alkanes, naphthenes and monoaromatics than the normal phase columns set (i.e., non-

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polar × polar) 15,27, in addition to the enhanced separation of compound classes due to the extraction

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of the high resolution extracted ion chromatograms (EICs). 2D-CHEMSIC was applied to GC×GC–

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HRMS data using EICs from the different groups of compounds contained in diesels that are already

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investigated for diagnostic information about the spills. The data from all the spill cases were

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preprocessed by retention time shift corrections, normalization and then submitted to pixel-based

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analysis using Weighted Principal Component Analysis (WPCA), aiming to find the most probable

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source(s) of the spilled diesels that were susceptible to different degrees of weathering. Because of

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the inherent absence of recalcitrant biomarkers, the modeling strategy was focused on: i)

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distinguishing different sources of similar diesels, ii) identifying the weathering effects in the diesel

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spills, and iii) proposing modelling strategies to minimize or handle the influence of the weathering

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effects in the chromatographic fingerprints. The matching criterion for overall chemical similarities

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between spills and suspected sources were based on statistical hypothesis tests in the reduced PC

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subspace 28,29. The 2D-CHEMSIC method was validated according to the diagnostics obtained by the

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standardized CEN-Tr method for forensic investigation of oil spills, and the advantages of our method

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were highlighted.

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2. Materials and Methods



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2.1.

Samples

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The sample set included 32 diesel samples of 13 potential source diesel candidates and 19

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samples of spills split into 12 real cases of diesel spills into marine environments (Table 1). The spills

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and the source diesels were obtained from the collaboration within the Bonn Agreement Oil Spill

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Identification Network Group (BonnOSInet). The spill samples were grouped according to the

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presence or absence of the diesel source suspect(s) (A or B, respectively), the i-th case number and

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the j-th spill sample from the i-th case, while the diesels source oils were coded for each k-th sample

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(k = 1 to 13) A quality control (QC) sample was prepared by mixing equal volumes of seven sample

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extracts (5 source diesels and 2 spill samples). The composition of the QC sample provided a good

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target chromatogram for the retention time alignment step (Section 3.1. Preprocessing) and for daily

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GC column performance quality assurance.

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2.2.

Sample preparation

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Samples from oil cases can arrive at the lab as pure gasoil from fuel tanks, as thick layers on

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water, or as a small amount of oil taken with an EFTE net. Nets are used to collect oil from the surface

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water, from surfaces on board of a ship and from fuel and bilge tanks. Pure gasoil samples were

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diluted (15 µl into 10 ml DCM =1.26 mg/ml) and 1 µl was used for the analysis. The net samples

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arrived in a pot. 30 ml of DCM were added and the pot was shaken for 5 min to extract the oil from

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the net. Based on visual appearance, 1 µl of the extract was directly analyzed in a test run with GC-

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FID or injected after dilution. Black samples were first cleaned over a small column filled with

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sodium sulfate and Florisil before injection 30. Based on the GC-FID test run results, the concentration

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of oil in the net samples was adjusted to a concentration between 1.0 to 1.5 mg/ml.


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2.3.

GC×GC – (HR)QTOFMS

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The sample set included the 32 sample extracts of diesel source oil and spill samples, analytical

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replicates of 10 randomly selected sample extracts, 5 QC replicates and 3 solvent blanks. The runs

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were divided into 3 batches that contained no more than 17 sample extracts, incl. analytical replicates,

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2 QC samples and 1 solvent blank to check for cross-contamination. The extracts were analyzed on

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an Agilent 7890B GC System modified with a secondary column oven and a Zoex ZX2 cryogenic

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loop-type modulator (Zoex Corporation, Houston, TX – USA), which was interfaced with a 7200

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Accurate Mass GC/QTOF mass spectrometer (Agilent Technologies, Palo Alto, CA – USA). The MS

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system was operating in electron ionization mode. An inverse GC×GC column set was installed: 1D

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column was an intermediate polarity ZB-50 column (60 m, 0.25 mm i.d., 0.25 μm) and the 2D column

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was a non-polar ZB-5 column (1.5 m, 0.15 mm i.d., 0.15 μm) (Phenomenex, Torrance, CA – USA).

