Assessment and Optimization of
ASTM D7096 Simulated Distillation
for Quantifying Heavy Hydrocarbons
in Gasoline
£% United States
Environmental Protect
Agency
-------
Assessment and Optimization of
ASTM D7096 Simulated Distillation
for Quantifying Heavy Hydrocarbons
This technical report does not necessarily represent final EPA decisions
or positions. It is intended to present technical analysis of issues using
data that are currently available. The purpose in the release of such
reports is to facilitate the exchange of technical information and to
inform the public of technical developments.
in Gasoline
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
4>EPA
United States
Environmental Protection
Agency
EPA-420-R-23-009
April 2023
-------
Assessment and Optimization of ASTM D7096 Simulated Distillation
for Quantifying Heavy Hydrocarbons in Gasoline
Table of Contents
1. Executive Summary 2
2. Introduction / Purpose.............................................................................................................3
2.1. Background 3
2.2. Motivation for Assessment and Validation of Simulated Distillation Method 3
2.3. SimDis Investigation Overview 7
3. Simulated Distillation (SimDis) Methodology Improvements 9
3.1. Overview of ASTM D7096 9
3.2. Lab Work to Enhance ASTM D7096 9
3.3. Sample Exchange Validation 15
4. Application of SimDis C ut-Points and PM Index Analysis to IIS Market Gasolines 20
4.1 SimDi s Results 20
4.2 DHA and PMI Analysis 23
4.3 SimDis Cut-Point Temperatures and PM Index Improvement in US Market Gasoline 26
5. Summary and Conclusions....................................................................................................29
Acknowledgement 30
References 30
Appendix A: Simulated Distillation for Heavy Aromatics Laboratory Procedure
l
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I. Executive Summary
Multiple studies have shown that the chemical makeup of gasoline has a major influence on combustion-related
particulate matter (PM) emissions from vehicles, and that combustion of heavy aromatic compounds in
particular is a major PM contributor [3, 4, 5, 6, 7], An analysis of a large sample of US market gasolines has
shown that the high-boiling tail contains a large fraction of aromatics; for example, the heaviest 10 v% is over
80% aromatics [12],
ASTM D7096 SimDis is a gas-chromatography method that can provide a relatively precise volatility profile of
a gasoline sample. Given the highly aromatic makeup of the tail of typical gasoline, quantification of high-
boiling material by SimDis may provide a good surrogate for more rigorous PM predictors such as PM Index.
The present work explored sources of variability in SimDis results and developed several procedural
recommendations to improve repeatability and reproducibility within the method as written, focusing on the
high-boiling (T90+) tail of gasoline. Validation studies done at EPA and GM laboratories showed that
reproducibility values well below those published by ASTM can be achieved.
Following the method improvement work, SimDis as well as ASTM D6730 DHA were run on 80 gasoline
samples taken from the US market in 2021-22. The contribution to PM Index and volume percent was assessed
by boiling range and molecular class. This analysis shows that the heavy tail of gasoline contains a large
proportion of aromatics that have high leverage on PM Index, findings that are consistent with previous work
[12], The correlation between PM Index values and a range of heavy-end SimDis T-numbers (%-off fractions)
was also assessed. Results indicated that the highest correlation occurred in the range of SimDis T95-T98 with a
Pearson coefficient of around 0.83.
Finally, the impact on PM Index of applying several SimDis endpoint limits to trim heavy-end material from
market gasoline was assessed. Starting from the survey sample average PMI of 1.6, this analysis suggests a PMI
reduction of about 0.5 (31%) could be achieved by applying a SimDis endpoint limit of 430°F. This would
correspond to removing the heaviest 1.7 v% of an average market fuel, and supports the conclusion that a
relatively large reduction in PM Index could be achieved by trimming a small amount of high-boiling material
from gasoline.
2
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2. Introduction / Purpose
2.1. Background
Particulate matter (PM) pollution has been linked to a multitude of health problems [1,2]. Particles smaller than
2.5 micrometers in diameter, referred to as PM2.5, pose the greatest risk because they can penetrate deep into
the lungs and enter the bloodstream. Exposure to PM2.5 increases the risk of premature death and can impair
lung growth in children. For individuals with preexisting health challenges, PM2.5 can increase the risk of
cardiovascular and respiratory disease. The United States Environmental Protection Agency (US EPA)'s 2017
National Emissions Inventory estimates that gasoline-fueled vehicles and nonroad equipment contribute 31.9%
of the total mobile source primary PM2.5 emissions [1],
Multiple studies have shown that the chemical makeup of gasoline has a major influence on combustion-related
PM emissions [3, 4, 5, 6, 7], Combustion of heavy aromatic compounds in particular is a major PM contributor.
The heavy end of gasoline consists almost exclusively of aromatics, and the heaviest several percent of those
species have a disproportionally large impact on the amount of PM emitted.
Advancements continue in engine and aftertreatment technology to mitigate PM emissions. However, these
improvements only affect new products. Over 250 million gasoline-powered on-road vehicles and about 150
million nonroad vehicles and pieces of equipment exist in the United States [8, 9], with many of them expected
to remain in use for decades to come. Changes in fuel composition can affect this entire population of
equipment, resulting in an immediate air-quality benefit.
2.2. Motivation for Assessment and Validation of Simulated Distillation Method
A number of test methods are available for assessing properties of gasoline that correlate with PM emissions,
but each leaves room for improvement [10, 11], Quantifying heavy aromatics (e.g., with molecular size of ten
carbons and above) would be ideal but getting this level of detail for a fuel sample requires results from a
relatively rigorous method. One such method is detailed hydrocarbon analysis (DHA) by ASTM D6730. This
method runs for 2-3 hours and produces a chromatogram that must be interpreted by an experienced analyst,
making it difficult to standardize and automate. Calculations using data from DHA form the basis of the PM
Index, which has been shown to be directly proportional to tailpipe PM emissions [4, 6],
Distillation by ASTM D86 has been part of market gasoline standards for decades, and therefore the equipment
and expertise to run the method are widespread. However, correlation of distillation points such as T80 or T90
with heavy aromatic content, a key driver of PM emissions, is only mediocre. A comparison of D86 results with
those of DHA illustrate that D86 does a relatively poor job of separating compounds by volatility and
underestimates the final boiling point significantly [12], These analyses indicate that ASTM D86 may lack the
needed precision.
There are a few alternative ASTM chromatography methods that are simpler and faster to run than DHA, which
may be better candidates for quantifying heavy aromatics. ASTM D8071 uses a chromatographic column with a
vacuum-UV (VUV) spectroscopic detector to produce results by molecular type and carbon number in about 35
minutes. It doesn't quantify individual species but produces percent by carbon number and molecular type (i.e.,
aromatics, paraffins, naphthenes) that agree well with DHA without requiring the level of operator effort and
3
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expertise as DHA. However, it is a relatively new and unfamiliar method to many labs. Another method is
D5769, which quantifies a range of aromatics species using a mass spectrometer but does not offer a complete
accounting of all aromatic material.
Simulated distillation (SimDis) by D7096 is a promising option. Unlike D6730 or D8071, this method does not
separate the constituents by molecular type but produces a profile of volume (or mass) by boiling point that can
be sufficiently precise to quantify the heavy tail of a fuel sample. SimDis was developed in the 1980s to quickly
assess the boiling point range of petroleum samples and has been in use in refinery process control for many
years. In a lab setting, D7096 runs in about 15 minutes and can easily be incorporated into an automated
workflow. Given the data showing that the heavy tail of market gasoline is highly aromatic, this method can act
as a promising surrogate for PM-forming compounds.
Figure 2.1 illustrates the highly aromatic nature of the tail of US gasoline based on DHA data from 708 summer
regular-grade E10 gasoline market samples [12], To characterize the distribution of aromatic species, these data
were grouped into three categories: total aromatics, monocyclic aromatics (substituted benzenes), and bicyclic
aromatics such as naphthalenes. Their percent content in the tail end of the fuels is presented as a function of
boiling temperature, with lines indicating the fraction of material boiling above the "cut-off temperature" on the
x-axis.
