Quantitation, Detection and Measurement Precision of Organic Molecular
Markers in Urban Particulate Matter
Min Li1*, Stephen R. McDow2, David J. Tollerud3 and Monica A. Mazurek1
1
Department of Civil & Environmental Engineering, Rutgers University, Piscataway, NJ
Environmental Characterization and Apportionment Branch, U.S. EPA, Research
Triangle Park, NC
3
School of Public Health and Information Sciences, University of Louisville, Louisville, KY
*
Corresponding author
2
Abstract
This work focuses on analysis of organic molecular markers in airborne particulate matter (PM)
by Gas Chromatography/Ion Trap Mass Spectrometry (GC/IT MS). The particulate samples used
in the method development were collected as PM10 in metropolitan Philadelphia area during
2000. The analytical method emphases a detailed compound identification procedure by ion trap
mass spectrometry, five-point mass calibration for compound quantitation and estimates of
measurement uncertainty of ambient particulate samples. A systematic procedure for describing
measurement precision of the organic marker compounds is critical input to current source
apportionment models.
INTRODUCTION
Particulate organic matter accounts for a large fraction of ambient fine particulate matter
in most urban and rural locations in the United States (EPA 2002). The operation of the
Environmental Protection Agency’s (EPA) Speciation Trends Network (STN) since 2001 has
provided a considerable amount of data on magnitudes, trends and patterns of urban ambient
concentrations from multiple sites (EPA 2002). Particulate organic matter is composed of a large
number of individual compounds with widely differing chemical and physical properties.
Consequently, atmospheric behavior, health impacts, and mass estimates are likely to vary
considerably with location, season, and time of day. Further speciation of particulate organic
matter by analysis of its individual components is valuable for a number of purposes, including
prediction of atmospheric behavior and potential health impacts. Because some major sources of
fine particulate matter are mainly organic, analysis of individual organic molecular markers by
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gas chromatography/mass spectrometry (GCMS) also has great potential for source
apportionment applications.
Organic markers for source apportionment have been quantified in atmospheric
particulate matter by gas chromatography/mass spectrometry (GCMS) for nearly two decades
(Mazurek et al. 1987, Rogge et al. 1993, Schauer et al. 1996, Zheng et al. 2000, ManchesterNeesvig et al. 2003). Results from these studies have demonstrated clearly the value of organic
markers for source apportionment of fine particulate matter. Further work that would improve
this approach includes continued investigation of which markers to analyze, additional
development of sampling and analysis methods, and estimates of uncertainties to establish
realistic measurement quality expectations.
Uncertainties associated with organic markers are difficult to assess. Measurement
procedures are time consuming, and there are multiple potential sources of uncertainty, including
sampling, shipping, extraction, concentration, storage, and GCMS analysis. This circumstance
complicates the use of organic markers for source apportionment, since measurement uncertainty
is a required model input and the value used for a marker’s uncertainty can critically influence
whether sources truly can be resolved (Paatero and Hopke 2003).
In this paper we describe the analytical approach developed for analysis of organic
molecular markers for particulate matter collected in metropolitan Philadelphia, PA. Ambient
particulate matter with size less than 10 µm (PM10) was collected for 24 hours from 1/20/00 –
2/6/00, 3/28/00 – 4/20/00, 7/31/00 – 8/12/00, and 10/16/00 – 11/2/00 at the City of Philadelphia’s
Air Management Service’s North Broad Street site. A total of 71 samples were acquired using a
high volume Anderson PM10 sampler at a flow rate of 38 ft3 / min on pre-combusted 8 × 10 inch
Whatman quartz fiber filters (Whatman product No. 1851-101, Clifton, New Jersey). The
complete Philadelphia results are reported elsewhere (Li et al. 2004). In addition, these ambient
samples were used to evaluate measurement uncertainty and data quality, which we report in the
present paper.
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Although GC/MS analysis of organic molecular markers is a fairly common approach
applied to ambient particle analysis little information about the precision of these measurements
has been provided specifically for the parts-per-billion determinations of single organic marker
compounds in urban particular matter (PM). Such information is critical input to current source
apportionment models since the uncertainty of analytical measurement itself is the primary
quantifiable uncertainty in source receptor models (Schauer et al., 1996). The uncertainty of the
analytical measurements has been estimated only as ±20% for all the molecular markers due to
lack of accurate measurement of the analytical precision (Schauer et al., 1996; Zheng et al., 2002).
The problem with this estimation is that different molecular markers have different analytical
uncertainties because of their various volatilities and chemical structures within the analysis due
to molecular properties such as volatility, molecular weight and structure, and presence of
heteroatoms (e.g., O, N, S). If the molecular markers have significantly different analytical
uncertainties, the ±20% estimation is not likely to generate accurate air pollution source
apportionment results using current receptor models. In this case, knowledge of the analytical
uncertainty for every molecular marker is critical input to source receptor models. Therefore, an
approach is needed to evaluate the measurement precision of molecular markers in ambient
particulate matter using GC/MS analysis.