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A SilTite™ µ-Union ferrule (SGE Analytical Science, Wetherill Park, NSW - Australia) was used

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for the column connection. Aliquots of 1μL were injected in split mode 1:20 and injection temperature

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of 300 oC, using the following oven temperature gradient: 60 oC held for 10 min, 5 oC min−1 to 300

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oC

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the secondary oven containing the 2D column. Helium (≥ 99.9999 % purity) was used as carrier gas

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at a flow rate of 2.0 ml min-1. The modulation was performed with the Zoex ZX2 cryogenic loop-

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type modulator using the final 0.7 m length of the first column as the loop. An independent cooling

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system provided cold jets of N2(g) at approximately -70 oC, while the hot jets (pressure = 20 psi) were

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obtained using N2(g) heated at a constant temperature offset of +60 oC from the primary oven. The

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modulation period was adjusted to 7.0 s, holding the hot jet for 800 ms each cycle. The electron

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ionization source was operated at 230 oC with emission current of 35 μA and 70 eV. The QTOF mass

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analyzer was operated in the TOF mode with transfer line temperature of 300 oC and quadrupole

and held for 5 min. A constant temperature offset of +10 oC from the primary oven was used in



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temperature of 150 oC. Mass calibration was performed before each run using perfluorotributylamine

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as instrument internal standard. Data acquisition was performed in the m/z range of 60 – 500 Da with

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25 spectra s-1. MassHunter ver. B.06.00 was used for instrument control (Agilent Technologies).

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3. Data Treatment

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3.1.

Preprocessing

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A graphical scheme about the preprocessing steps can be seen in the upper part of Fig. 1. The

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MassHunter data files were converted to netCDF files using the AIA File Translator program from

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Agilent, which inevitably bins the m/z values according to an accuracy of ±0.025 Da. An in-house

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script was written to import specific high-resolution (1rt × 2rt) 2D-EICs, where 1rt and 2rt are the

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retention times in the 1D and 2D, respectively, into Matlab R2016a (Matworks, Natick, MA – USA).

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A total of 54 2D-EICs that describe the most relevant groups of hydrocarbons found in diesels, i.e.

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straight and branched alkanes, cyclic alkanes, olefins, monoaromatic hydrocarbons and two- to four

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rings non- and alkylsubstituted PAHs were imported for each sample (Table S1 in the Supporting

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Information). The 2D-EIC corresponding to the same type of compounds (e.g.; m/z = 113.15, 127.15,

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183.20 and 197.25 for branched alkanes, see Table S1) were summed into a new single 2D - summed

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EIC (2D-sEIC) per group, to reduce redundant signals that describe the same group of compounds.

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The 2D-sEICs were phase-corrected for each sample to account for rigid retention time shifts due to

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the synchronization gaps between the instrument and the starting of the modulation, and regions of

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each 2D-sEIC containing only baseline noise were visually identified and excluded to reduce the data

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size. The 2D correlation optimized warping algorithm (2D-COW)

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sEICs to a common target. The optimal warping parameters: segment length and slack (how much

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was used to align of the 2D-


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each segment is allowed to change in the alignment) were obtained using an in-house script in Matlab

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that performs grid search in the parameters space

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dimensions. The 2D-COW aligns each 2D-sEIC individually, here using the chromatogram of a QC

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replicate obtained in the middle of the analytical sequence as target. The aligned 2D-sEICs were

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unfolded and combined row-wise for each sample, resulting in an augmented data matrix D (K × L),

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where K are the objects (i.e. samples, replicates and QC samples) and L the variables containing the

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aligned EICs. Some slight peak misalignments persisting only in the 2D was corrected using the

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icoshift v. 3.0 algorithm 33 (downloaded from http://models.life.ku.dk). Finally, D was normalized to

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unitary Euclidian norm to remove the differences in the injected oil concentrations between samples

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and to reduce the effects of variations in the instrument sensitivity along the analysis of the batches.

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herein accounting for both chromatographic

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3.2.

