Analysis of Polycyclic Aromatic Hydrocarbons in Ambient Aerosols by Using One-Dimensional and Comprehensive Two-Dimensional Gas Chromatography Combined with Mass Spectrometric Method: A Comparative Study.
Human health research associated with polycyclic aromatic hydrocarbons (PAHs) has raised concerns because certain PAHs are classified as probable human carcinogens [1-4] and have shown tumorigenic activity and endocrine disrupting activity in mammals . The US EPA has included 16 of them in the list of priority pollutants and has established a maximum contaminant level of 0.2 [micro]g/L for benzo[a]pyrene in drinking water . In the European Union (EU), eight PAHs have been identified as priority hazardous substances in the field of water policy . The EPA priority 16 PAHs and two additional PAHs are now being monitored by European agencies, and they have sought to quantify the individual concentrations of benzo[e] pyrene and perylene in environmental samples . PAHs are found in ambient air in the gas phase and as sorbents to aerosols . Thus, air monitoring of PAHs to quantify inhalation exposure and to identify other organic compounds is important for insight into photochemical reactions. The quantification and identification of organic compounds in air samples is an important feature of atmospheric chemistry and represents some demanding analytical challenges .
For these reasons, a key issue in current analytical methods is the ability to measure a large number of compounds with quantitative analysis for target analytes. Comprehensive two-dimensional gas chromatography (GCxGC) coupled with mass spectrometry (MS) can screen for nontarget compounds with fast identification of the compounds in an entire sample . Therefore, previous studies applied GCxGC-MS for the identification of numerous compounds present in air samples [11-13]. However, there are limitations on the validation of simultaneous quantification and identification of analytes in air samples. Correspondingly, a validation of simultaneous identification and quantification of PAHs and other compounds in air samples by GCxGC-MS is required. A TOF mass spectrometer was used to acquire sufficient data from a comprehensive two-dimensional chromatographic technique that generated multiple narrow peaks from the short secondary column [14, 15]. Generally, GC coupled with quadrupole MS (GC-qMS) in the selected ion monitoring (SIM) mode has been used for quantitative analysis of PAHs in air samples because of its selective detection for specific target compounds [16, 17]. However, a GCxGC-TOFMS validated method suitable for the quantification of target PAHs in an aerosol sample compared with GC-qMS in the SIM mode has not yet been reported. The aim of this study was to evaluate the effectiveness of GCxGC-TOFMS in the quantitative analysis of target PAHs as well as the fast identification of multiple compounds for aerosol samples. The validity of the quantitative results obtained by both GCxGC-TOFMS and GC-qMS in the SIM mode was demonstrated by several method performance parameters such as linearity, accuracy, and repeatability.
2.1. Air Sampling. The total suspended particle (TSP) samples were collected at Asan Engineering Building, Ewha Womans University, Seoul, South Korea (37.56[degrees]N, 126.94[degrees]E, 20 m above ground level), with a PUF sampler (Tisch, TE-1000) on a quartz fiber filter (Quartz fiber filter, QFF, 010.16 cm, Whatman, UK). The sampling site is located in the mixed resident area, commercial area, forest area, and nearby roadside. A total of 67 filter samples were obtained during summer (August 12-30, 2013) and winter (January 27-February 16, 2014) and day (9 a.m.~6 p.m.) and night (8 p.m.~6 a.m.). Prior to sampling, the quartz fiber filters were baked for 8 h in an electric oven at 550[degrees]C to remove possible organic contaminants. The sampled filters were wrapped in aluminum foils and stored in a freezer at -20[degrees]C until analysis.
