Screening of sample extracts
Substantial concentration differences were observed between UTM and FSK samples. In Figure 2, the 2D-BPCs of facilitator samples for FSK (AnQCFSK) and UTM (AnQCUTM) are compared. The 2D-BPC of AnQCFSK contains > 1000 peaks while 2D-BPC of AnQCUTM is much less populated, which demonstrates that the overall concentration of compounds in the two combined extracts is significantly higher in the fortress channel (FSK) compared to the lake (UTM). Some peaks in AnQCFSK expressed wraparound after 30 min, viz. peaks that should have retention in 2D higher than the modulation period of 6 s. Fortunately, these wraparounds did not co-elute in 2D with other peaks. Moreover, a detector overload can be seen in the 2D-BPC of AnQCFSK (#58, large yellow blob after 35 min in Figure 2). This peak or cluster of peaks (which is difficult to assess due to the detector overload) was identified as phthalic acid esters (Table 1) because of the major fragment and characteristic base peak at m/z 149.0235. The most common member of the class of phthalates is bis(2-ethylhexyl) phthalate (DEHP); it is often found in environmental samples due to its excessive overuse in countless products and has been listed as a priority substance by the EU Parliament 4, 23. Several potential candidates of phthalates are listed in SI2. The detector saturation was observed only in a few FSK samples but in none from UTM. As the detector saturation would have exacerbated the pixel-based analysis, the cut-off for modelling was set to ≤ 35 min.
Target analysis was performed for the spiked compounds listed in Table S1 in the GC×GC-HRMS chromatograms of standard mixtures and the two facilitator samples from the third batch. In the standard mixture, 67 out of 109 compounds were detected; 25 out of the 67 were also found in the facilitator samples (Table S1). Some of these compounds represent a group of isomers with the same monoisotopic mass and molecular formula, and thereby enabled the identification of groups of these isomers in other samples. Aromatic and fatty acids and steroids were not detected because of, e.g., degradation at high temperatures, high polarity or low volatility of the compounds; while (alkylated) four-ring PAHs (and higher) were not detected because of the lower maximum oven temperature in the GC×GC-HRMS method compared to GC-MS methods.
Pixel-based analysis
The enhanced peak capacity and sensitivity of GC×GC-HRMS allows the separation of thousands of compounds with different physicochemical properties in such complex environmental samples. Compound identification, however, can be cumbersome still, because of the often overwhelming number of peaks and large datasets in GC×GC-HRMS. Prioritisation is often unavoidable and is based, for example, on signal intensity, peaks with a specific isotopic pattern or mass defect, to name a few 6, 16. Often, some samples do not add meaningful information, especially when many samples are collected for spatial and temporal investigations. The pixel-based PCA provides information on the highest variation in a dataset, and thus, helps to focus the identification only on the samples with the highest variation and unique chemical fingerprints. Furthermore, the positive and negative signals in a loading plot can be used to identify peaks of high relevance.
Global model
The global model includes all measured samples from both sampling sites and describes the overall variation within and between the two sampling sites. The score plot in Figure 3 is a projection of the samples in the WPCA model (Figure S2 for loading plots). Thus, the chemical similarities between pair or group of samples can be assessed while comparing the distances of their coordinates in this variable-reduced space spanned by the PCs. The first PC explains 73.10 % of the total variance, whereas PC2 describes 10.81 % (Figure 3). Samples from FSK showed a more considerable variation along the PCs subspace compared to the UTM samples, which also demonstrates that the FSK samples contain a more substantial chemical heterogeneity across the sampling site. In general, the samples were separated in the WPCA model according to sampling location. However, there is an overlap along PC1 between UTM samples collected close to the outlet of the channel (Region I) and close to the road (Region II) (Figure 3). A reasonable hypothesis is that the samples collected in these specific locations of the lake are affected by chemical inputs from FSK and the urban areas delimited by Region II. In contrast, the UTM samples from the centre of the lake (Region III) are less affected by the chemical inputs from both
FSK and Region II, due to dilution in the lake.