Data modeling using WPCA

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The graphical scheme about the data modeling is shown in the lower part of Fig. 1. The pixel-

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based analysis should facilitate the comparison of source oils and spill samples to distinguish oils

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from different sources and to identify the source of the spilled oils. Chromatograms contain variations

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that are unrelated to the chemical composition such as instrumental noise, residual retention time

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shifts and chemical noise (e.g., column bleeding and peak saturation), which will negatively affect

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the PC model quality. To mitigate these effects, WPCA was used where the weight vector w contains

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the inverses of the pixel-by-pixel relative analytical standard deviations (RSDA), using only the EICs

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from the analytical replicates and the QC samples (Fig. S1 in the Supplementary Material). Since all

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elements in each column of D have the same weight, WPCA can be obtained via preprocessing

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(namely, scaling each column by the corresponding element of w) 34,29. The extraction of information

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from D was therefore improved by downscaling signals or pixels that contain little inherent chemical



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information and up-scaling pixels with low analytical variation. PCA was performed on the scaled

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and mean-centered D excluding sample replicates (‘training set’ consisting samples and QCs). The

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10 analytical replicates were used for model validation (‘validation set’) to check for absence of

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model overfitting, outliers during the chromatographic analysis and to calculate the accuracy of the

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method (i.e. the confidence interval of the samples in the WPCA models). The number of PCs in the

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models was chosen after considering only the PCs containing relevant chemical information,

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neglecting the next PCs describing practically instrumental noise. The sample uncertainties in the

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PCA models were estimated from the pooled average error of the scores (weighted by the explained

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variance of each PC) from the QC samples and the predicted analytical replicates (validation set),

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considering the respective degrees of freedom for their replicates. Initially, PCA models were fitted

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considering each preprocessed (unfolded) 2D-EIC separately, and an F-test was performed to

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statistically compare the overall variance of the models with the error variance. The 2D-sEICs with

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low diagnostic power (i.e. when the overall variance and the error variance for that 2D-sEIC are not

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significantly different, at the 95% confidence level) were excluded from the dataset. The relevant

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unfolded 2D-sEICs were then re-combined into a new data matrix DN (K × N), where N contains only

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the relevant signals, which was then modeled using WPCA for the forensic oil spill investigations.

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The hypothesis about one spill potentially belonging to one suspected source was done in the PCs

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subspace, after computing the averaged p-value (weighted by the explained variance of each PC)

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from a two-sample hypothesis t-test performed using the corresponding scores (i.e. spill × source

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diesel) from each relevant PC 28. The pooled standard deviation was calculated from the scores of the

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analytical replicates and the QC samples. The null-hypothesis, i.e. the spill cannot be distinguished

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from one suspected source with reasonable doubt, was accepted for p-value > 0.05 (i.e. confidence

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level = 95 %). Due to the previous weighting of the variables in DN before WPCA, the


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chromatographic loadings were re-scaled (i.e. element-wise multiplying by the corresponding

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weights vector wN) for data interpretation 35.

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4. Results and Discussions

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4.1.

Identifying the evaporation weathered diesel spills

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The first screening of the dataset was performed using WPCA on DN after excluding two non-

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diagnostic EICs: m/z 180 (C1–Fluorenes) and m/z 184.05 (Dibenzothiophene), for which the overall

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variance of the samples was not significantly larger than the error variance. Therefore, DN consisted

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of a total of 30 unfolded and combined 2D-sEICs. The 4 component WPCA model explains 77.4%

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of the variance, and, along PC1, spills are separated according to the degree of evaporation while

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source diesels form three clusters characterized by different heaviness (boiling point region – Fig. 2).

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The refolded PC1 2D-loadings (Fig. S2 – Supplementary material) are positive for naphthenes,

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adamantanes and monoaromatics < C14, including the C0-, C1- and C2-naphtalenes, and C0-fluorene.

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In contrast, compounds ≥ C14, incl. the targeted bicyclic sesquiterpanes (drimanes), and the higher

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molecular weight PAHs have negative PC1 loadings (blue pixels). Therefore, the samples with the

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largest negative PC1 scores (e.g., spills B-C7-s2, B-C8-s1 and B-C6-s1) have the lowest relative

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concentration of the lighter compounds with < n-C15 and are the samples most affected by

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evaporation. In contrast, samples with positive PC1 scores (e.g., B-C2-s2, B-C5-s2 and B-C5-s1)

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have a higher than relative concentration of the lighter compounds and can therefore be concluded to

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be less affected by evaporative weathering. The heaviness of spills not affected by evaporation can

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also be described through their corresponding scores in this PC. For instance, the source diesels can

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also be distinguished on this PC according to their relative abundance of light and heavy

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hydrocarbons. No evidence of evaporation weathering is found for the diesels spills that are not 11 ACS Paragon Plus Environment


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completely separated from the source diesels in the PC1 scores plot, e.g.; A-C3-s1, A-C3-s2 and A-

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C4-s1. Therefore, two types of information are confounded in PC1, both are related to the difference

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between the relative concentration of the heavier (> n-C15) and the lighter (≤ n-C15) hydrocarbons

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in the diesel fingerprints: the most prominent regards the evaporation weathering of spill samples, the

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other relates to the heaviness of the diesel oils.

