TECHNICAL PAPER
ISSN:1047-3289 J. Air & Waste Manage. Assoc. 57:721–740
DOI:10.3155/1047-3289.57.6.721
Copyright 2007 Air & Waste Management Association
Evaluations of the Chemical Mass Balance Method for
Determining Contributions of Gasoline and Diesel Exhaust to
Ambient Carbonaceous Aerosols
Eric M. Fujita, David E. Campbell, William P. Arnott, Judith C. Chow, and Barbara Zielinska
Division of Atmospheric Sciences, Desert Research Institute, Reno, NV
ABSTRACT
The US. Department of Energy Gasoline/Diesel PM Split
Study was conducted to assess the sources of uncertainties
in using an organic compound-based chemical mass balance receptor model to quantify the relative contributions
of emissions from gasoline (or spark ignition [SI]) and
diesel (or compression ignition [CI]) engines to ambient
concentrations of fine particulate matter (PM2.5) in California’s South Coast Air Basin (SOCAB). In this study,
several groups worked cooperatively on source and ambient sample collection and quality assurance aspects of the
study but worked independently to perform chemical
analysis and source apportionment. Ambient sampling
included daily 24-hr PM2.5 samples at two air qualitymonitoring stations, several regional urban locations, and
along freeway routes and surface streets with varying proportions of automobile and truck traffic. Diesel exhaust
was the dominant source of total carbon (TC) and elemental carbon (EC) at the Azusa and downtown Los Angeles, CA, monitoring sites, but samples from the central
part of the air basin showed nearly equal apportionments
of CI and SI. CI apportionments to TC were mainly dependent on EC, which was sensitive to the analytical
method used. Weekday contributions of CI exhaust were
higher for Interagency Monitoring of Protected Visual
Environments (IMPROVE; 41 ⫾ 3.7%) than Speciation
Trends Network (32 ⫾ 2.4%). EC had little effect on SI
apportionment. SI apportionments were most sensitive to
higher molecular weight polycyclic aromatic hydrocarbons (indeno[123-cd]pyrene, benzo(ghi)perylene, and
IMPLICATIONS
The apportionments of CI and SI exhaust were most sensitive to the specific CI and SI source profiles that are used
and the analytical method for EC. The IMPROVE carbon
fractions (four OC and three EC fractions) could not be used
for apportionment because of rapid changes in the atmosphere that altered the distributions among the carbon
fractions. The apportionment of CI was mainly dependent
on EC and could not be obtained by organic markers alone.
However, CI vehicles were the dominant source of EC, and
EC was a reasonable surrogate of PM emissions from CI
vehicles in the SOCAB. Semivolatile organic compounds,
which are major components of ultrafine particles and are
precursors of secondary organic aerosols, were emitted in
far greater quantities than particulate organic compounds
by both SI and CI vehicles.
Volume 57 June 2007
coronene) and several steranes and hopanes, which were
associated mainly with high emitters. Apportionments
were also sensitive to choice of source profiles. CI contributions varied from 30% to 60% of TC when using individual source profiles rather than the composites used in
the final apportionments. The apportionment of SI vehicles varied from 1% to 12% of TC depending on the
specific profile that was used. Up to 70% of organic carbon (OC) in the ambient samples collected at the two
fixed monitoring sites could not be apportioned to directly emitted PM emissions.
INTRODUCTION
Receptor models have been widely used to estimate the
contributions of various sources to measured airborne
particulate matter (PM) concentrations.1–3 More recent
applications have explored the use of particulate organic
markers,4 – 6 as well as combined particulate and gaseous
markers.7 Several studies have been conducted recently to
characterize the emission rates and organic speciation of
PM from various combustion sources.4 – 6,8 –17 Although
source composition data for directly emitted carbonaceous aerosols were developed for these studies, the effects of variations in experimental approach, methods,
and conditions on source composition and ambient apportionment have not been characterized. Emission rates
and chemical composition of gaseous and particle-phase
pollutants from diesel and gasoline vehicles depend on
many factors, which include state of maintenance, vehicle age and mileage, fuel, lubricating oil, emission control
technology, vehicle operating mode (cold start or hot
stabilized), load, and ambient temperature. Of particular
importance in this regard are the factors affecting the
relative emissions of organic carbon (OC) and elemental
carbon (EC; e.g., speed and acceleration in the test cycle
and sampling temperature) and variations in sampling
and analytical methods (e.g., thermal evolution carbon
measurement protocol and method of pyrolysis corrections).
We present the results of the U.S. Department of
Energy (DOE) Gasoline/Diesel PM Split Study conducted
during the summer of 2001 in Los Angeles. The impetus
for the study was the disparate conclusions obtained from
receptor modeling studies of the relative contributions of
SI and CI vehicles to ambient concentrations of fine particles in the Los Angeles area4 and the Denver, CO, area.5,6
White18 reviewed the details of these two studies and
developed recommendations for a study to obtain a better
Journal of the Air & Waste Management Association 721
Fujita, Campbell, Arnott, Chow, and Zielinska
understanding of the uncertainties associated with applying
the chemical mass balance (CMB) receptor model to quantify contributions of spark ignition (SI) and compression
ignition (CI) exhaust to ambient fine PM (PM2.5) concentrations. The current study did not necessarily seek to reconcile
the results of the previous studies but was intended to examine the range of uncertainties that may be associated
with the methods and procedures for sample collection,
chemical analysis, and source apportionment.
Source testing included 59 light-duty vehicles (including 2 diesel vehicles) and 34 heavy-duty diesel vehicles. Ambient sampling included daily 24-hr PM2.5 samples for 28 days during summer 2001 at two air qualitymonitoring stations in California’s South Coast Air Basin
(SOCAB) plus samples at several regional urban locations
and along freeway routes and surface streets with varying
proportions of automobile and truck traffic. Key components of the design for this study included characterization of the variations in exhaust composition within vehicle categories, the differences in determination of
“elemental” carbon by thermal/optical reflectance (TOR)
method using the Interagency Monitoring of Protected
Visual Environments (IMPROVE) temperature/oxygen cycle19 and thermal/optical transmittance (TOT) method
using the Speciation Trends Network (STN) protocol,20
and comparability between multiple laboratories in the
analysis of organic species. The source apportionment
results obtained by the Desert Research Institute (DRI)
and associated experimental methods are described here,
whereas the DRI source characterization results are summarized by Fujita et al.21
EXPERIMENTAL WORK
Bevilacqua-Knight, Inc. (BKI)/U.S. Environmental Protection Agency (EPA) and West Virginia University (WVU)
conducted dynamometer tests of light-duty gasolinepowered vehicles and heavy-duty diesel-powered vehicles, respectively, in support of this apportionment study.
Researchers from DRI and University of Wisconsin-Madison (UWM) collected and chemically analyzed both
source and ambient samples in parallel in a manner that
could support independent receptor-modeling calculations of the source contributions of gasoline-powered and
diesel-powered engines and characterization of the uncertainties associated with the apportionments. The two
groups worked cooperatively on sample collection and
quality assurance aspects of the study but worked independently on chemical analysis and data analysis. Speciation data include PM2.5 mass, elements by X-ray fluorescence, ions by ion chromatography, OC and EC by both
IMPROVE TOR and STN TOT protocols, and organic speciation by gas chromatography with mass spectrometry
(polycyclic aromatic hydrocarbons [PAHs], hopanes, steranes, alkanes, and polar organic compounds). The sampling and chemical analysis methods and derivation of
exhaust composition profiles are described elsewhere.21
These analytical methods also apply to the ambient samples described here.
Because EC and OC are operationally defined by the
method, the specific instrument used, details of its operation, and choice of thermal evolution protocol can
722 Journal of the Air & Waste Management Association
greatly influence the split between EC and OC. In addition to the differences in methods of pyrolysis correction
and temperature and oxygen cycles that are implemented
in IMPROVE and STN, it should also be noted that the
STN TOT measurements by DRI for this study were made
using the DRI Model 2001 carbon analyzer (Atmoslytic).
The DRI instrument differs in several ways from the Sunset Laboratory carbon analyzers used in STN. These differences include (1) temperature ramping rates, (2) laser
intensity, (3) position of the thermocouple that monitors
sample temperature, (4) carrier gas pressure and flow rate,
and (5) size of the quartz filter punch. For some samples,
especially ambient samples by STN, transmittance exceeded the original value before the addition of oxygen.
This “negative” pyrolysis correction can occur if the particle mixture includes mineral oxides or polar organic
compounds that can supply oxygen to neighboring carbon particles at the higher nonoxidizing temperature
stages. The STN temperature cycle is more prone to negative pyrolysis corrections because of the higher temperature at the final nonoxidizing stage. As in the Sunset
Laboratory implementation of the STN protocol, any negative pyrolysis correction was added to the total pyrolysis
correction during data processing.
Source Samples
The vehicle emission tests were conducted for the Gasoline/Diesel PM Split Study at the Ralph’s Supermarket
distribution center in Riverside, CA, during summer 2001
from June 2 to June 23 for light-duty vehicles and from
July 20 to September 19 for heavy-duty vehicles. The
dynamometer systems, test vehicle characteristics, and
test protocols are described by EPA22 and WVU.23 Fine
particle mass and black carbon (BC) concentrations were
continuously monitored by light scattering and photoacoustic methods,24,25 respectively, providing immediate
feedback about the state of the dynamometer exhaust
dilution system, the nature of the emissions from vehicles, and identification of the portions of a driving cycle
where particle emissions were greatest and least.
BKI conducted dynamometer tests on their transportable Clayton Model CTE-50-0 chassis dynamometer. A
positive displacement pump-constant volume sampler
(PDP-CVS) system was used to quantitatively dilute exhaust gas from the vehicle operating on the dynamometer. The PDP-CVS system used an 8-in. diameter stainless
steel dilution tunnel with particulate-filtered inlet air. BKI
tested 57 light-duty gasoline vehicles and two light-duty
diesel vehicles in 11 combined model-year and mileage
categories. Each vehicle was tested on the modified unified driving cycle that consisted of a phase 1 plus phase 2
from a cold start, a 10-minute soak, followed immediately
by a repeat of the phase 1 (i.e., phase 3) plus phase 2 from
a warm start. Separate integrated PM samples were collected for the “cold” (phases 1 and 2) and “warm” (phases
3 and 4) portions of the test cycle. Media composite
samples (i.e., multiple vehicle tests collected on the same
sampling media) were collected for the four newest model
year and lowest mileage categories. Samples from the
remaining categories were either analyzed as laboratory
composites (extracts combined from different tests),
based on gravimetric mass and BC-to-PM ratios from the
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
continuous measurements or individually for several high
emitters. Other relevant chemical characterizations included lubricating oils from each vehicle and representative fuel samples from nearby service stations. The lubrication oil samples were analyzed by DRI for organic
constituents and by Gregory Poole Laboratories for elements by inductively coupled plasma analysis.
WVU tested heavy-duty diesel trucks and diesel buses
on their transportable heavy-duty vehicle emissions testing laboratory. For the largest vehicles, WVU used a
heavy-duty dynamometer unit with twin flywheel sets
and twin power absorbers. For light heavy-duty vehicles,
the WVU medium-duty vehicle chassis dynamometer
with a single power absorber and a single flywheel set was
used. The systems were capable of transient loading of
heavy-duty vehicles and incorporated a full-scale dilution
tunnel for emission measurement. Thirty trucks were
tested in the 12 combined vehicle weight (light-heavy,
medium-heavy, and heavy-heavy) and model year categories. Fifteen trucks were newer model year, well-maintained fleet vehicles. The remaining 15 trucks were a mix
of vehicles in typical service. Two transit buses were also
tested with one transit bus representing older engine technology and one representing newer engine technology.
All 30 of the trucks were operated over three duty cycles
for purposes of developing composition profiles, the CitySuburban Heavy Vehicle Route (CSHVR), the highway
cycle (HW), and idle operation. The two buses were operated through the CSHVR, an idle period, and the Manhattan test cycle. Time-integrated samples were collected
in parallel by DRI and UWM for each test.
Four composite compression-ignition and six composite spark-ignition exhaust chemical source profiles
normalized to total carbon (TC), shown in Table 1, were
created by combining the speciated emission from samples with comparable PM emission rates, EC/TC ratios,
and relative abundances of hopanes, steranes, and
high-molecular-weight PAHs that are potential markers
for SI exhaust (e.g., benzo[ghi]perylene, indeno[1,2,3cd]pyrene, and coronene). The light-duty gasoline vehicle (LDGV) composite profiles consist of high and low
emitters for both “cold” (SI_LC and SI_HC) and “warm”
(SI_LW and SI_HW) portions of the test cycle. A separate pair of composite profiles was also derived for vehicles with higher proportions of EC (SI_BC and
SI_BW). Incremental cold-start profiles were obtained
by subtracting the warm samples from the corresponding cold samples, but the analytical uncertainties are
too high for them to be applied in CMB analysis. MDD
is the composite of speciation data for light-heavy and
medium-heavy trucks. HCS and HW are composites
exhaust profiles for heavy-heavy trucks on the citysuburban and highway driving cycles, respectively.
HDD is the composite of the HCS and HW profiles.
Ambient Samples
The 24-hr ambient PM2.5 samples were collected with DRI
sequential filter samplers for 28 days (June 20 –26, 2001
and July 7–27, 2001) at the South Coast Air Quality Management District monitoring stations at Azusa and Los
Angeles-North Main. Samples from the 3-week period in
July were analyzed as composites by day-of-week as
Volume 57 June 2007
shown in Table 2. The monitoring station in Los Angeles
is located north of the civic center at the Los Angeles
Department of Water and Power facility. The land use in
the area is a mix of industrial and commercial with limited amount of residential. The site is located at the center
point of a triangle formed by three freeways with each
side of the triangle measuring approximately 3 km. Interstate (I) 5 runs north-south on the east side, US Route 101
runs west-east along the south side, and State Route (SR)
110 runs southwest-northeast. North Main St. is located
approximately 40 m from the sampling probe with a daily
vehicle count of 10,000. Diesel traffic is moderate on this
street during work hours. Rail tracks for the Metrolink
regional train (diesel locomotives) system and Amtrak are
located approximately 100 m to the east along the Los
Angeles River. The tracks terminate 1.5 km to the south at
Union Station and also circle the monitoring site to the
west. Azusa is located in the central basin ⬃30 km east
(downwind) of downtown Los Angeles. The air monitoring station is located ⬃0.7 km north of I-210 in a commercial/industrial area of Azusa.
