Journal of Chromatography A, 1364 (2014) 223–233
Contents lists available at ScienceDirect
Journal of Chromatography A
j ournal homepage: www.elsevier.com/locate/chroma
Two-dimensional gas chromatography/mass spectrometry, physical
property modeling and automated production of component maps to
assess the weathering of pollutants
a a b a,∗
Patrick M. Antle , Christian D. Zeigler , Dimitri G. Livitz , Albert Robbat
a
Tufts University Department of Chemistry, United States
b
University of Massachusetts, Amherst, United States
a r t a b
i s
c l e i n f o t r a c t
Article history: Local conditions influence how pollutants will weather in subsurface environments and sediment, and
Received 3 June 2014
many of the processes that comprise environmental weathering are dependent upon these substances’
Received in revised form 7 August 2014
physical and chemical properties. For example, the effects of dissolution, evaporation, and organic phase
Accepted 10 August 2014
partitioning can be related to the aqueous solubility (SW), vapor pressure (VP), and octanol–water par-
Available online 16 August 2014
tition coefficient (KOW), respectively. This study outlines a novel approach for estimating these physical
properties from comprehensive two-dimensional gas chromatography–mass spectrometry (GC × GC/MS)
Keywords:
retention index-based polyparameter linear free energy relationships (LFERs). Key to robust correlation
GC × GC/MS
between GC measurements and physical properties is the accurate and precise generation of retention
Environmental forensics
Weathering indices. Our model, which employs isovolatility curves to calculate retention indices, provides improved
Component maps retention measurement accuracy for families of homologous compounds and leads to better estimates
Retention index of their physical properties. Results indicate that the physical property estimates produced from this
PAH approach have the same error on a logarithmic-linear scale as previous researchers’ log–log estimates,
yielding a markedly improved model. The model was embedded into a new software program, allowing
for automated determination of these properties from a single GC × GC analysis with minimal model
training and parameter input. This process produces component maps that can be used to discern the
mechanism and progression of how a particular site weathers due to dissolution, organic phase parti-
tioning, and evaporation into the surrounding environment.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction of pollution effects. Because site-specific weathering processes can
dramatically change the chemical composition of fossil fuel mix-
Fossil fuel pollution is the result of a number of factors, including tures, even at the isomer level [1], it is important to assess these
naturally occurring and unintended seepages; collection, trans- changes as a function of each component’s physical and chemi-
port and storage activities; and incomplete combustion. When cal properties [2]. Once known, one can use this information to
fossil fuels and their by-products are released into the environ- determine if natural attenuation is sufficient to reduce pollutant
ment, they are subject to a number of weathering factors. These impact on the environment or if active remediation is required. To
weathering factors include physical (evaporation, adsorption, dis- make this determination, the compositional effects of dissolution,
solution, and emulsification), biological (microbial degradation), organic phase partitioning, and evaporation must be known; each
and chemical (photo- and oxidative degradation) processes, all of which one can examine by studying the aqueous solubility (SW),
of which can significantly change the chemical composition of octanol–water partition coefficient (KOW), and vapor pressure (VP)
the substance over time. Understanding how local environments of sample components, respectively [3,4].
impact weathering is critical to determining whether the local The measurement of aqueous solubility and octanol–water
ecosystem is capable of remediation, i.e., the natural attenuation partition coefficient of hydrophobic fossil fuel components such
as benzene, polycyclic aromatic hydrocarbons (PAH) and sulfur
heterocycles (PASH) and their substituted homologues is time-
∗ consuming, challenging, and susceptible to error [5]. For these
Corresponding author at: 62 Talbot Avenue, Medford, MA 02155, United States.
reasons, gas chromatographic (GC) retention indices (RIs) are often
Tel.: +1 6176273474.
E-mail address: [email protected] (A. Robbat). used to estimate these properties [6,7]. GC provides the means to
http://dx.doi.org/10.1016/j.chroma.2014.08.033
0021-9673/© 2014 Elsevier B.V. All rights reserved.