Precision and measurement bias have become a big concern to state and local air quality
managers and the U.S.EPA (National Academy of Sciences, 1998, 1999, 2001, 2002).
Regulatory groups must understand underlying measurement and precision factors relating to
organic marker ambient mass concentrations before requiring and implementing any control
strategies on specific urban sources of PM. This work addresses the analytical precision and bias
for the measurements of the organic molecular source markers in atmospheric particles with
nominal particle diameters <10 μm (PM10). Measurement precision and bias can be used to
evaluate analytical results of molecular marker abundance in urban PM10 samples, indicating the
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quality of the measurements and to what extent the measurements can be used reliably for policy
and regulatory decisions.
For this study an ion trap mass spectrometry interfaced with gas chromatography (GC/IT
MS) was used to identify and quantify molecular markers. The majority of ambient molecular
marker studies associated with PM have used quadrupole mass spectrometry to analyze the
organic compounds of interest (Schauer et al., 1996; Rogge et al., 1993; Mazurek et al., 1989).
Ion trap mass spectrometry is generally 5 to 10 times more sensitive than a quadrupole mass
spectrometry in the full-scan mode (Wong and Cooks, 1997). The enhanced sensitivity of ion
trap mass spectrometry is based on the trap’s ability to accumulate ions, therefore increasing the
signal-to-noise ratio of the chromatographic retention band containing the marker compound.
Spectra generated from ion trap and quadrupole mass analyzers also are somewhat different for a
given compound. Therefore, it is essential to use authentic standards run on each system before
accurate identification and quantitation of that marker compound can be reported in ambient PM
measurements.
1. Experimental Methods
1.1 Operating Conditions for Gas Chromatograph/Ion Trap Mass Spectrometer (GC/IT MS)
Analyses of all samples, including derivatized portions, were carried out on a Varian
3800 capillary column gas chromatograph interfaced to a Saturn 2000 ion trap mass spectrometer
(GC/MS). A fused silica capillary column coated with DB-1701was used (30 m, 0.25 μm coating
thickness and 0.25 mm internal diameter, J&W Scientific, Wilmington, Delaware). The DB1701
coating consists 7% cyanopropyl, 7%phenyl, 86% dimethylpolysiloxane and is used widely for
compounds with low to mid-polarity. The DB1701 phase provides greater separation between
benzo[b]fluoranthene and benzo[k]fluoranthene compared to DB-5 column based on analysis of
authentic standards. The GC analytical method was programmed for 60.5 minutes and consisted
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of the following steps: 1) started at 50C isothermal for 3 minutes; 2) a temperature ramp of 20C
/min up to 150C; 3) isothermal hold for 3 minutes; 4) temperature ramp of 4C/min until 280C;
and 5) isothermal hold of 17 minutes to end of run (60.5 min).
1.2 Standards
Perdeuterated n-tetracosane (C24D50) was added as a surrogate standard addition to all
sample filters prior to solvent extraction. This standard also was added to the entire set of
ambient molecular marker standards to determine relative response factors for each of these
compounds needed to quantify the marker compound when detected in the ambient PM sample
extracts. nC24D50 has been used extensively as an internal standard for quantifying organic
fraction of aerosol samples (Mazurek et al., 1987; Rogge et al., 1993a). For this study of ambient
PM, three additional perdeuterated standards, C30D62, pyrene-d10, and lauric acid (C12D23HO2),
were added to the sample filters before extraction to estimate recovery of these specific
compound classes.
An organic acid standard mixture was prepared in acetone at 25.0-27.0 g/ml with
monocarboxylic acids, ranging from C10 to C30 and dicarboxylic acids from C3 to C9 (SigmaAldrich, St. Louis, MO). The acidic standard mixture was derivatized by freshly prepared
diazomethane prior to analysis by GC/IT MS. The n-alkane standard mixture consisted of C25 to
C32 homologues in dichloromethane at 10.0 g/ml (Sigma-Aldrich). The PAH standard contained
5 compounds with molecular weight of 252 amu and above and was prepared in dichloromethane
at 10.0 g/ml (Sigma-Aldrich). Generally, compounds with amu > 252 exist at particle phase at
80% or greater throughout the year in the Northeastern U.S. (Baek et al., 1991a; Gardner et al.,
1995). One hopane standard, 17α(H),21β(H)-Hopane in dichloromethane at 3.0 g/ml (Chiron,
Trondheim, Norway) comprised the only hopane standard due to the commercial unavailability
of the other homologues.
1.3 Five-point Mass Calibration
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Developing an accurate mass calibration method is a critical task for the analytical
procedure. A successful mass calibration underlies precise and accurate analytical measurements.