Table 2 also lists samples that were collected at several
regional urban background locations and along freeway
routes and surface streets with varying proportions of
automobile and truck traffic. A class C motor home was
used as a mobile sampling platform. Sampling duration
ranged from 2 to 6 hr. Quartz and Teflon filters were
sampled concurrently at 55 L/min each through a Bendix
240 cyclone separator with a cutoff diameter of 2.5 ␮m
located inside a 75-L residence chamber. A DRI fine particulate/semivolatile organic compounds sampler was
used to collect samples for speciation of organic compounds. The Teflon-impregnated glass-fiber filters were
followed by polyurethane foam and XAD-4 resin sandwich cartridges (PUF/XAD/PUF). The air sample was
drawn through the residence chamber via a PM2.5 cyclone
operating at 113 L/min.
Particle light absorption was monitored with a photoacoustic instrument.24,25 Light from a 1047-nm laser is
power modulated at the operating frequency of an acoustical resonator. Light-absorbing aerosols (BC) drawn continuously through the resonator absorb some of the laser
power, slightly heating the aerosol. The heat transfers
rapidly from the aerosol to the surrounding air and the
local pressure increases, contributing to the standing
acoustic wave in the resonator. The acoustic wave is measured with a microphone as a measure of the light absorption, which is linearly proportional to the mass concentration of the BC aerosol in the sample air. Light
absorption (in Mm⫺1) can be interpreted as BC (in ␮g/m3)
when divided by an assumed mass absorption efficiency
(in m2/g). A mass absorption efficiency of 5 m2/g was
assumed for these conversions based on previous comparisons of EC by the IMPROVE TOR method with photoacoustic absorption on diesel exhaust (for 1047 nm laser).26 The resolution of the instrument for the 10-sec
averaging time used in the study corresponds with approximately 0.2 ␮g/m3 for BC mass concentration using
the assumed absorption efficiency.
The TSI DustTrak nephelometer monitored light scattering that was interpreted as PM mass. The DustTrak
Journal of the Air & Waste Management Association 723
Mnemonic
PM mass and IMPROVE-TOR carbon (mg/mi)
PM2.5 mass
MSGC
TC
TC
OC
OCTC
ECa
ECTC
OC 1
O1TC
OC 2
O2TC
OC 3
O3TC
OC 4 ⫹ pyrolyzed OC
O4_OP
EC 1-pyrolyzed OC
E1_OP
EC 2
E2TC
EC 3
E3TC
STN-TOT carbon (mg/mi)
OC
OC_STN
EC
EC_STN
Elements (mg/mi)
Chloride
CLIC
Nitrate
N3IC
Sulfate
S4IC
Ammonium
N4CC
Soluble potassium
KPAC
Sodium (qualitative)
NAXC
Magnesium (qualitative)
MGXC
Aluminum
ALXC
Silicon
SIXC
Phosphorous
PHXC
Sulfur
SUXC
Chlorine
CLXC
Potassium
KPXC
Calcium
CAXC
Chromium
CRXC
Manganese
MNXC
Iron
FEXC
Nickel
NIXC
Copper
CUXC
Zinc
ZNXC
Bromine
BRXC
Rubidium
RBXC
Strontium
SRXC
Molybdenum
MOXC
Barium
BAXC
Lead
PBXC
PAHs (␮g/mi)
Naphthalene
NAPHTH
Methyl naphthalenes
MNAPH
Biphenyl
BIPHEN
1⫹2ethylnaphthalene
ENAP12
Dimethyl naphthalenes
DMNAPH
Methylbiphenyls
MBPH
Bibenzyl
BIBENZ
Species Description
80.9 ⫾ 8.1
100.0 ⫾ 10.0
34.5 ⫾ 4.2
65.5 ⫾ 6.5
13.7 ⫾ 1.6
6.5 ⫾ 0.7
10.0 ⫾ 1.7
4.4 ⫾ 0.5
20.0 ⫾ 4.2
45.5 ⫾ 4.5
0.0 ⫾ 0.0
31.4 ⫾ 3.1
35.1 ⫾ 17.8
0.26 ⫾ 0.17
0.00 ⫾ 0.06
1.29 ⫾ 0.16
0.33 ⫾ 0.07
0.11 ⫾ 0.05
0.07 ⫾ 0.07
0.08 ⫾ 0.03
0.11 ⫾ 0.05
0.98 ⫾ 0.11
0.12 ⫾ 0.01
0.62 ⫾ 0.13
0.06 ⫾ 0.03
0.09 ⫾ 0.01
0.51 ⫾ 0.05
0.00 ⫾ 0.01
0.00 ⫾ 0.00
0.70 ⫾ 0.36
0.00 ⫾ 0.00
0.02 ⫾ 0.01
0.34 ⫾ 0.08
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.01
0.23 ⫾ 0.15
0.01 ⫾ 0.01
3.111 ⫾ 1.243
0.259 ⫾ 0.026
0.037 ⫾ 0.004
0.090 ⫾ 0.025
0.219 ⫾ 0.022
2.483 ⫾ 1.524
0.0000 ⫾ 0.0439
36.5 ⫾ 9.3
28.7 ⫾ 15.7
0.18 ⫾ 0.14
0.09 ⫾ 0.26
2.15 ⫾ 2.89
0.61 ⫾ 0.87
0.12 ⫾ 0.05
0.13 ⫾ 0.10
0.07 ⫾ 0.07
0.14 ⫾ 0.15
1.01 ⫾ 0.44
0.14 ⫾ 0.06
0.82 ⫾ 0.76
0.04 ⫾ 0.03
0.09 ⫾ 0.08
0.61 ⫾ 0.43
0.00 ⫾ 0.01
0.00 ⫾ 0.00
0.55 ⫾ 0.34
0.00 ⫾ 0.00
0.02 ⫾ 0.01
0.32 ⫾ 0.10
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.14 ⫾ 0.11
0.01 ⫾ 0.01
1.584 ⫾ 1.825
0.252 ⫾ 0.054
0.032 ⫾ 0.013
0.072 ⫾ 0.025
0.239 ⫾ 0.051
1.376 ⫾ 1.549
0.0000 ⫾ 0.0654
HCS
82.7 ⫾ 30.5
100.0 ⫾ 10.0
39.0 ⫾ 5.7
61.0 ⫾ 6.1
14.3 ⫾ 1.9
7.4 ⫾ 1.0
12.2 ⫾ 2.7
5.2 ⫾ 1.0
21.2 ⫾ 3.9
39.7 ⫾ 6.7
0.1 ⫾ 0.2
HDD
724 Journal of the Air & Waste Management Association
0.057 ⫾ 0.115
0.244 ⫾ 0.081
0.027 ⫾ 0.019
0.054 ⫾ 0.005
0.259 ⫾ 0.068
0.269 ⫾ 0.072
0.0000 ⫾ 0.1232
0.10 ⫾ 0.07
0.18 ⫾ 0.37
3.01 ⫾ 4.17
0.89 ⫾ 1.25
0.13 ⫾ 0.06
0.20 ⫾ 0.10
0.06 ⫾ 0.10
0.16 ⫾ 0.22
1.04 ⫾ 0.66
0.16 ⫾ 0.09
1.02 ⫾ 1.10
0.02 ⫾ 0.02
0.09 ⫾ 0.13
0.70 ⫾ 0.63
0.00 ⫾ 0.01
0.00 ⫾ 0.01
0.41 ⫾ 0.30
0.00 ⫾ 0.00
0.01 ⫾ 0.01
0.30 ⫾ 0.13
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.01
0.05 ⫾ 0.16
0.01 ⫾ 0.01
41.6 ⫾ 11.4
22.3 ⫾ 12.3
84.5 ⫾ 46.6
100.0 ⫾ 10.0
43.5 ⫾ 4.4
56.5 ⫾ 5.6
14.9 ⫾ 2.4
8.2 ⫾ 0.9
14.4 ⫾ 1.9
6.0 ⫾ 0.6
22.3 ⫾ 5.5
33.9 ⫾ 3.4
0.3 ⫾ 0.2
HW
MDD
3.026 ⫾ 2.416
0.128 ⫾ 0.060
0.003 ⫾ 0.006
0.079 ⫾ 0.033
0.143 ⫾ 0.047
0.311 ⫾ 0.254
0.0000 ⫾ 0.1132
0.20 ⫾ 0.47
0.28 ⫾ 0.54
4.29 ⫾ 2.38
1.58 ⫾ 0.89
0.11 ⫾ 0.04
0.33 ⫾ 0.20
0.13 ⫾ 0.07
0.17 ⫾ 0.23
0.78 ⫾ 0.57
0.12 ⫾ 0.05
1.77 ⫾ 0.92
0.09 ⫾ 0.02
0.09 ⫾ 0.12
0.47 ⫾ 0.37
0.00 ⫾ 0.01
0.00 ⫾ 0.01
0.43 ⫾ 0.44
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.19 ⫾ 0.07
0.09 ⫾ 0.07
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.01
0.30 ⫾ 0.21
0.00 ⫾ 0.01
37.4 ⫾ 11.4
19.4 ⫾ 19.3
63.2 ⫾ 17.3
100.0 ⫾ 10.0
52.2 ⫾ 15.1
47.8 ⫾ 15.1
14.7 ⫾ 2.7
12.3 ⫾ 4.3
15.4 ⫾ 5.1
9.8 ⫾ 4.1
14.3 ⫾ 5.0
33.1 ⫾ 13.7
0.4 ⫾ 0.3
Table 1. Composite profiles (weighted % normalized to TC) used in CMB receptor modeling.
40.309 ⫾ 17.703
22.297 ⫾ 11.346
0.904 ⫾ 0.532
1.209 ⫾ 0.642
5.166 ⫾ 3.095
0.569 ⫾ 0.508
2.9707 ⫾ 2.7788
0.50 ⫾ 0.18
2.33 ⫾ 1.81
15.34 ⫾ 13.77
6.64 ⫾ 6.00
0.06 ⫾ 0.04
0.10 ⫾ 0.08
0.24 ⫾ 0.10
0.11 ⫾ 0.05
7.19 ⫾ 4.52
0.18 ⫾ 0.22
5.93 ⫾ 5.13
0.59 ⫾ 0.42
0.04 ⫾ 0.02
0.37 ⫾ 0.13
0.01 ⫾ 0.01
0.00 ⫾ 0.00
0.28 ⫾ 0.10
0.01 ⫾ 0.00
0.04 ⫾ 0.02
0.32 ⫾ 0.33
0.08 ⫾ 0.07
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.05 ⫾ 0.11
0.02 ⫾ 0.01
60.3 ⫾ 11.2
36.5 ⫾ 12.1
143.4 ⫾ 38.0
100.0 ⫾ 10.0
46.4 ⫾ 5.3
53.6 ⫾ 5.4
17.4 ⫾ 6.8
10.6 ⫾ 4.0
8.9 ⫾ 2.1
9.5 ⫾ 3.3
18.1 ⫾ 6.6
35.2 ⫾ 10.0
0.3 ⫾ 0.3
SI_BC
15.333 ⫾ 6.288
6.717 ⫾ 4.537
0.316 ⫾ 0.077
0.369 ⫾ 0.290
1.485 ⫾ 0.779
0.091 ⫾ 0.336
2.2877 ⫾ 2.0958
0.49 ⫾ 0.24
4.04 ⫾ 3.38
9.81 ⫾ 8.06
5.10 ⫾ 4.41
0.06 ⫾ 0.02
0.02 ⫾ 0.13
0.17 ⫾ 0.17
0.11 ⫾ 0.07
13.56 ⫾ 10.15
0.63 ⫾ 1.30
3.72 ⫾ 2.98
0.20 ⫾ 0.09
0.07 ⫾ 0.05
1.94 ⫾ 3.71
0.01 ⫾ 0.01
0.00 ⫾ 0.01
0.70 ⫾ 0.39
0.01 ⫾ 0.00
0.10 ⫾ 0.07
0.61 ⫾ 0.76
0.04 ⫾ 0.04
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.00 ⫾ 0.01
0.30 ⫾ 0.20
0.02 ⫾ 0.01
70.3 ⫾ 7.0
26.7 ⫾ 4.3
152.7 ⫾ 53.2
100.0 ⫾ 10.0
47.2 ⫾ 5.5
52.8 ⫾ 5.5
8.1 ⫾ 6.6
11.3 ⫾ 2.4
16.2 ⫾ 3.5
11.6 ⫾ 3.6
32.1 ⫾ 14.0
20.2 ⫾ 11.4
0.5 ⫾ 0.4
SI_BW
11.014 ⫾ 11.230
7.358 ⫾ 9.454
0.240 ⫾ 0.258
0.380 ⫾ 0.467
1.531 ⫾ 1.702
0.089 ⫾ 0.142
0.1754 ⫾ 0.3881
0.49 ⫾ 0.69
0.77 ⫾ 0.88
7.93 ⫾ 14.56
3.19 ⫾ 6.04
0.06 ⫾ 0.05
0.13 ⫾ 0.21
0.17 ⫾ 0.08
0.06 ⫾ 0.03
6.89 ⫾ 11.04
0.37 ⫾ 0.30
2.86 ⫾ 4.89
0.38 ⫾ 0.67
0.05 ⫾ 0.04
0.66 ⫾ 0.40
0.01 ⫾ 0.02
0.00 ⫾ 0.00
0.34 ⫾ 0.30
0.00 ⫾ 0.00
0.06 ⫾ 0.06
0.48 ⫾ 0.39
0.02 ⫾ 0.04
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.01 ⫾ 0.01
0.06 ⫾ 0.11
0.03 ⫾ 0.02
79.7 ⫾ 10.2
4.3 ⫾ 3.6
132.4 ⫾ 56.1
100.0 ⫾ 10.0
82.4 ⫾ 8.2
17.6 ⫾ 6.3
43.2 ⫾ 22.8
16.8 ⫾ 8.7
