Oligomer-Specific, Short Chain Linear Alcohol Ethoxylate

Letter Cite This: Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

pubs.acs.org/journal/estlcu

Oligomer-Specific, Short Chain Linear Alcohol Ethoxylate Quantification via Comprehensive Two-Dimensional Gas Chromatography Brian D. Drollette,†,‡ Rebecca J. Brenneis,†,§ and Desiree L. Plata*,†,§ †

Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06511, United States Exponent, Inc., Maynard, Massachusetts 01754, United States § Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States Environ. Sci. Technol. Lett. Downloaded from pubs.acs.org by SAINT LOUIS UNIV on 08/27/18. For personal use only.

‡

S Supporting Information *

ABSTRACT: Non-ionic linear alcohol ethoxylated surfactants (LAEs) make up a broadly utilized class of compounds commonly employed in unconventional hydraulic fracturing activities. However, current detection and quantification methods fail to operate in relevant concentration ranges and are limited in specificity because of a lack of access to all analytical standards for individual LAE oligomers. Here, we present a novel extraction and quantification technique for aqueous samples containing both petroleum hydrocarbons and short chain LAEs with alkyl chain lengths of 6−10 carbons and 0−9 ethylene oxide groups. Using liquid−liquid extraction and two-dimensional gas chromatography with flame ionization detection, the method provided over 80 and 100% recovery of LAEs and n-alkane hydrocarbons, respectively. Individual LAE oligomers were quantified using calibration curves of n-alkanes and adjusted response factors based on the effective carbon number (ECN) concept, and we present a detailed framework for calculating ECN using primary alcohol standards and ether functional group contributions. This method was applied to a centralized waste treatment facility effluent discharging directly into a local river in Pennsylvania and indicated parts per million-level discharges of individual oligomers. This first demonstration of LAE and petroleum hydrocarbon quantification will gain utility as researchers seek to understand the environmental fate of these industrially important chemicals.

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INTRODUCTION

the most abundant industrial chemical classes cannot be monitored accurately at meaningful environmental levels. Accurate LAE characterization and quantitation are complicated by the fact that LAEs are produced in polydisperse mixtures and with limited availability as pure authentic standards.7 In spite of this, several methods have been developed to try to quantify LAEs in aqueous samples, each with limitations and applied across varying ranges of oligomer sizes (Table S1).7−16 Some methods rely on derivatization to report the total EO content (e.g., via the cobalt thiocyanate colorimetric method14) or bulk mass of LAEs with particular alkyl-tail lengths (e.g., via the HBr fission method11). Others are oligomer-specific and require extraction, derivatization, and calibration using commercially available technical mixtures via liquid chromatography with mass spectrometric detection (LC−MS).7,17,18 While these methods offer several advantages,

The linear alcohol ethoxylate (LAE) non-ionic surfactants, characterized by the molecular formula CxH2x+1(OCH2CH2)nOH, where x denotes the length of the linear alkyl tail and n is the number of ethylene oxide (EO) groups, make up one of the most abundantly manufactured non-ionic surfactants.1 Nearly 1 million tons are used annually,1,2 often in detergents, herbicides, and lubricants and as solubilizers in enhanced oil recovery.3,4 While the main source of environmental release is “down the drain” disposal, activated sludge treatment can remove up to 99% of the total LAE mass.5 However, when such treatments are absent, LAE releases have been documented in surface waters from centralized waste treatment facilities (CWTFs) that historically accepted oil and gas wastewaters.6 Such disposal contributes to biochemical oxygen demand and can enhance transport of nonpolar organic compounds. Nevertheless, LAE discharges are not regulated, and there is no robust quantitative method for individual LAE oligomers with relevant detection limits (e.g., parts per billion to parts per million). As a result, one of © XXXX American Chemical Society

Received: July 13, 2018 Revised: August 9, 2018 Accepted: August 13, 2018

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DOI: 10.1021/acs.estlett.8b00358 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