The most common mass calibration method in organic aerosol analysis is a single-point
calibration, which has been used widely in studies over the past 15 years (Mazurek et al., 1987,
1989, Rogge et al., 1993). A single-point calibration is less time-consuming relative to a fivepoint calibration when used for compound quantitation. The limitation to a single-point
calibration is that the range of analyte concentration in real PM samples must be within a small
window or fall within an order of magnitude. This assumption was fairly accurate for PM2.5
studies in metropolitan Los Angles (Mazurek et al., 1987, 1989, Rogge et al., 1993). However, a
single-point calibration standard response may not generate reproducible results because it is
subject to more system analytical bias during analysis than a five-point calibration. The
concentration of the standard in a single-point calibration may not be representative to the actual
concentrations the molecular markers in the samples, which can vary by one or two orders of
magnitude in different seasons based on preliminary ambient sample analyses. Unlike a singlepoint calibration, a five-point calibration covers a wide range of concentrations in actual ambient
samples and establishes the range of linear response for the ion trap mass analyzer. In the present
work, five-point calibration curves were produced for all standards to obtain the concentrations of
the molecular markers in the PM samples.
A five-point calibration curve was generated with molecular marker calibration standards
with various concentrations, but maintaining the same internal standard concentration (nC24D50).
The quantitative basis of mass calibration and analysis is expressed in Equations (1) and (2)
(Table 1). Equation (1) was used to calculate the standard response of the calibration curves,
while (2) was for computing the concentration value of individual organic compound in samples
after calibration.
Calibration curves were plotted using the area ratio (AS/AIS) versus the concentration
ratio (CS/CIS) and single graphing application software (e.g., Microsoft Excel). Thus, the slope of
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the curve is the relative response factor (RRF) (Equation (1)). Once the ratio of (AX/AIS) is found
for a single compound by integrating the peak area from the GC/IT MS total ion current or
selected mass-to-charge (m/z) response, the (CX/CIS) is determined by fitting the ratio of (AX/AIS)
into the curve (Equation (2)). The concentrations of the marker compounds in real PM samples
are found by multiplying the concentration of internal standard CIS by the RRF.
The initial calibration of the Varian Saturn GC/IT MS was performed in December 2002
prior to the ambient PM sample analysis at the following five levels (four levels for hopanes): 0.5,
5.0, 10.0, 20.0 and 50.0 µg/ml for n-alkanes calibration standards; 1.0, 5.0, 10.0, 15.0 and 20.0
µg/ml for PAH calibration standards; 0.5, 1.0, 3.0 and 6.0 µg/ml for hopanes calibration standards;
5.0, 10.0, 25.0, 50.0 and 75.0 µg/ml for dicarboxylic acids calibration standards; 3.0, 9.0, 27.0,
51.0 and 75.0 µg/ml for n-alkanoic acids calibration standards. Levels of calibration standards
were selected based on the mass concentrations from 24-hr ambient test samples. Response
factors were calculated for all analytes at each concentration level. Concentration ratios were
determined for all calibration standards relative to the internal standard (CS/CIS). Area ratios were
calculated for the same calibration standards relative to the internal standard (AS/AIS). Both
(CS/CIS) and (AS/AIS) were used to generate five-point mass calibration curves for every
molecular marker measured in the PM samples. Figure 1 shows examples of calibration curves
generated in this study, including calibration curve of 17α,21β-hopane, n-nonacosane,
benzo[b]fluoranthene, n-hexadecanoic acid and azelaic acid (nonadioic acid).
Calibration curves for most of the molecular markers are highly linear with correlation
coefficients (R2) greater than 0.97 for the n-alkanes, 0.9998 for the hopane, 0.95 for the nalkanoic acids and 0.97 for the dicarboxylic acids. PAH compounds have calibration curves with
slightly less linearity as R2 values ranging from 0.86 for indeno[1,2,3-cd]pyrene to 0.95 for
benzo[b]fluoranthene. The linearity of the calibration curves for all standard compounds indicate
high consistency and reproducibility of the GC chromatographic and MS analyses.
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1.4 Compound Identification and Quantification
Identification of the organic molecular source markers associated with atmospheric
particulate matter is challenging since the markers are generally present at low ppb trace levels in
the PM solvent-soluble mixture. Because the PM solvent-soluble fraction is a complex mixture
that is unresolved by the chromatographic step, it is usually the case that molecular markers
coelute with other compounds, thereby combining mass spectra and complicating automated
library searches and peak purity fits for identifying target compounds. Consequently, it is useful
to employ selected ion monitoring (SIM) to screen the MS response (total ion current) for massto-charge (m/z) fragments that are characteristic for an individual marker compound or
homologous series (e.g., n-alkanes, C25 to C32). For PAH, diacids and hopanes, the most
abundant ion m/z serves as the quantification ion. For the n-alkanes and the n-alkanoic acids,
however, several m/z fragments are monitored for each homolog series. For example, m/z 57, 71
and 85 are of almost the equivalent abundance for n-alkane series, but only m/z 85 was selected
as quantification ion due to less interference with other coeluting compounds at this m/z. For nalkanoic acid methyl esters, m/z 74, 87 and 43 were screened with quantitation based on m/z 74.