11.9 ⫾ 6.7
10.5 ⫾ 9.6
8.5 ⫾ 6.2
8.6 ⫾ 6.7
0.6 ⫾ 0.6
SI_HC
38.554 ⫾ 38.612
21.710 ⫾ 24.125
0.980 ⫾ 0.908
1.058 ⫾ 0.941
4.700 ⫾ 4.687
0.363 ⫾ 0.742
0.8290 ⫾ 1.2777
0.49 ⫾ 0.47
1.27 ⫾ 1.96
3.27 ⫾ 5.44
1.46 ⫾ 2.55
0.16 ⫾ 0.27
0.14 ⫾ 0.25
0.27 ⫾ 0.14
0.07 ⫾ 0.07
8.97 ⫾ 15.16
0.32 ⫾ 0.27
1.45 ⫾ 2.05
0.07 ⫾ 0.05
0.04 ⫾ 0.03
0.73 ⫾ 0.57
0.02 ⫾ 0.03
0.00 ⫾ 0.00
0.29 ⫾ 0.19
0.00 ⫾ 0.00
0.06 ⫾ 0.10
0.41 ⫾ 0.25
0.01 ⫾ 0.02
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.01 ⫾ 0.01
0.25 ⫾ 0.31
0.03 ⫾ 0.03
77.2 ⫾ 13.7
2.2 ⫾ 2.2
115.8 ⫾ 50.7
100.0 ⫾ 10.0
82.9 ⫾ 9.7
17.1 ⫾ 9.7
45.4 ⫾ 26.3
12.1 ⫾ 5.1
14.1 ⫾ 8.0
11.3 ⫾ 11.2
4.8 ⫾ 3.0
11.6 ⫾ 9.8
0.8 ⫾ 0.9
SI_HW
27.308 ⫾ 9.981
17.505 ⫾ 9.831
0.576 ⫾ 0.321
0.724 ⫾ 0.501
2.441 ⫾ 1.575
0.037 ⫾ 0.200
0.4185 ⫾ 0.7543
0.79 ⫾ 0.73
2.51 ⫾ 1.94
14.42 ⫾ 12.65
6.47 ⫾ 5.64
0.12 ⫾ 0.08
0.09 ⫾ 0.07
0.24 ⫾ 0.09
0.19 ⫾ 0.09
4.69 ⫾ 1.68
0.36 ⫾ 0.18
5.53 ⫾ 4.57
0.72 ⫾ 0.89
0.12 ⫾ 0.11
0.87 ⫾ 0.53
0.01 ⫾ 0.01
0.00 ⫾ 0.00
0.69 ⫾ 0.44
0.02 ⫾ 0.01
0.07 ⫾ 0.04
0.55 ⫾ 0.32
0.12 ⫾ 0.11
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.02 ⫾ 0.02
0.19 ⫾ 0.20
0.03 ⫾ 0.02
60.5 ⫾ 17.1
7.0 ⫾ 2.3
126.4 ⫾ 33.1
100.0 ⫾ 10.0
68.6 ⫾ 6.9
31.4 ⫾ 6.9
17.4 ⫾ 11.5
20.5 ⫾ 5.4
20.4 ⫾ 5.7
10.3 ⫾ 2.7
18.1 ⫾ 1.8
12.7 ⫾ 6.7
0.7 ⫾ 0.4
SI_LC
23.370 ⫾ 24.494
11.219 ⫾ 11.989
0.476 ⫾ 0.501
0.384 ⫾ 0.366
1.426 ⫾ 1.255
0.023 ⫾ 0.352
0.4160 ⫾ 1.0574
(Table continues)
0.75 ⫾ 0.18
3.72 ⫾ 2.43
13.41 ⫾ 12.88
6.07 ⫾ 5.79
0.10 ⫾ 0.03
0.26 ⫾ 0.11
0.26 ⫾ 0.17
0.19 ⫾ 0.06
4.68 ⫾ 2.01
0.32 ⫾ 0.07
5.26 ⫾ 4.70
0.40 ⫾ 0.51
0.10 ⫾ 0.07
0.74 ⫾ 0.34
0.02 ⫾ 0.01
0.01 ⫾ 0.01
0.63 ⫾ 0.21
0.02 ⫾ 0.01
0.05 ⫾ 0.01
0.42 ⫾ 0.15
0.08 ⫾ 0.07
0.00 ⫾ 0.00
0.00 ⫾ 0.00
0.01 ⫾ 0.01
0.28 ⫾ 0.21
0.03 ⫾ 0.01
60.9 ⫾ 19.8
7.0 ⫾ 4.5
109.0 ⫾ 36.4
100.0 ⫾ 10.0
69.2 ⫾ 9.7
30.8 ⫾ 9.7
16.5 ⫾ 16.5
16.9 ⫾ 2.8
23.6 ⫾ 7.7
12.3 ⫾ 4.0
15.5 ⫾ 11.6
14.6 ⫾ 7.7
0.7 ⫾ 0.4
SI_LW
Fujita, Campbell, Arnott, Chow, and Zielinska
Volume 57 June 2007
Dibenzofuran
Trimethyl naphthalenes
Ethyl methyl naphthalenes
Acenaphthylene
Acenaphthene
Fluorene
Phenanthrene
Methyl fluorene
9-Fluorenone
Xanthone
Acenaphthenequinone
Perinaphthenone
Methyl phenanthrenes
Di-methyl phenanthrenes
Anthracenea
Fluoranthenea
Pyrene
Retene
Methylpyrenesa
Benzo(c)phenanthrene
Benz(a)anthracene
Chrysenea
Benzo(b⫹j⫹k)fluoranthene
BeP
BaP
Indeno(123-cd)pyrenea
Benzo(ghi)perylenea
Dibenzo(ah⫹ac)anthracene
Coronenea
Alkanes (␮g/mi)
Norfarnesane
Farnesene
Norpristane
Pristane
Phytane
Sum of cyclohexanes
Tridecanoic acid (c13)
Phthalic acid
Glutaric acid (d-c5)
Succinic acid (d-c4)
Steranes (␮g/mi)
C27–20S-13ß(H),17a(H)diasterane
C27–20R-13ß(H),17a(H)diasterane
C28–20S-13ß(H),17a(H)diasterane
C27–20S5a(H),14a(H)cholestane
C27–20R5a(H),14ß(H)cholestanea
Species Description
Table 1. Cont.
0.0178 ⫾ 0.0021
0.1191 ⫾ 0.0119
0.0655 ⫾ 0.0287
0.0352 ⫾ 0.0155
0.0139 ⫾ 0.0059
0.0396 ⫾ 0.0142
0.0782 ⫾ 0.0287
0.0212 ⫾ 0.0104
0.2768 ⫾ 0.1701
0.0040 ⫾ 0.0022
0.0041 ⫾ 0.0027
0.0589 ⫾ 0.0135
0.0378 ⫾ 0.0038
0.0224 ⫾ 0.0065
0.0045 ⫾ 0.0017
0.1245 ⫾ 0.0830
0.1446 ⫾ 0.0870
0.0004 ⫾ 0.0007
0.0301 ⫾ 0.0169
0.0001 ⫾ 0.0005
0.0208 ⫾ 0.0134
0.0143 ⫾ 0.0090
0.0038 ⫾ 0.0026
0.0005 ⫾ 0.0011
0.0017 ⫾ 0.0034
0.0001 ⫾ 0.0009
0.0000 ⫾ 0.0012
0.0000 ⫾ 0.0013
0.0000 ⫾ 0.0004
0.0169 ⫾ 0.0160
0.0081 ⫾ 0.0161
0.0466 ⫾ 0.0047
0.0246 ⫾ 0.0159
0.0332 ⫾ 0.0061
0.3234 ⫾ 0.1428
0.0009 ⫾ 0.0019
0.1888 ⫾ 0.0976
0.0236 ⫾ 0.0140
0.0254 ⫾ 0.0170
0.0045 ⫾ 0.0015
0.0023 ⫾ 0.0014
0.0001 ⫾ 0.0004
0.0022 ⫾ 0.0012
0.0031 ⫾ 0.0014
0.0174 ⫾ 0.0052
0.1264 ⫾ 0.0599
0.0549 ⫾ 0.0231
0.0317 ⫾ 0.0109
0.0070 ⫾ 0.0083
0.0486 ⫾ 0.0184
0.0712 ⫾ 0.0206
0.0221 ⫾ 0.0066
0.2589 ⫾ 0.1456
0.0038 ⫾ 0.0024
0.0020 ⫾ 0.0028
0.0553 ⫾ 0.0125
0.0333 ⫾ 0.0082
0.0162 ⫾ 0.0090
0.0034 ⫾ 0.0016
0.1130 ⫾ 0.0711
0.1271 ⫾ 0.0745
0.0002 ⫾ 0.0004
0.0231 ⫾ 0.0139
0.0001 ⫾ 0.0004
0.0106 ⫾ 0.0140
0.0115 ⫾ 0.0073
0.0019 ⫾ 0.0026
0.0009 ⫾ 0.0019
0.0017 ⫾ 0.0032
0.0000 ⫾ 0.0006
0.0000 ⫾ 0.0008
0.0000 ⫾ 0.0009
0.0000 ⫾ 0.0002
0.0231 ⫾ 0.0238
0.0159 ⫾ 0.0340
0.0678 ⫾ 0.0267
0.0123 ⫾ 0.0167
0.0395 ⫾ 0.0147
0.4103 ⫾ 0.2061
0.0007 ⫾ 0.0012
0.1053 ⫾ 0.1116
0.0128 ⫾ 0.0150
0.0172 ⫾ 0.0184
0.0047 ⫾ 0.0017
0.0030 ⫾ 0.0018
0.0012 ⫾ 0.0014
0.0026 ⫾ 0.0014
0.0029 ⫾ 0.0011
NORFARN
FARNES
NORPRIS
PRIST
PHYTAN
NCYHEXS
TDECAC
PHTHAC
GLUAC
SUCAC
STER35
STER36
STER39
STER42
STER43
DBZFUR
TMNAPH
EMNAPH
ACNAPY
ACNAPE
FLUORE
PHENAN
MFLUOR
FL9ONE
XANONE
ACQUONE
PNAPONE
MPHEN
DMPHEN
ANTHRA
FLUORA
PYRENE
RETENE
MFLPYR
BZCPHEN
BAANTH
CHRYSN
BBJKFL
BEPYRN
BAPYRN
INCDPY
BGHIPE
DBANTH
CORONE
HCS
HDD
Mnemonic
Volume 57 June 2007
0.0028 ⫾ 0.0009
0.0031 ⫾ 0.0017
0.0023 ⫾ 0.0013
0.0038 ⫾ 0.0021
0.0049 ⫾ 0.0020
0.0292 ⫾ 0.0311
0.0238 ⫾ 0.0476
0.0890 ⫾ 0.0213
0.0000 ⫾ 0.0003
0.0458 ⫾ 0.0199
0.4971 ⫾ 0.2421
0.0006 ⫾ 0.0013
0.0217 ⫾ 0.0300
0.0019 ⫾ 0.0038
0.0091 ⫾ 0.0181
0.0169 ⫾ 0.0076
0.1337 ⫾ 0.0906
0.0443 ⫾ 0.0111
0.0282 ⫾ 0.0028
0.0001 ⫾ 0.0017
0.0576 ⫾ 0.0193
0.0642 ⫾ 0.0065
0.0230 ⫾ 0.0083
0.2411 ⫾ 0.1404
0.0035 ⫾ 0.0030
0.0000 ⫾ 0.0003
0.0518 ⫾ 0.0122
0.0288 ⫾ 0.0100
0.0100 ⫾ 0.0068
0.0023 ⫾ 0.0010
0.1015 ⫾ 0.0675
0.1097 ⫾ 0.0675
0.0000 ⫾ 0.0005
0.0162 ⫾ 0.0101
0.0002 ⫾ 0.0005
0.0003 ⫾ 0.0011
0.0087 ⫾ 0.0049
0.0000 ⫾ 0.0005
0.0013 ⫾ 0.0027
0.0018 ⫾ 0.0036
0.0000 ⫾ 0.0008
0.0000 ⫾ 0.0011
0.0000 ⫾ 0.0012
0.0000 ⫾ 0.0003
HW
0.0018 ⫾ 0.0012
0.0014 ⫾ 0.0012
0.0019 ⫾ 0.0012
0.0025 ⫾ 0.0024
0.0033 ⫾ 0.0031
0.0287 ⫾ 0.0319
0.1407 ⫾ 0.0830
0.1253 ⫾ 0.0251
0.0017 ⫾ 0.0047
0.1351 ⫾ 0.0529
0.3962 ⫾ 0.2123
0.0000 ⫾ 0.0012
0.0624 ⫾ 0.1765
0.0045 ⫾ 0.0127
0.0443 ⫾ 0.1253
0.0150 ⫾ 0.0031
0.0853 ⫾ 0.0304
0.0359 ⫾ 0.0281
0.0244 ⫾ 0.0141
0.0163 ⫾ 0.0073
0.0472 ⫾ 0.0209
0.0435 ⫾ 0.0120
0.0662 ⫾ 0.0353
0.1509 ⫾ 0.0717
0.0007 ⫾ 0.0018
0.0000 ⫾ 0.0005
0.0198 ⫾ 0.0143
0.0089 ⫾ 0.0063
0.0132 ⫾ 0.0072
0.0014 ⫾ 0.0025
0.0382 ⫾ 0.0108
0.0580 ⫾ 0.0236
0.0000 ⫾ 0.0007
0.0153 ⫾ 0.0130
0.0000 ⫾ 0.0006
0.0227 ⫾ 0.0082
0.0084 ⫾ 0.0031
0.0000 ⫾ 0.0008
0.0000 ⫾ 0.0005
0.0001 ⫾ 0.0015
0.0000 ⫾ 0.0013
0.0000 ⫾ 0.0017
0.0000 ⫾ 0.0019
0.0000 ⫾ 0.0005
MDD
0.0042 ⫾ 0.0022
0.0003 ⫾ 0.0009
0.0022 ⫾ 0.0008
0.0025 ⫾ 0.0009
0.0043 ⫾ 0.0018
0.0064 ⫾ 0.5000
0.0136 ⫾ 0.5000
0.0038 ⫾ 0.5000
0.0085 ⫾ 0.5000
0.0044 ⫾ 0.5000
0.0614 ⫾ 1.2543
0.0019 ⫾ 0.0046
0.0000 ⫾ 0.0062
0.0000 ⫾ 0.0055
0.0000 ⫾ 0.0119
0.3073 ⫾ 0.1826
1.9958 ⫾ 1.3767
0.5107 ⫾ 0.3228
4.0512 ⫾ 1.3457
0.2504 ⫾ 0.5057
0.8527 ⫾ 0.3554
1.5526 ⫾ 0.9571
0.3271 ⫾ 0.2039
0.7511 ⫾ 0.5378
0.0568 ⫾ 0.0423
0.0016 ⫾ 0.0029
0.0282 ⫾ 0.0181
0.2884 ⫾ 0.1851
0.0859 ⫾ 0.0514
0.2954 ⫾ 0.1802
0.1970 ⫾ 0.1159
0.1588 ⫾ 0.0754
0.0000 ⫾ 0.0008
0.0317 ⫾ 0.0811
0.0035 ⫾ 0.0026
0.0082 ⫾ 0.0013
0.0122 ⫾ 0.0054
0.0515 ⫾ 0.0174
0.0260 ⫾ 0.0034
0.0285 ⫾ 0.0093
0.0543 ⫾ 0.0238
0.1353 ⫾ 0.0452
0.0018 ⫾ 0.0017
0.1090 ⫾ 0.0362
SI_BC
0.0056 ⫾ 0.0024
0.0025 ⫾ 0.0053
0.0041 ⫾ 0.0019
0.0067 ⫾ 0.0055
0.0119 ⫾ 0.0060
0.0000 ⫾ 0.5000
0.0018 ⫾ 0.5000
0.0000 ⫾ 0.5000
0.0087 ⫾ 0.5000
0.0000 ⫾ 0.5000
0.5706 ⫾ 1.3132
0.0036 ⫾ 0.0052
0.0000 ⫾ 0.0211
0.0000 ⫾ 0.0048
0.0000 ⫾ 0.0190
0.1597 ⫾ 0.0349
0.4923 ⫾ 0.1409
0.3369 ⫾ 0.3437
0.7986 ⫾ 0.5683
0.0129 ⫾ 0.0221
0.3526 ⫾ 0.0969
0.9381 ⫾ 0.4072
0.1010 ⫾ 0.0386
0.8653 ⫾ 0.4945
0.1108 ⫾ 0.0620
0.0055 ⫾ 0.0144
0.0864 ⫾ 0.0485
0.3576 ⫾ 0.1430
0.7228 ⫾ 1.5365
0.1769 ⫾ 0.0831
0.3259 ⫾ 0.1385
0.3181 ⫾ 0.1541
0.0147 ⫾ 0.0293
0.3708 ⫾ 0.6050
0.0039 ⫾ 0.0027
