Identification of Potential Novel Bioaccumulative and Persistent

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Identification of Potential Novel Bioaccumulative and Persistent Chemicals in Sediments from Ontario (Canada) Using Scripting Approaches with GC×GC-TOF MS Analysis Miren Pena-Abaurrea,*,†,‡,§ Karl J. Jobst,§ Ralph Ruffolo,§ Li Shen,§ Robert McCrindle,∥,⊥ Paul A. Helm,§ and Eric J. Reiner†,§ †

Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada Conservation Ontario, Newmarket, Ontario L3Y 2P5, Canada § Ontario Ministry of the Environment and Climate Change, Toronto, Ontario M9P 3V6, Canada ∥ Wellington Laboratories Inc., Guelph, Ontario N1G 3M5, Canada ⊥ Department of Chemistry, University of Guelph, Guelph, Ontario N1G 2W1, Canada ‡

S Supporting Information *

ABSTRACT: This work describes a single and fast approach using a filtering script as a means of prioritizing sample processing of data acquired by GC×GC-TOF MS for the identification of potentially novel persistent and bioaccumulative halogenated chemicals. The proposed script is based on the recognition of a generic halogenated isotope cluster pattern that allows for the simultaneous detection of chlorinated, brominated, or mixed halogensubstituted compounds in a single classification. Once developed, the script was applied to the identification of organohalogens in stream sediments collected across the southern region of Ontario (Canada). Classified peaks were first compared with available analytical standards and reference libraries to confirm the known chemicals. Unknown potential persistent organic pollutants (POPs) were evaluated for occurrence within the samples and high resolution mass spectrometry was used in order to identify some of the most prevalent compounds in the samples and resulting in the identification of three decachlorinated dechlorane analogs (C18H14Cl10), two undecachlorinated dechlorane species (C18H13Cl11), and a novel mixed chloro/bromo-carbazole (C12H5NCl2Br2) in a number of sediments analyzed. Relative peak abundances of these unknown halogenated compounds were in the same order of magnitude or slightly higher than levels observed for conventional POPs detected in the samples.

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INTRODUCTION Simultaneous multicomponent analysis of organic pollutants in complex environmental matrices can be accomplished using multidimensional gas chromatography (MDGC).1−4 MDGC techniques, such as comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF MS), combine enhanced chromatographic and mass spectral resolution for the confident identification of individual compounds.5,6 These analyses usually generate large data files containing hundreds to thousands of peaks. Analytical standards, reference libraries and group-type separations facilitate the determination of known and class-related chemicals.5,7 However, if the study is focused on the identification of new metabolites, degradation products or other analytes not previously detected, the analyst faces a much more challenging task. A number of GC×GC studies examining the occurrence of novel bioaccumulating halogenated natural products (HNPs) as well as new isomers or metabolites of some families of contaminants have been published in recent years.7−9 Efforts to develop analytical strategies for the early identification of © 2014 American Chemical Society

potentially bioaccumulative, persistent, and/or toxic compounds would facilitate future environmental investigations. Therefore, the determination of assessment criteria for these types of chemicals is important. Recent studies have shown threshold classification criteria for potential persistent, bioaccumulative, and long-range transport chemicals based on physicochemical properties.10−12 Interestingly, the majority of the compounds classified in the group of “potential POPs” were halogenated. In particular, chlorinated, brominated, and mixed halogenated-substituted compounds represented approximately two-thirds of compounds in the group, while fluorinated chemicals accounted for the rest. Considering this particular trend, focused strategies that target halogenated compounds can give information about both conventional and unknown novel POPs present in the samples. Approaches for this kind of analyses include the use of halogenReceived: Revised: Accepted: Published: 9591