Table 2 lists the m/z ions used to screen the total ion current output for individual marker
compounds and homologous compound series in the solvent-soluble PM complex mixtures.
The molecular markers were identified by comparing the retention times and mass
spectra with authentic standards and National Institute of Standards and Technology (NIST) mass
spectral reference library. The retention time of the target compound in the complex mixture was
required to fall within a range of + 0.1 second. The mass spectrometric plot of the marker
compound was required to have the quantitation ion(s) present in addition to other key m/z ions
for that compound determined from standard runs of the GC/IT MS. Finally, additional
confirmation of the target compound was achieved by establishing and verifying the typical ratios
in the MS plot for several most abundant ions in the molecule using either authentic standard MS
spectra or the NIST MS library. This sequence of steps comprises the compound verification
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procedure for molecular markers in urban PM. Figure 2 is an example of the ion trap mass
spectra for several individual molecular markers based on the authentic standards. Positive
identification of an unknown compound in the PM extracts was possible only when the mass
spectrum of the compound had identical mass spectrum and ion-ion ratios relative to the authentic
standard spectrum.
Positive identification of all molecular markers was employed with the complete sets of
authentic standards with an exception of some hopanes. The hopanes for which authentic
standards are not available were identified by referring to the retention time of 17α,21β-hopane
standard (Chiron, Norway) and the unique distribution pattern to published mass spectra and
chromatographic information (e.g., relative retention time to known compounds within the PM
extract mixture (Fraser et al., 1999; Philp, 1985). These hopane homologs were quantified by
applying the response factor of 17α,21β-hopane.
2. Precision and Bias of Analytical Measurements
2.1 Analysis Precision of the Samples
The analytical precision of molecular marker method was determined by duplicating the
analysis for every tenth sample, according to the Quality Assurance Project Plan (McDow, 2002).
Precision is expressed as the average relative range (relative percent difference) of duplicate
analyses (Equation (3), Table 1).
The concentrations of hopanes and n-alkanes in each duplicate analysis and the analytical
precision are listed in Table 3. The analytical precision of the two classes of compounds ranges
from 0.025 for n-octacosanes to 0.081 for n-triacontane. Six out of nine hopanes and three out of
eight n-alkanes have measurement precision less than 0.05. Graphical representations of the
analytical precision are shown in Figure 3 for the duplicate measurements and their mean values
of the hopanes. This figure demonstrates the analytical precision for the duplicate analyses of the
7 sample pairs. For example, the measurements of 18α(H)22,29,30-trisnorneohopane (Figure 3,
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(a))seem to be more precise relative to 18α(H)-29-norneohopane (Figure 3, (d))because the
deviation is relatively small between the duplicate analyses of the former compound for most
samples. Correspondingly, the former compound has a lower analytical precision p value of
0.031, while the latter gives a higher p value of 0.062.
2.2 Analysis Precision of RRF
Response factors for GC/IT MS have a strong influence on the analytical results of the
molecular markers identified in ambient PM. It is important to monitor the variation of the
response factors throughout the analytical process. In the case of many ambient samples, for
example this study (71 samples), it is not feasible to establish an entire set of five-point mass
calibration curves for the analysis of each single marker compound for each sample analyzed.
The approach in this study constructs a second five-point calibration set for compounds of interest
by the end of the analysis of all samples (3 months later) and then estimates the deviation
between the two sets of the response factors. The quantitation of the marker compounds in the 71
samples was based on the initial set of response factors. There was no need for correction since
the variation between the two sets of response factor was less than certain value, i.e. 40% in this
work (Table 4). {I don’t know what you mean here.-mm}
Table 4 shows the comparison of two sets of response factors obtained from the fivepoint calibration experiments determined 3 months apart. The RRF are highly reproducible
throughout the entire sample analysis period for most molecular markers tested. Low RSD were
observed for n-alkanes with carbon number less than 32 (RSD<6.5%), hopanes (RSD=6.3%), and
some PAHs, like BbF and BkF (RSD <8.3%). In comparison, relatively high RSD were
measured for palmitic acid (C16) and BeP with RSD equal to 32.0% and 48.5%, respectively.
Overall, the five-point calibration response factors give fairly consistent results for those marker
compounds determined as part of the ambient sample analysis.