Figure 2.1 also shows the volume fraction of all identified species in the 708 fuels boiling at or above the cut-
off temperature. This analysis illustrates that aromatic species dominate the heavy end of US market gasolines,
exceeding 90 v% at a cut-off temperature of 380°F. Bicyclic aromatics, which are most prone to the generation
of PM emissions, dominate its heaviest, least volatile fractions. It is important to note that these heaviest
fractions also contain a significant amount of unidentified species, which are not accounted for in a DHA-based
PM Index determination. A SimDis-based approach does not have this problem, given that all component peaks
(above a certain noise threshold) are included in the integration with no identification required.
4
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<
50
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Np
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tt=
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13
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40
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80 §
D
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CL)
60 o
JD
TO
40
c
OJ
c
o
u
20 c
03
E
o
180 220 260 300 340 380
Cut-off temperature (°F)
420
460
Figure 2.1. The content of aromatic species in the tail ends of US summer
E1Q regular-grade market gasoline. [12]
Correlating Fuel Properties with PM Emissions - The Traditional PM Index Approach
The PM Index is currently the parameter most frequently used to characterize the propensity of gasoline to
generate PM emissions. It was proposed in 2010 by Aikawa and colleagues [3] The PM Index requires the use
of a detailed hydrocarbon analysis (DHA) of the fuel and is calculated using the following equation:
PM Index = ^
(DBE +1)
v ' -x Wt
VP(443K).
where DBE; is the double bond equivalent of compound i, VP(443K); is the vapor pressure of compound i at
443 K, and Wti is the weight percent of compound i in the fuel.
DBEi is related to the degree of saturation of each compound, and therefore to its sooting tendency while the VP
term is related to the volatility of each component. In this way the chemical and physical attributes,
respectively, of each compound are considered. Heavy aromatic compounds—such as naphthalenes—are highly
unsaturated and have low vapor pressures. Considering the equation above, it is clear why such fuel components
are main contributors to the PM Index values of commercial gasolines.
However, the complexity and time burden of the required DHA make the PM Index determination more
suitable for a research laboratory environment. It would be impractical for routine use in many industrial
settings, such as a refinery laboratory.
5
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Correlating Fuel Properties with PM Emissions - The SimDis Approach
The efficacy of the SimDis cut-point (also called endpoint or final boiling point) concept was simulated using
the aforementioned DHA database of 708 fuels. Since both SimDis and DHA methods use a GC-FID system to
separate a sample by volatility, this exercise provides useful insight. An analysis was undertaken to assess the
impact of a range of SimDis cut-points on the average PM Index of US summer, regular-grade E10 gasoline
[12], This activity involved the following steps:
1. Unidentified species were accounted for in the DHA data, and the calculated PM Index was adjusted
accordingly for each fuel. This involved the estimation of boiling points, vapor pressures at 443K, and
DBEs for the unidentified compounds.
2. Various SimDis cut-points were applied to the 708 gasoline datasets. This involved mathematical
trimming of the material boiling at and above the assumed cut-off temperature.
3. To compensate for octane loss, the mass percentage contents of all monocyclic aromatic species boiling
below the cut-point were proportionally increased to equal the total mass of heavy material trimmed
from each fuel. This procedure also compensated for the loss of mass.
The results of this analysis1 are shown in Figure 2.2. As this figure demonstrates, a simulation of relatively
modest SimDis trims produced significant reductions in the average PM Index of US summer, regular-grade
E10 gasoline. For example, a 25% drop in the PM Index from 1.95 to 1.46 could be achieved with a volume
trim of about 1%.
1 Figure 2.2 represents supplemental results from methods described in reference 12. Original data source: 2008-2012 US market fuel DHA results
provided by Honda R&D Americas, Inc.
6
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Numbers in red indicate vol% cut
from typical summer gasoline
Q.
3
01
J*
(O
1.7
425 450 475
DHA Cutpoint Temperature, °F
Figure 2.2. Estimated average PM Index of US summer, regular-grade E10 gasoline after SimDis trim
and octane makeup as a function of tail cutpoint temperature.
2.3. SimDis Investigation Overview
A research program was conducted by several auto manufacturers, a private laboratory, and the US EPA. The
program was designed to confirm several assumptions of the SimDis analytical approach. These assumptions
are listed in Table 2.1 along with associated project activity. The remainder of this paper will present the
SimDis methodology program activities in detail.
7
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Table 2.1. Objectives of the SimDis methodology investigation program.
Assumption
SimDis Program Activity to Confirm
• The SimDis method can be sufficiently
precise to allow reproducible inter- and
intra-laboratory determination of the
resulting distillation curve, especially in
the heavy tail region of the fuel.
• Enhance ASTM D7096 interlaboratory
precision through improvements to
procedures, sample handling and
instrument parameters while staying
within the bounds of the ASTM method
itself.
• Perform an interlaboratory study to
confirm the resulting improvement in
precision.
• SimDis data parameters correlate with
DHA-based PM Index data, thereby
confirming analysis by SimDis to be a
compelling alternative method for
assessing the PM-formation propensity of
a fuel.
• Acquire multiple market fuels and
perform DHA and SimDis analyses.
• Analyze the correlation between the PM
Index of the fuels and various parameters
derived from resulting SimDis data.
8
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3. Simulated Distillation (SimDis) Methodology Improvements
3.1. Overview of ASTM D7096
Several simulated distillation methods have been developed and standardized by ASTM. Simulated distillation
is used to determine the boiling point distribution of crude oil and petroleum refining fractions through the use
of gas chromatography (GC). Typically, hydrocarbon samples are eluted from a nonpolar column in boiling-
point order by temperature programming until the entire sample is eluted. For gasoline simulated distillation
several standardized procedures exist, including ASTM D7096. This method provides a rapid determination of a
gasolines chemical profile based on boiling point ranges using a large-bore, non-polar separation column. The
SimDis methodology is analogous to physical distillation, which is more time consuming and doesn't provide
fine resolution in the boiling point distribution due to low (single plate simulation) efficiency. A typical
gasoline fuel sample may contain up to 1000+ compounds, with many of them being isomers with azeotropic
boiling behavior. This makes deeper analysis of low-efficiency distillation (e.g., ASTM D86) data difficult.
Analysis by GC typically has better precision, higher throughput, less hands-on time, and lower cost per sample.
SimDis also requires considerably less sample for analysis; less than 1 ml as comparing to D86 which requires a
minimum of 100 ml.
ASTM D7096 is among the newest SimDis methods for gasoline. The combination of large-bore, thick film
columns and flame ionization detection yields a robust, reproducible analysis. Extracting distillation curves
from GC data starts by slicing a chromatogram into very small segments and integrating the area under the
signal. Boiling points for each slice are interpolated from reference standards eluting before and after the slice.
The cumulative sum of volumes of individual slices are plotted against the boiling points of those slices to yield
a boiling point curve.
3.2. Lab Work to Enhance ASTM D7096
Despite the advantages of simulated distillation by GC, the method has not been widely adopted by analytical
laboratories. Part of this is due to the fact that most fuel specifications require distillation data be obtained
through ASTM D86, which is the more traditional method. However, with advancements in both hardware,
software, and methodology, SimDis can provide more precise and detailed information for a given sample. In
an effort to evaluate and improve the usefulness of this method, work was performed to further specify ASTM
D7096 analytical conditions, sample, and data handling to improve both inter and intra-laboratory precision
while also remaining within the bounds of the ASTM method itself.
This work was done in three stages between the GM Pontiac and US EPA NVFEL laboratories followed by
sample exchanges with other labs running the D7096 method with proposed enhancements to validate
improvements to the method.
3.2.1 Stage One - Initial Investigation
Stage One was composed of an initial investigation into method reproducibility using the currently in-use
methods at the GM Pontiac and US EPA laboratories. Five samples were exchanged, and each lab analyzed the
samples using the method their lab currently uses for ASTM D7096 analysis, generating distillation curves. As
this project focuses on heavy gasoline components, results below the 50% distilled mark were calculated but are
not presented. Resulting absolute differences for each of the six Stage One samples can be seen in Table 3.1
alongside the ASTM D7096 method precision (ASTM R).