Letter

Environmental Science & Technology Letters

water was used to generate a solution with the desired salt content which was then spiked with nC10−nC28 even alkanes (25 μg; Restek 31064) and then extracted three times using 150 mL of dichloromethane (DCM). Funnels were gently inverted for several minutes to avoid formation of an emulsion that would otherwise result from vigorous agitation (Figure S1). The organic phase and the organic−water interface were collected, and triplicate organic phases were combined, dried with anhydrous Na2SO4, and rotary evaporated until the volume reached 1 mL. The LAE recovery was calculated as the total peak area relative to GC×GC-FID-injected solutions of the same concentration. The alkane recovery was measured with explicit calibration curves using authentic standards. We note that this extraction method was applied to aqueous fluids only, and samples of solids or sediments would require different extraction techniques designed for those matrices. Field Sample. Grab samples of effluents were collected from the Josephine CWTF (National Pollutant Discharge Elimination System (NPDES) Permit PA0095273) in Blacklick Creek, PA, in January 2016. During this time, the Blacklick Creek was flowing at relatively high flow rates for the stream (approximately 190−646 million gal day−1).22 The Josephine CWTF is permitted for a maximum daily discharge of 0.155 million gal day−1 of conventional, industrial oil-produced, and industrial gas-produced waters only,6 with over 4.2 million gal treated in from January to December 2016. Samples were collected in pre-combusted amber glass jars. Surface water samples from Blacklick Creek were collected approximately 15 m upstream, 15 m downstream, and directly from the effluent pipe. Samples were collected with no headspace, adjusted to pH below 2 with 50% (v/v) HCl, and stored at 4 °C until they were extracted within 3 days following the protocol described above (without addition of LAE, alkanes, or salts). The 3-day holding time likely reduces the uncertainty introduced by preservation issues for these types of compounds, considering the Environmental Protection Agency (EPA) has used holding times of 14 days for similar structures (i.e., glycols).23 Instrument Parameters. GC-FID analyses were performed on a Thermo Trace 1310 GC-FID instrument, and GC×GC analyses were performed on a LECO Pegasus 4D instrument using TOF-MS and FID. Briefly, we employed a nonpolar column for the GC-FID analyses and a nonpolar primary and polar secondary column configuration for GC×GC (see the Supporting Information for details). Quality Assurance. All extraction glassware was precleaned with an acetone rinse and water wash and then baked at 450 °C for 8 h. The Teflon stopcocks used with the separatory funnels were cleaned with five separate 15 min sonications in water, methanol, acetone, methylene chloride, and hexane. To rule out potential artifacts associated with a GC analysis for these low-molecular weight LAEs, we evaluated injection efficiency and column carryover. Injection inefficiency could result from thermal destabilization of the LAEs or carryover in the GC inlet. To test for losses, repeated injections of the LAE mixture and an alkane standard (n-octane) were made at increasing inlet temperatures (200−300 °C). The total oligomer peak area was normalized to the alkane and examined across the temperature range (Figure S2). Column carryover was assessed by analyzing a solvent blank between each injection.

such as low detection limits and efficacy over a wide range of homologues, the methods are limited in their extraction recovery (as low as 33%), are uncertain due to derivatization inefficiencies, and rely on manufacturer-reported estimations of oligomer distributions in technical mixtures as a means of calibration when pure standards are not available. These methods also assume homogeneous ionization efficiencies across all oligomers, which is a poor approximation (see Getzinger et al.6 for adduct-dependent variations in the oligomer distribution). Therefore, techniques that can confirm individual LAE identity and distribution without the use of derivatization and with more robust quantitation are needed. A common procedure for quantifying organic compounds in the absence of analytical standards is the effective carbon number (ECN) concept employed in gas chromatography with flame ionization detection (GC-FID). This technique takes advantage of the broad linear range of the FID response in proportion to combusted hydrocarbon content and relates FID signal enhancement or suppression to the presence of heteroatoms.19 Thus, systematically quantifying the effect of heteroatom-containing functional groups on a known reference standard’s response factor (e.g., in relation to an alkane of equal carbons) enables the calculation of the unknown analyte’s own response factor. Ultimately, this provides a route to quantitation of an analyte of a known chemical structure without a comprehensive set of analytical standards for each oligomer. Previous deployments of the ECN concept for GC-amenable LAE quantification have relied on derivatization and independently published values for relative signal suppression of the oxygen-containing functional groups,8,20 which could be a flawed approach.21 Currently, there is no broad analytical method for extracting, detecting, and quantifying LAEs in environmental samples, especially those impacted by oil and gas wastes. Here, we developed an ECN method to extract and quantify GCamenable LAEs from aqueous samples while maintaining the ability to quantify other extractable hydrocarbons using liquid−liquid extraction (LLE) and comprehensive twodimensional gas chromatography with FID (GC×GC-FID). The extraction efficiency was assessed for hydrocarbons and low-molecular weight LAEs under variable concentrations and salinities that might reflect those observed in oil and gas residual fluids. LAE oligomers were identified with time-offlight mass spectrometry (TOF-MS) for elution patterns and then quantified using n-alkane standards and ECN-adjusted response factors via GC×GC-FID. Finally, the method was applied to an oil and gas residual wastewater. This is the first demonstrated example of an extraction procedure for the quantification of a suite of petroleum hydrocarbons and lowmolecular weight LAEs in a single instrument run at relevant LAE concentrations.