2.2. Chemicals. All organic solvents were of GC grade and purchased from Burdick and Jackson (Phillipsburg, NJ, USA). Standard solutions of target PAHs (Table 1 for their full chemical names and information) except Per and BeP for quantitative analysis were purchased as a mixture at a concentration of 2000 [micro]g/mL in dichloromethane from Supelco (Bellefonte, PA, USA). Per and BeP standards (>99%) were purchased from Aldrich (St. Louis, MI, USA), and a standard mixture of eighteen PAHs was prepared at a concentration of 1000 [micro]g/mL. Deuterium-labeled internal standards of seven PAHs were purchased from Aldrich (St. Louis, MI, USA) and Chiron (Trondheim, Norway) and used for the spiking test as listed in Table 1. Working standard solutions (0.01 ~ 10 [micro]g/mL) were prepared and then stored at -20[degrees]C prior to use.
2.3. Preparation of Samples. Air sampling filters were extracted with a mixture of dichloromethane and methanol (3: 1, v/v) two times using an accelerated solvent extractor (ASE) (Dionex ASE-200) at 40[degrees]C and 1700 psi for 5 min. Prior to the extraction, seven deuterated internal standards (Nap-d8, Ace-d10, Phen-d10, Fla-d10, Chr-d12, Per-d12, and BghiPer-d12) were spiked in the filters to compensate for matrix effects during the extraction procedure. Extracts were blown down to 1 mL using a nitrogen evaporator (TurboVap II, Caliper Life Sciences). GCxGC-TOFMS analysis was carried out using an Agilent GC (Wilmington, Delaware, USA)-Quad-jet thermal modulation Pegasus 4D TOFMS (LECO, St. Joseph, MI, USA). The sample was injected in the splitless mode at 300[degrees]C. The GCxGC columns were as follows: DB-5MS (30 m x 0.25 mm ID, film thickness of 0.25[micro]m) and 1.17m DB-17MS (0.18mm OD, 0.18[micro]m film). The operating conditions of GC-MS and GCxGCTOFMS are summarized in Table 2.
3. Results and Discussion
3.1. GC-qMS and GCxGC-TOFMS for Characterization of Aerosol Samples. In most studies, separation and quantification of PAHs in aerosol samples have been analyzed using a conventional GC-qMS . Flame ionization detection (FID) has also been widely used for quantification as it features a higher response to PAHs which contain only carbon and hydrogen, while oxygenates and other species that contain heteroatoms tend to have a lower response factor . However, this nonspecific detector may not distinguish inferences, which include a large fraction of aliphatic and aromatic compounds in aerosol samples from alkylated PAH homologues. The coupling of GC with MS is increasingly becoming the analytical tool of choice in this regard because of its superior selectivity and sensitivity. Among the most common analyzers including TOF , ion trap, and qMS [21, 22], qMS is the most widely adopted technique for routine analysis of PAHs . GC-qMS data acquisition takes advantages of both a full mass scan range (scan mode) and specific ion masses for target analytes (SIM mode). The sensitivity in the SIM mode is higher than that in the scan mode of GC-qMS due to the increased dwell time on each monitored ion for trace analysis in some matrices such as in atmospheric aerosols [24, 25]. GC-TOFMS has a much faster spectral acquisition rate than GC-qMS does, which is up to 500 full mass scans per second . Consequently, this system is able to widen the application of GCxGC techniques providing very narrow chromatographic peaks, typically 50~600ms at the baseline with sufficient density of data points per chromatographic peak . Environmental samples are generally complex, often with more than hundreds of compounds containing structural isomers and homologues spread over a wide range of concentration and volatility. Accordingly, multidimensional separation is an advanced technique offering the possibility of greatly enhanced selectivity using different separation mechanisms for the analysis of complex environmental samples [28-30]. In this study, a set of columns DB-5xDB-17 ms was applied to increase the resolution and peak capacity. The fast scanning Pegasus 4D TOFMS system was combined to allow efficient processing of data acquisition, handling, peak detection, and deconvolution. In the one-dimensional column, a 30m-long DB-5ms (5% diphenyl/95% dimethyl polysiloxane) stationary phase was used to separate analytes based on volatility and combined with a 1.17m-long DB-17ms column (50% diphenyl/50% dimethyl polysiloxane) allowing relative polarity-based separation. Figure 1 shows GCxGC-TOFMS chromatograms of aerosol samples collected at day and night during winter in Seoul, South Korea. To compare the identification ability of GCxGC-TOFMS with GC-qMS, analysis with GCqMS in the scan mode was performed. A comparison of the one-dimensional chromatograms of the same samples obtained by GC-qMS is shown in Figure 2. 2D chromatograms enable the visual classification of chemically related compounds into groups. It was rare to see that the early-eluting analytes have an extreme volatility in the chromatogram, as shown in Figure 2. Because of the large losses of these analytes during sample extraction and concentration, particle-associated semivolatile analytes were mainly detected and classified according to their aromatic and aliphatic hydrocarbon groups.