The precision of five-point calibration runs was evaluated by the relative standard
deviation (RSD) of the slopes, regression and the intercept of the calibration curves of all
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molecular markers (Equations (5)-(7), Table 1). 17α,21β-hopane has the smallest RSD for its
relative response factor, which is as low as 1.08%, followed by n-octacosane (C28) with 2.5% of
RSD (Table 5). Most RRF for the marker compounds evaluated have RSD values less than 10%
except for two low molecular weight n-alkanoic acids (n-decanoic acid, n-dodecanoic acid) and
all the PAH compounds. n-Decanoic acid and n-dodecanoic acid have RSD of 13.3% and 10.3%,
respectively. All the RRF for the PAH compounds show high RSD, ranging from 13.5% to
23.7%, which are greater than any other markers.
The high RSD of the RRF for PAH compounds are consistent with the lower degree of
linear correlations for the calibration curves. The correlation coefficient R2 of PAH calibration is
as low as 0.85, while R2 of most other markers are greater than 0.95. This low correlation
coefficient or high RSD of the calibration curves of the PAH compounds indicates these analyses
were less reproducible at different concentration levels, particularly at the low levels. It is not
known precisely why the PAH compounds as a class demonstrate greater measurement variability
in the calibration on tests.
2.3 Analysis Bias
Measurement accuracy for the molecular marker GC/IT MS method was examined by
analyzing a PAH standard certified by NIST, named NIST 1491. Table 6 shows the results of the
accuracy tests which are reported as analytical bias of the PAH standard with five-point
calibration relative to their certified values, and are calculated from Equation (8) in Table 1.
Four PAH of interest in NIST 1491 were quantified by the five-point calibrated response
factors that were used for the analysis of the Philadelphia PM10 samples, including
benzo[b]fluoranthene, benzo[k]fluoranthene, bezo[e]pyrene and indeno[1,2,3-cd]pyrene.
Benzo[k]fluoranthene has the lowest analysis bias of 7.1%, while benzo[b]fluoranthene has the
highest bias of 33.5%.
The 33.5% bias for benzo[b]fluoranthene is the highest among all the molecular marker
compounds. The high measurement bias for benzo[b]fluoranthene is related to difficulties
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associated with identification and the relatively low reproducibility of the response factors of
PAH from the 5-point calibration curves. PAH have the highest relative standard deviation (RSD)
of the response factors from the calibration curves and the lowest reproducibility of the response
factors among all the molecular markers. Overall, the analytical bias for molecular marker
compounds evaluated in this GC/IT MS study should be less than 33.5%.
2.4 Recovery Tests
Recovery of a compound is the ratio of the mass detected by GC/IT MS to the mass
present in the sample. Recoveries of the organic markers are expected to vary because of
differences in compound volatility, adsorption on glassware during extraction, extract
concentration prior to analysis, and other sample handling steps, for example. The internal
standard added to samples prior to extraction monitors the cumulative loss processes throughout
the analysis steps. However, the internal standard must have properties similar to the compounds
of interest for this assumption to be accurate. Alternatively, the internal standard can have
different properties from the marker compounds as long as the differences in their recoveries are
reproducible.
Four recovery standards representing a range of volatilities and functional groups were
added to each sample filter in the amount of 10.0 μg for each recovery standard prior to the
extraction of all samples. The standards were nC24D50 (perdeuterated n-tetracosane), nC30D62
(perdeuterated n-triacontane), pyrene-d10 (perdeuterated pyrene) and C12D23 (perduterated lauric
acid). The reproducibility of using nC24D50 as an internal standard for quantifying less volatile
alkane homologs (carbon number between 25 and 32) was tested by comparing the area ratios of
nC30D62 and nC24D50. Similar tests were carried out with pyrene-d10 and C12D23 to test the
suuitability of nC24D50 as an internal standard for PAH and n-alkanoic acids. The purpose of
these tests was to assess the analytical error when an internal standard with different volatility
relative to a marker compound was used to monitor the marker compound recovery.
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The area ratios of C30D62, pyrene-d10 and C12D23 to C24D50 in the samples are listed in
Table 7. Even with a large sample number (n>60), the area ratios of the recovery standards to the
internal standard show relatively high coefficient of variation of 34.2% to 40.9%. These
comparisons to C24D50 alone indicate the quantification of target compounds using a single
internal standard (C24D50) is not highly reproducible, which could in turn affect the precision and
accuracy of the analytical measurements.
An alternative way to increase recovery reproducibility is to use more than one internal
standard for sample analysis. The addition of other internal standards can be adjusted to target
compounds exhibiting wider ranges of volatility and functional group composition. This would
allow more options for matching target compound chemical properties with to an internal
standard that is most similar in volatility and functional group composition. Although using
additional internal standards might improve the reproducibility to some extent, the degree of the
improvement needs to be estimated. The use of multiple internal standards must be evaluated
carefully in terms of some disadvantages, such as more interference due to adding new
compounds into the samples and extra analysis time.