0.0074 ⫾ 0.0104
0.0105 ⫾ 0.0078
0.0385 ⫾ 0.0241
0.0176 ⫾ 0.0165
0.0194 ⫾ 0.0262
0.0552 ⫾ 0.0380
0.1739 ⫾ 0.1646
0.0013 ⫾ 0.0037
0.1794 ⫾ 0.2044
SI_BW
0.0114 ⫾ 0.0095
0.0040 ⫾ 0.0055
0.0038 ⫾ 0.0035
0.0049 ⫾ 0.0025
0.0042 ⫾ 0.0052
0.0381 ⫾ 0.0426
0.0046 ⫾ 0.0107
0.0034 ⫾ 0.0055
0.0015 ⫾ 0.0028
0.0014 ⫾ 0.0032
0.0390 ⫾ 0.0463
0.0010 ⫾ 0.0017
0.0059 ⫾ 0.0155
0.0000 ⫾ 0.0027
0.0000 ⫾ 0.0020
0.0985 ⫾ 0.1007
0.4854 ⫾ 0.6551
0.1712 ⫾ 0.1754
0.7537 ⫾ 0.9443
0.0602 ⫾ 0.1082
0.2863 ⫾ 0.4722
0.3739 ⫾ 0.5032
0.0712 ⫾ 0.1232
0.2398 ⫾ 0.2568
0.0179 ⫾ 0.0153
0.0034 ⫾ 0.0057
0.0214 ⫾ 0.0250
0.0966 ⫾ 0.1431
0.0291 ⫾ 0.0422
0.0861 ⫾ 0.1351
0.1134 ⫾ 0.1750
0.1180 ⫾ 0.1816
0.0001 ⫾ 0.0004
0.0213 ⫾ 0.0200
0.0012 ⫾ 0.0023
0.0008 ⫾ 0.0010
0.0015 ⫾ 0.0017
0.0195 ⫾ 0.0083
0.0143 ⫾ 0.0129
0.0147 ⫾ 0.0119
0.0208 ⫾ 0.0134
0.0552 ⫾ 0.0308
0.0004 ⫾ 0.0010
0.0568 ⫾ 0.0504
SI_HC
0.0205 ⫾ 0.0138
0.0000 ⫾ 0.0006
0.0078 ⫾ 0.0069
0.0076 ⫾ 0.0064
0.0096 ⫾ 0.0053
0.1525 ⫾ 0.1756
0.1535 ⫾ 0.2020
0.1965 ⫾ 0.2751
0.0016 ⫾ 0.0020
0.0640 ⫾ 0.0665
0.5550 ⫾ 0.7516
0.0493 ⫾ 0.0907
0.3239 ⫾ 0.6129
0.0445 ⫾ 0.0883
0.0000 ⫾ 0.0198
0.2801 ⫾ 0.2191
1.1592 ⫾ 1.1208
0.4817 ⫾ 0.4083
1.6180 ⫾ 1.6878
0.0286 ⫾ 0.0687
0.7286 ⫾ 0.7954
1.0654 ⫾ 0.8917
0.2367 ⫾ 0.2904
0.8675 ⫾ 0.6199
0.0945 ⫾ 0.0628
0.0023 ⫾ 0.0040
0.1709 ⫾ 0.2209
0.5999 ⫾ 0.6339
0.3470 ⫾ 0.4851
0.2624 ⫾ 0.2620
0.4850 ⫾ 0.5250
0.5446 ⫾ 0.5511
0.0006 ⫾ 0.0011
0.0893 ⫾ 0.1198
0.0030 ⫾ 0.0043
0.0132 ⫾ 0.0118
0.0052 ⫾ 0.0061
0.0149 ⫾ 0.0146
0.0121 ⫾ 0.0073
0.0113 ⫾ 0.0115
0.0097 ⫾ 0.0073
0.0235 ⫾ 0.0194
0.0005 ⫾ 0.0020
0.0324 ⫾ 0.0293
SI_HW
0.0098 ⫾ 0.0099
0.0024 ⫾ 0.0018
0.0020 ⫾ 0.0011
0.0024 ⫾ 0.0029
0.0048 ⫾ 0.0017
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0019 ⫾ 0.0031
0.0000 ⫾ 0.0122
0.0000 ⫾ 0.0039
0.0000 ⫾ 0.0084
0.1984 ⫾ 0.1330
0.6195 ⫾ 0.3708
0.3123 ⫾ 0.1673
1.0810 ⫾ 0.6755
0.0762 ⫾ 0.1143
0.3245 ⫾ 0.1402
0.6661 ⫾ 0.4809
0.1025 ⫾ 0.0786
0.6376 ⫾ 0.4997
0.0442 ⫾ 0.0310
0.0000 ⫾ 0.0001
0.0567 ⫾ 0.0607
0.2241 ⫾ 0.1966
0.0858 ⫾ 0.0851
0.1316 ⫾ 0.1015
0.1788 ⫾ 0.1733
0.2149 ⫾ 0.2615
0.0038 ⫾ 0.0029
0.0264 ⫾ 0.0766
0.0011 ⫾ 0.0018
0.0108 ⫾ 0.0059
0.0044 ⫾ 0.0023
0.0334 ⫾ 0.0237
0.0273 ⫾ 0.0200
0.0158 ⫾ 0.0190
0.0723 ⫾ 0.0541
0.1923 ⫾ 0.1404
0.0022 ⫾ 0.0036
0.1496 ⫾ 0.1126
SI_LC
(Table continues)
0.0105 ⫾ 0.0054
0.0049 ⫾ 0.0073
0.0023 ⫾ 0.0028
0.0041 ⫾ 0.0027
0.0099 ⫾ 0.0058
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0000 ⫾ 0.0100
0.0071 ⫾ 0.0116
0.0091 ⫾ 0.0240
0.0000 ⫾ 0.0064
0.0000 ⫾ 0.0167
0.1499 ⫾ 0.1420
0.3123 ⫾ 0.1688
0.2031 ⫾ 0.2531
0.4629 ⫾ 0.4612
0.0000 ⫾ 0.0025
0.2470 ⫾ 0.0936
0.6158 ⫾ 0.4674
0.0540 ⫾ 0.0503
0.7823 ⫾ 0.5144
0.0707 ⫾ 0.0463
0.0018 ⫾ 0.0029
0.0793 ⫾ 0.0988
0.2273 ⫾ 0.1974
0.1126 ⫾ 0.1158
0.1137 ⫾ 0.0890
0.2633 ⫾ 0.2301
0.2998 ⫾ 0.3070
0.0041 ⫾ 0.0067
0.0366 ⫾ 0.1103
0.0011 ⫾ 0.0019
0.0149 ⫾ 0.0123
0.0065 ⫾ 0.0029
0.0209 ⫾ 0.0080
0.0201 ⫾ 0.0079
0.0082 ⫾ 0.0108
0.0720 ⫾ 0.0604
0.1843 ⫾ 0.1422
0.0000 ⫾ 0.0024
0.1539 ⫾ 0.1252
SI_LW
Fujita, Campbell, Arnott, Chow, and Zielinska
Journal of the Air & Waste Management Association 725
726 Journal of the Air & Waste Management Association
HCS
0.0002 ⫾ 0.0004
0.0002 ⫾ 0.0004
0.0002 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0001 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0001 ⫾ 0.0004
0.0001 ⫾ 0.0004
0.0001 ⫾ 0.0004
0.0059 ⫾ 0.0031
0.0000 ⫾ 0.0004
0.0136 ⫾ 0.0076
0.0063 ⫾ 0.0035
0.0000 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0000 ⫾ 0.0004
0.0199 ⫾ 0.0110
0.0130 ⫾ 0.0037
HDD
0.0051 ⫾ 0.0066
0.0024 ⫾ 0.0027
0.0002 ⫾ 0.0003
0.0000 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0031 ⫾ 0.0036
0.0001 ⫾ 0.0002
0.0111 ⫾ 0.0063
0.0050 ⫾ 0.0029
0.0001 ⫾ 0.0004
0.0000 ⫾ 0.0002
0.0000 ⫾ 0.0002
0.0000 ⫾ 0.0002
0.0000 ⫾ 0.0002
0.0163 ⫾ 0.0089
0.0226 ⫾ 0.0141
STER44
STER45_
40
STER47
STER48
STER49
STER50
STER51
STER52
STER53
HOP13
HOP15
HOP17
HOP19
HOP21
HOP22
HOP24
HOP25
HOP26
HOPANES
STERANE
Mnemonic
0.0126 ⫾ 0.0054
0.0322 ⫾ 0.0143
0.0000 ⫾ 0.0003
0.0000 ⫾ 0.0003
0.0000 ⫾ 0.0003
0.0000 ⫾ 0.0003
0.0037 ⫾ 0.0017
0.0003 ⫾ 0.0003
0.0085 ⫾ 0.0044
0.0001 ⫾ 0.0003
0.0004 ⫾ 0.0007
0.0001 ⫾ 0.0003
0.0002 ⫾ 0.0003
0.0002 ⫾ 0.0003
0.0001 ⫾ 0.0003
0.0001 ⫾ 0.0003
0.0000 ⫾ 0.0003
0.0002 ⫾ 0.0003
0.0047 ⫾ 0.0019
0.0101 ⫾ 0.0061
HW
0.0065 ⫾ 0.0064
0.0222 ⫾ 0.0164
0.0000 ⫾ 0.0012
0.0000 ⫾ 0.0012
0.0001 ⫾ 0.0012
0.0004 ⫾ 0.0012
0.0040 ⫾ 0.0054
0.0002 ⫾ 0.0012
0.0018 ⫾ 0.0015
0.0000 ⫾ 0.0012
0.0003 ⫾ 0.0012
0.0001 ⫾ 0.0012
0.0000 ⫾ 0.0012
0.0013 ⫾ 0.0012
0.0007 ⫾ 0.0012
0.0029 ⫾ 0.0038
0.0000 ⫾ 0.0012
0.0017 ⫾ 0.0012
0.0037 ⫾ 0.0026
0.0011 ⫾ 0.0012
MDD
0.0387 ⫾ 0.0691
0.0346 ⫾ 0.0055
0.0015 ⫾ 0.0014
0.0000 ⫾ 0.0002
0.0001 ⫾ 0.0002
0.0111 ⫾ 0.0294
0.0214 ⫾ 0.0371
0.0000 ⫾ 0.0003
0.0030 ⫾ 0.0053
0.0014 ⫾ 0.0025
0.0002 ⫾ 0.0004
0.0013 ⫾ 0.0023
0.0025 ⫾ 0.0022
0.0028 ⫾ 0.0006
0.0008 ⫾ 0.0015
0.0038 ⫾ 0.0054
0.0011 ⫾ 0.0015
0.0005 ⫾ 0.0006
0.0049 ⫾ 0.0012
0.0045 ⫾ 0.0013
SI_BC
0.0973 ⫾ 0.1680
0.0679 ⫾ 0.0109
0.0010 ⫾ 0.0026
0.0000 ⫾ 0.0007
0.0010 ⫾ 0.0018
0.0330 ⫾ 0.0874
0.0485 ⫾ 0.0832
0.0000 ⫾ 0.0008
0.0084 ⫾ 0.0049
0.0054 ⫾ 0.0077
0.0068 ⫾ 0.0180
0.0019 ⫾ 0.0032
0.0081 ⫾ 0.0051
0.0011 ⫾ 0.0024
0.0013 ⫾ 0.0023
0.0086 ⫾ 0.0130
0.0013 ⫾ 0.0023
0.0016 ⫾ 0.0029
0.0126 ⫾ 0.0054
0.0026 ⫾ 0.0045
SI_BW
0.1924 ⫾ 0.2061
0.1014 ⫾ 0.0417
0.0079 ⫾ 0.0074
0.0053 ⫾ 0.0046
0.0063 ⫾ 0.0067
0.0426 ⫾ 0.0722
0.0692 ⫾ 0.0814
0.0151 ⫾ 0.0126
0.0422 ⫾ 0.0291
0.0037 ⫾ 0.0052
0.0062 ⫾ 0.0037
0.0074 ⫾ 0.0053
0.0118 ⫾ 0.0082
0.0114 ⫾ 0.0060
0.0054 ⫾ 0.0028
0.0083 ⫾ 0.0087
0.0057 ⫾ 0.0051
0.0053 ⫾ 0.0019
0.0180 ⫾ 0.0085
0.0072 ⫾ 0.0051
SI_HC
0.2286 ⫾ 0.2290
0.1818 ⫾ 0.1555
0.0042 ⫾ 0.0100
0.0036 ⫾ 0.0077
0.0061 ⫾ 0.0111
0.0358 ⫾ 0.0864
0.1182 ⫾ 0.1243
0.0106 ⫾ 0.0198
0.0309 ⫾ 0.0568
0.0192 ⫾ 0.0246
0.0042 ⫾ 0.0046
0.0061 ⫾ 0.0077
0.0229 ⫾ 0.0257
0.0112 ⫾ 0.0103
0.0046 ⫾ 0.0040
0.0394 ⫾ 0.0748
0.0054 ⫾ 0.0066
0.0071 ⫾ 0.0067
0.0327 ⫾ 0.0156
0.0129 ⫾ 0.0097
SI_HW
0.0128 ⫾ 0.0099
0.0863 ⫾ 0.0364
0.0022 ⫾ 0.0036
0.0011 ⫾ 0.0018
0.0011 ⫾ 0.0018
0.0000 ⫾ 0.0001
0.0000 ⫾ 0.0001
0.0011 ⫾ 0.0018
0.0035 ⫾ 0.0048
0.0038 ⫾ 0.0062
0.0005 ⫾ 0.0013
0.0029 ⫾ 0.0019
0.0136 ⫾ 0.0175
0.0022 ⫾ 0.0017
0.0020 ⫾ 0.0014
0.0263 ⫾ 0.0288
0.0000 ⫾ 0.0001
0.0040 ⫾ 0.0026
0.0129 ⫾ 0.0064
0.0038 ⫾ 0.0027
SI_LC
0.0061 ⫾ 0.0155
0.2102 ⫾ 0.1701
0.0000 ⫾ 0.0014
0.0000 ⫾ 0.0007
0.0010 ⫾ 0.0026
0.0000 ⫾ 0.0002
0.0000 ⫾ 0.0002
0.0000 ⫾ 0.0012
0.0020 ⫾ 0.0052
0.0031 ⫾ 0.0078
0.0000 ⫾ 0.0005
0.0011 ⫾ 0.0019
0.0023 ⫾ 0.0037
0.0010 ⫾ 0.0026
0.0021 ⫾ 0.0028
0.1072 ⫾ 0.1284
0.0051 ⫾ 0.0023
0.0227 ⫾ 0.0260
0.0293 ⫾ 0.0143
0.0090 ⫾ 0.0074
SI_LW
Notes: Carbon fractions in the IMPROVE method correspond with temperature steps of 120 °C (OC1), 250 °C (OC2), 450 °C (OC3), and 550 °C (OC4) in a nonoxidizing helium atmosphere and 550 °C (EC1), 700 °C (EC2),
and 850 °C (EC3) in an oxidizing atmosphere. The temperature steps in the STN thermal evolution protocol are 310 °C, 480 °C, 615 °C, and 900 °C in a nonoxidizing helium atmosphere and 600 °C, 675 °C, and 825
°C in an oxidizing atmosphere. The STN method uses fixed hold times of 45–120 sec at each heating stage, and IMPROVE method uses variable hold times of 150 –580 sec so that carbon responses return to baseline
values. aFitting species used in the CMB calculations.