9
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Table 3.1. Absolute Differences Exchange Samples Compared with ASTM Reproducibility (°F).
T-number (%-off)
ASTM R
Fuel S1-1
Fuel S1-2
Fuel S1-3
Fuel S1-4
Fuel S1-5
FuelS 1-6
50
17.8
0.8
7.2
3.1
2.8
3.4
1
70
18
11.8
8.6
0.1
1.9
5
5.5
80
3.2
1.4
90
6.7
0.9
3.3
95
8.3
2.4
3.6
3.5
FBP
18.5
15
4.1
15.9
While some data points show good alignment to the documented ASTM D7096 reproducibility, a large majority
of the data above T80% falls outside the current method precision. Since the focus of this work is on the heavy
end components, the source of this variation should be reduced. Various elements of the method were
investigated to find and reduce the variation as much as possible.
The first step was to review each lab's procedures on paper to determine any areas of difference. Differences
found included sample handling, analytical sample sequence order, hold time at the end of a run (260°C),
injection blanks used for baseline subtraction, and whether a lab was using a pre-sequence column bake or not.
All of these differences, though seemingly incidental, can result in analytical variances. As a result, both labs
deemed it necessary to conduct further investigation through a variety of studies aimed at choosing the most
reliable methodology to obtain more precision.
3.2.2 Stage Two - Method Refinement
In Stage Two, a second sample exchange was conducted with the goal of isolating sources of variation and
eliminating them. A sample preparation procedure was agreed upon, wherein samples would be shipped same-
day on ice, refrigerated on arrival, aliquoted cold into 2 mL GC vials and sealed with crimp caps and analyzed
within a week of receipt. The sample exchange included a single gasoline source, shipped in a 1 L bottle to each
lab. The lab then aliquoted the sample into thirty 2 mL vials and shipped half to each other. In this way, any
sample preparation variations could be deduced if present. A diagram demonstrating sample prep can be seen
below in Figure 3.1.
Figure 3.1. Sample Exchange #2 sampling diagram.
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Results of the sample exchange can be seen below in Table 3.2. Samples prepared by the GM lab are shown on
the left while samples prepared by the EPA lab are shown on the right. Notice that although the results when
compared between labs show a large absolute difference, when comparing data within the same lab, the
repeatability is very good regardless of which lab prepared the samples. This indicates there is a method
difference causing the variability and not a sample handling issue.
Table 3.2. Stage Two Sample Exchange Results. Green boxes denote results within ASTM R, yellow
boxes denote within 1°F of R and red boxes denote results outside the R.
Sample Prep by GM
Sample Prep by EPA
GM EPA
Analyzed Analyzed
Average Average
value (°F) value (°F) Difference
GM EPA
Analyzed Analyzed
Average Average
value (°F) value (°F) Difference
IBP
46.5
28.2
18.3
4.4
13.9
5.2
22.7
IBP
46.3
27.6
18.7
4.8
17.4
5
54.7
50.3
5
54.5
49.7
15
78.6
64.7
15
72.6
55.2
25
83.6
88.7
25
83.3
84.9
1.6
35
97.6
120.2
35
97.3
106.0
8.7
45
139.7
154.2
14.5
45
139.0
150.4
11.4
55
157.9
190.8
33
16.3
55
156.7
186.7
30
14.3
11.9
12.6
13.5
65
196.8
213.1
65
195.0
209.3
75
230.9
249.1
18.2
75
230.3
242.3
85
282.6
299.2
16.6
85
281.1
293.7
90
335.2
341.9
6.7
90
327.8
341.3
95
384.6
387.9
3.3
95
376.2
383.9
7.7
FBP
496.1
474.8
21.3
FBP
491.6
474.3
17.4
11
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3.2.3 Stage Three - Further Refinement, Harmonized Method
Going into Stage Three, the following parameters were examined through a series of studies: injection volume,
inlet temperature, and final temperature hold time. The studies and their results are paraphrased below.
A. Inlet Temp Study
Objective: Determine whether raising inlet temperature to 325°C reduces carryover between runs.
• How: Inject 0.5 |iL standard followed by two method blank injections (no physical injection) at
300°C and 325°C.
• What to measure: Compare first blanks with one another and with second blanks.
• Expected Outcome: Determine most consistently clean blank.
• Result: 325°C increased septum bleed peaks and carryover (Figure 3.2). Use 300°C for inlet
temperature.
blank with inlet at 300°C (red trace) and second blank with inlet at 325°C (green trace). Larger peaks
at a higher inlet temperature imply more septum bleed is occurring.
B. Injection Volume Study
• Objective: Determine the effect of injection volume on carryover and initial boiling point (IBP).
• How: Use 3X air injection blanks, then inject 0.1, 0.25, 0.5 and 1 |iL of standard followed by two
more air injection blanks. Inject reference fuel at each level. Use 0.5 |iL syringe for 0.1 and 0.25 |iL.
Use 5 |iL syringe for 0.5 and 1 |iL.
• What to measure: determine IBP and final boiling point (FBP) for each fuel sample, measure peak
area of standard and in blank immediately following standard.
• Expected Outcome: Determine if lower volume decreases ratio of carryover. Determine effect on IBP
and FBP.
• Result: Lower injection volumes increase relative carryover and r (Figure 3.3). Use a 1 |iL injection.
12
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Figure 3.3. Absolute difference in reference fuel at different injection volumes with ± ASTM r for
reference. 1 jiL injections showed the least variability.
C. Syringe wash study
Objective: Determine the effect of different syringe wash programs on carryover using carbon
disulfide (CS2) and dichloromethane (DCM) wash solvents.
• How: 3X method blanks, THEN Inject 0.5 |iL standard followed by two more method blank
injections for: wash vial A only (CS2 and DCM), wash vials A&B (CS2, 3 and 6 washes) and vial A
(CS2) then vial B (DCM) washes. Inject samples from wash bottles to determine transfer.
• What to measure: Measure the peak area of C15 and Ci6 in the sample and in the blank immediately
following the standard, inspect 1st blank vs. 2nd blank.
• Outcome: Determine conditions that minimize carryover.
• Results: A dual solvent system with DCM followed by CS2 using 6 solvent rinses performed best,
though all double-wash procedures performed better than single wash (Figure 3.4).
13
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0.12
¦ C15 blank 1 ¦C15blank2 I C16 blank 1 nC16blank2
Figure 3.4. Results of the syringe wash study. A dual wash system generally works best and 6 washes
in each vial works better than only 3 washes.
D. Blank Study
Objective: Determine whether to use solvent blank, method blank, or air blank between sample
injections.
• How: 3X method blanks, then inject 0.5 |iL standard followed by two more method blank injections
of a) air, b) nothing and c) CS2.
• What to measure: Compare first blanks with one another and with second blanks.
Outcome: Determine most consistent blank.
Caveat: Operator noticed an issue with the air blanks wherein standard compound carryover was
evident and greater with the second blank, suggesting syringe carryover into air-blank vial caused
contamination.
• Results: DCM solvent peak dominates and doesn't seem to provide any improvements over CS2. Two
CS2 blanks provide least amount of sample-to-sample carryover.
After collating and implementing learnings from the studies conducted in Stages One and Two above, a final
method was decided upon. This method was written up as a standard operating procedure covering GC method
parameters, blank selections, and sample handling and storage requirements. See Appendix A for the procedure
distributed to laboratories participating in sample exchanges.