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PC2 describes chemical differences that can be used for source identification as samples with

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highest negative PC2 scores have the highest relative concentration of PAHs (except for C0-

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napthalene), mono-aromatic hydrocarbons > C16, and the lowest relative concentrations of FAMEs

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(Fig. S3 in the Supplementary Material), vice-versa for samples with positive PC2 scores. FAMEs

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are the main component of biodiesels blended with mineral diesels to reduce environmental pollution

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36.

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to the order; SD-3, SD-4, SD-5 < SD-6, SD-9, SD-11, SD-7, SD-12, SD-8 < SD-13, SD-1, SD-2, SD-

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10.

The chemical fingerprints described by PC2 is ranking the environmental-friendly fuels according

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The capability of 2D-CHEMSIC to sort the correlations among the chromatographic signals into

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the PCs facilitated the identification of the main weathering effect and source information along the

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combined 2D-sEICs simultaneously, without the need to evaluate the large amount of peaks obtained

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by GC×GC individually.

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4.2.

Handling evaporation weathering for spill-source correlations in diesels spills

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To address evaporation in forensic spill-source correlation of diesel spills using 2D-CHEMSIC

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the affected chromatographic sections were gradually excluded from the chromatograms according

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to the number of carbons, fitting successive WPCA models after each step (Fig. S4 in the

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Supplementary Material). The visual inspection of the samples in the PC1 and PC2 scores plot from


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the models revealed the gradual mitigation of the evaporation weathering for all the affected spills.

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The score plots of the model obtained after excluding chromatographic signals < n-C18, i.e. those

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more susceptible to the evaporation weathering in the spills, are shown in Fig. 3. The chemical

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similarity of the highly evaporated spills and the source diesels is enhanced after excluding the signals

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from the most volatile hydrocarbons, because the distances between the corresponding scores in the

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PCs subspace decreased. This result is very evident for the spills A-C2-s1, B-C1-s1, B-C6-s1, B-C7-

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s1, B-C7-s2, B-C7-s3 and B-C8-s1, which were de-cluttered in the first WPCA model that was fitted

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using the signals from the entire hydrocarbons range of the diesels.

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Furthermore, source diesels from the same origin and spills from the same source and similar

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degrees of weathering can be identified accordingly as their chemical similarities are still preserved

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for the compounds eluting after n-C18; for example, source diesels SD-3, SD-4 and SD-5, and spills

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B-C3-s1 and B-C3-s2 are still not statistically distinguished (confidence level of 95%) in the new

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WPCA model (see Fig. 2 and Fig. 3). Interestingly, spill-source pairs that were different before the

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mitigation of the evaporation weathering on the spills could be matched in this new WPCA model.

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The potential matching of spill-source pairs was investigated while considering all the successive

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WPCA models (see Fig. 3), and the individual spill cases are discussed in the sections 4.2.1 and 4.2.2.

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4.2.1. Statistical spill-source forensic correlations for the diesel spills with potential suspects

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The weathering effects on the spills for forensic oil-source correlations was initially evaluated

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for the cases with specific diesel source suspects. The extent of the weathering was assessed based

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on the distances between spill and source in the PCs subspace of the successive WPCA models, and

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therefore without the necessity to evaluate individual peaks for each sample. In a positive match, the

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scores distances between spills and sources in the WPCA models are expected to decrease whether



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excluding more variables susceptible to weathering, until the spill – source pair becomes

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indistinguishable for the models (i.e. the samples cannot be separated in the multivariate space

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according to the analytical method’s accuracy). This trend will only be valid for highly-weathered

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spills mostly affected by evaporation, after gradually excluding the chromatographic signals from the

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most susceptible lighter hydrocarbons; conversely, spills subjected to the remaining weathering

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effects that also affect heavier compounds, e.g.; biodegradation, dissolution and photo-oxidation, can

