The First Quantitative Analysis of Alkylated PAH and PASH by GCxGC/MS and its Implications on Weathering Studies

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1 The First Quantitative Analysis of Alkylated PAH and PASH by GCxGC/MS and its Implications on Weathering Studies INEF Penn State Conference 2013 Albert Robbat, Jr. and Patrick Antle Tufts University, Chemistry Department Center for Field Analytical Studies & Technology Medford, MA

2 Figure 1-1. Example polycyclic aromatic compounds. Clockwise from top left: Importance of Polycyclic Aromatic Compounds naphthalene, 1-ethylphenanthrene, 5-H-indeno[1,2-b]pyridine, dibenzofuran, Forensics Weathering Source Allocation Liability phenanthro[4,5-bcd]thiophene. Toxicology Food chain Aquatic/Terrestrial/Benthic Flora/Fauna PAH 34 = 16 Parent PAH + 18 Alkyl PAH S S Remedial investigations Soil, sediment, water Background comparison studies U.S. EPA, Office of Research & Development, Washington, DC, EPA/600/R/02/013 Nov. 2003

3 Sample Complexity Coal tar and crude oil contain thousands of organic compounds The matrix (unresolved chromatogram) interferes with forensic analyses GC/MS alone cannot produce clean mass spectra nor differentiate one target compound from another if compounds coelute and/or have same mass spectra Coal Tar unresolved chromatograms Crude Oil

4 The Problem with Current Methods of Analysis A 15-year review of the literature shows 65% of PAH studies rely on selected ion monitoring (SIM) to obtain data Only 35% of the studies employ full scan mass spectrometry Of the SIM studies, the majority used the molecular ion (1-ion) to detect PAH, the remainder used a second ion (2-ion) to confirm identity Commercial labs use SIM exclusively and rely on retention windows and pattern recognition to identify alkylated PAH, see ASTM and NOAA methods Matrix compounds, PAH and PASH interfere with one another, leading to false positives and inaccurate concentration estimates.

5 Other Problems and Solution No libraries or standards exist to obtain the mass spectral patterns of all alkylated PAH and PASH 100% One fragmentation pattern cannot capture all of the isomers of a homologue Using coal tar and crude oil as PAH and PASH sources, we found many unpublished alkylated PAH and PASH fragmentation patterns by GC- GC/MS Once the library was established the same target compounds were found by GC/MS using the Ion Analytics spectral deconvolution software

6 Combinatorial Library Building of PAH and PASH Retention Windows and Spectra by 2D GC-GC/MS/PFPD Coal Tar - FID Crude Oil - FID Coal Tar Cut 21 Coal Tar Cut 35 Coal Tar Cut 47 C 2 2-ring C 3 2-ring C 2 3-ring C 3 3-ring C 2 3-ring C 3 3-ring Sulfur Response Sulfur Response Sulfur Response

7 1D vs. 2D Spectra GC-GC/MS GC/MS

8 GC GC/MS Much shorter 2 nd columns and modulation times and continuous sample transfer produce space compressed 2 nd dimension peaks up to 50-times narrower than 1 st dimension peaks, increasing measurement sensitivity Since 2 nd column runtimes finish before subsequent transfers occur, an additional set of chromatograms on a 1-dimensional time-scale are produced In theory, the resulting multidimensional chromatogram increases separation space and visualization of sample components in complex mixtures However, suboptimal column conditions and non-orthogonal stationary phases limit peak capacity improvements MS identification requires reconciling narrow 2 nd column peaks with obtaining sufficient invariant peak scans a problem for low resolution mass filters Additionally, the modulation ratio (the number of sample portions transferred per 1 st dimension peak), and modulation phase (the sampling start time with respect to peak shape) can alter detector signal, increasing quantitative errors

9 GC GC

10 Need TOF, peaks too narrow for quad to produce invariant scans across the peak Modulation phase and ratio affect peak area leads to ion skewing Geometric summation of 3D blob areas Most analyses based on ion extraction of molecular ion and not spectral patterns of target compounds Current mathematical attempts to deconvolve quadrupole data unsuccessful Historical Quantitative Challenges Solved

11 Ion Analytics Spectral Deconvolution f Ri ( t) A L ( t) 1 m t i ( ) Transformed Signal Transformed Signal transformation of peak signal for simple visualization F1 ( t) max[ fi ( t)] min[ fi ( t)] i N j N F N 1 N f ( t) i i 1 j i 1 2( t) N 1 i 1 i f j ( t) F 3 ( t) max i N dfi ( t) dt min j N df j dt ( t) Target Compound Identities Confirmed When Equations Produce Scan-to-Scan Relative Errors Less Than Analyst Established Values

12 Deconvolved PAH and PASH Spectral Patterns

13 Measurement Sensitivity GC/MS vs. GC GC/MS GC GC/MS 5-times more sensitive than GC/MS

14 Compound/Homologue GC GC/MS (μg/g) GC/MS (μg/g) % RPD 2.5 AL (μg/g) and 1 (RPD) GC GC/MS Precision meets EPA criteria for when comparing different methods Sensitivity meets EPA action levels for site cleanup Naphthalene (100) C 1 Naphthalenes C 2 Naphthalenes C 3 Naphthalenes C 4 Naphthalenes Fluorene (100) C 1 Fluorenes C 2 Fluorenes C 3 Fluorenes C 4 Fluorenes 1.6 ND N/A Phenanthrene/Anthracene (100) C 1 Phenanthrenes C 2 Phenanthrenes C 3 Phenanthrenes C 4 Phenanthrenes Benzo(a)anthracene/Chrysene (60) C 1 Chrysenes C 2 Chrysenes C 3 Chrysenes 0.8 ND N/A C 4 Chrysenes ND ND N/A Pyrene/Fluoranthene (100) C 1 Pyrenes C 2 Pyrenes C 3 Pyrenes C 4 Pyrenes 1.1 ND N/A C 1 2-Ring PASH C 2 2-Ring PASH C 3 2-Ring PASH C 4 2-Ring PASH Dibenzothiophene C 1 3-Ring PASH C 2 3-Ring PASH C 3 3-Ring PASH C 4 3-Ring PASH C 1 4-Ring Fused PASH C 2 4-Ring Fused PASH C 3 4-Ring Fused PASH 0.7 ND N/A C 4 4-Ring Fused PASH C 1 4-Ring Condensed PASH C 2 4-Ring Condensed PASH C 4-Ring Condensed PASH

15 When concentrations are calculated from the same data file, nearly 40% of PAH and PASH homologues are significantly overestimated (> 50%) by 1-ion analysis compared to MFPPH

16 Double Ratio Plot

17 Weathering Maps and Property Estimation

18 Conclusions GC-GC/MS-PFPD (sulfur-specific detector) was used to obtain the mass spectral patterns for the different structural isomers of C 1 to C 4 alkylated PAH and PASH and to establish the correct homologue retention windows The Ion Analytics spectral deconvolution software was used to quantify these target compounds in coal tar contaminated sediments and soils and in crude oils by GC/MS and GC GC/MS GC GC/MS produced 30-fold lower quantitation and detection limits compared to GC/MS and weathering maps that can be used to predict the extent of weathering that has occurred on site Most importantly, both the GC/MS and GC GC/MS data make clear that SIM analysis based on the detection of 1-ion or even 2-ions to identify all of the C 1 to C 4 PAH and PASH produces dramatic overestimation of some homologue concentrations compared to using 4 ions per spectral pattern and as many fragmentation patterns as needed (MFPPH)

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