Summary of proposed final method and sample procedure:
• Oven: 40°C 1 min, 25 °C/min to 260 °C, 4 min hold, 6 min post run @40 °C
• Injection Volume: 1 |iL
• Wash 6X DCM (or C S2), 6X C S2
• Injection: 300°C, Split 50:1, Focus liner w/glass wool
• Carrier Gas: He, 5 mL/min for 0.6 min, 30 mL/min2 to 20 mL/min
• Detector: FID, 300°C, 30 ml/min combined makeup
• Batch sequence order controlled
• Run 3 method blanks before sample injections and use third blank for baseline subtraction
14
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• Two CS2 blanks run between each sample
• Calibration performed with each batch of samples
• Use crimp cap vials and ensure proper sealing before analysis
• Store and aliquot samples cold (0 - 4°C when transferring liquid)
3.3. Sample Exchange Validation
3.3.1 Sample Exchange Validation
A sample exchange study was performed between the GM Pontiac Chemistry Laboratory and the EPA lab,
incorporating what was learned in previous steps. The results are presented in Table 3.3 and Figure 3.5 and
show:
• Both labs improved repeatability over previous stages
• Average results from EPA and GM within repeatability (r) of one another at 12/13 points
• < 2 °F difference
• Within reproducibility (R) at 13/13 points
• Deviation at 2 points with no calculated statistics
• T25 and T98
T25 GM found 2 replicates at 122 °F and 1 at 134 °F
• T25 EPA found 1 replicate at 122 °F and 5 at 134 °F
Table 3.3. Distillation data from the same fuel performed at both EPA and GM Laboratories.
EPA Average GM Average Abs. Dif.
°F °F °F
IBP
27.80
27.83
0.03
5
50.10
51.43
1.33
10
52.50
54.03
1.53
15
81.05
81.00
0.05
20
96.95
97.07
0.12
25
132.25
126.23
6.02
30
139.02
139.50
0.48
35
148.53
148.47
0.07
40
150.60
150.83
0.23
45
158.18
158.07
0.12
50
203.30
201.87
1.43
55
219.17
218.87
0.30
60
230.38
230.07
0.32
65
232.05
231.87
0.18
70
248.07
247.27
0.80
75
277.38
277.33
0.05
80
282.08
282.23
0.15
85
339.67
340.50
0.83
90
342.47
343.70
1.23
95
360.12
360.13
0.02
98
380.38
374.37
6.02
FBP
500.77
498.73
2.03
15
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Table 3.4. Final fuel property data after blending.
Base Fuel ^Typical
Fuel A PaseFuel w/3.1
Fuel B ^2
07435
-
II 74T
-
J "74"'4
0 7428
0 "4C5
Ff 1! in jp a j3qp CH»
aqn LHr*
±0.1
1.5
1 49
2.66
1.58
1 5
1.46
1 50
1.42
1 41
Ethanoi
±0.2
10
9 54
9.7
n
9.7
n 24
9.7
; 2:
14.9
14 "5
other uxvaenates
T 42 It
maximum
0.1
0.0
0.1
n n
0.1
n n
0.1
0.0
0.1
0>.qen
HdC* c i
.
3.54
.
^4.
.
2 4.
3 42
.
5 43
RON
.
D2699
.
91.1
.
n 1 4
.
11 "
.
91.2
.
M Ui
-
D27Q0
-
83.2
-
oo c
-
83.7
-
83.7
-
if + n _
-
J2099027DC
±0.3
37.2
87.2
87,4
E
87.5
87.6
87.2
87.4
89.2
30 2
it
7-9
-
7.9
-
7.9
7.5
Q 2
D FE
psi
35191 ,EP^
±0.2
9.0
9.2
8.5
9.1
8.7
8.9
8.7
9.0
8.7
9.1
Distillation
X
r;:~~
iBP
.
95.1
.
qr p
.
96.1
.
oc: c;
.
T5
-
117.0
-
11n "
-
118.9
-
1 18 3
-
119 4
T10
± 5
127.6
125 n
128.6
128.9
127.2
128.1
126 2
129.3
12" 2
T20
-
125 6
-
1 37 v
-
137.7
-
1 36 5
-
7 -- -
T30
±5
146.1
144 0
147.3
14- 4
147.5
146.9
146.5
145 a
148.3
14" 2
T40
-
152 0
-
1^47
-
155.6
52 5
155 0
T50
°F
±5
19B.7
102 9
207.2
1
206.3
204.8
202.1
199 8
161.7
" 5
160
D86
-
222 0
-
24ii m
-
237.7
-
212 E
-
2 :
170
±5
256.8
2:: f
264.3
2M P
260.0
259.7
255.1
255 7
253.6
252
T80
-
2.80.7
-
^12
-
283.2
-
2"9 9
-
2" i
T90
± 5
313.9
312.0
331.5
1 1
313.3
315.6
314,1
3 12 0
311.6
"112
T95
-
344.1
-
2^" "1
-
345.4
-
344 4
-
:_21
FBF
maximum
437
380.1
437
4211 ^
437
382.0
437
383 2
437
132 2
Re: "! e : <
maximum
1.3
1-0
1.3
1.1
1.3
1.0
1.3
1 0
13
Re:o*pr\
%¥
-
97-4
-
"
-
97.8
98.1
Loss
-
1.5
-
1.5
-
1.6
-
1.5
-
1.5
Total Aro matics
±1
25.0
24.6
27.3
2i 8
27.5
27.3
24.2
24.1
23.6
Benzene
± 0.2
0.6
0.6
0.6
II h
0.6
0.6
0.6
0.6
0.6
Toluene
± 1
6.3
6.2
6.1
fin
7.1
7.3
6.1
6.0
7.9
C8 Aromatics
%V
(Gage DHAI
±1
8.1
8.1
7.9
7 8
9.0
8 9
7.8
7.9
5.9
- i
C9 Aromatics
± 1
5.5
5,3
5,4
5.2
6.3
5 0
5.4
5-2
5.2
C10 aromatics
±1
3.4
3.3
4.9
4.7
3.5
3 3
3.3
3-2
3.2
C11+ Aromatics
±0.3
1.1
1.2
2.4
2.5
1.1
1 2
1.1
1.2
1.0
1 2
Pf.l Inde< b>, D6729
-
-
1.43
-
2.28
-
1 45
-
1.44
-
1
Total Aromatics
-
25.1
-
27.7
-
2":
-
24.8
Beniene
-
0.6
-
0.6
-
0.6
-
0.6
-
Toluene
D6729
-
6.7
-
6.5
-
" E
-
6.3
-
I 4
C3 Aromatics
%v
-
7.9
-
7.7
-
3 :
-
7.8
-
7.5
C i -nmatu
-
5.4
-
5.4
-
: :
-
5.4
-
5.2
C1ii -r iiidti -
-
3.0
-
4.5
-
3.0
3.1
-
2.9
C11 + -r ifiitizs
-
1-6
-
3.0
-
1.4
1.6
1-4
PF i In Jp C6730-1X
-
-
1.48
-
0 00
-
1.53
-
1.45
-
1 41
Ti tdl ^r mati s
-
24.7
-
0 0
-
27.4
-
24.0
2: 4
Beraene
-
0.6
-
0.0
-
0.6
-
0.6
-
: :
Ti lut-nn
D6730-1X
-
6.3
-
0.0
-
7.4
-
6.1
-
5"
C-> «nmati
%v
-
7.9
-
0.0
-
8.8
-
7.7
-
:
-
5.3
-
0.0
-
6.0
-
5.2
-
5.1
C1 i -r mati i
-
3-0
-
0 0
-
3.0
-
2-9
-
2.8
C11+ -r initios
-
1.5
-
0 0
-
1.5
1.5
1.5
Olefins
%m
:
±3
7
8.7
7
Ei 5
7
8.7
7
8.4
7
8.1
Sulfur
ma/kq
D5452
±3
7
6.3
7
6 0
7
6.0
7
6.1
7
5.8
Carbon iFart of D4809)
mass
%
D52U1
-
82.62
-
8298
-
82.99
-
82.82
-
80.86
H drooen i F art of
D4609I
mass
%
D5291
-
13.72
-
13,49
-
13.44
-
13.70
-
13.61
Carbon
mass
%
D3343M
-
82.70
-
82.98
-
82.96
82.71
-
1 ">">
H rrogen
mass
%
D3343M
-
13.64
-
13.5
-
13.47
-
13.81
-
k 41
Water Content
mq/kq
E1064
.
1315
-
1259
.