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still be distinguishable from a true source in this variable-reduction approach. However, positive

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matches for spill – source pairs can be obtained if the chemical changes of the spill can be explained

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exclusively by weathering effects 1. The averaged scores distances between spills and the

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corresponding suspected source(s) for these cases are depicted in Fig. 4. The mean distances of the

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spill-source pairs decreased while excluding the signals up to n-C15 for spills A-C1-s1 (Fig. 4a) and

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A-C2-s1 (Fig. 4b), but they are not statistically different along the successive WPCA models

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according to the corresponding error bars. These profiles are coherent with evaporation weathering

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in these spills affecting mostly the hydrocarbons up to n-C15, and a minor effect on the hydrocarbons

319

after n-C18. This diagnostic is corroborated with the increase of the chemical similarities of the

320

corresponding spill – source pairs when considering hydrocarbons > n-C18 exclusively. The two-

321

samples t-test statistics after considering only the hydrocarbons > n-C18 suggests both spills possibly

322

come from the respective suspected sources (p-value = 0.700 and 0.501 for A-C1-s1 and A-C2-s1,

323

respectively, confidence level 95%), see Fig. 4a) and Fig. 4b).

324

The case involving three spills (A-C3-s1, A-C3-s2 and A-C3-s3) and the similar source diesels

325

SD-3, SD-4 and SD-5 (i.e. they cannot be distinguished in the WPCA models) resulted that A-C3-s1

326

and A-C3-s2 spills differ from the source diesel suspects after n-C15, contrary to A-C3-s1 spill that

327

remains similar. The slight increase of the chemical similarity between A-C3-s1 and the suspect

328

source diesels after n-C17 (p-value > 0.05, at 95% confidence level) suggests the presence of only


Page 15 of 29


329

short evaporation for this spill (Fig. 4c). Therefore, the positive match between this spill-source pair

330

cannot be confirmed without reasonable doubt, because the fingerprint of the hydrocarbons ≤ n-C18

331

(incl. pristane and phytane) are very different.

332

The scores distances computed for A-C4-s1 spill from the four suspected sources (i.e. SD-6, SD-

333

7, SD-8, SD-9) reveal that the chemical composition of the spill differs significantly from the diesels

334

sources after n-C19, except for SD-9, Fig. 4d). Contrary to the above-mentioned cases, there is no

335

evidence of remarkable evaporation weathering while comparing the scores distances along the

336

WPCA models. The hydrocarbon fingerprints of spill and the suspected sources cannot be

337

distinguished in the WPCA models until ≤ n-C17. In contrast, the corresponding spill – source pairs

338

differ for the SD-6, SD-7 and SD-8 (confidence level of 95%) for the WPCA models that considers

339

only the ≥ n-C17 hydrocarbons. The high chemical similarity between A-C4-s1 spill and the SD-9

340

diesel source along the entire hydrocarbon range indicates a positive spill-source match only for this

341

suspect (p-value = 0.299 ± 0.235). The drastic increase of the chemical similarity between the spill

342

and all four suspects after considering only the signals ≥ n-C23 is due to the low diagnostic power

343

occurring in this small hydrocarbon range when comparing the diesels.

344

While PC1 discriminates spills from different sources after excluding hydrocarbons most

345

susceptible to evaporation (i.e. ≤ n-C17), PC2 explains the photo-oxidation weathering in the spills.

346

The photo-oxidation effects on the spills can be mostly identified in the chromatograms by the

347

degradation of the pyrenes (yellow loadings for C0-, C1- and C2-pyrenes in PC2) and naphthenic

348

one-ring aromatics, and the differences between isomers distribution for the C2- and C3-

349

phenanthrenes 26, Fig. 5. The A-C1-s1 spill show a slight level of photo-oxidation in relation to SD1.

350

The A-C3-s2 spill shows the highest influence of photo-oxidation effects in case 3 (i.e. it contains the

351

most negative score value in PC2). Furthermore, the n-alkanes, isoprenoids, naphthenes and the one-

352

ring aromatics were more resistant to photo-oxidation (blue pixels in Fig. 5), which is in accordance



Page 16 of 29

353

to laboratory experiments already published 37. Interestingly, the chromatographic loadings from the

354

one-ring aromatics showing interference of the naphthenic one-ring aromatics even in the HRMS data

355

demonstrate that GC×GC was crucial to separate these similar groups of hydrocarbons that may have

356

responded differently to photo-oxidation. Moreover, the more efficient analysis of the higher MW

357

hydrocarbons more resistant to photo-oxidation and to evaporation by GC×GC – HRMS is a

358

remarkable advantage when analyzing the whole fingerprint of the samples in oil spill investigations

359

of mid-distillates.