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(constant flow, 2 mL/min). A nitrogen quad-jet dual-stage modulator was used for sample focusing and reinjection in the secondary column. The temperature of the modulator was set 35 °C above that of the main oven. A modulation period of 6 s with hot pulses of 1 s was applied during modulation. The transfer line and ion source temperatures were set at 275 and 250 °C respectively. MS detection was performed in full scan range (50−1000 m/z); the EI energy was 70 eV, the voltage of the multiplier was 1600 eV and the data acquisition rate was set at 50 Hz. A compromised mass defect value of −60/100 Da was used in order to adjust the MS bin window during MS detection and to avoid distortion of the isotope cluster of the high halogenated-substituted compounds. ChromaToF software (version 4.50) was used during acquisition and sample processing. The optimized script was written in Microsoft Visual Basic language and applied during data processing of the samples. HRMS Experiments. HRMS experiments were performed on a GC-HRTOF MS (Waters; Milford, MA, U.S.A.) equipped with an Agilent 6890 GC using a 30 m Rtx-5MS column (0.25 mm i.d. × 0.10 μm d.f). The HRMS system was operated in the negative chemical ionization mode (NCI) with an electron energy of 70 eV. Methane was used as a reagent gas at a 60% of set flow. The instrument was tuned to have a 7000 resolving power (full width at half-maximum, fwhm) using perfluorotributylamine as the mass calibrant. Additional experiments were performed using a Varian GCTQ-FTMS (triple quadrupole-Fourier transform mass spectrometer) (Varian Inc., Walnut Creek, CA, U.S.A.) consisting of a Varian CP-3800 GC, a Varian J320-MS(TQ), a Varian 920MS (FTMS), and a Varian 9.4 T superconducting magnet. The FTMS system was operated in the EI mode (70 eV) at a mass resolution of 50 000−100 000 (fwhm). Mass spectra were obtained using arbitrary waveform excitation and detected in the 150−650 m/z range.

selective detection techniques, such as GC or GC×GC coupled with electron capture detection (ECD or micro-ECD)1,2 or more selective detection using MS techniques. In the latter approach, the analyst can recognize halogenated compounds by their characteristic isotope pattern13,14 or by their negative mass defect15 (in this case the use of high-resolution mass spectrometry HRMStechniques are required). Scripting filters, as they are usually applied to GC×GC-TOF MS, aim to classify compounds based on specific characteristics of their mass spectra and retention times. A few studies have reported nontarget approaches for organohalogenated compounds including scripts.13,14,16−18 In these studies, the analyte determinations were aided by using different script functions that identified the degree of halogenation14,16,19,20 or the type of halogen substitution (e.g., chlorine or bromine). Only a couple of these studies demonstrated the feasibility of using generic script functions to simplify the simultaneous detection of different halogenated homologues in the same class.14,20 However, none of these approaches allowed for the detection of mono and poly halogenated substituted chemicals under an unique threshold criteria. This study evaluates the use of scripts following GC×GCTOF MS analysis for the identification of unknown potentially bioaccumulative, toxic, and persistent contaminants in sediments collected in rural areas across the province of Ontario (Canada). A classification routine including a single script function has been developed for the simultaneous identification of chlorinated, brominated, and mixed halogenated chemicals (containing at least one chlorine or bromine) present in the investigated fraction. After identifying well-known POPs by comparison with reference standards and library matching (similarity), the classified unknown organohalogens were evaluated for occurrence and prevalence within samples. Those considered of potential interest because of their relative abundance and prevalence in the investigated sediments were further investigated with HRMS techniques.

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RESULTS AND DISCUSSION Scripting Details for Identification of Halogenated POPs. The detection of chemicals in samples is, understandably, limited by the methodology applied during sample preparation and the instrumental technique used for analysis. The extracts included in this study were previously analyzed for conventional nonpolar halogenated POPs with protocols optimized for the selective extraction of such nonpolar compounds followed by multilayered silica purification prior to the final GC analysis. Qualtity control (QC) data confirmed the quantitative extraction of halogenated POPs (PCBs, PBDEs, PCNs, and PCDD/Fs) (see SI Table S1). Thus, it is likely that halogenated metabolites or degradation products, as well as other nonpolar, nonionic, and semivolatile chemicals were present in the final purified extracts. Among the halogenated chemicals listed in previous inventory studies of potential POPs, the high volatility and somewhat polar nature of fluorinated analytes complicates their detection using routine POP GC screening methods.12 Chlorinated, brominated and mixed halogenated (Cl/F, Br/F, Cl/I, Br/I, and Cl/Br) compounds are the most likely substances to be detected using this analytical scheme. This work presents the optimization of a generic script designed to classify peaks based on the recognition of a distinctive and common isotope cluster distribution in the mass spectrum of any halogenated compound containing, at least, one chlorine or bromine in its molecular structure. The script