1251
-
1314
-
P25
Lead
q/1
D3237
.
s 0.013 q/i
< 0.0027
* 0-013 q/i
< 0.0027
£ 0.013 q/i
U ihC""
*0.013 q/i
< 0.0027
£ 0.013 qli
1 1U2"
!Jet Heat of
Combustion iD240,
MJ/kg
D240
-
41.58
-
41.63
-
4 1 -1
-
41.78
-
4li "4
Net Heat of Combustion -
D1319
MJ/kg
D3338
-
41.36
-
41.34
-
41.31
-
41.79
-
40.40
0-idation Stabiliti
minute
D525
minimum
240
> 1.000
240
> 1000
240
> 1000
240
> 1000
240
> 1000
Copper Strip Corrosion,
3ft at 122 F
-
D13G
maximum
No. 1
1A
No. 1
1A
No. 1
1A
No. 1
1A
No. 1
1A
Sohent 'A1 ashed Gum
Content
mg/10
0 ml
D381
maximum
5
< 0.5
5
< 0.5
5
< 0.5
5
< 0.5
5
: 5
T
°F
D5188
minimum
116
128.7
116
129.7
116
130.3
116
129.8
129 3
Drweahilit, inde\
D4814
maximum
1250
1101
1250
1159
1250
1143
1250
1123
•"25:
1022
17
-------
Table 3.5. Distillation data on five fuels.
Distillation, °F | Base Fuel A FuelB FueIC FuelD
Method Std
Avg.
Std
Avg.
Std
Avg.
Std
Avg.
Std
Avg.
0
D7096
0.283
28.4
0.308
28.7
0.367
28.6
0.418
28.5
0.427
28.6
5
D7096
0.827
50.8
0.850
51.1
0.780
51.1
0.965
50.9
0.852
50.8
10
D7096
0.931
53.4
0.840
53.8
0.860
53.7
0.860
53.7
0.351
53.1
20
D7096
0.500
94.6
0.458
95.5
0.441
95.3
0.531
95.0
7.628
85.7
30
D7096
10.487
126.2
8.937
133.2
2.360
136.6
10.315
131.5
12.371
105.4
40
D7096
2.453
152.2
2.745
155.2
2.455
154.7
2.822
153.8
1.302
149.2
50
D7096
3.576
201.1
0.418
204.5
0.374
204.4
0.838
203.5
5.729
196.2
60
D7096
5.540
214.3
4.828
227.8
3.909
228.0
7.874
218.9
3.201
209.2
70
D7096
1.860
251.0
7.624
269.0
7.846
256.7
1.832
250.4
0.750
248.5
80
D7096
0.332
281.2
3.981
289.8
0.550
281.9
0.425
281.1
0.103
280.8
90
D7096
5.451
335.3
5.231
355.4
3.852
338.4
6.114
335.5
2.390
331.2
95
D7096
4.842
373.9
1.740
401.6
5.891
370.2
5.018
373.8
2.179
371.0
100
D7096
2.132
424.2
7.199
506.1
1.673
423.8
1.826
425.2
0.641
423.9
Distillation, °F
Method
Std
Avg.
Std
Avg.
Std
Avg.
Std
Avg.
Std
Avg.
0
D86
1.169
95.5
2.114
95.7
1.997
95.4
1.877
95.7
0.453
94.3
5
D86
0.673
117.2
2.144
117.6
0.696
119.2
2.535
119.5
0.297
119.8
10
D86
0.273
124.9
0.666
126.8
0.321
127.3
1.640
126.9
0.467
127.4
20
D86
1.913
136.6
0.831
136.8
0.129
137.7
1.255
137.0
0.693
137.9
30
D86
1.096
144.2
4.154
144.1
0.452
147.0
0.930
146.0
1.004
147.2
40
D86
2.957
150.4
1.896
153.3
0.423
155.2
2.123
154.7
0.764
154.9
50
D86
22.603
179.6
12.687
198.0
0.867
205.5
0.262
199.9
0.566
161.2
60
D86
21.870
220.7
5.613
237.3
1.152
237.8
1.299
234.3
1.442
219.6
70
D86
8.597
250.0
3.179
262.8
0.589
259.9
1.222
256.3
0.764
253.0
80
D86
6.493
276.9
2.706
291.5
0.621
283.1
1.100
280.2
0.509
278.1
90
D86
4.203
308.3
2.794
330.7
0.924
315.1
2.956
311.2
0.099
310.6
95
D86
6.173
340.6
2.291
366.1
1.594
344.5
1.315
343.2
1.541
341.6
100
D86
11.111
375.6
10.013
414.7
0.356
383.0
4.418
381.9
1.358
382.8
In general, the standard deviations from D7096 SimDis analysis on the five fuels from two labs across three
operators are much better than from conventional D86. However, it must be noted there are larger than expected
deviations in the 20-40% distillation point range using the enhanced D7096 as compared to the current ASTM
D7096 precision. Further study is ongoing with the ASTM D7096 team and the method developer on
improvements to the calibration strategy and potential software updates to enable a more reproducible method.
Even so, SimDis provides much better resolution and compound separation for gasolines when compared with
ASTM D86. A graphical comparison of the D86 vs D7096 distillation profiles for two of the test fuels is shown
in Figure 3.6 below. Each data point is averaged across all labs that ran each sample.
18
-------
Figure 3.6. Fuel A and Fuel B distillation profiles from D7096 and D86
As SimDis methodology utilizes capillary column technology and ramping oven temperatures to separate
individual fuel components based on boiling point, it provides better resolution and fewer azeotropic
interferences throughout the entire distillation profile as can be seen in the figures above. The improved
resolution is due to samples being injected into a sealed inlet by micro syringe, resulting in all compounds (light
or heavy) making it onto the column with minimal loss. By contrast, in the more traditional D86 distillation,
resolution is lost at the back end due to isotropic hydrocarbon interactions and at the front end due to
unavoidable sample handling compromises. As a result, the beginning and end of the D86 distillation profile are
biased towards the average boiling point of the fuel. The better resolution from SimDis provides a much better
option for rapidly determining a fuel's volatility characteristics.
19
-------
4. Application of SimDis Cut-Points and PM Index Analysis to US Market Gasolines
With the improved resolution of the heavy tail of gasoline provided by these SimDis procedures, it becomes
feasible to correlate specific SimDis parameters with PMI and other emission indices 113, 14|. In this section,
we examine the correlation of SimDis results with PM Index for a set of 80 samples taken from a US retail
market survey conducted during 2021-22. These samples were analyzed by both DHA (D6730-X1) and SimDis
(D7096). The SimDis analyses were carried out in General Motors Pontiac Chem Lab and the DHA analyses
were performed at Southwest Research Institute.
4.1 SimDis Results
Figure 4.1 shows four example distillation profiles from SimDis with PMI values ranging from 1.01 to 2.55. A
key observation is that points between T90 and T98 fall reliably in order of PMI, with the distance between the
lines being roughly proportional to the differences in PMI. This is consistent with the heavy tail material being a
primary driver of the overall PMI value.
690
590
490
V 390
=3
4—1
CD
l—
CD
£ 290
,
-------
Figure 4.2 and Figure 4.3 plot SimDis temperature versus PM Index for T95 through final boiling point (FBP).
A positive correlation is clear for all the series plotted, with correlation appearing tightest for T97 and T98.
Scatter expands markedly for the FBP series.
500
4gQ • SimDis_T98
450 • SimDis_T97
,-..440 • SimDis_T95 ^
^ 420 • •* •/%%«*** " •
3 . • v. /WC
ts 400 9 •
^ 380 :• •• •
! * • • '.•
•" 360 •• • • * * * •
•
340 •. ••••* s ••
320
300
0.8 1.0 1.3 1.5 1.8 2.0 2.3
PM Index
Figure 4.2. Plot of SimDis T95-T98 temperature versus PM Index for 80 US market fuels.
• SimDis
_T98
• SimDis.
_T97
•
• SimDis.
_T95
• *
• • .
S •
• v t » • -• •
•
• *
_•
•
•
•
•
t • «H
V: <
- - •
• • •
. •• *
• * .
"1* -
•
•
• J
• • *
•
•
• • •
•
•
.8 1.0 1.3 1.5 1.8 2.0 2.
PM Index
21
-------
650
600
550
£ 500
¦M
ro
§.450
E
£
400
350
300
• SimDis
_FBP
•
• SimDis
_T99
•
•
•
• • • 1
• J
•
. J*
»'•
•
• • •
• v •• ,*
p •••
> «i • •
•
• .