3. Comparison of Spectra from GC/IT MS and GC/MS Quadrupole
The mass spectra of the authentic standards from the Varian ion trap analyzer used in this study
were found to be very similar to the NIST quadrupole mass spectra. Figure 4 shows the
comparisons of ion trap and quadrupole mass spectra using hexadecaoinc acid methyl ester,
azelaic acid dimethyl ester and benzo[b]fluoranthene as examples. The pairs of mass spectra
show the same base ions m/z for each compound (74 for n-hexadecanoic acid methyl ester, 152
for azelaic acid dimethyl ester, and 252 for benzo[b]fluoranthene) and some other abundant ions
m/z. However, the relationship of m/z intensities for a single compound show some variation
between the two o two types of mass spectrometers . The intensity of the second most abundant
ion m/z 87 in n-hexadecanoic acid methyl ester by NIST is 69.7%, while the intensity is 82.7% by
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ion trap analyzer (Table 8). {Min, how does the relative intensity of m/z fragments compare?
For example is the order of intensity the same for c16 fame for ion trap and quadrupole
and are all the major ions present in both spectra? This should be discussed. -MM}Most
of the abundant ions m/z have different intensity in NIST quadrupole and Varian ion trap mass
spectrometer. Therefore, NIST mass spectra determined through electron impact quadrupole MS
is a useful reference guide for identifying organic compounds identified by GC/Ion Trap MS
when authentic standards are not available. Using as NIST mass spectra for verifying the
identification of the marker compounds should be avoided. {WHY??? Should say explicitly. mm}
SUMMARY and Conclusions Section
Should make a list first of each topic and then add the major findings based on this work. The conclusion
should basically say or recommend statistical tests that should be part of the routine MS molecular marker
protocol that currently is not reported. Also, the type and frequency of MS detector calibration needed for
marker compounds.
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Table 1: Statistical Equations Used to Estimate of Precision and Bias in Determining Molecular Markers from PM Samples
Compound
Quantitation
Equations
Analytical
Precision of
Samples
Relative
Standard
Deviation
Standard
Deviation
of
Regression
Standard
Deviation
of Slope
A S C IS

A IS CS
C
A
C X  X  IS
A IS RRF
RRF 
p
 (C
i,ha
(1)
(2)
 C i,la) /C i,avg
i
Ci,ha and Ci,la are the highest and lowest of the two
duplicate analyses of concentration measurements for the
same sample i, respectively, Ci,avg is the average of the two
duplicate measurements of sample i, and n is the total
number of duplicate measurements taken.
(3)
n
% RSD
RRF is relative response factor; A is integrated area; C is
concentration; subscript S is standard, IS is internal
standard, X is the unknown compound.
 100 
S
m
RSD: Relative Response Factor. S is the standard
deviation, m is the mean value.
(4)
S yy  m S xx
2
sr 
sm 
sb  sr
Analytical
Bias
%bias  100 
S yy   (y i  y)   y 
2
s r2
S xx
Standard
Deviation
of Intercept
S xx   (x i  x) 2   x i2 
(5)
N 2
(6)
x
2
i
2
i
( x i )
(7)
(8)
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(Skoog, West and
Holler, 2004)
2
N
( y i ) 2
N
S xy   (x i  x)(y i  y)   x i y i 
N  x i2  ( x i ) 2
C x  Cs
Cs
McDow, 2002
x y
i
i
N
where xi and yi are individual pairs of data for x and y, N is
the number of pairs of data used in preparing the
calibration curve, and x and y are the average values for
the variables, m is the slope of the regression line.
Where Cx is analytical result, Cs is the certified value.