Meanwhile, analytes from the GC-qMS chromatogram were separated based on their vapor pressures or boiling points. The GCxGC technique is rather well suited for group separations, and classifying compounds into chemical-related groups could be useful for source identification of atmospheric aerosols by means of the large amount of chemical data handling. The combined use with TOFMS provides rapid and reliable identification of analytes using their deconvoluted pure mass spectra. The major limitation of qMS is its limited scan rate; therefore, quantification and identification is seriously compromised because of the mass spectral skew due to the variations in ion abundances at different regions of a chromatographic peak [31, 32]. The numbers of identified chromatographic peaks analyzed by GC-qMS using the same signal threshold setting from the aerosol samples collected at day and night were 35 and 64, respectively. In the case of results obtained by GCxGCTOFMS, 251 and 297 peaks from the day- and night-time aerosol samples were, respectively, assigned by individual spectral deconvolution. As a result, phthalic anhydride and 1.2- naphthalic anhydride as the markers of secondary formation for gas-phase PAH reactions were identified in the aerosol sample, as shown in Figure 3. Since the products formed through photochemical reactions are often more toxic than their parent PAHs in atmosphere , significant efforts have been expended to identify the photochemical products with PAHs in the fields of atmospheric or environmental sciences. In the case of results obtained using GC-qMS, phthalic anhydride and 1.2- naphthalic anhydride were not detected in the same sample. Limitations of one-dimensional separation have been reported for these photochemical products and complex mixtures of the aerosol sample because of their diverse polarities in a single run [33, 34]. Contrastively, two anhydrides associated with secondary organic aerosol formation were clearly separated and detected by GCxGCTOFMS. Therefore, it showed advantages for nontarget screening to identify molecular markers or chemical patterns more representative of the aerosol state observed in ambient air.
3.2. Validation of GC-qMS and GCxGC-TOFMS for Quantification of PAHs. GC-qMS and GCxGC-TOFMS were tested individually in order to evaluate their analytical performances. The calibration linearity (regression coefficient, R2) and relative response factor (RRF) are presented in Table 3. The RRF is the ratio between a signal produced by an individual native analyte and the corresponding isotopically labeled analogue of the analyte (as an internal standard). For calculating RRF, 2 ng of each target PAH and each corresponding deuterated internal standard was spiked, and the relative sensitivity in both the methods was compared. Despite the high-speed scanning performance of GCxGC-TOFMS, the RRFs obtained by this method were approximately equivalent to those obtained by GC-qMS. RRF expresses the sensitivity of a detector for a given substance relative to a standard substance [35, 36]. Thus, it indicated that the sensitivity of GCxGC-TOFMS relative to target PAHs is comparable in quantitative analysis. Calibration curves were generated using the peak area for the 18 PAHs at seven concentrations ranging from 0.01 to 10 [micro]g/mL. The linearity was assessed by calculating the regression equation and the correlation coefficient by the least squares method, as shown in Table 3. The [R.sup.2] values were greater than 0.999 for GC-qMS and 0.99 for GCxGC-TOFMS. Although data processing for quantification by GCxGC-TOFMS was derived from the combined peak areas for the slices of modulated peaks in contrast to production of the single measured peak by GC-qMS, the results meet the criteria for acceptable linearity within this calibration range.