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MATERIALS AND METHODS Liquid−Liquid Extraction Test Matrix. Extraction efficiencies of LAEs and n-alkane hydrocarbons were determined in triplicate across a range of surfactant (Alfonic 610-3.5 at 0, 10−4, 10−3, and 10−2 % (v/v)) and salt (NaCl at 0, 0.01, 0.1, and 1.0 M) concentrations. Alfonic 610-3.5 (Sasol North America) is a “100% active” technical mixture of LAE characterized as having alkyl tails with 6−10 carbons and an average of 3.5 moles of ethylene oxide (EO) groups. Total mixture volumes of 500 mL were prepared in 1-L glass separatory funnels with Teflon stopcocks. Milli Q (18 MOhm) B


Letter


Figure 1. Linear alcohol ethoxylate (LAE) chromatographic elution patterns and solvent extraction efficiency. (A) GC×GC separation of LAE oligomers in a technical mixture (Alfonic 610-3.5) and n-alkanes (nC10−nC28) concentrated from aqueous samples via liquid−liquid extraction with dichloromethane. Primary alcohol retention times were confirmed with analytical standards, and LAE oligomers were confirmed with mass spectra (for low-EO molecules, see the Supporting Information) and refined by previously reported literature detailing GC×GC elution patterns of LAEs. (B) Across a range of surfactant loadings, recoveries of surfactant and hydrocarbons were 73−86 and 97−122%, respectively, irrespective of the widely varying salt content.

Calibration. Oligomers were quantified with calibration curves of n-alkanes (five-point calibration from 25 to 1000 ng with an r2 of greater than 0.999) after the oligomer peak area was adjusted according to its ECN. Using a signal-to-noise (S/ N) instrumental limitof 25, method detection limits ranged from 0.4 to 42 μg L−1 depending on the oligomer (Table S2).

Effective Carbon Number Calculations. The effective carbon number (ECN) for LAEs can be calculated for any oligomer using eq 1 ECNLAE = ncarbons + nEO × dECNEO + dECNOH

(1)

where nc is the number of total carbon atoms in the molecule, nEO is the number of EO groups, dECNEO is the difference in FID signal due to the EO group ether oxygen relative to a reference alkane, and dECNOH is the relative signal decrease due to the primary alcohol. Here, dECNOH was determined explicitly through the use of primary alcohol standards and dECNEO was calculated iteratively (see Results and Discussion). Knowing the target molecule’s ECN, one can calculate its response factor relative to an alkane with an equal number of carbon atoms via eqs 2 and 319 ECNanalyte =

RFanalyte =

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RESULTS AND DISCUSSION GC×GC Analysis of the LAE Technical Mixture. Complete resolution of each expected oligomer was achieved with good peak shape and minimal tailing on both columns via GC×GC (Figure 1A), even without derivatization techniques such as trimethylsilylation.24,25 Oligomers eluted predictably, with longer alkyl-tail molecules eluting later in the first dimension and increasing EO content molecules eluting later in the second dimension. At total carbon contents above 10, oligomers eluted in clusters of three and were grouped with the same total number of carbon atoms in the structure (e.g., C10EO0, C8EO1, and C6EO2, defining a shorthand of CxEOn for all, where x denotes the alkyl (C) chain length and n is the ethylene oxide (EO) number). These patterns were identified in previous work,25,26 and mass spectral matching provided further confirmation (Figures S3−S9). Injection efficiencies improved at a high temperature (300 °C), and no carryover was observed (Figure S2). Extraction Efficiency. The gentle inversion LLE technique and capture of the solvent−water interface resulted in good