224 P.M. Antle et al. / J. Chromatogr. A 1364 (2014) 223–233
employed alkane standards to estimate the properties of aromatic
targets, RIs are calculated and physical properties are estimated
using 2-, 3-, 4-, and 5-ring PAH as bracketing compounds. Since PAH
are mutagenic [18], carcinogenic [19], and persistent [20] organic
pollutants, they serve as important model compounds for this
study. We and others have shown that when target analytes, in this
case alkylated PAH and PASH, are bracketed by structurally similar
compounds, accurate measurement of their separation is obtained
under linear temperature-programmed conditions [21–24]. This,
in turn, leads to more robust linear free energy relationships and
Fig. 1. Schematic representation of a component map; arrows correspond to weath-
ering processes. more accurate physical property estimates. This property estima-
tion process has been encoded in a new software program, allowing
for automated determination of physical properties from one anal-
not only estimate these properties, but also to assess the extent ysis, with minimal model training and parameter input. Since coal
to which natural attenuation has occurred, all without the need to tar is predominantly aromatic [25], it serves as an ideal model mix-
identify each sample component or directly measure their prop- ture to test this hypothesis, especially considering our experience
erties. Both 1-dimensional GC and 2-dimensional GC × GC can be with analysis of C1- to C4-alkylated PAH and PASH homologues
used to estimate SW, KOW, and VP via linear or logarithmic free [17,26–30].
energy relationships (LFERs). GC × GC is often used in weather-
ing studies because it provides a visual depiction of the differences 2. Experimental
between fresh and weathered sample chromatograms, and ortho-
gonal column pairings provide the means to generate LFERs to 2.1. Standards and reagents
estimate these properties simultaneously [4]. Arey and coworkers
[8,9] derived an empirical expression for the isothermal parti- Airgas (Salem, NH, USA) supplied the ultra-high purity helium
tion coefficient (K), then used this information to estimate VP, and nitrogen used in this study. Chromatography-grade toluene
SW, and the air–water and octanol–water partition coefficients and dichloromethane were purchased from Sigma–Aldrich (St.
from the 1st and 2nd dimension retention indices of diesel fuel. Louis, MO, USA). The 16 EPA priority pollutant PAH, diben-
Based on the assumption that the partitioning of the solute is zothiophene, and internal standards 1.4-dichlorobenzene-d4,
primarily controlled by size and polarizability, a two-component naphthalene-d8, phenanthrene-d10, chrysene-d12, and perylene-
LFER, with the 1st dimension retention index (RI1D) conveying d12 were obtained from Restek (Bellefonte, PA, USA). Supelco
information about size and the 2nd dimension polarizability, envi- (Bellefonte, PA, USA) supplied the base/neutral surrogate
×
ronmental researchers have used GC GC to produce contour maps spike mix (2-fluorobiphenyl, nitrobenzene-d5, p-terphenyl-
[10,11] and air and water mass transfer models [12], and estimate d14) as well as a number of neat standards: anthracene,
phase-transfer properties, phospholipid membrane–water parti- benzo(b)thiophene, fluorene, fluoranthene, hexylbenzene, pyrene,
tion coefficients and corresponding narcosis toxicity [13]. 1-phenyloctane, 1-methylnaphthalene, 2-methylnaphthalene, and
This line of research is not without its challenges – for exam- 1,7-dimethylnaphthalene. Also purchased were neat standards of
ple, the calculation of meaningful 2nd dimension retention indices, n-decylbenzene and 2,6-dimethylnaphthalene from Ultra Scien-
since either the 2nd dimension hold-up time (tM,2D) or the retention tific (North Kingstown, RI, USA) and Crescent Chemical (Islandia,
of bracketing compounds at different temperatures across the sepa- NY, USA), respectively.