In summary, the global WPCA model was able to show that i) FSK and UTM sampling sites contain distinct chemical fingerprints and ii) chemical inputs to UTM may come from FSK (Region I) and the urban areas delimited by Region II. To assess the chemical composition of the samples from each sampling site individually, and to elucidate the contamination sources, local WPCA models for FSK and UTM sample sets were calculated. The local WPCA models were used to prioritise a subset of samples within each site in order to reduce the identification workload.
Local models – Channel (FSK) site
The local model of the FSK sampling site explained 91.27 % of the total variation and was built using six PCs. Chemical interpretation of the loading coefficients of PC1 to PC6 was performed to assess chemically relevant patterns and the presence of modelling artefacts (Figure 4). For example, a negative score in the PC1 loading, such as the dark blue peak #7 in Figure 4, and a negative score in the scores plot (Figure 5) indicates that that particular sample (2C1) has a high relative concentration (due to the nature of the normalisation) of that compound compared to the samples with high positive PC1 scores. PC3 and PC4 loadings majorly describe residual retention time shifts that could not be fully removed during pre-processing (Figure S3). Therefore they do not explain relevant chemical information from this site and were not discussed further.
Chemical interpretation of the PC1 loading (explained variance of 44.80 %) together with the BPCs of the particular samples with the highest negative and highest positive scores (2C1 and 2C3, respectively, Figure 5) revealed a higher relative input of natural chemical occurring compounds as a function of the depth in the sediment. An increase in complexity and the number of peaks is noticeable with increasing depth when investigating the GC×GC-HRMS chromatograms of these samples (Figure S4). Many peaks were identified as monoterpenes (#47), sesquiterpenes (#61) or diterpenes (#39). These compounds are commonly derived biosynthetically from diverse plants, fungi and animals 24. Because of the nature of the extraction solvent mixture (DCM:acetone [3:1]) and the absence of an in-cell adsorbent, many mid-polar components were also extracted, such as diterpenes. Typically, these naturally occurring components are removed in sample extraction and clean-up 14, 25. Here, terpenes were associated with a natural input chemical fingerprint.
The PC2 loading (Figure 4) describes a weathered oil fingerprint (blue peaks with a negative score) 26, 27 because of the occurrence of many hydrocarbons, naphthenes and alkylated mono- and polycyclic aromatic hydrocarbons, some of which were confirmed with standards (Level 1 in Table 1, Table S1). They indicate a continuous input of mineral oil products in this particular region of the fortress channel (negatively scoring samples 1C to 2C) in the past. The top-layer (2C1, 0-10 cm) positively scores in PC2, thus, there is relatively less of these oil components within the entire chromatographic fingerprint of these samples. Therefore, it could be presumed that the spill did not occur recently. The retained oil compounds were weathered and potentially biodegraded in the lower sediment levels (2C2 to 2C3, 10-30 cm) seen by the many alkylated compounds such as the C4 – C6-benzenes (#10.4 – 10.6), naphthalenes (#48.1 – 48.4), PAHs and dibenzothiophenes (#30.1 – 30.2). Alkylphenols (#7) and butylated hydroxytoluene (#19) also occurred with negative PC2 coefficients (Figure 5). They have a wide variety of applications in consumer products such as detergents or cleaning products; butylated hydroxytoluene is known as an antioxidant and is widely used in fuels to prevent oxidation. The most common alkylated phenol is nonylphenol which is primarily used for the production of nonylphenol ethoxylate surfactants or a degradation product of them. They are defined as endocrine disrupters, and are persistent and bioaccumulative, and have been found ubiquitously in the aquatic environment 28. The positive PC2 loading coefficients are mostly described by a few peaks (#39 diterpenes and #64.d tetralin derivatives) which, as in the positive PC1 loading, is indicative for a dominating natural chemical fingerprint (Figure 4).