MWanalyte × ECNref MWref × RFanalyte

(2)

area ref × massanalyte areaanalyte × massref

(3)

where massanalyte and areaanalyte are the mass and recorded peak area of the target molecule, respectively, massref and arearef are the mass and peak area of the reference alkane, respectively, MW is the molecular weight, and ECNref is the effective carbon number of the reference alkane (e.g., octane’s ECN is 8). C


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Figure 2. Calculation of ECN and influence on LAE distribution. (A) Measured ECN of the primary alcohols (red, spaced dash) and calculated ECN of the LAEs (blue, dashed) relative to an alkane of equal carbons (black, solid). The mean experimentally measured dECN for the primary alcohols (nC6−12OH) was −1.63 ± 0.34. (B) Iterative calculations of the ether functional group deviation from the theoretical effective carbon number (dECN) resulted in a decrease of FID signal equal to −2.08 ± 0.06 carbon units per ether group. (C) Distributions of the technical mixture Alfonic 610-3.5 by alkyl chain length and ethylene oxide (EO) group distribution skew toward the lower EO content before ECN adjustment (i.e., assuming a homogeneous FID response for all LAEs). (D) After adjusting on the basis of the calculated ECN, the peak distribution shifted toward three to five EO groups.

7.4%, respectively, going from 0 to 1.0 M NaCl (Figure S10 and Table S4)). ECN: Primary Alcohol Signal Contribution. The decrease in the intensity of the signal for a primary alcohol relative to an equal carbon number alkane (dECNOH) was experimentally measured with analytical standards using four primary alcohols and their corresponding alkanes on GC×GCFID via eqs 2 and 3, where dECNOH was −1.63 ± 0.34 (Figure 2A; n = 5 injections). Others have used a single alkane reference standard (e.g., octane) to calculate ECNs for all primary alcohols,31 and following that approach would have yielded a less precise ECNOH (dECNOH = −1.56 ± 0.66). Critically, we note that the ECN values should be treated like response factors for a calibration curve and account for the variation in experimental conditions. Previously reported dECNOH values range from −0.72 to −0.5,19,31 varying substantially from the values measured here. Indeed, Kallai et al.21 suggested that accurate calculation of ECN values relies not only on the carbon number of the reference and target compound but also on instrumental parameters such as detector temperature, geometry, and fuel source, as well as injection temperature and column selection. For example, analysis of the same standards on a traditional one-dimensional

recovery of both bulk LAEs and alkane hydrocarbons, ranging from 73 to 86% for LAEs and from 97 to 122% for alkanes (Figure 1B and Table S3). These relatively high extraction efficiencies were enabled by clearly delineated aqueous and organic layers, a systematic effort to capture the interface between the two, and the avoidance of an emulsion. Note that following EPA Method 3510C guidance to shake the separatory funnel “vigorously for 1−2 min” resulted in an undesirable, thick emulsion layer (see Figure S1).27 If an emulsion is expected, other applications have shown that the addition of salt can enhance recovery of more polar analytes.28 However, in our experiments, the LAE recovery did not increase significantly with the addition of NaCl (Figure 1) because of high organic-solvent-to-water volume ratios used. For example, the difference in the fraction of the C6EO2 oligomer (log Kow = 2.01)29,30 in the organic phase would increase by less than 2% (from 96.9 to 98.3%) with the addition of 1.0 M NaCl (see the Supporting Information). Nevertheless, relative standard deviations (RSDs) for the extractions were improved with salt addition, but only at high surfactant loadings (e.g., at 10−3 and 10−2 % (v/v) LAE, RSD values improved from 7.6 to 5.4% and from 19.9 to D