360

The profiles of score distances along the WPCA models is an interesting approach to simplify

361

the main sources of variance due to weathering that are affecting the chromatographic signals of the

362

spills for the individual cases. Additionally, the complex chromatographic fingerprints of the spills

363

can be compared more easily among the cases.

364 365

4.2.2. Validation of the cases with potential suspects according to the CEN/Tr method

366

The samples studied in this paper are derived from oil cases originally assessed with the

367

conventional CEN/Tr method 23 for oil spill identification. Similarly to the 2D-CHEMSIC method, a

368

match has been concluded between the samples A-C1-s1 and SD1, in which the spill showed some

369

evaporation up to n-C12, and pyrene and the C1- and C2-pyrenes are reduced to 70-80% by photo-

370

oxidation.

371

A non-match has been concluded between the samples A-C2-s1 and SD-2. Differences were

372

found above n-C30 for the alkanes and between the biomarkers present at a low concentration, which

373

suggests a small contamination of heavy oil in the spill sample. N-alkanes before n-C14 were reduced

374

by evaporation and compounds sensitive for photo-oxidation were slightly reduced. Because the


Page 17 of 29


375

biomarkers were not studied with 2D-CHEMSIC the results are in accordance with the conclusion

376

based on Fig. 4b.

377

A match has been found between the diesel sources SD-3, -4 and -5. Samples were taken from

378

two diesel tanks and from the diesel daily tank. Compared with A-C3-s1, - s2 and –s3 a non-match

379

has been found for all spill samples. Fig. 4c shows clear differences for A-C3-s1 and –s2, but some

380

similarity with A-C3-s3. A-C3-s3 is not weathered by evaporation or photo-oxidation, but shows

381

clear differences for the isoprenoid ratios (n-C17/pristane, n-C18/ and pristane/phytane) and some other

382

compounds eluting around the same retention time section. This is confirmed by Fig. 4c in which

383

clear difference have been found above n-C14.

384

A match has been found between SD-9 and A-C4-s1. The spill sample showed some evaporation

385

up to n-C12, no indication of photo-oxidation and non-matches for the other 3 source samples. These

386

conclusions were also obtained with the 2D-CHEMSIC method.

387 388

4.2.3. Statistical spill-source correlations for the diesel spills without potential suspects

389

The spill cases without specific potential suspects were evaluated to find the diesel source(s)

390

candidate in our dataset that could be similar candidate in a forensic investigation. The diagnostics of

391

these spill cases without specific sources provided by 2D-CHEMSIC are supported by the previous

392

validation done with the CEN-Tr method for the cases with suspected sources. Diesel spill samples

393

from cases 2, 4, 5, and 7 (Group B) cluster in the scores plot of the WPCA model in Fig. 2. This may

394

indicate that the source of the spill samples are very similar and they have similar degrees of

395

evaporation weathering, e.g.; B-C2-s1 and B-C2-s2 (case 2), B-C3-s1 and B-C3-s2 (case 3), B-C5-

396

s1 and B-C5-s2 (case 5), B-C7-s1 and B-C7-s3 (case 7). Moreover, B-C1-s1 and B-C4-s1 cannot be

397

distinguished to the spill samples from cases 3 and 7, respectively, despite they belong to different



Page 18 of 29

398

cases. The B-C2-s1 and B-C2-s2 spills most probably come from a diesel source similar to SD-6, and

399

no potential suspect could be identified for the spills in case 5 (i.e. B-C5-s1 and B-C5-s2).

400

Interestingly, the photo-oxidation weathering described by PC2 emphasizes the different photo-

401

oxidation levels between the spills in case 2, in which B-C2-s1 is more photo-oxidized than B-C2-s2

402

even they have similar evaporation levels. The spills from cases 1, 3, 4 and 7 are chemically similar

403

after minimizing the influence of the evaporation weathering, according to the MPCA models; they

404

most probably come from diesel source(s) similar to SD-9 or SD-12 (Fig. 2), with no evidence of

405

high photo-oxidation.