PC5 and PC6 explain merely 3.16 % and 2.43 % of the variation, respectively. However, they can still be useful to differentiate samples, e.g., with a higher relative abundance of lighter compounds, alkylphenols (#7), butylated hydroxytoluene (#19) and dibenzothiophene (#30) in the positive PC5 loading; and samples with a dominating range of peaks between 20-25 min in 1D (Figure 4). It seems that specific types of diterpenes (#39) and tetralin derivatives (#64.d) can be distinguished in the positive and negative PC6 loading. However, examinations of the raw chromatograms revealed that peaks within the dotted white circle in PC5 and PC6 (Figure 4) were misaligned in 2D during data pre-processing. These misaligned regions therefore do not represent chemical variation of the data.
Figure 5 shows the score plot along the flow direction of the channel with all relevant PCs, viz., PC1, PC2, PC5 and PC6 with the highest scoring samples in each PC indicated by an arrow. No particular pattern along the flow direction of the channel could be recognised according to the scores (Figure 5). Error bars are the standard deviation of the sample preparation duplicates and demonstrate high repeatability of the sample preparation and an acceptable pre-processing as part of the WPCA modelling 20. Single sampling spots highlight that there were variations within the same sampling grid, e.g., samples 6S01, 02, 04 and 05 range from -0.055 to 0.038 in PC1 (Figure 5). The loading of PC3 (positive direction) describes mainly sample 6S05 (Figure S3). Composite samples, on the other hand, describe an average of five sampling spots per sampling grid, such as for sampling grids 3C0 or 7C0. The considerable variation within one sampling grid is significant with respect to the sampling strategy. Therefore, the chromatograms highlight the importance of the adequate sampling strategy, particularly for sediments and soil samples containing high chemical heterogeneity and immobility of non-polar contaminants 29.
Six out of 18 samples were prioritised for compound identification according to their scoring in the particular loadings, namely 2C1, 2C2, 2C3, 6S0-5, 8S0-2 and 8S0-3. These samples build the corners or of the dataset in the particular loadings. Alkanes (#5), dichloro-diphenyl-dichloroethane (DDD, #24), polychlorinated biphenyls (PBCs, #51) and PAHs were observed in higher relative concentrations in-depth (Figure 4). Dichloro-diphenyl-trichloroethane (DDT) and PCBs are POPs and have been phased out in Europe in the 1970s. Therefore, it comes to no surprise to find these pollutants at relatively higher concentrations in-depth. However, this was difficult to recognise from the loading plots alone (Figure 4). Other compounds in Table 1 are not less important but were found in several samples, e.g., decalin and its derivatives (#26 in Table 1) which are industrial solvents used in fuel additives.
Sample 2C1 (0-10 cm) had a high relative concentration of alkylphenols (#7), alkylated benzenes (#10.4), non-alkylated styrene (#62) and tetralin (#64) (Table 1). Sample 2C2 was relatively different from the rest of the samples which is implied by its relatively large negative score (between -0.052 and ‑0.055) in PC2 compared to the rest of the samples (Figure 5). The only diterpenes that could be identified at Level 3 were 10,18-bisnorabieta-8,11,13-triene (C18H26, #39.1) and Methyl-10,18-bisnorabieta-8,11,13-triene (C19H28, #39.2). The NIST library search suggested polycyclic musk (included in the term ‘tetralin derivatives’, #64.d) such as tonalide or versalide (used in personal care products), of which the former is associated with long-term adverse effects to the aquatic life. It was not possible, however, to confirm the tentative identification without the target analysis with standards.
Local models –
Lake (UTM) site
The model for the lake site was built with four PCs and described 65.0% of the explained variance. The PC1 and PC3 scores and loading plots are shown in Figure 6 and Figure 7, respectively. These PCs are the most descriptive PCs without retention time shifts as it was the case for PC2. Samples collected close to the channel outlet (samples 1F and 1G, including replicates, Figure 1) and samples 12A and 6F-R, have a negative PC1 score and describe a very similar weathered oil fingerprint as samples 1C0 and 2C2 from the channel, which confirms the observations from PC2 from the global model. The GC×GC-HRMS raw chromatograms of 1F and 12A confirmed this (not shown). Most of the samples from the centre of the lake (Region III) have very similar chemical fingerprints. That represents the averaged chemical fingerprint of all the samples from this site, contrary to the samples in the corners in Figure 6. Samples close to a road (Region II) demonstrate a high variation in the score plot (Figure 6) such as 1S and 4T (closest to the road on the east of the lake) and 12A and 9B-RI (from the south of the lake).