Letter


Figure 3. LAEs detected in the Josephine CWTF, Blacklick Creek, PA. (A) GC×GC chromatogram of the Josephine CWTF effluent extract, annotated to show LAE oligomers and the Diesel Range Organic compound (DRO) elution range. Here, CxEn indicates a linear alcohol ethoxylate where x denotes the alkyl (C) chain length and n is the ethylene oxide (EO) number. Quantified oligomers in the effluent (B) were as high as 19000 ± 4000 ppb, while the (C) downstream sample had concentrations of up to 99 ± 16 ppb with a similar molecular distribution. Some oligomers with ethoxymer numbers of 6 or 7 were nondetectable (see Table S6 for data and Table S2 for detection limits); e.g., we highlight the fact that the method is not very sensitive to C10EO7.

GC-FID in our lab yielded a dECNOH of −5.98 ± 0.63. Clearly, instrumental differences affect ECN, and we discourage the use of dECN values from reported literature, rather than one’s own measurement. ECN: Ethylene Oxide (EO) Signal Contribution. Analytically pure standards of individual LAEs are not readily available for all oligomers at reasonable costs for routine analysis (Table S5). Therefore, the experimental approach used for the dECNOH measurement was not possible for the ether functional group within the LAE. To address this limitation, we took an iterative approach (Figure S11) to determine the contribution of the ether oxygen to the molecule’s FID signal suppression using a solution of LAEs (Alfonic 610-3.5) and n-alkanes (n = 6 injections). Beginning with an initial assumption of peak proportionality without ECN correction, we estimated specific oligomer masses. Those masses were used to determine response factors relative to alkanes with equal carbons (eq 2), and the overall ECN was determined for each oligomer (eq 3). The mean difference in measured ECN and total carbon number from known the molecular structure, the dECNEO per ether group, was calculated for each oligomer. That is, knowing the alkyl-tail and primary alcohol contributions, one assumes any extra FID

signal suppression is due to the ether oxygen functional group, and we distributed the signal response change for the entire oligomer across the number of EO groups within it. Then, a new ECN for each oligomer was calculated using the dECNEO and dECNOH (eq 1), and that ECN was used to generate a new response factor (eq 2) that was multiplied by the peak area to determine the corresponding estimated masses. This process was repeated until the mean dECNEO converged, where the contribution of one ether group to FID signal suppression was −2.08 ± 0.06 (Figure 2B). With this information and following these steps, one can calculate the ECN for any oligomer (following eq 1). Without any adjustment of the peak areas of the technical mixture solution using ECN corrections, the raw FID peak areas (and assumed homogeneous FID responses) would imply an oligomer distribution skewed heavily toward low EO content (i.e., where there is the least signal suppression, as expected from the chemical structure (Figure 2C)). After the ECN and associated response factors had been applied, the distribution shifted and centered around three to five EO groups (Figure 2D). This adjusted distribution is consistent with the reported distribution of the technical mixture (3.5 average EO content) and highlights the potential error E


Letter


was largely the result of LAEs, and signal suppression without response factor correction is artificially underestimating the total amount of EPA-defined DRO. Simultaneously, the amount of diesel range hydrocarbons, which the measurement is intended to report, is overestimated because of the GC amenability of LAEs and their elution within this range. Because of these artifacts, we suggest caution when applying DRO measurements to samples suspected to contain compounds that are not definitively diesel hydrocarbons, such as LAEs. Quality Assurance, Limitations, and Method Application. This method is valid for low-molecular weight LAEs (from C6EO0 to C10EO9) and potentially other GC-amenable surfactants, whereas higher-molecular weight LAEs and other surfactants such as alkylphenol ethoxylates may fall outside the chromatographic window. In addition, samples containing higher-molecular weight LAEs could have a greater potential for emulsion formation and may impede recovery during extraction if care is not taken to avoid emulsion formation. Nevertheless, this approach overcomes common inaccuracies introduced by assumptions related to ionization efficiency and assumed technical mixture distributions to give oligomerspecific LAE quantitation. Enabled future studies include elucidation of previously untraceable biodegradation pathways in high-salinity environments or the oligomer-specific fate or effects in natural or engineered (i.e., waste treatment) systems. Finally, regulatory agencies could now monitor this compound class more accurately at environmentally relevant levels and have an improved understanding of the true composition of DRO simultaneously.