ration space is required. Researchers have examined column bleed
[14] and employed continuous injections of an unretained com- 2.2. Samples and sample preparation procedure
pound [15] to assess tM,2D, and created “hypothetical” bracketing
compounds [8,9] and isovolatility curves [16]. Isovolatility curves, Pure coal tar and impacted soils were obtained from a utility in
the result of continuous solute elution over a prolonged period, Illinois. We modified EPA method 3550C [30–32]: 15 g of sample
can provide the means to obtain retention information at differ- was spiked with surrogate mix and sonicated for 10 min in 8 mL
ent elution temperatures by creating curved elution lines that cut of 50% (v/v) toluene/dichloromethane (Branson Ultrasonics, Dan-
across the 2nd dimension separation space. Unlike time-of-flight bury, CT), and the procedure was repeated eight times to obtain
(TOF) mass filters, only recently have quadrupole mass spectrome- maximum extraction efficiency. Activated copper and anhydrous
ters provided the scan speeds sufficient for invariant spectra across sodium sulfate were used to eliminate elemental sulfur and water
narrow 2nd dimension peaks and, in turn, accurate quantitative from the extracts, which were concentrated under a stream of nitro-
analysis of components of complex mixtures, such as PAH, PASH gen prior to the addition of 10 g/mL of internal standards.
and their alkylated homologs in coal tar and crude oil [17].
Fig. 1 shows a weathering map that can be used to inform 2.3. Instrumentation
remedial decisions and to delineate site-specific weathering pro-
×
cesses [10]. The axes correspond to volatility and solubility and GC GC/MS analyses were performed using an Agilent Tech-
provide important sample information. For example, sites that con- nologies (Santa Clara, CA, USA) 6890/5975C GC/MS with Gerstel
tain a large number of volatile and highly soluble compounds will (Mülheim an der Ruhr, Germany) MPS2 autosampler and CIS6
continue to pose risk to the environment, as opposed to highly injector. Since most sample components in coal tar are aro-
weathered sites in which only non-volatile and insoluble compo- matic members of homologous families, instrumental conditions
nents remain. If these compounds are also biologically inaccessible, were chosen to maximize utilization of 2D separation space
the pollution no longer poses risk to the environment. This is espe- and to increase data granularity. Following the example of Arey
cially important since local weathering conditions, even at the same and coworkers [8,9], two different GC × GC/MS methods were
site, may attenuate pollution differently. employed to evaluate if the accuracy of physical property estimates
In this study, we report for the first time the use of isovolatility was method-dependent. These included differences in column
curves to generate retention indices in both GC × GC dimensions manufacturer and size, split ratio, temperature program, flow
at every point in 2D space. In contrast with other studies that rate, and modulation time; instrumental parameters for both
P.M. Antle et al. / J. Chromatogr. A 1364 (2014) 223–233 225
Table 1
times obtained from the bracketing compound isovolatility curves,
GC × GC method parameters.
see Fig. 2A. As fitting the correct curve to the 2D chromatogram
Method A Method B is essential for obtaining accurate retention indices, the MATLAB
curve fitting toolbox was utilized to test a number of possible mod-
GC parameters
Injection mode 20:1 split Splitless els. During the training step of each regression, the curve toolbox’s
Injection 1 1 “fit” function was used to fit a curve based on a specific model (lin-
volume (L)
ear, quadratic, power, etc.), with the x and y values obtained from
− ◦ ◦ − ◦ ◦
Injection 20 C, 12 C/min to 20 C, 12 C/min to
◦ ◦ the 1D and 2D retention times of the isovolatility curve bracketing
temperature 320 C, hold for 5 min 320 C, hold for 5 min
compounds. The resulting curve was stored and the goodness of
or PTV
program fit recorded. This process is repeated for all curve types, and the
Flow 1.2 mL/min, constant 1.0 mL/min, constant
best fit is selected for further use. In order to use these isovolatility
flow flow
curves for retention index calculation and physical property esti-
Carrier gas Helium
◦ mation, a second spreadsheet was used to input the 1st and 2nd
Transfer line 300 C
temperature dimension retention times and literature physical property val-
× × × ×
Column 1 30 m 0.25 mm 0.25 m 30 m 0.25 mm 1.0 m ues of training compounds that spanned the retention space and
Rxi-5SilMS (Restek) DB-5 UI (Agilent)
physical property estimation limits of the sample, see Fig. 2B. This
Column 2 1.0 m × 0.25 mm × 0.25 m 1.5 m × 0.18 mm × 0.36 m
sheet also contained the retention times of the user-defined target
Rxi-17SilMS (Restek) Rxi-17SilMS (Restek) compounds.