Despite the 1:5 dilution of the channel samples, compound concentrations in the lake were still considerably lower than compared to the fortress channel samples. The prominent peaks (e.g., #1, #11 and #31) in the PC3 loading (Figure 7) indicate potential insufficient remobilisation in 2D of these compounds or that these compounds were tailing. Nevertheless, compound identification was possible due to the presence of only a few peaks in the 2D-BPC, which in combination with the insufficient remobilisation in 2D makes it difficult to identify compounds.
As it was the case for the channel samples, alkylphenols (#7) were present at a high relative concentration in samples close to the outlet of the fortress channel (Region I) as it can be seen by the negative PC1 and PC3 loadings (Figure 7). Interestingly, this was not the case for samples from the east, which is indicative of dilution from the channel towards the lake (Figure 1). Diterpenes (#39) were observed only at low relative concentrations in Region I. Further, fatty acid methyl esters (#40) were detected in higher relative concentrations in this region as were alkylated benzenes (#10.4), naphthalenes (#48-48.4) and many compounds that were also identified in the fortress channel.
Samples with positive PC3 scores (such as 1S, 4T and 9B-RII) contain a higher relative concentration of heterocyclic compounds, including 2-mercaptobenzothiazole (#1), benzoic acid esters (#11), benzothiazoles (#12), dibenzylamine (#31), alkylated naphthothiophenes (#49.2), hydroxylated or sulphur-containing sesquiterpenes (#61) and vanillins (#67). Most of these compounds have been reported elsewhere in freshwater sediments 14, 30. Many of these compounds are natural plant metabolites; others such as the alkylated naphthothiophenes are potentially derived from a petroleum source. Benzothiazoles and 2-mercaptobenzothiazole are used in various industrial processes, such as for rubber vulcanisation or as a corrosion inhibitor. These compounds are biologically active and potential aquatic toxins 31. Tire wear particles were identified as a potentially significant source in the environment 31. Dibenzylamine (#31) is also an additive and by-product from the production of rubber. Benzothiazoles, its derivatives and dibenzylamine were detected in sediment samples and associated with a rubber production factory in China 32. The higher relative abundance of these compounds in samples 1S and 4T is most likely linked to the proximity to the highway, which is one of the four major routes to Copenhagen and among the ten busiest highways in Denmark 33. The contamination source and, to a large extent, the chemical fingerprint in PC 3 loading, could therefore be defined as traffic-related, perhaps even more specifically to tire wear particles. The impact on tire wear particles in the aquatic environment was recently reviewed by Wagner et al. 34.
Table 1. Tentatively identified compounds from both sampling sites (UTM and FSK). Excel sheet in SI2 with more detailed information, incl. RI and chemical identifiers. Numbering (#) related to the loading plots in Figure 4 and 7.