Naturally, the development of quantitative GCxGC studies based on the quantitative results associated with sophisticated implementation for modulated peaks has been delayed compared with qualitative reports. Recently, the approach to quantifying multiple analytes at once with comprehensive two-dimensional GC has been extensively studied in accordance with the improvement of data processing for the integration of modulated peaks [37, 38]. In this study, the modulated peaks of each PAH was automatically combined and integrated by the ChromaTOF software based on a similarity of spectra within an allowable time difference between the second dimension peaks in the neighboring slices of the chromatogram. Recovery test was performed by spiking known amounts of the 18 PAH compounds in a prebaked clean filter at a final concentration of 2 [micro]g/mL and analyses of each through all the experiment procedures were compared using the two different methods. Six duplicate tests were performed, and the results of the recovery are shown in Table 4. The average recoveries were in the range of 90.3 to 158% with relative standard deviations (RSDs) ranging from 3.9 to 28% for GC-qMS, while the recoveries were from 86.3 to 135% for GCxGC-TOFMS, with RSDs ranging from 5.7 to 45%. Most of the targeted PAH compounds were afforded acceptable recoveries, excluding F and Nap by using the two analytical methods due to the high volatility of these compounds. Compared with the reproducibility as expressed in %RSDs, the values obtained by GC-qMS were slightly lower than those obtained by GCxGC-TOFMS; however, the %RSD values of the targeted PAHs excluding F and Nap were acceptable (<20% RSD). These observations may vary for the versatile GCxGC technique, since the reproducibility of the modulation phase is dependent on the type of modulator, the stability of the stationary phases, and the chemistry of the analyte, regarding interaction with the stationary phase as presented in several prior studies [39, 40]. The LOD and LOQ were determined based on the standard deviation (SD) of the intersection of the analytical curve (s) and the slope of the curve (S) as LOD = 3.3 x (s/S) and LOQ = 10 x (s/S). The LOD and LOQ for each PAH compound obtained from both the methods are shown in Table 4. The LOD and LOQ values of the 18 PAH compounds obtained by GC-qMS were similar to the results of previous studies [10, 41, 42]. Thus, the suitability of GCxGC-TOFMS for quantification of PAHs was proven by comparing the results with those obtained using GC-qMS.
A fast scanning GCxGC-TOFMS was compared to a GC-qMS for the determination of PAHs in aerosol samples. For separation, identification, and characterization, GCxGC-TOFMS was advantageous over GC-qMS owing to the increased peak capacity, and its results showed enhanced detectability and structured chromatograms for nontarget analysis. The qualitative mass separation by TOFMS combined with an automated peak-finding capability provided the resolution of complex mixed mass spectra, resulting from overlapping chromatographic peaks and spectral deconvolution of individual mass spectra for unknown analytes. Furthermore, the obtained quantitative results such as LODs, LOQs, and recoveries of the 18 target PAHs were approximately equivalent for both the analytical methods. Thus, GCxGC-TOFMS had advantages for the simultaneous quantification and qualification of PAHs and other organic compounds in a single run. Because of its high degree of separation and capability of spectral deconvolution of overlapping peaks in highly complex samples, comprehensive GCxGC-TOFMS may become a useful platform in many other fields of research.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This research was supported by the Bio-Synergy Research Project (no. NRF-2017M3A9C4065961) of the Ministry of Science, ICT, and Future Planning through the National Research Foundation and the Korea Basic Science Institute Grant (no. C37705). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1A2B4015143)
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Yun Gyong Ahn (iD), (1) So Hyeon Jeon, (1) Hyung Bae Lim, (2) Na Rae Choi, (3) Geum-Sook Hwang, (1) Yong Pyo Kim, (4) and Ji Yi Lee (iD) (3)
(1) Western Seoul Center, Korea Basic Science Institute, Seoul 03759, Republic of Korea
(2) Air Quality Research Division, National Institute of Environmental Research, Incheon 22689, Republic of Korea
(3) Department of Environmental Science and Engineering, Ewha Womans University, Seoul 03759, Republic of Korea
(4) Department of Chemical Engineering and Material Science, Ewha Womans University, Seoul 03760, Republic of Korea
Correspondence should be addressed to Ji Yi Lee; firstname.lastname@example.org
Received 14 December 2017; Revised 6 February 2018; Accepted 19 February 2018; Published 1 April 2018
Academic Editor: Federica Bianchi
Caption: FIGURE 1: GCxGC-TOFMS plots of aerosol samples collected during day (a) and night (b) of winter in Seoul, Korea. A total of 251 and 297 peaks were identified in aerosol samples collected during day (a) and night (b), respectively. Aromatic and aliphatic classes were drawn to divide two regions for ease of viewing.