EXPERIMENTAL SECTION Sample Analysis. In this study, 69 purified extracts of rural sediments previously prepared for the routine analysis of a suite of POPs were reanalyzed by using GC×GC-TOF MS in order to investigate the presence of unknown and potentially persistent and toxic new chemicals. Additional method blanks (n = 8) were included in the investigation of unknown halogenated substances to provide assurance that the analytical protocols did not introduce contamination that interfered with identification of unknowns. A detailed description of the sediment collection campaign, sample preparation and quality control analysis is included in the Supporting Information (SI, see page S2). GC×GC Analysis. Analyses were performed using a Pegasus 4D (Leco Corp., St. Joseph, MI, U.S.A.) consisting of an updated GC×GC Agilent 6890 and a TOF-MS with electron ionization (EI) mode. Samples were injected in the hot splitless mode (1 μL, 280 °C) into a Rtx-5MS × BPX-50 column set (30 m × 0.25 mm internal diameter (i.d.) × 0.25 μm film thickness (d.f.) and 1.6 m × 0.15 mm i.d. × 0.15 μm d.f., respectively). Columns were purchased from Restek (Bellefonte, PA, U.S.A.) and SGE (Melbourne, Australia) respectively. The main oven was programmed as follows: from 80 °C (5 min) to 190 °C at 15 °C/min and then to 310 °C (15 min) at 3 °C/min. The secondary oven was kept 20 °C above the program of the main oven. Helium was used as carrier gas 9592

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Figure 1. (A) Examples of different molecular clusters of pentahalogen-substituted biphenyls, (B) Calculated Y+2/Y mass ratio values for mono- to deca- substituted organohalogens, (C) Molecular cluster observed for a sulfur compound (S7), and (D) Molecular cluster registered for a misidentified compound.

applies a sequential evaluation of different mass variables to every peak identified with the peak find criteria (i.e., a peak width threshold of 10 s for the 1D and 0.15 s for the 2D, and a signal-to-noise ratio of 10:1). Script variables were predetermined by ChromaToF and analyzed the “true” spectrum data of each compound (i.e., deconvoluted spectrum). Since the script was written in Microsoft Visual Basic, this approach can also be applied to exported mass spectral data acquired from other related MS instruments. In brief, the sequential conditions evaluated included (see SI Table S2 for the detailed function description): (a) Identif ication of the highest m/z halogenated isotope cluster in the mass spectra of the compound based on a minimum intensity and abundance threshold of the base ion of the cluster (Y). Figure 1A. illustrates an example of the identification of Y in different penta-halogenated clusters of substituted biphenyls. (b) Evaluation of the relative abundances (mass ratio, MR) of a number of surrounding masses of Y (bordered ion bars in Figure 1A): a MR2 range (Y+2/Y ratio) between 0.23 (as calculated for the lower theoretical value plus 10% of standard deviation) and 1 allowed for the simultaneous classification of all chlorinated, brominated and mixed Cl/Br substituted chemicals in the same function. Figure 1B shows an example of theoretical calculated MR2 values for mono- to deca-substituted congeners. Values for higher substituted chemicals are not shown in the figure but were calculated and confirmed to be captured within the evaluation range. However, because of the 30 m-first dimension column used in this experiment, higher

brominated chemicals (more than 8 bromines) may degrade on the GC column before MS detection. (c) Evaluation of false positives corresponding to sulf ur homologues: Some false positives due to the similar isotope distribution of elemental sulfur compounds to the mono-Cl substituted clusters (see Figure 1C) were detected when running the script detailed in SI Table S2. Therefore, an additional loop evaluating the “mono-Cllike” isotope pattern observed for the resulting sulfur fragments in the mass spectrum of elemental sulphur was added to remove those false positive filtered peaks (see SI Table S3). In order to validate the scripting function, a standard solution containing 120 halogenated chemicals (including both chlorinated and brominated standards) was subjected to GC×GC analysis and further data processing using the classification script. Two different concentrations were evaluated, 100 and 500 pg/μL, and in all cases 1 μL of solution was injected. Once analyzed and processed, the classification table for the least concentrated solution (100 pg/ μL) identified 110 compounds. A number of misidentifications (10) owed to some missing masses in the molecular clusters were detected (MR1 and MR3 script threshold conditions were not met, see Figure 1D). Processed data for the most concentrated standard (500 pg/μL) showed 118 classified peaks. In this case, only two (2) peaks were not classified because of failure to meet the MR1 and MR3 threshold criteria. In order to reproduce routine scenarios of processing of complex samples, the proposed filter was applied during the processing of a sediment sample randomly selected from those 9593

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Figure 2. Extracted ion chromatogram (m/z 238) for (A) sample 10, (B) sample 31, and (C) technical DP mixture. Mass spectrum collected for (D) unknown peaks 1, 2, and 5, (E) peaks 3 and 4, and (F) aCl11DP standard.