#
|
Compound/ Compound group
|
Molecular formula
|
Monoisotopic mass [Da]
|
Mass error [ppm]*
|
1D tR
[min]
|
ID
level†
|
1
|
2-Mercaptobenzothiazole
|
C7H5NS2
|
166.9863
|
11.0
|
29.5
|
1
|
2
|
Acenaphthylene
|
C12H8
|
152.0626
|
10.5
|
21.9
|
1
|
3
|
Acenaphthene
|
C12H10
|
154.0783
|
13.3
|
22.5
|
1
|
4
|
Acetylides
|
C18H34
|
250.2661
|
7.0
|
25.85
|
4
|
5
|
Alkanes
|
CnH2n+2
|
|
7.2
|
|
3
|
6
|
Alkylphenylketone (Phenacylidene diacetate)
|
C12H12O5
|
236.0685
|
13.1
|
11.7
|
4
|
7
|
Alkylphenols
|
|
|
5.7
|
24.1-25.3
|
3
|
8
|
Anthracene
|
C14H10
|
178.0783
|
5.3
|
27.3
|
1
|
8.1
|
Anthracenes, C1-
|
C15H12
|
192.0939
|
7.6
|
28.9-29.1
|
2
|
8.d
|
Anthracene derivative
|
C15H14
|
194.1096
|
8.0
|
28.0
|
3
|
9
|
Azulene
|
C10H8
|
128.0626
|
5.5
|
18.1
|
4
|
9.3
|
Azulene, C3-
|
C13H14
|
170.1096
|
5.6
|
23.1
|
4
|
10.3
|
Benzenes, C3-
|
C9H12
|
120.0939
|
11.5
|
11.7-13.1
|
3
|
10.4
|
Benzenes, C4-
|
C10H14
|
134.1096
|
6.2
|
13.1-16.0
|
3
|
10.5
|
Benzenes, C5-
|
C11H16
|
148.1252
|
8.3
|
14.8-17.7
|
3
|
10.6
|
Benzenes, C6-
|
C12H18
|
162.1409
|
7.6
|
16.7-20.1
|
3
|
10.7
|
Benzenes, C7-
|
C13H20
|
176.1565
|
10.2
|
20.0
|
3
|
10.8
|
Benzenes, C8-
|
C14H22
|
190.1722
|
5.9
|
21.8-21.9
|
3
|
11
|
Benzoic acid ester (Methyl benzoate)
|
C8H8O2
|
136.0511
|
9.8
|
14.7
|
3
|
12
|
Benzothiazole
|
C7H5NS
|
135.0143
|
10.1
|
17.6
|
3
|
13
|
Benzoyl hydrazine
|
C7H8N2O
|
136.0524
|
14.2
|
14.8
|
4
|
14
|
Benzyl 2-chloroethyl sulfone
|
C9H11ClO2S
|
218.0168
|
14.5
|
20.2
|
4
|
15
|
Benzyl isocyanate
|
C8H7NO
|
133.0528
|
17.0
|
15.4
|
3
|
16
|
Benzyl thiocyanate
|
C8H7NS
|
149.0299
|
6.2
|
20.5
|
4
|
17
|
Benzylidenebenzylamine
|
C14H13N
|
195.1048
|
10.8
|
26.4
|
3
|
18
|
Biphenyl
|
C12H10
|
154.0783
|
10.1
|
20.5
|
3
|
18.1
|
Biphenyls, C1-
|
C13H12
|
168.0939
|
10.9
|
22.3-23.7
|
4
|
18.2
|
Biphenyls, C2-
|
C14H14
|
182.1096
|
10.7
|
24.0-24.2
|
3
|
19
|
Butylated hydroxytoluene
|
C15H24O
|
220.1827
|
6.0
|
22.6
|
2
|
20
|
Calamenes
|
C15H22
|
202.1722
|
4.9
|
21.8
|
4
|
21
|
Chloronaphthalenes
|
C10H7Cl
|
162.0236
|
9.4
|
20.6
|
3
|
22
|
Cyclic organosulphur
|
|
|
9.8
|
|
2
|
23
|
Cycloalkane
|
C13H26
|
182.2035
|
8.0
|
17.4
|
3
|
24
|
DDD
|
C14H10Cl4
|
317.9537
|
8.6
|
32.0-32.6
|
3
|
25
|
DDMS
|
C14H11Cl3
|
283.9926
|
5.8
|
31.2
|
4
|
26
|
Decalin
|
C10H18
|
138.1409
|
7
|
14.0
|
3
|
26.1
|
Decalins, C1-
|
C11H20
|
152.1565
|
8.5
|
15.2
|