Caption: FIGURE 2: Total ion chromatograms of aerosol samples collected in day (a) and night (b) of winter in Seoul, Korea, obtained by GC-qMS. A total of 35 and 64 peaks were identified in aerosol samples collected during day (a) and night (b), respectively. The analytes were separated based on their boiling points.
Caption: FIGURE 3: GCxGC chromatograms and mass spectrums of phthalic anhydride (marked as green) and 1,2-naphthalic anhydride (marked as yellow) in the aerosol sample. GCxGC chromatograms of phthalic anhydride and 1,2-naphthalic anhydride were certified by molecular ions of m/z 148 and 198, respectively.
TABLE 1: Information of target PAHs in the study. Compound Abbreviation CAS number Naphthalene-[d.sub.8] (a) Nap-[d.sub.8] 1146-65-2 Naphthalene Nap 91-20-3 Acenaphthylene Acy 208-96-8 Acenaphthene-[d.sub.10] (a) Ace-[d.sub.10] 15067-26-2 Acenaphthene Ace 83-32-9 Fluorene F 86-73-7 Phenanthrene-[d.sub.10] (a) Phen-[d.sub.10] 1518-22-2 Phenanthrene Phen 85-01-8 Anthracene Ant 120-12-7 Fluoranthene-[d.sub.10] (a) Fla-[d.sub.10] 93951-69-0 Fluoranthene Fla 206-44-0 Pyrene Pyr 129-00-0 Benz[a]anthracene BaA 56-55-3 Chrysene-[d.sub.12] (a) Chr-[d.sub.12] 1719-03-5 Chrysene Chr 218-01-9 Benzo[b]fluoranthene BbF 205-99-2 Benzo[k]fluoranthene BkF 207-08-9 Benzo[e]pyrene BeP 192-97-2 Benzo[a]pyrene BaP 50-32-8 Perylene-[d.sub.12] (a) Per-[d.sub.12] 1520-96-3 Perylene Per 198-55-0 Indeno[1,2,3-cd] IP 193-39-5 pyrene Dibenz[a,h]anthracene DBahAnt 53-70-3 Benzo[ghi] BghiPer-[d.sub.12] 93951-66-7 perylene-[d.sub.12] (a) Benzo[ghi]perylene BghiPer 191-24-2 Compound Molecular MW formula Naphthalene-[d.sub.8] (a) [C.sub.10][D.sub.8] 136.2 Naphthalene [C.sub.10][H.sub.8] 128.2 Acenaphthylene [C.sub.12][H.sub.8] 152.2 Acenaphthene-[d.sub.10] (a) [C.sub.12][D.sub.10] 164.2 Acenaphthene [C.sub.12][H.sub.10] 154.2 Fluorene [C.sub.13][H.sub.10] 166.2 Phenanthrene-[d.sub.10] (a) [C.sub.14][D.sub.10] 188.2 Phenanthrene [C.sub.14][H.sub.10] 178.2 Anthracene [C.sub.14][H.sub.10] 178.2 Fluoranthene-[d.sub.10] (a) [C.sub.16][D.sub.10] 212.2 Fluoranthene [C.sub.16][H.sub.10] 202.2 Pyrene [C.sub.16][H.sub.10] 202.2 Benz[a]anthracene [C.sub.18][H.sub.12] 228.2 Chrysene-[d.sub.12] (a) [C.sub.18][D.sub.12] 240.3 Chrysene [C.sub.18][H.sub.12] 228.3 Benzo[b]fluoranthene [C.sub.20][H.sub.12] 252.3 