C27–20S5a(H),14ß(H),
17ß(H)-cholestane
C27–20R5a(H),14a(H),
17a(H)-cholestane&C29–
20S13ß(H), 17a(H)diasteranea
C28–20R5a(H),14ß(H),
17ß(H)-ergostane
C28–20S5a(H),14ß(H),
17ß(H)-ergostanea
C28–20R5a(H),14a(H),
17a(H)-ergostanea
C29–20S5a(H),14a(H),
17a(H)-stigmastanea
C29–20R5a(H),14ß(H),
17ß(H)-stigmastanea
C29–20S5a(H),14ß(H),
17ß(H)-stigmastanea
C29–20R5a(H),14a(H),
17a(H)-stigmastanea
Hopanes (␮g/mi)
18a(H),21ß(H)-22,29,30Trisnorhopane
17a(H),21ß(H)-22,29,30Trisnorhopane
17a(H),21ß(H)-30Norhopanea
17a(H),21ß(H)-Hopanea
22S-17a(H),21ß(H)-30Homohopanea
22R-17a(H),21ß(H)-30Homohopanea
22S-17a(H),21ß(H)-30,31Bishomohopane
22R-17a(H),21ß(H)-30,31Bishomohopane
22S-17a(H),21ß(H)-30,31,
32-Trisomohopane
Sum of hopanes
Sum of steranes
Species Description
Table 1. Cont.
Fujita, Campbell, Arnott, Chow, and Zielinska
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
Table 2. List of ambient samples.
Ambient
Composite
Fixed ambient sites
Azusa Sun
Azusa Mon
Azusa Tue
Azusa Wed
Azusa Thu
Azusa Fri
Azusa Sat
LANM Sun
LANM Mon
LANM Tue
LANM Wed
LANM Thu
LANM Fri
LANM Sat
Mobile sampler
M1F_Sun
Location
Azusa SCAQMD Station
Los Angeles North Main SCAQMD Station
West basin freeway loop during morning commute
period
M1F_WD
M2F_Sun
Midbasin freeway loop along major truck routes
during midday
M2F_WD
CI - TI and TS
CI - Cajon Pass
SurfSt1
SurfSt2
SILoad
Cold start
Regional W
Regional C
CI-dominated, Terminal Island, diesel traffic
I10/I15 Truck Stop (diesel idle and acceleration)
I-15 Cajon Pass from San Bernardino to Hesperia
Surface streets in Compton
South Gate surface streets/Freeway Loop
Surface streets in South Central Los Angeles
Surface streets in downtown Los Angeles
Surface streets in Venice/Santa Monica
SI dominated under load 405 Freeway from Sunset to
Burbank, side trip on
Parking lot after sporting event: Rose Bowl (cold
starts) ⫹ freeway
Venice Rennie St. Liggett Residence
RV park at Frank Bonelli Regional Park in San Dimas
Date(s)
Day of the
Week
Start Time
Stop Time
7/8, 7/15, 7/22
7/9, 7/16, 7/23
7/10, 7/17, 7/24
7/11, 7/18, 7/25
7/12, 7/19, 7/26
7/13, 7/20, 7/27
7/7, 7/14, 7/21
7/8, 7/15
7/16, 7/23
7/10, 7/17, 7/24
7/11, 7/25
7/12, 7/19, 7/26
7/13, 7/20, 7/27
7/7, 7/21
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
Tue
Wed
Thu
Fri
Sat
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
12:00 a.m.
07/08/01
07/15/01
07/09/01
07/11/01
07/08/01
07/15/01
07/09/01
07/11/01
07/10/01
07/16/01
07/13/01
07/12/01
07/12/01
07/12/01
07/10/01
07/07/01
07/15/01
Sun
Sun
Mon
Wed
Sun
Sun
Mon
Wed
Tue
Mon
Fri
Thu
Thu
Thu
Tue
Sat
Sun
7:54 a.m.
7:20 a.m.
7:35 a.m.
7:13 a.m.
12:30 p.m.
9:45 a.m.
10:45 a.m.
10:00 a.m.
11:05 a.m.
20:00 a.m.
9:10 a.m.
9:40 a.m.
12:50 p.m.
6:48 p.m.
4:50 p.m.
6:00 p.m.
1:16 p.m.
10:02 a.m.
9:20 a.m.
9:35 a.m.
9:16 a.m.
2:28 p.m.
11:45 a.m.
1:26 p.m.
12:00 p.m.
1:00 p.m.
11:00 p.m.
11:18 a.m.
11:52 a.m.
3:13 p.m.
8:34 p.m.
6:51 p.m.
8:05 p.m.
3:32 p.m.
07/14/01
Sat
8:30 p.m.
10:08 p.m.
07/07/01
07/09/01
07/11/01
Sat
Mon
Wed
12:40 p.m.
2:39 p.m.
1:00 p.m.
4:40 p.m.
6:30 p.m.
5:00 p.m.
Notes: SCAQMD ⫽ South Coast Air Quality Management District; LANM, Los Angeles North Main.
Aerosol Monitor is a portable, battery-operated, laser photometer that measures 90 ° light scattering (different from
the total light scattering measured by an integrating
nephelometer) and reports it as estimated PM mass concentration. The laser diode used by the DustTrak has a
wavelength of 780 nm, which limits the smallest detectable particle to approximately 0.1 ␮m. The reported PM
mass concentration is factory calibrated using the respirable fraction of an Arizona Road Dust standard (ISO
12103–1, A1). The mass scattering efficiency depends on
particle shapes, size distribution, and composition (index
of refraction). The ISO 12103–1, A1 standard consists of
primarily silica particles (70%) that are provided with
some particle size specifications. By volume, the standard
consists of 1–3% particles with diameter less than 1 ␮m,
36 – 44% with diameter less than 4 ␮m, 83– 88% with
diameter less than 7 ␮m, and 97–100% with diameter less
Volume 57 June 2007
than 10 ␮m. This standard contains a larger quantity of
coarse (above 2.5 ␮m) particles than are usually found in
ambient aerosol. PM2.5 has a higher mass scattering efficiency, so the DustTrak overestimates PM2.5 for smaller,
chain aggregate soot particles.20 For the present study, the
DustTrak was found to exceed gravimetric mass concentrations of the mobile ambient samples by a factor of 2.24
with an R2 of 0.75.
Figure 1 shows the locations of the mobile samples.
Samples were collected during the morning commute period in predominantly automobile traffic along a freeway
loop in the western basin (I-110, I-405, and I-10) and later in
the morning along the major truck routes within the basin
(I-710, SR-91, I-605, and SR-60). Samples were also collected
in the port area (Terminal Island), at a truck stop, and along
surface streets in downtown Los Angeles, South Central Los
Angeles, and in the coastal communities of Santa Monica
Journal of the Air & Waste Management Association 727
Fujita, Campbell, Arnott, Chow, and Zielinska
Figure 1. Location of fixed and mobile ambient samples. Daily
24-hr samples were collected at Los Angeles North Main and Azusa
during four consecutive weeks and composited by day of week for
each site. Mobile sampling included urban background location and
freeway and surface streets with varying proportions of gasoline and
diesel traffic.
and Venice. On-road samples were collected on both weekends and weekdays. Urban background samples were collected at a regional park in San Dimas (mix of gasoline and
diesel) and near the coast in Venice (predominantly gasoline). Measurements were made in the parking lot of the
Pasadena Rose Bowl during and after a professional soccer
match to determine the effect of LDGV cold-start emissions
on ambient PM concentrations. On-road measurements
were made on I-405 between Westwood and the San Fernando Valley to detect differences in PM concentrations
along the uphill and downhill portions of the route.
Source Apportionment Method and Procedures
The ambient source apportionments were obtained by
applying the effective variance solution implemented in
version 8 of the CMB receptor model.27 The CMB model
consists of a least-squares solution to a set of linear equations that expresses each receptor concentration of a
chemical species as a linear sum of products of source
profile species and source contributions. The source profile species (the fractional amount of each species in the
emissions from a given source type) and the receptor
concentrations, each with uncertainty estimates, serve as
input data to the CMB model. The output consists of the
contributions of each source type to both total and individual ambient pollutant concentrations. The model calculates values for contributions from each source and the
uncertainties of those values. Input data uncertainties are
used both to weigh the relative importance of the input
data to the model solution and to estimate uncertainties
of the source contributions.
The CMB analysis involves several procedural
choices. These include the methods for derivation of
source composition profiles and uncertainties and selection of profiles and fitting species in the CMB calculations. One objective for this study was to determine the
sensitivity of the CMB results to these choices. TC, TC
minus the OC1 fraction, and reconstructed mass were
used to derive alternatively normalized profiles (in weight
fractions), which were applied in CMB and evaluated.
TC-OC1 was used to examine potential effects of varying
retention of more volatile OC species for source and ambient samples. The composite profiles were also derived
728 Journal of the Air & Waste Management Association
by alternatively averaging weight fractions or emission
rates. The former approach gives equal weight to all members of the composite, whereas the latter method gives
greater weight to high emitters. Variations in source contributions among alternative individual and composite
profiles were examined for both averaging approaches.
The final apportionments were obtained after determining the sensitivity of the CMB output to that above
perturbations and effect on model stability and performance. The final apportionments were obtained by applying the composite profiles in Table 1. Two sets of
profiles were derived, one using IMPROVE-TOR carbon
data and the second using STN-TOT. The profiles are
given in weight percentages normalized to TC and are
averaged by weight fractions. TC was used to normalize
the profile, because it was measured consistently by both
IMPROVE and STN methods. SI profiles were grouped into
six composites based on emission rate, abundances of BC,
and vehicle test cycle. Therefore, individual profiles
within these composites were averaged by weight fractions to give equal influence to each profile. Comparable
level of compositing for CI exhaust was not possible because of collinearity among the profiles and higher analytical uncertainty for the medium heavy-duty trucks because of overdilution of the exhaust. Uncertainties are 1-␴
variations in fractional abundances among members of
the composite or the propagated root mean squares of the
analytical uncertainties, whichever is larger. The analytical uncertainties are square roots of the sum of the squares
of the product of the replicate precision and analyte concentration plus the minimum detection limit. A footnote
in Table 1 identifies the species that were included in the
default set of fitting species. This set includes total EC
(IMPROVE TOR or STN TOT), seven particle-phase PAHs,
four hopanes, and eight steranes. OC was not used for
reasons that will be given below. These 20 species are
known components of motor vehicle exhaust, are present
in both ambient and source samples at analytically significant levels, and are sufficiently stable in the atmosphere
for receptor modeling.4
PAHs are present in emissions from all combustion
sources, and the abundances of different PAH compounds
in emissions from a given source may vary over several
orders of magnitude. Data from the present study as well
as the DOE Comparative Toxicity Study28 and the Northern Front Range Air Quality Study5,6 show that SI exhaust
abundances for certain PAHs are greater than those for CI
exhaust. SI vehicles, whether low or high emitters, have
higher emission rates of the high-molecular-weight particulate PAHs, benzo(ghi)perylene, indeno(1,2,3-cd)
pyrene, and coronene. These PAHs are also found in used
gasoline vehicle motor oil but not in fresh oil and not in
fresh or used diesel engine oil.21 Their concentration in
gasoline vehicle motor oil increases with age of the oil.28
Although relative abundances of these PAHs to each other
are reasonably consistent in gasoline vehicle exhaust irrespective of emission rate, their emissions relative to TC
are more varied. CI vehicles also emit particle-phase
PAHs, but in lower relative proportions to other PAHs,
especially two- to four-ring semivolatile methylated PAHs
(dimethylnaphthalenes, methyl- and dimethylphenanthrenes, and methylfluorenes), which are relatively more
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
abundant in CI exhaust compared with SI exhaust. These
semivolatile PAHs were not used in the CMB calculations.