3
|
26.2
|
Decalins, C2-
|
C12H22
|
166.1722
|
6.7
|
16.3-17.6
|
3
|
27
|
Dialkyl ethers
|
CnH2n+4O
|
|
6.1
|
|
4
|
28
|
Dibenzofuran
|
C12H8O
|
168.0575
|
1.9
|
23.0
|
1
|
29
|
Dibenzopyrans
|
C13H10O
|
182.0732
|
9.4
|
24.7-24.9
|
3
|
30
|
Dibenzothiophene
|
C12H8S
|
184.0347
|
3.6
|
26.8
|
1
|
30.1
|
Dibenzothiophenes, C1-
|
C13H10S
|
198.0503
|
8.5
|
28.7-28.9
|
2
|
30.2
|
Dibenzothiophenes, C2-
|
C14H12S
|
212.0660
|
8.1
|
29.6-30.4
|
2
|
31
|
Dibenzylamine
|
C14H15N
|
197.1204
|
2.8
|
26.1
|
3
|
32
|
Dicarboxylic acid derivatives
|
C16H30O4
|
286.2144
|
15.4
|
15.8
|
4
|
33
|
Dichlorobenzenes
|
C6H4Cl2
|
145.9690
|
6.2
|
12.7-13.4
|
3
|
34
|
Dichlorobisphenyl
|
C13H10Cl2
|
236.0160
|
7.8
|
28.1
|
3
|
35
|
Dichloroisocyanatobenzenes
|
C7H3Cl2NO
|
186.9592
|
7.0
|
19.1-19.4
|
4
|
36
|
Dichloronaphthalenes
|
C10H6Cl2
|
195.9847
|
9.7
|
23.7-24.1
|
3
|
37.3
|
Dihydronaphthalenes, C3-
|
C13H16
|
172.1252
|
5.2
|
20.0-20.3
|
3
|
38
|
Diphenyl sulfone
|
C12H10O2S
|
218.0402
|
12.2
|
29.2
|
4
|
39
|
Diterpenes
|
|
|
5.5
|
28.9-31.3
|
3
|
39.1
|
10,18-Bisnorabieta-8,11,13-triene
|
C18H26
|
242.2035
|
6.0
|
30.6
|
3
|
39.2
|
10,18-Bisnorabieta-8,11,13-triene, C1-
|
C19H28
|
256.2191
|
7.4
|
32.1
|
4
|
40
|
Fatty acid methyl esters
|
|
|
8.4
|
26.6-31.2
|
3
|
41
|
Fatty alcohol (cis-9-Eicosen-1-ol)
|
C20H40O
|
296.3079
|
9.8
|
28.7
|
4
|
42
|
Fluoranthene
|
C16H10
|
202.0783
|
2.2
|
31.9
|
1
|
43
|
Fluorene
|
C13H10
|
166.0783
|
12.3
|
24.1
|
1
|
43.1
|
Fluorenes, C1-
|
C14H12
|
180.0939
|
7.8
|
25.9-26.4
|
2
|
43.2
|
Fluorenes, C2-
|
C15H14
|
194.1096
|
6.8
|
27.7-28.2
|
3
|
44.1
|
Indanes, C1-
|
C10H12
|
132.0939
|
10.2
|
16.0
|
3
|
44.2
|
Indanes, C2-
|
C11H14
|
146.1096
|
10.3
|
16.7-18.0
|
3
|
44.3
|
Indanes, C3-
|
C12H16
|
160.1252
|
10.5
|
18.3-19.4
|
3
|
44.5
|
Indanes, C5-
|
C14H20
|
188.1565
|
10.1
|
22.9
|
3
|
45.6
|
Indanones, C6-
|
C18H24O
|
256.1827
|
6.2
|
31.4
|
3
|
46.2
|
Indenes, C2- (1-Ethylidene-1H-indene)
|
C11H10
|
142.0783
|
12.3
|
19.3
|
3
|
46.4
|
Indenes, C4-
|
C13H16
|
172.1252
|
10.5
|
21.5
|
4
|
47
|
Monoterpenes
|
C15H24
|
204.1878
|
6.2
|
20.8-22.9
|
3
|
48
|
Naphthalene
|
C10H8
|
128.0626
|
5.5
|
16.8
|
1
|
48.1
|
Naphthalenes, C1-
|
C11H10
|
142.0783
|
7.7
|
18.9-19.0
|
2
|
48.2
|
Naphthalenes, C2-
|
C12H12
|
156.0939
|
8.4
|
20.8-21.9
|
2
|
48.3
|
Naphthalenes, C3-
|
C13H14
|
170.1096
|
6.7
|
21.9-23.7
|
2
|
48.4