'¥• •
•
•
•
0.8 1.0 1.3 1.5 1.8 2.0 2.3
PM Index
Figure 4.3. Plot of SimDis T99 and final boiling point (FBP) temperature versus PM Index for 80 US
market fuels.
Table 4.1 shows a matrix of Pearson correlation coefficients for SimDis values T90 through T99. Correlation
with PM Index is also shown in the first column, values that correspond to the two figures above. All distillation
points from T90 to T99 exhibit reasonably good correlation to the PM Index, with T98 having the highest
coefficient (p=0.832) in the 80-sample study.
Table 4.1. Pearson Correlation of Distillation Points vs PM Index for a dataset of 80 US market fuels.
80 Samples
PMI(SwRIDHA)
T90
T91
T92
T93
T94
T95
T96
T97
T98 T99
PMI(SwRIDHA
1
T90
0.765
1
T91
0.751
0.962
1
T92
0.781
0.933
0.955
1
T93
0.797
0.929
0.945
0.984
1
T94
0.799
0.899
0.935
0.974
0.990
1
T95
0.829
0.887
0.906
0.952
0.973
0.981
1
T96
0.817
0.903
0.908
0.939
0.960
0.960
0.984
1
T97
0.827
0.915
0.922
0.927
0.944
0.943
0.964
0.985
1
T98
0.832
0.888
0.897
0.906
0.918
0.919
0.938
0.962
0.981
1
T99
0.710
0.762
0.767
0.760
0.758
0.751
0.760
0.806
0.846
0.900 1
22
-------
4.2 DHA and PMI Analysis
Table 4.2 and Table 4.3 summarize the PMI contribution and volume contribution from specific groups of
compounds in the gasoline survey data. Since unidentified components can't be included in the PM Index
calculation, they are not reported in Table 4.2. The volumes as identified from the DHAs are organized by
temperature ranges, as defined in [14],
Table 4.2. Average PMI% contribution by chemical groups in selected temperature ranges from DHA,
where I is isoparaffins, A is aromatics, N is naphthene, O is olefins, and Ox is oxygenates and P is
paraffins, and U is unidentified.
T°C Range
0-110
110-111
135-145
150-182
182-221
221-420
PMI%
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
A
0.30
0.11
4.32
1.82
-
-
11.87
4.38
-
-
19.97
3.00
23.17
3.73
29.01
6.98
1
1.54
0.40
-
-
0.90
0.30
0.47
0.13
0.05
0.02
0.84
0.31
0.49
0.18
0.16
0.08
N
1.31
0.42
-
-
1.06
0.38
0.12
0.05
0.12
0.04
0.49
0.17
0.06
0.02
-
-
0
0.49
0.24
-
-
0.12
0.06
0.10
0.05
0.10
0.05
0.11
0.05
0.10
0.05
-
-
Ox
0.78
0.17
-
-
-
-
-
-
-
-
-
-
-
-
-
-
P
0.56
0.16
-
-
0.19
0.06
-
-
-
-
0.31
0.11
0.24
0.12
0.63
0.31
U
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
There were on average about 1.3 v% of each sample unidentified in the DHA. Since there is no information for
the unknown compounds, no DBE or boiling points can be assigned and contributions to PMI are ignored. (It
should be noted that unidentified components are accounted in SimDis results.)
Table 4.3. Average v% contribution by chemical groups in selected temperature ranges from DHA,
where I is isoparaffins, A is aromatics, N is naphthene, O is olefins, and Ox is oxygenates and P is
paraffins, and U is unidentified.
T°C Range
0-110
110-111
135-145
150-182
182-221
221-420
Vol%
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
A
0.63
0.18
4.63
1.57
-
-
6.49
1.86
-
-
5.41
0.93
3.14
0.72
0.70
0.29
1
27.05
3.47
-
-
5.02
1.49
1.50
0.29
0.13
0.05
1.39
0.45
0.44
0.16
0.05
0.03
N
6.67
1.30
-
-
2.17
0.70
0.16
0.07
0.15
0.05
0.46
0.15
0.02
0.01
-
-
0
5.57
2.11
0.02
0.01
0.33
0.13
0.17
0.08
0.13
0.06
0.10
0.04
0.04
0.02
-
-
Ox
9.44
1.08
-
-
-
-
-
-
-
-
-
-
-
-
-
-
P
14.93
3.84
-
-
0.91
0.15
-
-
-
-
0.61
0.17
0.16
0.08
0.06
0.03
U
0.02
0.01
-
-
0.10
0.05
0.05
0.03
0.02
0.01
0.39
0.13
0.49
0.21
0.27
0.12
Figure 4.4 and Figure 4.5 summarize the data shown in the previous two tables. It makes clear that the heavy
tails of market fuels are dominated by aromatics and therefore have strong leverage on the PMI value (blue
bars), despite the fact that the contribution of this material to overall gasoline volume is very small (orange
circles). Two specific observations are:
• Fuel components with boiling points >360°F account for over half of the PMI value, but only 5% of the
volume.
• Fuel components with boiling points >430°F account for almost 30% of the PMI value, but only 1% of
the volume.
23
-------
30%
£ 25%
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20%
15%
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£ 10%
o
u
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so
0s-
5%
0%
•¦=>
w
Li Paraffins
u Oxygenates
H Olefins
U Naphthenes
M Isoparaffins
B Aromatics
« Volume % Fuel
= i
L
1-
m 1 .
!
I .
II
70%
60%
50%
40%
30%
oS
430°F
Boiling Point Temperature Range from DHA (ASTM D6730)
Figure 4.4. Percent PMI contribution by compound class and DHA-derived boiling point range.
60%
50%
40%
JS
u
"U
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3
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20%
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0%
y Paraffins
u Oxygenates
HI Olefins
M Naphthenes
H Isoparaffins
SI Aromatics
32-230°F 230-232°F 232-275°F 275-293°F 293-302°F 302-360°F 360-430°F
Boiling Point Temperature Range from DHA (ASTM D6730)
>430°F
Figure 4.5. Percent PMI contribution per volume by compound class and DHA-derived boiling point
range.
24
-------
Further breakdown of aromatics by carbon number are presented in Table 4.4 and Table 4.5. Both PMI and
volume percent are normalized from overall samples.
Table 4.4. Average PMI contribution by aromatics by carbon number in selected temperature ranges
from DHA.
T°C Range
0-110
110-111
135-145
150-182
182-221
221-420
PMI
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
A6
0.005
0.001
-
-
-
-
-
-
-
-
-
-
A7
-
-
0.066
0.022
-
-
-
-
-
-
-
-
A8
-
-
-
-
0.181
0.049
-
-
-
-
-
-
A9
-
-
-
-
-
-
0.271
0.046
-
-
-
-
A10
-
-
-
-
-
-
0.025
0.006
0.230
0.047
0.054
0.017
All
-
-
-
-
-
-
0.004
0.002
0.112
0.035
0.187
0.086
A12
-
-
-
-
-
-
0.012
0.006
0.028
0.013
0.154
0.085
A13
-
-
-
-
-
-
-
-
-
-
0.053
0.026
A14
-
-
-
-
-
-
-
-
-
-
0.029
0.012
Table 4.5. Average v% by aromatics by carbon number in selected temperature ranges from DHA.
T°C Range
0-110
110-111
135-145
150-182
182-221
221-420
Vol%
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
Mean
Std
A6
0.628
0.176
-
-
-
-
-
-
-
-
-
-
A7
-
-
4.624
1.566
-
-
-
-
-
-
-
-
A8
-
-
-
-
6.488
1.857
-
-
-
-
-
-
A9
-
-
-
-
-
-
4.944
0.896
-
-
-
-
A10
-
-
-
-
-
-
0.355
0.083
2.104
0.423
0.124
0.039
All
-
-
-
-
-
-
0.043
0.022
0.857
0.262
0.357
0.160
A12
-
-
-
-
-
-
0.065
0.034
0.172
0.076
0.176
0.093
A13
-
-
-
-
-
-
0.001
0.002
-
-
0.031
0.015
A14
-
-
-
-
-
-
-
-
-
-
0.008
0.004
Figure 4.6 summarizes the data shown in the two tables above. It is similar in format to Figure 4.4, but only the
aromatic contribution is shown, and the aromatics are further grouped by carbon number (e.g., CIO means 10-
carbon molecules). Aromatics boiling above 430°F—primarily CI 1 and above—account for 29% of the PMI
value but only 0.7% of the fuel volume.