Modulation 12 8
time (s) Using this data, the program compares the RT from the train-
Hot jet 310
ing compounds to those of the isovolatility curves to determine
temperature
which bracketing standards to use for each training compound.
(◦C)
Next, it calculates the 1D and 2D retention indices for each training
Cryogen flow 15
(L/min) compound and regresses the known physical properties against the
◦ ◦
Temperature 60 C for 1 min, 60 C for 1 min, RI values. The literature vapor pressures, octanol–water partition
◦ ◦ ◦ ◦
program 4.5 C/min to 320 C, hold 6.5 C/min to 320 C, hold
◦ coefficients, and aqueous solubilities of these training compounds
−
for 5 min, 20 C/min to for 5 min
◦ are used to construct free energy relationships that output VP, KOW,
300 C, hold for 5 min
and S estimates for each point in the 2D chromatogram based
Run time (min) 65.77 76.77 W
MS parameters solely upon retention indices, see Fig. 2C. These estimates are used
Solvent delay 10 17
to “map” the entire chromatogram. Partition lines for both vapor
(min)
pressure and aqueous solubility are evaluated in half-minute inter-
Mass range 50–300 50–350
(m/z) vals across the 2D chromatogram to delineate sections of similar
Scan rate 23.7 19.8 properties. The two resulting surfaces are plotted as a contour map
(scans/s) on top of the 2D chromatogram, with the contour lines correspond-
Quadrupole 150 150
ing to physical property log integer values. The model then outputs
temperature
V , K , and S estimates for each target compound. Any com-
(◦C) P OW W
pound in the sample can be evaluated using this approach. If the
Ion source 230 230
temperature physical property literature values of compounds in the sample
(◦C)
are known, the model-generated estimates are compared to pre-
viously measured values. In this study, however, there was limited
opportunity for these comparisons, given the paucity of KOW and
×
GC GC/MS methods are found (Table 1). Columns were connected
SW literature for coal tar components beyond the 16 PAH listed by
using Restek press-fit connectors or VICI Valco (Houston, TX, USA)
EPA as target compounds [33–37]. In cases where literature val-
low mass external union connectors. The GC × GC cryogenic and
ues are available, the program generates an error analysis (model
thermal modulation hardware was provided by Zoex Corporation
versus literature value estimate) for each LFER. This error analy-
(Houston, TX, USA). GC Image (Lincoln, NE, USA) supplied the soft-
sis is displayed in the output spreadsheet and each compound is
ware to create the three-dimensional chromatograms. Unknown
plotted on the component map with a user-defined color that cor-
sample components were identified using Ion Analytics (Andover,
responds to the magnitude of the error. Importantly, only a single
MA, USA) spectral deconvolution software.
GC × GC analysis is required to produce the contour map and the
2D retention indices were confirmed by analyzing standards
corresponding property estimates.
under isothermal conditions; i.e., we compared our GC × GC RI2D
to 1D GC/MS RI results for the same compounds on the same col-
3. Theory
umn. These 1-dimensional GC/MS analyses were performed using a
Shimadzu (Columbia, MD, USA) GC2010/QP2010+ instrument on a
As outlined by Curvers and expressed in Eq. (1), retention, r,
30 m × 0.25 mm × 0.25 m RXI-17MS column provided by Restek.
as a function of temperature, T, is controlled by two factors: (1)
These runs were performed at temperatures between 100 and
◦ ◦ thermodynamics, as described by the bracketed terms; and (2) fluid
300 C at 25 C increments. Methane was used to measure tM.
dynamics, as described by the t0(T) term: T
R dT
2.4. Software programming and functionality r T
( ) = (1)
T t (T) 1 + (exp( S/R)/ ) exp( H/RT)
0 0
The automated physical property program was constructed
where:
using the R and Octave software environments within MATLAB
(Mathworks, Natick, MA, USA). The software is available for down- 3 2
(P − 1) 128 × L
t0(T) = × × ( (T)) (2)
load in supporting information. Basic functionality is described 4 2 2
P − P +
2 1 3 × po × dc
below, and a point-by-point list of instructions and illustrative fig-