Benzo[k]fluoranthene [C.sub.20][H.sub.12] 252.3 Benzo[e]pyrene [C.sub.20][H.sub.12] 252.3 Benzo[a]pyrene [C.sub.20][H.sub.12] 252.3 Perylene-[d.sub.12] (a) [C.sub.20][D.sub.12] 264.3 Perylene [C.sub.20][H.sub.12] 252.3 Indeno[1,2,3-cd] [C.sub.22][H.sub.12] 276.3 pyrene Dibenz[a,h]anthracene [C.sub.22][H.sub.14] 278.3 Benzo[ghi] [C.sub.22][D.sub.12] 288.3 perylene-[d.sub.12] (a) Benzo[ghi]perylene [C.sub.22][H.sub.12] 276.3 Compound Quantitative Qualifier ion ion Naphthalene-[d.sub.8] (a) 136 137 Naphthalene 128 129 Acenaphthylene 152 153 Acenaphthene-[d.sub.10] (a) 162 164 Acenaphthene 153 154 Fluorene 166 165 Phenanthrene-[d.sub.10] (a) 188 189 Phenanthrene 178 179 Anthracene 178 179 Fluoranthene-[d.sub.10] (a) 212 213 Fluoranthene 202 203 Pyrene 202 203 Benz[a]anthracene 228 226 Chrysene-[d.sub.12] (a) 240 236 Chrysene 228 226 Benzo[b]fluoranthene 252 253 Benzo[k]fluoranthene 252 253 Benzo[e]pyrene 252 253 Benzo[a]pyrene 252 253 Perylene-[d.sub.12] (a) 264 260 Perylene 252 253 Indeno[1,2,3-cd] 276 277 pyrene Dibenz[a,h]anthracene 278 279 Benzo[ghi] 288 284 perylene-[d.sub.12] (a) Benzo[ghi]perylene 276 277 Retention time GCxGC-TOFMS Compound GC-qMS (min) [t.sub.r1] [t.sub.r2] (min) (s) Naphthalene-[d.sub.8] (a) 12.25 13.40 1.34 Naphthalene 12.34 13.47 1.35 Acenaphthylene 19.08 19.56 1.55 Acenaphthene-[d.sub.10] (a) 19.47 20.12 1.52 Acenaphthene 19.61 20.28 1.52 Fluorene 21.61 22.28 1.53 Phenanthrene-[d.sub.10] (a) 25.91 25.96 1.68 Phenanthrene 26.01 26.04 1.71 Anthracene 26.14 26.20 1.68 Fluoranthene-[d.sub.10] (a) 30.99 30.68 1.84 Fluoranthene 31.08 30.68 1.87 Pyrene 32.25 31.56 1.98 Benz[a]anthracene 37.20 36.28 2.21 Chrysene-[d.sub.12] (a) 37.42 36.28 2.27 Chrysene 37.54 36.44 2.26 Benzo[b]fluoranthene 41.55 40.12 2.76 Benzo[k]fluoranthene 41.65 40.28 2.74 Benzo[e]pyrene 42.90 41.08 3.16 Benzo[a]pyrene 43.09 41.24 3.23 Perylene-[d.sub.12] (a) 43.46 41.40 3.36 Perylene 43.57 41.48 3.47 Indeno[1,2,3-cd] 48.09 45.48 0.71 pyrene Dibenz[a,h]anthracene 48.12 45.64 0.82 Benzo[ghi] 49.92 46.52 1.54 perylene-[d.sub.12] (a) Benzo[ghi]perylene 50.13 46.68 1.78 (a) Internal standard. TABLE 2: GC-qMS and GCxGC-TOFMS operating conditions. Parameters GC-qMS Injector settings Injection volume 1 [micro]L Inlet mode Splitless Carrier gas He (99.999%) Carrier gas flow 1.0 mL*[min.sup.