occurred when minor analytes were close to a S/N of 10:1 and/ or when analytes were close to the matrix band and deconvolution was not perfectly performed. Identification of Unknown Halogenated Compounds in Stream Sediments. The sediment samples and analytical blank extracts collected were subjected to GC×GC-TOF MS analysis. Sample processing including peak identification, library comparison and peak classification (using script filtering) was later applied to the acquired files. SI Figure S2A,B show an example of a typical TIC (total ion chromatogram) and an EIC (extracted ion chromatogram) for a processed sample file. Over a thousand peaks were individually identified in the contour plot. Black dots highlight peaks chromatographically detected within the peak find criteria while white dots classify peaks that fulfill both the peak find threshold and the script filter. Simplified contour plots were obtained after removing the nonclassified peaks (SI Figure S2C). Several (on the order of tens) unknown halogenated analytes were displayed in the contour plot after filtering known organohalogens included in our user-defined libraries (see SI Figure S2D). This simplification allowed for the straightforward mass spectral

collected in the present study. Two parallel processing methods were applied: the first was based on a manual review and for the second the halogen filter script was applied. In the first scenario, a peak-by-peak review of every peak chromatographically identified was carried out (this manual characterization could take a couple of hours). After a careful interpretation, the analyst compared those peaks selected due to their apparently halogenated breakdown profile to those automatically detected using the proposed script. Endogenous compounds automatically classified with the script method included 180 chemicals. Five analytes previously not captured in the manual review were detected in this experiment. As before, these misidentifications were due to the poor isotopic ratios of molecular cluster collected, probably due to their lower concentration levels. After analyzing all the collected sediments, the estimated level of accurate identification of organohalogens by using the develop script filter was observed to range between 90 and 95% and was dependent on the relative signal-to-noise (S/N) of the identified peaks in the samples. The occurrence of misidentified peaks and false positives in the final acid-purified extracts 9594

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Figure 3. NCI-HRMS extracted ion chromatograms obtained for sample 10 for the theoretical base ion of clusters of (A) C18H14Cl10•− (corresponding to dihydro-DP molecular formula), (B) C18H13Cl11•− (monohydro-DPs), and (C) C18H12Cl12•− (DPs).

samples and a commercial technical DP mixture indicated a strong match between the RTs and mass spectra for the unknown peaks 2, 3, 4, and 5 (Figure 2C). The mass spectrum for each of the unknown peaks showed a common dominant pentachlorinated cluster at m/z 238 and a less abundant tetrachlorinated cluster around m/z 203 (see spectra depicted in Figure 2D for peaks 1, 2, and 5 and in Figure 2E for peaks 3 and 4). Peaks 3 and 4 showed a minor cluster around m/z 272. These spectra looked to have a similar pattern to that collected for the monohydro-analog standard of DP: anti-undecachloropentacyclooctadecadiene (aCl11DP) (Figure 2F). However, the RT of the aCl11DP in the available analytical standard did not match any of the classified unknown compounds. On the basis of previous articles which reported the presence of detectable levels of monohydro- and dihydro-analogs of DP in sediments and dust samples,25,26 we suspected that these peaks could also correspond to (−1Cl + 1H) and (−2Cl + 2H) analogs of DP. In order to confirm this hypothesis, some additional full scan HRMS experiments working in the NCI mode were carried out. Figure 3A−C illustrates an example of the extracted ion chromatogram obtained for the base ion of the molecular cluster of the dihydro-DP analogs (C18H14Cl10•−), monohydroDP analogs (C18H13Cl11•−) and DP (C18H12Cl12•−), respectively, in sample 31. Anti- and syn-DP were observed when extracting the calculated mass for the DP isomers (m/z

analysis of the unknown analytes. Classified unknowns were carefully analyzed based on their chromatographic separation and mass spectral behavior. Those considered potentially environmentally relevant because of their relative abundance (in the same or higher response range of other conventional POPs, i.e., PCBs, PBDEs) and prevalence in samples were subjected to additional HRMS experiments to further elucidate their identity. Two examples of such compounds are detailed below. Identification of Deca- and Undeca-Chlorinated Dechlorane Analogs. In the first example, a series of compounds eluting in an area close to the anti- and synDechlorane Plus (DP) flame retardants was identified in six of the sixty-nine sediments studied (samples 6, 10, 16, 22, 31, 34, see SI Figure S1 for sample locations). DP is a chlorinated flame retardant incorporated into a range of polymers and applications and has been manufactured in the Great Lakes region in Niagara Falls, New York.21,22 DP, analogs, and structurally similar compounds arising from impurities, and other related Dechlorane flame retardants (e.g., Dechloranes 602, 603, 604) have been found throughout the Great Lakes region in lake and tributary sediments.23−25 Five peaks in the series with similar mass spectra were found to have peak intensities in the same order as anti-DP levels detected in the corresponding samples (especially peaks 2, 4, and 5, see Figure 2A,B). Comparison between the sediment 9595