25
-------
30%
c 25%
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1 15%
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u C14
HC13
U C12
yen
H CIO
HC9
UC8
HC7
HC6
• Volume'
I
32-230°F 230-232T 275-293°F 302-360°F 360-430°F >430°F
Boiling Point Temperature Range from DHA (ASTM D6730)
Figure 4.6. Percent aromatic contribution to PMI by carbon number and DHA-derived boiling point
range.
4.3 SimDis Cut-Point Temperatures and PM Index Improvement in US Market Gasoline
The SimDis cut-off temperatures (endpoints or final boiling point limits) can be applied to the US market
gasoline sample set as shown in Figure 4.7, Figure 4.9, and Figure 4.8. Based on the SimDis profiles, the
targeted cut temperatures are defined and PM index is then adjusted using the DHA data after removing the cut
off volume as described by Sobotowski, etal [15], 2 The adjusted PMI can then be plotted with cut
temperatures as shown in Figure 4.7 and Figure 4.8. The data points on the graph indicate the cut-off (or trim)
volumes. Distributions of adjusted PMI are presented in Figure 4.9
As shown in Table 4.5, the heavy ends are comprised mostly of aromatic compounds. As a result, reduction of
PMI with minimum removal of heavy ends can be achieved. It should be noted that the DHA method typically
leaves 0.5 to 0.8 v% reported as unidentified, meaning those compounds not included in the computation of
PMI. However, their contribution to the heavy tail and PM formation is captured by SimDis.
2 This analysis does not reflect reformulation of the remaining fuel volume to make up for loss of octane in material that was trimmed out. Octane
make-up would be expected to produce a small reduction in the PMI impact shown. More detail is available in Sobotowski, et al. [15]
26
-------
SimDis cut-off Temperature (°F)
Figure 4.7. Estimated average PM Index adjustment of US market gasoline after SimDis cut-points.
Figure 4.8. Estimated PM Index reduction of US market gasoline after SimDis cut-points.
27
-------
Figure 4.9 shows the distribution of PMI values across the 80-sample market fuel survey before and after
application of several SimDis FBP limits. PMI 392 denotes adjusted PMIs of samples with a SimDis cut-point
temperature of 392°F, PMI 405 denoting adjusted PMIs of samples with a SimDis cut-point temperature of
405°F, etc.
Mean
StDev
N
PMI
392F
0.9639
0.1017
80
— —
PMI
405F
1.018
0.1066
80
PMI
41 OF
1.056
0.1106
80
PMI
420F
1.075
0.1115
80
— -
PMI
42 5F
1.083
0.1129
80
PMI
430F
1.084
0.1129
80
— —
PMI
1.586
0.2780
80
—I ! 1 —=-=-i—
1.2 1.6 2.0 2.4
PMI Distribution With SimDis Cut Temperatures
Figure 4.9. Distribution of adjusted PMI of US gasoline after SimDis cut-points.
Starting from the survey sample average PMI of 1.6, this analysis indicates that a reduction in the market
average PMI of 0.5 could be achieved by applying a SimDis FBP limit of 430°F, which would correspond to
removing the heaviest 1.7 v% of the average gasoline sample.
28
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5. Summary and C onclusions
ASTM D7096 SimDis is a gas-chromatography method that provides a relatively precise volatility profile of a
gasoline sample. Detailed hydrocarbon analysis (DHA) of market gasoline shows that the high-boiling tail is
comprised primarily of aromatics that have high leverage on PM emissions. Thus, quantification of high-boiling
material by SimDis could be a useful surrogate for DHA-based parameters such as PM Index if good
repeatability and correlation can be demonstrated.
This work explored sources of variability in SimDis results and developed several procedural recommendations
to improve repeatability and reproducibility within the method as written, focusing on the high-boiling (T90+)
tail of gasoline. Validation studies done at EPA and GM laboratories showed that reproducibility values well
below those published by ASTM can be achieved.
Following the method improvement work, SimDis as well as ASTM D6730 DHA were run on 80 gasoline
samples taken from the US market in 2021-22. Contribution to PM Index and volume percent was assessed by
boiling range and molecular class. This analysis shows that the heavy tail of gasoline contains a large proportion
of aromatics that have high leverage on PM Index, findings that are consistent with previous work [12],
Correlation between PM Index values and a range of heavy-end SimDis T-numbers (or %-ofif values) was also
assessed. Results indicated that the highest correlation occurred in the range of SimDis T95-T98 with a Pearson
coefficient around 0.83.
Finally, the impact on PM Index of applying several SimDis cut-points (or endpoints) to trim heavy-end
material from market gasoline was assessed. Starting from the survey sample average PMI of 1.6, this analysis
suggests a PMI reduction of 0.5 (31%) could be achieved by applying a SimDis final boiling point limit of
430°F. This would correspond to removing the heaviest 1.7 v% of the average market fuel, and supports the
conclusion that a relatively large reduction in PM Index could be achieved with removal of a small amount of
high-boiling material from gasoline.
29
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Acknowledgement
The U.S. EPA Office of Transportation and Air Quality acknowledges significant contributions by General
Motors staff in producing fuel chemistry data and analysis for this project.
References
1. USEPA Technical Support Document, "2017 National Emissions Inventory: January 2021 Updated
Release, Technical Support Document," January 2021, accessed 29 October 2022,
https://www.epa.eov/sites/default/ files/2 cuments/nei2017 tsd full i an2021 .pdf.
2. American Lung Association, "Particle Pollution," accessed 29 October 2022,
https://www.lime.ore/clean-air/oiitdoors/what-makes-air-imhealthv/particle-pollution
3. Aikawa, K., Sakurai, K., and Jetter, J. J., "Development of a Predictive Model for Gasoline Vehicle
Particulate Matter Emissions," SAE Technical Paper 2010-01-2115. 2010, https://
doi.c
4. Aikawa, K., & Jetter, J. J., "Impact of Gasoline Composition on Particulate Matter Emissions from a
Direct-Injection Gasoline Engine: Applicability of the Particulate Matter Index," International Journal
of Engine Research, 15, 298 - 306, 2014, https://doi.ore/10.1177/146808
5. Sobotowski, R.A., Butler, A.D., and Guerra, Z., "A Pilot Study of Fuel Impacts on PM Emissions from
Light-duty Gasoline Vehicles," SAE Int. J. Fuels Lubr. 8, no. 1 (2015): 214-233,
https://doi.ore/10.4271/2015-01-9071.
6. Butler, A.D., Sobotowski, R.A., Hoffman, G.J., and Machiele, P., "Influence of Fuel PM Index and
Ethanol Content on Particulate Emissions from Light-Duty Gasoline Vehicles," SAE Technical Paper
2015-01-1072. 2015, https://doi.o
7. Coordinating Research Council, "Evaluation and Investigation of Fuel Effects on Gaseous and
Particulate Emissions on S1D1 In-Use Vehicles," Report No. E-94-2, March 2017, 3^
20955 E94-2FinalReport-Revlb.pdf fcrcao.
8. USEPA, "Population and Activity of Onroad Vehicles in MOVES3," Technical Report EPA-420-R-21-
012, April 2021, https://nepis.epa.eov/Exe/ZyPDF.t ^ i umi 11 - PDF?Dockev= " 101 I U s n )F
9. USEPA, "Nonroad Engine Population Growth Estimated in MOVES2014b," Technical Report EPA-
420-R-18-010, July 2018, https://nepis.epa.eov/Exe/ZyPDF.cei?Dockev=P 100UXJK.pdf.