Hopanes and steranes are present in engine lubricating oil and are not present in gasoline or diesel fuels.
Emission rates of hopanes and steranes are highest for
both gasoline and diesel high-emitting vehicles. CI exhaust contains higher amounts of lower molecular weight
hopanes and steranes, whereas SI exhaust contains a more
even distribution by molecular weight. The differences in
the ratios of hopanes plus steranes to “elemental” carbon
have been used to quantify the combined contributions
of gasoline-powered and diesel-powered vehicles.29
Other than the particulate carbon that is apportioned
to engine exhaust, a residual fraction exceeding measurement uncertainties is unexplained. Primary particle emitters that might contribute to this unexplained fraction
include meat cooking, vegetative burning, and biological
material. Excluding these and other sources of fine particulate carbon from the CMB can result in overestimation
of the contributions of motor vehicle exhaust if the excluded sources contain species in common with engine
exhaust. In this study, the apportionments of engine exhaust and the split between gasoline and diesel contributions are determined by the abundances of EC, hopanes,
steranes, and particle-phase PAHs in the source profiles
relative to their presence in ambient samples. Restricting
the apportionment calculation to these fitting species
tends to minimize the potential for overestimation because of the presence of nonengine sources in the ambient sample. The contributions of other sources, such as
meat cooking, vegetative burning, and biological material,
to TC may be determined by including composition profiles
and relevant fitting species in the CMB calculations.
Secondary organic aerosols (SOAs) can contribute
to fine particle carbon during the summer periods in
the presence of gaseous precursors. Secondary components of organic aerosols are complex and may partition between the gas and particle phases with relative
amounts that vary by region and season. Laboratory
and theoretical studies of organic PM formation from
precursor gases indicate that secondary compounds are
composed of aliphatic and aromatic compounds, including carboxylic acids, alcohols, carbonyls, nitrates,
and other single and multifunctional oxygenated compounds. Schauer et al.7 found that, in a sample taken
during a severe smog episode in 1993 in the greater Los
Angeles area, as much as 67% of the fine particle organic concentration could not be explained by primary
source contributions. These authors assumed that
much of the unidentified organic component was SOA.
In a similar analysis, Zheng et al.30 showed that the
highest fraction of unidentified OC occurred during
summer and early fall. Because the ambient samples for
this study were collected in the Los Angeles area during
midsummer, the presence of SOA in our ambient samples is highly likely. The practical consequence of this
expectation is that OC should not be used unless an
SOA profile is also included. A profile containing only
OC abundance is sometimes used to make up the difference between calculated and measured values as an
estimate of the SOA contribution. Because OC coefficient of variations in the source profiles and ambient
Volume 57 June 2007
data are often less than those for the organic fitting
species (e.g., PAHs, hopanes, and steranes), including
OC without an SOA profile may bias the source contribution estimates.
RESULTS
Contributions of Gasoline and Diesel Exhaust to
Ambient Fine Particle Carbon
Sensitivity tests were conducted before the CMB source
apportionment to examine the variations in source contributions with alternative SI and CI exhaust profiles.
Figure 2a shows the variations in source contribution
estimates of SI engine exhaust to total particulate carbon
for each of the six alternative composite SI profiles and
the individual profiles that compose the composites. Each
alternative SI profile and the default composite CI profile
(HDD) were applied to a midweek ambient sample from
Azusa and Los Angeles North Main. Despite large variations in the relative abundances of any single fitting species, the six alternative composite SI profiles yielded similar source contributions for SI exhaust at Azusa of 3% of
total ambient particulate carbon with 1-␴ uncertainties of
approximately 1.6%. SI contributions for individual profiles ranged from 0.4 ⫾ 0.2 to 6.5 ⫾ 2.3% of total particulate carbon. The highest contributions were obtained
with the “high-warm” profiles. The SI contributions at Los
Angeles North Main ranged from 1.6% ⫾ 1.5% to 8.4% ⫾
4.6% of particulate carbon for the six composite profiles
and ⫺1.9% to 11.8% ⫾ 5.9% for the individual profiles.
Figure 2b similarly shows the variations in source contributions of CI engine exhaust for each of the composite
and individual CI profiles using SI_HW as the default SI
profile for the sensitivity tests. CI contributions for the
HDD composite profile were 38.1% ⫾ 6.8% at Azusa and
41.9% ⫾ 7.8% at Los Angeles North Main. The MDD
composite profile yielded higher CI contributions of
45.4% ⫾ 14.3% and 50.5% ⫾ 16.5%, respectively, at the
two sites. Uncertainties are twice as high for MDD than
HDD, and the MDD profile is substantially collinear with
HDD.
The set of source profiles that was used in the final
source apportionments included HDD and all six of the SI
composite profiles. The source elimination option in the
CMB version 8 software was turned on so that collinear SI
profiles were automatically eliminated in successive iterations of the least-square calculations. SI_LC ⫹ SI_HW
and SI_HC ⫹ SI_HW were the most common combinations of SI profiles in the CMB results for the two fixed
monitoring stations. Table 3 shows the source contributions to ambient total particulate carbon, OC, and EC at
Azusa and Los Angeles North Main, urban background
sites in the western (Venice) and central basin (San Dimas), and two LDGV-dominated samples (Rose Bowl and
I-405). The source contribution estimates in the upper
half of the table are based on IMPROVE TOR carbon data,
and the bottom half is based on STN TOT carbon data for
both source and ambient measurements. Figure 3 compares the fractional source contributions of CI and SI
derived from the CMB analysis using IMPROVE and STN
carbon data.
CI exhaust contributions to TC (IMPROVE TOR) in
the Sunday, weekday, and Saturday composite ambient
Journal of the Air & Waste Management Association 729
Fujita, Campbell, Arnott, Chow, and Zielinska
(a)
(b)
Figure 2. (a) Variations in CMB source contributions to ambient total particulate carbon with alternative composition profiles for spark-ignition
vehicle exhaust for compression-ignition vehicle exhaust (b). Sensitivity tests were applied to midweek 24-hr PM sample from Azusa and Los
Angeles North Main.
samples were 29%, 39%, and 43%, respectively, at Azusa
and 30%, 43%, and 35%, respectively, at Los Angeles.
Although we expect generally lower CI apportionment on
Sunday because of substantially lower diesel traffic, dayof-week variations in the relative apportionment of CI
730 Journal of the Air & Waste Management Association
emissions may vary with meteorology variations that affect pollutant transport and daily carry-over of emissions.
The corresponding SI contributions were 3%, 6%, and 5%
at Azusa and 7%, 7%, and 12% at Los Angeles North
Main. The CI/SI ratios of the contributions to TC, OC, and
Volume 57 June 2007
Volume 57 June 2007
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
15–19
13–17
21–23
13–15
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
00–24
15–19
13–17
21–23
13–15
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Mon
Sat
Sat
Sun
Period
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Mon
Sat
Sat
Sun
Day
0.95
0.92
0.90
0.91
0.90
0.86
0.90
0.88
0.95
0.95
0.91
0.94
0.89
0.93
0.88
0.58
0.97
0.80
0.95
0.92
0.88
0.91
0.90
0.84
0.89
0.95
0.94
0.97
0.92
0.91
0.85
0.95
0.97
0.81
0.99
0.98
R2
0.19
0.39
0.50
0.43
0.49
0.71
0.49
0.24
0.18
0.21
0.24
0.28
0.60
0.30
0.45
0.70
0.04
0.18
0.21
0.37
0.54
0.48
0.48
0.81
0.55
0.20
0.25
0.17
0.32
0.43
0.83
0.25
0.44
0.80
0.11
0.28
c2
0.14 ⫾ 0.09
0.29 ⫾ 0.15
0.39 ⫾ 0.14
0.28 ⫾ 0.27
0.38 ⫾ 0.26
0.41 ⫾ 0.19
0.28 ⫾ 0.23
0.24 ⫾ 0.14
0.27 ⫾ 0.15
0.46 ⫾ 0.32
0.33 ⫾ 0.20
0.41 ⫾ 0.18
0.42 ⫾ 0.18
0.58 ⫾ 0.24
2.21 ⫾ 0.93
1.06 ⫾ 0.52
1.78 ⫾ 1.10
0.94 ⫾ 1.02
0.17 ⫾ 0.17
0.33 ⫾ 0.23
0.39 ⫾ 0.22
0.28 ⫾ 0.20
0.36 ⫾ 0.23
0.40 ⫾ 0.17
0.29 ⫾ 0.23
0.29 ⫾ 0.23
0.38 ⫾ 0.26
0.46 ⫾ 0.28
0.53 ⫾ 0.33
0.54 ⫾ 0.28
0.51 ⫾ 0.23
0.65 ⫾ 0.41
1.92 ⫾ 0.78
0.96 ⫾ 0.44
1.47 ⫾ 1.47
0.94 ⫾ 1.06
0.70 ⫾ 0.12
1.31 ⫾ 0.19
1.25 ⫾ 0.18
1.41 ⫾ 0.20
1.94 ⫾ 0.27
2.38 ⫾ 0.34
1.81 ⫾ 0.25
0.67 ⫾ 0.17
1.11 ⫾ 0.20
1.44 ⫾ 0.23
1.32 ⫾ 0.29
1.54 ⫾ 0.23
1.93 ⫾ 0.28
1.28 ⫾ 0.24
1.46 ⫾ 0.86
nd
nd
0.33 ⫾ 2.61
SI
1.25 ⫾ 0.18
1.79 ⫾ 0.27
2.09 ⫾ 0.30
2.12 ⫾ 0.30
2.70 ⫾ 0.38
3.28 ⫾ 0.47
2.53 ⫾ 0.34
1.01 ⫾ 0.16
1.80 ⫾ 0.26
2.21 ⫾ 0.32
2.04 ⫾ 0.30
2.46 ⫾ 0.36
2.42 ⫾ 0.36
1.75 ⫾ 0.28
2.15 ⫾ 0.68
0.09 ⫾ 0.35
0.77 ⫾ 0.70
1.69 ⫾ 0.44
CI
2.64 ⫾ 0.41
2.61 ⫾ 0.52
2.70 ⫾ 0.52
3.17 ⫾ 0.56
3.55 ⫾ 0.68
4.53 ⫾ 0.82
3.04 ⫾ 0.61
1.81 ⫾ 0.40
2.06 ⫾ 0.48
2.08 ⫾ 0.54
2.52 ⫾ 0.62
2.46 ⫾ 0.58
3.15 ⫾ 0.67
2.18 ⫾ 0.63
2.77 ⫾ 1.32
1.71 ⫾ 0.63
0.75 ⫾ 1.77
0.52 ⫾ 2.91
2.88 ⫾ 0.29
2.78 ⫾ 0.38
2.99 ⫾ 0.43
3.25 ⫾ 0.49
3.48 ⫾ 0.55
4.34 ⫾ 0.64
3.09 ⫾ 0.51
2.15 ⫾ 0.26
1.88 ⫾ 0.35
2.11 ⫾ 0.50
2.76 ⫾ 0.43
2.54 ⫾ 0.48
3.69 ⫾ 0.51
2.61 ⫾ 0.43
2.47 ⫾ 1.21
1.93 ⫾ 0.82
0.61 ⫾ 1.69
0.15 ⫾ 1.36
Residual
Notes: SCE ⫽ source contribution estimates; nd ⫽ not detected; L.A. ⫽ Los Angeles; N. ⫽ North.