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pounds 2, 4 and 5) could result from impurities (secondary products) in the DP technical mixture as well as products formed from UV degradation. Our findings suggest that further studies are merited that consider the fate of DP, and particularly its dechlorinated impurities and/or degradation products, as they may be bioavailable and have differing properties and toxicity than the parent DP. Identification of a Mixed Halogenated Carbazole. In the second example, a mixed halogenated compound eluting close to the octachlorinated PCBs and with a fragmentation pattern showing losses of chlorines and bromines (see Figure 5A), was detected in 26 of the 69 samples analyzed by GC×GC (samples 14, 17, 19, 23, 25, 30, 31, 34, 36, 37, 38, 40, 42, 43, 44, 45, 46, 49, 51, 52, 54, 55, 56, 60, 64, and 65, see SI Figure S1). The suspected molecular ion cluster showed a base peak at m/z 393 (corresponding, presumably, to the M+2 ion of the compound). This odd numbered ion suggested the presence of at least one nitrogen or phosphorus atom in the molecular structure. This analyte eluted at the same first dimension RT (1DRT) as PCB 180 and the molecular ion cluster of the mixed halogenated analyte matched with a fragment observed in the mass spectrum of the octa-PCB (Figure 5B,C). Identification of the unknown Cl/Br compound using monodimensional GCMS techniques would be challenging but possible if working with full scan HRMS, but not possible with low resolution MS detection. Additional experiments with GC/FTMS were used to identify the exact molecular mass of the mixed Cl/Brsubstituted compound. The experimentally measured molecular mass for the unknown chemical was determined to be m/z 390.8162. The most logical molecular formula obtained from the elemental composition calculator was: C12H5NCl2Br2 (error of 0.438 ppm). With this information and the fragmentation pattern observed in the mass spectrum, a mixed halogenated carbazole was the most probable structure proposed for the unknown chemical. In order to confirm this proposed structure, standard solutions of a number of dibromo/dichloro-carbazole isomers were synthesized and analyzed under the same GC×GC conditions as the investigated sediments. The mass spectra collected for all the carbazole standards matched that of the newly identified compound. Further, the RT observed for the unknown compound using GC×GC analysis closely matched one particular isomer, 1,8-dibromo-3,6-dichlorocarbazole (see structure in Figure 5D). To further support the proposed structure, a fortification experiment was carried out. Co-injection experiments confirmed a strong chromatographic match between the new compound and the 1,8dibromo-3,6-dichloro-substituted carbazole (see Figure 5E). The fortified sample was processed, including the optimized script filter, and a unique deconvoluted peak was automatically detected. The slight shift in the 1DRT could be due to the complex matrix background of the collected sediments. There are 114 possible dibromo-dichloro substituted congeners, of which we have synthesized and further analyzed only eight based on a recent paper that uses modeling prediction of the most energetically stable spatial conformation of the halogens for the carbazole.27 Therefore, we cannot be certain that the compound identified is not another congener, however, the close RT, mass spectral similarities and expected distribution of halogens for the 1,8-dibromo-3,6-dichlorocarbazole indicate that it is the most likely structure for the novel identified analyte.

653.7113). Three peaks corresponding to the unknown compounds # 3 and 4 and the aCl11DP were present when plotting the base mass corresponding to the monohydro-DP analogs (m/z 617.7532). Unknown chemicals # 1, 2, and 5 were captured after extracting the calculated mass for the (−2Cl + 2H) analogs (m/z 583.7922). Conclusions drawn from GC×GC-MS and HRMS experiments confirm the identification of two monohydro- and three dihydro-DP analogs. According to the fragmentation pattern observed for peaks 1, 2, and 5 (usually resulting from a retroDiels−Alder breakdown),23 the most probable generic molecular distribution for these compounds is shown in Figure 4A, with one chlorine in each bridge carbon. This distribution

Figure 4. Generic molecular structure proposed for the novel (A) decachlorinated and (B) undecachlorinated dechlorane analogs.