406

The highly evaporated B-C6-s1 spill most probably comes from a suspect diesel sources similar

407

to SD-10 (p-value = 0.251 ± 0.149 after excluding ≤ n-C17), while B-C8-s1 spill should be similar to

408

the SD-1 (p-value = 0.351 ± 0.261, after excluding ≥ n-C18) or SD-13 (p-value = 0.310 ± 0.247, after

409

excluding ≥ n-C18). There is no evidence of significant photo-oxidation effects on these spills.

410 411

Acknowledgments

412

The São Paulo Research Foundation (FAPESP, grants 2015/08201-0 and 2016/18453-0) is

413

acknowledged for the research funding.

414 415

Supporting Information

416

The supplementary material contains the diagnostic m/z from the main type of compounds in the

417

diesels, the weight vector for each chromatographic pixel, the loadings from the general WPCA

418

model, and the cutoffs used to mitigate the evaporation weathering effects in the diesels.

419


Page 19 of 29

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Identification of Spilled Light Refined Oils. Environ. Forensics 2012, 13 (4), 298–311.

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Malmborg, J. Examining the Weathering Susceptibility of Light Biomarkers. Environ. Forensics 2017, 18 (3), 197–206.

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Stout, S. A.; Uhler, A. D.; McCarthy, K. J. Middle Distillate Fuel Fingerprinting Using

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Mogollón, N. G. S.; Prata, P. S.; dos Reis, J. Z.; Neto, E. V. dos S.; Augusto, F.

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Spectrometry. Org. Geochem. 2017, 106, 93–104.

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Casilli, A.; Silva, R. C.; Laakia, J.; Oliveira, C. J. F.; Ferreira, A. A.; Loureiro, M. R. B.;

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Azevedo, D. A.; Aquino Neto, F. R. High Resolution Molecular Organic Geochemistry

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Assessment of Brazilian Lacustrine Crude Oils. Org. Geochem. 2014, 68, 61–70.

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Type Quantification of Petroleum Middle Distillates Based on Comprehensive Two-

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Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry (GC×GC-TOFMS) and

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Visual Basic Scripting. Energy & Fuels 2014, 28 (9), 5670–5681.

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Fingerprinting Analysis Using Gas Chromatography-Quadrupole Time-of-Flight (GC-QTOF).

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(CHEMometric Analysis of Selected Ion Chromatograms). J. Chromatogr. A 2012, 1235, 149–

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Sediments from Khuzestan Province, Iran. Mar. Pollut. Bull. 2016, 110 (1), 584–590.

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Analytical Methodology and Interpretation of Results Based on GC-FID and GC-MS Low

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527 528 529 530


Page 23 of 29


Table 1. Source diesel and spills samples. Sample code n-Ci-sj refers to the n-th group (A or B), i-th case within the same group and the j-th sample of the i-th case. SD-k refers to the k-th diesel source. Case Group

A

531

Sample A-C1-s1 SD-1 A-C2-s1 SD-2 A-C3-s1 A-C3-s2 A-C3-s3 SD-3 SD-4 SD-5 A-C4-s1 SD-6 SD-7 SD-8 SD-9

Type

Sampling*

Spill Source Diesel Spill Source Diesel Spill Spill Spill Source Diesel Source Diesel Source Diesel Spill Source Diesel Source Diesel Source Diesel Source Diesel

surface water as gasoil water using the EFTE net

water using the EFTE net

water using the EFTE net

SD-10 Source Diesel SD-11 Source Diesel SD-12 Source Diesel SD-13 Source Diesel B-C1-s1 Spill water using the EFTE net B-C2-s1 Spill water using the EFTE net B-C2-s2 Spill B-C3-s1 Spill water using the EFTE net B B-C3-s2 Spill bilge water from ship using the EFTE net B-C4-s1 Spill bilge water from ship using the EFTE net B-C5-s1 Spill water using the EFTE net B-C5-s2 Spill B-C6-s1 Spill water using the EFTE net B-C7-s1 Spill B-C7-s2 Spill water using the EFTE net B-C7-s3 Spill B-C8-s1 Spill water using the EFTE net * Source Diesels were obtained directly from tanks as gasoil.