IMPROVE-TOR
Azusa
Azusa
Azusa
Azusa
Azusa
Azusa
Azusa
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
San Dimas
Venice
Rose Bowl
I-405
STN-TOT
Azusa
Azusa
Azusa
Azusa
Azusa
Azusa
Azusa
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
L.A. N. Main
San Dimas
Venice
Rose Bowl
I-405
Site
TC, SCE (mg/m3)
0.29 ⫾ 0.04
0.54 ⫾ 0.07
0.52 ⫾ 0.07
0.58 ⫾ 0.08
0.80 ⫾ 0.11
0.98 ⫾ 0.14
0.74 ⫾ 0.10
0.28 ⫾ 0.04
0.46 ⫾ 0.06
0.59 ⫾ 0.08
0.54 ⫾ 0.08
0.64 ⫾ 0.09
0.80 ⫾ 0.11
0.53 ⫾ 0.07
0.60 ⫾ 0.08
nd
nd
0.14 ⫾ 0.02
0.49 ⫾ 0.07
0.70 ⫾ 0.10
0.82 ⫾ 0.12
0.83 ⫾ 0.12
1.05 ⫾ 0.15
1.28 ⫾ 0.19
0.99 ⫾ 0.15
0.40 ⫾ 0.06
0.70 ⫾ 0.10
0.86 ⫾ 0.13
0.80 ⫾ 0.12
0.96 ⫾ 0.14
0.94 ⫾ 0.14
0.68 ⫾ 0.10
0.84 ⫾ 0.12
0.03 ⫾ 0.00
0.30 ⫾ 0.04
0.66 ⫾ 0.10
CI
0.15 ⫾ 0.01
0.30 ⫾ 0.02
0.35 ⫾ 0.03
0.26 ⫾ 0.02
0.32 ⫾ 0.03
0.36 ⫾ 0.04
0.26 ⫾ 0.02
0.26 ⫾ 0.02
0.34 ⫾ 0.03
0.41 ⫾ 0.04
0.47 ⫾ 0.04
0.49 ⫾ 0.04
0.45 ⫾ 0.03
0.58 ⫾ 0.04
1.61 ⫾ 0.16
0.83 ⫾ 0.10
1.27 ⫾ 0.10
0.86 ⫾ 0.07
0.11 ⫾ 0.01
0.24 ⫾ 0.03
0.32 ⫾ 0.03
0.23 ⫾ 0.02
0.31 ⫾ 0.03
0.34 ⫾ 0.03
0.23 ⫾ 0.02
0.20 ⫾ 0.02
0.21 ⫾ 0.02
0.38 ⫾ 0.04
0.28 ⫾ 0.03
0.33 ⫾ 0.04
0.31 ⫾ 0.02
0.44 ⫾ 0.04
1.52 ⫾ 0.15
0.74 ⫾ 0.10
1.22 ⫾ 0.12
0.75 ⫾ 0.07
SI
OC, SCE (mg/m3)
2.64 ⫾ 0.31
2.60 ⫾ 0.35
2.68 ⫾ 0.36
3.16 ⫾ 0.41
3.53 ⫾ 0.48
4.40 ⫾ 0.59
3.07 ⫾ 0.42
1.85 ⫾ 0.24
2.06 ⫾ 0.29
2.11 ⫾ 0.32
2.52 ⫾ 0.36
2.43 ⫾ 0.37
3.06 ⫾ 0.44
2.21 ⫾ 0.34
2.70 ⫾ 0.53
1.70 ⫾ 0.94
0.78 ⫾ 2.23
0.19 ⫾ 1.62
2.86 ⫾ 0.20
2.72 ⫾ 0.22
2.89 ⫾ 0.26
3.19 ⫾ 0.26
3.40 ⫾ 0.30
4.04 ⫾ 0.36
3.10 ⫾ 0.29
2.15 ⫾ 0.15
1.86 ⫾ 0.17
2.12 ⫾ 0.21
2.73 ⫾ 0.23
2.44 ⫾ 0.24
3.55 ⫾ 0.31
2.63 ⫾ 0.21
2.44 ⫾ 0.36
1.91 ⫾ 0.54
0.61 ⫾ 1.07
0.14 ⫾ 0.76
Residual
0.41 ⫾ 0.04
0.77 ⫾ 0.08
0.74 ⫾ 0.07
0.83 ⫾ 0.08
1.14 ⫾ 0.11
1.40 ⫾ 0.14
1.06 ⫾ 0.11
0.40 ⫾ 0.04
0.65 ⫾ 0.07
0.84 ⫾ 0.08
0.78 ⫾ 0.08
0.91 ⫾ 0.09
1.14 ⫾ 0.11
0.75 ⫾ 0.08
0.86 ⫾ 0.09
nd
nd
0.20 ⫾ 0.02
0.76 ⫾ 0.08
1.09 ⫾ 0.11
1.27 ⫾ 0.13
1.30 ⫾ 0.13
1.64 ⫾ 0.16
2.00 ⫾ 0.20
1.55 ⫾ 0.15
0.62 ⫾ 0.06
1.10 ⫾ 0.11
1.35 ⫾ 0.13
1.24 ⫾ 0.12
1.50 ⫾ 0.15
1.48 ⫾ 0.15
1.06 ⫾ 0.11
1.31 ⫾ 0.13
0.05 ⫾ 0.01
0.47 ⫾ 0.05
1.03 ⫾ 0.10
CI
0.02 ⫾ 0.01
0.03 ⫾ 0.02
0.04 ⫾ 0.03
0.03 ⫾ 0.01
0.04 ⫾ 0.02
0.05 ⫾ 0.03
0.03 ⫾ 0.01
0.03 ⫾ 0.01
0.04 ⫾ 0.02
0.05 ⫾ 0.03
0.06 ⫾ 0.03
0.06 ⫾ 0.03
0.06 ⫾ 0.02
0.08 ⫾ 0.02
0.30 ⫾ 0.06
0.13 ⫾ 0.10
0.20 ⫾ 0.04
0.08 ⫾ 0.05
0.03 ⫾ 0.01
0.05 ⫾ 0.03
0.07 ⫾ 0.02
0.05 ⫾ 0.01
0.07 ⫾ 0.02
0.07 ⫾ 0.03
0.05 ⫾ 0.02
0.04 ⫾ 0.02
0.05 ⫾ 0.02
0.08 ⫾ 0.03
0.06 ⫾ 0.03
0.08 ⫾ 0.04
0.11 ⫾ 0.02
0.14 ⫾ 0.04
0.70 ⫾ 0.15
0.33 ⫾ 0.10
0.56 ⫾ 0.12
0.19 ⫾ 0.07
SI
EC, SCE (mg/m3)
0.00 ⫾ 0.07
0.02 ⫾ 0.11
0.02 ⫾ 0.11
0.01 ⫾ 0.12
0.02 ⫾ 0.17
0.13 ⫾ 0.21
-0.02 ⫾ 0.15
-0.02 ⫾ 0.10
0.00 ⫾ 0.12
-0.03 ⫾ 0.14
0.00 ⫾ 0.18
0.02 ⫾ 0.14
0.10 ⫾ 0.17
-0.04 ⫾ 0.14
0.06 ⫾ 0.49
0.12 ⫾ 1.37
0.06 ⫾ 2.70
0.34 ⫾ 1.68
0.03 ⫾ 0.11
0.06 ⫾ 0.16
0.10 ⫾ 0.19
0.07 ⫾ 0.19
0.08 ⫾ 0.24
0.29 ⫾ 0.31
0.00 ⫾ 0.22
0.00 ⫾ 0.09
0.03 ⫾ 0.16
-0.01 ⫾ 0.20
0.04 ⫾ 0.19
0.10 ⫾ 0.23
0.14 ⫾ 0.23
-0.02 ⫾ 0.16
0.02 ⫾ 0.29
0.01 ⫾ 0.13
0.00 ⫾ 0.23
0.01 ⫾ 0.19
Residual
Table 3. Source contribution estimates in ␮g/m3 to ambient TC, OC, and EC (IMPROVE and STN protocols) at Azusa and Los Angeles-North Main stations, urban background sites, and source-dominated locations
by CMB receptor modeling.
Fujita, Campbell, Arnott, Chow, and Zielinska
Journal of the Air & Waste Management Association 731
Fujita, Campbell, Arnott, Chow, and Zielinska
(c)
0.8
0.6
0.4
0.2
0.0
Organic Carbon (STN)
1.0
0.8
0.6
0.4
0.2
(f)
Gasoline
I-405 Sun
Venice Sat
Rose Bowl Sat
San Dimas Mon
LANM Fri
LANM Sat
LANM Thu
LANM Tue
LANM Wed
LANM Sun
LANM Mon
Azusa Fri
Day of Week
Day of Week
Di e se l
Azusa Sat
Azusa Thu
Azusa Tue
Azusa Wed
Elemental Carbon (STN)
Azusa Sun
1.0
0.8
0.6
0.4
0.2
0.0
Azusa Mon
I-405 Sun
Venice Sat
Rose Bowl Sat
San Dimas Mon
LANM Fri
LANM Sat
LANM Thu
LANM Tue
LANM Wed
LANM Sun
LANM Mon
Azusa Fri
Azusa Sat
Azusa Thu
Azusa Tue
Azusa Wed
Azusa Sun
Fractional SCE
Elemental Carbon (IMPROVE)
Azusa Mon
Fractional SCE
1.0
0.8
0.6
0.4
0.2
0.0
0.0
(e)
1.0
0.8
0.6
0.4
0.2
0.0
Total Carbon (STN)
(d)
Organic Carbon (IMPROVE)
1.0
Fractional SCE
Fractional SCE
(b)
Total Carbon (IMPROVE)
1.0
0.8
0.6
0.4
0.2
0.0
Fractional SCE
Fractional SCE
(a)
Residual
Diesel
Gasoline
Residual
Figure 3. Fractional source contribution estimates to ambient TC, OC, and EC (using IMPROVE, a, c, and e, respectively; and using STN, b,
d, and f, respectively) at Azusa and Los Angeles-North Main stations, urban background sites, and source-dominated locations by CMB receptor
modeling.
EC at Azusa were 7.4 ⫾ 0.5, 3.6 ⫾ 0.3, and 24.5 ⫾ 1.7,
respectively. The corresponding ratios at Los Angeles
North Main were 5.2 ⫾ 0.5, 2.6 ⫾ 0.2, and 16.1 ⫾ 1.8,
respectively. The relative CI and SI apportionments for
the four samples collected at other locations stress the
importance of accounting for spatial and temporal patterns in PM emissions. SI exhaust was the dominant
source of TC, OC, and EC at the upwind background
sample from Venice and the sample collected after the
soccer match at the Rose Bowl. The regionally representative sample from San Dimas had nearly equal contributions of SI and CI exhaust to TC. The analytical uncertainties are higher for the mobile laboratory samples
compared with the composite samples from the two fixed
sites because of the shorter sampling times.
Using STN-TOT, OC, and EC in ambient concentrations and source profiles leads to lower CI contributions
than those obtained by using IMPROVE-TOR carbon data,
because EC is consistently higher with IMPROVE, and the
CI apportionments are influenced primarily by EC, as
shown below. The CI/SI ratios of the contributions to TC,
OC, and EC at Azusa were 4.8 ⫾ 0.4, 2.2 ⫾ 0.2, and 28.1 ⫾
3, respectively. The corresponding ratios at Los Angeles
North Main were 2.8 ⫾ 0.2, 1.3 ⫾ 0.1, and 15.2 ⫾ 1,
respectively. The samples from Venice and the Rose Bowl
both showed undetectable contributions from CI vehicles
for TC, OC, and EC, and the sample from San Dimas
showed greater contributions from SI for both TC and OC.
Figure 4 depicts the source contributions for SI and CI
vehicle exhaust to ambient total particulate carbon at
Azusa and Los Angeles North Main using IMPROVE TOR
732 Journal of the Air & Waste Management Association
versus STN TOT carbon data. Use of STN carbon data in
the profile and ambient data yielded lower contributions
of CI exhaust than with IMPROVE carbon data, with an
average ratio of approximately 0.7. The SI apportionment
was determined largely by organic species and is relatively
invariant to the carbon measurement method.
During weekdays, the residual, unexplained fractions
accounted for 55% and 50% of the total particulate carbon and 73% and 68% of the OC at Azusa and Los Angeles, respectively. The actual contributions of motor vehicles and other CI and SI engines to ambient PM is
expected to be greater to the extent that volatile and
semivolatile organic compound emissions from CI and SI
exhaust contribute to the formation of SOA. Contribution
of direct emissions from CI and SI engines would be larger
during winter as a result of lower contributions of SOA
and higher emission rates because of extended cold-start
operations at lower ambient temperatures, as well as
home heating.
Good model performance was obtained for the CMB
calculations. R2 typically averaged above 0.9, and ␹2 values are mostly between 0.2 and 0.6.31 Using either STN or
IMPROVE EC gave similar model performance. Figure 5
shows reasonable correlations between measured and
CMB-predicted ambient concentrations of indeno(123cd)pyrene, benzo(ghi)perylene, and coronene and strong
correlation for total EC (TOR-IMPROVE). EC has a large
influence on the estimates, because its coefficient of variation is smaller in source profiles and ambient data than
the CVS for the other fitting species consisting of PAHs,
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
(a)
(b)
Figure 4. Source contributions for (a) CI and (b) SI vehicle exhaust to ambient total particulate carbon at Azusa and Los Angeles North Main
(LANM) using IMPROVE-TOR vs. STN-TOT carbon data.
hopanes, and steranes. If the CMB is run with and without EC in the fit, the results in Figure 6 show that SI source
contribution estimates are approximately 15–35% higher
without EC. The reasonable correlations between the two
sets of SI contributions show that the organic species are
exerting an influence on the apportionment. This is also
supported by the transpose of the normalized modified
pseudoinverse matrix (MPIN), a diagnostic tool in CMB
that indicates the degree of influence that each species
concentration has on the contribution and standard of
error of the corresponding source category. MPIN is normalized such that it takes on values from ⫺1 to 1. Species
with MPIN absolute values of 1 to 0.5 are associated with
influential species. Noninfluential species have MPIN absolute values of 0.3 or less. In a typical CMB run, approximately 5 of the 19 organic species had MPIN absolute
values above 0.5, and an additional 5 to 6 species had
values between 0.2 and 0.5 for the CI source. The apportionments for SI were, therefore, influenced by organic
species. In contrast, poor correlations of the apportionment with and without EC for CI show that the CI apportionments were highly dependent on EC (Figure 6).
Another sensitivity test involved the effect of alternative calculations of the average composite SI and CI exhaust profiles and associated uncertainties on the source
contribution estimates. The default method derived composite profiles using averages of weight fractions and
pooled root mean square (RMS) analytical uncertainties or
standard deviations of the members of the composites,
whichever was larger. The alternative methods are based
on average weight fractions and RMS error only or average
emission rates and RMS errors. The three alternative approaches yielded similar results as shown in Figure 7.
Differences between IMPROVE and STN EC for
Source and Ambient Samples
Considerable ongoing discussion centers on the issue of
carbon analysis by thermal methods and the definition of
EC and OC.20,31 This issue was examined in the present
study. EC measurements by IMPROVE-TOR and STN-TOT
Volume 57 June 2007
(a)
(b)
Figure 5. Correlations between measured and CMB-predicted
ambient concentrations of (a) indeno(123-cd)pyrene, benzo(ghi)perylene, coronene and (b) IMPROVE-TOR EC.