15
Figure 1: Calibration Curves of the Molecular Markers (5 figures in total)
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Table 2: Ions Mass-to-charge (m/z) Used for Compound
Identification and Quantitation
Compound or Class
n-Alkanes
BbF, BkF, BeP
InF, InP
Hopanes
n-Alkanoic acids
Dicarboxylic acids
Quantitation m/z
85
252
276
191
74
Compound MW
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Screened m/z
85, 71 and 57
252 or 126
276
191, 95
74, 43 and 87
Compound MW
17
(a) Total Ion Current of an Ambient Sample
n-Nonacosane
17α,21β,Hopane
(b) Mass Spectrum of n-Nonacosane
(c) Mass Spectrum of 17α,21β,Hopane
Figure 2: Gas Chromatograph and Ion Trap Mass Spectra of
Individual Molecular Markers
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Table 3: Analysis Precision of 7 Ambient Samples
Concentration of Duplicate Measurements (ng/m3)
Hopanes
Carbon Number
18α(H)22,29,30-Trisnorneohopane
17α(H)-22,29,30-Trisnorhopane
17α(H),21β(H)-29-Norhopane
18α(H)-29-Norneohopane
17α(H),21β(H)-Hopane
22S,17α(H),21β(H)-30-Homohopane
22R,17α(H),21β(H)-30-Homohopane
22S,17α(H),21β(H)-30-Bishomohopane
22R,17α(H),21β(H)-30-Bishomohopane
N9
N31
N55
0.81
0.59
2.10
0.56
2.14
1.08
0.72
0.47
0.40
N89
N105
0.70
0.73
2.84
0.61
3.00
1.10
0.93
0.63
0.41
N117
C27
C27
C29
C29
C30
C31
C31
C32
C32
0.56
0.53
1.87
0.22
1.65
0.65
0.49
0.34
0.31
0.56
0.56
1.85
0.22
1.71
0.77
0.48
0.39
0.30
0.79
0.88
2.49
0.46
2.23
0.85
0.63
0.58
0.49
0.82
0.77
2.47
0.45
2.71
0.98
0.63
0.60
0.59
0.78
0.54
1.97
0.50
2.17
0.98
0.76
0.55
0.41
0.27
0.38
1.26
0.27
1.47
0.60
0.41
0.36
0.37
0.29
0.41
1.28
0.28
1.43
0.60
0.44
0.39
0.29
0.65
0.72
2.58
0.71
2.93
1.20
0.85
0.56
0.43
0.41
0.50
1.54
0.26
1.63
0.85
0.67
0.55
0.48
0.45
0.52
1.67
0.38
1.67
1.00
0.68
0.74
0.47
C25
C26
C27
C28
C29
C30
C31
C32
4.75
3.46
4.53
2.83
7.72
2.54
7.99
6.22
5.66
3.74
5.10
2.71
8.53
2.39
9.35
9.12
6.75
4.58
4.36
4.31
4.68
3.06
7.86
9.39
5.50 5.17 5.03
4.27 4.03 3.39
3.98 5.59 6.16
4.30 2.98 3.14
4.68 9.58 11.61
3.62 2.29 3.02
6.93 13.00 14.27
7.23 6.08 6.10
2.06
2.53
2.77
2.52
4.10
2.97
6.03
6.17
2.28
2.96
2.66
2.71
4.19
3.24
6.37
7.26
3.47 4.10
4.26 4.89
3.36 4.29
5.23 5.27
3.35 4.54
7.37 7.33
1.68 3.04
7.88 8.08
5.49 7.22 14.83 14.96
2.43 2.80
5.95 4.55
8.41 15.28 30.26 28.37
7.92 11.86 16.90 16.23
N131
1.27
1.25
5.26
0.96
5.26
2.25
1.66
1.33
1.02
Precision
1.13
1.09
4.33
0.86
4.61
1.95
1.79
1.38
1.20
0.031
0.041
0.033
0.062
0.033
0.058
0.026
0.061
0.050
4.26 4.89
5.23 5.27
7.37 7.33
7.88 8.08
14.83 14.96
5.95 4.55
30.26 28.37
16.90 16.23
0.062
0.055
0.051
0.025
0.048
0.081
0.046
0.074
n-Alkanes
n-Pentacosane
n-Hexacosane
n-Heptacosane
n-Octacosane
n-Nonacosane
n-Triacontane
n-Hentriacontane
n-Dotriacontane
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19
(a)
(c)
(e)
(b)
(d)
(f)
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20
Table 4: Reproducibility of Five-point RRF Over Months
Molecular Markers
Nov, 2002
Feb, 2003
%RSD
n-Pentacosane
n-Hexacosane
n-Heptacosane
n-Octacosane
n-Nonacosane
n-Triacontane
n-Hentriacontane
n-Dotriacontane
1.11
1.03
0.928
0.816
0.805
0.795
0.516
0.216
1.13
0.992
0.942
0.842
0.809
0.855
0.566
0.283
1.42
2.36
1.07
2.23
0.32
5.15
6.53
19.0
benzo[b]fluoranthene
benzo[k]fluoranthene
benzo[e]pyrene
0.620
0.698
0.705
0.698
0.645
0.345
8.30
5.54
48.5
17α,21β,hopane
1.29
1.20
5.02
Palmitic acid (C16)
1.21
0.762
32.0
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Table 5: RSD of the Five-point Mass Calibration
n-Alkanes
C25
C26
C27
C28
C29
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RRF
slope
m
1.11
1.03
0.93
0.82
0.80
intercept
b
-0.21
-0.21
-0.15
-0.14
-0.23