|
Naphthalenes, C4-
|
C14H16
|
184.1252
|
4.2
|
24.2-26.4
|
2
|
48.d
|
Naphthalene derivatives
|
|
|
9.9
|
|
4
|
49.1
|
Naphthothiophenes, C1-
|
C13H10S
|
198.0503
|
6.2
|
28.2
|
3
|
49.2
|
Naphthothiophenes, C2-
|
C14H12S
|
212.0660
|
6.5
|
30.3
|
4
|
50
|
Nonyl pentyl sulfite
|
C14H30O3S
|
278.1916
|
5.8
|
13.9
|
4
|
51
|
PCBs
|
C12H4Cl6
|
357.8444
|
6.5
|
33.2-34.5
|
3
|
52
|
Pentafluorinated depsides
|
C14H7F5O2
|
302.0366
|
6.2
|
27.3
|
4
|
53
|
Phenalene
|
C13H10
|
166.0783
|
6.3
|
24.2
|
3
|
54
|
Phenanthrene
|
C14H10
|
178.0783
|
1.4
|
27.2
|
1
|
54.1
|
Phenanthrenes, C1-
|
C15H12
|
192.0939
|
6.0
|
28.8-29.2
|
2
|
54.2
|
Phenanthrenes, C2-
|
C16H14
|
206.1096
|
5.1
|
30.2-30.8
|
2
|
54.4
|
Phenanthrenes, C4-
|
C18H18
|
234.1409
|
3.6
|
32.8
|
3
|
54.d
|
Phenanthrene derivatives
|
|
|
11.3
|
|
4
|
55
|
Phenylacetaldehydes
|
C9H10O
|
134.0732
|
12.4
|
13.5
|
3
|
56
|
Phenylglyoxal
|
C8H6O2
|
134.0368
|
13.3
|
11.8
|
4
|
57
|
Phenylnaphthalenes
|
C16H12
|
204.0939
|
7.1
|
29.7-29.8
|
4
|
58
|
Phthalic acid ester
|
|
|
5.4
|
35.5-36.7
|
3
|
59
|
Polycyclic hydrocarbons
|
|
|
6.8
|
|
4
|
60
|
Pyrene
|
C16H10
|
202.0783
|
6.7
|
31.9
|
1
|
61
|
Sesquiterpenes
|
|
|
6.6
|
20.0-26.8
|
3
|
62
|
Styrenes
|
C10H12
|
132.0939
|
10.6
|
14.5-15.3
|
3
|
63
|
Tetrachloroethane
|
C2H2Cl4
|
165.8911
|
6.4
|
10.5
|
3
|
64
|
Tetralin
|
C10H12
|
132.0939
|
7.6
|
16.2
|
3
|
64.1
|
Tetralins, C1-
|
C11H14
|
146.1096
|
11.5
|
18.1-18.9
|
3
|
64.2
|
Tetralins, C2-
|
C12H16
|
160.1252
|
10.8
|
19.3-20.8
|
3
|
64.3
|
Tetralins, C3-
|
C13H18
|
174.1409
|
7.2
|
20.1-22.4
|
3
|
64.4
|
Tetralins, C4-
|
C14H20
|
188.1565
|
11.7
|
19.9-22.4
|
3
|
64.d
|
Tetralins derivative (Versalide)
|
C18H26O
|
258.1984
|
3.7
|
29.6
|
3
|
65
|
Thiophene, 3-methyl-2-3,7,11-trimethyldodecyl
|
C20H36S
|
308.25377
|
9.0
|
31.4
|
3
|
66
|
Unsaturated hydrocarbons
|
|
|
8.3
|
|
3
|
67
|
Vanillins
|
C8H8O3
|
152.0473
|
13.4
|
20.7-21.1
|
3
|
*) If several compounds were identified for a compound group, the average of the mass error was calculated.
†) Confidence ID levels from Schymanski et al. 22. Retention time (1D tR) and index (RI) were extracted from the facilitator samples AnQCFSK and AnQCUTM and calculated based on n-alkane elution in the reference oil and Florida mix (Table S6). If the calculated RI deviated ≥ 100 from the NIST library, the confidence ID level was set down to 4.