Journal of the Air & Waste Management Association 733
Fujita, Campbell, Arnott, Chow, and Zielinska
(b)
(a)
Figure 6. Source contributions for (a) CI and (b) SI vehicle exhaust to ambient total particulate carbon at Azusa and Los Angeles North Main
(LANM) with and without EC (IMROVE-TOR) used as fitting species. CMBs without EC are based only on particulate PAHs, hopanes, and
steranes.
protocols agreed well with the photoacoustic (␭ ⫽ 1047)
particle absorption measurements converted to BC assuming a 5-m2/g mass absorption efficiency for CI vehicles at higher sample loading with slope near unity and
correlation coefficients above 0.95. Many of the filter
samples were opaque, however, so some of the TOT and
TOR optical corrections were uncertain. However, the
high abundance of EC in these samples made errors seem
smaller than they would be in ambient samples where EC
is usually a small fraction of TC. The SI exhaust samples
show greater scatter between thermal/optical total EC and
(a)
BC at higher and lower loadings, and EC was consistently
greater than BC at higher loadings by approximately 40 –
50% for both IMPROVE and STN data. The difference
between IMPROVE EC and BC increases with decreasing
loadings but remains constant for STN EC versus BC. In
contrast, the IMPROVE TOR EC2 fractions were in good
agreement with photoacoustic BC emission rates with the
BC/EC2 slope of 0.99 at the higher loadings.
Figure 8 shows correlations of average continuous
photoacoustic BC measurements against the corresponding integrated National Institute for Occupational Safety
(b)
Figure 7. Correlations show effects of alternative calculations of (a) CI exhaust and (b) average composite SI profiles and associated
uncertainties on source contribution estimates. The reference method derives composite profiles using averages of weight fractions and pooled
RMS analytical uncertainties or standard deviations of the members of the composites, whichever is larger. The alternative methods use average
weight fractions and RMS error only and average emission rates and RMS errors.
734 Journal of the Air & Waste Management Association
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
(a)
(b)
Figure 8. Correlations of average continuous photoacoustic BC measurements against corresponding integrated thermal evolution STN-TOT
EC and IMPROVE-TOR EC and EC2 fraction for fixed and mobile ambient samples: (a) on-road and (b) urban.
and Health TOT (STN) and IMPROVE TOR total EC and
EC2 fractions for ambient samples collected along roadways and at fixed urban locations. The highest concentrations of EC and BC were measured along roadways
with higher fractions of diesel truck traffic (e.g., Terminal Island and weekday samples along major truck
routes). The agreement between BC with assumed
5-m2/g mass absorption efficiency and EC by STN TOT
has a slope of 1.01 and an R2 of 0.99. EC by IMPROVE
TOR also has a high coefficient of variation (R2 ⫽ 0.99),
with a slope of 1.2. The IMPROVE TOR slope increases
to 1.4 at urban sites that are located away from major
roadways, whereas the slope for EC by STN TOT is 0.88.
The increasing ratio of IMPROVE TOR EC to STN TOT
with increasing photochemical age of the emissions
may be related to greater retention in the IMPROVE
protocol of OC before the oxidizing stages and either
underestimation of the pyrolysis correction by TOR
because of pyrolyzed material beneath the surface of
the filter or overestimation of the TOT pyrolysis correction because of the higher absorption efficiency of pyrolyzed carbon.
Temporal and Spatial Characterization of
Ambient Carbonaceous Particles
Time series plots were used to characterize the spatial and
temporal variations in BC and DustTrak PM mass data
relative to expected dominance of diesel or gasoline vehicle traffic based on qualitative observations of passing
vehicles near the monitoring sites. Figure 9 shows observed variations and the conclusions that they suggest.
The first pair of plots in Figure 9a shows BC and PM time
series in diesel-dominated locations. The time series in the
upper panel is from several traverses in the port area from
the west end of Terminal Island to approximately 1 mi
north of the island on I-710. BC was closely correlated to
the DustTrak PM with peak 10-sec average BC levels exceeding 80 ␮g/m3. The peak 10-sec PM concentration in
Volume 57 June 2007
the Terminal Island area of 90 ␮g/m3 was 10 times the
local background PM concentration of approximately 9
␮g/m3. The bottom panel shows the middaytime series
for a freeway loop along the major truck routes in the
basin on a weekday when the proportion of truck traffic
was high. The time series is similar to Terminal Island in
the range of peak BC and PM concentrations and BC/PM
ratios. The peak BC and PM concentrations coincided
with times when the mobile sampler was in close proximity to the exhaust plume of diesel trucks. Diesel trucks
were clearly the dominant source of ambient PM in both
cases.
The two time series in Figure 9b are from freeway
loops during times when traffic consisted predominantly
of light-duty SI vehicles. The top panel is from a weekday
morning with mostly commuter traffic. The few diesel
trucks that were encountered along this route produced
occasional spikes in BC and PM concentrations. Note that
the correlations at higher concentrations in these plots
resemble the previous two plots. The lower panel shows
the BC and PM concentrations on a Sunday along the
same truck route loop shown in the bottom panel of
Figure 9a. Diesel trucks were seldom encountered during
this sampling period. Changes in PM concentrations are
more gradual than in the previous three time series and
are not correlated with BC, which was near the typical
urban background levels of 1–2 ␮g/m3 throughout the
entire sampling period.
Figure 9c examines the two specific cases where our
instruments should have responded to PM emission from
predominantly SI vehicles. The top panel shows the
changes in ambient BC and PM at the Pasadena Rose Bowl
parking lot starting a few minutes before the end of a
professional soccer match to approximately 1 hr after the
match when the parking lot had nearly emptied. PM
concentrations increased immediately after the vehicles
began leaving the parking lot and gradually increased
while the vehicles cleared the lot. However, there was no
Journal of the Air & Waste Management Association 735
Fujita, Campbell, Arnott, Chow, and Zielinska
(a)
Figure 9. (a) Time series plots of photoacoustic BC and DustTrak PM2.5 in diesel-dominated locations (top, Terminal Island; bottom, truck
routes during weekday morning). (b) Time series plots of photoacoustic BC and DustTrak PM2.5 in predominately LDGV traffic (top, weekday
morning commute; bottom, truck route during Sunday morning). (c) Time series plots of photoacoustic BC and DustTrak PM2.5 in predominately
LDGV traffic (top, Rose Bowl parking lot after soccer match; bottom, traverses between Westwood and Van Nuys on I-405).
corresponding increase in concentrations of BC. This observation differs from measurements during dynamometer tests, which typically show that gasoline vehicles emit
more EC during cold starts. The vehicles were not in true
cold-start conditions, because they had been in the parking lot for periods less than 3 hr. However, in this particular situation, the ambient temperature was warm, and
the vehicles left the area at very low speeds. The situation
illustrated in the lower panel specifically examined the
instrument response to gasoline vehicles at high speed
and load. The time series covers several traverses over the
hill between Westwood and Van Nuys along I-405 on a
Sunday afternoon when diesel truck traffic was low. The
small oscillations in the PM concentrations appear to
correspond with the alternating uphill and downhill portions of the traverse. However, this pattern may also be
explained by variations in the local ambient PM concentrations between Westwood and Van Nuys. More significantly, BC concentrations were consistently near typical
736 Journal of the Air & Waste Management Association
urban background levels, and only small increases in BC
were observed during the uphill portions of the traverse.
The distributions of OC and EC fractions at the
Azusa and Los Angeles North Main ambient air quality
monitoring stations are shown in Figure 10 relative to
the source-dominated ambient samples (e.g., along
freeways and surface arterials) with varying proportions
of diesel and gasoline vehicle traffic. The mean ambient
PM2.5 concentrations during the study period at the
two monitoring sites were 13.8, 17.8, and 18 ␮g/m3 on
Sundays, weekdays, and Saturdays, respectively. The
corresponding mean ratios of TC to PM2.5 were 0.28,
0.32, and 0.30, and the mean ratios of EC (IMPROVE) to
TC were 0.19, 0.28, and 0.26. PM concentrations are
higher in diesel-dominated locations (e.g., samples
M2F_WD and CI-TI and CI-TS), with typical EC/TC
ratios between 0.6 and 0.7, which are consistent with
the EC/TC ratios in four composite CI exhaust profiles
in Table 1.
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
(b)
Figure 9. Cont.
Although EC (IMPROVE-TOR) in diesel exhaust is
mostly EC2 in dynamometer samples, ambient samples
have a greater fraction of EC1 relative to EC2. The mean
EC2/EC ratios at Azusa and Los Angeles North Main are
0.07 and 0.14, respectively, and EC1 is the dominant EC
fraction. In contrast, substantially higher EC2/EC ratios
are seen for both source-dominated ambient samples
(ranging from 0.21 to 0.90 with mean of 0.50) and CI and
SI source test samples (see Table 1, 0.6 – 0.7 for CI and
0.40 – 0.66 for SI). In contrast to total EC, it appears that
the relative fraction of EC1 and EC2 in ambient samples is
not consistent with the predicted fractions, which were
derived from the gasoline and diesel exhaust profiles. This
difference may occur because vehicle operating conditions used in the source tests and within the sourcedominated ambient samples may differ from those near
the air quality monitoring sites. Oxidizing material or
catalyst from other sources could also cause the E2 to
oxidize at lower temperatures, thereby resulting in a shift
to other carbon fractions. These observations may explain
why the use of carbon fractions was unsuccessful in apportioning CI and SI exhaust and was not applied in this
study.
Volume 57 June 2007
DISCUSSION
The Gasoline/Diesel PM Split Study was conducted to
assess the sources of uncertainties in quantifying the relative contributions of emissions from gasoline and diesel
engines to the ambient concentrations of PM2.5. Determination of the specific contributions of CI and SI exhaust
to ambient PM was not the objective of this study. Four
composite CI and six composite SI composition profiles
were derived and applied in CMB to ambient samples
from both fixed monitoring sites and other ambient samples having different proportions of gasoline and diesel
vehicle traffic. Source composition profiles for other
sources, such as wood and meat combustion, were not
included in the analysis, because the inclusion of profiles
of unknown relevance to the study area would add additional uncertainties that may be difficult to quantify. The
default fitting species included seven particle-phase PAHs,
four hopanes, and eight steranes and total EC as measured
by two alternative thermal/optical carbon methods (IMPROVE TOR or STN TOT).
The relative apportionments of SI and CI vehicles to
particulate carbon were highly dependent on the mix of
Journal of the Air & Waste Management Association 737
Fujita, Campbell, Arnott, Chow, and Zielinska
(c)
Figure 9. Cont.
traffic near the monitoring site. Although CI engine exhaust was the dominant source of TC and EC at the air
monitoring stations at Azusa and Los Angeles North
Main, the apportionments of samples from other regional
urban sites in the Los Angeles area indicate that the overall contributions of SI engine exhaust were greater than
that suggested by the samples collected at Azusa and Los
Angeles North Main. Samples from a regional park in San
Dimas, which is located in the central part of the SOCAB,
showed nearly equal apportionment of CI and SI. Up to
70% of OC in the ambient samples at the two fixed
monitoring sites could not be apportioned to directly
emitted PM emissions from motor vehicles during highly
photochemical summer conditions in the SOCAB.
The apportionments of CI exhaust were strongly influenced by EC and were relatively insensitive to organic
species. Therefore, the method used to measure OC and
EC can lead to differences in the CI source contributions.
Using IMPROVE TOR EC rather than STN TOT EC in the
CMB fit resulted in approximately 40% higher CI contributions to ambient particulate carbon. These differences
exist because, whereas the IMPROVE protocol typically
yields higher EC values than the STN protocol for ambient
738 Journal of the Air & Waste Management Association
samples,32,33 both protocols gave comparable EC values
for highly loaded samples from dynamometer tests of CI
vehicles. The positive bias of IMPROVE TOR EC data
relative to photoacoustic BC increases for ambient samples with aged emissions relative to samples with fresh
emissions, whereas STN TOT EC compares well with photoacoustic BC for samples with either aged or fresh emissions. Therefore, the expectation that receptor models
that are based on different carbon measurement methods
will give comparable results as long as the same method is
used for both source and ambient measurements was not
supported by the results of this study. The expectation
that the organic markers by themselves differentiate CI
contributions from other sources was also not supported
by this study.
The apportionment of SI exhaust is most sensitive
to indeno(123-cd)pyrene, benzo(ghi)perylene, and coronene and was insensitive to EC. These six- to seven-ring
PAHs are not detected in most diesel exhaust samples, in
diesel fuel, or in lubrication oil. They are presently in used
SI engine lubrication oil in similar proportions to one
another but in concentrations that tends to increase with
the age of the oil. Proportions of the three PAHs to TC are
Volume 57 June 2007
Fujita, Campbell, Arnott, Chow, and Zielinska
Figure 10. Distributions of IMPROVE-TOR OC and EC fractions at Azusa and Los Angeles North Main ambient air quality monitoring stations
relative to source-dominated ambient samples (e.g., along freeways and surface arterials) and regional urban background locations. a, fixed
ambient sites; b, mobile ambient sampling locations.
higher during cold starts and higher in normal emitters
than higher emitters. Sensitivity runs using alternative
composite and individual profiles showed coefficients of
variation in the SI apportionments of approximately 50%
and 75–100% for Azusa and Los Angeles, respectively. The
uncertainties in this study should be considered a lower
bound of the uncertainties when the source profiles from
this study are applied in CMB analysis to ambient samples
from other locations without determining local source
profiles.
ACKNOWLEDGMENTS
This work was support by the Department of Energy Office of Heavy Vehicles Technologies and FreedomCAR
Vehicles Technologies through the National Renewable
Energy Laboratory (NREL). The authors gratefully acknowledge the technical support provided by Douglas
Lawson of NREL. They acknowledge the vehicle emissions
tests performed by Bevilacqua Knight, Inc., and West Virginia University. They also acknowledge the following
Desert Research Institute personnel for their assistance:
Kelly Fitch for field sampling, Mark McDaniel and Anna
Cunningham for the organic speciation analysis, and
Steven Kohl, Barbara Hinsvark, and Dale Crow for analysis of inorganic species. They acknowledge Drs. Warren
White and Richard Gunst for their contributions to the
study design. Finally, they also thank John Watson for his
comments on the paper and valuable scientific discussions.
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About the Authors
Eric Fujita is a research professor, David Campbell is an
assistant research scientist, Barbara Zielinska is a research
professor, and Judith Chow is a research professor in the
Division of Atmospheric Sciences at the Desert Research
Institute (Nevada System of Higher Education). William
“Pat” Arnott is an associate professor in the Department of
Physics at the University of Nevada, Reno, NV. Please
address correspondence to: Eric M. Fujita, Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512; phone: ⫹1-775-674-7084;
fax: ⫹1-775-674-7060; e-mail: [email protected].
Volume 57 June 2007