10. Leach, F., Chapman, E., Jetter, J., Rubino, L. et al., "A Review and Perspective on Particulate Matter
Indices Linking Fuel Composition to Particulate Emissions from Gasoline Engines," SAE Int. J. Fuels
Lubr. 15(l):3-28, 2022. https://doi.ore/10.4271/
11. Coordinating Research Council, "Assessment of the Relative Accuracy of the PM Index and Related
Methods," Report No. RW-107, April 15, 2019, http://crcsite.wpengine.com/wp-
content/uploads/2019/05/CRC-RW~107~Final~Repc
30
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12. Sobotowski, R., Butler, A., Loftis, K., and Wyborny, L., "A Method of Assessing and Reducing the
Impact of Heavy Gasoline Fractions on Particulate Matter Emissions from Light-Duty Vehicles," SAE
Int. J. Fuels Lubr. 15(3):2022, https://doi.org/i 0.427 i/04-15-03-0015
13. Geng, Pat, Reilly, Veronica, Collin, Will, "A New Predictive Vehicle Particulate Emissions Index Based
on Gasoline Simulated Distillation", SAE Technical Paper 2022-01-0489. 2022,
https://doi.org/10.4271/20z ;
14. Reilly, Veronica, Geng, Pat, Salyers, John, Goralski, Sarah "Correlation of Detailed Hydrocarbon
Analysis with Simulated Distillation of US Market Gasoline Samples and its Effect on the PEI-SimDis
Equation of Calculated Vehicle Particulate Emission", SAE Technical Paper 2023-01-0298
31
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APPENDIX A:
Simulated Distillation for Heavy Aromatics
Laboratory Procedure
Version - 01
A-1
-------
Simulated Distillation for Heavy Aromatics
Version:
01
1. Scope
This method is based off ASTM test method D70961, and is intended to measure the distillation
curves of gasoline and gasoline-ethanol blends with a boiling point range within that of nC3 - nC16
hydrocarbons. This method further specifies analytical conditions set forth in ASTM D7096 with the
goal of increasing the interlaboratory analytical precision.
2. Summary of Method
This method uses a wide bore non-polar GC column to separate gasoline samples according to
boiling point. Retention times and response factors of a calibration sample are used to calculate the
volume percent eluted at a given time - and by extension boiling point. Cumulative volume percent
and boiling temperature are plotted to yield a distillation curve.
3. Significance
4. Definitions
control sample - a reference gasoline sample is used to verify both the chromatography and
calculation process
final boiling point - the point at which the cumulative volume counts is equal to 99.5 % of the total
volume counts under the chromatogram is obtained
initial boiling point - the point at which the cumulative volume counts is equal to 0.5 % of the total
volume counts under the chromatogram is obtained
5. Interferences and/or Limitations
Ethanol/oxygenates response factors are known to differ significantly from other gasoline
components. The presence of high amounts of oxygenates is likely to interfere with accurate volume
estimation. Samples up to 10% ethanol have been analyzed by this method without undue bias. The
bias imposed by other oxygenates and increased ethanol content have not been investigated.
1 ASTM Standard D7096, 2016, "Standard Test Method for the Determination of the Boiling Range Distribution of Gasoline by
Wide-Bore Capillary Gas Chromatography", ASTM International, West Conshohocken, PA, 2016, DOI: 10.1520/D7096-16.
A-2
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Simulated Distillation for Heavy Aromatics
Version:
01
6. Apparatus
Agilent gas chromatograph (GC) equipped with an flame ionization detector (FID) and a 30 m x
0.53 mm 100 % polydimethylsiloxane (PDMS) column film thickness of 5 |im (or comparable
column with equivalent stationary phase and length) is used in this test procedure.
Chemstation software is used for data acquisition. Separation Systems, Inc. SimDis Expert 10 (or
equivalent software package capable of automating the necessary calculations) is used for data
analysis.
Sample introducing systems (autosampler, microliter syringe and injection port) capable of
introducing 1.0 [xL into the split inlet device of the gas chromatograph.
Equipment Maintenance:
In response to problems with instrument functionality or out-of-tolerance events, a number of local
maintenance or troubleshooting activities may be performed to resolve the problem. These include
the investigation of:
Auto Sampler
Power Supply
Computer
Mobile Phase
Performance Test Mixture Integrity
Reagent Purity
Instrument Setup
Data Entry
Sample Integrity
Injector Valve
Detector
Analytical Column
Sampling Needle
Temperature Controller
Pressure Controller
Sampling Loop
7. Reagents and Materials
• 2-mL GC sample vials and aluminum crimp caps with rubber septa are used as samples
containers for the auto-sampler.
• Disposable glass transfer pipettes are used to transfer samples GC sample vial.
• Standardization Standard mixtures can be purchased and should span the nC3 - nC16
hydrocarbon boiling range and include several aromatic compounds in that boiling range.
• calibration standard - a mixture of pure hydrocarbons that possess boiling points over the range
of that expected for the samples (i.e. C3 - C 16). A single calibration standard may be used for
both, retention time calibration and relative response factor validation. It is necessary to know
the identity and amount of each component in the calibration standard.
• calibration standard with oxygenates - in the event that samples contain oxygenates, the
calibration standard shall also contain the oxygenates, in addition to the hydrocarbons. The
concentration of each oxygenate in the calibration standard should approximate that of samples.
A-3
-------
Simulated Distillation for Heavy Aromatics
Version:
01
• control sample - with each batch (see 9.3) of samples analyzed, a reference gasoline sample
should be analyzed, allowing for verification of system integrity. This sample should be
available in relatively large quantity and be similar in composition to fuels regularly analyzed.
8. Sampling
Samples should be cold (0-4 °C) when transferring.
Fuel is sampled by pipetting into a 2 mL crimp-top vial with PTFE-lined septa to 90% maximum
volume.
When transporting fuel for analysis, samples should be transported in volumes of 1 L or greater.
9. Analytical Procedure
9.1 Sample Handling:
Sample handling is critical to achieving acceptable repeatability and reproducibility. Whenever
possible, efforts should be taken to decrease potential loss of high volatility sample components.
This includes storing and aliquoting samples cold, minimizing storage container and sample vial
headspace, limiting sample exposure to heat sources and ensuring proper sealing of all closures.
When a sample needs to be stored for a long period it is best to do so with a large (e.g. 1 L)
container.
9.2 Operating Conditions:
The GC operating conditions are:
Oven: 40 °C 1 min, 25 °C/min to 260 °C, 4 min hold 6 min post run @40 °C
Injection Volume: 1 |iL
Wash 6X DCM (or CS2), 6X CS2
Injection: 300 °C, Split 50:1, Focus liner w/glass wool
Carrier Gas: He, 5 mL/min for 0.6 min, 30 mL/min2 to 20 mL/min
Detector: FID, 300 °C, 30 ml/min combined makeup
9.3 Batch Order:
A sample batch should include standards, blanks and samples in the following injection order,
repeating 6 and 7 as needed according to the number of samples to be analyzed:
1. System Blank (repeat 3 times)
2. Standard
3. Solvent Blank (CS2, repeat 2 times)
4. Control Sample
5. Solvent Blank (CS2, repeat 2 times)
A-4
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Simulated Distillation for Heavy Aromatics
Version:
01
6. Sample
7. Solvent Blank (CS2, repeat 2 times)
8. Control Sample
9.4 Calibration
Retention Time Calibration - Prior to the analysis of samples, a calibration must be performed,
defining the correlation between retention time and boiling point. Calibration is performed by
analyzing a mixture of known hydrocarbons covering the boiling point range expected. For samples
containing oxygenates, the retention time calibration mixture must contain those oxygenates.
10. Processing, Calculations, and Reporting
10.1 Sample Calculations
Sample calculations are carried out automatically by post-processing software. The third system
blank of the batch should be used for baseline correction of all subsequent samples. Report volume
percent data in 0.5 °C increments and include initial and final boiling points.
10.2 QC Evaluation
QC sample: Use ASTM r to compare repeatability.
Calibration performance: Use relative volume response factor (measured) and compare to relative
volume response factor (theoretical). Values should agree to within 10% of theoretical values. Refer
to ASTM D7096 for resolution, column selectivity and peak skewing.
A-5
------- |