introduced when using peak areas as a proxy for compound abundance. Comparison to Calibration with Authentic Standards: Quantitative Efficacy. The ECN method was compared to traditional standard calibration curves for quantitative effectiveness. Calibration curves for two oligomers (C6EO2 and C8EO4) were generated using compounds purchased from Sigma-Aldrich (C6EO2, product no. 449393, and C8EO4, product no. T3394), and these were used to quantify C6EO2 and C8EO4 in a test solution of the Alfonic 610-3.5 surfactant. On-column masses quantified via standard calibration curves and the ECN-calculated response factors for the C6EO2 oligomer were 95 ± 1 and 103 ± 1.4 μg, respectively, and for C8EO4 oligomers were 214 ± 2.6 and 203 ± 2.4 μg, respectively (Figure S12). While significantly different from one another, the errors were generally small (7.8% low and 5.4% high for the C6EO2 for C8EO4 oligomers, respectively (i.e., there was no observed systematic bias for the two tested oligomers)), and agreement between the two methods was strong. LAE Quantification in an Environmental Sample. Josephine Centralized Waste Treatment Facility. LAEs with C6−C10 alkyl-tail oligomers with 0−7 EOs were detected and quantified in the Josephine CWTF effluent and in the downstream grab sample (Figure 3A and Table S6). Concentrations in the effluent sample ranged from 400 ± 100 μg L−1 (C6EO7) to 19000 ± 4000 μg L−1 (C6EO5), and the distributions centered around three to five EO groups (Figure 3B). The downstream sample exhibited concentrations up to 100 ± 20 μg L−1, and the distribution resembled that of the effluent with a slight shift toward the higher-EO content oligomers, possibly because of sorptive losses of the low-EO content oligomers coupled with some dilution (Figure 3C and Figure S13). These distributions are similar to LAEs measured previously via LC−high-resolution MS (HRMS), where three to five EO groups were most common for the 6-, 8-, and 10carbon alkyl-tail oligomers.6 Finally, no LAEs were detected in the upstream sample at the 0.4−42 ppb detection limits (Table S2). Concentrations of individual oligomers should be considered when evaluating discharge permitting requirements, as the ecological risk varies with oligomer structure.32−34 For example, while oligomer-specific exposure has been limited to date, computation and structure−activity relationships have predicted higher toxicity for longer alkyl-tail and lower-EO content oligomers.5 Considering that the methodology described here provides oligomer-specific quantitation, compound-specific effects, as well as synergistic interactions, can now be evaluated and individual, short-chain oligomers monitored. Comparison to Previous DRO Measurements. Previous characterizations of the Josephine effluent required at least two methods: LC−HRMS for LAE identification and GC-FID for diesel range organic (DRO) compound quantification.6 Calculated DRO values were 3080 μg L−1 (within the range of measurements from other studies (380−3300 μg L−1)),6 suggesting a remarkably high level of diesel-like compounds for an aqueous, surface water sample. However, the enhanced separation capability of GC×GC revealed that most discernible peaks within the DRO range were actually LAEs (Figure 3A), rather than petroleum hydrocarbons. Of the 21 LAEs quantified, 19 were within the DRO range and contributed 67% of the total DRO (2075 μg L−1).35 Thus, the DRO signal

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.estlett.8b00358. This includes: review of previous analytical methods, photographs of emulsion layers, analytical details, injection quality control data, oligomer-specific detection limits, annotated two-dimensional chromatogram and paired mass spectra and library matches, extraction recoveries and relative standard deviations as a function of salt and surfactant concentration, discussion of the salting out effect, diagram of the iterative process for calculating ECN, market availability of individual LAE standards, comparison between ECN and authentic standards, and LAE concentrations in the Josephine CWTF (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 617-258-8596. ORCID

Brian D. Drollette: 0000-0003-2746-7279 Desiree L. Plata: 0000-0003-0657-7735 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the National Science Foundation (NSF) for CBET Grant 1336702, Yale University for support, and the anonymous reviewers for efforts to improve this work. F


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