Graphic Abstract 84x42mm (300 x 300 DPI)

ACS Paragon Plus Environment

Page 24 of 29

Page 25 of 29


Figure 1. Scheme of the data preprocessing (section 3.2) and next data modeling by pixel-based analysis using WPCA (section 3.3). The 2D-EIC (rt1D, rt2D) groups are described in Table S1. 2D-COW (2D – Correlation Optimized Warping) and icoshift (interval Co-Shifting) are algorithms used to correct retention time shifts among the samples chromatograms. D(K, L) is the data matrix for WPCA containing the unfolded and next row-wisely concatenated 2D-sEIC groups, which is plotted and colorized for each sample. The signals from the first three 2D-sEICs (i.e. I, II and III) and from all the PAH in D are also highlighted in the figure. The dimensions K and L are the total number of samples and variables (i.e. chromatographic pixels from all the 2D-sEICs), respectively. Data was normalized to the Euclidian norm = 1 (i.e. total area of the row-wise chromatograms) before the data analysis. w(1, L) is the weights vector computed as pixels-bypixel 1/RSD(QCs), RSD = relative standard deviation and QCs = (unfolded) 2D-sEICs from the QC samples. DN(K, N) is the data matrix containing only the 2D-sEICs with significant S/N ratio, N = number of variables. 136x128mm (300 x 300 DPI)



Figure 2. PC1 and PC2 scores plot from the WPCA model while identifying the different degrees of weathering of the spills and distinguishing the source diesels. The explained variances of the PCs are expressed between the brackets. The samples were assigned as i-Cj-sk; i refers to the group of spill cases in which the suspected source(s) within each case is(are) present (A) or absent (B). j and k are the number of the case in the corresponding group and the number of the sample within the j-th case, respectively. SD-l refers to the l-th diesel source. 293x89mm (300 x 300 DPI)


Page 26 of 29

Page 27 of 29


Figure 3. PC1 and PC2 scores plot from the WPCA model after excluding the chromatographic signals < nC18. The areas inside the solid rectangles were zoomed for clarification (dashed rectangles). The explained variances of the PCs are shown in the brackets. The samples were assigned as i-Cj-sk; i refers to the group of spill cases in which the suspected source(s) within each case is(are) present (A) or absent (B). j and k are the number of the case in the corresponding group and the number of the sample within the j-th case, respectively. SD-l refers to the l-th diesel source. 242x113mm (300 x 300 DPI)



Figure 4. Averaged scores distances (weighted by the percentage of the variance explained by each PC) between spills and the suspected source(s) in the successive WPCA models computed after gradually excluding the chromatographic signals from the lighter hydrocarbons more susceptible to evaporation weathering. “all” represents WPCA models fitted using the entire 2D chromatograms. The values in the plot are the averaged p-value computed from a two-sample Student’s t-test for the spill belonging to the corresponding source. a) A-C1-s1 (spill) paired with SD-1 (source diesel), b) A-C2-s1 (spill) paired with SD2 (source diesel), c) A-C3-s1, A-C3-s2 and A-C3-s3 (spills) paired with SD-3, SD-4 or SD-5 (source diesels); here, the p-values are the averages from the 3 source diesels that cannot be distinguished by the method, i.e. they most probably come from a common source. d) A-C4-s1 (spill) paired with SD-6, SD-7, SD-8 or SD-9 (source diesels). The error bars in the plots are the averaged errors of the score values computed from the analytical replicates and QC samples (weighted by the percentage of the explained variance of each PC). The samples were assigned as i-Cj-sk; i refers to the group of spill cases in which the suspected diesel source(s) within each case is(are) present (A) or absent (B). j and k are the number of the case in the corresponding group and the number of the sample within the j-th case, respectively. SD-l refers to the l-th diesel source. 330x252mm (300 x 300 DPI)


Page 28 of 29

Page 29 of 29


Figure 5. Two-dimensional chromatographic PC2 loadings highlighting the degradation of phenanthrene isomers and pyrenes by photo-oxidation (i.e. yellow pixels in the corresponding loadings) from WPCA after excluding chromatographic signals from hydrocarbons < n-C18, which were more susceptible to the evaporation weathering. 236x105mm (300 x 300 DPI)


Forensic Investigations of Diesel Oil Spills in the Environment Using

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