-1] Inlet temperature 280[degrees]C GC oven temperature Initial temperature 1 min at 60[degrees]C First rate 6[degrees]C/min to 310[degrees]C Isothermal pause 15 min at 310[degrees]C 2nd oven temperature -- offset Modulator Modulator temperature -- offset Modulator period -- Hot pulse time -- Cool time between stages -- MS Mass range 40-550 Electron energy 70 eV Ion source temperature 230[degrees]C Parameters GCxGC-TOFMS Injector settings Injection volume 1 [micro]L Inlet mode Splitless Carrier gas He (99.999%) Carrier gas flow 1.3 mL*[min.sup.-1] Inlet temperature 300[degrees]C GC oven temperature Initial temperature 1 min at 60[degrees]C First rate 6[degrees]C/min to 300[degrees]C Isothermal pause 15 min at 300[degrees]C 2nd oven temperature 5[degrees]C, relative offset temperature Modulator Modulator temperature 15[degrees]C, relative offset to the 2nd oven temperature Modulator period 4.00s Hot pulse time 1.00s Cool time between stages 1.40s MS Mass range 40-550 Electron energy 70 eV Ion source temperature 230[degrees]C TABLE 3: Relative response factors (RRFs) and calibrations of 18 PAHs obtained by the compared methods. GC-qMS Compound RRF (a) Slope Intercept [R.sup.2] Nap 1.04 0.515 -0.003 0.9999 Acy 1.57 0.808 -0.004 1.0000 Ace 1.03 0.439 0.009 0.9999 F 1.29 0.667 -0.006 1.0000 Phe 1.17 0.572 -0.004 0.9998 Ant 0.98 0.547 -0.017 0.9992 Fla 1.30 0.678 -0.001 1.0000 Pyr 1.31 0.686 -0.004 0.9999 BaA 0.98 0.575 -0.019 0.9997 Chr 1.06 0.563 -0.003 1.0000 BbF 0.99 0.535 -0.008 0.9999 BkF 1.11 0.576 -0.011 0.9998 BeP 0.91 0.455 -0.007 0.9996 BaP 0.88 0.505 -0.014 0.9997 Per 0.89 0.475 -0.008 0.9998 IP 1.37 0.717 -0.023 0.9995 DBahAnt 1.24 0.629 -0.019 0.9996 BghiPer 1.24 0.594 -0.010 1.000 GCxGC-TOFMS Compound RRF Slope Intercept [R.sup.2] Nap 1.69 0.560 0.049 0.9971 Acy 1.95 1.026 -0.012 0.9999 Ace 1.16 0.553 -0.006 0.9994 F 1.11 0.609 -0.021 0.9997 Phe 1.47 0.763 -0.039 0.9979 Ant 0.93 0.431 -0.010 0.9982 Fla 1.43 0.802 -0.021 0.9995 Pyr 1.62 0.910 -0.055 0.9972 BaA 1.42 0.574 -0.004 0.9998 Chr 1.26 0.651 -0.005 0.9998 BbF 1.69 0.848 -0.028 0.9993 BkF 0.88 0.327 -0.012 0.9977 BeP 0.90 0.530 -0.014 0.9997 BaP 0.83 0.477 -0.030 0.9985 Per 1.11 0.521 -0.023 0.9979 IP 1.25 0.660 -0.062 0.9922 DBahAnt 1.16 0.490 -0.074 0.9898 BghiPer 1.53 0.710 -0.036 0.9991 (a) RRF expresses the sensitivity of a