RRF
RSDm
4.35
3.55
3.05
2.50
4.90
22
% RSD
regression
RSDr
17.20
14.02
12.06
9.87
19.38
intercept
RSDb
10.70
8.72
7.50
6.14
12.06
C30
C31
C32
0.80
0.52
0.22
-0.20
-0.13
-0.06
9.92
6.35
5.09
39.21
25.09
20.11
24.40
15.61
12.51
PAH
Benzo[b]fluoranthene
Benzo[k]fluoranthene
Benzo[e]pyrene
Indeno[1,2,3-cd]fluoranthene
Indeno[1,2,3-cd]pyrene
0.62
0.70
0.70
0.50
0.51
-0.18
-0.22
-0.21
-0.16
-0.18
13.49
16.00
16.03
23.67
23.53
20.49
24.30
24.35
35.97
35.74
16.53
19.61
19.65
29.01
28.83
Hopane
17a,21B,hopane
1.29
-0.07
1.08
0.47
0.37
n-Alkanoic Acids
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
0.80
1.04
1.10
1.18
1.20
1.22
1.21
1.10
1.04
0.95
0.96
0.77
0.67
0.59
0.61
0.50
0.44
0.03
-0.15
-0.02
-0.15
-0.30
-0.34
-0.39
-0.44
-0.43
-0.46
-0.40
-0.37
-0.34
-0.28
-0.33
-0.26
-0.27
13.34
9.11
10.34
9.13
8.14
9.13
9.90
9.32
7.52
8.47
6.30
5.68
5.62
3.57
5.32
3.99
5.06
80.05
54.63
62.07
54.77
48.85
54.77
59.40
55.89
45.13
50.82
37.82
34.11
33.69
21.40
31.94
23.97
30.34
56.74
38.73
44.00
38.83
34.63
38.83
42.11
39.62
31.99
36.02
26.81
24.18
23.89
15.17
22.64
16.99
21.51
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C27
C28
C29
C30
0.42
0.42
0.37
0.31
-0.25
-0.27
-0.24
-0.20
4.97
5.88
7.00
9.91
29.81
35.25
42.00
59.46
4.10
9.57
9.49
9.24
8.30
8.07
6.00
4.50
8.30
7.05
24.03
56.03
55.58
54.12
48.62
47.26
35.13
26.35
48.59
41.31
21.13
24.99
29.78
42.15
Table 5 (continued)
Dicarboxylic Acids
malonic
succinic
methyl succinic
glutaric
malic
adipic
suberic
phthalic
isophathalic
azelaic
0.64
1.39
0.96
1.32
0.51
0.75
0.87
5.71
5.47
1.05
-0.32
-0.49
-0.19
-0.68
-0.27
-0.49
-0.51
-1.24
-2.94
-0.57
Table 6: Analysis Bias with Five-point Calibration
BbF
BkF
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Certified
Analyzed
Conc.(μg/ml) Conc.( μg/ml)
5.25
7.01
5.57
5.96
24
%bias
33.5
7.1
RRF
5-point
0.620
0.698
17.29
40.30
39.98
38.93
34.97
34.00
25.27
18.95
34.95
29.72
BeP
5.62
InP
6.29
BbF: benzo[b]fluoranthene; BkF: benzo[k]fluoranthene;
BeP: benzo[e]pyrene; InP: indeno[1,2,3-cd]pyrene
6.15
4.94
9.5
-21.5
0.705
0.514
Table 7: Reproducibility of the Recovery Standards
Area Ratio of Recovery
Stds/Internal Std
Analysis Period
pyrene-d10/ C24D50
C30D62/ C24D50
C12D23/ C24D50
11/22-12/10/2002
11/22-12/10/2002
12/11/2002-1/2/2003
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Sample Number
25
n=62
n=64
n=72
Mean coefficient
of variation
%
0.880
40.9
1.03
37.1
0.585
34.2
(a)
(b)
(c)
(d)
(e)
(f))
Figure 4: Mass Spectra Comparison for Quadropole and Ion Trap Analyzer
(a), (b) n-Hexadecanoic acid methyl ester NIST quadropole mass spectrum, ion trap mass spectrum, respectively; (c), (d) Azelaic acid, dimethyl
ester NIST quadropole mass spectrum, ion trap mass spectrum, respectively; (e), (f) Benzo[b]fluoranthene NIST quadropole mass spectrum, ion
trap mass spectrum, respectively.
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Table 8: Ion (m/z) Intensity of Marker Compounds from NIST Quadrupole and Ion Trap Mass Spectrometer (this study)
Hexanaoic acid, methyl ester
NIST
Ion Trap
Ion m/z Intensity
Ion m/z Intensity
74
99.9
74
100
87
69.7
87
82.7
43
41.5
43
77.5
41
33.3
55
62.9
55
30
143
56.3
75
21.4
41
46.8
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Azelaic acid, dimethyl ester
NIST
Ion Trap
Ion m/z Intensity
Ion m/z Intensity
152
99.9
152
100
55
80
55
80.5
111
80
83
72.3
74
75
124
50.9
83
66
185
44.3
185
61
43
37.6
27
Benzo[b]fluoranthene
NIST
Ion Trap
Ion m/z Intensity
Ion m/z Intensity
252
99.9
252
100
253
21.6
253
25.5
250
18.2
251
12.1
126
17
126
10.1
125
10.9
113
6.9
113
6.9
125
5.3
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