supports both the similar fragmentation pattern observed between the deca- and undeca-chlorinated dechlorane analogs (see Figure 4B) and the lack of the minor cluster around m/z 272 in the EI-MS experiments. The different chromatographic peaks could correspond to different molecular stereoisomers depending on the position of the chlorines on the bridge carbons and the chair-boat configuration of the cyclooctadiene. In the case of the undecachlorinated analogs (peaks #3 and 4), the two identified isomers could also correspond to different stereoisomers than the available analytical aCl11DP standard. Two studies reported detectable levels of monohydro- and dihydro-analogs of DP in sediments and dust samples.25,26 In these studies, only one monohydro compound showed levels in the range of the DP while the rest of peaks were minor impurities. The authors suggested ultraviolet (UV) degradation as the potential source of these isomers. In the present manuscript, relative abundances observed for peaks 2, 4, and 5 were higher than those detected for the anti-DP in the samples. In particular, a decachlorinated isomer (peak # 2) showed the highest levels. The strong match between some of the novel DP isomers in the sediments and certain DP-analog products detected in the DP technical mixture, suggests that the observed dechlorane isomer pattern (containing significant contribution of com9596

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Figure 5. (A) Mass spectrum collected for the unknown mixed halogenated compound and (B) PCB #180. (C) Reconstructed 2D contour plot illustrating the elution area. (D) Mass spectrum and structure for 3,6-diCl-1,8-diBr-carbazole standard and (E) 3D peak observed for the coinjection experiment.

To the best of our knowledge, this is the first time that a mixed chloro/bromocarbazole has been identified in environmental samples. To date, only a few studies have shown the occurrence of halogenated carbazoles in environmental samples, and in all cases, their occurrence was reported in abiotic samples, especially soils.28−30 These studies included mono, dichloro-, dibromo-, and tetrabromo-substituted carbazoles. In our study, none of the mono or dihalogenated substituted carbazoles were observed in the analyzed extracts. Only a few samples showed measurable levels of the 1,3,6,8tetrabrominated carbazole. As observed in some fortification experiments with commercial standards of carbazoles, mono-, di-, and tetra-substituted organohalogenated carbazoles substituted in positions other than the 1 or 8 positions on the carbazole ring would irreversibly bind to acidic phases during sample preparation. Halogen substitutions in the 1 and 8 positions of the benzenes could act as a steric shield over the NH bond and would explain the unique presence of the 1,3,6,8-substituted halogenated carbazoles in the final eluate. A comprehensive literature search for the determination of the potential sources of synthesis and/or emission of this novel compound was carried out. Although no source of this compound has been reported, a recent report points to a potential natural bacteriological synthesis of these halogenated chemicals.27

It is also worth noting that the 3,6-dichlorocarbazole isomer has already been identified as a dioxin-like compound30 and, consequently, future toxicological studies for the evaluation of the inherent toxic activity of the mixed halogenated carbazole are highly recommended in order to have a complete chemical description of this compound. As described in the Results and Discussion section, the enhanced benefits of using script classifications can significantly reduce processing times and minimize human error during the manual interpretation of the data collected in nontarget GC×GC environmental analysis of complex samples. Interpretation of the mass spectra of prevalent unknown analytes classified by GC×GC-TOF MS processing methods, combined with additional HRMS analyses and subject to confirmation with authentic standards, can facilitate the identification of emerging and potential persistent, bioaccumulative, and toxic chemicals in complex environmental samples.

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ASSOCIATED CONTENT

S Supporting Information *

Detailed information on experimental protocols and quality assurance results, and the scripting function used in this study in order to reproduce experiments. A map of the sampling area and selected examples of classified chromatograms obtained after applying the developed scripting filter. This material is available free of charge via the Internet at http://pubs.acs.org. 9597

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1-416-235-6154; fax: +1-416-235-5744; e-mail: [email protected]. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding

This study was supported by Conservation Ontario and the Ontario Ministry of Northern Development and Mines (Southern Ontario Stream Sediment Project). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Authors thank Conservation Ontario and the Ministry of Northern Development and Mines (SOSSP project) for financial support and Richard Dyer and Rachael Fletcher for project management support. M.P.-A. also thanks Robert Salemi (MOE) for technical support during script optimization and Sri Chaudhuri for GIS support. The authors thank Dr. Vince Taguchi (MOE) for valuable discussions and technical assistance with the FTMS experiments.

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REFERENCES

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