detector for a given analyte relative to its corresponding deuterated internal standards; RRF = ([A.sub.x] [C.sub.is])/([A.sub.is] [C.sub.x]), where [A.sub.x] is the peak area of a quantifying ion for a given analyte being measured; [A.sub.is] is the peak area of a quantifying ion for its corresponding internal standard; [C.sub.x] is the concentration of a given analyte; and [C.sub.is] is the concentration of its corresponding internal standard. TABLE 4: Limits of detection and quantification and recoveries of 18 PAHs obtained by the compared methods. Compound LOD (a) (ng) LOQ (b) (ng) GC-qMS GCxGC-TOFMS GC-qMS GCxGC-TOFMS Nap 0.07 0.40 0.21 1.19 Acy 0.17 0.07 0.51 0.22 Ace 0.05 0.17 0.16 0.52 F 0.04 0.15 0.13 0.44 Phe 0.10 0.34 0.31 1.03 Ant 0.19 0.31 0.58 0.92 Fla 0.05 0.14 0.16 0.41 Pyr 0.08 0.36 0.25 1.09 BaA 0.12 0.09 0.37 0.27 Chr 0.04 0.08 0.13 0.24 BbF 0.05 0.18 0.15 0.53 BkF 0.09 0.35 0.28 1.05 BeP 0.13 0.13 0.40 0.38 BaP 0.12 0.24 0.37 0.72 Per 0.11 0.34 0.32 1.02 IP 0.15 0.65 0.16 1.94 DBahAnt 0.13 1.05 0.40 3.14 BghiPer 0.09 0.22 0.27 0.66 Compound Recovery [+ or -] RSD (%) GC-qMS GCxGC-TOFMS Nap 94.4 [+ or -] 4.2 135 [+ or -] 45 Acy 19 [+ or -] 12 116 [+ or -] 15 Ace 105 [+ or -] 5.3 105 [+ or -] 7.8 F 158 [+ or -] 28 130 [+ or -] 29 Phe 94.5 [+ or -] 5.3 86.3 [+ or -] 16 Ant 90.4 [+ or -] 4.6 95.1 [+ or -] 20 Fla 90.3 [+ or -] 3.9 105 [+ or -] 13 Pyr 97.4 [+ or -] 5.3 97.2 [+ or -] 13 BaA 93.4 [+ or -] 4.9 86.9 [+ or -] 8.2 Chr 95.8 [+ or -] 5.8 101 [+ or -] 16 BbF 96.1 [+ or -] 5.7 92.3 [+ or -] 10 BkF 94.2 [+ or -] 6.5 105 [+ or -] 12 BeP 92.6 [+ or -] 5.8 92.7 [+ or -] 5.7 BaP 93.6 [+ or -] 5.3 104 [+ or -] 9.0 Per 93.0 [+ or -] 5.5 92.5 [+ or -] 8.6 IP 95.0 [+ or -] 5.4 93.9 [+ or -] 8.5 DBahAnt 94.9 [+ or -] 5.5 95.8 [+ or -] 5.7 BghiPer 94.6 [+ or -] 6.0 87.0 [+ or -] 8.5 (a) LOD, smallest amount of analyte that is statistically different from the blank; bLOQ, smallest amount of analyte that can be measured with reasonable accuracy.
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|Title Annotation:||Research Article|
|Author:||Ahn, Yun Gyong; Jeon, So Hyeon; Lim, Hyung Bae; Choi, Na Rae; Hwang, Geum-Sook; Kim, Yong Pyo; Lee,|
|Publication:||Journal of Analytical Methods in Chemistry|
|Date:||Jan 1, 2018|
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